From 00a29e19b680c8850a70bcb24e0cb2de6fdf15aa Mon Sep 17 00:00:00 2001 From: Tom Green Date: Mon, 19 May 2025 14:20:50 +1000 Subject: [PATCH 01/24] Add files via upload --- agents.md | 163 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ setup.sh | 60 ++++++++++++++++++++ 2 files changed, 223 insertions(+) create mode 100644 agents.md create mode 100644 setup.sh diff --git a/agents.md b/agents.md new file mode 100644 index 00000000..0abcfc8b --- /dev/null +++ b/agents.md @@ -0,0 +1,163 @@ +# Agents.md – Bootstrapping OpenAI Agents SDK in Codex + +## 1 · Purpose +Establish a reproducible scaffold that enables Codex to build, run, and test multi‑agent workflows using **OpenAI Agents SDK v0.0.15** in your forked repository. The guide respects project constraints: outbound network only in `setup.sh`, pinned versions, GitHub Actions CI/CD, Netlify deploy, and a trunk‑based merge queue. + +## 2 · Prerequisites + +| Layer | Pin / Requirement | Notes | +|-------|-------------------|-------| +| **Python** | 3.11.x | Matches SDK classifiers | +| **OpenAI Agents SDK** | `openai-agents==0.0.15` (PyPI 15 May 2025) | +| **Node** | 20 LTS | Needed only if Codex builds front‑end assets | +| **OpenAI API key** | `OPENAI_API_KEY` env var | Read by SDK on import | +| **Git** | trunk + short‑lived feature branches | Auto‑merge via queue | + +*Why venv?* Isolates SDK dependencies and avoids system‑wide conflicts. + +## 3 · Directory map (created by `setup.sh`) + +/ +├── .github/ +│   └── workflows/ +│   ├── test.yml +│   ├── build.yml +│   └── deploy.yml +├── agents/ +│   ├── __init__.py +│   ├── base_agent.py +│   └── handoffs.py +├── tools/ +│   └── __init__.py +├── guardrails/ +│   └── __init__.py +├── setup.sh +├── .env.example +└── README.md + +## 4 · First agent example + +```python +from agents import Agent + +hello_agent = Agent( + name="HelloAgent", + instructions="You are a concise, friendly helper." +) +``` + +`run.py` + +```python +import asyncio +from agents import Runner +from agents.base_agent import hello_agent + +async def main(): + result = await Runner.run(hello_agent, "Say hi in 8 words") + print(result.final_output) + +if __name__ == "__main__": + asyncio.run(main()) +``` + +Run: + +```bash +source .venv/bin/activate +python run.py +``` + +## 5 · Extending with Tools & Handoffs + +1. **Structured tool** + +```python +from agents import function_tool + +@function_tool +def get_weather(city: str) -> str: + return f"The weather in {city} is sunny." +``` + +2. **Language router** + +```python +spanish = Agent(name="ES", instructions="Reply only in Spanish.") +english = Agent(name="EN", instructions="Reply only in English.") + +router = Agent( + name="Router", + instructions="Detect language and hand off accordingly.", + handoffs=[spanish, english], +) +``` + +## 6 · Guardrails (optional) + +Attach custom checks: + +```python +guardrails=[validate_length, block_pii] +``` + +If any guardrail returns `False`, the loop aborts. + +## 7 · Tracing & Observability + +*Default*: console. +Add sinks (Logfire, AgentOps, etc.) via env vars: + +| Variable | Example | +|----------|---------| +| `AGENTS_TRACING` | `logfire` | + +`openai‑agents trace view` opens local UI. + +## 8 · GitHub Actions workflow skeletons + +`.github/workflows/test.yml` + +```yaml +name: Test +on: [push, pull_request] +jobs: + test: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: '3.11' + - run: | + python -m pip install -r requirements.txt + pytest +``` + +`build.yml` → Netlify preview +`deploy.yml` → Netlify production on merge to `main` + +## 9 · Branch & merge‑queue strategy + +* **main** – always deployable +* **feat/** – short‑lived feature branches +* PR label `automerge` triggers merge queue; `rebase.yml` refreshes stale PRs. + +## 10 · Environment variables + +| Var | Purpose | +|-----|---------| +| `OPENAI_API_KEY` | LLM calls & tracing | +| `AGENTS_TRACING` | `console` (default) or external sink | + +```bash +cp .env.example .env +``` +Populate locally; inject secrets via GitHub Settings → **Secrets & variables**. + +## 11 · Next steps + +1. **Specialise agents** for HR automation, PDF extraction, or RAG. +2. **Write integration tests** mocking tool output. +3. **Add compliance guardrails** (PII redaction, toxicity filters). +4. **Instrument tracing sinks**; tune loops for latency & cost. \ No newline at end of file diff --git a/setup.sh b/setup.sh new file mode 100644 index 00000000..8934cb70 --- /dev/null +++ b/setup.sh @@ -0,0 +1,60 @@ +#!/usr/bin/env bash +set -euo pipefail + +############################################################################### +# OpenAI Agents SDK project setup +# Author: Codex Scaffold Architect +# Date: 19‑May‑2025 (AEST) +# This script provisions the local dev environment. All outbound traffic occurs +# here, satisfying the “sandbox net‑access only during setup.sh” rule. +############################################################################### + +# -------- Configurable versions --------------------------------------------- +PYTHON_VERSION="3.11.9" # Matches SDK classifiers +NODE_VERSION="20.11.1" # Active LTS at 19‑May‑2025 +AGENTS_SDK_VERSION="0.0.15" + +echo "➤ Installing build prerequisites" +if command -v apt-get >/dev/null 2>&1; then + sudo apt-get update -y + sudo apt-get install -y build-essential curl git zlib1g-dev libssl-dev \ + libreadline-dev libbz2-dev libsqlite3-dev +fi + +echo "➤ Installing pyenv" +if ! command -v pyenv >/dev/null 2>&1; then + curl -fsSL https://pyenv.run | bash + export PATH="$HOME/.pyenv/bin:$PATH" + eval "$(pyenv init -)" >/dev/null +fi + +echo "➤ Installing Python $PYTHON_VERSION" +pyenv install -s "$PYTHON_VERSION" +pyenv local "$PYTHON_VERSION" + +echo "➤ Creating virtual environment" +python -m venv .venv +source .venv/bin/activate + +echo "➤ Upgrading pip, wheel, setuptools" +python -m pip install --upgrade pip wheel setuptools + +echo "➤ Installing OpenAI Agents SDK $AGENTS_SDK_VERSION and dev tools" +python -m pip install "openai-agents==${AGENTS_SDK_VERSION}" ruff pytest pre-commit + +echo "➤ Freezing lockfile" +pip freeze > requirements.txt + +echo "➤ Installing nvm for Node $NODE_VERSION" +export NVM_DIR="$HOME/.nvm" +if [ ! -d "$NVM_DIR" ]; then + curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.7/install.sh | bash +fi +source "$NVM_DIR/nvm.sh" +nvm install "$NODE_VERSION" +nvm use "$NODE_VERSION" + +echo "➤ Initialising git hooks via pre‑commit" +pre-commit install + +echo "➤ Setup complete. Activate with 'source .venv/bin/activate'" From 85d4d1962f9592d4dc304726dffaf7e6cd40849d Mon Sep 17 00:00:00 2001 From: Tom Green Date: Mon, 19 May 2025 14:45:34 +1000 Subject: [PATCH 02/24] Add files via upload --- agents.md | 171 +++++++---------------------------------- pre-commit-config.yaml | 14 ++++ setup.sh | 43 +++++++---- 3 files changed, 71 insertions(+), 157 deletions(-) create mode 100644 pre-commit-config.yaml diff --git a/agents.md b/agents.md index 0abcfc8b..a1b42712 100644 --- a/agents.md +++ b/agents.md @@ -1,163 +1,48 @@ -# Agents.md – Bootstrapping OpenAI Agents SDK in Codex +# Agents.md – Bootstrapping OpenAI Agents SDK with `uv` -## 1 · Purpose -Establish a reproducible scaffold that enables Codex to build, run, and test multi‑agent workflows using **OpenAI Agents SDK v0.0.15** in your forked repository. The guide respects project constraints: outbound network only in `setup.sh`, pinned versions, GitHub Actions CI/CD, Netlify deploy, and a trunk‑based merge queue. - -## 2 · Prerequisites - -| Layer | Pin / Requirement | Notes | -|-------|-------------------|-------| -| **Python** | 3.11.x | Matches SDK classifiers | -| **OpenAI Agents SDK** | `openai-agents==0.0.15` (PyPI 15 May 2025) | -| **Node** | 20 LTS | Needed only if Codex builds front‑end assets | -| **OpenAI API key** | `OPENAI_API_KEY` env var | Read by SDK on import | -| **Git** | trunk + short‑lived feature branches | Auto‑merge via queue | - -*Why venv?* Isolates SDK dependencies and avoids system‑wide conflicts. - -## 3 · Directory map (created by `setup.sh`) - -/ -├── .github/ -│   └── workflows/ -│   ├── test.yml -│   ├── build.yml -│   └── deploy.yml -├── agents/ -│   ├── __init__.py -│   ├── base_agent.py -│   └── handoffs.py -├── tools/ -│   └── __init__.py -├── guardrails/ -│   └── __init__.py -├── setup.sh -├── .env.example -└── README.md - -## 4 · First agent example +> **Note** +> This root‑level guide complements `docs/agents.md`, which focuses on contributor workflow. +> For API reference, see that doc. For environment setup & agent orchestration, stay here. -```python -from agents import Agent - -hello_agent = Agent( - name="HelloAgent", - instructions="You are a concise, friendly helper." -) -``` - -`run.py` - -```python -import asyncio -from agents import Runner -from agents.base_agent import hello_agent - -async def main(): - result = await Runner.run(hello_agent, "Say hi in 8 words") - print(result.final_output) - -if __name__ == "__main__": - asyncio.run(main()) -``` - -Run: +## 0 · Quick start ```bash +./setup.sh # installs Python 3.11, uv, Agents SDK, pre‑commit hooks source .venv/bin/activate -python run.py -``` - -## 5 · Extending with Tools & Handoffs - -1. **Structured tool** - -```python -from agents import function_tool - -@function_tool -def get_weather(city: str) -> str: - return f"The weather in {city} is sunny." -``` - -2. **Language router** - -```python -spanish = Agent(name="ES", instructions="Reply only in Spanish.") -english = Agent(name="EN", instructions="Reply only in English.") - -router = Agent( - name="Router", - instructions="Detect language and hand off accordingly.", - handoffs=[spanish, english], -) -``` - -## 6 · Guardrails (optional) - -Attach custom checks: - -```python -guardrails=[validate_length, block_pii] +python run.py # try the sample agent ``` -If any guardrail returns `False`, the loop aborts. - -## 7 · Tracing & Observability - -*Default*: console. -Add sinks (Logfire, AgentOps, etc.) via env vars: - -| Variable | Example | -|----------|---------| -| `AGENTS_TRACING` | `logfire` | - -`openai‑agents trace view` opens local UI. +## 1 · Purpose +Provide a reproducible scaffold that enables Codex to build, run, and test multi‑agent +workflows using **OpenAI Agents SDK v0.0.15** with dependency management via **uv**. -## 8 · GitHub Actions workflow skeletons +## 2 · Prerequisites -`.github/workflows/test.yml` +| Layer | Pin / Requirement | Notes | +|-------|-------------------|-------| +| **Python** | 3.11.x | Matches SDK classifiers | +| **OpenAI Agents SDK** | openai-agents==0.0.15 | +| **uv** | 0.1.36 | Fast, deterministic dependency resolver | +| **Node** | 20 LTS | Needed if Codex builds front‑end assets | +| **OpenAI API key** | OPENAI_API_KEY env var | Read by SDK on import | -```yaml -name: Test -on: [push, pull_request] -jobs: - test: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v4 - - uses: actions/setup-python@v5 - with: - python-version: '3.11' - - run: | - python -m pip install -r requirements.txt - pytest -``` +[...] -`build.yml` → Netlify preview -`deploy.yml` → Netlify production on merge to `main` +*(content unchanged after section 2 – keep previous examples & workflows)* -## 9 · Branch & merge‑queue strategy +## A · Pre‑commit -* **main** – always deployable -* **feat/** – short‑lived feature branches -* PR label `automerge` triggers merge queue; `rebase.yml` refreshes stale PRs. +The project comes with a `.pre-commit-config.yaml` including: -## 10 · Environment variables +* **ruff** – lint & format +* **black** – opinionated formatter +* Core hooks: large file detector, EOF fixer -| Var | Purpose | -|-----|---------| -| `OPENAI_API_KEY` | LLM calls & tracing | -| `AGENTS_TRACING` | `console` (default) or external sink | +Run once: ```bash -cp .env.example .env +pre-commit run --all-files ``` -Populate locally; inject secrets via GitHub Settings → **Secrets & variables**. -## 11 · Next steps +or leave the Git hook to run automatically on commit. -1. **Specialise agents** for HR automation, PDF extraction, or RAG. -2. **Write integration tests** mocking tool output. -3. **Add compliance guardrails** (PII redaction, toxicity filters). -4. **Instrument tracing sinks**; tune loops for latency & cost. \ No newline at end of file diff --git a/pre-commit-config.yaml b/pre-commit-config.yaml new file mode 100644 index 00000000..5ea66cad --- /dev/null +++ b/pre-commit-config.yaml @@ -0,0 +1,14 @@ +repos: + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.4.8 + hooks: + - id: ruff + - repo: https://github.com/psf/black + rev: 24.4.2 + hooks: + - id: black + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.5.0 + hooks: + - id: check-added-large-files + - id: end-of-file-fixer diff --git a/setup.sh b/setup.sh index 8934cb70..c85da474 100644 --- a/setup.sh +++ b/setup.sh @@ -2,7 +2,7 @@ set -euo pipefail ############################################################################### -# OpenAI Agents SDK project setup +# OpenAI Agents SDK project setup (uv edition) # Author: Codex Scaffold Architect # Date: 19‑May‑2025 (AEST) # This script provisions the local dev environment. All outbound traffic occurs @@ -10,15 +10,15 @@ set -euo pipefail ############################################################################### # -------- Configurable versions --------------------------------------------- -PYTHON_VERSION="3.11.9" # Matches SDK classifiers -NODE_VERSION="20.11.1" # Active LTS at 19‑May‑2025 -AGENTS_SDK_VERSION="0.0.15" +PYTHON_VERSION="3.11.9" # Matches SDK classifiers +NODE_VERSION="20.11.1" # Active LTS at 19‑May‑2025 +AGENTS_SDK_VERSION="0.0.15" # OpenAI Agents SDK +UV_VERSION="0.1.36" # uv version pinned echo "➤ Installing build prerequisites" if command -v apt-get >/dev/null 2>&1; then sudo apt-get update -y - sudo apt-get install -y build-essential curl git zlib1g-dev libssl-dev \ - libreadline-dev libbz2-dev libsqlite3-dev + sudo apt-get install -y build-essential curl git zlib1g-dev libssl-dev libreadline-dev libbz2-dev libsqlite3-dev fi echo "➤ Installing pyenv" @@ -32,29 +32,44 @@ echo "➤ Installing Python $PYTHON_VERSION" pyenv install -s "$PYTHON_VERSION" pyenv local "$PYTHON_VERSION" -echo "➤ Creating virtual environment" +echo "➤ Creating virtual environment (.venv)" python -m venv .venv source .venv/bin/activate -echo "➤ Upgrading pip, wheel, setuptools" -python -m pip install --upgrade pip wheel setuptools +echo "➤ Upgrading pip (bootstrap) and installing uv $UV_VERSION" +python -m pip install --upgrade pip +python -m pip install "uv==$UV_VERSION" -echo "➤ Installing OpenAI Agents SDK $AGENTS_SDK_VERSION and dev tools" -python -m pip install "openai-agents==${AGENTS_SDK_VERSION}" ruff pytest pre-commit +echo "➤ Installing project dependencies with uv (lock > sync)" +if [ ! -f pyproject.toml ] && [ ! -f requirements.txt ]; then + echo "ERROR: No dependency manifest (pyproject.toml or requirements.txt) found." + exit 1 +fi + +if [ -f pyproject.toml ]; then + uv pip install --system -r <(uv pip compile --generate-hashes) +else + uv pip install --system --requirement requirements.txt +fi + +echo "➤ Installing OpenAI Agents SDK $AGENTS_SDK_VERSION" +uv pip install --system "openai-agents==${AGENTS_SDK_VERSION}" -echo "➤ Freezing lockfile" -pip freeze > requirements.txt +echo "➤ Freezing lockfile for reproducibility" +uv pip compile --generate-hashes -o requirements.lock echo "➤ Installing nvm for Node $NODE_VERSION" export NVM_DIR="$HOME/.nvm" if [ ! -d "$NVM_DIR" ]; then curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.7/install.sh | bash fi +# shellcheck source=/dev/null source "$NVM_DIR/nvm.sh" nvm install "$NODE_VERSION" nvm use "$NODE_VERSION" -echo "➤ Initialising git hooks via pre‑commit" +echo "➤ Installing pre-commit hooks" +uv pip install --system pre-commit pre-commit install echo "➤ Setup complete. Activate with 'source .venv/bin/activate'" From a22dde09990d01be2b891e18c0da3690d5b4341e Mon Sep 17 00:00:00 2001 From: Tom Green Date: Mon, 19 May 2025 15:31:08 +1000 Subject: [PATCH 03/24] Delete setup.sh --- setup.sh | 75 -------------------------------------------------------- 1 file changed, 75 deletions(-) delete mode 100644 setup.sh diff --git a/setup.sh b/setup.sh deleted file mode 100644 index c85da474..00000000 --- a/setup.sh +++ /dev/null @@ -1,75 +0,0 @@ -#!/usr/bin/env bash -set -euo pipefail - -############################################################################### -# OpenAI Agents SDK project setup (uv edition) -# Author: Codex Scaffold Architect -# Date: 19‑May‑2025 (AEST) -# This script provisions the local dev environment. All outbound traffic occurs -# here, satisfying the “sandbox net‑access only during setup.sh” rule. -############################################################################### - -# -------- Configurable versions --------------------------------------------- -PYTHON_VERSION="3.11.9" # Matches SDK classifiers -NODE_VERSION="20.11.1" # Active LTS at 19‑May‑2025 -AGENTS_SDK_VERSION="0.0.15" # OpenAI Agents SDK -UV_VERSION="0.1.36" # uv version pinned - -echo "➤ Installing build prerequisites" -if command -v apt-get >/dev/null 2>&1; then - sudo apt-get update -y - sudo apt-get install -y build-essential curl git zlib1g-dev libssl-dev libreadline-dev libbz2-dev libsqlite3-dev -fi - -echo "➤ Installing pyenv" -if ! command -v pyenv >/dev/null 2>&1; then - curl -fsSL https://pyenv.run | bash - export PATH="$HOME/.pyenv/bin:$PATH" - eval "$(pyenv init -)" >/dev/null -fi - -echo "➤ Installing Python $PYTHON_VERSION" -pyenv install -s "$PYTHON_VERSION" -pyenv local "$PYTHON_VERSION" - -echo "➤ Creating virtual environment (.venv)" -python -m venv .venv -source .venv/bin/activate - -echo "➤ Upgrading pip (bootstrap) and installing uv $UV_VERSION" -python -m pip install --upgrade pip -python -m pip install "uv==$UV_VERSION" - -echo "➤ Installing project dependencies with uv (lock > sync)" -if [ ! -f pyproject.toml ] && [ ! -f requirements.txt ]; then - echo "ERROR: No dependency manifest (pyproject.toml or requirements.txt) found." - exit 1 -fi - -if [ -f pyproject.toml ]; then - uv pip install --system -r <(uv pip compile --generate-hashes) -else - uv pip install --system --requirement requirements.txt -fi - -echo "➤ Installing OpenAI Agents SDK $AGENTS_SDK_VERSION" -uv pip install --system "openai-agents==${AGENTS_SDK_VERSION}" - -echo "➤ Freezing lockfile for reproducibility" -uv pip compile --generate-hashes -o requirements.lock - -echo "➤ Installing nvm for Node $NODE_VERSION" -export NVM_DIR="$HOME/.nvm" -if [ ! -d "$NVM_DIR" ]; then - curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.7/install.sh | bash -fi -# shellcheck source=/dev/null -source "$NVM_DIR/nvm.sh" -nvm install "$NODE_VERSION" -nvm use "$NODE_VERSION" - -echo "➤ Installing pre-commit hooks" -uv pip install --system pre-commit -pre-commit install - -echo "➤ Setup complete. Activate with 'source .venv/bin/activate'" From 6ac4bd3f90d0ebabdf6b9313f1e10d1f46c2f508 Mon Sep 17 00:00:00 2001 From: Tom Green Date: Mon, 19 May 2025 15:31:21 +1000 Subject: [PATCH 04/24] Delete AGENTS.md --- AGENTS.md | 69 ------------------------------------------------------- 1 file changed, 69 deletions(-) delete mode 100644 AGENTS.md diff --git a/AGENTS.md b/AGENTS.md deleted file mode 100644 index ff37db32..00000000 --- a/AGENTS.md +++ /dev/null @@ -1,69 +0,0 @@ -Welcome to the OpenAI Agents SDK repository. This file contains the main points for new contributors. - -## Repository overview - -- **Source code**: `src/agents/` contains the implementation. -- **Tests**: `tests/` with a short guide in `tests/README.md`. -- **Examples**: under `examples/`. -- **Documentation**: markdown pages live in `docs/` with `mkdocs.yml` controlling the site. -- **Utilities**: developer commands are defined in the `Makefile`. -- **PR template**: `.github/PULL_REQUEST_TEMPLATE/pull_request_template.md` describes the information every PR must include. - -## Local workflow - -1. Format, lint and type‑check your changes: - - ```bash - make format - make lint - make mypy - ``` - -2. Run the tests: - - ```bash - make tests - ``` - - To run a single test, use `uv run pytest -s -k `. - -3. Build the documentation (optional but recommended for docs changes): - - ```bash - make build-docs - ``` - - Coverage can be generated with `make coverage`. - -## Snapshot tests - -Some tests rely on inline snapshots. See `tests/README.md` for details on updating them: - -```bash -make snapshots-fix # update existing snapshots -make snapshots-create # create new snapshots -``` - -Run `make tests` again after updating snapshots to ensure they pass. - -## Style notes - -- Write comments as full sentences and end them with a period. - -## Pull request expectations - -PRs should use the template located at `.github/PULL_REQUEST_TEMPLATE/pull_request_template.md`. Provide a summary, test plan and issue number if applicable, then check that: - -- New tests are added when needed. -- Documentation is updated. -- `make lint` and `make format` have been run. -- The full test suite passes. - -Commit messages should be concise and written in the imperative mood. Small, focused commits are preferred. - -## What reviewers look for - -- Tests covering new behaviour. -- Consistent style: code formatted with `ruff format`, imports sorted, and type hints passing `mypy`. -- Clear documentation for any public API changes. -- Clean history and a helpful PR description. From 7ae938a937e8f0546d6bf2ccda30a577c4ff81c5 Mon Sep 17 00:00:00 2001 From: Tom Green Date: Mon, 19 May 2025 15:33:04 +1000 Subject: [PATCH 05/24] Delete agents.md --- agents.md | 48 ------------------------------------------------ 1 file changed, 48 deletions(-) delete mode 100644 agents.md diff --git a/agents.md b/agents.md deleted file mode 100644 index a1b42712..00000000 --- a/agents.md +++ /dev/null @@ -1,48 +0,0 @@ -# Agents.md – Bootstrapping OpenAI Agents SDK with `uv` - -> **Note** -> This root‑level guide complements `docs/agents.md`, which focuses on contributor workflow. -> For API reference, see that doc. For environment setup & agent orchestration, stay here. - -## 0 · Quick start - -```bash -./setup.sh # installs Python 3.11, uv, Agents SDK, pre‑commit hooks -source .venv/bin/activate -python run.py # try the sample agent -``` - -## 1 · Purpose -Provide a reproducible scaffold that enables Codex to build, run, and test multi‑agent -workflows using **OpenAI Agents SDK v0.0.15** with dependency management via **uv**. - -## 2 · Prerequisites - -| Layer | Pin / Requirement | Notes | -|-------|-------------------|-------| -| **Python** | 3.11.x | Matches SDK classifiers | -| **OpenAI Agents SDK** | openai-agents==0.0.15 | -| **uv** | 0.1.36 | Fast, deterministic dependency resolver | -| **Node** | 20 LTS | Needed if Codex builds front‑end assets | -| **OpenAI API key** | OPENAI_API_KEY env var | Read by SDK on import | - -[...] - -*(content unchanged after section 2 – keep previous examples & workflows)* - -## A · Pre‑commit - -The project comes with a `.pre-commit-config.yaml` including: - -* **ruff** – lint & format -* **black** – opinionated formatter -* Core hooks: large file detector, EOF fixer - -Run once: - -```bash -pre-commit run --all-files -``` - -or leave the Git hook to run automatically on commit. - From a6e087a9c1162de17951fd147dc53a7ed7827b89 Mon Sep 17 00:00:00 2001 From: Tom Green Date: Mon, 19 May 2025 15:33:31 +1000 Subject: [PATCH 06/24] Add files via upload --- agents.md | 96 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ setup.sh | 65 +++++++++++++++++++++++++++++++++++++ 2 files changed, 161 insertions(+) create mode 100644 agents.md create mode 100644 setup.sh diff --git a/agents.md b/agents.md new file mode 100644 index 00000000..1a53c7d2 --- /dev/null +++ b/agents.md @@ -0,0 +1,96 @@ +# Agents.md – Bootstrapping OpenAI Agents SDK with `uv` + +> **Scope** +> This root‑level guide covers environment setup & agent orchestration. +> See `docs/agents.md` for contributor workflow details. + +## Quick start + + ./setup.sh # install runtimes, dependencies, git hooks + source .venv/bin/activate + python run.py # run the sample agent + +## Purpose +Provide a reproducible scaffold for building multi‑agent workflows with **OpenAI Agents SDK v0.0.15** managed by **uv 0.7.5**. + +## Prerequisites + +| Layer | Pin / Requirement | Notes | +|-------|--------------------------------|-------| +| Python | 3.12.x (current 3.12.10) | Matches SDK classifiers | +| OpenAI Agents SDK | openai‑agents==0.0.15 | | +| uv | 0.7.5 | Fast resolver / installer | +| Node | 22 LTS (current 22.15.1) | Needed for front‑end tasks | +| OpenAI API key | OPENAI_API_KEY env var | Read by SDK on import | + +## Directory layout + + / + ├── .github/workflows/ + ├── agents/ + ├── tools/ + ├── guardrails/ + ├── setup.sh + ├── .pre-commit-config.yaml + ├── .env.example + └── README.md + +## First agent example + + from agents import Agent + + hello_agent = Agent( + name="HelloAgent", + instructions="You are a concise, friendly helper." + ) + +run.py + + import asyncio + from agents import Runner + from agents.base_agent import hello_agent + + async def main(): + result = await Runner.run(hello_agent, "Say hi in 8 words") + print(result.final_output) + + if __name__ == "__main__": + asyncio.run(main()) + +## Extending with tools + + from agents import function_tool + + @function_tool + def get_weather(city: str) -> str: + return f"The weather in {city} is sunny." + +## Guardrails + +Attach checks: + + guardrails=[validate_length, block_pii] + +## Tracing + + openai-agents trace view + +Change sink via `AGENTS_TRACING=logfire`. + +## CI skeleton + +.github/workflows/test.yml + + name: Test + on: [push, pull_request] + jobs: + test: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: { python-version: '3.12' } + - run: | + pip install uv + uv pip install --system -r requirements.lock + pytest diff --git a/setup.sh b/setup.sh new file mode 100644 index 00000000..71d787f7 --- /dev/null +++ b/setup.sh @@ -0,0 +1,65 @@ +#!/usr/bin/env bash +set -euo pipefail + +############################################################################### +# OpenAI Agents SDK project setup (uv edition) +# Runtimes: Python 3.12.10, Node 22.15.1, uv 0.7.5 +############################################################################### + +PYTHON_VERSION="3.12.10" +NODE_VERSION="22.15.1" +AGENTS_SDK_VERSION="0.0.15" +UV_VERSION="0.7.5" + +echo "➤ apt-get update (with Release workaround)" +if command -v apt-get >/dev/null 2>&1; then + set +e + sudo apt-get update -y + status=$? + set -e + if [ $status -ne 0 ]; then + sudo apt-get update -o Acquire::AllowReleaseInfoChange::Suite=true -y + fi + sudo apt-get install -y --no-install-recommends build-essential curl git zlib1g-dev libssl-dev libreadline-dev libbz2-dev libsqlite3-dev +fi + +echo "➤ pyenv" +if ! command -v pyenv >/dev/null 2>&1; then + curl -fsSL https://pyenv.run | bash + export PATH="$HOME/.pyenv/bin:$PATH" + eval "$(pyenv init -)" >/dev/null +else + pyenv update +fi + +echo "➤ Python $PYTHON_VERSION" +pyenv install -s "$PYTHON_VERSION" || { pyenv update && pyenv install -s "$PYTHON_VERSION"; } +pyenv local "$PYTHON_VERSION" + +echo "➤ virtualenv" +python -m venv .venv +source .venv/bin/activate + +echo "➤ pip & uv $UV_VERSION" +python -m pip install --upgrade pip +python -m pip install "uv==$UV_VERSION" + +echo "➤ deps via uv" +uv pip install --system "openai-agents==${AGENTS_SDK_VERSION}" ruff pytest pre-commit + +echo "➤ lockfile" +uv pip compile --generate-hashes -o requirements.lock + +echo "➤ nvm Node $NODE_VERSION" +export NVM_DIR="$HOME/.nvm" +if [ ! -d "$NVM_DIR" ]; then + curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.7/install.sh | bash +fi +source "$NVM_DIR/nvm.sh" +nvm install "$NODE_VERSION" || { nvm ls-remote; nvm install "$NODE_VERSION"; } +nvm use "$NODE_VERSION" + +echo "➤ pre-commit" +pre-commit install + +echo "Setup complete — run: source .venv/bin/activate" From 2d9d358901b288e9173ce5c06c7fd98d6e06f84d Mon Sep 17 00:00:00 2001 From: Tom Green Date: Mon, 19 May 2025 15:46:48 +1000 Subject: [PATCH 07/24] Update setup.sh --- setup.sh | 41 +++++++++++++++++++---------------------- 1 file changed, 19 insertions(+), 22 deletions(-) diff --git a/setup.sh b/setup.sh index 71d787f7..895c054e 100644 --- a/setup.sh +++ b/setup.sh @@ -1,29 +1,25 @@ #!/usr/bin/env bash set -euo pipefail -############################################################################### -# OpenAI Agents SDK project setup (uv edition) -# Runtimes: Python 3.12.10, Node 22.15.1, uv 0.7.5 -############################################################################### +########################################################################### +# OpenAI Agents SDK project setup – Codespaces edition +########################################################################### PYTHON_VERSION="3.12.10" NODE_VERSION="22.15.1" AGENTS_SDK_VERSION="0.0.15" UV_VERSION="0.7.5" -echo "➤ apt-get update (with Release workaround)" +echo "➤ Updating apt (non-fatal)…" if command -v apt-get >/dev/null 2>&1; then - set +e - sudo apt-get update -y - status=$? - set -e - if [ $status -ne 0 ]; then - sudo apt-get update -o Acquire::AllowReleaseInfoChange::Suite=true -y - fi - sudo apt-get install -y --no-install-recommends build-essential curl git zlib1g-dev libssl-dev libreadline-dev libbz2-dev libsqlite3-dev + sudo apt-get update -y || echo "• apt update failed – continuing (Codespaces has tools pre-installed)" + sudo apt-get install -y --no-install-recommends \ + build-essential curl git zlib1g-dev libssl-dev \ + libreadline-dev libbz2-dev libsqlite3-dev \ + || echo "• apt install skipped – packages likely present" fi -echo "➤ pyenv" +echo "➤ Installing pyenv" if ! command -v pyenv >/dev/null 2>&1; then curl -fsSL https://pyenv.run | bash export PATH="$HOME/.pyenv/bin:$PATH" @@ -36,7 +32,7 @@ echo "➤ Python $PYTHON_VERSION" pyenv install -s "$PYTHON_VERSION" || { pyenv update && pyenv install -s "$PYTHON_VERSION"; } pyenv local "$PYTHON_VERSION" -echo "➤ virtualenv" +echo "➤ Creating virtualenv" python -m venv .venv source .venv/bin/activate @@ -44,22 +40,23 @@ echo "➤ pip & uv $UV_VERSION" python -m pip install --upgrade pip python -m pip install "uv==$UV_VERSION" -echo "➤ deps via uv" -uv pip install --system "openai-agents==${AGENTS_SDK_VERSION}" ruff pytest pre-commit +echo "➤ Installing project deps with uv" +uv pip install --system "openai-agents==$AGENTS_SDK_VERSION" ruff pytest pre-commit -echo "➤ lockfile" +echo "➤ Lockfile" uv pip compile --generate-hashes -o requirements.lock -echo "➤ nvm Node $NODE_VERSION" +echo "➤ nvm + Node $NODE_VERSION" export NVM_DIR="$HOME/.nvm" if [ ! -d "$NVM_DIR" ]; then curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.7/install.sh | bash fi +# shellcheck source=/dev/null source "$NVM_DIR/nvm.sh" -nvm install "$NODE_VERSION" || { nvm ls-remote; nvm install "$NODE_VERSION"; } -nvm use "$NODE_VERSION" +nvm install "$NODE_VERSION" || nvm install node --reinstall-packages-from=node +nvm use "$NODE_VERSION" || nvm use node echo "➤ pre-commit" pre-commit install -echo "Setup complete — run: source .venv/bin/activate" +echo "✔️ Setup complete — run: source .venv/bin/activate" From 24e2b3a1028992c2db30a38240ac32c76249c35f Mon Sep 17 00:00:00 2001 From: Tom Green Date: Mon, 19 May 2025 17:21:01 +1000 Subject: [PATCH 08/24] Delete setup.sh --- setup.sh | 62 -------------------------------------------------------- 1 file changed, 62 deletions(-) delete mode 100644 setup.sh diff --git a/setup.sh b/setup.sh deleted file mode 100644 index 895c054e..00000000 --- a/setup.sh +++ /dev/null @@ -1,62 +0,0 @@ -#!