diff --git a/.github/workflows/python-merge-tests.yml b/.github/workflows/python-merge-tests.yml index fd18a60836..3c5e58ec7a 100644 --- a/.github/workflows/python-merge-tests.yml +++ b/.github/workflows/python-merge-tests.yml @@ -237,6 +237,8 @@ jobs: fail-on-empty: true title: Test results + # TODO: Add python-tests-lab + python-integration-tests-check: if: always() runs-on: ubuntu-latest diff --git a/.github/workflows/python-tests.yml b/.github/workflows/python-tests.yml index ef31032840..0b979a1a7d 100644 --- a/.github/workflows/python-tests.yml +++ b/.github/workflows/python-tests.yml @@ -102,6 +102,35 @@ jobs: name: coverage-${{ matrix.OS }}-${{ matrix.python-version }}-${{ env.PACKAGE_NAME }} path: ./python/coverage_${{ matrix.OS }}_${{ matrix.python-version }}_${{ env.PACKAGE_NAME }}.xml + # Lab package tests + - name: Set environment variables - lab - win + if: ${{ matrix.os == 'windows-latest' }} + run: | + echo "PACKAGE_NAME=lab" | Out-File -FilePath $env:GITHUB_ENV -Append + - name: Set environment variables - lab + if: ${{ matrix.os != 'windows-latest' }} + run: | + echo "PACKAGE_NAME=lab" >> $GITHUB_ENV + - name: Test with pytest - lab - gaia + run: uv run poe --directory ./packages/${{ env.PACKAGE_NAME }}/gaia test -n logical --dist loadfile --dist worksteal --junitxml=coverage.xml + working-directory: ./python + - name: Test with pytest - lab - tau2 + run: | + uv run poe --directory ./packages/${{ env.PACKAGE_NAME }}/tau2 setup-data + uv run poe --directory ./packages/${{ env.PACKAGE_NAME }}/tau2 test -n logical --dist loadfile --dist worksteal --junitxml=coverage.xml + uv run poe --directory ./packages/${{ env.PACKAGE_NAME }}/tau2 purge-data + working-directory: ./python + - name: Move coverage file - lab + run: | + mv ./packages/${{ env.PACKAGE_NAME }}/gaia/coverage.xml coverage_${{ matrix.OS }}_${{ matrix.python-version }}_${{ env.PACKAGE_NAME }}_gaia.xml + mv ./packages/${{ env.PACKAGE_NAME }}/tau2/coverage.xml coverage_${{ matrix.OS }}_${{ matrix.python-version }}_${{ env.PACKAGE_NAME }}_tau2.xml + working-directory: ./python + - name: Upload coverage artifact - lab + uses: actions/upload-artifact@v4 + with: + name: coverage-${{ matrix.OS }}-${{ matrix.python-version }}-${{ env.PACKAGE_NAME }} + path: ./python/coverage_${{ matrix.OS }}_${{ matrix.python-version }}_${{ env.PACKAGE_NAME }}_*.xml + # Surface failing tests - name: Surface failing tests if: always() diff --git a/.gitignore b/.gitignore index 34decd349e..a1decf54e0 100644 --- a/.gitignore +++ b/.gitignore @@ -203,3 +203,6 @@ data_gaia_hub/ **/data_gaia_hub/ python/packages/lab/gaia/**/*.jsonl +# TAU2 data directories +python/packages/lab/tau2/**/data/ +python/packages/lab/tau2/**/results/ diff --git a/python/packages/lab/cookiecutter-agent-framework-lab/{{cookiecutter.package_name}}/pyproject.toml b/python/packages/lab/cookiecutter-agent-framework-lab/{{cookiecutter.package_name}}/pyproject.toml index f9b0b66149..ae45ae4e41 100644 --- a/python/packages/lab/cookiecutter-agent-framework-lab/{{cookiecutter.package_name}}/pyproject.toml +++ b/python/packages/lab/cookiecutter-agent-framework-lab/{{cookiecutter.package_name}}/pyproject.toml @@ -36,6 +36,7 @@ requires = ["setuptools>=64", "wheel"] build-backend = "setuptools.build_meta" [tool.setuptools] +package-dir = {"" = "src"} packages = ["agent_framework_lab_{{ cookiecutter.package_name }}", "agent_framework.lab.{{ cookiecutter.package_name }}"] [tool.setuptools.package-data] @@ -66,7 +67,7 @@ warn_return_any = true warn_unreachable = true show_error_codes = true implicit_reexport = true -packages = ["agent_framework_lab_{{ cookiecutter.package_name }}"] +packages = ["src.agent_framework_lab_{{ cookiecutter.package_name }}"] {% if cookiecutter.within_microsoft_agent_framework_repo == "y" %} [tool.poe] @@ -86,7 +87,7 @@ mypy = "mypy agent_framework_lab_{{ cookiecutter.package_name }}" [tool.pytest.ini_options] testpaths = ["tests"] -pythonpath = ["."] +pythonpath = ["src"] addopts = "--strict-markers --strict-config" markers = [ "unit: marks tests as unit tests", diff --git a/python/packages/lab/cookiecutter-agent-framework-lab/{{cookiecutter.package_name}}/agent_framework/__init__.py b/python/packages/lab/cookiecutter-agent-framework-lab/{{cookiecutter.package_name}}/src/agent_framework/__init__.py similarity index 100% rename from python/packages/lab/cookiecutter-agent-framework-lab/{{cookiecutter.package_name}}/agent_framework/__init__.py rename to python/packages/lab/cookiecutter-agent-framework-lab/{{cookiecutter.package_name}}/src/agent_framework/__init__.py diff --git a/python/packages/lab/cookiecutter-agent-framework-lab/{{cookiecutter.package_name}}/agent_framework/lab/__init__.py b/python/packages/lab/cookiecutter-agent-framework-lab/{{cookiecutter.package_name}}/src/agent_framework/lab/__init__.py similarity index 100% rename from python/packages/lab/cookiecutter-agent-framework-lab/{{cookiecutter.package_name}}/agent_framework/lab/__init__.py rename to python/packages/lab/cookiecutter-agent-framework-lab/{{cookiecutter.package_name}}/src/agent_framework/lab/__init__.py diff --git a/python/packages/lab/cookiecutter-agent-framework-lab/{{cookiecutter.package_name}}/agent_framework/lab/{{cookiecutter.package_name}}/__init__.py b/python/packages/lab/cookiecutter-agent-framework-lab/{{cookiecutter.package_name}}/src/agent_framework/lab/{{cookiecutter.package_name}}/__init__.py similarity index 100% rename from python/packages/lab/cookiecutter-agent-framework-lab/{{cookiecutter.package_name}}/agent_framework/lab/{{cookiecutter.package_name}}/__init__.py rename to python/packages/lab/cookiecutter-agent-framework-lab/{{cookiecutter.package_name}}/src/agent_framework/lab/{{cookiecutter.package_name}}/__init__.py diff --git a/python/packages/lab/cookiecutter-agent-framework-lab/{{cookiecutter.package_name}}/agent_framework_lab_{{cookiecutter.package_name}}/__init__.py b/python/packages/lab/cookiecutter-agent-framework-lab/{{cookiecutter.package_name}}/src/agent_framework_lab_{{cookiecutter.package_name}}/__init__.py similarity index 58% rename from python/packages/lab/cookiecutter-agent-framework-lab/{{cookiecutter.package_name}}/agent_framework_lab_{{cookiecutter.package_name}}/__init__.py rename to python/packages/lab/cookiecutter-agent-framework-lab/{{cookiecutter.package_name}}/src/agent_framework_lab_{{cookiecutter.package_name}}/__init__.py index d2d082187c..78d327cbe0 100644 --- a/python/packages/lab/cookiecutter-agent-framework-lab/{{cookiecutter.package_name}}/agent_framework_lab_{{cookiecutter.package_name}}/__init__.py +++ b/python/packages/lab/cookiecutter-agent-framework-lab/{{cookiecutter.package_name}}/src/agent_framework_lab_{{cookiecutter.package_name}}/__init__.py @@ -4,13 +4,18 @@ {{ cookiecutter.package_description }} """ +import importlib.metadata + # Import your main exports here # from .main_module import MainClass, main_function +try: + __version__ = importlib.metadata.version(__name__) +except importlib.metadata.PackageNotFoundError: + __version__ = "0.0.0" # Fallback for development mode + __all__ = [ # List your exports here # "MainClass", # "main_function", ] - -__version__ = "{{ cookiecutter.version }}" diff --git a/python/packages/lab/cookiecutter-agent-framework-lab/{{cookiecutter.package_name}}/agent_framework_lab_{{cookiecutter.package_name}}/py.typed b/python/packages/lab/cookiecutter-agent-framework-lab/{{cookiecutter.package_name}}/src/agent_framework_lab_{{cookiecutter.package_name}}/py.typed similarity index 100% rename from python/packages/lab/cookiecutter-agent-framework-lab/{{cookiecutter.package_name}}/agent_framework_lab_{{cookiecutter.package_name}}/py.typed rename to python/packages/lab/cookiecutter-agent-framework-lab/{{cookiecutter.package_name}}/src/agent_framework_lab_{{cookiecutter.package_name}}/py.typed diff --git a/python/packages/lab/gaia/agent_framework_lab_gaia/__init__.py