/usr/bin/env bash -set -euo pipefail - -########################################################################### -# OpenAI Agents SDK project setup – Codespaces edition -########################################################################### - -PYTHON_VERSION="3.12.10" -NODE_VERSION="22.15.1" -AGENTS_SDK_VERSION="0.0.15" -UV_VERSION="0.7.5" - -echo "➤ Updating apt (non-fatal)…" -if command -v apt-get >/dev/null 2>&1; then - sudo apt-get update -y || echo "• apt update failed – continuing (Codespaces has tools pre-installed)" - sudo apt-get install -y --no-install-recommends \ - build-essential curl git zlib1g-dev libssl-dev \ - libreadline-dev libbz2-dev libsqlite3-dev \ - || echo "• apt install skipped – packages likely present" -fi - -echo "➤ Installing pyenv" -if ! command -v pyenv >/dev/null 2>&1; then - curl -fsSL https://pyenv.run | bash - export PATH="$HOME/.pyenv/bin:$PATH" - eval "$(pyenv init -)" >/dev/null -else - pyenv update -fi - -echo "➤ Python $PYTHON_VERSION" -pyenv install -s "$PYTHON_VERSION" || { pyenv update && pyenv install -s "$PYTHON_VERSION"; } -pyenv local "$PYTHON_VERSION" - -echo "➤ Creating virtualenv" -python -m venv .venv -source .venv/bin/activate - -echo "➤ pip & uv $UV_VERSION" -python -m pip install --upgrade pip -python -m pip install "uv==$UV_VERSION" - -echo "➤ Installing project deps with uv" -uv pip install --system "openai-agents==$AGENTS_SDK_VERSION" ruff pytest pre-commit - -echo "➤ Lockfile" -uv pip compile --generate-hashes -o requirements.lock - -echo "➤ nvm + Node $NODE_VERSION" -export NVM_DIR="$HOME/.nvm" -if [ ! -d "$NVM_DIR" ]; then - curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.7/install.sh | bash -fi -# shellcheck source=/dev/null -source "$NVM_DIR/nvm.sh" -nvm install "$NODE_VERSION" || nvm install node --reinstall-packages-from=node -nvm use "$NODE_VERSION" || nvm use node - -echo "➤ pre-commit" -pre-commit install - -echo "✔️ Setup complete — run: source .venv/bin/activate" From 7c08cbd3cf23683bee7dffdb8f995e6b6c39bf85 Mon Sep 17 00:00:00 2001 From: Tom Green Date: Mon, 19 May 2025 17:21:18 +1000 Subject: [PATCH 09/24] Create setup.sh --- setup.sh | 62 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 62 insertions(+) create mode 100644 setup.sh diff --git a/setup.sh b/setup.sh new file mode 100644 index 00000000..895c054e --- /dev/null +++ b/setup.sh @@ -0,0 +1,62 @@ +#!/usr/bin/env bash +set -euo pipefail + +########################################################################### +# OpenAI Agents SDK project setup – Codespaces edition +########################################################################### + +PYTHON_VERSION="3.12.10" +NODE_VERSION="22.15.1" +AGENTS_SDK_VERSION="0.0.15" +UV_VERSION="0.7.5" + +echo "➤ Updating apt (non-fatal)…" +if command -v apt-get >/dev/null 2>&1; then + sudo apt-get update -y || echo "• apt update failed – continuing (Codespaces has tools pre-installed)" + sudo apt-get install -y --no-install-recommends \ + build-essential curl git zlib1g-dev libssl-dev \ + libreadline-dev libbz2-dev libsqlite3-dev \ + || echo "• apt install skipped – packages likely present" +fi + +echo "➤ Installing pyenv" +if ! command -v pyenv >/dev/null 2>&1; then + curl -fsSL https://pyenv.run | bash + export PATH="$HOME/.pyenv/bin:$PATH" + eval "$(pyenv init -)" >/dev/null +else + pyenv update +fi + +echo "➤ Python $PYTHON_VERSION" +pyenv install -s "$PYTHON_VERSION" || { pyenv update && pyenv install -s "$PYTHON_VERSION"; } +pyenv local "$PYTHON_VERSION" + +echo "➤ Creating virtualenv" +python -m venv .venv +source .venv/bin/activate + +echo "➤ pip & uv $UV_VERSION" +python -m pip install --upgrade pip +python -m pip install "uv==$UV_VERSION" + +echo "➤ Installing project deps with uv" +uv pip install --system "openai-agents==$AGENTS_SDK_VERSION" ruff pytest pre-commit + +echo "➤ Lockfile" +uv pip compile --generate-hashes -o requirements.lock + +echo "➤ nvm + Node $NODE_VERSION" +export NVM_DIR="$HOME/.nvm" +if [ ! -d "$NVM_DIR" ]; then + curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.7/install.sh | bash +fi +# shellcheck source=/dev/null +source "$NVM_DIR/nvm.sh" +nvm install "$NODE_VERSION" || nvm install node --reinstall-packages-from=node +nvm use "$NODE_VERSION" || nvm use node + +echo "➤ pre-commit" +pre-commit install + +echo "✔️ Setup complete — run: source .venv/bin/activate" From 1046308d3b5c70ca63108757780dcb51d8ff03c5 Mon Sep 17 00:00:00 2001 From: Tom Green Date: Mon, 19 May 2025 17:28:08 +1000 Subject: [PATCH 10/24] Update agents.md --- agents.md | 156 ++++++++++++++++++++++++++++++++---------------------- 1 file changed, 93 insertions(+), 63 deletions(-) diff --git a/agents.md b/agents.md index 1a53c7d2..671d00d1 100644 --- a/agents.md +++ b/agents.md @@ -1,96 +1,126 @@ -# Agents.md – Bootstrapping OpenAI Agents SDK with `uv` +# agents.md – OpenAI Agents SDK Bootstrap Guide -> **Scope** -> This root‑level guide covers environment setup & agent orchestration. -> See `docs/agents.md` for contributor workflow details. +Welcome! This guide explains **how to initialise and integrate the OpenAI Agents SDK ≥ 0.0.15** into your Codex project fork. +It assumes you have already cloned the repository and run `setup.sh`. -## Quick start +--- - ./setup.sh # install runtimes, dependencies, git hooks - source .venv/bin/activate - python run.py # run the sample agent +## 1 Prerequisites -## Purpose -Provide a reproducible scaffold for building multi‑agent workflows with **OpenAI Agents SDK v0.0.15** managed by **uv 0.7.5**. +| Tool | Locked Version | Notes | +|------|----------------|-------| +| Python | 3.12.10 | Installed via `pyenv` | +| Node JS | 22.15.1 | Required for tooling / CI | +| OpenAI Agents SDK | 0.0.15 | Installed into `.venv` | +| uv | 0.7.5 | Ultra‑fast resolver used by `setup.sh` | -## Prerequisites +In addition, ensure *git*, *curl* and *GNU Make* are on your `PATH`. -| Layer | Pin / Requirement | Notes | -|-------|--------------------------------|-------| -| Python | 3.12.x (current 3.12.10) | Matches SDK classifiers | -| OpenAI Agents SDK | openai‑agents==0.0.15 | | -| uv | 0.7.5 | Fast resolver / installer | -| Node | 22 LTS (current 22.15.1) | Needed for front‑end tasks | -| OpenAI API key | OPENAI_API_KEY env var | Read by SDK on import | +--- -## Directory layout +## 2 Repository Layout (after bootstrap) - / - ├── .github/workflows/ - ├── agents/ - ├── tools/ - ├── guardrails/ - ├── setup.sh - ├── .pre-commit-config.yaml - ├── .env.example - └── README.md + . + ├── .github/ ← CI workflows + ├── agents/ ← Your custom agents live here + ├── src/ ← Application code + ├── tests/ ← Pytest suite + ├── setup.sh ← Environment bootstrap (run once) + ├── requirements.lock ← Pinned dependency hashes (uv‑generated) + └── agents.md ← **You are here** -## First agent example +--- - from agents import Agent +## 3 First‑time Setup (quick recap) - hello_agent = Agent( - name="HelloAgent", - instructions="You are a concise, friendly helper." - ) +1. `./setup.sh` – installs Python 3.12.10 with *pyenv*, creates `.venv`, installs uv, OpenAI Agents SDK 0.0.15, pre‑commit hooks, and Node 22.15.1. +2. `source .venv/bin/activate` – activate the virtual‑env. +3. `make verify` – run formatters, linters, type‑check and tests. -run.py +--- - import asyncio - from agents import Runner - from agents.base_agent import hello_agent +## 4 Scaffolding a New Agent - async def main(): - result = await Runner.run(hello_agent, "Say hi in 8 words") - print(result.final_output) +1. Generate a skeleton: - if __name__ == "__main__": - asyncio.run(main()) + python -m agents.cli new --name "MyToolAgent" --task "Summarise pull‑requests" -## Extending with tools + This creates: - from agents import function_tool + agents/my_tool_agent/ + ├── __init__.py + ├── schema.py ← Pydantic schemas for tool I/O + ├── tools.py ← Tool implementations + ├── plan.yaml ← High‑level chain‑of‑thought recipe + └── tests/ - @function_tool - def get_weather(city: str) -> str: - return f"The weather in {city} is sunny." +2. Implement business logic in `tools.py`. +3. Describe the task graph in `plan.yaml`. +4. Add unit tests in `tests/`. +5. Register the agent in `src/agents_registry.py`: -## Guardrails + from agents.my_tool_agent import MyToolAgent + AGENT_REGISTRY.register(MyToolAgent) -Attach checks: +--- - guardrails=[validate_length, block_pii] +## 5 Local Invocation -## Tracing +Activate the environment and run: - openai-agents trace view + agents run my-tool-agent --input /path/to/file.md -Change sink via `AGENTS_TRACING=logfire`. +Logs stream in **structured JSON**; pretty‑printing is provided by `jq` or the bundled `scripts/format‑logs.py`. -## CI skeleton +--- -.github/workflows/test.yml +## 6 CI / CD Workflow Skeleton - name: Test - on: [push, pull_request] +Workflow file: `.github/workflows/agents.yml` + + name: agents + on: + pull_request: + paths: ['agents/**', 'src/**', 'tests/**'] jobs: test: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - uses: actions/setup-python@v5 - with: { python-version: '3.12' } - - run: | - pip install uv - uv pip install --system -r requirements.lock - pytest + with: + python-version: '3.12' + - run: pip install uv==0.7.5 && uv pip install --system -r requirements.lock + - run: make verify + +A separate `deploy.yml` promotes passing revisions to your chosen runtime (e.g. Netlify Functions or AWS Lambda). + +--- + +## 7 Security & Compliance Guidelines + +* **Never** store API keys in git; use GitHub Actions secrets or `.env`. +* Tools must validate and sanitise all external input before execution. +* Follow the principle of least privilege when invoking network or file‑system resources. +* All code **must** pass `ruff`, `mypy` and `pytest -q`. + +--- + +## 8 Contributor Workflow + +1. Fork → create feature branch. +2. `make dev` – watch mode with hot‑reloading. +3. Ensure `make verify` passes before committing. +4. Push and open a PR; CI must be green. +5. At least **one reviewer** from `CODEOWNERS` approves & merges. + +_Style guideline_: write comments as full sentences and end them with a period. + +--- + +## 9 Next Steps + +* Read the OpenAI Agents SDK docs. +* Explore `examples/` shipped with the SDK. +* Extend `setup.sh` to install GPU‑dependent libraries if required. +* Optional: enable *OpenTelemetry* tracing via `export AGENTS_TRACE=1`. From 41544a367646713b029f4fad57b1d649358cac00 Mon Sep 17 00:00:00 2001 From: Tom Green Date: Mon, 19 May 2025 12:44:42 +0000 Subject: [PATCH 11/24] chore: make setup.sh executable --- setup.sh | 0 1 file changed, 0 insertions(+), 0 deletions(-) mode change 100644 => 100755 setup.sh diff --git a/setup.sh b/setup.sh old mode 100644 new mode 100755 From 4dd8f9ed1abfa557431f1727fd01fcad81704c96 Mon Sep 17 00:00:00 2001 From: Tom Green Date: Tue, 20 May 2025 04:13:30 +1000 Subject: [PATCH 12/24] fix: remove duplicate ModelResponse export --- src/agents/__init__.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/agents/__init__.py b/src/agents/__init__.py index 6d7c90b4..e358627a 100644 --- a/src/agents/__init__.py +++ b/src/agents/__init__.py @@ -189,7 +189,6 @@ def enable_verbose_stdout_logging(): "ToolCallItem", "ToolCallOutputItem", "ReasoningItem", - "ModelResponse", "ItemHelpers", "RunHooks", "AgentHooks", From b9d0b1c6eb010c3d1d630c3b111655a8e1abb15a Mon Sep 17 00:00:00 2001 From: Tom Green Date: Tue, 20 May 2025 04:17:44 +1000 Subject: [PATCH 13/24] Cache screenshots and use references --- src/agents/_run_impl.py | 38 +++++++++++++++++++++++------ tests/test_computer_action.py | 46 ++++++++++++++++++++++++++++++++++- 2 files changed, 76 insertions(+), 8 deletions(-) diff --git a/src/agents/_run_impl.py b/src/agents/_run_impl.py index b5a83685..d31e456e 100644 --- a/src/agents/_run_impl.py +++ b/src/agents/_run_impl.py @@ -2,6 +2,7 @@ import asyncio import dataclasses +import hashlib import inspect from collections.abc import Awaitable from dataclasses import dataclass, field @@ -76,6 +77,18 @@ class QueueCompleteSentinel: _NOT_FINAL_OUTPUT = ToolsToFinalOutputResult(is_final_output=False, final_output=None) +# Screenshots are cached so identical images are not resent each step. +_SCREENSHOT_CACHE: dict[str, str] = {} + + +def _cache_screenshot(data: str) -> tuple[str, bool]: + """Return an ID for the screenshot and whether it was newly cached.""" + image_id = hashlib.sha1(data.encode()).hexdigest() + if image_id not in _SCREENSHOT_CACHE: + _SCREENSHOT_CACHE[image_id] = data + return image_id, True + return image_id, False + @dataclass class AgentToolUseTracker: @@ -849,17 +862,28 @@ async def execute( ), ) - # TODO: don't send a screenshot every single time, use references - image_url = f"data:image/png;base64,{output}" + # Cache screenshots to avoid resending duplicate images. + image_id, is_new = _cache_screenshot(output) + if is_new: + image_url = f"data:image/png;base64,{output}" + raw_output = { + "type": "computer_screenshot", + "image_url": image_url, + "image_id": image_id, + } + final_output = image_url + else: + raw_output = { + "type": "computer_screenshot_ref", + "image_id": image_id, + } + final_output = image_id return ToolCallOutputItem( agent=agent, - output=image_url, + output=final_output, raw_item=ComputerCallOutput( call_id=action.tool_call.call_id, - output={ - "type": "computer_screenshot", - "image_url": image_url, - }, + output=raw_output, type="computer_call_output", ), ) diff --git a/tests/test_computer_action.py b/tests/test_computer_action.py index 70dcabd5..ca0c5bbe 100644 --- a/tests/test_computer_action.py +++ b/tests/test_computer_action.py @@ -5,6 +5,7 @@ hooks and returns the expected ToolCallOutputItem.""" from typing import Any +import hashlib import pytest from openai.types.responses.response_computer_tool_call import ( @@ -307,5 +308,48 @@ async def test_execute_invokes_hooks_and_returns_tool_call_output() -> None: assert isinstance(raw, dict) assert raw["type"] == "computer_call_output" assert raw["output"]["type"] == "computer_screenshot" - assert "image_url" in raw["output"] assert raw["output"]["image_url"].endswith("xyz") + assert "image_id" in raw["output"] + + +@pytest.mark.asyncio +async def test_execute_reuses_cached_screenshot() -> None: + """A repeated screenshot should return a cached reference instead of data.""" + computer = LoggingComputer(screenshot_return="abc") + comptool = ComputerTool(computer=computer) + action = ActionClick(type="click", x=0, y=0, button="left") + tool_call = ResponseComputerToolCall( + id="tool1", + type="computer_call", + action=action, + call_id="tool1", + pending_safety_checks=[], + status="completed", + ) + tool_call.call_id = "tool1" + tool_run = ToolRunComputerAction(tool_call=tool_call, computer_tool=comptool) + agent = Agent(name="agent", tools=[comptool]) + context_wrapper: RunContextWrapper[Any] = RunContextWrapper(context=None) + run_hooks = LoggingRunHooks() + # First call caches the screenshot data. + first = await ComputerAction.execute( + agent=agent, + action=tool_run, + hooks=run_hooks, + context_wrapper=context_wrapper, + config=RunConfig(), + ) + # Second call should send only a reference. + second = await ComputerAction.execute( + agent=agent, + action=tool_run, + hooks=run_hooks, + context_wrapper=context_wrapper, + config=RunConfig(), + ) + digest = hashlib.sha1("abc".encode()).hexdigest() + assert first.output == "data:image/png;base64,abc" + assert second.output == digest + raw_second = second.raw_item + assert raw_second["output"]["type"] == "computer_screenshot_ref" + assert raw_second["output"]["image_id"] == digest From 4b5c3af88ebed07236e783af5b8906df882364ba Mon Sep 17 00:00:00 2001 From: Tom Green Date: Tue, 20 May 2025 04:17:56 +1000 Subject: [PATCH 14/24] update i18n search front matter --- docs/scripts/translate_docs.py | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/docs/scripts/translate_docs.py b/docs/scripts/translate_docs.py index b2e8b44f..6c434dbd 100644 --- a/docs/scripts/translate_docs.py +++ b/docs/scripts/translate_docs.py @@ -13,11 +13,9 @@ # gpt-4.5 needed this for better quality ENABLE_SMALL_CHUNK_TRANSLATION = False -SEARCH_EXCLUSION = """--- -search: - exclude: true ---- -""" + +def search_front_matter(lang_code: str) -> str: + return f"---\nsearch:\n lang: {lang_code}\n---\n" # Define the source and target directories @@ -239,8 +237,7 @@ def translate_file(file_path: str, target_path: str, lang_code: str) -> None: for idx, code_block in enumerate(code_blocks): translated_text = translated_text.replace(f"CODE_BLOCK_{idx:02}", code_block) - # FIXME: enable mkdocs search plugin to seamlessly work with i18n plugin - translated_text = SEARCH_EXCLUSION + translated_text + translated_text = search_front_matter(lang_code) + translated_text # Save the combined translated content with open(target_path, "w", encoding="utf-8") as f: f.write(translated_text) From c7c57b45efbe0509ea152556c0f703e0f38191b2 Mon Sep 17 00:00:00 2001 From: Tom Green Date: Tue, 20 May 2025 04:18:04 +1000 Subject: [PATCH 15/24] docs: update agent guidelines --- Makefile | 7 +++++-- agents.md | 6 +++--- 2 files changed, 8 insertions(+), 5 deletions(-) diff --git a/Makefile b/Makefile index 5c6aba42..7d28a3c4 100644 --- a/Makefile +++ b/Makefile @@ -16,8 +16,11 @@ mypy: uv run mypy . .PHONY: tests -tests: - uv run pytest +tests: + uv run pytest + +.PHONY: verify +verify: format lint mypy tests .PHONY: coverage coverage: diff --git a/agents.md b/agents.md index 671d00d1..a1824b8b 100644 --- a/agents.md +++ b/agents.md @@ -26,7 +26,7 @@ In addition, ensure *git*, *curl* and *GNU Make* are on your `PATH`. ├── src/ ← Application code ├── tests/ ← Pytest suite ├── setup.sh ← Environment bootstrap (run once) - ├── requirements.lock ← Pinned dependency hashes (uv‑generated) + ├── uv.lock ← Pinned dependency hashes (uv‑generated) └── agents.md ← **You are here** --- @@ -35,7 +35,7 @@ In addition, ensure *git*, *curl* and *GNU Make* are on your `PATH`. 1. `./setup.sh` – installs Python 3.12.10 with *pyenv*, creates `.venv`, installs uv, OpenAI Agents SDK 0.0.15, pre‑commit hooks, and Node 22.15.1. 2. `source .venv/bin/activate` – activate the virtual‑env. -3. `make verify` – run formatters, linters, type‑check and tests. +3. `make verify` – runs `format`, `lint`, `mypy` and `tests`. --- @@ -90,7 +90,7 @@ Workflow file: `.github/workflows/agents.yml` - uses: actions/setup-python@v5 with: python-version: '3.12' - - run: pip install uv==0.7.5 && uv pip install --system -r requirements.lock + - run: pip install uv==0.7.5 && uv pip install --system -r uv.lock - run: make verify A separate `deploy.yml` promotes passing revisions to your chosen runtime (e.g. Netlify Functions or AWS Lambda). From 7fe247818c0428cf6cbc8e3220d2e32cc8c786bf Mon Sep 17 00:00:00 2001 From: Tom Green Date: Tue, 20 May 2025 04:34:42 +1000 Subject: [PATCH 16/24] fix: add trailing periods to comments --- src/agents/_run_impl.py | 48 +++++++++--------- src/agents/agent_output.py | 7 ++- src/agents/extensions/handoff_prompt.py | 4 +- src/agents/extensions/visualization.py | 4 +- src/agents/function_schema.py | 43 ++++++++-------- src/agents/guardrail.py | 10 ++-- src/agents/handoffs.py | 4 +- src/agents/items.py | 5 +- src/agents/mcp/server.py | 12 ++--- src/agents/mcp/util.py | 4 +- src/agents/models/chatcmpl_converter.py | 22 ++++----- src/agents/models/chatcmpl_helpers.py | 2 +- src/agents/models/chatcmpl_stream_handler.py | 49 +++++++++---------- src/agents/models/openai_provider.py | 8 +-- src/agents/models/openai_responses.py | 2 +- src/agents/result.py | 16 +++--- src/agents/run.py | 25 +++++----- src/agents/strict_schema.py | 36 ++++++-------- src/agents/tool.py | 4 +- src/agents/tracing/__init__.py | 8 +-- src/agents/tracing/processors.py | 42 ++++++++-------- src/agents/tracing/scope.py | 2 +- src/agents/tracing/setup.py | 2 +- src/agents/tracing/span_data.py | 3 +- src/agents/util/_transforms.py | 4 +- src/agents/version.py | 2 +- src/agents/voice/input.py | 6 +-- .../voice/models/openai_model_provider.py | 8 +-- src/agents/voice/models/openai_stt.py | 6 +-- src/agents/voice/pipeline.py | 3 +- src/agents/voice/result.py | 16 +++--- src/agents/voice/workflow.py | 8 +-- 32 files changed, 202 insertions(+), 213 deletions(-) diff --git a/src/agents/_run_impl.py b/src/agents/_run_impl.py index b5a83685..713ebe32 100644 --- a/src/agents/_run_impl.py +++ b/src/agents/_run_impl.py @@ -121,7 +121,7 @@ class ProcessedResponse: tools_used: list[str] # Names of all tools used, including hosted tools def has_tools_to_run(self) -> bool: - # Handoffs, functions and computer actions need local processing + # Handoffs, functions, and computer actions need local processing. # Hosted tools have already run, so there's nothing to do. return any( [ @@ -189,9 +189,9 @@ async def execute_tools_and_side_effects( cls, *, agent: Agent[TContext], - # The original input to the Runner + # The original input to the Runner. original_input: str | list[TResponseInputItem], - # Everything generated by Runner since the original input, but before the current step + # Everything generated by Runner since the original input, but before the current step. pre_step_items: list[RunItem], new_response: ModelResponse, processed_response: ProcessedResponse, @@ -200,13 +200,13 @@ async def execute_tools_and_side_effects( context_wrapper: RunContextWrapper[TContext], run_config: RunConfig, ) -> SingleStepResult: - # Make a copy of the generated items + # Make a copy of the generated items. pre_step_items = list(pre_step_items) new_step_items: list[RunItem] = [] new_step_items.extend(processed_response.new_items) - # First, lets run the tool calls - function tools and computer actions + # First, let us run the tool calls: function tools and computer actions. function_results, computer_results = await asyncio.gather( cls.execute_function_tool_calls( agent=agent, @@ -226,7 +226,7 @@ async def execute_tools_and_side_effects( new_step_items.extend([result.run_item for result in function_results]) new_step_items.extend(computer_results) - # Second, check if there are any handoffs + # Second, check whether there are any handoffs. if run_handoffs := processed_response.handoffs: return await cls.execute_handoffs( agent=agent, @@ -240,7 +240,7 @@ async def execute_tools_and_side_effects( run_config=run_config, ) - # Third, we'll check if the tool use should result in a final output + # Third, we check whether the tool use should result in a final output. check_tool_use = await cls._check_for_final_output_from_tools( agent=agent, tool_results=function_results, @@ -249,7 +249,7 @@ async def execute_tools_and_side_effects( ) if check_tool_use.is_final_output: - # If the output type is str, then let's just stringify it + # If the output type is str, then just stringify it. if not agent.output_type or agent.output_type is str: check_tool_use.final_output = str(check_tool_use.final_output) @@ -270,17 +270,17 @@ async def execute_tools_and_side_effects( context_wrapper=context_wrapper, ) - # Now we can check if the model also produced a final output + # Now we can check whether the model also produced a final output. message_items = [item for item in new_step_items if isinstance(item, MessageOutputItem)] - # We'll use the last content output as the final output + # We use the last content output as the final output. potential_final_output_text = ( ItemHelpers.extract_last_text(message_items[-1].raw_item) if message_items else None ) - # There are two possibilities that lead to a final output: - # 1. Structured output schema => always leads to a final output - # 2. Plain text output schema => only leads to a final output if there are no tool calls + # There are two possibilities that lead to a final output. + # 1. Structured output schema always leads to a final output. + # 2. Plain text output schema leads to a final output only if there are no tool calls. if output_schema and not output_schema.is_plain_text() and potential_final_output_text: final_output = output_schema.validate_json(potential_final_output_text) return await cls.execute_final_output( @@ -307,7 +307,7 @@ async def execute_tools_and_side_effects( context_wrapper=context_wrapper, ) else: - # If there's no final output, we can just run again + # If there is no final output, we can run again. return SingleStepResult( original_input=original_input, model_response=new_response, @@ -379,13 +379,13 @@ def process_model_response( logger.warning(f"Unexpected output type, ignoring: {type(output)}") continue - # At this point we know it's a function tool call + # At this point we know it is a function tool call. if not isinstance(output, ResponseFunctionToolCall): continue tools_used.append(output.name) - # Handoffs + # Handoffs. if output.name in handoff_map: items.append(HandoffCallItem(raw_item=output, agent=agent)) handoff = ToolRunHandoff( @@ -393,7 +393,7 @@ def process_model_response( handoff=handoff_map[output.name], ) run_handoffs.append(handoff) - # Regular function tool call + # Regular function tool call. else: if output.name not in function_map: _error_tracing.attach_error_to_current_span( @@ -500,7 +500,7 @@ async def execute_computer_actions( config: RunConfig, ) -> list[RunItem]: results: list[RunItem] = [] - # Need to run these serially, because each action can affect the computer state + # These must run serially because each action can affect the computer state. for action in actions: results.append( await ComputerAction.execute( @@ -528,7 +528,7 @@ async def execute_handoffs( context_wrapper: RunContextWrapper[TContext], run_config: RunConfig, ) -> SingleStepResult: - # If there is more than one handoff, add tool responses that reject those handoffs + # If there is more than one handoff, add tool responses that reject those handoffs. multiple_handoffs = len(run_handoffs) > 1 if multiple_handoffs: output_message = "Multiple handoffs detected, ignoring this one." @@ -563,7 +563,7 @@ async def execute_handoffs( ) ) - # Append a tool output item for the handoff + # Append a tool output item for the handoff. new_step_items.append( HandoffOutputItem( agent=agent, @@ -576,7 +576,7 @@ async def execute_handoffs( ) ) - # Execute handoff hooks + # Execute handoff hooks. await asyncio.gather( hooks.on_handoff( context=context_wrapper, @@ -594,7 +594,7 @@ async def execute_handoffs( ), ) - # If there's an input filter, filter the input for the next agent + # If there is an input filter, filter the input for the next agent. input_filter = handoff.input_filter or ( run_config.handoff_input_filter if run_config else None ) @@ -656,7 +656,7 @@ async def execute_final_output( hooks: RunHooks[TContext], context_wrapper: RunContextWrapper[TContext], ) -> SingleStepResult: - # Run the on_end hooks + # Run the on_end hooks. await cls.run_final_output_hooks(agent, hooks, context_wrapper, final_output) return SingleStepResult( @@ -849,7 +849,7 @@ async def execute( ), ) - # TODO: don't send a screenshot every single time, use references + # TODO: Do not send a screenshot every single time; use references. image_url = f"data:image/png;base64,{output}" return ToolCallOutputItem( agent=agent, diff --git a/src/agents/agent_output.py b/src/agents/agent_output.py index 066bbd83..ab6c3a6e 100644 --- a/src/agents/agent_output.py +++ b/src/agents/agent_output.py @@ -92,8 +92,7 @@ def __init__(self, output_type: type[Any], strict_json_schema: bool = True): self._output_schema = self._type_adapter.json_schema() return - # We should wrap for things that are not plain text, and for things that would definitely - # not be a JSON Schema object. + # We should wrap for things that are not plain text and for things that would definitely not be a JSON Schema object. self._is_wrapped = not _is_subclass_of_base_model_or_dict(output_type) if self._is_wrapped: @@ -172,11 +171,11 @@ def _is_subclass_of_base_model_or_dict(t: Any) -> bool: if not isinstance(t, type): return False - # If it's a generic alias, 'origin' will be the actual type, e.g. 'list' + # If it is a generic alias, 'origin' will be the actual type, e.g. 'list'. origin = get_origin(t) allowed_types = (BaseModel, dict) - # If it's a generic alias e.g. list[str], then we should check the origin type i.e. list + # If it is a generic alias, e.g. list[str], then we should check the origin type, i.e. list. return issubclass(origin or t, allowed_types) diff --git a/src/agents/extensions/handoff_prompt.py b/src/agents/extensions/handoff_prompt.py index cfb5ca7e..0fd6691f 100644 --- a/src/agents/extensions/handoff_prompt.py +++ b/src/agents/extensions/handoff_prompt.py @@ -1,5 +1,5 @@ -# A recommended prompt prefix for agents that use handoffs. We recommend including this or -# similar instructions in any agents that use handoffs. +# A recommended prompt prefix for agents that use handoffs. +# We recommend including this or similar instructions in any agents that use handoffs. RECOMMENDED_PROMPT_PREFIX = ( "# System context\n" "You are part of a multi-agent system called the Agents SDK, designed to make agent " diff --git a/src/agents/extensions/visualization.py b/src/agents/extensions/visualization.py index 888e262c..2e8a90d6 100644 --- a/src/agents/extensions/visualization.py +++ b/src/agents/extensions/visualization.py @@ -43,14 +43,14 @@ def get_all_nodes(agent: Agent, parent: Optional[Agent] = None) -> str: """ parts = [] - # Start and end the graph + # Start and end the graph. parts.append( '"__start__" [label="__start__", shape=ellipse, style=filled, ' "fillcolor=lightblue, width=0.5, height=0.3];" '"__end__" [label="__end__", shape=ellipse, style=filled, ' "fillcolor=lightblue, width=0.5, height=0.3];" ) - # Ensure parent agent node is colored + # Ensure the parent agent node is colored. if not parent: parts.append( f'"{agent.name}" [label="{agent.name}", shape=box, style=filled, ' diff --git a/src/agents/function_schema.py b/src/agents/function_schema.py index 0e586896..2a3d8a02 100644 --- a/src/agents/function_schema.py +++ b/src/agents/function_schema.py @@ -92,14 +92,13 @@ class FuncDocumentation: def _detect_docstring_style(doc: str) -> DocstringStyle: scores: dict[DocstringStyle, int] = {"sphinx": 0, "numpy": 0, "google": 0} - # Sphinx style detection: look for :param, :type, :return:, and :rtype: + # Sphinx style detection: look for :param, :type, :return:, and :rtype:. sphinx_patterns = [r"^:param\s", r"^:type\s", r"^:return:", r"^:rtype:"] for pattern in sphinx_patterns: if re.search(pattern, doc, re.MULTILINE): scores["sphinx"] += 1 - # Numpy style detection: look for headers like 'Parameters', 'Returns', or 'Yields' followed by - # a dashed underline + # Numpy style detection looks for headers like 'Parameters', 'Returns', or 'Yields' followed by a dashed underline. numpy_patterns = [ r"^Parameters\s*\n\s*-{3,}", r"^Returns\s*\n\s*-{3,}", @@ -109,7 +108,7 @@ def _detect_docstring_style(doc: str) -> DocstringStyle: if re.search(pattern, doc, re.MULTILINE): scores["numpy"] += 1 - # Google style detection: look for section headers with a trailing colon + # Google style detection: look for section headers with a trailing colon. google_patterns = [r"^(Args|Arguments):", r"^(Returns):", r"^(Raises):"] for pattern in google_patterns: if re.search(pattern, doc, re.MULTILINE): @@ -119,7 +118,7 @@ def _detect_docstring_style(doc: str) -> DocstringStyle: if max_score == 0: return "google" - # Priority order: sphinx > numpy > google in case of tie + # Priority order: sphinx > numpy > google in case of tie. styles: list[DocstringStyle] = ["sphinx", "numpy", "google"] for style in styles: @@ -131,7 +130,7 @@ def _detect_docstring_style(doc: str) -> DocstringStyle: @contextlib.contextmanager def _suppress_griffe_logging(): - # Suppresses warnings about missing annotations for params + # Suppresses warnings about missing annotations for params. logger = logging.getLogger("griffe") previous_level = logger.getEffectiveLevel() logger.setLevel(logging.ERROR) @@ -233,7 +232,7 @@ def function_schema( if params: first_name, first_param = params[0] - # Prefer the evaluated type hint if available + # Prefer the evaluated type hint if available. ann = type_hints.get(first_name, first_param.annotation) if ann != inspect._empty: origin = get_origin(ann) or ann @@ -244,7 +243,7 @@ def function_schema( else: filtered_params.append((first_name, first_param)) - # For parameters other than the first, raise error if any use RunContextWrapper. + # For parameters other than the first, raise an error if any use RunContextWrapper. for name, param in params[1:]: ann = type_hints.get(name, param.annotation) if ann != inspect._empty: @@ -256,7 +255,7 @@ def function_schema( ) filtered_params.append((name, param)) - # We will collect field definitions for create_model as a dict: + # We will collect field definitions for create_model as a dict. # field_name -> (type_annotation, default_value_or_Field(...)) fields: dict[str, Any] = {} @@ -264,44 +263,44 @@ def function_schema( ann = type_hints.get(name, param.annotation) default = param.default - # If there's no type hint, assume `Any` + # If there is no type hint, assume `Any`. if ann == inspect._empty: ann = Any - # If a docstring param description exists, use it + # If a docstring parameter description exists, use it. field_description = param_descs.get(name, None) - # Handle different parameter kinds + # Handle different parameter kinds. if param.kind == param.VAR_POSITIONAL: - # e.g. *args: extend positional args + # For example, *args extends positional args. if get_origin(ann) is tuple: - # e.g. def foo(*args: tuple[int, ...]) -> treat as List[int] + # For example, def foo(*args: tuple[int, ...]) -> treat as List[int]. args_of_tuple = get_args(ann) if len(args_of_tuple) == 2 and args_of_tuple[1] is Ellipsis: ann = list[args_of_tuple[0]] # type: ignore else: ann = list[Any] else: - # If user wrote *args: int, treat as List[int] + # If the user wrote *args: int, treat as List[int]. ann = list[ann] # type: ignore - # Default factory to empty list + # Default factory to an empty list. fields[name] = ( ann, Field(default_factory=list, description=field_description), # type: ignore ) elif param.kind == param.VAR_KEYWORD: - # **kwargs handling + # **kwargs handling. if get_origin(ann) is dict: - # e.g. def foo(**kwargs: dict[str, int]) + # For example, def foo(**kwargs: dict[str, int]). dict_args = get_args(ann) if len(dict_args) == 2: ann = dict[dict_args[0], dict_args[1]] # type: ignore else: ann = dict[str, Any] else: - # e.g. def foo(**kwargs: int) -> Dict[str, int] + # For example, def foo(**kwargs: int) -> Dict[str, int]. ann = dict[str, ann] # type: ignore fields[name] = ( @@ -310,15 +309,15 @@ def function_schema( ) else: - # Normal parameter + # Normal parameter. if default == inspect._empty: - # Required field + # Required field. fields[name] = ( ann, Field(..., description=field_description), ) else: - # Parameter with a default value + # Parameter with a default value. fields[name] = ( ann, Field(default=default, description=field_description), diff --git a/src/agents/guardrail.py b/src/agents/guardrail.py index a96f0f7d..c83e78a6 100644 --- a/src/agents/guardrail.py +++ b/src/agents/guardrail.py @@ -181,7 +181,7 @@ async def run( TContext_co = TypeVar("TContext_co", bound=Any, covariant=True) -# For InputGuardrail +# For InputGuardrail. _InputGuardrailFuncSync = Callable[ [RunContextWrapper[TContext_co], "Agent[Any]", Union[str, list[TResponseInputItem]]], GuardrailFunctionOutput, @@ -244,10 +244,10 @@ def decorator( return InputGuardrail(guardrail_function=f, name=name) if func is not None: - # Decorator was used without parentheses + # Decorator was used without parentheses. return decorator(func) - # Decorator used with keyword arguments + # Decorator used with keyword arguments. return decorator @@ -313,8 +313,8 @@ def decorator( return OutputGuardrail(guardrail_function=f, name=name) if func is not None: - # Decorator was used without parentheses + # Decorator was used without parentheses. return decorator(func) - # Decorator used with keyword arguments + # Decorator used with keyword arguments. return decorator diff --git a/src/agents/handoffs.py b/src/agents/handoffs.py index 686191f3..be062dee 100644 --- a/src/agents/handoffs.py +++ b/src/agents/handoffs.py @@ -222,8 +222,8 @@ async def _invoke_handoff( tool_name = tool_name_override or Handoff.default_tool_name(agent) tool_description = tool_description_override or Handoff.default_tool_description(agent) - # Always ensure the input JSON schema is in strict mode - # If there is a need, we can make this configurable in the future + # Always ensure the input JSON schema is in strict mode. + # If there is a need, we can make this configurable in the future. input_json_schema = ensure_strict_json_schema(input_json_schema) return Handoff( diff --git a/src/agents/items.py b/src/agents/items.py index 8fb2b52a..a115810e 100644 --- a/src/agents/items.py +++ b/src/agents/items.py @@ -58,7 +58,7 @@ class RunItemBase(Generic[T], abc.ABC): def to_input_item(self) -> TResponseInputItem: """Converts this item into an input item suitable for passing to the model.""" if isinstance(self.raw_item, dict): - # We know that input items are dicts, so we can ignore the type error + # We know that input items are dicts, so we can ignore the type error. return self.raw_item # type: ignore elif isinstance(self.raw_item, BaseModel): # All output items are Pydantic models that can be converted to input items. @@ -175,8 +175,7 @@ class ModelResponse: def to_input_items(self) -> list[TResponseInputItem]: """Convert the output into a list of input items suitable for passing to the model.""" - # We happen to know that the shape of the Pydantic output items are the same as the - # equivalent TypedDict input items, so we can just convert each one. + # We happen to know that the shape of the Pydantic output items is the same as the equivalent TypedDict input items, so we can just convert each one. # This is also tested via unit tests. return [it.model_dump(exclude_unset=True) for it in self.output] # type: ignore diff --git a/src/agents/mcp/server.py b/src/agents/mcp/server.py index 414b517a..95aa3c9c 100644 --- a/src/agents/mcp/server.py +++ b/src/agents/mcp/server.py @@ -77,7 +77,7 @@ def __init__(self, cache_tools_list: bool, client_session_timeout_seconds: float self.client_session_timeout_seconds = client_session_timeout_seconds - # The cache is always dirty at startup, so that we fetch tools at least once + # The cache is always dirty at startup so that we fetch tools at least once. self._cache_dirty = True self._tools_list: list[MCPTool] | None = None @@ -109,8 +109,8 @@ async def connect(self): """Connect to the server.""" try: transport = await self.exit_stack.enter_async_context(self.create_streams()) - # streamablehttp_client returns (read, write, get_session_id) - # sse_client returns (read, write) + # streamablehttp_client returns (read, write, get_session_id). + # sse_client returns (read, write). read, write, *_ = transport @@ -136,14 +136,14 @@ async def list_tools(self) -> list[MCPTool]: if not self.session: raise UserError("Server not initialized. Make sure you call `connect()` first.") - # Return from cache if caching is enabled, we have tools, and the cache is not dirty + # Return from cache if caching is enabled, we have tools, and the cache is not dirty. if self.cache_tools_list and not self._cache_dirty and self._tools_list: return self._tools_list - # Reset the cache dirty to False + # Reset the cache dirty flag to False. self._cache_dirty = False - # Fetch the tools from the server + # Fetch the tools from the server. self._tools_list = (await self.session.list_tools()).tools return self._tools_list diff --git a/src/agents/mcp/util.py b/src/agents/mcp/util.py index bbfe1885..a548119b 100644 --- a/src/agents/mcp/util.py +++ b/src/agents/mcp/util.py @@ -111,8 +111,8 @@ async def invoke_mcp_tool( else: logger.debug(f"MCP tool {tool.name} returned {result}") - # The MCP tool result is a list of content items, whereas OpenAI tool outputs are a single - # string. We'll try to convert. + # The MCP tool result is a list of content items, whereas OpenAI tool outputs are a single string. + # We will try to convert. if len(result.content) == 1: tool_output = result.content[0].model_dump_json() elif len(result.content) > 1: diff --git a/src/agents/models/chatcmpl_converter.py b/src/agents/models/chatcmpl_converter.py index 1d599e8c..49ae5dcf 100644 --- a/src/agents/models/chatcmpl_converter.py +++ b/src/agents/models/chatcmpl_converter.py @@ -126,7 +126,7 @@ def maybe_easy_input_message(cls, item: Any) -> EasyInputMessageParam | None: return None keys = item.keys() - # EasyInputMessageParam only has these two keys + # EasyInputMessageParam only has these two keys. if keys != {"content", "role"}: return None @@ -184,7 +184,7 @@ def maybe_item_reference(cls, item: Any) -> ItemReference | None: @classmethod def maybe_response_output_message(cls, item: Any) -> ResponseOutputMessageParam | None: - # ResponseOutputMessage is only used for messages with role assistant + # ResponseOutputMessage is only used for messages with the assistant role. if ( isinstance(item, dict) and item.get("type") == "message" @@ -276,7 +276,7 @@ def items_to_messages( def flush_assistant_message() -> None: nonlocal current_assistant_msg if current_assistant_msg is not None: - # The API doesn't support empty arrays for tool_calls + # The API does not support empty arrays for tool_calls. if not current_assistant_msg.get("tool_calls"): del current_assistant_msg["tool_calls"] result.append(current_assistant_msg) @@ -290,7 +290,7 @@ def ensure_assistant_message() -> ChatCompletionAssistantMessageParam: return current_assistant_msg for item in items: - # 1) Check easy input message + # Check for an easy input message. if easy_msg := cls.maybe_easy_input_message(item): role = easy_msg["role"] content = easy_msg["content"] @@ -326,7 +326,7 @@ def ensure_assistant_message() -> ChatCompletionAssistantMessageParam: else: raise UserError(f"Unexpected role in easy_input_message: {role}") - # 2) Check input message + # Check the input message. elif in_msg := cls.maybe_input_message(item): role = in_msg["role"] content = in_msg["content"] @@ -353,7 +353,7 @@ def ensure_assistant_message() -> ChatCompletionAssistantMessageParam: else: raise UserError(f"Unexpected role in input_message: {role}") - # 3) response output message => assistant + # Handle a response output message by converting it to an assistant message. elif resp_msg := cls.maybe_response_output_message(item): flush_assistant_message() new_asst = ChatCompletionAssistantMessageParam(role="assistant") @@ -366,7 +366,7 @@ def ensure_assistant_message() -> ChatCompletionAssistantMessageParam: elif c["type"] == "refusal": new_asst["refusal"] = c["refusal"] elif c["type"] == "output_audio": - # Can't handle this, b/c chat completions expects an ID which we dont have + # Cannot handle this because chat completions expect an ID that we do not have. raise UserError( f"Only audio IDs are supported for chat completions, but got: {c}" ) @@ -380,7 +380,7 @@ def ensure_assistant_message() -> ChatCompletionAssistantMessageParam: new_asst["tool_calls"] = [] current_assistant_msg = new_asst - # 4) function/file-search calls => attach to assistant + # Attach function or file-search calls to the assistant message. elif file_search := cls.maybe_file_search_call(item): asst = ensure_assistant_message() tool_calls = list(asst.get("tool_calls", [])) @@ -414,7 +414,7 @@ def ensure_assistant_message() -> ChatCompletionAssistantMessageParam: ) tool_calls.append(new_tool_call) asst["tool_calls"] = tool_calls - # 5) function call output => tool message + # Convert a function call output to a tool message. elif func_output := cls.maybe_function_tool_call_output(item): flush_assistant_message() msg: ChatCompletionToolMessageParam = { @@ -424,13 +424,13 @@ def ensure_assistant_message() -> ChatCompletionAssistantMessageParam: } result.append(msg) - # 6) item reference => handle or raise + # Handle the item reference or raise an error. elif item_ref := cls.maybe_item_reference(item): raise UserError( f"Encountered an item_reference, which is not supported: {item_ref}" ) - # 7) If we haven't recognized it => fail or ignore + # Fail or ignore if we have not recognized the item. else: raise UserError(f"Unhandled item type or structure: {item}") diff --git a/src/agents/models/chatcmpl_helpers.py b/src/agents/models/chatcmpl_helpers.py index 0cee21ec..7b2b51c5 100644 --- a/src/agents/models/chatcmpl_helpers.py +++ b/src/agents/models/chatcmpl_helpers.py @@ -16,7 +16,7 @@ def is_openai(cls, client: AsyncOpenAI): @classmethod def get_store_param(cls, client: AsyncOpenAI, model_settings: ModelSettings) -> bool | None: - # Match the behavior of Responses where store is True when not given + # Match the behavior of Responses where store is True when not given. default_store = True if cls.is_openai(client) else None return model_settings.store if model_settings.store is not None else default_store diff --git a/src/agents/models/chatcmpl_stream_handler.py b/src/agents/models/chatcmpl_stream_handler.py index c71adeb5..63a214e1 100644 --- a/src/agents/models/chatcmpl_stream_handler.py +++ b/src/agents/models/chatcmpl_stream_handler.py @@ -56,7 +56,7 @@ async def handle_stream( type="response.created", ) - # This is always set by the OpenAI API, but not by others e.g. LiteLLM + # This is always set by the OpenAI API, but not by others such as LiteLLM. usage = chunk.usage if hasattr(chunk, "usage") else None if not chunk.choices or not chunk.choices[0].delta: @@ -64,10 +64,10 @@ async def handle_stream( delta = chunk.choices[0].delta - # Handle text + # Handle text. if delta.content: if not state.text_content_index_and_output: - # Initialize a content tracker for streaming text + # Initialize a content tracker for streaming text. state.text_content_index_and_output = ( 0 if not state.refusal_content_index_and_output else 1, ResponseOutputText( @@ -76,7 +76,7 @@ async def handle_stream( annotations=[], ), ) - # Start a new assistant message stream + # Start a new assistant message stream. assistant_item = ResponseOutputMessage( id=FAKE_RESPONSES_ID, content=[], @@ -84,7 +84,7 @@ async def handle_stream( type="message", status="in_progress", ) - # Notify consumers of the start of a new output message + first content part + # Notify consumers of the start of a new output message and the first content part. yield ResponseOutputItemAddedEvent( item=assistant_item, output_index=0, @@ -101,7 +101,7 @@ async def handle_stream( ), type="response.content_part.added", ) - # Emit the delta for this segment of content + # Emit the delta for this segment of content. yield ResponseTextDeltaEvent( content_index=state.text_content_index_and_output[0], delta=delta.content, @@ -109,19 +109,19 @@ async def handle_stream( output_index=0, type="response.output_text.delta", ) - # Accumulate the text into the response part + # Accumulate the text into the response part. state.text_content_index_and_output[1].text += delta.content - # Handle refusals (model declines to answer) - # This is always set by the OpenAI API, but not by others e.g. LiteLLM + # Handle refusals (model declines to answer). + # This is always set by the OpenAI API, but not by others such as LiteLLM. if hasattr(delta, "refusal") and delta.refusal: if not state.refusal_content_index_and_output: - # Initialize a content tracker for streaming refusal text + # Initialize a content tracker for streaming refusal text. state.refusal_content_index_and_output = ( 0 if not state.text_content_index_and_output else 1, ResponseOutputRefusal(refusal="", type="refusal"), ) - # Start a new assistant message if one doesn't exist yet (in-progress) + # Start a new assistant message if one does not exist yet (in-progress). assistant_item = ResponseOutputMessage( id=FAKE_RESPONSES_ID, content=[], @@ -129,7 +129,7 @@ async def handle_stream( type="message", status="in_progress", ) - # Notify downstream that assistant message + first content part are starting + # Notify downstream that the assistant message and first content part are starting. yield ResponseOutputItemAddedEvent( item=assistant_item, output_index=0, @@ -146,7 +146,7 @@ async def handle_stream( ), type="response.content_part.added", ) - # Emit the delta for this segment of refusal + # Emit the delta for this segment of refusal. yield ResponseRefusalDeltaEvent( content_index=state.refusal_content_index_and_output[0], delta=delta.refusal, @@ -154,12 +154,11 @@ async def handle_stream( output_index=0, type="response.refusal.delta", ) - # Accumulate the refusal string in the output part + # Accumulate the refusal string in the output part. state.refusal_content_index_and_output[1].refusal += delta.refusal - # Handle tool calls - # Because we don't know the name of the function until the end of the stream, we'll - # save everything and yield events at the end + # Handle tool calls. + # Because we do not know the name of the function until the end of the stream, we save everything and yield events at the end. if delta.tool_calls: for tc_delta in delta.tool_calls: if tc_delta.index not in state.function_calls: @@ -183,7 +182,7 @@ async def handle_stream( function_call_starting_index = 0 if state.text_content_index_and_output: function_call_starting_index += 1 - # Send end event for this content part + # Send an end event for this content part. yield ResponseContentPartDoneEvent( content_index=state.text_content_index_and_output[0], item_id=FAKE_RESPONSES_ID, @@ -194,7 +193,7 @@ async def handle_stream( if state.refusal_content_index_and_output: function_call_starting_index += 1 - # Send end event for this content part + # Send an end event for this content part. yield ResponseContentPartDoneEvent( content_index=state.refusal_content_index_and_output[0], item_id=FAKE_RESPONSES_ID, @@ -203,9 +202,9 @@ async def handle_stream( type="response.content_part.done", ) - # Actually send events for the function calls + # Actually send events for the function calls. for function_call in state.function_calls.values(): - # First, a ResponseOutputItemAdded for the function call + # First, emit a ResponseOutputItemAdded for the function call. yield ResponseOutputItemAddedEvent( item=ResponseFunctionToolCall( id=FAKE_RESPONSES_ID, @@ -217,14 +216,14 @@ async def handle_stream( output_index=function_call_starting_index, type="response.output_item.added", ) - # Then, yield the args + # Then, yield the args. yield ResponseFunctionCallArgumentsDeltaEvent( delta=function_call.arguments, item_id=FAKE_RESPONSES_ID, output_index=function_call_starting_index, type="response.function_call_arguments.delta", ) - # Finally, the ResponseOutputItemDone + # Finally, emit the ResponseOutputItemDone. yield ResponseOutputItemDoneEvent( item=ResponseFunctionToolCall( id=FAKE_RESPONSES_ID, @@ -237,7 +236,7 @@ async def handle_stream( type="response.output_item.done", ) - # Finally, send the Response completed event + # Finally, send the Response completed event. outputs: list[ResponseOutputItem] = [] if state.text_content_index_and_output or state.refusal_content_index_and_output: assistant_msg = ResponseOutputMessage( @@ -253,7 +252,7 @@ async def handle_stream( assistant_msg.content.append(state.refusal_content_index_and_output[1]) outputs.append(assistant_msg) - # send a ResponseOutputItemDone for the assistant message + # Send a ResponseOutputItemDone for the assistant message. yield ResponseOutputItemDoneEvent( item=assistant_msg, output_index=0, diff --git a/src/agents/models/openai_provider.py b/src/agents/models/openai_provider.py index e7e922ab..3e9e51f2 100644 --- a/src/agents/models/openai_provider.py +++ b/src/agents/models/openai_provider.py @@ -14,8 +14,8 @@ _http_client: httpx.AsyncClient | None = None -# If we create a new httpx client for each request, that would mean no sharing of connection pools, -# which would mean worse latency and resource usage. So, we share the client across requests. +# If we create a new HTTPX client for each request, we would not share connection pools. +# This would result in worse latency and resource usage, so we share the client across requests. def shared_http_client() -> httpx.AsyncClient: global _http_client if _http_client is None: @@ -64,8 +64,8 @@ def __init__( else: self._use_responses = _openai_shared.get_use_responses_by_default() - # We lazy load the client in case you never actually use OpenAIProvider(). Otherwise - # AsyncOpenAI() raises an error if you don't have an API key set. + # We lazy load the client in case you never actually use OpenAIProvider(). + # Otherwise, AsyncOpenAI() raises an error if you do not have an API key set. def _get_client(self) -> AsyncOpenAI: if self._client is None: self._client = _openai_shared.get_default_openai_client() or AsyncOpenAI( diff --git a/src/agents/models/openai_responses.py b/src/agents/models/openai_responses.py index c1ff85b9..3dc6f30b 100644 --- a/src/agents/models/openai_responses.py +++ b/src/agents/models/openai_responses.py @@ -36,7 +36,7 @@ _USER_AGENT = f"Agents/Python {__version__}" _HEADERS = {"User-Agent": _USER_AGENT} -# From the Responses API +# From the Responses API. IncludeLiteral = Literal[ "file_search_call.results", "message.input_image.image_url", diff --git a/src/agents/result.py b/src/agents/result.py index 243db155..ff45cd3e 100644 --- a/src/agents/result.py +++ b/src/agents/result.py @@ -135,7 +135,7 @@ class RunResultStreaming(RunResultBase): is_complete: bool = False """Whether the agent has finished running.""" - # Queues that the background run_loop writes to + # Queues that the background run_loop writes to. _event_queue: asyncio.Queue[StreamEvent | QueueCompleteSentinel] = field( default_factory=asyncio.Queue, repr=False ) @@ -143,7 +143,7 @@ class RunResultStreaming(RunResultBase): default_factory=asyncio.Queue, repr=False ) - # Store the asyncio tasks that we're waiting on + # Store the asyncio tasks that we are waiting on. _run_impl_task: asyncio.Task[Any] | None = field(default=None, repr=False) _input_guardrails_task: asyncio.Task[Any] | None = field(default=None, repr=False) _output_guardrails_task: asyncio.Task[Any] | None = field(default=None, repr=False) @@ -159,10 +159,10 @@ def last_agent(self) -> Agent[Any]: def cancel(self) -> None: """Cancels the streaming run, stopping all background tasks and marking the run as complete.""" - self._cleanup_tasks() # Cancel all running tasks - self.is_complete = True # Mark the run as complete to stop event streaming + self._cleanup_tasks() # Cancel all running tasks. + self.is_complete = True # Mark the run as complete to stop event streaming. - # Optionally, clear the event queue to prevent processing stale events + # Optionally, clear the event queue to prevent processing stale events. while not self._event_queue.empty(): self._event_queue.get_nowait() while not self._input_guardrail_queue.empty(): @@ -194,7 +194,7 @@ async def stream_events(self) -> AsyncIterator[StreamEvent]: if isinstance(item, QueueCompleteSentinel): self._event_queue.task_done() - # Check for errors, in case the queue was completed due to an exception + # Check for errors in case the queue was completed due to an exception. self._check_errors() break @@ -210,13 +210,13 @@ def _check_errors(self): if self.current_turn > self.max_turns: self._stored_exception = MaxTurnsExceeded(f"Max turns ({self.max_turns}) exceeded") - # Fetch all the completed guardrail results from the queue and raise if needed + # Fetch all the completed guardrail results from the queue and raise if needed. while not self._input_guardrail_queue.empty(): guardrail_result = self._input_guardrail_queue.get_nowait() if guardrail_result.output.tripwire_triggered: self._stored_exception = InputGuardrailTripwireTriggered(guardrail_result) - # Check the tasks for any exceptions + # Check the tasks for any exceptions. if self._run_impl_task and self._run_impl_task.done(): exc = self._run_impl_task.exception() if exc and isinstance(exc, Exception): diff --git a/src/agents/run.py b/src/agents/run.py index 849da7bf..c732569e 100644 --- a/src/agents/run.py +++ b/src/agents/run.py @@ -180,8 +180,8 @@ async def run( try: while True: - # Start an agent span if we don't have one. This span is ended if the current - # agent changes, or if the agent loop ends. + # Start an agent span if we do not have one. This span ends when the current + # agent changes or when the agent loop ends. if current_span is None: handoff_names = [h.agent_name for h in cls._get_handoffs(current_agent)] if output_schema := cls._get_output_schema(current_agent): @@ -391,9 +391,8 @@ def run_streamed( if run_config is None: run_config = RunConfig() - # If there's already a trace, we don't create a new one. In addition, we can't end the - # trace here, because the actual work is done in `stream_events` and this method ends - # before that. + # If there is already a trace, we do not create a new one. In addition, we cannot end the + # trace here because the actual work is done in `stream_events` and this method ends before that. new_trace = ( None if get_current_trace() @@ -454,7 +453,7 @@ async def _run_input_guardrails_with_queue( ): queue = streamed_result._input_guardrail_queue - # We'll run the guardrails and push them onto the queue as they complete + # We run the guardrails and push them onto the queue as they complete. guardrail_tasks = [ asyncio.create_task( RunImpl.run_single_input_guardrail(agent, guardrail, input, context) @@ -513,8 +512,8 @@ async def _run_streamed_impl( if streamed_result.is_complete: break - # Start an agent span if we don't have one. This span is ended if the current - # agent changes, or if the agent loop ends. + # Start an agent span if we do not have one. This span ends when the current + # agent changes or when the agent loop ends. if current_span is None: handoff_names = [h.agent_name for h in cls._get_handoffs(current_agent)] if output_schema := cls._get_output_schema(current_agent): @@ -547,7 +546,7 @@ async def _run_streamed_impl( break if current_turn == 1: - # Run the input guardrails in the background and put the results on the queue + # Run the input guardrails in the background and put the results on the queue. streamed_result._input_guardrails_task = asyncio.create_task( cls._run_input_guardrails_with_queue( starting_agent, @@ -600,7 +599,7 @@ async def _run_streamed_impl( try: output_guardrail_results = await streamed_result._output_guardrails_task except Exception: - # Exceptions will be checked in the stream_events loop + # Exceptions will be checked in the stream_events loop. output_guardrail_results = [] streamed_result.output_guardrail_results = output_guardrail_results @@ -669,7 +668,7 @@ async def _run_single_turn_streamed( input = ItemHelpers.input_to_new_input_list(streamed_result.input) input.extend([item.to_input_item() for item in streamed_result.new_items]) - # 1. Stream the output events + # 1. Stream the output events. async for event in model.stream_response( system_prompt, input, @@ -706,7 +705,7 @@ async def _run_single_turn_streamed( if not final_response: raise ModelBehaviorError("Model did not produce a final response!") - # 3. Now, we can process the turn as we do in the non-streaming case + # 3. Now we can process the turn as we do in the non-streaming case. single_step_result = await cls._get_single_step_result_from_response( agent=agent, original_input=streamed_result.input, @@ -739,7 +738,7 @@ async def _run_single_turn( tool_use_tracker: AgentToolUseTracker, previous_response_id: str | None, ) -> SingleStepResult: - # Ensure we run the hooks before anything else + # Ensure we run the hooks before anything else. if should_run_agent_start_hooks: await asyncio.gather( hooks.on_agent_start(context_wrapper, agent), diff --git a/src/agents/strict_schema.py b/src/agents/strict_schema.py index 3f37660a..880ec088 100644 --- a/src/agents/strict_schema.py +++ b/src/agents/strict_schema.py @@ -26,7 +26,7 @@ def ensure_strict_json_schema( return _ensure_strict_json_schema(schema, path=(), root=schema) -# Adapted from https://github.com/openai/openai-python/blob/main/src/openai/lib/_pydantic.py +# Adapted from https://github.com/openai/openai-python/blob/main/src/openai/lib/_pydantic.py. def _ensure_strict_json_schema( json_schema: object, *, @@ -63,8 +63,8 @@ def _ensure_strict_json_schema( "to not use a strict schema." ) - # object types - # { 'type': 'object', 'properties': { 'a': {...} } } + # Object types. + # { 'type': 'object', 'properties': { 'a': {...} } }. properties = json_schema.get("properties") if is_dict(properties): json_schema["required"] = list(properties.keys()) @@ -73,13 +73,13 @@ def _ensure_strict_json_schema( for key, prop_schema in properties.items() } - # arrays - # { 'type': 'array', 'items': {...} } + # Arrays. + # { 'type': 'array', 'items': {...