b/python/packages/lab/gaia/agent_framework_lab_gaia/__init__.py index d746acb4bb..3c5902758a 100644 --- a/python/packages/lab/gaia/agent_framework_lab_gaia/__init__.py +++ b/python/packages/lab/gaia/agent_framework_lab_gaia/__init__.py @@ -4,9 +4,16 @@ GAIA benchmark module for Agent Framework. """ +import importlib.metadata + from ._types import Evaluation, Evaluator, Prediction, Task, TaskResult, TaskRunner from .gaia import GAIA, GAIATelemetryConfig, gaia_scorer, viewer_main +try: + __version__ = importlib.metadata.version(__name__) +except importlib.metadata.PackageNotFoundError: + __version__ = "0.0.0" # Fallback for development mode + __all__ = [ "GAIA", "GAIATelemetryConfig", @@ -19,5 +26,3 @@ __all__ = [ "TaskRunner", "Evaluator", ] - -__version__ = "0.1.0b1" diff --git a/python/packages/lab/gaia/pyproject.toml b/python/packages/lab/gaia/pyproject.toml index 6896c2c8a4..0824b02c54 100644 --- a/python/packages/lab/gaia/pyproject.toml +++ b/python/packages/lab/gaia/pyproject.toml @@ -72,7 +72,9 @@ packages = ["agent_framework_lab_gaia"] [tool.poe] executor.type = "uv" include = "../../../shared_tasks.toml" + [tool.poe.tasks] +test = "pytest --cov=agent_framework_lab_gaia --cov-report=term-missing:skip-covered tests" [tool.pytest.ini_options] testpaths = ["tests"] diff --git a/python/packages/lab/lightning/agent_framework_lab_lightning/__init__.py b/python/packages/lab/lightning/agent_framework_lab_lightning/__init__.py index f3aac855ac..2a726e805e 100644 --- a/python/packages/lab/lightning/agent_framework_lab_lightning/__init__.py +++ b/python/packages/lab/lightning/agent_framework_lab_lightning/__init__.py @@ -4,6 +4,13 @@ RL Module for Microsoft Agent Framework """ +import importlib.metadata + +try: + __version__ = importlib.metadata.version(__name__) +except importlib.metadata.PackageNotFoundError: + __version__ = "0.0.0" # Fallback for development mode + # Import your main exports here # from .main_module import MainClass, main_function @@ -12,5 +19,3 @@ __all__ = [ # "MainClass", # "main_function", ] - -__version__ = "0.1.0b1" diff --git a/python/packages/lab/tau2/LICENSE b/python/packages/lab/tau2/LICENSE new file mode 100644 index 0000000000..22aed37e65 --- /dev/null +++ b/python/packages/lab/tau2/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) Microsoft Corporation. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/python/packages/lab/tau2/README.md b/python/packages/lab/tau2/README.md new file mode 100644 index 0000000000..0761247dec --- /dev/null +++ b/python/packages/lab/tau2/README.md @@ -0,0 +1,190 @@ +# Agent Framework Lab - τ²-bench + +τ²-bench implements a simulation framework for evaluating customer service agents across various domains. + +The framework orchestrates conversations between two AI agents: +- **Customer Service Agent**: Follows domain-specific policies and has access to tools (e.g., booking systems, databases) +- **User Simulator**: Simulates realistic customer behavior with specific goals and scenarios + +Each evaluation runs a multi-turn conversation where the user simulator presents a customer service scenario, and the agent must resolve it following the domain policy while using available tools appropriately. The results are evaluated using τ²'s comprehensive evaluation system. + +## Supported Domains + +| Domain | Status | Description | +|--------|--------|-------------| +| **airline** | ✅ Supported | Customer service for airline booking, changes, and support | +| **retail** | 🚧 In Development | E-commerce customer support scenarios | +| **telecom** | 🚧 In Development | Telecommunications service support | + +*Note: Currently only the airline domain is fully supported.* + +## Installation + +```bash +pip install agent-framework-lab-tau2 +``` + +Download data from [Tau2-Bench](https://github.com/sierra-research/tau2-bench): + +```bash +git clone https://github.com/sierra-research/tau2-bench.git +mv tau2-bench/data/ . +rm -rf tau2-bench +``` + +Export the data directory to `TAU2_DATA_DIR` environment variable: + +```bash +export TAU2_DATA_DIR="data" +``` + +## Quick Start + +### Running a Single Task + +```python +import asyncio +from agent_framework.openai import OpenAIChatClient +from agent_framework_lab_tau2 import TaskRunner +from tau2.domains.airline.environment import get_tasks + +async def run_single_task(): + # Initialize the task runner + runner = TaskRunner(max_steps=50) + + # Set up your LLM clients + assistant_client = OpenAIChatClient( + base_url="https://api.openai.com/v1", + api_key="your-api-key", + ai_model_id="gpt-4o" + ) + user_client = OpenAIChatClient( + base_url="https://api.openai.com/v1", + api_key="your-api-key", + ai_model_id="gpt-4o-mini" + ) + + # Get a task and run it + tasks = get_tasks() + task = tasks[0] # Run the first task + + conversation = await runner.run(task, assistant_client, user_client) + reward = runner.evaluate(task, conversation, runner.termination_reason) + + print(f"Task completed with reward: {reward}") + +# Run the example +asyncio.run(run_single_task()) +``` + +### Running the Full Benchmark + +Use the provided script to run the complete benchmark: + +```bash +# Run with default models (gpt-4.1 for both agent and user) +python samples/run_benchmark.py + +# Use custom models +python samples/run_benchmark.py --assistant gpt-4o --user gpt-4o-mini + +# Debug a specific task +python samples/run_benchmark.py --debug-task-id task_001 --assistant gpt-4o + +# Limit conversation length +python samples/run_benchmark.py --max-steps 20 +``` + +## Results (on Airline Domain) + +The following results are reproduced from our implementation of τ²-bench with `samples/run_benchmark.py`. It shows the average success rate over the dataset of 50 tasks. + +| Agent Model | User Model | Success Rate | +|-------------|------------|----------| +| gpt-5 | gpt-4.1 | 62.0% | +| gpt-5-mini | gpt-4.1 | 52.0% | +| gpt-4.1 | gpt-4.1 | 60.0% | +| gpt-4.1-mini | gpt-4.1 | 50.0% | +| gpt-4.1 | gpt-4o-mini | 42.0% | +| gpt-4o | gpt-4.1 | 42.0% | +| gpt-4o-mini | gpt-4.1 | 26.0% | + +## Advanced Usage + +### Environment Configuration + +Set required environment variables: + +```bash +export OPENAI_BASE_URL="https://api.openai.com/v1" +export OPENAI_API_KEY="your-api-key" + +# Optional: for custom endpoints +export OPENAI_BASE_URL="https://your-custom-endpoint.com/v1" +``` + +### Custom Agent Implementation + +```python +from agent_framework_lab_tau2 import TaskRunner +from agent_framework import ChatAgent + +class CustomTaskRunner(TaskRunner): + def assistant_agent(self, assistant_chat_client): + # Override to customize the assistant agent + return ChatAgent( + chat_client=assistant_chat_client, + instructions="Your custom system prompt here", + # Add custom tools, temperature, etc. + ) + + def user_simulator(self, user_chat_client, task): + # Override to customize the user simulator + return ChatAgent( + chat_client=user_chat_client, + instructions="Custom user simulator prompt", + ) +``` + +### Custom Workflow Integration + +```python +from agent_framework._workflow import WorkflowBuilder, AgentExecutor +from agent_framework_lab_tau2 import TaskRunner + +class WorkflowTaskRunner(TaskRunner): + def build_conversation_workflow(self, assistant_agent, user_simulator_agent): + # Build a custom workflow + builder = WorkflowBuilder() + + # Create agent executors + assistant_executor = AgentExecutor(assistant_agent, id="assistant_agent") + user_executor = AgentExecutor(user_simulator_agent, id="user_simulator") + + # Add workflow edges and conditions + builder.set_start_executor(assistant_executor) + builder.add_edge(assistant_executor, user_executor) + builder.add_edge(user_executor, assistant_executor, condition=self.should_not_stop) + + return builder.build() +``` + +### Utility Functions + +```python +from agent_framework_lab_tau2 import patch_env_set_state, unpatch_env_set_state + +# Enable compatibility patches for τ²-bench integration +patch_env_set_state() + +# Disable