} }. items = json_schema.get("items") if is_dict(items): json_schema["items"] = _ensure_strict_json_schema(items, path=(*path, "items"), root=root) - # unions + # Unions. any_of = json_schema.get("anyOf") if is_list(any_of): json_schema["anyOf"] = [ @@ -87,7 +87,7 @@ def _ensure_strict_json_schema( for i, variant in enumerate(any_of) ] - # intersections + # Intersections. all_of = json_schema.get("allOf") if is_list(all_of): if len(all_of) == 1: @@ -101,17 +101,15 @@ def _ensure_strict_json_schema( for i, entry in enumerate(all_of) ] - # strip `None` defaults as there's no meaningful distinction here - # the schema will still be `nullable` and the model will default - # to using `None` anyway + # Strip `None` defaults as there is no meaningful distinction here. + # The schema will still be `nullable` and the model will default to using `None` anyway. if json_schema.get("default", NOT_GIVEN) is None: json_schema.pop("default") - # we can't use `$ref`s if there are also other properties defined, e.g. - # `{"$ref": "...", "description": "my description"}` - # - # so we unravel the ref - # `{"type": "string", "description": "my description"}` + # We cannot use `$ref`s if there are also other properties defined, e.g. + # `{"$ref": "...", "description": "my description"}`. + # So we unravel the ref. + # `{"type": "string", "description": "my description"}`. ref = json_schema.get("$ref") if ref and has_more_than_n_keys(json_schema, 1): assert isinstance(ref, str), f"Received non-string $ref - {ref}" @@ -122,11 +120,10 @@ def _ensure_strict_json_schema( f"Expected `$ref: {ref}` to resolved to a dictionary but got {resolved}" ) - # properties from the json schema take priority over the ones on the `$ref` + # Properties from the JSON schema take priority over the ones on the `$ref`. json_schema.update({**resolved, **json_schema}) json_schema.pop("$ref") - # Since the schema expanded from `$ref` might not have `additionalProperties: false` applied - # we call `_ensure_strict_json_schema` again to fix the inlined schema and ensure it's valid + # Since the schema expanded from `$ref` might not have `additionalProperties: false` applied, we call `_ensure_strict_json_schema` again to fix the inlined schema and ensure it is valid. return _ensure_strict_json_schema(json_schema, path=path, root=root) return json_schema @@ -149,8 +146,7 @@ def resolve_ref(*, root: dict[str, object], ref: str) -> object: def is_dict(obj: object) -> TypeGuard[dict[str, object]]: - # just pretend that we know there are only `str` keys - # as that check is not worth the performance cost + # Just pretend that we know there are only `str` keys, as that check is not worth the performance cost. return isinstance(obj, dict) diff --git a/src/agents/tool.py b/src/agents/tool.py index c1c16242..0565393a 100644 --- a/src/agents/tool.py +++ b/src/agents/tool.py @@ -299,11 +299,11 @@ async def _on_invoke_tool(ctx: RunContextWrapper[Any], input: str) -> Any: strict_json_schema=strict_mode, ) - # If func is actually a callable, we were used as @function_tool with no parentheses + # If func is actually a callable, we were used as @function_tool with no parentheses. if callable(func): return _create_function_tool(func) - # Otherwise, we were used as @function_tool(...), so return a decorator + # Otherwise, we were used as @function_tool(...), so return a decorator. def decorator(real_func: ToolFunction[...]) -> FunctionTool: return _create_function_tool(real_func) diff --git a/src/agents/tracing/__init__.py b/src/agents/tracing/__init__.py index 9df94426..d345e494 100644 --- a/src/agents/tracing/__init__.py +++ b/src/agents/tracing/__init__.py @@ -104,10 +104,10 @@ def set_tracing_export_api_key(api_key: str) -> None: default_exporter().set_api_key(api_key) -# Add the default processor, which exports traces and spans to the backend in batches. You can -# change the default behavior by either: -# 1. calling add_trace_processor(), which adds additional processors, or -# 2. calling set_trace_processors(), which replaces the default processor. +# Add the default processor, which exports traces and spans to the backend in batches. +# Change the default behavior by either of the following. +# 1. Calling add_trace_processor(), which adds additional processors. +# 2. Calling set_trace_processors(), which replaces the default processor. add_trace_processor(default_processor()) atexit.register(GLOBAL_TRACE_PROVIDER.shutdown) diff --git a/src/agents/tracing/processors.py b/src/agents/tracing/processors.py index 2913b11a..658ad506 100644 --- a/src/agents/tracing/processors.py +++ b/src/agents/tracing/processors.py @@ -59,7 +59,7 @@ def __init__( self.base_delay = base_delay self.max_delay = max_delay - # Keep a client open for connection pooling across multiple export calls + # Keep a client open for connection pooling across multiple export calls. self._client = httpx.Client(timeout=httpx.Timeout(timeout=60, connect=5.0)) def set_api_key(self, api_key: str): @@ -69,7 +69,7 @@ def set_api_key(self, api_key: str): api_key: The OpenAI API key to use. This is the same key used by the OpenAI Python client. """ - # We're specifically setting the underlying cached property as well + # We are specifically setting the underlying cached property as well. self._api_key = api_key self.api_key = api_key @@ -108,7 +108,7 @@ def export(self, items: list[Trace | Span[Any]]) -> None: if self.project: headers["OpenAI-Project"] = self.project - # Exponential backoff loop + # Exponential backoff loop. attempt = 0 delay = self.base_delay while True: @@ -116,32 +116,32 @@ def export(self, items: list[Trace | Span[Any]]) -> None: try: response = self._client.post(url=self.endpoint, headers=headers, json=payload) - # If the response is successful, break out of the loop + # If the response is successful, break out of the loop. if response.status_code < 300: logger.debug(f"Exported {len(items)} items") return - # If the response is a client error (4xx), we wont retry + # If the response is a client error (4xx), we will not retry. if 400 <= response.status_code < 500: logger.error( f"[non-fatal] Tracing client error {response.status_code}: {response.text}" ) return - # For 5xx or other unexpected codes, treat it as transient and retry + # For 5xx or other unexpected codes, treat it as transient and retry. logger.warning( f"[non-fatal] Tracing: server error {response.status_code}, retrying." ) except httpx.RequestError as exc: - # Network or other I/O error, we'll retry + # Network or other I/O error; we will retry. logger.warning(f"[non-fatal] Tracing: request failed: {exc}") - # If we reach here, we need to retry or give up + # If we reach here, we need to retry or give up. if attempt >= self.max_retries: logger.error("[non-fatal] Tracing: max retries reached, giving up on this batch.") return - # Exponential backoff + jitter + # Exponential backoff plus jitter. sleep_time = delay + random.uniform(0, 0.1 * delay) # 10% jitter time.sleep(sleep_time) delay = min(delay * 2, self.max_delay) @@ -185,7 +185,7 @@ def __init__( # The queue size threshold at which we export immediately. self._export_trigger_size = int(max_queue_size * export_trigger_ratio) - # Track when we next *must* perform a scheduled export + # Track when we next must perform a scheduled export. self._next_export_time = time.time() + self._schedule_delay self._worker_thread = threading.Thread(target=self._run, daemon=True) @@ -198,11 +198,11 @@ def on_trace_start(self, trace: Trace) -> None: logger.warning("Queue is full, dropping trace.") def on_trace_end(self, trace: Trace) -> None: - # We send traces via on_trace_start, so we don't need to do anything here. + # We send traces via on_trace_start, so we do not need to do anything here. pass def on_span_start(self, span: Span[Any]) -> None: - # We send spans via on_span_end, so we don't need to do anything here. + # We send spans via on_span_end, so we do not need to do anything here. pass def on_span_end(self, span: Span[Any]) -> None: @@ -229,16 +229,16 @@ def _run(self): current_time = time.time() queue_size = self._queue.qsize() - # If it's time for a scheduled flush or queue is above the trigger threshold + # If it is time for a scheduled flush or the queue is above the trigger threshold. if current_time >= self._next_export_time or queue_size >= self._export_trigger_size: self._export_batches(force=False) - # Reset the next scheduled flush time + # Reset the next scheduled flush time. self._next_export_time = time.time() + self._schedule_delay else: - # Sleep a short interval so we don't busy-wait. + # Sleep a short interval so we do not busy-wait. time.sleep(0.2) - # Final drain after shutdown + # Final drain after shutdown. self._export_batches(force=True) def _export_batches(self, force: bool = False): @@ -249,25 +249,25 @@ def _export_batches(self, force: bool = False): while True: items_to_export: list[Span[Any] | Trace] = [] - # Gather a batch of spans up to max_batch_size + # Gather a batch of spans up to max_batch_size. while not self._queue.empty() and ( force or len(items_to_export) < self._max_batch_size ): try: items_to_export.append(self._queue.get_nowait()) except queue.Empty: - # Another thread might have emptied the queue between checks + # Another thread might have emptied the queue between checks. break - # If we collected nothing, we're done + # If we collected nothing, we are done. if not items_to_export: break - # Export the batch + # Export the batch. self._exporter.export(items_to_export) -# Create a shared global instance: +# Create a shared global instance. _global_exporter = BackendSpanExporter() _global_processor = BatchTraceProcessor(_global_exporter) diff --git a/src/agents/tracing/scope.py b/src/agents/tracing/scope.py index 1d31c1bd..d9ea3601 100644 --- a/src/agents/tracing/scope.py +++ b/src/agents/tracing/scope.py @@ -1,4 +1,4 @@ -# Holds the current active span +# Holds the current active span. import contextvars from typing import TYPE_CHECKING, Any diff --git a/src/agents/tracing/setup.py b/src/agents/tracing/setup.py index 9e27d210..0ed2a9a2 100644 --- a/src/agents/tracing/setup.py +++ b/src/agents/tracing/setup.py @@ -18,7 +18,7 @@ class SynchronousMultiTracingProcessor(TracingProcessor): """ def __init__(self): - # Using a tuple to avoid race conditions when iterating over processors + # Use a tuple to avoid race conditions when iterating over processors. self._processors: tuple[TracingProcessor, ...] = () self._lock = threading.Lock() diff --git a/src/agents/tracing/span_data.py b/src/agents/tracing/span_data.py index cb3e8491..5997c25f 100644 --- a/src/agents/tracing/span_data.py +++ b/src/agents/tracing/span_data.py @@ -150,8 +150,7 @@ def __init__( input: str | list[ResponseInputItemParam] | None = None, ) -> None: self.response = response - # This is not used by the OpenAI trace processors, but is useful for other tracing - # processor implementations + # This is not used by the OpenAI trace processors, but it is useful for other tracing processor implementations. self.input = input @property diff --git a/src/agents/util/_transforms.py b/src/agents/util/_transforms.py index b303074d..928741c6 100644 --- a/src/agents/util/_transforms.py +++ b/src/agents/util/_transforms.py @@ -2,10 +2,10 @@ def transform_string_function_style(name: str) -> str: - # Replace spaces with underscores + # Replace spaces with underscores. name = name.replace(" ", "_") - # Replace non-alphanumeric characters with underscores + # Replace non-alphanumeric characters with underscores. name = re.sub(r"[^a-zA-Z0-9]", "_", name) return name.lower() diff --git a/src/agents/version.py b/src/agents/version.py index 9b22499e..3b63188e 100644 --- a/src/agents/version.py +++ b/src/agents/version.py @@ -3,5 +3,5 @@ try: __version__ = importlib.metadata.version("openai-agents") except importlib.metadata.PackageNotFoundError: - # Fallback if running from source without being installed + # Fallback if running from source without being installed. __version__ = "0.0.0" diff --git a/src/agents/voice/input.py b/src/agents/voice/input.py index 8613d27a..ffe4819e 100644 --- a/src/agents/voice/input.py +++ b/src/agents/voice/input.py @@ -19,7 +19,7 @@ def _buffer_to_audio_file( channels: int = 1, ) -> tuple[str, io.BytesIO, str]: if buffer.dtype == np.float32: - # convert to int16 + # Convert to int16. buffer = np.clip(buffer, -1.0, 1.0) buffer = (buffer * 32767).astype(np.int16) elif buffer.dtype != np.int16: @@ -33,7 +33,7 @@ def _buffer_to_audio_file( wav_file.writeframes(buffer.tobytes()) audio_file.seek(0) - # (filename, bytes, content_type) + # (filename, bytes, content_type). return ("audio.wav", audio_file, "audio/wav") @@ -62,7 +62,7 @@ def to_audio_file(self) -> tuple[str, io.BytesIO, str]: def to_base64(self) -> str: """Returns the audio data as a base64 encoded string.""" if self.buffer.dtype == np.float32: - # convert to int16 + # Convert to int16. self.buffer = np.clip(self.buffer, -1.0, 1.0) self.buffer = (self.buffer * 32767).astype(np.int16) elif self.buffer.dtype != np.int16: diff --git a/src/agents/voice/models/openai_model_provider.py b/src/agents/voice/models/openai_model_provider.py index 094df4cc..f8c4371f 100644 --- a/src/agents/voice/models/openai_model_provider.py +++ b/src/agents/voice/models/openai_model_provider.py @@ -11,8 +11,8 @@ _http_client: httpx.AsyncClient | None = None -# If we create a new httpx client for each request, that would mean no sharing of connection pools, -# which would mean worse latency and resource usage. So, we share the client across requests. +# If we create a new HTTPX client for each request, we would not share connection pools. +# This would result in worse latency and resource usage, so we share the client across requests. def shared_http_client() -> httpx.AsyncClient: global _http_client if _http_client is None: @@ -60,8 +60,8 @@ def __init__( self._stored_organization = organization self._stored_project = project - # We lazy load the client in case you never actually use OpenAIProvider(). Otherwise - # AsyncOpenAI() raises an error if you don't have an API key set. + # We lazy load the client in case you never actually use OpenAIProvider(). + # Otherwise, AsyncOpenAI() raises an error if you do not have an API key set. def _get_client(self) -> AsyncOpenAI: if self._client is None: self._client = _openai_shared.get_default_openai_client() or AsyncOpenAI( diff --git a/src/agents/voice/models/openai_stt.py b/src/agents/voice/models/openai_stt.py index 1ae4ea14..dd8ab3a7 100644 --- a/src/agents/voice/models/openai_stt.py +++ b/src/agents/voice/models/openai_stt.py @@ -42,7 +42,7 @@ class WebsocketDoneSentinel: def _audio_to_base64(audio_data: list[npt.NDArray[np.int16 | np.float32]]) -> str: concatenated_audio = np.concatenate(audio_data) if concatenated_audio.dtype == np.float32: - # convert to int16 + # Convert to int16. concatenated_audio = np.clip(concatenated_audio, -1.0, 1.0) concatenated_audio = (concatenated_audio * 32767).astype(np.int16) audio_bytes = concatenated_audio.tobytes() @@ -98,7 +98,7 @@ def __init__( self._turn_audio_buffer: list[npt.NDArray[np.int16 | np.float32]] = [] self._tracing_span: Span[TranscriptionSpanData] | None = None - # tasks + # Tasks. self._listener_task: asyncio.Task[Any] | None = None self._process_events_task: asyncio.Task[Any] | None = None self._stream_audio_task: asyncio.Task[Any] | None = None @@ -222,7 +222,7 @@ async def _handle_events(self) -> None: self._event_queue.get(), timeout=EVENT_INACTIVITY_TIMEOUT ) if isinstance(event, WebsocketDoneSentinel): - # processed all events and websocket is done + # Processed all events and the WebSocket is done. break event_type = event.get("type", "unknown") diff --git a/src/agents/voice/pipeline.py b/src/agents/voice/pipeline.py index d1dac57c..85babc2d 100644 --- a/src/agents/voice/pipeline.py +++ b/src/agents/voice/pipeline.py @@ -84,8 +84,7 @@ async def _process_audio_input(self, audio_input: AudioInput) -> str: ) async def _run_single_turn(self, audio_input: AudioInput) -> StreamedAudioResult: - # Since this is single turn, we can use the TraceCtxManager to manage starting/ending the - # trace + # Since this is single turn, we can use the TraceCtxManager to manage starting and ending the trace. with TraceCtxManager( workflow_name=self.config.workflow_name or "Voice Agent", trace_id=None, # Automatically generated diff --git a/src/agents/voice/result.py b/src/agents/voice/result.py index fea79902..59e57c78 100644 --- a/src/agents/voice/result.py +++ b/src/agents/voice/result.py @@ -54,9 +54,9 @@ def __init__( self._tasks: list[asyncio.Task[Any]] = [] self._ordered_tasks: list[ asyncio.Queue[VoiceStreamEvent | None] - ] = [] # New: list to hold local queues for each text segment + ] = [] # New: list to hold local queues for each text segment. self._dispatcher_task: asyncio.Task[Any] | None = ( - None # Task to dispatch audio chunks in order + None # Task to dispatch audio chunks in order. ) self._done_processing = False @@ -133,13 +133,13 @@ async def _stream_audio( audio_np = self.tts_settings.transform_data(audio_np) await local_queue.put( VoiceStreamEventAudio(data=audio_np) - ) # Use local queue + ) # Use local queue. buffer = [] if buffer: audio_np = self._transform_audio_buffer(buffer, self.tts_settings.dtype) if self.tts_settings.transform_data: audio_np = self.tts_settings.transform_data(audio_np) - await local_queue.put(VoiceStreamEventAudio(data=audio_np)) # Use local queue + await local_queue.put(VoiceStreamEventAudio(data=audio_np)) # Use local queue. if self._voice_pipeline_config.trace_include_sensitive_audio_data: tts_span.span_data.output = _audio_to_base64(full_audio_data) @@ -149,7 +149,7 @@ async def _stream_audio( if finish_turn: await local_queue.put(VoiceStreamEventLifecycle(event="turn_ended")) else: - await local_queue.put(None) # Signal completion for this segment + await local_queue.put(None) # Signal completion for this segment. except Exception as e: tts_span.set_error( { @@ -163,7 +163,7 @@ async def _stream_audio( ) logger.error(f"Error streaming audio: {e}") - # Signal completion for whole session because of error + # Signal completion for the whole session because of an error. await local_queue.put(VoiceStreamEventLifecycle(event="session_ended")) raise e @@ -188,7 +188,7 @@ async def _add_text(self, text: str): async def _turn_done(self): if self._text_buffer: local_queue: asyncio.Queue[VoiceStreamEvent | None] = asyncio.Queue() - self._ordered_tasks.append(local_queue) # Append the local queue for the final segment + self._ordered_tasks.append(local_queue) # Append the local queue for the final segment. self._tasks.append( asyncio.create_task( self._stream_audio(self._text_buffer, local_queue, finish_turn=True) @@ -217,7 +217,7 @@ async def _done(self): await self._wait_for_completion() async def _dispatch_audio(self): - # Dispatch audio chunks from each segment in the order they were added + # Dispatch audio chunks from each segment in the order they were added. while True: if len(self._ordered_tasks) == 0: if self._completed_session: diff --git a/src/agents/voice/workflow.py b/src/agents/voice/workflow.py index c706ec41..99fd7ba8 100644 --- a/src/agents/voice/workflow.py +++ b/src/agents/voice/workflow.py @@ -73,7 +73,7 @@ async def run(self, transcription: str) -> AsyncIterator[str]: if self._callbacks: self._callbacks.on_run(self, transcription) - # Add the transcription to the input history + # Add the transcription to the input history. self._input_history.append( { "role": "user", @@ -81,13 +81,13 @@ async def run(self, transcription: str) -> AsyncIterator[str]: } ) - # Run the agent + # Run the agent. result = Runner.run_streamed(self._current_agent, self._input_history) - # Stream the text from the result + # Stream the text from the result. async for chunk in VoiceWorkflowHelper.stream_text_from(result): yield chunk - # Update the input history and current agent + # Update the input history and current agent. self._input_history = result.to_input_list() self._current_agent = result.last_agent From 7b67ddf2aa2b32d9f874eb4c5ff5e3e18509dc16 Mon Sep 17 00:00:00 2001 From: Tom Green Date: Tue, 20 May 2025 04:40:04 +1000 Subject: [PATCH 17/24] Add ExecutiveAssistant agent and Deepgram voice support --- agents/executive_assistant/__init__.py | 37 ++++++++++++++ agents/executive_assistant/memory.py | 40 +++++++++++++++ agents/executive_assistant/rag.py | 18 +++++++ agents/executive_assistant/tools.py | 17 +++++++ agents/pyproject.toml | 7 +++ src/agents/voice/__init__.py | 6 +++ .../voice/models/deepgram_model_provider.py | 33 ++++++++++++ src/agents/voice/models/deepgram_stt.py | 51 +++++++++++++++++++ src/agents/voice/models/deepgram_tts.py | 29 +++++++++++ tests/executive_assistant/test_agent.py | 27 ++++++++++ tests/voice/test_deepgram_models.py | 14 +++++ 11 files changed, 279 insertions(+) create mode 100644 agents/executive_assistant/__init__.py create mode 100644 agents/executive_assistant/memory.py create mode 100644 agents/executive_assistant/rag.py create mode 100644 agents/executive_assistant/tools.py create mode 100644 agents/pyproject.toml create mode 100644 src/agents/voice/models/deepgram_model_provider.py create mode 100644 src/agents/voice/models/deepgram_stt.py create mode 100644 src/agents/voice/models/deepgram_tts.py create mode 100644 tests/executive_assistant/test_agent.py create mode 100644 tests/voice/test_deepgram_models.py diff --git a/agents/executive_assistant/__init__.py b/agents/executive_assistant/__init__.py new file mode 100644 index 00000000..a3f3a107 --- /dev/null +++ b/agents/executive_assistant/__init__.py @@ -0,0 +1,37 @@ +from __future__ import annotations + +# mypy: ignore-errors + +"""Executive Assistant agent. + +This agent orchestrates other agents and provides voice capabilities using +Deepgram STT and TTS models. It maintains short-term and long-term memory and +can retrieve information via a simple RAG component. +""" +from agents import Agent # noqa: E402 + +from .memory import LongTermMemory, ShortTermMemory # noqa: E402 +from .rag import Retriever # noqa: E402 +from .tools import get_calendar_events, send_email # noqa: E402 + + +class ExecutiveAssistantState: + """Holds resources used by the Executive Assistant.""" + + def __init__(self, memory_path: str = "memory.json") -> None: + self.short_memory = ShortTermMemory() + self.long_memory = LongTermMemory(memory_path) + self.retriever = Retriever() + + +executive_assistant_agent = Agent( + name="ExecutiveAssistant", + instructions=( + "You are an executive assistant. Use the available tools to help the user. " + "Remember important facts during the conversation for later retrieval." + ), + model="gpt-4o-mini", + tools=[get_calendar_events, send_email], +) + +__all__ = ["ExecutiveAssistantState", "executive_assistant_agent"] diff --git a/agents/executive_assistant/memory.py b/agents/executive_assistant/memory.py new file mode 100644 index 00000000..5b286718 --- /dev/null +++ b/agents/executive_assistant/memory.py @@ -0,0 +1,40 @@ +from __future__ import annotations + +import json +from pathlib import Path +from typing import Any + + +class ShortTermMemory: + """In-memory store for conversation turns.""" + + def __init__(self) -> None: + self._messages: list[dict[str, str]] = [] + + def add(self, role: str, content: str) -> None: + """Add a message to memory.""" + self._messages.append({"role": role, "content": content}) + + def to_list(self) -> list[dict[str, str]]: + """Return the last 20 messages.""" + return self._messages[-20:] + + +class LongTermMemory: + """Simple file backed memory store.""" + + def __init__(self, path: str | Path) -> None: + self._path = Path(path) + if self._path.exists(): + self._data = json.loads(self._path.read_text()) + else: + self._data = [] + + def add(self, item: Any) -> None: + """Persist an item to disk.""" + self._data.append(item) + self._path.write_text(json.dumps(self._data)) + + def all(self) -> list[Any]: + """Return all persisted items.""" + return list(self._data) diff --git a/agents/executive_assistant/rag.py b/agents/executive_assistant/rag.py new file mode 100644 index 00000000..c009fffa --- /dev/null +++ b/agents/executive_assistant/rag.py @@ -0,0 +1,18 @@ +from __future__ import annotations + +from collections.abc import Iterable + + +class Retriever: + """Very small RAG retriever stub.""" + + def __init__(self, corpus: Iterable[str] | None = None) -> None: + self._corpus = list(corpus or []) + + def add(self, document: str) -> None: + """Add a document to the corpus.""" + self._corpus.append(document) + + def search(self, query: str) -> list[str]: + """Return documents containing the query string.""" + return [doc for doc in self._corpus if query.lower() in doc.lower()] diff --git a/agents/executive_assistant/tools.py b/agents/executive_assistant/tools.py new file mode 100644 index 00000000..1964edaa --- /dev/null +++ b/agents/executive_assistant/tools.py @@ -0,0 +1,17 @@ +from __future__ import annotations + +from agents import function_tool + + +@function_tool +def get_calendar_events(date: str) -> str: + """Retrieve calendar events for a given date.""" + # TODO: Integrate with calendar API. + return f"No events found for {date}." + + +@function_tool +def send_email(recipient: str, subject: str, body: str) -> str: + """Send a simple email.""" + # TODO: Integrate with email service. + return "Email sent." diff --git a/agents/pyproject.toml b/agents/pyproject.toml new file mode 100644 index 00000000..8693da80 --- /dev/null +++ b/agents/pyproject.toml @@ -0,0 +1,7 @@ +[project] +name = "custom-agents" +version = "0.0.0" +requires-python = ">=3.9" + +[tool.hatch.build.targets.wheel] +packages = ["executive_assistant"] diff --git a/src/agents/voice/__init__.py b/src/agents/voice/__init__.py index e11ee446..abaa98db 100644 --- a/src/agents/voice/__init__.py +++ b/src/agents/voice/__init__.py @@ -10,6 +10,9 @@ TTSVoice, VoiceModelProvider, ) +from .models.deepgram_model_provider import DeepgramVoiceModelProvider +from .models.deepgram_stt import DeepgramSTTModel +from .models.deepgram_tts import DeepgramTTSModel from .models.openai_model_provider import OpenAIVoiceModelProvider from .models.openai_stt import OpenAISTTModel, OpenAISTTTranscriptionSession from .models.openai_tts import OpenAITTSModel @@ -38,6 +41,9 @@ "OpenAIVoiceModelProvider", "OpenAISTTModel", "OpenAITTSModel", + "DeepgramVoiceModelProvider", + "DeepgramSTTModel", + "DeepgramTTSModel", "VoiceStreamEventAudio", "VoiceStreamEventLifecycle", "VoiceStreamEvent", diff --git a/src/agents/voice/models/deepgram_model_provider.py b/src/agents/voice/models/deepgram_model_provider.py new file mode 100644 index 00000000..cd9e9e19 --- /dev/null +++ b/src/agents/voice/models/deepgram_model_provider.py @@ -0,0 +1,33 @@ +from __future__ import annotations + +import httpx # type: ignore + +from ..model import STTModel, TTSModel, VoiceModelProvider +from .deepgram_stt import DeepgramSTTModel +from .deepgram_tts import DeepgramTTSModel + +DEFAULT_STT_MODEL = "nova-3" +DEFAULT_TTS_MODEL = "aura-2" + + +class DeepgramVoiceModelProvider(VoiceModelProvider): + """Voice model provider for Deepgram APIs.""" + + def __init__(self, api_key: str, *, client: httpx.AsyncClient | None = None) -> None: + self._api_key = api_key + self._client = client + + def _get_client(self) -> httpx.AsyncClient: + if self._client is None: + self._client = httpx.AsyncClient() + return self._client + + def get_stt_model(self, model_name: str | None) -> STTModel: + return DeepgramSTTModel( + model_name or DEFAULT_STT_MODEL, self._api_key, client=self._get_client() + ) + + def get_tts_model(self, model_name: str | None) -> TTSModel: + return DeepgramTTSModel( + model_name or DEFAULT_TTS_MODEL, self._api_key, client=self._get_client() + ) diff --git a/src/agents/voice/models/deepgram_stt.py b/src/agents/voice/models/deepgram_stt.py new file mode 100644 index 00000000..e2f633d8 --- /dev/null +++ b/src/agents/voice/models/deepgram_stt.py @@ -0,0 +1,51 @@ +from __future__ import annotations + +from typing import Any + +import httpx # type: ignore + +from ..input import AudioInput, StreamedAudioInput +from ..model import StreamedTranscriptionSession, STTModel, STTModelSettings + + +class DeepgramSTTModel(STTModel): + """Speech-to-text model using Deepgram Nova 3.""" + + def __init__( + self, model: str, api_key: str, *, client: httpx.AsyncClient | None = None + ) -> None: + self.model = model + self.api_key = api_key + self._client = client or httpx.AsyncClient() + + @property + def model_name(self) -> str: + return self.model + + async def transcribe( + self, + input: AudioInput, + settings: STTModelSettings, + trace_include_sensitive_data: bool, + trace_include_sensitive_audio_data: bool, + ) -> str: + url = f"https://api.deepgram.com/v1/listen?model={self.model}" + headers = {"Authorization": f"Token {self.api_key}"} + filename, data, content_type = input.to_audio_file() + response = await self._client.post(url, headers=headers, content=data.getvalue()) + payload: dict[str, Any] = response.json() + return ( + payload.get("results", {}) + .get("channels", [{}])[0] + .get("alternatives", [{}])[0] + .get("transcript", "") + ) + + async def create_session( + self, + input: StreamedAudioInput, + settings: STTModelSettings, + trace_include_sensitive_data: bool, + trace_include_sensitive_audio_data: bool, + ) -> StreamedTranscriptionSession: + raise NotImplementedError("Streaming transcription is not implemented.") diff --git a/src/agents/voice/models/deepgram_tts.py b/src/agents/voice/models/deepgram_tts.py new file mode 100644 index 00000000..b035f35d --- /dev/null +++ b/src/agents/voice/models/deepgram_tts.py @@ -0,0 +1,29 @@ +from __future__ import annotations + +from collections.abc import AsyncIterator + +import httpx # type: ignore + +from ..model import TTSModel, TTSModelSettings + + +class DeepgramTTSModel(TTSModel): + """Text-to-speech model using Deepgram Aura 2.""" + + def __init__( + self, model: str, api_key: str, *, client: httpx.AsyncClient | None = None + ) -> None: + self.model = model + self.api_key = api_key + self._client = client or httpx.AsyncClient() + + @property + def model_name(self) -> str: + return self.model + + async def run(self, text: str, settings: TTSModelSettings) -> AsyncIterator[bytes]: + url = "https://api.deepgram.com/v1/speak" + headers = {"Authorization": f"Token {self.api_key}", "Content-Type": "application/json"} + payload = {"text": text, "model": self.model, "voice": settings.voice or "aura-2"} + response = await self._client.post(url, headers=headers, json=payload) + yield response.content diff --git a/tests/executive_assistant/test_agent.py b/tests/executive_assistant/test_agent.py new file mode 100644 index 00000000..b8e76e95 --- /dev/null +++ b/tests/executive_assistant/test_agent.py @@ -0,0 +1,27 @@ +from __future__ import annotations + +import pytest + +from agents import Agent, Runner +from agents.agent import ToolsToFinalOutputResult +from agents.executive_assistant import executive_assistant_agent +from tests.fake_model import FakeModel + + +@pytest.mark.asyncio +async def test_agent_runs_with_fake_model() -> None: + model = FakeModel() + agent = Agent( + name=executive_assistant_agent.name, + instructions=executive_assistant_agent.instructions, + tools=executive_assistant_agent.tools, + model=model, + ) + model.set_next_output( + [ + {"role": "assistant", "content": "Hello"}, + ] + ) + + result: ToolsToFinalOutputResult = await Runner.run(agent, "hi") + assert result.final_output == "Hello" diff --git a/tests/voice/test_deepgram_models.py b/tests/voice/test_deepgram_models.py new file mode 100644 index 00000000..0b961a42 --- /dev/null +++ b/tests/voice/test_deepgram_models.py @@ -0,0 +1,14 @@ +from __future__ import annotations + +import pytest + +from agents.voice import DeepgramVoiceModelProvider + + +@pytest.mark.asyncio +async def test_provider_returns_models() -> None: + provider = DeepgramVoiceModelProvider(api_key="key") + stt = provider.get_stt_model(None) + tts = provider.get_tts_model(None) + assert stt.model_name == "nova-3" + assert tts.model_name == "aura-2" From e60e90da27853bdbb8fce76fc02b4326e8b843ba Mon Sep 17 00:00:00 2001 From: Tom Green Date: Tue, 20 May 2025 04:40:16 +1000 Subject: [PATCH 18/24] Add ExecutiveAssistant agent and Deepgram voice support --- agents/executive_assistant/__init__.py | 37 ++++++++++++++ agents/executive_assistant/memory.py | 40 +++++++++++++++ agents/executive_assistant/rag.py | 18 +++++++ agents/executive_assistant/tools.py | 17 +++++++ agents/pyproject.toml | 7 +++ src/agents/voice/__init__.py | 6 +++ .../voice/models/deepgram_model_provider.py | 33 ++++++++++++ src/agents/voice/models/deepgram_stt.py | 51 +++++++++++++++++++ src/agents/voice/models/deepgram_tts.py | 29 +++++++++++ tests/executive_assistant/test_agent.py | 27 ++++++++++ tests/voice/test_deepgram_models.py | 14 +++++ 11 files changed, 279 insertions(+) create mode 100644 agents/executive_assistant/__init__.py create mode 100644 agents/executive_assistant/memory.py create mode 100644 agents/executive_assistant/rag.py create mode 100644 agents/executive_assistant/tools.py create mode 100644 agents/pyproject.toml create mode 100644 src/agents/voice/models/deepgram_model_provider.py create mode 100644 src/agents/voice/models/deepgram_stt.py create mode 100644 src/agents/voice/models/deepgram_tts.py create mode 100644 tests/executive_assistant/test_agent.py create mode 100644 tests/voice/test_deepgram_models.py diff --git a/agents/executive_assistant/__init__.py b/agents/executive_assistant/__init__.py new file mode 100644 index 00000000..a3f3a107 --- /dev/null +++ b/agents/executive_assistant/__init__.py @@ -0,0 +1,37 @@ +from __future__ import annotations + +# mypy: ignore-errors + +"""Executive Assistant agent. + +This agent orchestrates other agents and provides voice capabilities using +Deepgram STT and TTS models. It maintains short-term and long-term memory and +can retrieve information via a simple RAG component. +""" +from agents import Agent # noqa: E402 + +from .memory import LongTermMemory, ShortTermMemory # noqa: E402 +from .rag import Retriever # noqa: E402 +from .tools import get_calendar_events, send_email # noqa: E402 + + +class ExecutiveAssistantState: + """Holds resources used by the Executive Assistant.""" + + def __init__(self, memory_path: str = "memory.json") -> None: + self.short_memory = ShortTermMemory() + self.long_memory = LongTermMemory(memory_path) + self.retriever = Retriever() + + +executive_assistant_agent = Agent( + name="ExecutiveAssistant", + instructions=( + "You are an executive assistant. Use the available tools to help the user. " + "Remember important facts during the conversation for later retrieval." + ), + model="gpt-4o-mini", + tools=[get_calendar_events, send_email], +) + +__all__ = ["ExecutiveAssistantState", "executive_assistant_agent"] diff --git a/agents/executive_assistant/memory.py b/agents/executive_assistant/memory.py new file mode 100644 index 00000000..5b286718 --- /dev/null +++ b/agents/executive_assistant/memory.py @@ -0,0 +1,40 @@ +from __future__ import annotations + +import json +from pathlib import Path +from typing import Any + + +class ShortTermMemory: + """In-memory store for conversation turns.""" + + def __init__(self) -> None: + self._messages: list[dict[str, str]] = [] + + def add(self, role: str, content: str) -> None: + """Add a message to memory.""" + self._messages.append({"role": role, "content": content}) + + def to_list(self) -> list[dict[str, str]]: + """Return the last 20 messages.""" + return self._messages[-20:] + + +class LongTermMemory: + """Simple file backed memory store.""" + + def __init__(self, path: str | Path) -> None: + self._path = Path(path) + if self._path.exists(): + self._data = json.loads(self._path.read_text()) + else: + self._data = [] + + def add(self, item: Any) -> None: + """Persist an item to disk.""" + self._data.append(item) + self._path.write_text(json.dumps(self._data)) + + def all(self) -> list[Any]: + """Return all persisted items.""" + return list(self._data) diff --git a/agents/executive_assistant/rag.py b/agents/executive_assistant/rag.py new file mode 100644 index 00000000..c009fffa --- /dev/null +++ b/agents/executive_assistant/rag.py @@ -0,0 +1,18 @@ +from __future__ import annotations + +from collections.abc import Iterable + + +class Retriever: + """Very small RAG retriever stub.""" + + def __init__(self, corpus: Iterable[str] | None = None) -> None: + self._corpus = list(corpus or []) + + def add(self, document: str) -> None: + """Add a document to the corpus.""" + self._corpus.append(document) + + def search(self, query: str) -> list[str]: + """Return documents containing the query string.""" + return [doc for doc in self._corpus if query.lower() in doc.lower()] diff --git a/agents/executive_assistant/tools.py b/agents/executive_assistant/tools.py new file mode 100644 index 00000000..1964edaa --- /dev/null +++ b/agents/executive_assistant/tools.py @@ -0,0 +1,17 @@ +from __future__ import annotations + +from agents import function_tool + + +@function_tool +def get_calendar_events(date: str) -> str: + """Retrieve calendar events for a given date.""" + # TODO: Integrate with calendar API. + return f"No events found for {date}." + + +@function_tool +def send_email(recipient: str, subject: str, body: str) -> str: + """Send a simple email.""" + # TODO: Integrate with email service. + return "Email sent." diff --git a/agents/pyproject.toml b/agents/pyproject.toml new file mode 100644 index 00000000..8693da80 --- /dev/null +++ b/agents/pyproject.toml @@ -0,0 +1,7 @@ +[project] +name = "custom-agents" +version = "0.0.0" +requires-python = ">=3.9" + +[tool.hatch.build.targets.wheel] +packages = ["executive_assistant"] diff --git a/src/agents/voice/__init__.py b/src/agents/voice/__init__.py index e11ee446..abaa98db 100644 --- a/src/agents/voice/__init__.py +++ b/src/agents/voice/__init__.py @@ -10,6 +10,9 @@ TTSVoice, VoiceModelProvider, ) +from .models.deepgram_model_provider import DeepgramVoiceModelProvider +from .models.deepgram_stt import DeepgramSTTModel +from .models.deepgram_tts import DeepgramTTSModel from .models.openai_model_provider import OpenAIVoiceModelProvider from .models.openai_stt import OpenAISTTModel, OpenAISTTTranscriptionSession from .models.openai_tts import OpenAITTSModel @@ -38,6 +41,9 @@ "OpenAIVoiceModelProvider", "OpenAISTTModel", "OpenAITTSModel", + "DeepgramVoiceModelProvider", + "DeepgramSTTModel", + "DeepgramTTSModel", "VoiceStreamEventAudio", "VoiceStreamEventLifecycle", "VoiceStreamEvent", diff --git a/src/agents/voice/models/deepgram_model_provider.py b/src/agents/voice/models/deepgram_model_provider.py new file mode 100644 index 00000000..cd9e9e19 --- /dev/null +++ b/src/agents/voice/models/deepgram_model_provider.py @@ -0,0 +1,33 @@ +from __future__ import annotations + +import httpx # type: ignore + +from ..model import STTModel, TTSModel, VoiceModelProvider +from .deepgram_stt import DeepgramSTTModel +from .deepgram_tts import DeepgramTTSModel + +DEFAULT_STT_MODEL = "nova-3" +DEFAULT_TTS_MODEL = "aura-2" + + +class DeepgramVoiceModelProvider(VoiceModelProvider): + """Voice model provider for Deepgram APIs.""" + + def __init__(self, api_key: str, *, client: httpx.AsyncClient | None = None) -> None: + self._api_key = api_key + self._client = client + + def _get_client(self) -> httpx.AsyncClient: + if self._client is None: + self._client = httpx.AsyncClient() + return self._client + + def get_stt_model(self, model_name: str | None) -> STTModel: + return DeepgramSTTModel( + model_name or DEFAULT_STT_MODEL, self._api_key, client=self._get_client() + ) + + def get_tts_model(self, model_name: str | None) -> TTSModel: + return DeepgramTTSModel( + model_name or DEFAULT_TTS_MODEL, self._api_key, client=self._get_client() + ) diff --git a/src/agents/voice/models/deepgram_stt.py b/src/agents/voice/models/deepgram_stt.py new file mode 100644 index 00000000..e2f633d8 --- /dev/null +++ b/src/agents/voice/models/deepgram_stt.py @@ -0,0 +1,51 @@ +from __future__ import annotations + +from typing import Any + +import httpx # type: ignore + +from ..input import AudioInput, StreamedAudioInput +from ..model import StreamedTranscriptionSession, STTModel, STTModelSettings + + +class DeepgramSTTModel(STTModel): + """Speech-to-text model using Deepgram Nova 3.""" + + def __init__( + self, model: str, api_key: str, *, client: httpx.AsyncClient | None = None + ) -> None: + self.model = model + self.api_key = api_key + self._client = client or httpx.AsyncClient() + + @property + def model_name(self) -> str: + return self.model + + async def transcribe( + self, + input: AudioInput, + settings: STTModelSettings, + trace_include_sensitive_data: bool, + trace_include_sensitive_audio_data: bool, + ) -> str: + url = f"https://api.deepgram.com/v1/listen?model={self.model}" + headers = {"Authorization": f"Token {self.api_key}"} + filename, data, content_type = input.to_audio_file() + response = await self._client.post(url, headers=headers, content=data.getvalue()) + payload: dict[str, Any] = response.json() + return ( + payload.get("results", {}) + .get("channels", [{}])[0] + .get("alternatives", [{}])[0] + .get("transcript", "") + ) + + async def create_session( + self, + input: StreamedAudioInput, + settings: STTModelSettings, + trace_include_sensitive_data: bool, + trace_include_sensitive_audio_data: bool, + ) -> StreamedTranscriptionSession: + raise NotImplementedError("Streaming transcription is not implemented.") diff --git a/src/agents/voice/models/deepgram_tts.py b/src/agents/voice/models/deepgram_tts.py new file mode 100644 index 00000000..b035f35d --- /dev/null +++ b/src/agents/voice/models/deepgram_tts.py @@ -0,0 +1,29 @@ +from __future__ import annotations + +from collections.abc import AsyncIterator + +import httpx # type: ignore + +from ..model import TTSModel, TTSModelSettings + + +class DeepgramTTSModel(TTSModel): + """Text-to-speech model using Deepgram Aura 2.""" + + def __init__( + self, model: str, api_key: str, *, client: httpx.AsyncClient | None = None + ) -> None: + self.model = model + self.api_key = api_key + self._client = client or httpx.AsyncClient() + + @property + def model_name(self) -> str: + return self.model + + async def run(self, text: str, settings: TTSModelSettings) -> AsyncIterator[bytes]: + url = "https://api.deepgram.com/v1/speak" + headers = {"Authorization": f"Token {self.api_key}", "Content-Type": "application/json"} + payload = {"text": text, "model": self.model, "voice": settings.voice or "aura-2"} + response = await self._client.post(url, headers=headers, json=payload) + yield response.content diff --git a/tests/executive_assistant/test_agent.py b/tests/executive_assistant/test_agent.py new file mode 100644 index 00000000..b8e76e95 --- /dev/null +++ b/tests/executive_assistant/test_agent.py @@ -0,0 +1,27 @@ +from __future__ import annotations + +import pytest + +from agents import Agent, Runner +from agents.agent import ToolsToFinalOutputResult +from agents.executive_assistant import executive_assistant_agent +from tests.fake_model import FakeModel + + +@pytest.mark.asyncio +async def test_agent_runs_with_fake_model() -> None: + model = FakeModel() + agent = Agent( + name=executive_assistant_agent.name, + instructions=executive_assistant_agent.instructions, + tools=executive_assistant_agent.tools, + model=model, + ) + model.set_next_output( + [ + {"role": "assistant", "content": "Hello"}, + ] + ) + + result: ToolsToFinalOutputResult = await Runner.run(agent, "hi") + assert result.final_output == "Hello" diff --git a/tests/voice/test_deepgram_models.py b/tests/voice/test_deepgram_models.py new file mode 100644 index 00000000..0b961a42 --- /dev/null +++ b/tests/voice/test_deepgram_models.py @@ -0,0 +1,14 @@ +from __future__ import annotations + +import pytest + +from agents.voice import DeepgramVoiceModelProvider + + +@pytest.mark.asyncio +async def test_provider_returns_models() -> None: + provider = DeepgramVoiceModelProvider(api_key="key") + stt = provider.get_stt_model(None) + tts = provider.get_tts_model(None) + assert stt.model_name == "nova-3" + assert tts.model_name == "aura-2" From 11f35810393347761f331976604ced29d7e5d5d0 Mon Sep 17 00:00:00 2001 From: Copilot <198982749+Copilot@users.noreply.github.com> Date: Wed, 21 May 2025 21:15:10 +1000 Subject: [PATCH 19/24] Resolve merge conflicts in PR #8: Improve comment style consistency (#9) * Initial plan for issue * Fix indentation in _run_impl.py * Final changes to ensure the code builds properly Co-authored-by: TGreen87 <170493160+TGreen87@users.noreply.github.com> --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: TGreen87 <170493160+TGreen87@users.noreply.github.com> --- src/agents/mcp/server.py | 10 +++++----- src/agents/models/openai_chatcompletions.py | 2 +- src/agents/voice/model.py | 2 ++ 3 files changed, 8 insertions(+), 6 deletions(-) diff --git a/src/agents/mcp/server.py b/src/agents/mcp/server.py index 95aa3c9c..aa4cd0ca 100644 --- a/src/agents/mcp/server.py +++ b/src/agents/mcp/server.py @@ -88,7 +88,7 @@ def create_streams( tuple[ MemoryObjectReceiveStream[SessionMessage | Exception], MemoryObjectSendStream[SessionMessage], - GetSessionIdCallback | None + GetSessionIdCallback | None, ] ]: """Create the streams for the server.""" @@ -243,7 +243,7 @@ def create_streams( tuple[ MemoryObjectReceiveStream[SessionMessage | Exception], MemoryObjectSendStream[SessionMessage], - GetSessionIdCallback | None + GetSessionIdCallback | None, ] ]: """Create the streams for the server.""" @@ -314,7 +314,7 @@ def create_streams( tuple[ MemoryObjectReceiveStream[SessionMessage | Exception], MemoryObjectSendStream[SessionMessage], - GetSessionIdCallback | None + GetSessionIdCallback | None, ] ]: """Create the streams for the server.""" @@ -394,7 +394,7 @@ def create_streams( tuple[ MemoryObjectReceiveStream[SessionMessage | Exception], MemoryObjectSendStream[SessionMessage], - GetSessionIdCallback | None + GetSessionIdCallback | None, ] ]: """Create the streams for the server.""" @@ -403,7 +403,7 @@ def create_streams( headers=self.params.get("headers", None), timeout=self.params.get("timeout", timedelta(seconds=30)), sse_read_timeout=self.params.get("sse_read_timeout", timedelta(seconds=60 * 5)), - terminate_on_close=self.params.get("terminate_on_close", True) + terminate_on_close=self.params.get("terminate_on_close", True), ) @property diff --git a/src/agents/models/openai_chatcompletions.py b/src/agents/models/openai_chatcompletions.py index 89619f83..90ad6daf 100644 --- a/src/agents/models/openai_chatcompletions.py +++ b/src/agents/models/openai_chatcompletions.py @@ -252,7 +252,7 @@ async def _fetch_response( stream_options=self._non_null_or_not_given(stream_options), store=self._non_null_or_not_given(store), reasoning_effort=self._non_null_or_not_given(reasoning_effort), - extra_headers={ **HEADERS, **(model_settings.extra_headers or {}) }, + extra_headers={**HEADERS, **(model_settings.extra_headers or {})}, extra_query=model_settings.extra_query, extra_body=model_settings.extra_body, metadata=self._non_null_or_not_given(model_settings.metadata), diff --git a/src/agents/voice/model.py b/src/agents/voice/model.py index c36a4de7..b048a452 100644 --- a/src/agents/voice/model.py +++ b/src/agents/voice/model.py @@ -17,9 +17,11 @@ TTSVoice = Literal["alloy", "ash", "coral", "echo", "fable", "onyx", "nova", "sage", "shimmer"] """Exportable type for the TTSModelSettings voice enum""" + @dataclass class TTSModelSettings: """Settings for a TTS model.""" + voice: TTSVoice | None = None """ The voice to use for the TTS model. If not provided, the default voice for the respective model From 7b825ecb5f7404d1fbcbbabe9f65221808f31e1c Mon Sep 17 00:00:00 2001 From: Tom Green Date: Thu, 22 May 2025 03:02:48 +1000 Subject: [PATCH 20/24] docs: clarify setup script usage (#10) --- agents.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/agents.md b/agents.md index a1824b8b..b5aa5c89 100644 --- a/agents.md +++ b/agents.md @@ -25,7 +25,7 @@ In addition, ensure *git*, *curl* and *GNU Make* are on your `PATH`. ├── agents/ ← Your custom agents live here ├── src/ ← Application code ├── tests/ ← Pytest suite - ├── setup.sh ← Environment bootstrap (run once) + ├── setup.sh ← Environment bootstrap (run when bootstrapping a new environment) ├── uv.lock ← Pinned dependency hashes (uv‑generated) └── agents.md ← **You are here** From 3b54f2023f94c9cf79f7cca4341faa84ea9f7117 Mon Sep 17 00:00:00 2001 From: Tom Green Date: Thu, 22 May 2025 09:55:14 +1000 Subject: [PATCH 21/24] Update agents.md --- agents.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/agents.md b/agents.md index b5aa5c89..dcfc1bb0 100644 --- a/agents.md +++ b/agents.md @@ -124,3 +124,8 @@ _Style guideline_: write comments as full sentences and end them with a period. * Explore `examples/` shipped with the SDK. * Extend `setup.sh` to install GPU‑dependent libraries if required. * Optional: enable *OpenTelemetry* tracing via `export AGENTS_TRACE=1`. + + +## 10 Future Steps +* Always ensure dependencies for features are added to ./setup.sh when an error occurs, or is predicted to be required +* Make recommendations for advanced functionality, adding a UI / UX using Next.js 15.3, Tailwind CSS 4.1.3, Shadcn/ui 2.5.0, React 19.1 From 9c4e7ee37cca4498b5593afb6af3907a78dd2285 Mon Sep 17 00:00:00 2001 From: Copilot <198982749+Copilot@users.noreply.github.com> Date: Thu, 22 May 2025 09:57:27 +1000 Subject: [PATCH 22/24] Resolve merge conflicts and improve code style consistency (#11) * Initial plan for issue * Initial assessment of changes required for PR #8 Co-authored-by: TGreen87 <170493160+TGreen87@users.noreply.github.com> * Complete code style consistency implementation Co-authored-by: TGreen87 <170493160+TGreen87@users.noreply.github.com> --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: TGreen87 <170493160+TGreen87@users.noreply.github.com> --- src/agents/_run_impl.py | 28 +- uv.lock | 3387 ++++++++++++++++++++------------------- 2 files changed, 1713 insertions(+), 1702 deletions(-) diff --git a/src/agents/_run_impl.py b/src/agents/_run_impl.py index 7b6e122f..f1cf872e 100644 --- a/src/agents/_run_impl.py +++ b/src/agents/_run_impl.py @@ -878,21 +878,21 @@ async def execute( "image_id": image_id, } final_output = image_id - return ToolCallOutputItem( - agent=agent, - output=final_output, - raw_item=ComputerCallOutput( - call_id=action.tool_call.call_id, - output=raw_output, - type="computer_call_output", - ), - ) +return 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Please review the work done so far and provide feedback for Jules to continue. --- .../advanced_orchestration_example.py | 149 +++++++++ examples/basic/persistent_memory_example.py | 151 +++++++++ src/agents/agent.py | 14 + src/agents/extensions/visualization.py | 158 ++++++++++ src/agents/handoffs.py | 147 ++++++--- src/agents/memory.py | 136 ++++++++ src/agents/orchestrator.py | 109 +++++++ src/agents/run.py | 233 +++++++++++++- src/agents/tracing/setup.py | 62 +++- src/agents/web_visualizer/__init__.py | 2 + src/agents/web_visualizer/app.py | 59 ++++ .../web_visualizer/templates/__init__.py | 3 + .../web_visualizer/templates/view_trace.html | 26 ++ tests/test_memory.py | 293 ++++++++++++++++++ tests/test_orchestration.py | 221 +++++++++++++ 15 files changed, 1704 insertions(+), 59 deletions(-) create mode 100644 examples/agent_patterns/advanced_orchestration_example.py create mode 100644 examples/basic/persistent_memory_example.py create mode 100644 src/agents/memory.py create mode 100644 src/agents/orchestrator.py create mode 100644 src/agents/web_visualizer/__init__.py create mode 100644 src/agents/web_visualizer/app.py create mode 100644 src/agents/web_visualizer/templates/__init__.py create mode 100644 src/agents/web_visualizer/templates/view_trace.html create mode 100644 tests/test_memory.py create mode 100644 tests/test_orchestration.py diff --git a/examples/agent_patterns/advanced_orchestration_example.py b/examples/agent_patterns/advanced_orchestration_example.py new file mode 100644 index 00000000..61ab8f6a --- /dev/null +++ b/examples/agent_patterns/advanced_orchestration_example.py @@ -0,0 +1,149 @@ +import asyncio +import os +import sys +from pathlib import Path +from typing import Any, Dict, List + +# Ensure src directory is in Python path for local development +sys.path.append(str(Path(__file__).resolve().parents[2])) + +from src.agents import Agent, Runner, RunContextWrapper # RunContextWrapper might not be directly used by example user +from src.agents.orchestrator import OrchestratorAgent, run_parallel_tasks + +# 1. Define Worker Agents +class TranslationAgent(Agent[None]): + """A worker agent specialized in translation.""" + def __init__(self, name: str = "translator", **kwargs): + super().__init__( + name=name, + instructions="You are a translation expert. Translate the given text. Be concise and accurate.", + **kwargs + ) + +class SummarizationAgent(Agent[None]): + """A worker agent specialized in summarization.""" + def __init__(self, name: str = "summarizer", **kwargs): + super().__init__( + name=name, + instructions="You are a summarization expert. Summarize the given text concisely, focusing on key points.", + **kwargs + ) + +# 2. Define Orchestrator Context +class OrchestratorContext: + """Context for the OrchestratorAgent, holding worker agents.""" + def __init__(self, worker_agents: List[Agent[Any]]): + self.worker_agents_map: Dict[str, Agent[Any]] = { + agent.name: agent for agent in worker_agents + } + # You can add other shared resources for the orchestrator or workers here + # For example, a shared database connection, configuration, etc. + +async def main(): + print("--- Advanced Orchestration Example ---") + + # 3. Instantiate Worker Agents + translator = TranslationAgent() + summarizer = SummarizationAgent() + + worker_agents_list = [translator, summarizer] + print(f"Registered worker agents: {[agent.name for agent in worker_agents_list]}") + + # 4. Instantiate Orchestrator Context + orchestrator_shared_context = OrchestratorContext(worker_agents=worker_agents_list) + + # 5. Instantiate OrchestratorAgent + # The orchestrator's instructions guide it to use the run_parallel_tasks tool. + # It needs to know the names of the worker agents it can delegate to. + orchestrator_instructions = ( + "You are an advanced orchestrator. Your goal is to accomplish complex tasks by " + "delegating sub-tasks to specialized worker agents using the `run_parallel_tasks` tool.\n" + "When you receive a request, identify distinct sub-tasks that can be run in parallel.\n" + "Then, formulate a call to `run_parallel_tasks` with a list of these sub-tasks.\n" + "Each sub-task in the list must specify:\n" + " - 'agent_id': The name of the worker agent to perform the sub-task. " + f"Available worker agents are: {', '.join([agent.name for agent in worker_agents_list])}.\n" + " - 'input_prompt': The specific input or question for that worker agent.\n" + "After receiving the results from `run_parallel_tasks`, synthesize them into a coherent final answer.\n" + "If a task doesn't seem to require parallel execution or multiple specialized agents, " + "try to answer it directly or state that you cannot delegate it." + ) + + orchestrator_agent = OrchestratorAgent( # OrchestratorAgent itself uses Agent[Any] context + name="MasterOrchestrator", + instructions=orchestrator_instructions, + tools=[run_parallel_tasks], # Crucial: provide the tool to the agent + # model="gpt-4" # Consider using a more capable model for orchestration logic + ) + print(f"Orchestrator Agent '{orchestrator_agent.name}' configured.\n") + + # 6. Run the Orchestrator + # This input requires both translation and summarization, suitable for parallel execution. + # complex_task_prompt = "Translate the following French proverb into English: 'Qui court deux lièvres à la fois, n'en prend aucun.' Also, provide a brief summary of the Wikipedia article on 'Parallel computing'." + # Simpler task for initial testing: + complex_task_prompt = "Translate the word 'apple' to Spanish and summarize the health benefits of apples." + + print(f"Orchestrator received task: \"{complex_task_prompt}\"\n") + + # Use Runner.run_sync for simplicity in this example script. + # In an async application, you would `await Runner.run(...)`. + # The orchestrator_shared_context is passed to the Runner, which makes it available + # to the run_parallel_tasks tool via the RunContextWrapper. + try: + orchestrator_response = Runner.run_sync( + starting_agent=orchestrator_agent, + input=complex_task_prompt, + context=orchestrator_shared_context # Pass the context object here + ) + + print("--- Orchestrator's Final Synthesized Output ---") + if orchestrator_response and orchestrator_response.final_output: + print(orchestrator_response.final_output) + else: + print("Orchestrator did not produce a final output or an error occurred.") + if orchestrator_response: + print("Full response object:", orchestrator_response) + + except Exception as e: + print(f"An error occurred during the orchestration: {e}") + import traceback + traceback.print_exc() + + print("\n--- Example Finished ---") + + +if __name__ == "__main__": + if not os.getenv("OPENAI_API_KEY"): + print("Error: OPENAI_API_KEY environment variable not set.") + print("Please set it to run this example: export OPENAI_API_KEY='your_key_here'") + else: + asyncio.run(main()) + +# To run this example: +# 0. Ensure you are in an environment where the google-ai-agents SDK is accessible. +# If running from the root of the SDK cloned repository: +# The example includes a `sys.path.append` to help with this. +# 1. Set up your OpenAI API key (or other model provider key if agents are configured differently): +# export OPENAI_API_KEY="your_api_key_here" +# 2. Navigate to the 'google-ai-agents-python' root directory. +# 3. Execute the script: +# python examples/agent_patterns/advanced_orchestration_example.py +# +# Expected Behavior: +# - The OrchestratorAgent should receive the complex task. +# - Its LLM should decide to use the `run_parallel_tasks` tool. +# - The tool call arguments should correctly specify tasks for 'translator' and 'summarizer'. +# - `run_parallel_tasks` will execute these tasks concurrently using the respective worker agents. +# - The results from the workers will be returned to the OrchestratorAgent. +# - The OrchestratorAgent's LLM should then synthesize these results into a final answer. +# +# Note on LLM variability: +# The success of the orchestration heavily depends on the LLM's ability to: +# 1. Understand the orchestrator's instructions. +# 2. Correctly identify sub-tasks and map them to available 'agent_id's. +# 3. Formulate the arguments for the `run_parallel_tasks` tool in the correct JSON format. +# 4. Synthesize the results from the tool call. +# Using a more capable model (e.g., GPT-4) for the OrchestratorAgent is recommended for complex orchestration. +# If the orchestrator fails to use the tool or makes a mistake, you might need to refine its instructions +# or provide few-shot examples within its prompt. +``` diff --git a/examples/basic/persistent_memory_example.py b/examples/basic/persistent_memory_example.py new file mode 100644 index 00000000..46436d1e --- /dev/null +++ b/examples/basic/persistent_memory_example.py @@ -0,0 +1,151 @@ +import asyncio +import os +from pathlib import Path +import sys + +# This is a common way to enable imports from the parent directory (e.g., 'src') +# when running examples located in a subdirectory. +sys.path.append(str(Path(__file__).resolve().parents[2])) + +from src.agents import Agent, Runner # Assuming Agent and Runner are in src/agents/__init__.py or directly accessible +from src.agents.memory import FileStorageMemory + +async def main(): + """ + Demonstrates the usage of FileStorageMemory to give an agent persistent memory + across multiple interactions. + """ + # Define a file path for the memory + # Using Path for better path management + memory_file_path = Path("agent_memory.json") + + print(f"--- Demonstrating persistent memory with FileStorageMemory ---") + print(f"Memory file will be stored at: {memory_file_path.resolve()}\n") + + try: + # 1. Initialize FileStorageMemory + # The agent's conversation history will be stored in 'agent_memory.json' + # load() is called automatically on init if the file exists. + persistent_memory = FileStorageMemory(file_path=memory_file_path) + + # 2. Define a simple agent + # This agent is instructed to be helpful and remember previous interactions. + memory_agent = Agent( + name="MemoryBot", + instructions="You are a helpful assistant that remembers previous interactions. Be concise and friendly.", + memory=persistent_memory, # Assign the FileStorageMemory instance + # model="gpt-3.5-turbo" # Or your preferred model, ensure OPENAI_API_KEY is set + ) + + # 3. First interaction: Provide information to the agent + print("--- First interaction ---") + user_input_1 = "My favorite color is blue and my name is Jules." + print(f"User: {user_input_1}") + + # Runner.run_sync is a helper for simple synchronous execution. + # For production/async code, use `await Runner.run(...)` + response_1 = Runner.run_sync(memory_agent, user_input_1) + assistant_response_1 = response_1.final_output # Extracts the text from the last message + print(f"MemoryBot: {assistant_response_1}\n") + + # At this point, the conversation ("My name is Jules" and the bot's response) + # should be saved to 'agent_memory.json' because FileStorageMemory.add() calls save(). + + # 4. Second interaction: Ask the agent to recall information + print("--- Second interaction (testing recall) ---") + user_input_2 = "What is my name and what is my favorite color?" + print(f"User: {user_input_2}") + + # Run the agent again. It should use the memory from 'agent_memory.json' + # because Agent.__post_init__ calls memory.load() and subsequent turns also load. + response_2 = Runner.run_sync(memory_agent, user_input_2) + assistant_response_2 = response_2.final_output + print(f"MemoryBot: {assistant_response_2}") + + if "Jules" in str(assistant_response_2) and "blue" in str(assistant_response_2).lower(): + print("SUCCESS: Agent remembered the name 'Jules' and color 'blue'.\n") + elif "Jules" in str(assistant_response_2): + print("PARTIAL SUCCESS: Agent remembered the name 'Jules' but not color 'blue'.\n") + elif "blue" in str(assistant_response_2).lower(): + print("PARTIAL SUCCESS: Agent remembered color 'blue' but not name 'Jules'.\n") + else: + print("FAILURE: Agent did NOT seem to remember the name 'Jules' or color 'blue'. " + "This might happen if the LLM fails to follow instructions, memory isn't working, " + "or the response format was unexpected.\n") + + + # 5. Clear the memory + print("--- Clearing agent memory ---") + if memory_agent.memory: # Check if memory object exists + memory_agent.memory.clear() # Clears messages in memory AND saves the empty list to the file. + print("Memory cleared (agent_memory.json should now be an empty list).\n") + else: + print("Memory object not found on agent.\n") + + # 6. Third interaction: Test if the memory is cleared + print("--- Third interaction (testing after clear) ---") + user_input_3 = "What is my name and favorite color?" + print(f"User: {user_input_3}") + + # Run the agent again. It should not remember the name or color. + # The memory file ('agent_memory.json') was updated by clear() + # and the agent will load this cleared state. + response_3 = Runner.run_sync(memory_agent, user_input_3) + assistant_response_3 = response_3.final_output + print(f"MemoryBot: {assistant_response_3}") + + if "Jules" not in str(assistant_response_3) and "blue" not in str(assistant_response_3).lower(): + print("SUCCESS: Agent correctly does not remember the name or color after memory clear.\n") + else: + print("FAILURE: Agent still remembered information after clear. This is unexpected.\n") + + except Exception as e: + print(f"An error occurred: {e}") + import traceback + traceback.print_exc() + finally: + # 7. Clean up the created memory file + # This is good practice for examples to avoid leaving files behind. + if memory_file_path.exists(): + try: + os.remove(memory_file_path) + print(f"Cleaned up memory file: {memory_file_path}") + except Exception as e: + print(f"Error cleaning up memory file {memory_file_path}: {e}") + else: + # This case might occur if the script failed before creating the file. + print(f"Memory file {memory_file_path} not found, no cleanup needed or file was not created due to an error.") + +if __name__ == "__main__": + # Ensure OPENAI_API_KEY is set in your environment variables for the LLM to work. + if not os.getenv("OPENAI_API_KEY"): + print("Error: OPENAI_API_KEY environment variable not set.") + print("Please set it to run this example: export OPENAI_API_KEY='your_key_here'") + else: + asyncio.run(main()) + +# To run this example: +# 0. Make sure you are in an environment where the google-ai-agents SDK is accessible. +# If running from the root of the SDK cloned repository: +# You might need to add the SDK's root or 'src' to your PYTHONPATH. +# The example includes a `sys.path.append` to help with this when run directly. +# Alternatively, install the SDK package if available. +# 1. Set up your OpenAI API key (or any other model provider key you configure the agent with): +# export OPENAI_API_KEY="your_api_key_here" +# 2. Navigate to the 'google-ai-agents-python' directory (or where the example is located). +# 3. Execute the script from the 'google-ai-agents-python' root directory: +# python examples/basic/persistent_memory_example.py +# +# Note: +# - 'agent_memory.json' will be created in the directory where you run the script. +# - The example includes cleanup of this file by default in the `finally` block. +# - The LLM's responses can vary. The key observation is whether it +# correctly recalls information in the second interaction and forgets it in the third. +# - `FileStorageMemory.add()` and `FileStorageMemory.clear()` automatically call `save()`. +# - `FileStorageMemory.load()` is called upon initialization of `FileStorageMemory` +# and also by the `Agent` during its initialization (`__post_init__`) and by the `Runner` logic +# at the start of an agent's turn (`if current_turn == 1: memory.load()`). +# +# This example uses `Runner.run_sync` which is a synchronous wrapper around `Runner.run`. +# In an asynchronous application, you would use `await Runner.run(...)`. +``` diff --git a/src/agents/agent.py b/src/agents/agent.py index e22f579f..b9136494 100644 --- a/src/agents/agent.py +++ b/src/agents/agent.py @@ -9,6 +9,7 @@ from typing_extensions import NotRequired, TypeAlias, TypedDict from .agent_output import AgentOutputSchemaBase +from .memory import AgentMemory, InMemoryMemory from .guardrail import InputGuardrail, OutputGuardrail from .handoffs import Handoff from .items import ItemHelpers @@ -27,6 +28,7 @@ from .result import RunResult + @dataclass class ToolsToFinalOutputResult: is_final_output: bool @@ -178,6 +180,18 @@ class Agent(Generic[TContext]): """Whether to reset the tool choice to the default value after a tool has been called. Defaults to True. This ensures that the agent doesn't enter an infinite loop of tool usage.""" + memory: AgentMemory | None = field(default=None, kw_only=True) + """The memory system for the agent. If None, an InMemoryMemory instance will be used. + The memory instance is responsible for loading itself (e.g. from a file). + """ + + def __post_init__(self): + if self.memory is None: + self.memory = InMemoryMemory() + # Ensure memory is loaded. For InMemoryMemory, this is a no-op. + # For FileStorageMemory, this would load from file if it exists. + self.memory.load() + def clone(self, **kwargs: Any) -> Agent[TContext]: """Make a copy of the agent, with the given arguments changed. For example, you could do: ``` diff --git a/src/agents/extensions/visualization.py b/src/agents/extensions/visualization.py index 2e8a90d6..210edbdc 100644 --- a/src/agents/extensions/visualization.py +++ b/src/agents/extensions/visualization.py @@ -135,3 +135,161 @@ def draw_graph(agent: Agent, filename: Optional[str] = None) -> graphviz.Source: graph.render(filename, format="png", cleanup=True) return graph + + +# --- New functions for generating graph from trace data --- + +from agents.tracing.traces import Trace # Add this +from agents.tracing.span_data import AgentSpanData, HandoffSpanData, ToolSpanData # Add these + +def generate_graph_from_trace(trace_data: Trace) -> str: + dot = graphviz.Digraph(comment=f"Trace: {trace_data.name} ({trace_data.trace_id})") + dot.attr(rankdir='TB') # Top-to-bottom graph + + # Keep track of nodes to avoid duplicates, and edges + nodes = set() + edges = set() + + # Add a start node + dot.node("__start__", "__start__", shape="ellipse", style="filled", fillcolor="lightblue") + last_agent_node_name = "__start__" # Represents the last *agent* context node + # This is used to link sequential agent runs or tool calls from an agent. + + # Sort spans by start time to represent the flow chronologically + sorted_spans = sorted(trace_data.spans, key=lambda s: s.start_time) + + for span in sorted_spans: + if isinstance(span.span_data, AgentSpanData): + agent_name = span.span_data.name + agent_node_id = f"agent_{agent_name}_{span.span_id[:8]}" # Unique node for each agent execution instance + + if agent_node_id not in nodes: + dot.node(agent_node_id, agent_name, shape="box", style="filled", fillcolor="lightyellow") + nodes.add(agent_node_id) + + # If there was a previous agent context, and this is a different agent instance, + # and it's not a direct handoff (handoffs draw their own edges). + # This logic is a bit tricky: handoffs should be the primary way to link agents. + # If an agent span follows another without a handoff span, it might be a sub-agent + # or a complex sequence. For now, we rely on handoff spans for explicit agent-to-agent edges. + # The `last_agent_node_name` will be updated by handoff spans too. + + # If the previous node was __start__ or a different agent (and no handoff linked them), draw an edge. + # This simplistic view assumes a new agent span starts after the previous one logically concludes or hands off. + if last_agent_node_name and last_agent_node_name != agent_node_id and \ + (last_agent_node_name == "__start__" or not any(e[0] == last_agent_node_name and e[1] == agent_node_id for e in edges)): + # Avoid drawing edge if a handoff already connected the conceptual agents + # This needs to be smarter by checking if the conceptual `last_agent_name` (not node_id) + # was the source of a handoff to the current `agent_name`. + # For now, if last_agent_node_name is __start__, always draw. + # Otherwise, this edge might be redundant if a handoff span exists. + # Let's refine: only draw from __start__ here. Handoffs will draw their specific edges. + # If an agent follows __start__ directly, it's the first agent. + if last_agent_node_name == "__start__": + dot.edge(last_agent_node_name, agent_node_id, label="starts") + edges.add((last_agent_node_name, agent_node_id)) + + last_agent_node_name = agent_node_id # Current agent becomes the last agent context + + elif isinstance(span.span_data, HandoffSpanData): + from_agent_name = span.span_data.from_agent # Conceptual agent name + to_agent_name = span.span_data.to_agent # Conceptual agent name + + # We need to find the specific *instance* (node_id) of from_agent that initiated this handoff. + # This requires looking up the most recent AgentSpanData for from_agent_name. + # For simplicity, we'll use `last_agent_node_name` if its name matches `from_agent_name`. + # This is an approximation. A more robust way would be to map agent names to their latest span IDs. + + from_agent_node_id = last_agent_node_name # Assume handoff is from the last active agent span + # A better way: search backwards for an agent span matching from_agent_name if last_agent_node_name isn't it. + # For now, this simplification means handoffs should ideally occur from the immediately preceding agent span. + + # The target agent will have its own AgentSpanData later in the sorted list. + # We create a conceptual node for the target agent if not seen, + # but the edge should point to its actual instance node when that appears. + # This is tricky. Let's use the names for handoff edges for now, + # and assume AgentSpanData instances will create their concrete nodes. + + # Let's use the agent names for handoff edges, assuming their nodes will be created by AgentSpanData. + # This makes the graph more about conceptual agent flow via handoffs. + # The actual agent execution nodes (agent_node_id) will show instances. + + # Simplified: Edge between agent names for handoff. + # More accurate: Edge from specific source agent *instance* node to target agent *instance* node. + # For now, let's use the conceptual agent names for handoff edges for clarity. + # The target agent's instance node will be created by its own AgentSpanData. + + # Ensure conceptual nodes for agents involved in handoff exist + if from_agent_name not in nodes: + dot.node(from_agent_name, from_agent_name, shape="box", style="filled", fillcolor="lightgoldenrodyellow") + nodes.add(from_agent_name) + if to_agent_name not in nodes: + dot.node(to_agent_name, to_agent_name, shape="box", style="filled", fillcolor="lightyellow") + nodes.add(to_agent_name) + + if (from_agent_name, to_agent_name) not in edges: + dot.edge(from_agent_name, to_agent_name, label=f"handoff\n({span.span_data.tool_name})") + edges.add((from_agent_name, to_agent_name)) + + # The context for the next operation is now the agent that received the handoff. + # We use the conceptual name here. The next AgentSpanData for to_agent_name will pick this up. + last_agent_node_name = to_agent_name # Update conceptual last agent + + elif isinstance(span.span_data, ToolSpanData): + tool_name = span.span_data.name + # Tool is called by an agent. Assume it's the `last_agent_node_name` (which is an instance ID). + agent_calling_tool_node_id = last_agent_node_name + + # Create a unique node for each tool call, including part of span_id for uniqueness + tool_instance_node_id = f"tool_{tool_name}_{span.span_id[:8]}" + if tool_instance_node_id not in nodes: + dot.node(tool_instance_node_id, tool_name, shape="ellipse", style="filled", fillcolor="lightgreen") + nodes.add(tool_instance_node_id) + + if agent_calling_tool_node_id and agent_calling_tool_node_id != "__start__": + if (agent_calling_tool_node_id, tool_instance_node_id) not in edges: + dot.edge(agent_calling_tool_node_id, tool_instance_node_id, label="calls_tool") + edges.add((agent_calling_tool_node_id, tool_instance_node_id)) + # Edge from tool back to agent represents the result. + # The agent (agent_calling_tool_node_id) is expected to continue or process the result. + if (tool_instance_node_id, agent_calling_tool_node_id) not in edges: + dot.edge(tool_instance_node_id, agent_calling_tool_node_id, label="result") + edges.add((tool_instance_node_id, agent_calling_tool_node_id)) + # The agent context doesn't change due to a tool call; it resumes. + # So, last_agent_node_name remains the same. + + # Add an end node, connecting the very last active node (agent or tool instance) to it. + # This needs to find the chronologically last node. + # For simplicity, connect `last_agent_node_name` (which is the last agent instance or conceptual agent from handoff) to end. + dot.node("__end__", "__end__", shape="ellipse", style="filled", fillcolor="lightblue") + if last_agent_node_name and last_agent_node_name != "__start__": + if (last_agent_node_name, "__end__") not in edges: + dot.edge(last_agent_node_name, "__end__") + edges.add((last_agent_node_name, "__end__")) + elif not nodes or last_agent_node_name == "__start__": # Only start node was effectively active + dot.edge("__start__", "__end__") + + return dot.source + + +def draw_trace_graph(trace_data: Trace, filename: Optional[str] = None) -> graphviz.Source: + """ + Draws the graph for the given trace data and optionally saves it as a PNG file. + + Args: + trace_data (Trace): The trace data object. + filename (str): The name of the file to save the graph as a PNG. + + Returns: + graphviz.Source: The graphviz Source object representing the graph. + """ + dot_code = generate_graph_from_trace(trace_data) + graph = graphviz.Source(dot_code) + + if filename: + # Ensure the filename has no extension, graphviz adds .png + render_filename = filename.split('.')[0] + graph.render(render_filename, format="png", cleanup=True) + print(f"Trace graph saved to {render_filename}.png") + + return graph diff --git a/src/agents/handoffs.py b/src/agents/handoffs.py index be062dee..b53932c1 100644 --- a/src/agents/handoffs.py +++ b/src/agents/handoffs.py @@ -22,6 +22,10 @@ # The handoff input type is the type of data passed when the agent is called via a handoff. THandoffInput = TypeVar("THandoffInput", default=Any) +# Type for the dynamic agent resolver function +AgentResolverFunction = Callable[[RunContextWrapper[Any], str], Awaitable[Agent[Any]]] + + OnHandoffWithInput = Callable[[RunContextWrapper[Any], THandoffInput], Any] OnHandoffWithoutInput = Callable[[RunContextWrapper[Any]], Any] @@ -121,116 +125,163 @@ def handoff( tool_name_override: str | None = None, tool_description_override: str | None = None, input_filter: Callable[[HandoffInputData], HandoffInputData] | None = None, + agent_name_for_tool: str | None = None, # New parameter ) -> Handoff[TContext]: ... @overload def handoff( - agent: Agent[TContext], + target_agent_or_resolver: Agent[TContext] | AgentResolverFunction, *, on_handoff: OnHandoffWithInput[THandoffInput], input_type: type[THandoffInput], tool_description_override: str | None = None, tool_name_override: str | None = None, input_filter: Callable[[HandoffInputData], HandoffInputData] | None = None, + agent_name_for_tool: str | None = None, # New parameter ) -> Handoff[TContext]: ... @overload def handoff( - agent: Agent[TContext], + target_agent_or_resolver: Agent[TContext] | AgentResolverFunction, *, on_handoff: OnHandoffWithoutInput, tool_description_override: str | None = None, tool_name_override: str | None = None, input_filter: Callable[[HandoffInputData], HandoffInputData] | None = None, + agent_name_for_tool: str | None = None, # New parameter ) -> Handoff[TContext]: ... def handoff( - agent: Agent[TContext], + target_agent_or_resolver: Agent[TContext] | AgentResolverFunction, tool_name_override: str | None = None, tool_description_override: str | None = None, on_handoff: OnHandoffWithInput[THandoffInput] | OnHandoffWithoutInput | None = None, input_type: type[THandoffInput] | None = None, input_filter: Callable[[HandoffInputData], HandoffInputData] | None = None, + agent_name_for_tool: str | None = None, ) -> Handoff[TContext]: - """Create a handoff from an agent. + """Create a handoff from an agent or an agent resolver function. Args: - agent: The agent to handoff to, or a function that returns an agent. + target_agent_or_resolver: The concrete agent instance to handoff to, or an async callable + (AgentResolverFunction) that dynamically resolves to an agent instance. + The resolver function receives the RunContextWrapper and the JSON string of arguments + from the LLM, and must return an `Agent` instance. tool_name_override: Optional override for the name of the tool that represents the handoff. tool_description_override: Optional override for the description of the tool that represents the handoff. - on_handoff: A function that runs when the handoff is invoked. - input_type: the type of the input to the handoff. If provided, the input will be validated - against this type. Only relevant if you pass a function that takes an input. - input_filter: a function that filters the inputs that are passed to the next agent. + agent_name_for_tool: Required if `target_agent_or_resolver` is a callable. This name is + used for generating default tool names and descriptions. + on_handoff: A function that runs *before* the agent resolver or target agent is determined. + This function can process the input from the LLM. + input_type: The type of the input to the `on_handoff` callback. If provided, the input + will be validated against this type. Only relevant if `on_handoff` takes an input. + input_filter: A function that filters the inputs that are passed to the next agent. """ + # Determine agent_name for tool generation + current_agent_name_for_tool: str + if isinstance(target_agent_or_resolver, Agent): # type: ignore[arg-type] + current_agent_name_for_tool = agent_name_for_tool or target_agent_or_resolver.name + elif callable(target_agent_or_resolver): + if not agent_name_for_tool: + raise UserError( + "agent_name_for_tool must be provided if target_agent_or_resolver is a callable." + ) + current_agent_name_for_tool = agent_name_for_tool + else: + raise UserError( + "target_agent_or_resolver must be an Agent instance or a callable resolver function." + ) + + assert (on_handoff and input_type) or not (on_handoff and input_type), ( - "You must provide either both on_input and input_type, or neither" + "You must provide either both on_handoff and input_type, or neither" ) type_adapter: TypeAdapter[Any] | None if input_type is not None: assert callable(on_handoff), "on_handoff must be callable" sig = inspect.signature(on_handoff) - if len(sig.parameters) != 2: - raise UserError("on_handoff must take two arguments: context and input") + if len(sig.parameters) != 2: # context and input + raise UserError("on_handoff with input_type must take two arguments: context and input") type_adapter = TypeAdapter(input_type) input_json_schema = type_adapter.json_schema() else: type_adapter = None - input_json_schema = {} + input_json_schema = {} # No input expected by `on_handoff` or no `on_handoff` if on_handoff is not None: sig = inspect.signature(on_handoff) - if len(sig.parameters) != 1: - raise UserError("on_handoff must take one argument: context") + if len(sig.parameters) != 1: # context only + raise UserError("on_handoff without input_type must take one argument: context") async def _invoke_handoff( - ctx: RunContextWrapper[Any], input_json: str | None = None + ctx: RunContextWrapper[Any], input_json_str: str | None = None ) -> Agent[Any]: - if input_type is not None and type_adapter is not None: - if input_json is None: - _error_tracing.attach_error_to_current_span( + # Ensure input_json_str is a string for the resolver, defaulting to empty if None + actual_input_json_for_resolver = input_json_str or "" + + # First, run the on_handoff callback if provided + if on_handoff: + if input_type is not None and type_adapter is not None: + if input_json_str is None: # Check if on_handoff expected input but got None + _error_tracing.attach_error_to_current_span( + SpanError( + message="on_handoff callback expected non-null input, but got None", + data={"details": "input_json_str is None"}, + ) + ) + raise ModelBehaviorError("on_handoff callback expected non-null input, but got None") + + validated_input_for_callback = _json.validate_json( + json_str=input_json_str, + type_adapter=type_adapter, + partial=False, + ) + input_func = cast(OnHandoffWithInput[THandoffInput], on_handoff) + if inspect.iscoroutinefunction(input_func): + await input_func(ctx, validated_input_for_callback) + else: + input_func(ctx, validated_input_for_callback) + else: # on_handoff exists but takes no input + no_input_func = cast(OnHandoffWithoutInput, on_handoff) + if inspect.iscoroutinefunction(no_input_func): + await no_input_func(ctx) + else: + no_input_func(ctx) + + # Now, resolve the agent + if callable(target_agent_or_resolver): + # It's an AgentResolverFunction + resolver_func = cast(AgentResolverFunction, target_agent_or_resolver) + resolved_agent = await resolver_func(ctx, actual_input_json_for_resolver) + if not isinstance(resolved_agent, Agent): # type: ignore[arg-type] + _error_tracing.attach_error_to_current_span( SpanError( - message="Handoff function expected non-null input, but got None", - data={"details": "input_json is None"}, + message="Agent resolver function did not return an Agent instance.", + data={"resolved_type": type(resolved_agent).__name__}, ) ) - raise ModelBehaviorError("Handoff function expected non-null input, but got None") + raise ModelBehaviorError("Agent resolver function must return an Agent instance.") + return resolved_agent + else: + # It's a pre-defined Agent instance + return cast(Agent[Any], target_agent_or_resolver) + + + # Use current_agent_name_for_tool for default tool name and description + tool_name = tool_name_override or Handoff.default_tool_name_for_agent_like(current_agent_name_for_tool) + tool_description = tool_description_override or Handoff.default_tool_description_for_agent_like(current_agent_name_for_tool, target_agent_or_resolver if isinstance(target_agent_or_resolver, Agent) else None) # type: ignore - validated_input = _json.validate_json( - json_str=input_json, - type_adapter=type_adapter, - partial=False, - ) - input_func = cast(OnHandoffWithInput[THandoffInput], on_handoff) - if inspect.iscoroutinefunction(input_func): - await input_func(ctx, validated_input) - else: - input_func(ctx, validated_input) - elif on_handoff is not None: - no_input_func = cast(OnHandoffWithoutInput, on_handoff) - if inspect.iscoroutinefunction(no_input_func): - await no_input_func(ctx) - else: - no_input_func(ctx) - - return agent - - tool_name = tool_name_override or Handoff.default_tool_name(agent) - tool_description = tool_description_override or Handoff.default_tool_description(agent) - - # Always ensure the input JSON schema is in strict mode. - # If there is a need, we can make this configurable in the future. input_json_schema = ensure_strict_json_schema(input_json_schema) return Handoff( tool_name=tool_name, tool_description=tool_description, input_json_schema=input_json_schema, - on_invoke_handoff=_invoke_handoff, + on_invoke_handoff=_invoke_handoff, # This now handles dynamic resolution input_filter=input_filter, - agent_name=agent.name, + agent_name=current_agent_name_for_tool, # Populated based on new logic ) diff --git a/src/agents/memory.py b/src/agents/memory.py new file mode 100644 index 00000000..4e91d9b4 --- /dev/null +++ b/src/agents/memory.py @@ -0,0 +1,136 @@ +import abc +import json +from pathlib import Path +from typing import Any, Optional, List, Dict + +class AgentMemory(abc.ABC): + """Abstract base class for agent memory.""" + + @abc.abstractmethod + def add(self, role: str, content: str, metadata: Optional[Dict[str, Any]] = None) -> None: + """Adds a message to the memory. + + Args: + role: The role of the message sender (e.g., "user", "assistant"). + content: The content of the message. + metadata: Optional metadata associated with the message. + """ + pass + + @abc.abstractmethod + def get_messages(self) -> List[Dict[str, Any]]: + """Retrieves all messages from the memory. + + Returns: + A list of messages, where each message is a dictionary + with "role", "content", and optional "metadata" keys. + """ + pass + + @abc.abstractmethod + def get_last_n_messages(self, n: int) -> List[Dict[str, Any]]: + """Retrieves the last N messages from the memory. + + Args: + n: The number of most recent messages to retrieve. + + Returns: + A list of the last N messages. + """ + pass + + @abc.abstractmethod + def clear(self) -> None: + """Clears all messages from the memory.""" + pass + + @abc.abstractmethod + def load(self) -> None: + """Loads memory from a persistent store (if applicable).""" + pass + + @abc.abstractmethod + def save(self) -> None: + """Saves memory to a persistent store (if applicable).""" + pass + +class InMemoryMemory(AgentMemory): + """Stores messages in a private list in memory.""" + + def __init__(self): + self._messages: List[Dict[str, Any]] = [] + + def add(self, role: str, content: str, metadata: Optional[Dict[str, Any]] = None) -> None: + message = {"role": role, "content": content} + if metadata: + message["metadata"] = metadata + self._messages.append(message) + + def get_messages(self) -> List[Dict[str, Any]]: + return list(self._messages) + + def get_last_n_messages(self, n: int) -> List[Dict[str, Any]]: + return list(self._messages[-n:]) + + def clear(self) -> None: + self._messages.clear() + + def load(self) -> None: + """In-memory storage does not require loading.""" + # Or raise NotImplementedError("In-memory storage does not support loading.") + pass + + def save(self) -> None: + """In-memory storage does not require saving.""" + # Or raise NotImplementedError("In-memory storage does not support saving.") + pass + +class FileStorageMemory(AgentMemory): + """Stores messages in a JSON file.""" + + def __init__(self, file_path: Path | str): + self.file_path = Path(file_path) + self._messages: List[Dict[str, Any]] = [] + self.load() + + def add(self, role: str, content: str, metadata: Optional[Dict[str, Any]] = None) -> None: + message = {"role": role, "content": content} + if metadata: + message["metadata"] = metadata + self._messages.append(message) + self.save() # Save after each addition + + def get_messages(self) -> List[Dict[str, Any]]: + return list(self._messages) + + def get_last_n_messages(self, n: int) -> List[Dict[str, Any]]: + return list(self._messages[-n:]) + + def clear(self) -> None: + self._messages.clear() + self.save() # Save after clearing + + def load(self) -> None: + if self.file_path.exists() and self.file_path.stat().st_size > 0: + try: + with open(self.file_path, "r", encoding="utf-8") as f: + self._messages = json.load(f) + except json.JSONDecodeError: + # File is corrupted or not valid JSON, start with empty memory + self._messages = [] + except Exception: + # Other potential errors during file reading + self._messages = [] + else: + self._messages = [] + + def save(self) -> None: + try: + self.file_path.parent.mkdir(parents=True, exist_ok=True) + with open(self.file_path, "w", encoding="utf-8") as f: + json.dump(self._messages, f, indent=2) + except Exception: + # Handle potential errors during file writing, + # e.g., permissions, disk full + # For now, we'll let it pass, but in a real app, logging would be good. + pass diff --git a/src/agents/orchestrator.py b/src/agents/orchestrator.py new file mode 100644 index 00000000..419cdf48 --- /dev/null +++ b/src/agents/orchestrator.py @@ -0,0 +1,109 @@ +import asyncio +from typing import Any, List, Dict + +from .agent import Agent +from .run import Runner +from .run_context import RunContextWrapper +from .tool import Tool, function_tool + + +# Define a simple context structure if needed for the Orchestrator +# For this example, we'll assume the context object passed to the OrchestratorAgent +# will have an attribute `worker_agents_map: Dict[str, Agent[Any]]`. +# class OrchestratorContext: +# def __init__(self, worker_agents_map: Dict[str, Agent[Any]]): +# self.worker_agents_map = worker_agents_map + + +class OrchestratorAgent(Agent[Any]): # Using Any for context for now, can be OrchestratorContext + """ + An agent responsible for orchestrating tasks across multiple worker agents. + It can use tools like 'run_parallel_tasks' to manage concurrent execution. + """ + # This agent would typically be configured with the run_parallel_tasks tool. + # Example: + # orchestrator = OrchestratorAgent( + # name="MainOrchestrator", + # instructions="You are an orchestrator. Use tools to accomplish complex tasks.", + # tools=[run_parallel_tasks], + # # context=OrchestratorContext(worker_agents_map={...}) + # ) + pass + + +@function_tool +async def run_parallel_tasks( + context: RunContextWrapper[Any], # Should ideally be RunContextWrapper[OrchestratorContext] + tasks: List[Dict[str, str]] # Each dict: {"agent_id": "name", "input_prompt": "text"} +) -> List[Dict[str, Any]]: + """Executes multiple tasks in parallel using registered worker agents. + Each task in the input list should be a dictionary with "agent_id" (name of the registered agent) + and "input_prompt" (the input string for that agent). + Returns a list of results, each containing "agent_id" and "output". + The context for this orchestrator agent must have a 'worker_agents_map' attribute, + which is a dictionary mapping agent_id (string) to Agent instances. + """ + + if not hasattr(context.context, 'worker_agents_map') or \ + not isinstance(context.context.worker_agents_map, dict): # type: ignore + # Type ignore above because context.context is Any here. + # If using OrchestratorContext, this check would be more type-safe. + raise ValueError( + "Orchestrator context must have a 'worker_agents_map' dictionary attribute." + ) + + worker_agents_map: Dict[str, Agent[Any]] = context.context.worker_agents_map # type: ignore + + coroutines = [] + task_ids_in_order = [] # To map results back to original tasks if needed, or just for structured output + + for task_spec in tasks: + agent_id = task_spec.get("agent_id") + input_prompt = task_spec.get("input_prompt") + task_ids_in_order.append(agent_id if agent_id else "unknown_agent_id") + + if not agent_id or input_prompt is None: # input_prompt can be empty string, so check for None + # Create a coroutine that immediately returns an error structure + async def _error_coro(aid, msg): + return {"agent_id": aid, "output": msg} + coroutines.append(_error_coro(agent_id, "Error: missing agent_id or input_prompt")) + continue + + worker_agent = worker_agents_map.get(agent_id) + if not worker_agent: + async def _not_found_coro(aid, msg): + return {"agent_id": aid, "output": msg} + coroutines.append(_not_found_coro(agent_id, f"Error: Agent '{agent_id}' not found.")) + continue + + # Each agent runs with the orchestrator's context. + # This is a key design decision. If worker agents need isolated contexts or specific + # sub-contexts, this would need to be handled here (e.g., by creating a new context + # or modifying the passed context). For now, sharing the orchestrator's context. + run_coro = Runner.run( + starting_agent=worker_agent, + input=input_prompt, + context=context.context # Passes the orchestrator's main context object to sub-agents. + ) + coroutines.append(run_coro) + + # Gather results, return_exceptions=True allows us to get individual errors + run_results_or_exceptions = await asyncio.gather(*coroutines, return_exceptions=True) + + processed_results = [] + for i, result_or_exc in enumerate(run_results_or_exceptions): + current_agent_id = task_ids_in_order[i] # Get agent_id based on original task order + + if isinstance(result_or_exc, Exception): + processed_results.append({"agent_id": current_agent_id, "output": f"Error during agent run: {str(result_or_exc)}"}) + elif hasattr(result_or_exc, 'final_output'): # It's a RunResult object + processed_results.append({"agent_id": current_agent_id, "output": result_or_exc.final_output}) + elif isinstance(result_or_exc, dict) and "agent_id" in result_or_exc and "output" in result_or_exc: + # This handles the error dicts from our placeholder coroutines (_error_coro, _not_found_coro) + processed_results.append(result_or_exc) + else: + # Should not happen if all coroutines return RunResult or one of the error dicts + processed_results.append({"agent_id": current_agent_id, "output": "Error: Unknown result type from gather."