patches when done +unpatch_env_set_state() +``` + +## Contributing + +This package is part of the Microsoft Agent Framework Lab. Please see the main repository for contribution guidelines. + +## License + +This project is licensed under the MIT License - see the LICENSE file for details. \ No newline at end of file diff --git a/python/packages/lab/tau2/agent_framework_lab_tau2/__init__.py b/python/packages/lab/tau2/agent_framework_lab_tau2/__init__.py new file mode 100644 index 0000000000..a99b5b1911 --- /dev/null +++ b/python/packages/lab/tau2/agent_framework_lab_tau2/__init__.py @@ -0,0 +1,21 @@ +# Copyright (c) Microsoft. All rights reserved. + +""" +Tau2 Benchmark for Agent Framework. +""" + +import importlib.metadata + +from ._tau2_utils import patch_env_set_state, unpatch_env_set_state +from .runner import TaskRunner + +try: + __version__ = importlib.metadata.version(__name__) +except importlib.metadata.PackageNotFoundError: + __version__ = "0.0.0" # Fallback for development mode + +__all__ = [ + "TaskRunner", + "patch_env_set_state", + "unpatch_env_set_state", +] diff --git a/python/packages/lab/tau2/agent_framework_lab_tau2/_message_utils.py b/python/packages/lab/tau2/agent_framework_lab_tau2/_message_utils.py new file mode 100644 index 0000000000..999432d691 --- /dev/null +++ b/python/packages/lab/tau2/agent_framework_lab_tau2/_message_utils.py @@ -0,0 +1,112 @@ +# Copyright (c) Microsoft. All rights reserved. + +from agent_framework._types import ChatMessage, Contents, Role +from loguru import logger + + +def flip_messages(messages: list[ChatMessage]) -> list[ChatMessage]: + """Flip message roles between assistant and user for role-playing scenarios. + + Used in agent simulations where the assistant's messages become user inputs + and vice versa. Function calls are filtered out when flipping assistant + messages to user messages (since users typically don't make function calls). + """ + + def filter_out_function_calls(messages: list[Contents]) -> list[Contents]: + """Remove function call content from message contents.""" + return [content for content in messages if content.type != "function_call"] + + flipped_messages = [] + for msg in messages: + if msg.role == Role.ASSISTANT: + # Flip assistant to user + contents = filter_out_function_calls(msg.contents) + if contents: + flipped_msg = ChatMessage( + role=Role.USER, + # The function calls will cause 400 when role is user + contents=contents, + author_name=msg.author_name, + message_id=msg.message_id, + ) + flipped_messages.append(flipped_msg) + elif msg.role == Role.USER: + # Flip user to assistant + flipped_msg = ChatMessage( + role=Role.ASSISTANT, contents=msg.contents, author_name=msg.author_name, message_id=msg.message_id + ) + flipped_messages.append(flipped_msg) + elif msg.role == Role.TOOL: + # Skip tool messages + pass + else: + # Keep other roles as-is (system, tool, etc.) + flipped_messages.append(msg) + return flipped_messages + + +def log_messages(messages: list[ChatMessage]) -> None: + """Log messages with colored output based on role and content type. + + Provides visual debugging by color-coding different message roles and + content types. Escapes HTML-like characters to prevent log formatting issues. + """ + _logger = logger.opt(colors=True) + for msg in messages: + # Handle different content types + if hasattr(msg, "contents") and msg.contents: + for content in msg.contents: + if hasattr(content, "type"): + if content.type == "text": + escape_text = content.text.replace("<", r"\<") + if msg.role == Role.SYSTEM: + _logger.info(f"[SYSTEM] {escape_text}") + elif msg.role == Role.USER: + _logger.info(f"[USER] {escape_text}") + elif msg.role == Role.ASSISTANT: + _logger.info(f"[ASSISTANT] {escape_text}") + elif msg.role == Role.TOOL: + _logger.info(f"[TOOL] {escape_text}") + else: + _logger.info(f"[{msg.role.value.upper()}] {escape_text}") + elif content.type == "function_call": + function_call_text = f"{content.name}({content.arguments})" + function_call_text = function_call_text.replace("<", r"\<") + _logger.info(f"[TOOL_CALL] 🔧 {function_call_text}") + elif content.type == "function_result": + function_result_text = f"ID:{content.call_id} -> {content.result}" + function_result_text = function_result_text.replace("<", r"\<") + _logger.info(f"[TOOL_RESULT] 🔨 {function_result_text}") + else: + content_text = str(content).replace("<", r"\<") + _logger.info(f"[{msg.role.value.upper()}] ({content.type}) {content_text}") + else: + # Fallback for content without type + text_content = str(content).replace("<", r"\<") + if msg.role == Role.SYSTEM: + _logger.info(f"[SYSTEM] {text_content}") + elif msg.role == Role.USER: + _logger.info(f"[USER] {text_content}") + elif msg.role == Role.ASSISTANT: + _logger.info(f"[ASSISTANT] {text_content}") + elif msg.role == Role.TOOL: + _logger.info(f"[TOOL] {text_content}") + else: + _logger.info(f"[{msg.role.value.upper()}] {text_content}") + elif hasattr(msg, "text") and msg.text: + # Handle simple text messages + text_content = msg.text.replace("<", r"\<") + if msg.role == Role.SYSTEM: + _logger.info(f"[SYSTEM] {text_content}") + elif msg.role == Role.USER: + _logger.info(f"[USER] {text_content}") + elif msg.role == Role.ASSISTANT: + _logger.info(f"[ASSISTANT] {text_content}") + elif msg.role == Role.TOOL: + _logger.info(f"[TOOL] {text_content}") + else: + _logger.info(f"[{msg.role.value.upper()}] {text_content}") + else: + # Fallback for other message formats + text_content = str(msg).replace("<", r"\<") + _logger.info(f"[{msg.role.value.upper()}] {text_content}") diff --git a/python/packages/lab/tau2/agent_framework_lab_tau2/_sliding_window.py b/python/packages/lab/tau2/agent_framework_lab_tau2/_sliding_window.py new file mode 100644 index 0000000000..4148430251 --- /dev/null +++ b/python/packages/lab/tau2/agent_framework_lab_tau2/_sliding_window.py @@ -0,0 +1,131 @@ +# Copyright (c) Microsoft. All rights reserved. + +import json +from collections.abc import Sequence +from typing import Any + +import tiktoken +from agent_framework._threads import ChatMessageList +from agent_framework._types import ChatMessage, Role +from loguru import logger + + +class SlidingWindowChatMessageList(ChatMessageList): + """A token-aware sliding window implementation of ChatMessageList. + + Maintains two message lists: complete history and truncated window. + Automatically removes oldest messages when token limit is exceeded. + Also removes leading tool messages to ensure valid conversation flow. + """ + + def __init__( + self, + messages: Sequence[ChatMessage] | None = None, + max_tokens: int = 3800, + system_message: str | None = None, + tool_definitions: Any | None = None, + ): + super().__init__(messages) + self._truncated_messages = self._messages.copy() # Separate truncated view + self.max_tokens = max_tokens + self.system_message = system_message # Included in token count + self.tool_definitions = tool_definitions + # An estimation based on a commonly used vocab table + self.encoding = tiktoken.get_encoding("o200k_base") + + async def add_messages(self, messages: Sequence[ChatMessage]) -> None: + await super().add_messages(messages) + + self._truncated_messages = self._messages.copy() + self.truncate_messages() + + async def list_messages(self) -> list[ChatMessage]: + """Get the current list of messages, which may be truncated.""" + return self._truncated_messages + + async def list_all_messages(self) -> list[ChatMessage]: + """Get all messages from the store including the truncated ones.""" + return self._messages + + def truncate_messages(self) -> None: + while len(self._truncated_messages) > 0 and self.get_token_count() > self.max_tokens: + logger.warning("Messages exceed max tokens. Truncating oldest message.") + self._truncated_messages.pop(0) + # Remove leading tool messages + while len(self._truncated_messages) > 0 and self._truncated_messages[0].role == Role.TOOL: + logger.warning("Removing leading tool message because tool result cannot be the first message.") + self._truncated_messages.pop(0) + + def get_token_count(self) -> int: + """Estimate token count for a list of messages using tiktoken. + Args: + messages: List of ChatMessage objects + system_message: Optional system message to include in count + Returns: + Estimated token count + """ + total_tokens = 0 + + # Add system message tokens if provided + if self.system_message: + total_tokens += len(self.encoding.encode(self.system_message)) + total_tokens += 4 # Extra tokens for system message formatting + + for msg in self._truncated_messages: + # Add 4 tokens per message for role, formatting, etc. + total_tokens += 4 + + # Handle different content types + if hasattr(msg, "contents") and msg.contents: + for content in msg.contents: + if hasattr(content, "type"): + if content.type == "text": + total_tokens += len(self.encoding.encode(content.text)) + elif content.type == "function_call": + total_tokens += 4 + # Serialize function call and count tokens + func_call_data = { + "name": content.name, + "arguments": content.arguments, + } + total_tokens += self.estimate_any_object_token_count(func_call_data) + elif content.type == "function_result": + total_tokens += 4 + # Serialize function result and count tokens + func_result_data = { + "call_id": content.call_id, + "result": content.result, + } + total_tokens += self.estimate_any_object_token_count(func_result_data) + else: + # For other content types, serialize the whole content + total_tokens += self.estimate_any_object_token_count(content) + else: + # Content without type, treat as text + total_tokens += self.estimate_any_object_token_count(content) + elif hasattr(msg, "text") and msg.text: + # Simple text message + total_tokens += self.estimate_any_object_token_count(msg.text) + else: + # Skip it + pass + + if total_tokens > self.max_tokens / 2: + logger.opt(colors=True).warning( + f"Total tokens {total_tokens} is " + f"{total_tokens / self.max_tokens * 100:.0f}% " + f"of max tokens {self.max_tokens}" + ) + elif total_tokens > self.max_tokens: + logger.opt(colors=True).warning( + f"Total tokens {total_tokens} is over max tokens {self.max_tokens}. Will truncate messages." + ) + + return total_tokens + + def estimate_any_object_token_count(self, obj: Any) -> int: + try: + serialized = json.dumps(obj) + except Exception: + serialized = str(obj) + return len(self.encoding.encode(serialized)) diff --git a/python/packages/lab/tau2/agent_framework_lab_tau2/_tau2_utils.py b/python/packages/lab/tau2/agent_framework_lab_tau2/_tau2_utils.py new file mode 100644 index 0000000000..ec50bf77e4 --- /dev/null +++ b/python/packages/lab/tau2/agent_framework_lab_tau2/_tau2_utils.py @@ -0,0 +1,244 @@ +# Copyright (c) Microsoft. All rights reserved. + +import json +from collections.abc import Mapping +from copy import deepcopy +from typing import Any + +import numpy as np +from agent_framework._tools import AIFunction +from agent_framework._types import ChatMessage +from loguru import logger +from pydantic import BaseModel +from tau2.data_model.message import ( # type: ignore[import-untyped] + AssistantMessage, + Message, + SystemMessage, + ToolCall, + ToolMessage, + UserMessage, +) +from tau2.data_model.tasks import EnvFunctionCall, InitializationData # type: ignore[import-untyped] +from tau2.environment.environment import Environment # type: ignore[import-untyped] +from tau2.environment.tool import Tool # type: ignore[import-untyped] + +_original_set_state = Environment.set_state + + +def convert_tau2_tool_to_ai_function(tau2_tool: Tool) -> AIFunction[Any, Any]: + """Convert a tau2 Tool to an AIFunction for agent framework compatibility. + + Creates a wrapper that preserves the tool's interface while ensuring + results are deep-copied to prevent unintended mutations. + """ + + def wrapped_func(**kwargs: Any) -> Any: + result = tau2_tool(**kwargs) + # Deep copy to prevent mutations of returned data + if isinstance(result, BaseModel): + result = result.model_copy(deep=True) + else: + result = deepcopy(result) + return result + + return AIFunction( + name=tau2_tool.name, + description=tau2_tool._get_description(), + func=wrapped_func, + input_model=tau2_tool.params, + ) + + +def convert_agent_framework_messages_to_tau2_messages(messages: list[ChatMessage]) -> list[Message]: + """Convert agent framework ChatMessages to tau2 Message objects. + + Handles role mapping, text extraction, function calls, and function results. + Function results are converted to separate ToolMessage instances. + """ + + tau2_messages = [] + + for msg in messages: + role_str = str(msg.role) + + # Extract text content from all text-type contents + text_content = None + text_contents = [c for c in msg.contents if hasattr(c, "text") and hasattr(c, "type") and c.type == "text"] + if text_contents: + text_content = " ".join(c.text for c in text_contents) + + # Extract function calls and convert to ToolCall objects + function_calls = [c for c in msg.contents if hasattr(c, "type") and c.type == "function_call"] + tool_calls = None + if function_calls: + tool_calls = [] + for fc in function_calls: + arguments = fc.parse_arguments() or {} + tool_call = ToolCall( + id=fc.call_id, + name=fc.name, + arguments=arguments, + requestor="assistant" if role_str == "assistant" else "user", + ) + tool_calls.append(tool_call) + + # Extract function results for separate ToolMessage creation + function_results = [c for c in msg.contents if hasattr(c, "type") and c.type == "function_result"] + + # Create main message based on role + if role_str == "system": + tau2_messages.append(SystemMessage(role="system", content=text_content)) + elif role_str == "user": + tau2_messages.append(UserMessage(role="user", content=text_content, tool_calls=tool_calls)) + elif role_str == "assistant": + tau2_messages.append(AssistantMessage(role="assistant", content=text_content, tool_calls=tool_calls)) + elif role_str == "tool": + # Tool messages are handled as function results below + pass + + # Convert function results to separate ToolMessage instances + for fr in function_results: + dumpable_content = _dump_function_result(fr.result) + content = dumpable_content if isinstance(dumpable_content, str) else json.dumps(dumpable_content) + tool_msg = ToolMessage( + id=fr.call_id, + role="tool", + content=content, + requestor="assistant", # Most tool calls originate from assistant + error=fr.exception is not None, + ) + tau2_messages.append(tool_msg) + + return tau2_messages + + +def patch_env_set_state() -> None: + """Patch Environment.set_state to allow inconsistent tool call results. + + Modifies the original method to log warnings instead of raising errors + when actual tool results differ from expected results, enabling more + flexible testing and development workflows. + """ + + def set_state( + self: Any, + initialization_data: InitializationData | None, + initialization_actions: list[EnvFunctionCall] | None, + message_history: list[Message], + ) -> None: + if self.solo_mode: + if any(isinstance(message, UserMessage) for message in message_history): + raise ValueError("User messages are not allowed in solo mode") + + def get_actions_from_messages( + messages: list[Message], + ) -> list[tuple[ToolCall, ToolMessage]]: + """ + Get the actions from the messages. + """ + messages = deepcopy(messages)[::-1] + actions = [] + while messages: + message = messages.pop() + if isinstance(message, ToolMessage): + raise ValueError("Tool message not expected. Tool messages should always follow a tool call.") + if isinstance(message, (AssistantMessage, UserMessage)) and message.is_tool_call(): + tool_calls = message.tool_calls + if tool_calls is None: + raise ValueError("Tool message expected. Got None.") + for tc in tool_calls: + if len(messages) == 0: + raise ValueError("Tool message expected. Got None.") + tm = messages.pop() + if not