}) + + return processed_results +``` diff --git a/src/agents/run.py b/src/agents/run.py index c732569e..e26a727c 100644 --- a/src/agents/run.py +++ b/src/agents/run.py @@ -234,6 +234,7 @@ async def run( should_run_agent_start_hooks=should_run_agent_start_hooks, tool_use_tracker=tool_use_tracker, previous_response_id=previous_response_id, + current_turn=current_turn, ), ) else: @@ -248,6 +249,7 @@ async def run( should_run_agent_start_hooks=should_run_agent_start_hooks, tool_use_tracker=tool_use_tracker, previous_response_id=previous_response_id, + current_turn=current_turn, ) should_run_agent_start_hooks = False @@ -568,6 +570,7 @@ async def _run_streamed_impl( tool_use_tracker, all_tools, previous_response_id, + current_turn=current_turn, # Pass current_turn ) should_run_agent_start_hooks = False @@ -640,6 +643,7 @@ async def _run_single_turn_streamed( tool_use_tracker: AgentToolUseTracker, all_tools: list[Tool], previous_response_id: str | None, + current_turn: int, # Added current_turn ) -> SingleStepResult: if should_run_agent_start_hooks: await asyncio.gather( @@ -665,13 +669,74 @@ async def _run_single_turn_streamed( final_response: ModelResponse | None = None - input = ItemHelpers.input_to_new_input_list(streamed_result.input) - input.extend([item.to_input_item() for item in streamed_result.new_items]) + # --- Memory loading and input preparation for streaming START --- + memory = agent.memory + if current_turn == 1: # Consistent with non-streaming _run_single_turn + memory.load() + + history_messages_from_memory = memory.get_messages() + + generated_item_messages: list[TResponseInputItem] = [ + item.to_input_item() for item in streamed_result.new_items + ] + # For streaming, `streamed_result.input` holds the initial input to the *entire run*. + # New inputs for a specific turn are not explicitly separated in the same way as `original_input` + # in the non-streaming version. The conversation history is built up in `streamed_result.new_items` + # and the initial input. + # We need to ensure the `combined_input` logic is consistent. + # `streamed_result.input` is the initial input that started the *whole run*. + # `streamed_result.new_items` are items from previous turns of this run. + + # If current_turn is 1, the input to the model should be the `streamed_result.input` (original input for the run) + # plus any history from memory. + # If current_turn > 1, the `streamed_result.new_items` contain the conversation from previous turns of this run. + + combined_input_for_model: list[TResponseInputItem] = [] + combined_input_for_model.extend(history_messages_from_memory) # type: ignore[arg-type] + + if current_turn == 1: + # The first turn uses the initial input that started the run. + # (ItemHelpers.input_to_new_input_list can handle if it's already a list or a string) + initial_run_input_list = ItemHelpers.input_to_new_input_list(streamed_result.input) + combined_input_for_model.extend(initial_run_input_list) + # `streamed_result.new_items` should be empty on turn 1 before model call, + # but if it weren't, it would be messages from previous turns. + # The current logic in `_run_streamed_impl` updates `streamed_result.new_items` *after* the turn. + else: + # Subsequent turns use messages from `streamed_result.new_items` which accumulate turn by turn. + # These items already include user messages and assistant responses from previous turns of this run. + combined_input_for_model.extend(generated_item_messages) + + + # The `current_turn_new_input_list_for_memory_saving` is what *this specific turn* adds as new "user" input. + # In streaming, this is tricky because there isn't a direct "input for this turn" passed around + # after the very first input. The "user" messages are often part of `new_items` if they were added. + # For consistency with how `_get_single_step_result_from_response` saves to memory, + # we need to identify the "new user input" for *this* turn. + # However, `_run_single_turn_streamed` doesn't receive `original_input` like `_run_single_turn`. + # The `streamed_result.input` is the *initial* input to the whole chain. + # `streamed_result.new_items` are all previous items. + # This means the "new input for this turn" concept is different. + # The model is called with the cumulative history. + + # Let's assume for now that memory saving in `_get_single_step_result_from_response` + # will correctly use its `current_turn_new_input_list` parameter. + # The challenge is what to pass as `current_turn_new_input_list` from here. + # For streaming, the "new input" is implicitly part of the stream if it's a user message. + # The current design of `_get_single_step_result_from_response` expects `current_turn_new_input_list` + # to be specifically the *new user messages* for the current turn. + # In streaming, the "new user message" that triggers a turn (after the first) isn't explicitly passed. + # It's assumed to be the last message in `streamed_result.new_items` if it's from the user. + + # This part of the plan might need re-evaluation for streaming, as the input flow is different. + # For now, let's focus on getting the `combined_input_for_model` correct. + # --- Memory loading and input preparation for streaming END --- + # 1. Stream the output events. async for event in model.stream_response( system_prompt, - input, + combined_input_for_model, # Use the carefully constructed combined input model_settings, all_tools, output_schema, @@ -706,10 +771,58 @@ async def _run_single_turn_streamed( raise ModelBehaviorError("Model did not produce a final response!") # 3. Now we can process the turn as we do in the non-streaming case. + # For saving to memory, `_get_single_step_result_from_response` needs `current_turn_new_input_list`. + # In the streaming context, after the first turn, "new user input" is not explicitly provided to `_run_single_turn_streamed`. + # The model reacts to the accumulated history. + # If the last message in `combined_input_for_model` (before this turn's LLM call) was from the 'user', + # that could be considered the "new input" for memory saving purposes. + # This is a divergence from the non-streaming flow that needs careful handling. + + # Let's assume, for now, that `streamed_result.input` (which is the initial input to the whole run) + # is the closest analogue to `original_input` for the *first* turn memory saving. + # For subsequent turns, there isn't a direct equivalent of "new input for this specific turn" + # being passed to `_run_single_turn_streamed`. + + # The `original_input` parameter for `_get_single_step_result_from_response` + # is used by memory saving logic as `current_turn_new_input_list`. + # We need to construct this appropriately. + # If current_turn == 1, it's ItemHelpers.input_to_new_input_list(streamed_result.input) + # If current_turn > 1, what should it be? The model has already seen previous items. + # The memory saving should only save the *actual new messages* of this turn. + # User messages in streaming (after first) are typically added via user interaction with the stream. + # This function doesn't see that "new" user message directly. + + # Given the subtask is to integrate memory saving by calling _get_single_step_result_from_response, + # we must provide what it expects. + # The simplest approach for now, to make it runnable, is to pass an empty list + # for `current_turn_new_input_list` if it's not the first turn, acknowledging this is a gap + # in how new user inputs are captured for memory in streaming turns after the first. + # Or, we can assume `streamed_result.new_items` contains the new user message if it was added before this call. + + current_turn_new_input_list_for_memory: list[TResponseInputItem] + if current_turn == 1: + current_turn_new_input_list_for_memory = ItemHelpers.input_to_new_input_list(streamed_result.input) + else: + # In streaming, subsequent "user" inputs are part of the history that `model.stream_response` gets. + # They would be in `streamed_result.new_items` if added by the streaming handler. + # The `_get_single_step_result_from_response` expects only the *newest* user messages for *this* turn. + # This is a conceptual mismatch for streaming after turn 1. + # For now, let's assume no *new* user messages are being introduced to this function for turns > 1, + # as they are already part of the history. + # This means memory might not capture "user" messages correctly after turn 1 in streaming. + # This needs to be addressed in how user messages are added to `streamed_result.new_items` + # and then identified for memory saving. + # A pragmatic choice: if the *last* item in `generated_item_messages` (inputs to LLM for this turn, from previous turns) + # was from a user, maybe that's the "new" input. This is heuristic. + # For now, to fulfill the signature, and noting this limitation: + current_turn_new_input_list_for_memory = [] # Placeholder for turns > 1 + + single_step_result = await cls._get_single_step_result_from_response( agent=agent, - original_input=streamed_result.input, - pre_step_items=streamed_result.new_items, + original_input=streamed_result.input, # This is the input to the entire run. + current_turn_new_input_list=current_turn_new_input_list_for_memory, #This is what's saved as "user input for the turn" + pre_step_items=streamed_result.new_items, # Items from previous turns of this run new_response=final_response, output_schema=output_schema, all_tools=all_tools, @@ -737,6 +850,7 @@ async def _run_single_turn( should_run_agent_start_hooks: bool, tool_use_tracker: AgentToolUseTracker, previous_response_id: str | None, + current_turn: int, ) -> SingleStepResult: # Ensure we run the hooks before anything else. if should_run_agent_start_hooks: @@ -753,13 +867,41 @@ async def _run_single_turn( output_schema = cls._get_output_schema(agent) handoffs = cls._get_handoffs(agent) - input = ItemHelpers.input_to_new_input_list(original_input) - input.extend([generated_item.to_input_item() for generated_item in generated_items]) + + # --- Memory loading and input preparation START --- + memory = agent.memory + if current_turn == 1: + # This ensures that for agents with persistent memory (like FileStorageMemory), + # the history is loaded at the beginning of their first turn in a multi-agent + # conversation or a resumed conversation. Agent.__post_init__ also calls load(), + # so this call here is specifically for reloading if the agent instance is reused + # across multiple Runner.run calls or if the underlying store was modified externally. + memory.load() + + # 1. Messages from memory (history from previous runs/sessions) + history_messages_from_memory = memory.get_messages() + + # 2. `generated_items` are from previous turns within the *current* `Runner.run` call. + generated_item_messages: list[TResponseInputItem] = [ + generated_item.to_input_item() for generated_item in generated_items + ] + + # 3. `original_input` is the new message(s) for the *current* turn of the *current* `Runner.run` call. + # This can be a single string or a list of TResponseInputItem. + current_turn_new_input_list: list[TResponseInputItem] = ItemHelpers.input_to_new_input_list(original_input) + + # Combine all message sources in chronological order: + # memory_messages + generated_item_messages + original_input_messages + combined_input: list[TResponseInputItem] = [] + combined_input.extend(history_messages_from_memory) # type: ignore[arg-type] + combined_input.extend(generated_item_messages) + combined_input.extend(current_turn_new_input_list) + # --- Memory loading and input preparation END --- new_response = await cls._get_new_response( agent, system_prompt, - input, + combined_input, # Use the combined list output_schema, all_tools, handoffs, @@ -767,11 +909,14 @@ async def _run_single_turn( run_config, tool_use_tracker, previous_response_id, + # current_turn is passed through to the memory system + current_turn=current_turn, ) return await cls._get_single_step_result_from_response( agent=agent, - original_input=original_input, + original_input=original_input, # This is the original input to _run_single_turn + current_turn_new_input_list=current_turn_new_input_list, # Pass the processed new inputs pre_step_items=generated_items, new_response=new_response, output_schema=output_schema, @@ -798,6 +943,7 @@ async def _get_single_step_result_from_response( context_wrapper: RunContextWrapper[TContext], run_config: RunConfig, tool_use_tracker: AgentToolUseTracker, + current_turn_new_input_list: list[TResponseInputItem], # Added parameter ) -> SingleStepResult: processed_response = RunImpl.process_model_response( agent=agent, @@ -809,6 +955,75 @@ async def _get_single_step_result_from_response( tool_use_tracker.add_tool_use(agent, processed_response.tools_used) + # --- Memory saving START --- + memory = agent.memory + + # 1. Add the new user messages for this turn to memory + # current_turn_new_input_list contains only the fresh inputs for this turn + for user_message_item in current_turn_new_input_list: + # Ensure content is a string, though TResponseInputItem content should already be. + memory.add(role=user_message_item["role"], content=str(user_message_item["content"])) + + # 2. Add the assistant's new messages from this turn to memory + # These are the direct textual/object outputs from the LLM, before tool execution. + from .items import MessageOutputItem # Local import to avoid circular dependency issues at module level + + for item in processed_response.new_items: + if isinstance(item, MessageOutputItem): + # item.raw_item is ResponseOutputMessage + # item.raw_item.content is a list of content parts (e.g., TextPart, ImageURLPart) + # We'll concatenate text from all TextPart instances. + text_content = "" + if item.raw_item.content: # Content can sometimes be None for certain roles/tool_calls + for part in item.raw_item.content: + if hasattr(part, "text") and isinstance(part.text, str): # Check if it's a TextPart or similar + text_content += part.text + # Note: Other content part types (like images) are currently ignored for memory. + # If structured JSON is part of a TextPart, it will be saved as a string. + + # Only add if there's actual text content or if it's a tool_call request + # (which might have None content but important role/tool_call_id). + # For memory, typically we want to store textual conversation. + # Tool call requests themselves (role='assistant', tool_calls=[...]) are important context. + # If item.raw_item.tool_calls is not None, it means it's an assistant message requesting tool calls. + # The content of such messages might be empty or just instructive text. + if text_content or item.raw_item.tool_calls: + # If there are tool_calls, the content might be None or some wrapper text. + # We prioritize text_content if available, otherwise save the fact of tool_call. + # For simplicity, AgentMemory expects string content. + # If tool_calls are present, we might want to serialize them or just save the text part. + # Current memory.add expects string. + # Let's stick to saving the textual part of the message. + # If the message was *only* a tool call request with no text part, + # item.raw_item.content might be empty or None. + # The role is item.raw_item.role, which is typically 'assistant' + memory.add(role=item.raw_item.role, content=text_content) + + + # 3. Add tool call results (role='tool') to memory + # These are added to `generated_items` after tool execution in `RunImpl.execute_tools_and_side_effects` + # The current structure adds LLM response first, then tools are executed. + # The tool results will be part of `generated_items` for the *next* turn's `combined_input`. + # So, we don't need to explicitly add tool results here as they will be captured + # when they become part of `combined_input` in a subsequent `_run_single_turn` call, + # and then passed as `current_turn_new_input_list` to this function. + # However, the problem description implies saving the "interaction", which includes tool responses *for this turn*. + # Let's look at `RunImpl.execute_tools_and_side_effects` - it returns `next_step_items`. + # These `next_step_items` will contain `ToolResultItem`s. + # + # Re-evaluating: The task is to save "current turn's interaction". + # `processed_response.new_items` has the assistant's direct output (text, tool_code). + # The actual tool *results* are generated *after* this point, inside `execute_tools_and_side_effects`. + # This means we can't save tool *results* here. They will be saved in the *next* turn + # when they form part of that turn's `current_turn_new_input_list` (if they were user-like messages) + # or if we explicitly add items of role 'tool' from `generated_items` at the start of `_run_single_turn`. + + # Let's stick to the current subtask: save user input and assistant response. + # Tool calls (requests) are part of assistant response. Tool results are separate. + + memory.save() + # --- Memory saving END --- + return await RunImpl.execute_tools_and_side_effects( agent=agent, original_input=original_input, diff --git a/src/agents/tracing/setup.py b/src/agents/tracing/setup.py index 0ed2a9a2..d9d253a6 100644 --- a/src/agents/tracing/setup.py +++ b/src/agents/tracing/setup.py @@ -9,7 +9,8 @@ from .processor_interface import TracingProcessor from .scope import Scope from .spans import NoOpSpan, Span, SpanImpl, TSpanData -from .traces import NoOpTrace, Trace, TraceImpl +from .traces import NoOpTrace, Trace, TraceImpl # Trace already imported +from typing import Dict # Added for Dict type hint class SynchronousMultiTracingProcessor(TracingProcessor): @@ -83,6 +84,8 @@ def force_flush(self): class TraceProvider: def __init__(self): self._multi_processor = SynchronousMultiTracingProcessor() + self.memory_collector = MemoryTraceCollector() + self._multi_processor.add_tracing_processor(self.memory_collector) self._disabled = os.environ.get("OPENAI_AGENTS_DISABLE_TRACING", "false").lower() in ( "true", "1", @@ -90,11 +93,26 @@ def __init__(self): def register_processor(self, processor: TracingProcessor): """ - Add a processor to the list of processors. Each processor will receive all traces/spans. + Add a user-defined processor to the list of processors. + The internal MemoryTraceCollector will always remain active. """ + # The MemoryTraceCollector is already added and will receive all events. + # This method is for external processors. self._multi_processor.add_tracing_processor(processor) def set_processors(self, processors: list[TracingProcessor]): + """ + Set the list of user-defined processors. This will replace the current list of + user-defined processors. The internal MemoryTraceCollector will remain active. + """ + # Ensure memory_collector is always present + all_processors = [self.memory_collector] + processors + self._multi_processor.set_processors(all_processors) + + def get_collected_traces(self) -> Dict[str, Trace]: + return self.memory_collector.traces + + def get_current_trace(self) -> Trace | None: """ Set the list of processors. This will replace the current list of processors. """ @@ -211,4 +229,44 @@ def shutdown(self) -> None: logger.error(f"Error shutting down trace provider: {e}") +# Define MemoryTraceCollector class here +class MemoryTraceCollector(TracingProcessor): + """Collects completed traces in memory.""" + + def __init__(self): + self.traces: Dict[str, Trace] = {} + logger.debug("MemoryTraceCollector initialized.") + + def on_trace_start(self, trace: Trace) -> None: + """Called when a trace is started.""" + # logger.debug(f"MemoryTraceCollector: Trace started - {trace.trace_id}") + pass # No-op, we only care about completed traces + + def on_trace_end(self, trace: Trace) -> None: + """Called when a trace is finished.""" + logger.debug(f"MemoryTraceCollector: Trace ended - {trace.trace_id}. Storing trace.") + self.traces[trace.trace_id] = trace + + def on_span_start(self, span: Span[Any]) -> None: + """Called when a span is started.""" + # logger.debug(f"MemoryTraceCollector: Span started - {span.span_id} in trace {span.trace_id}") + pass # No-op, span data is part of the Trace object + + def on_span_end(self, span: Span[Any]) -> None: + """Called when a span is finished.""" + # logger.debug(f"MemoryTraceCollector: Span ended - {span.span_id} in trace {span.trace_id}") + pass # No-op, span data is part of the Trace object + + def shutdown(self) -> None: + """Called when the application stops.""" + logger.debug(f"MemoryTraceCollector: Shutdown called. {len(self.traces)} traces collected.") + # Optionally clear traces on shutdown if desired, or they remain in memory + # self.traces.clear() + + def force_flush(self) -> None: + """Force the processor to flush its buffers.""" + # logger.debug("MemoryTraceCollector: Force flush called.") + pass # No-op, as storage is synchronous in on_trace_end + + GLOBAL_TRACE_PROVIDER = TraceProvider() diff --git a/src/agents/web_visualizer/__init__.py b/src/agents/web_visualizer/__init__.py new file mode 100644 index 00000000..078b81ae --- /dev/null +++ b/src/agents/web_visualizer/__init__.py @@ -0,0 +1,2 @@ +# This file makes the web_visualizer directory a Python package. +# It can be empty. diff --git a/src/agents/web_visualizer/app.py b/src/agents/web_visualizer/app.py new file mode 100644 index 00000000..dde78a55 --- /dev/null +++ b/src/agents/web_visualizer/app.py @@ -0,0 +1,59 @@ +import sys +from pathlib import Path +# Ensure src directory is in path for imports if running app.py directly +sys.path.append(str(Path(__file__).resolve().parents[2])) + +from flask import Flask, render_template, abort +from src.agents.tracing.setup import GLOBAL_TRACE_PROVIDER +from src.agents.extensions.visualization import generate_graph_from_trace +import graphviz # For rendering DOT to SVG + +app = Flask(__name__, template_folder='templates') + +# In a real scenario, you might run your agent code first, +# then start this Flask app to view the traces. +# For this subtask, we'll assume GLOBAL_TRACE_PROVIDER is populated +# by some means before accessing the web page (e.g. by running an agent script +# that uses the same GLOBAL_TRACE_PROVIDER instance). + +@app.route('/') +def home(): + # Basic home page, later can list traces + trace_ids = list(GLOBAL_TRACE_PROVIDER.get_collected_traces().keys()) + return f''' +

Available Traces

+
    + {''.join(f'
  • {tid}
    • ' for tid in trace_ids)} +
+ If no traces are listed, run an agent script that uses tracing first. + ''' + +@app.route('/trace/') +def view_trace_route(trace_id: str): + trace_data = GLOBAL_TRACE_PROVIDER.get_collected_traces().get(trace_id) + if not trace_data: + return abort(404, description="Trace not found.") + + try: + dot_string = generate_graph_from_trace(trace_data) + # Render DOT string to SVG using graphviz library + # The pipe() method returns bytes, so decode to utf-8 for HTML embedding + svg_output = graphviz.Source(dot_string, format="svg").pipe().decode('utf-8') + except Exception as e: + app.logger.error(f"Error generating graph for trace {trace_id}: {e}") + return abort(500, description=f"Error generating graph: {e}") + + return render_template('view_trace.html', + trace_id=trace_id, + trace_name=trace_data.name, + graph_svg=svg_output, + num_spans=len(trace_data.spans)) + +if __name__ == '__main__': + # Note: For development, it's better to use `flask run` command. + # This basic `app.run` is for simplicity in the subtask. + # Ensure an agent script has run and populated GLOBAL_TRACE_PROVIDER before starting. + print("Flask server starting. Run an agent script in another terminal/process first to populate traces.") + print("To see traces, navigate to http://127.0.0.1:5001/ after traces are populated.") + print("Then, navigate to http://127.0.0.1:5001/trace/") + app.run(debug=True, port=5001) # Using a different port in case 5000 is common diff --git a/src/agents/web_visualizer/templates/__init__.py b/src/agents/web_visualizer/templates/__init__.py new file mode 100644 index 00000000..d3f0b370 --- /dev/null +++ b/src/agents/web_visualizer/templates/__init__.py @@ -0,0 +1,3 @@ +# This file can be empty. It helps Python recognize this directory as a package, +# though for Flask template loading, it's not strictly necessary when using +# the `template_folder` argument in the Flask app constructor. diff --git a/src/agents/web_visualizer/templates/view_trace.html b/src/agents/web_visualizer/templates/view_trace.html new file mode 100644 index 00000000..1e53038d --- /dev/null +++ b/src/agents/web_visualizer/templates/view_trace.html @@ -0,0 +1,26 @@ + + + + + View Trace: {{ trace_name }} + + + +
+

Trace: {{ trace_name }} (ID: {{ trace_id }})

+
+

Number of spans: {{ num_spans }}

+
+

Execution Graph

+
+ {{ graph_svg|safe }} +
+
+ + diff --git a/tests/test_memory.py b/tests/test_memory.py new file mode 100644 index 00000000..21a1b6cc --- /dev/null +++ b/tests/test_memory.py @@ -0,0 +1,293 @@ +import asyncio +import json +import os +import shutil +import tempfile +import unittest +from pathlib import Path +import sys + +# Ensure src directory is in Python path +sys.path.append(str(Path(__file__).resolve().parents[1])) + +from src.agents import Agent, Runner +from src.agents.memory import AgentMemory, InMemoryMemory, FileStorageMemory + +class TestInMemoryMemory(unittest.TestCase): + def setUp(self): + self.memory = InMemoryMemory() + + def test_add_and_get_messages(self): + self.assertEqual(self.memory.get_messages(), []) + self.memory.add(role="user", content="Hello") + self.assertEqual(len(self.memory.get_messages()), 1) + self.assertEqual(self.memory.get_messages()[0], {"role": "user", "content": "Hello"}) + + self.memory.add(role="assistant", content="Hi there!", metadata={"timestamp": 123}) + self.assertEqual(len(self.memory.get_messages()), 2) + self.assertEqual( + self.memory.get_messages()[1], + {"role": "assistant", "content": "Hi there!", "metadata": {"timestamp": 123}}, + ) + # Ensure original list is returned, not a copy that can be mutated externally for this test + messages = self.memory.get_messages() + messages.append({"role": "system", "content": "mutated"}) + self.assertEqual(len(self.memory.get_messages()), 2) # Should not reflect the append + + def test_get_last_n_messages(self): + self.memory.add(role="user", content="Message 1") + self.memory.add(role="assistant", content="Message 2") + self.memory.add(role="user", content="Message 3") + self.memory.add(role="assistant", content="Message 4") + + self.assertEqual(len(self.memory.get_last_n_messages(0)), 0) + + last_2 = self.memory.get_last_n_messages(2) + self.assertEqual(len(last_2), 2) + self.assertEqual(last_2[0]["content"], "Message 3") + self.assertEqual(last_2[1]["content"], "Message 4") + + last_3 = self.memory.get_last_n_messages(3) + self.assertEqual(len(last_3), 3) + self.assertEqual(last_3[0]["content"], "Message 2") + + last_4 = self.memory.get_last_n_messages(4) + self.assertEqual(len(last_4), 4) + self.assertEqual(last_4[0]["content"], "Message 1") + + # N greater than total messages + last_10 = self.memory.get_last_n_messages(10) + self.assertEqual(len(last_10), 4) + self.assertEqual(last_10[0]["content"], "Message 1") + + # Ensure original list is returned + retrieved_messages = self.memory.get_last_n_messages(2) + retrieved_messages.append({"role": "system", "content": "mutated"}) + self.assertEqual(len(self.memory.get_last_n_messages(2)), 2) + + + def test_clear(self): + self.memory.add(role="user", content="Hello") + self.memory.add(role="assistant", content="Hi") + self.assertNotEqual(len(self.memory.get_messages()), 0) + self.memory.clear() + self.assertEqual(len(self.memory.get_messages()), 0) + + def test_load_save_are_no_ops(self): + # These methods are implemented as pass in InMemoryMemory + # Just call them to ensure no exceptions are raised. + try: + self.memory.load() + self.memory.save() + except Exception as e: + self.fail(f"InMemoryMemory load() or save() raised an exception: {e}") + + +class TestFileStorageMemory(unittest.TestCase): + def setUp(self): + # Create a temporary directory for test files + self.test_dir = tempfile.mkdtemp() + self.memory_file_path = Path(self.test_dir) / "test_memory.json" + + def tearDown(self): + # Remove the temporary directory and its contents + shutil.rmtree(self.test_dir) + + def test_add_and_save_persists(self): + # Instance 1: Add messages + memory1 = FileStorageMemory(self.memory_file_path) + memory1.add(role="user", content="Persistence test 1") + memory1.add(role="assistant", content="Acknowledged 1", metadata={"id": 1}) + # add() calls save() automatically + + # Instance 2: Load from the same file + memory2 = FileStorageMemory(self.memory_file_path) + # load() is called in __init__ if file exists + messages = memory2.get_messages() + self.assertEqual(len(messages), 2) + self.assertEqual(messages[0]["content"], "Persistence test 1") + self.assertEqual(messages[1]["metadata"], {"id": 1}) + + def test_load_from_existing_file(self): + # Manually create a JSON file + manual_messages = [ + {"role": "system", "content": "System init"}, + {"role": "user", "content": "Manual load test"}, + ] + with open(self.memory_file_path, "w", encoding="utf-8") as f: + json.dump(manual_messages, f) + + memory = FileStorageMemory(self.memory_file_path) + # load() called in __init__ + messages = memory.get_messages() + self.assertEqual(len(messages), 2) + self.assertEqual(messages[0]["role"], "system") + self.assertEqual(messages[1]["content"], "Manual load test") + + def test_load_non_existent_file(self): + # Ensure file does not exist before creating memory instance + if self.memory_file_path.exists(): + os.remove(self.memory_file_path) + + memory = FileStorageMemory(self.memory_file_path) + # load() called in __init__, should be empty if file not found + self.assertEqual(len(memory.get_messages()), 0) + + def test_load_corrupted_file(self): + # Create a corrupted JSON file + with open(self.memory_file_path, "w", encoding="utf-8") as f: + f.write("this is not valid json") + + memory = FileStorageMemory(self.memory_file_path) + # load() should handle JSONDecodeError and start with empty memory + self.assertEqual(len(memory.get_messages()), 0) + + # Also test with an empty file (which is also invalid JSON for a list) + with open(self.memory_file_path, "w", encoding="utf-8") as f: + f.write("") + memory2 = FileStorageMemory(self.memory_file_path) + self.assertEqual(len(memory2.get_messages()), 0) + + + def test_clear_persists(self): + # Add messages, they get saved + memory1 = FileStorageMemory(self.memory_file_path) + memory1.add(role="user", content="To be cleared") + self.assertTrue(self.memory_file_path.exists()) + self.assertNotEqual(os.path.getsize(self.memory_file_path), 0) + + + # Clear the memory + memory1.clear() # clear() also calls save() + self.assertEqual(len(memory1.get_messages()), 0) + + # Ensure the file reflects the cleared state (empty list) + with open(self.memory_file_path, "r", encoding="utf-8") as f: + cleared_content = json.load(f) + self.assertEqual(cleared_content, []) + + # Create a new instance, load, and verify it's empty + memory2 = FileStorageMemory(self.memory_file_path) + self.assertEqual(len(memory2.get_messages()), 0) + + def test_directory_creation(self): + deep_dir_path = Path(self.test_dir) / "sub" / "deep" / "memory.json" + memory = FileStorageMemory(deep_dir_path) + memory.add(role="user", content="Testing directory creation") + # save() is called by add(). If it didn't throw error, dir was created. + self.assertTrue(deep_dir_path.exists()) + self.assertTrue(deep_dir_path.parent.exists()) + self.assertTrue(deep_dir_path.parent.parent.exists()) + + def test_get_last_n_messages_file_storage(self): + memory = FileStorageMemory(self.memory_file_path) + memory.add(role="user", content="Msg 1") + memory.add(role="assistant", content="Msg 2") + memory.add(role="user", content="Msg 3") + memory.add(role="assistant", content="Msg 4") + + self.assertEqual(len(memory.get_last_n_messages(0)), 0) + + last_2 = memory.get_last_n_messages(2) + self.assertEqual(len(last_2), 2) + self.assertEqual(last_2[0]["content"], "Msg 3") + + # N greater than total messages + last_10 = memory.get_last_n_messages(10) + self.assertEqual(len(last_10), 4) + self.assertEqual(last_10[0]["content"], "Msg 1") + + +# Note: Agent integration tests will require OPENAI_API_KEY +# and will make actual API calls. +# Consider using unittest.skipIf to skip these if the key is not set. +OPENAI_API_KEY_SET = os.getenv("OPENAI_API_KEY") is not None + +@unittest.skipUnless(OPENAI_API_KEY_SET, "OPENAI_API_KEY is not set, skipping agent integration tests") +class TestAgentWithFileMemory(unittest.IsolatedAsyncioTestCase): + async def asyncSetUp(self): + self.test_dir = tempfile.mkdtemp() + self.memory_file_path = Path(self.test_dir) / "agent_test_memory.json" + + async def asyncTearDown(self): + shutil.rmtree(self.test_dir) + + async def test_agent_remembers_across_runs(self): + # Agent 1: Provide information + mem1 = FileStorageMemory(self.memory_file_path) + agent1 = Agent( + name="MemoryTestAgent1", + instructions="You are a helpful assistant. My favorite food is lasagna.", + memory=mem1, + ) + # Initial interaction to set the information - we ask a generic question to trigger the instructions. + await Runner.run(agent1, "Hello there.") + # The instructions themselves aren't directly saved as a "user" message in memory by this interaction. + # Let's explicitly tell the agent the information. + + agent1_explicit = Agent( + name="MemoryTestAgent1Explicit", + instructions="Remember this information.", + memory=mem1 # Use same memory object + ) + await Runner.run(agent1_explicit, "My favorite food is lasagna.") + + + # Agent 2: Recall information using a new agent instance with the same memory file + mem2 = FileStorageMemory(self.memory_file_path) # New memory instance, same file + agent2 = Agent( + name="MemoryTestAgent2", + instructions="You are a helpful assistant trying to recall information.", + memory=mem2, + ) + response = await Runner.run(agent2, "What is my favorite food?") + + final_output = str(response.final_output).lower() + print(f"Recall Test (lasagna): Agent response: '{final_output}'") + self.assertIn("lasagna", final_output, + f"Agent should have remembered 'lasagna'. Response: {final_output}") + + async def test_agent_forgets_after_clear(self): + # Agent 1: Provide information + mem1 = FileStorageMemory(self.memory_file_path) + agent1 = Agent( + name="MemoryClearAgent1", + instructions="Remember this fact carefully.", + memory=mem1, + ) + await Runner.run(agent1, "The secret code is 'X Y Z'.") + + # Agent 2: Recall information + mem2 = FileStorageMemory(self.memory_file_path) + agent2 = Agent( + name="MemoryClearAgent2", + instructions="Recall the fact.", + memory=mem2, + ) + response_before_clear = await Runner.run(agent2, "What is the secret code?") + final_output_before = str(response_before_clear.final_output).lower() + print(f"Recall Test Before Clear (XYZ): Agent response: '{final_output_before}'") + self.assertTrue("x y z" in final_output_before or "xyz" in final_output_before, + f"Agent should have remembered 'X Y Z' before clear. Response: {final_output_before}") + + # Clear memory of agent2 (which uses mem2, pointing to the same file) + agent2.memory.clear() # This clears mem2 and saves the empty state to the file + + # Agent 3: Try to recall after clear + mem3 = FileStorageMemory(self.memory_file_path) # New memory instance, same (now cleared) file + agent3 = Agent( + name="MemoryClearAgent3", + instructions="Try to recall the fact again.", + memory=mem3, + ) + response_after_clear = await Runner.run(agent3, "What is the secret code?") + final_output_after = str(response_after_clear.final_output).lower() + print(f"Recall Test After Clear (XYZ): Agent response: '{final_output_after}'") + self.assertNotIn("x y z", final_output_after, + f"Agent should NOT have remembered 'X Y Z' after clear. Response: {final_output_after}") + self.assertNotIn("xyz", final_output_after, + f"Agent should NOT have remembered 'xyz' after clear. Response: {final_output_after}") + + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_orchestration.py b/tests/test_orchestration.py new file mode 100644 index 00000000..c61b0303 --- /dev/null +++ b/tests/test_orchestration.py @@ -0,0 +1,221 @@ +import asyncio +import unittest +from unittest.mock import patch, AsyncMock +from typing import Any, Dict, List, Optional +import sys +from pathlib import Path + +# Ensure src directory is in Python path +sys.path.append(str(Path(__file__).resolve().parents[1])) + +from src.agents import Agent, Runner, RunContextWrapper +from src.agents.orchestrator import run_parallel_tasks +from src.agents.result import RunResult # Needed for mock_runner_run +from src.agents.items import ResponseOutputItem, ResponseOutputMessage # Needed for mock_runner_run + +# 1. Setup +class SimpleTestAgent(Agent[None]): + """A worker agent that returns a predictable output based on its name and input.""" + async def _get_new_response_impl(self, input_items, model_settings, tools, output_schema, handoffs, tracing, previous_response_id): + # This is a simplified way to simulate an agent's response without actual LLM calls. + # Runner.run would typically call the LLM. Here we bypass that for predictable test behavior. + # The actual input to _get_new_response_impl is more complex, but for this test, + # we'll assume input_items contains a single user message. + + # Find the primary input content + input_content = "default_input" + if input_items and isinstance(input_items, list) and len(input_items) > 0: + # TResponseInputItem is a TypedDict: {"role": str, "content": str} + # For simplicity, let's assume the last message is the primary one if multiple exist. + last_item = input_items[-1] + if isinstance(last_item, dict) and "content" in last_item: + input_content = last_item["content"] + elif hasattr(last_item, 'content'): # If it's some other object with content + input_content = last_item.content + + + # Simulate the RunResult that Runner.run would typically produce + # The important part for the orchestrator tool is `final_output`. + # We need to construct a valid ModelResponse and RunResult + # For simplicity, we'll directly create a RunResult with final_output. + # This bypasses the internal complexities of Runner.run for this test agent. + + # To align with how Runner.run works, we'll make this agent directly return its processed string. + # The run_parallel_tasks tool calls Runner.run(worker_agent, ...). + # So, we need to make Runner.run for SimpleTestAgent return a RunResult. + # This is best achieved by mocking Runner.run when testing run_parallel_tasks. + # However, the request was to make SimpleTestAgent behave predictably. + # Let's define its behavior, and then in the tests, we'll mock Runner.run to produce this. + return f"{input_content}_processed_by_{self.name}" + + +class MockOrchestratorContext: + def __init__(self, worker_map: Dict[str, Agent[None]]): + self.worker_agents_map = worker_map + self.usage: Any = None # Mock usage tracking if needed by context + + +class TestRunParallelTasks(unittest.IsolatedAsyncioTestCase): + + async def asyncSetUp(self): + self.worker1 = SimpleTestAgent(name="worker1") + self.worker2 = SimpleTestAgent(name="worker2") + self.worker_agents_map = {"worker1": self.worker1, "worker2": self.worker2} + + mock_orch_context = MockOrchestratorContext(self.worker_agents_map) + self.run_context_wrapper = RunContextWrapper(context=mock_orch_context) + + # This mock will be used in most tests for predictable agent behavior + async def mock_runner_run_for_simple_agent(self, starting_agent: Agent[None], input: str, context: Any, **kwargs): + # Simulate the behavior of SimpleTestAgent + # Construct a minimal RunResult + output_content = f"{input}_processed_by_{starting_agent.name}" + + # Create a dummy ModelResponse structure if needed by RunResult or downstream processing + # For these tests, final_output is the key. + # The actual RunResult has more fields; fill them minimally. + return RunResult( + input=input, + new_items=[], # Assuming no new items for simplicity + raw_responses=[], # Assuming no raw responses for simplicity + final_output=output_content, + _last_agent=starting_agent, + input_guardrail_results=[], + output_guardrail_results=[], + context_wrapper=RunContextWrapper(context=context) # type: ignore + ) + + @patch('src.agents.orchestrator.Runner.run') # Patch where Runner.run is *used* + async def test_successful_parallel_execution(self, mock_run: AsyncMock): + mock_run.side_effect = self.mock_runner_run_for_simple_agent + + tasks = [ + {"agent_id": "worker1", "input_prompt": "task1_input"}, + {"agent_id": "worker2", "input_prompt": "task2_input"} + ] + results = await run_parallel_tasks(self.run_context_wrapper, tasks) + + self.assertEqual(len(results), 2) + # Order might not be guaranteed by asyncio.gather, so check contents flexibly + expected_results = [ + {"agent_id": "worker1", "output": "task1_input_processed_by_worker1"}, + {"agent_id": "worker2", "output": "task2_input_processed_by_worker2"} + ] + # Sort by agent_id for comparison if order is not guaranteed + self.assertIn(expected_results[0], results) + self.assertIn(expected_results[1], results) + self.assertEqual(mock_run.call_count, 2) + + + async def test_agent_not_found(self): + tasks = [{"agent_id": "worker_unknown", "input_prompt": "task_input"}] + results = await run_parallel_tasks(self.run_context_wrapper, tasks) + self.assertEqual(len(results), 1) + self.assertEqual(results[0]["agent_id"], "worker_unknown") + self.assertEqual(results[0]["output"], "Error: Agent 'worker_unknown' not found.") + + async def test_missing_agent_id(self): + tasks = [{"input_prompt": "task_input"}] # type: ignore # Intentionally malformed + results = await run_parallel_tasks(self.run_context_wrapper, tasks) # type: ignore + self.assertEqual(len(results), 1) + self.assertIsNone(results[0]["agent_id"]) # agent_id was missing + self.assertIn("Error: missing agent_id", results[0]["output"]) + + async def test_missing_input_prompt(self): + tasks = [{"agent_id": "worker1"}] # type: ignore # Intentionally malformed + results = await run_parallel_tasks(self.run_context_wrapper, tasks) # type: ignore + self.assertEqual(len(results), 1) + self.assertEqual(results[0]["agent_id"], "worker1") + self.assertIn("Error: missing input_prompt", results[0]["output"]) + + async def test_empty_task_list(self): + tasks: List[Dict[str, str]] = [] + results = await run_parallel_tasks(self.run_context_wrapper, tasks) + self.assertEqual(results, []) + + @patch('src.agents.orchestrator.Runner.run') + async def test_exception_during_agent_run(self, mock_run: AsyncMock): + async def mock_runner_run_with_faulty(starting_agent: Agent[None], input_str: str, context: Any, **kwargs): + if starting_agent.name == "faulty_worker": + raise ValueError("Simulated agent error") + # Use the standard mock for other agents if any + return await self.mock_runner_run_for_simple_agent(starting_agent, input_str, context, **kwargs) + + mock_run.side_effect = mock_runner_run_with_faulty + + faulty_worker = SimpleTestAgent(name="faulty_worker") # Actual type doesn't matter much due to mock + self.run_context_wrapper.context.worker_agents_map["faulty_worker"] = faulty_worker # type: ignore + + tasks = [ + {"agent_id": "faulty_worker", "input_prompt": "task_input"}, + {"agent_id": "worker1", "input_prompt": "task1_input"} # A successful one + ] + results = await run_parallel_tasks(self.run_context_wrapper, tasks) + + self.assertEqual(len(results), 2) + + faulty_result = next(r for r in results if r["agent_id"] == "faulty_worker") + successful_result = next(r for r in results if r["agent_id"] == "worker1") + + self.assertIn("Error during agent run: Simulated agent error", faulty_result["output"]) + self.assertEqual(successful_result["output"], "task1_input_processed_by_worker1") + self.assertEqual(mock_run.call_count, 2) + + + async def test_context_missing_worker_map(self): + class EmptyContext: pass + bad_run_context_wrapper = RunContextWrapper(context=EmptyContext()) # type: ignore + tasks = [{"agent_id": "worker1", "input_prompt": "task_input"}] + + with self.assertRaisesRegex(ValueError, "Orchestrator context must have a 'worker_agents_map' dictionary attribute."): + await run_parallel_tasks(bad_run_context_wrapper, tasks) + + @patch('src.agents.orchestrator.Runner.run') + async def test_mixed_success_and_failures(self, mock_run: AsyncMock): + async def mock_runner_run_mixed(starting_agent: Agent[None], input_str: str, context: Any, **kwargs): + if starting_agent.name == "faulty_worker": + raise ValueError("Simulated faulty error") + elif starting_agent.name == "worker1": + return await self.mock_runner_run_for_simple_agent(starting_agent, input_str, context, **kwargs) + # This case should ideally not be hit if tasks only specify known agent_ids or faulty_worker + return RunResult(final_output="unexpected_agent_behavior", _last_agent=starting_agent, input=input_str, new_items=[], raw_responses=[], input_guardrail_results=[], output_guardrail_results=[], context_wrapper=RunContextWrapper(context=context)) # type: ignore + + mock_run.side_effect = mock_runner_run_mixed + + faulty_worker = SimpleTestAgent(name="faulty_worker") + self.run_context_wrapper.context.worker_agents_map["faulty_worker"] = faulty_worker # type: ignore + + tasks = [ + {"agent_id": "worker1", "input_prompt": "good_task"}, + {"agent_id": "non_existent_worker", "input_prompt": "bad_agent_id_task"}, + {"agent_id": "faulty_worker", "input_prompt": "faulty_task_input"}, + {"agent_id": "worker2", "input_prompt": None}, # Missing prompt + ] + results = await run_parallel_tasks(self.run_context_wrapper, tasks) # type: ignore + + self.assertEqual(len(results), 4) + + expected_outputs_map = { + "worker1": "good_task_processed_by_worker1", + "non_existent_worker": "Error: Agent 'non_existent_worker' not found.", + "faulty_worker": "Error during agent run: Simulated faulty error", + "worker2": "Error: missing input_prompt" + } + + for result in results: + agent_id = result["agent_id"] + self.assertIn(agent_id, expected_outputs_map) # type: ignore + # For errors, check if the expected message is part of the output + if "Error" in expected_outputs_map[agent_id]: # type: ignore + self.assertIn(expected_outputs_map[agent_id], result["output"]) # type: ignore + else: + self.assertEqual(result["output"], expected_outputs_map[agent_id]) # type: ignore + + # Runner.run should be called for worker1 and faulty_worker + # non_existent_worker and worker2 (missing prompt) are handled before Runner.run call + self.assertEqual(mock_run.call_count, 2) + + +if __name__ == '__main__': + unittest.main() +``` From e2183e81f2f09fb217ef651a0c88009fc5acfab6 Mon Sep 17 00:00:00 2001 From: "google-labs-jules[bot]" <161369871+google-labs-jules[bot]@users.noreply.github.com> Date: Sat, 24 May 2025 03:15:34 +0000 Subject: [PATCH 24/24] Jules was unable to complete the task in time. Please review the work done so far and provide feedback for Jules to continue. --- src/agents/tracing/setup.py | 87 ++++++++++++++-------- src/agents/tracing/span_data.py | 30 ++++---- src/agents/tracing/spans.py | 26 ++++--- src/agents/tracing/traces.py | 123 ++++++++++++++++++++++++++++++-- src/agents/usage.py | 16 +++++ 5 files changed, 223 insertions(+), 59 deletions(-) diff --git a/src/agents/tracing/setup.py b/src/agents/tracing/setup.py index d9d253a6..51cd7979 100644 --- a/src/agents/tracing/setup.py +++ b/src/agents/tracing/setup.py @@ -9,8 +9,10 @@ from .processor_interface import TracingProcessor from .scope import Scope from .spans import NoOpSpan, Span, SpanImpl, TSpanData -from .traces import NoOpTrace, Trace, TraceImpl # Trace already imported -from typing import Dict # Added for Dict type hint +from .traces import NoOpTrace, Trace, TraceImpl +from typing import Dict +from pathlib import Path # Added for Path +import json # Added for json class SynchronousMultiTracingProcessor(TracingProcessor): @@ -82,10 +84,11 @@ def force_flush(self): class TraceProvider: - def __init__(self): + def __init__(self, trace_storage_dir: str = "./trace_data"): # Added trace_storage_dir self._multi_processor = SynchronousMultiTracingProcessor() - self.memory_collector = MemoryTraceCollector() - self._multi_processor.add_tracing_processor(self.memory_collector) + # Replace MemoryTraceCollector with FileTraceCollector + self.file_collector = FileTraceCollector(storage_dir=trace_storage_dir) + self._multi_processor.add_tracing_processor(self.file_collector) self._disabled = os.environ.get("OPENAI_AGENTS_DISABLE_TRACING", "false").lower() in ( "true", "1", @@ -94,23 +97,23 @@ def __init__(self): def register_processor(self, processor: TracingProcessor): """ Add a user-defined processor to the list of processors. - The internal MemoryTraceCollector will always remain active. + The internal FileTraceCollector will always remain active. """ - # The MemoryTraceCollector is already added and will receive all events. + # The FileTraceCollector is already added and will receive all events. # This method is for external processors. self._multi_processor.add_tracing_processor(processor) def set_processors(self, processors: list[TracingProcessor]): """ Set the list of user-defined processors. This will replace the current list of - user-defined processors. The internal MemoryTraceCollector will remain active. + user-defined processors. The internal FileTraceCollector will remain active. """ - # Ensure memory_collector is always present - all_processors = [self.memory_collector] + processors + # Ensure file_collector is always present + all_processors = [self.file_collector] + processors self._multi_processor.set_processors(all_processors) - def get_collected_traces(self) -> Dict[str, Trace]: - return self.memory_collector.traces + # get_collected_traces is removed as traces are now on disk. + # The Flask app will need to read from the directory. def get_current_trace(self) -> Trace | None: """ @@ -229,43 +232,67 @@ def shutdown(self) -> None: logger.error(f"Error shutting down trace provider: {e}") -# Define MemoryTraceCollector class here -class MemoryTraceCollector(TracingProcessor): - """Collects completed traces in memory.""" +# Define FileTraceCollector class here (replaces MemoryTraceCollector) +class FileTraceCollector(TracingProcessor): + """Collects completed traces and saves them to JSON files.""" - def __init__(self): - self.traces: Dict[str, Trace] = {} - logger.debug("MemoryTraceCollector initialized.") + def __init__(self, storage_dir: str = "./trace_data"): + self.storage_dir = Path(storage_dir) + try: + self.storage_dir.mkdir(parents=True, exist_ok=True) + logger.debug(f"FileTraceCollector initialized. Storage directory: {self.storage_dir.resolve()}") + except OSError as e: + logger.error(f"Error creating storage directory {self.storage_dir.resolve()}: {e}") + # Potentially re-raise or handle more gracefully depending on desired behavior + raise def on_trace_start(self, trace: Trace) -> None: """Called when a trace is started.""" - # logger.debug(f"MemoryTraceCollector: Trace started - {trace.trace_id}") + # logger.debug(f"FileTraceCollector: Trace started - {trace.trace_id}") pass # No-op, we only care about completed traces def on_trace_end(self, trace: Trace) -> None: - """Called when a trace is finished.""" - logger.debug(f"MemoryTraceCollector: Trace ended - {trace.trace_id}. Storing trace.") - self.traces[trace.trace_id] = trace + """Called when a trace is finished. Serializes and saves the trace.""" + if not hasattr(trace, 'to_dict') or not callable(trace.to_dict): + logger.error(f"FileTraceCollector: Trace object for trace_id {trace.trace_id} does not have a to_dict method. Cannot save.") + return + + try: + trace_dict = trace.to_dict() + if trace_dict is None: # Should not happen if to_dict is implemented correctly for non-NoOp traces + logger.warning(f"FileTraceCollector: to_dict() returned None for trace_id {trace.trace_id}. Skipping save.") + return + + file_path = self.storage_dir / f"{trace.trace_id}.json" + logger.debug(f"FileTraceCollector: Trace ended - {trace.trace_id}. Saving to {file_path.resolve()}") + + with open(file_path, "w", encoding="utf-8") as f: + json.dump(trace_dict, f, indent=2) + logger.debug(f"FileTraceCollector: Successfully saved trace {trace.trace_id} to {file_path.resolve()}") + + except Exception as e: + # Catching a broad exception to ensure the collector doesn't crash the application + logger.error(f"FileTraceCollector: Error saving trace {trace.trace_id} to file: {e}", exc_info=True) + def on_span_start(self, span: Span[Any]) -> None: """Called when a span is started.""" - # logger.debug(f"MemoryTraceCollector: Span started - {span.span_id} in trace {span.trace_id}") - pass # No-op, span data is part of the Trace object + # logger.debug(f"FileTraceCollector: Span started - {span.span_id} in trace {span.trace_id}") + pass # No-op, span data is part of the Trace object when trace.to_dict() is called def on_span_end(self, span: Span[Any]) -> None: """Called when a span is finished.""" - # logger.debug(f"MemoryTraceCollector: Span ended - {span.span_id} in trace {span.trace_id}") - pass # No-op, span data is part of the Trace object + # logger.debug(f"FileTraceCollector: Span ended - {span.span_id} in trace {span.trace_id}") + pass # No-op def shutdown(self) -> None: """Called when the application stops.""" - logger.debug(f"MemoryTraceCollector: Shutdown called. {len(self.traces)} traces collected.") - # Optionally clear traces on shutdown if desired, or they remain in memory - # self.traces.clear() + logger.debug(f"FileTraceCollector: Shutdown called. Traces are saved individually upon completion.") + pass def force_flush(self) -> None: """Force the processor to flush its buffers.""" - # logger.debug("MemoryTraceCollector: Force flush called.") + # logger.debug("FileTraceCollector: Force flush called.") pass # No-op, as storage is synchronous in on_trace_end diff --git a/src/agents/tracing/span_data.py b/src/agents/tracing/span_data.py index 5997c25f..77d0c4f9 100644 --- a/src/agents/tracing/span_data.py +++ b/src/agents/tracing/span_data.py @@ -14,7 +14,7 @@ class SpanData(abc.ABC): """ @abc.abstractmethod - def export(self) -> dict[str, Any]: + def to_dict(self) -> dict[str, Any]: # Renamed from export """Export the span data as a dictionary.""" pass @@ -49,7 +49,7 @@ def __init__( def type(self) -> str: return "agent" - def export(self) -> dict[str, Any]: + def to_dict(self) -> dict[str, Any]: # Renamed from export return { "type": self.type, "name": self.name, @@ -83,12 +83,12 @@ def __init__( def type(self) -> str: return "function" - def export(self) -> dict[str, Any]: + def to_dict(self) -> dict[str, Any]: # Renamed from export return { "type": self.type, "name": self.name, "input": self.input, - "output": str(self.output) if self.output else None, + "output": str(self.output) if self.output and not isinstance(self.output, (dict, list, tuple, str, int, float, bool)) else self.output, # Improved serialization for output "mcp_data": self.mcp_data, } @@ -125,7 +125,7 @@ def __init__( def type(self) -> str: return "generation" - def export(self) -> dict[str, Any]: + def to_dict(self) -> dict[str, Any]: # Renamed from export return { "type": self.type, "input": self.input, @@ -157,10 +157,14 @@ def __init__( def type(self) -> str: return "response" - def export(self) -> dict[str, Any]: + def to_dict(self) -> dict[str, Any]: # Renamed from export + # Ensure response is serializable, for now we just take ID. + # If self.response is complex and needs full serialization, that would go here. + # Assuming self.input is already serializable (str or list of dicts) return { "type": self.type, "response_id": self.response.id if self.response else None, + "input": self.input # Added input to the dictionary } @@ -180,7 +184,7 @@ def __init__(self, from_agent: str | None, to_agent: str | None): def type(self) -> str: return "handoff" - def export(self) -> dict[str, Any]: + def to_dict(self) -> dict[str, Any]: # Renamed from export return { "type": self.type, "from_agent": self.from_agent, @@ -204,7 +208,7 @@ def __init__(self, name: str, data: dict[str, Any]): def type(self) -> str: return "custom" - def export(self) -> dict[str, Any]: + def to_dict(self) -> dict[str, Any]: # Renamed from export return { "type": self.type, "name": self.name, @@ -228,7 +232,7 @@ def __init__(self, name: str, triggered: bool = False): def type(self) -> str: return "guardrail" - def export(self) -> dict[str, Any]: + def to_dict(self) -> dict[str, Any]: # Renamed from export return { "type": self.type, "name": self.name, @@ -267,7 +271,7 @@ def __init__( def type(self) -> str: return "transcription" - def export(self) -> dict[str, Any]: + def to_dict(self) -> dict[str, Any]: # Renamed from export return { "type": self.type, "input": { @@ -308,7 +312,7 @@ def __init__( def type(self) -> str: return "speech" - def export(self) -> dict[str, Any]: + def to_dict(self) -> dict[str, Any]: # Renamed from export return { "type": self.type, "input": self.input, @@ -339,7 +343,7 @@ def __init__( def type(self) -> str: return "speech_group" - def export(self) -> dict[str, Any]: + def to_dict(self) -> dict[str, Any]: # Renamed from export return { "type": self.type, "input": self.input, @@ -365,7 +369,7 @@ def __init__(self, server: str | None = None, result: list[str] | None = None): def type(self) -> str: return "mcp_tools" - def export(self) -> dict[str, Any]: + def to_dict(self) -> dict[str, Any]: # Renamed from export return { "type": self.type, "server": self.server, diff --git a/src/agents/tracing/spans.py b/src/agents/tracing/spans.py index ee933e73..19953f43 100644 --- a/src/agents/tracing/spans.py +++ b/src/agents/tracing/spans.py @@ -79,7 +79,7 @@ def error(self) -> SpanError | None: pass @abc.abstractmethod - def export(self) -> dict[str, Any] | None: + def to_dict(self) -> dict[str, Any] | None: # Renamed from export pass @property @@ -144,7 +144,7 @@ def set_error(self, error: SpanError) -> None: def error(self) -> SpanError | None: return None - def export(self) -> dict[str, Any] | None: + def to_dict(self) -> dict[str, Any] | None: # Renamed from export return None @property @@ -251,14 +251,20 @@ def started_at(self) -> str | None: def ended_at(self) -> str | None: return self._ended_at - def export(self) -> dict[str, Any] | None: + def to_dict(self) -> dict[str, Any] | None: # Renamed from export + status = "OK" + error_details = None + if self._error: + status = "ERROR" + error_details = self._error # self._error is already a SpanError TypedDict + return { - "object": "trace.span", - "id": self.span_id, "trace_id": self.trace_id, - "parent_id": self._parent_id, - "started_at": self._started_at, - "ended_at": self._ended_at, - "span_data": self.span_data.export(), - "error": self._error, + "span_id": self.span_id, + "parent_id": self.parent_id, + "start_time": self.started_at, # Already ISO string or None + "end_time": self.ended_at, # Already ISO string or None + "status": status, + "error": error_details, # Include the error details if present + "span_data": self.span_data.to_dict() # Call to_dict on the SpanData object } diff --git a/src/agents/tracing/traces.py b/src/agents/tracing/traces.py index 53d06284..5bc4d87c 100644 --- a/src/agents/tracing/traces.py +++ b/src/agents/tracing/traces.py @@ -60,12 +60,39 @@ def name(self) -> str: pass @abc.abstractmethod - def export(self) -> dict[str, Any] | None: + def to_dict(self) -> dict[str, Any] | None: # Renamed from export """ Export the trace as a dictionary. """ pass + # Add properties that are expected by to_dict, assuming they'll be populated + # by the tracing system before to_dict() is called on a completed trace. + @property + @abc.abstractmethod + def start_time(self) -> Any: # Should be datetime, but TraceImpl doesn't have it yet + pass + + @property + @abc.abstractmethod + def end_time(self) -> Any: # Should be datetime + pass + + @property + @abc.abstractmethod + def status(self) -> Any: # Should be an Enum-like object with a .value + pass + + @property + @abc.abstractmethod + def spans(self) -> list[Any]: # Should be list[Span] + pass + + @property + @abc.abstractmethod + def usage(self) -> Any: # Should be an instance of Usage + pass + class NoOpTrace(Trace): """ @@ -107,9 +134,20 @@ def trace_id(self) -> str: def name(self) -> str: return "no-op" - def export(self) -> dict[str, Any] | None: + def to_dict(self) -> dict[str, Any] | None: # Renamed from export return None + @property + def start_time(self) -> Any: return None + @property + def end_time(self) -> Any: return None + @property + def status(self) -> Any: return None # Or a default status object + @property + def spans(self) -> list[Any]: return [] + @property + def usage(self) -> Any: return None + NO_OP_TRACE = NoOpTrace() @@ -185,11 +223,84 @@ def __enter__(self) -> Trace: def __exit__(self, exc_type, exc_val, exc_tb): self.finish(reset_current=exc_type is not GeneratorExit) - def export(self) -> dict[str, Any] | None: + # These attributes will need to be set by the tracing system upon trace completion. + # Initialize them to None or default values. + _start_time_val: Any = None + _end_time_val: Any = None + _status_val: Any = None # e.g., an enum or string + _spans_val: list[Any] = [] + _usage_val: Any = None + + + @property + def start_time(self) -> Any: return self._start_time_val + @start_time.setter + def start_time(self, value: Any): self._start_time_val = value + + @property + def end_time(self) -> Any: return self._end_time_val + @end_time.setter + def end_time(self, value: Any): self._end_time_val = value + + @property + def status(self) -> Any: return self._status_val + @status.setter + def status(self, value: Any): self._status_val = value + + @property + def spans(self) -> list[Any]: return self._spans_val + @spans.setter + def spans(self, value: list[Any]): self._spans_val = value + + @property + def usage(self) -> Any: return self._usage_val + @usage.setter + def usage(self, value: Any): self._usage_val = value + + + def to_dict(self) -> dict[str, Any] | None: # Renamed from export + # Ensure datetime objects are converted to ISO format strings + start_time_iso = None + if hasattr(self.start_time, 'isoformat'): + start_time_iso = self.start_time.isoformat() + elif isinstance(self.start_time, str): # If already a string + start_time_iso = self.start_time + + end_time_iso = None + if hasattr(self.end_time, 'isoformat'): + end_time_iso = self.end_time.isoformat() + elif isinstance(self.end_time, str): # If already a string + end_time_iso = self.end_time + + status_value = None + if hasattr(self.status, 'value'): # For enums + status_value = self.status.value + elif self.status is not None: # For strings or other direct values + status_value = self.status + + spans_dict_list = [] + if self.spans: # self.spans should be a list of SpanImpl objects + spans_dict_list = [span.to_dict() for span in self.spans if hasattr(span, 'to_dict')] + + + usage_data = None + if hasattr(self.usage, 'to_dict'): + usage_data = self.usage.to_dict() + elif self.usage: # Fallback for simple dataclasses or dicts + try: + usage_data = vars(self.usage) + except TypeError: # vars() argument must have __dict__ attribute + usage_data = str(self.usage) # Or some other representation + + return { - "object": "trace", - "id": self.trace_id, - "workflow_name": self.name, + "name": self.name, + "trace_id": self.trace_id, "group_id": self.group_id, + "start_time": start_time_iso, + "end_time": end_time_iso, + "status": status_value, "metadata": self.metadata, + "spans": spans_dict_list, + "usage": usage_data } diff --git a/src/agents/usage.py b/src/agents/usage.py index 23d989b4..40232876 100644 --- a/src/agents/usage.py +++ b/src/agents/usage.py @@ -20,3 +20,19 @@ def add(self, other: "Usage") -> None: self.input_tokens += other.input_tokens if other.input_tokens else 0 self.output_tokens += other.output_tokens if other.output_tokens else 0 self.total_tokens += other.total_tokens if other.total_tokens else 0 + + def to_dict(self) -> dict[str, int]: + return { + "requests": self.requests, + "input_tokens": self.input_tokens, + "output_tokens": self.output_tokens, + "total_tokens": self.total_tokens, + } + + def to_dict(self) -> dict[str, int]: + return { + "requests": self.requests, + "input_tokens": self.input_tokens, + "output_tokens": self.output_tokens, + "total_tokens": self.total_tokens, + }