isinstance(tm, ToolMessage): + raise ValueError(f"Tool message expected. Got {type(tm)}") + if tc.id != tm.id: + raise ValueError(f"Tool call id mismatch. Got {tc.id} and {tm.id}") + actions.append((tc, tm)) + + return actions + + if initialization_data is not None: + if initialization_data.agent_data is not None: + self.tools.update_db(initialization_data.agent_data) + if initialization_data.user_data is not None: + self.user_tools.update_db(initialization_data.user_data) + + if initialization_actions is not None: + for action in initialization_actions: + self.run_env_function_call(action) + + action_responses = get_actions_from_messages(message_history) + for tool_call, expected_response in action_responses: + response = self.get_response(tool_call) + content = _recursive_json_deserialize(response.content) + expected_content = _recursive_json_deserialize(expected_response.content) + if content != expected_content: + diff = f"Tool call:\n{tool_call}\n\nReturned:\n{response}\n\nExpected:\n{expected_response}" + if isinstance(content, str) and content.startswith("Error:"): + # If the tool call resulted in an error, the difference can be ignored + logger.warning(f"Tool call resulted in an error. Ignoring the difference.\n{diff}") + else: + raise ValueError( + f"Tool call:\n{tool_call}\n\nReturned:\n{response}\n\nExpected:\n{expected_response}" + ) + self.sync_tools() + + Environment.set_state = set_state + + +def unpatch_env_set_state() -> None: + Environment.set_state = _original_set_state + + +def _dump_function_result(result: Any) -> Any: + if isinstance(result, BaseModel): + return result.model_dump_json() + elif isinstance(result, list): + return [_dump_function_result(item) for item in result] + elif isinstance(result, dict): + return {k: _dump_function_result(v) for k, v in result.items()} + elif result is None: + return None + else: + return result + + +def _to_native(obj: Any) -> Any: + """Convert data retrieved from Panquet to data usable in AGL server.""" + # 1) Arrays -> list (then recurse) + if isinstance(obj, np.ndarray): + return _to_native(obj.tolist()) + + # 2) NumPy scalar types -> Python scalars + if isinstance(obj, np.generic): + return _to_native(obj.item()) + + # 3) Dict-like -> dict + if isinstance(obj, Mapping): + return {_to_native(k): _to_native(v) for k, v in obj.items()} + + # 4) Lists/Tuples/Sets -> list + if isinstance(obj, (list, tuple, set)): + return [_to_native(x) for x in obj] + + # 5) Anything else: leave as-is + return obj + + +def _recursive_json_deserialize(obj: Any) -> Any: + """ + Recursively deserialize a JSON object. + """ + if isinstance(obj, str): + try: + deserialized = json.loads(obj) + return _recursive_json_deserialize(deserialized) + except (json.JSONDecodeError, TypeError): + return obj + elif isinstance(obj, list): + return [_recursive_json_deserialize(item) for item in obj] + elif isinstance(obj, dict): + return {k: _recursive_json_deserialize(v) for k, v in obj.items()} + else: + return obj diff --git a/python/packages/lab/tau2/agent_framework_lab_tau2/py.typed b/python/packages/lab/tau2/agent_framework_lab_tau2/py.typed new file mode 100644 index 0000000000..e69de29bb2 diff --git a/python/packages/lab/tau2/agent_framework_lab_tau2/runner.py b/python/packages/lab/tau2/agent_framework_lab_tau2/runner.py new file mode 100644 index 0000000000..d6709530ca --- /dev/null +++ b/python/packages/lab/tau2/agent_framework_lab_tau2/runner.py @@ -0,0 +1,424 @@ +# Copyright (c) Microsoft. All rights reserved. + +import uuid +from typing import cast + +from agent_framework._agents import ChatAgent +from agent_framework._types import AgentRunResponse, ChatMessage, Role +from agent_framework._workflow import ( + AgentExecutor, + AgentExecutorRequest, + AgentExecutorResponse, + FunctionExecutor, + Workflow, + WorkflowBuilder, + WorkflowContext, +) +from agent_framework.openai import OpenAIChatClient +from loguru import logger +from tau2.data_model.simulation import SimulationRun, TerminationReason # type: ignore[import-untyped] +from tau2.data_model.tasks import Task # type: ignore[import-untyped] +from tau2.domains.airline.environment import get_environment # type: ignore[import-untyped] +from tau2.evaluator.evaluator import EvaluationType, RewardInfo, evaluate_simulation # type: ignore[import-untyped] +from tau2.user.user_simulator import ( # type: ignore[import-untyped] + OUT_OF_SCOPE, + STOP, + TRANSFER, + get_global_user_sim_guidelines, +) +from tau2.utils.utils import get_now # type: ignore[import-untyped] + +from ._message_utils import flip_messages, log_messages +from ._sliding_window import SlidingWindowChatMessageList +from ._tau2_utils import convert_agent_framework_messages_to_tau2_messages, convert_tau2_tool_to_ai_function + +# Agent instructions matching tau2's LLMAgent +ASSISTANT_AGENT_INSTRUCTION = """ +You are a customer service agent that helps the user according to the provided below. +In each turn you can either: +- Send a message to the user. +- Make a tool call. +You cannot do both at the same time. +Try to be helpful and always follow the policy. Always make sure you generate valid JSON only. +""".strip() + +# Default first message from agent (matching tau2) +DEFAULT_FIRST_AGENT_MESSAGE = "Hi! How can I help you today?" + +# Constants of Agent executor IDs +ASSISTANT_AGENT_ID = "assistant_agent" +USER_SIMULATOR_ID = "user_simulator" +ORCHESTRATOR_ID = "orchestrator" + + +class TaskRunner: + """Orchestrates task execution using agent framework workflows for tau2 benchmarks. + + Manages conversation flow between assistant agents and user simulators, + handles termination conditions, and evaluates performance using tau2 metrics. + + Only "airline" domain is supported for now. + """ + + # State tracking + step_count: int + full_conversation: list[ChatMessage] + termination_reason: TerminationReason | None + full_reward_info: RewardInfo | None + _final_user_message: list[ChatMessage] | None + _assistant_executor: AgentExecutor | None + _user_executor: AgentExecutor | None + + # Configuration + max_steps: int + assistant_sampling_temperature: float + assistant_window_size: int + + def __init__(self, max_steps: int, assistant_sampling_temperature: float = 0.0, assistant_window_size: int = 32768): + """Initialize the TaskRunner. + + Args: + max_steps: The maximum number of steps to run. + assistant_sampling_temperature: The sampling temperature for the assistant agent. + assistant_window_size: The window size for the assistant agent. + """ + self.assistant_sampling_temperature = assistant_sampling_temperature + self.assistant_window_size = assistant_window_size + self.max_steps = max_steps + self.reinit() + + def reinit(self) -> "TaskRunner": + """Reset all state for a new task run.""" + self.step_count = 0 + self.full_conversation = [] + self.termination_reason = None + self.full_reward_info = None + self._final_user_message = None + self._assistant_executor = None + self._user_executor = None + logger.info("TaskRunner has been re-initialized.") + return self + + def __repr__(self) -> str: + return ( + f"TaskRunner(max_steps={self.max_steps}, step_count={self.step_count}, " + f"full_conversation_length={len(self.full_conversation)}, " + f"termination_reason={self.termination_reason}, full_reward_info={self.full_reward_info})" + ) + + def should_not_stop(self, response: AgentExecutorResponse) -> bool: + """Based on the response, check whether we should or not stop the conversation.""" + + # Determine who sent this based on executor_id + is_from_agent = response.executor_id == ASSISTANT_AGENT_ID + is_from_user = response.executor_id == USER_SIMULATOR_ID + + self.step_count += 1 + + logger.opt(colors=True).info( + f"[Step {self.step_count}] Received the following response from " + f"{'assistant' if is_from_agent else 'user'}, " + f"routing to {'user' if is_from_agent else 'assistant'}:" + ) + log_messages(response.agent_run_response.messages) + + if self.step_count >= self.max_steps: + logger.info(f"Max steps ({self.max_steps}) reached - terminating conversation") + self.termination_reason = TerminationReason.MAX_STEPS + # Terminate the workflow + return False + + response_text = response.agent_run_response.text + if is_from_agent and self._is_agent_stop(response_text): + logger.info("Agent requested stop - terminating conversation") + self.termination_reason = TerminationReason.AGENT_STOP + return False + + if is_from_user and self._is_user_stop(response_text): + logger.info(f"User requested stop with message: '{response_text}' - terminating conversation") + self.termination_reason = TerminationReason.USER_STOP + # The final user message won't appear in the assistant's message store, + # because it will never arrive there. + # We need to store it because it's needed for evaluation. + self._final_user_message = flip_messages(response.agent_run_response.messages) + return False + + return True + + def _is_agent_stop(self, _: str) -> bool: + """Check if agent wants to stop the conversation.""" + # Could check for specific stop tokens if agent uses them + return False # Agent doesn't have explicit stop in this setup + + def _is_user_stop(self, text: str) -> bool: + """Check if user wants to stop the conversation.""" + return STOP in text or TRANSFER in text or OUT_OF_SCOPE in text + + def assistant_agent(self, assistant_chat_client: OpenAIChatClient) -> ChatAgent: + """Create an assistant agent. + + Users can override this method to provide a custom assistant agent. + + Args: + assistant_chat_client: The chat client for the assistant agent. + + Returns: + The assistant agent. + """ + + # Initialize tau2 environment and extract tools/policy + # This provides the domain-specific context (airline customer service in this case) + env = get_environment() + tools = env.get_tools() # Available actions the assistant can take + policy = env.get_policy() # Guidelines the assistant must follow + + logger.info( + f"Environment has {len(env.get_tools())} tools: {', '.join([tool.name for tool in env.get_tools()])}" + ) + + # Convert tau2 tools to agent framework AIFunction format + # This bridges the gap between tau2's tool system and agent framework's expectations + ai_functions = [convert_tau2_tool_to_ai_function(tool) for tool in tools] + + # Combines general customer service behavior with specific policy guidelines + assistant_system_prompt = f""" +{ASSISTANT_AGENT_INSTRUCTION} + + +{policy} +""" + + # Assistant agent has: + # - Access to all domain tools (booking, cancellation, etc.) + # - Sliding window memory to handle long conversations within token limits + # - Temperature-controlled response generation + return ChatAgent( + chat_client=assistant_chat_client, + instructions=assistant_system_prompt, + tools=ai_functions, # type: ignore + temperature=self.assistant_sampling_temperature, + chat_message_store_factory=lambda: SlidingWindowChatMessageList( + system_message=assistant_system_prompt, + tool_definitions=[tool.openai_schema for tool in tools], + max_tokens=self.assistant_window_size, + ), + ) + + def user_simulator(self, user_simuator_chat_client: OpenAIChatClient, task: Task) -> ChatAgent: + """Create a user simulator agent. + + Users can override this method to provide a custom user simulator agent. + + Args: + user_simuator_chat_client: The chat client for the user simulator agent. + task: The task to be executed. + + Returns: + The user simulator agent. + """ + + # User simulator follows tau2's guidelines for realistic customer behavior + # No tools available - users typically don't have direct system access + user_sim_guidelines = get_global_user_sim_guidelines(use_tools=False) + + # User simulator prompt combines general guidelines with task-specific scenario + user_sim_system_prompt = f"""{user_sim_guidelines} + +{task.user_scenario.instructions} +""" + + return ChatAgent( + chat_client=user_simuator_chat_client, + instructions=user_sim_system_prompt, + temperature=0.0, + # No sliding window for user simulator to maintain full conversation context + # TODO(yuge): Consider adding user tools in future for more realistic scenarios + ) + + async def conversation_orchestrator( + self, response: AgentExecutorResponse, ctx: WorkflowContext[AgentExecutorRequest] + ) -> None: + """Orchestrate conversation flow between assistant and user simulator. + + This is the central routing hub that: + + 1. Receives responses from either the assistant agent or user simulator + 2. Flips message roles to create proper conversation flow (assistant->user, user->assistant) + 3. Routes the flipped messages to the appropriate target agent + 4. Maintains the conversation loop until termination conditions are met + + Args: + response: The response from either assistant or user simulator agent + ctx: Workflow context for sending messages to other executors + """ + # Flip message roles for proper conversation flow + # Assistant messages become user messages and vice versa + flipped = flip_messages(response.agent_run_response.messages) + + # Determine source to route to correct target + is_from_agent = response.executor_id == ASSISTANT_AGENT_ID + + # Send flipped messages to the opposite agent + # Critical: Target ID must be specified to prevent broadcasting to both agents + await ctx.send_message( + AgentExecutorRequest(messages=flipped, should_respond=True), + target_id=USER_SIMULATOR_ID if is_from_agent else ASSISTANT_AGENT_ID, + ) + + def build_conversation_workflow(self, assistant_agent: ChatAgent, user_simulator_agent: ChatAgent) -> Workflow: + """Build the conversation workflow. + + Users can override this method to provide a custom conversation workflow. + + Args: + assistant_agent: The assistant agent. + user_simulator_agent: The user simulator agent. + + Returns: + The conversation workflow. + """ + + # STEP 1: Create workflow executors + # Each executor wraps an agent or function for workflow orchestration + self._assistant_executor = AgentExecutor(assistant_agent, id=ASSISTANT_AGENT_ID) + self._user_executor = AgentExecutor(user_simulator_agent, id=USER_SIMULATOR_ID) + orchestrator = FunctionExecutor(func=self.conversation_orchestrator, id=ORCHESTRATOR_ID) + + # STEP 2: Build the conversation workflow + # Creates a cyclic workflow: Orchestrator -> Assistant -> Orchestrator -> User -> Orchestrator... + # The orchestrator acts as a message router that flips roles and routes to appropriate agent + workflow = ( + WorkflowBuilder(max_iterations=10000) # Unlimited - we control termination via should_not_stop + .set_start_executor(orchestrator) # Orchestrator manages the conversation flow + .add_edge(orchestrator, self._assistant_executor) # Route messages to assistant + .add_edge( + self._assistant_executor, orchestrator, condition=self.should_not_stop + ) # Check termination after assistant + .add_edge(orchestrator, self._user_executor) # Route messages to user simulator + .add_edge(self._user_executor, orchestrator, condition=self.should_not_stop) # Check termination after user + .build() + ) + + return workflow + + async def run( + self, + task: Task, + assistant_chat_client: OpenAIChatClient, + user_simuator_chat_client: OpenAIChatClient, + ) -> list[ChatMessage]: + """Run a tau2 task using workflow-based agent orchestration. + + This method orchestrates a complex multi-agent simulation: + + 1. Sets up tau2 environment and converts tools for agent framework compatibility + 2. Creates two agents: assistant (with tools) and user simulator (without tools) + 3. Builds a workflow with orchestrated message routing between agents + 4. Manages conversation flow until termination conditions are met + 5. Returns complete conversation history for evaluation + + Args: + task: Tau2 task containing scenario, policy, and evaluation criteria + assistant_chat_client: LLM client for the assistant agent + user_simuator_chat_client: LLM client for the user simulator + + Returns: + Complete conversation history as ChatMessage list for evaluation + """ + + logger.info(f"Starting workflow agent for task {task.id}: {task.description.purpose}") # type: ignore[unused-ignore] + logger.info(f"Assistant chat client: {assistant_chat_client}") + logger.info(f"User simulator chat client: {user_simuator_chat_client}") + + # STEP 1: Create agents + assistant_agent = self.assistant_agent(assistant_chat_client) + user_simulator_agent = self.user_simulator(user_simuator_chat_client, task) + + # STEP 2: Create the conversation workflow + workflow = self.build_conversation_workflow(assistant_agent, user_simulator_agent) + + # STEP 3: Initialize conversation with standard greeting + # Matches tau2's expected conversation start pattern + logger.info(f"Starting workflow with hardcoded greeting: '{DEFAULT_FIRST_AGENT_MESSAGE}'") + + first_message = ChatMessage(Role.ASSISTANT, text=DEFAULT_FIRST_AGENT_MESSAGE) + initial_greeting = AgentExecutorResponse( + executor_id=ASSISTANT_AGENT_ID, + agent_run_response=AgentRunResponse(messages=[first_message]), + full_conversation=[ChatMessage(Role.ASSISTANT, text=DEFAULT_FIRST_AGENT_MESSAGE)], + ) + + # STEP 4: Execute the workflow and collect results + # The workflow runs until termination conditions are met (max steps, stop signals, etc.) + await workflow.run(initial_greeting) + + # STEP 5: Ensemble the conversation history needed for evaluation. + # It's coming from three parts: + # 1. The initial greeting + # 2. The assistant's message store (not just the truncated window) + # 3. The final user message (if any) + assistant_executor = cast(AgentExecutor, self._assistant_executor) + message_store = cast(SlidingWindowChatMessageList, assistant_executor._agent_thread.message_store) + full_conversation = [first_message] + await message_store.list_all_messages() + if self._final_user_message is not None: + full_conversation.extend(self._final_user_message) + + logger.opt(colors=True).info( + f"WORKFLOW COMPLETED WITH {len(full_conversation)} MESSAGES. " + f"Termination reason: {self.termination_reason}." + ) + log_messages(full_conversation) + + return full_conversation + + def evaluate( + self, task_input: Task, conversation: list[ChatMessage], termination_reason: TerminationReason | None + ) -> float: + """Evaluate agent performance using tau2's comprehensive evaluation system. + + Bridges agent framework conversation results with tau2's evaluation pipeline. + Considers task completion, policy adherence, conversation quality, and tool usage. + + Args: + task_input: Original tau2 task containing evaluation criteria + conversation: Complete conversation history from workflow execution + termination_reason: How/why the conversation ended (affects scoring) + + Returns: + Numeric reward score (0.0-1.0) representing overall performance + + Side Effects: + Stores detailed evaluation results in self.full_reward_info + """ + + # Handle missing termination reason (can happen with unexpected workflow endings) + if termination_reason is None: + termination_reason = TerminationReason.TOO_MANY_ERRORS + + # Convert agent framework ChatMessages to tau2 Message format for evaluation + tau2_messages = convert_agent_framework_messages_to_tau2_messages(conversation) + + # Package conversation and metadata for tau2's evaluation system + simulation = SimulationRun( + id=str(uuid.uuid4()), # Unique identifier for this evaluation run + task_id=task_input.id, # Links evaluation back to original task + start_time=get_now(), # Timestamp for evaluation records + end_time=get_now(), # Duration is 0 since this is post-hoc evaluation + duration=0.0, + termination_reason=termination_reason, # Context for how conversation ended + messages=tau2_messages, # The actual conversation to evaluate + ) + + # Run comprehensive multi-dimensional evaluation + # EvaluationType.ALL: evaluates task completion, policy adherence, conversation quality, ... + # solo_mode=False: indicates multi-agent conversation (assistant + user simulator) + self.full_reward_info = evaluate_simulation( + simulation=simulation, + task=task_input, + evaluation_type=EvaluationType.ALL, + solo_mode=False, + domain="airline", + ) + + logger.info(f"Evaluation completed - Reward: {self.full_reward_info.reward}, Info: {self.full_reward_info}") + return self.full_reward_info.reward # type: ignore[no-any-return] diff --git a/python/packages/lab/tau2/namespace/agent_framework/__init__.py b/python/packages/lab/tau2/namespace/agent_framework/__init__.py new file mode 100644 index 0000000000..1d3db512a4 --- /dev/null +++ b/python/packages/lab/tau2/namespace/agent_framework/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) Microsoft. All rights reserved. + +# This makes agent_framework a namespace package +__path__ = __import__("pkgutil").extend_path(__path__, __name__) diff --git a/python/packages/lab/tau2/namespace/agent_framework/lab/__init__.py b/python/packages/lab/tau2/namespace/agent_framework/lab/__init__.py new file mode 100644 index 0000000000..0d8d8fbaa4 --- /dev/null +++ b/python/packages/lab/tau2/namespace/agent_framework/lab/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) Microsoft. All rights reserved. + +# This makes agent_framework.lab a namespace package +__path__ = __import__("pkgutil").extend_path(__path__, __name__) diff --git a/python/packages/lab/tau2/namespace/agent_framework/lab/tau2/__init__.py b/python/packages/lab/tau2/namespace/agent_framework/lab/tau2/__init__.py new file mode 100644 index 0000000000..ef268ffd23 --- /dev/null +++ b/python/packages/lab/tau2/namespace/agent_framework/lab/tau2/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) Microsoft. All rights reserved. + +# Import and re-export from the actual implementation +from agent_framework_lab_tau2 import * # noqa: F403, F401 diff --git a/python/packages/lab/tau2/pyproject.toml b/python/packages/lab/tau2/pyproject.toml new file mode 100644 index 0000000000..6c49d0b3fc --- /dev/null +++ b/python/packages/lab/tau2/pyproject.toml @@ -0,0 +1,99 @@ +[project] +name = "agent-framework-lab-tau2" +description = "Tau2 Benchmark for Agent Framework." +authors = [{ name = "Microsoft", email = "SK-Support@microsoft.com"}] +readme = "README.md" +requires-python = ">=3.10" +version = "0.1.0b1" +license-files = ["LICENSE"] +urls.homepage = "https://learn.microsoft.com/en-us/semantic-kernel/overview/" +urls.source = "https://github.com/microsoft/agent-framework/tree/main/python" +urls.release_notes = "https://github.com/microsoft/agent-framework/releases?q=tag%3Apython-1&expanded=true" +urls.issues = "https://github.com/microsoft/agent-framework/issues" +classifiers = [ + "License :: OSI Approved :: MIT License", + "Development Status :: 2 - Pre-Alpha", + "Intended Audience :: Developers", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", +] +dependencies = [ + "agent-framework", + "pydantic>=2.0.0", + "tiktoken>=0.11.0", + "loguru>=0.7.3", + "tau2@git+https://github.com/sierra-research/tau2-bench@5ba9e3e56db57c5e4114bf7f901291f09b2c5619", +] + +[build-system] +requires = ["setuptools>=64", "wheel"] +build-backend = "setuptools.build_meta" + +[tool.setuptools] +packages = ["agent_framework_lab_tau2", "agent_framework.lab.tau2"] + +[tool.setuptools.package-dir] +"agent_framework.lab.tau2" = "namespace/agent_framework/lab/tau2" +"agent_framework_lab_tau2" = "agent_framework_lab_tau2" + +[tool.setuptools.package-data] +agent_framework_lab_tau2 = ["py.typed"] + +[tool.ruff] +line-length = 120 +target-version = "py310" +extend-exclude = ["tests", "__pycache__"] + +[tool.ruff.lint] +select = ["E", "F", "I", "W", "UP", "C4", "N"] +ignore = ["N803", "N806", "N999", "UP007"] + +[tool.ruff.format] +quote-style = "double" + +[tool.mypy] +python_version = "3.10" +strict = true +check_untyped_defs = true +disallow_untyped_defs = true +disallow_incomplete_defs = true +disallow_untyped_decorators = true +warn_redundant_casts = true +warn_unused_ignores = true +warn_return_any = true +warn_unreachable = true +show_error_codes = true +implicit_reexport = true +packages = ["agent_framework_lab_tau2"] +exclude = [ + "data", +] + +[tool.pyright] +exclude = ["**/data"] + + +[tool.poe] +executor.type = "uv" +include = "../../../shared_tasks.toml" + +[tool.poe.tasks] +test = "pytest --cov=agent_framework_lab_tau2 --cov-report=term-missing:skip-covered tests" +mypy = "mypy agent_framework_lab_tau2" +setup-data = "python tests/setup_data.py" +purge-data = "python tests/purge_data.py" + +[tool.pytest.ini_options] +testpaths = ["tests"] +pythonpath = ["."] +addopts = "--strict-markers --strict-config" +markers = [ + "unit: marks tests as unit tests", + "integration: marks tests as integration tests", +] +env = [ + "TAU2_DATA_DIR=data", +] diff --git a/python/packages/lab/tau2/samples/.env.example b/python/packages/lab/tau2/samples/.env.example new file mode 100644 index 0000000000..00dcf1dc03 --- /dev/null +++ b/python/packages/lab/tau2/samples/.env.example @@ -0,0 +1,4 @@ +TAU2_DATA_DIR=/path/to/your/data + +OPENAI_API_KEY=dummy +OPENAI_BASE_URL=http://127.0.0.1:12345/ diff --git a/python/packages/lab/tau2/samples/run_benchmark.py b/python/packages/lab/tau2/samples/run_benchmark.py new file mode 100644 index 0000000000..6462ad65e1 --- /dev/null +++ b/python/packages/lab/tau2/samples/run_benchmark.py @@ -0,0 +1,240 @@ +# Copyright (c) Microsoft. All rights reserved. + +import argparse +import asyncio +import json +import os +import traceback +from datetime import datetime +from typing import Any + +from agent_framework.openai import OpenAIChatClient +from loguru import logger +from tau2.domains.airline.environment import get_tasks + +from agent_framework_lab_tau2 import TaskRunner, patch_env_set_state + + +def to_dumpable(result: dict[str, Any]) -> dict[str, Any]: + """Convert benchmark result to JSONL-serializable format. + + Handles both successful runs and error cases, ensuring consistent output + format for downstream analysis. Converts Pydantic models to dictionaries + and extracts enum values for JSON compatibility. + """ + if "error" in result: + # Error case: minimal structure with zero reward + return { + "id": result["task"].id, + "error": result["error"], + "evaluation": { + "reward": 0.0, # Standard zero reward for failed runs + }, + "config": result["config"], + "task": result["task"].model_dump(), + } + else: + # Success case: full result structure + return { + "id": result["task"].id, + "evaluation": result["evaluation"].model_dump(), # Detailed evaluation metrics + "config": result["config"], # Model configuration used + "termination_reason": result["termination_reason"].value, # Enum to string + "messages": [m.model_dump() for m in result["messages"]], # Full conversation + "task": result["task"].model_dump(), # Task specification + } + + +async def run_benchmark(assistant_model: str, user_model: str, debug_task_id: str | None, max_steps: int): + """Run comprehensive tau2 benchmark evaluation using agent framework. + + This is the main function that: + + 1. Sets up output file handling (full benchmark vs debug mode) + 2. Loads tau2 task dataset and configures LLM clients + 3. Runs each task through the agent framework workflow + 4. Evaluates performance using tau2's multi-dimensional metrics + 5. Aggregates results and calculates overall benchmark scores + + Args: + assistant_model: Model ID for the customer service agent (e.g., "gpt-4o") + user_model: Model ID for the user simulator (e.g., "gpt-4o") + debug_task_id: Optional specific task ID to run (disables batch processing) + max_steps: Maximum conversation steps before forced termination + + Output: + Creates timestamped JSONL file with detailed results for analysis + Prints summary statistics to console with colored logging + """ + + # STEP 1: Configure output handling based on execution mode + result_fp = None + if debug_task_id is None: + # Full benchmark mode: create timestamped results file + timestamp = datetime.now().strftime("%m%d%H%M") # Format: MMDDHHMM + result_filename = f"results/{assistant_model}_user-{user_model}_{timestamp}.jsonl" + os.makedirs("results", exist_ok=True) + result_fp = open(result_filename, "a") # Append mode for resumability + logger.info(f"Results will be saved to: {result_filename}") + else: + # Debug mode: single task, no file output, verbose logging + logger.info(f"Debug mode: targeting task ID {debug_task_id}") + + # STEP 2: Load tau2 dataset and validate environment + tasks = get_tasks() # Loads all tau2 airline customer service tasks + logger.info(f"Found {len(tasks)} tasks in the dataset") + + _logger = logger.opt(colors=True) # Enable colored console output + + # Validate required OpenAI configuration + # Both models use the same endpoint but can be different model types + openai_base_url = os.getenv("OPENAI_BASE_URL") + if openai_base_url is None: + raise ValueError("OPENAI_BASE_URL must be set") + openai_api_key = os.getenv("OPENAI_API_KEY") + if openai_api_key is None: + raise ValueError("OPENAI_API_KEY must be set") + + # STEP 3: Initialize LLM clients for both agent roles + # Assistant: handles customer service with access to tools and policies + assistant_chat_client = OpenAIChatClient( + base_url=openai_base_url, + api_key=openai_api_key, + ai_model_id=assistant_model, + ) + + # User simulator: simulates realistic customer behavior and requests + user_simulator_chat_client = OpenAIChatClient( + base_url=openai_base_url, + api_key=openai_api_key, + ai_model_id=user_model, + ) + + # STEP 4: Filter task set for debug mode + if debug_task_id is not None: + tasks = [task for task in tasks if task.id == debug_task_id] + if not tasks: + logger.error(f"Task ID {debug_task_id} not found in dataset") + return + + # STEP 5: Initialize evaluation tracking + all_rewards: list[float] = [] # Stores reward scores for final statistics + task_runner = TaskRunner(max_steps=max_steps) # Reusable workflow orchestrator + + # STEP 6: Execute benchmark across all tasks + for task in tasks: + _logger.info(f"Testing task #{task.id}") + _logger.info(f"Purpose: {task.description.purpose}") # type: ignore + + # Initialize result structure for this task + result: dict[str, Any] = { + "config": { + "assistant": assistant_chat_client.ai_model_id, + "user": user_simulator_chat_client.ai_model_id, + }, + "task": task, + } + + # Log user scenario context for transparency + if task.user_scenario and task.user_scenario.instructions: + _logger.info(f"User scenario: {task.user_scenario.instructions.reason_for_call}") # type: ignore + + try: + # Execute the workflow: agent + user simulator conversation + conversation = await task_runner.run(task, assistant_chat_client, user_simulator_chat_client) + + # Evaluate performance using tau2's comprehensive metrics + reward_value = task_runner.evaluate(task, conversation, task_runner.termination_reason) + + # Store detailed results for analysis + result["evaluation"] = task_runner.full_reward_info # Full evaluation breakdown + result["messages"] = conversation # Complete conversation history + result["termination_reason"] = task_runner.termination_reason # How conversation ended + + # Log evaluation results (escape HTML for colored output) + reward_str = str(task_runner.full_reward_info).replace("<", r"\<") + _logger.info(f"Final evaluation: {reward_str}") + + except Exception as e: + # Robust error handling: capture all failures for analysis + _logger.error(f"Error testing task #{task.id}: {e}") + result["error"] = traceback.format_exc() # Full stack trace for debugging + + traceback.print_exc() # Console output for immediate debugging + reward_value = 0.0 # Zero score for failed runs + + # STEP 7: Persist results incrementally (enables partial analysis) + if result_fp is not None: + result_fp.write(json.dumps(to_dumpable(result), default=str) + "\n") + + all_rewards.append(reward_value) # Track for final statistics + + # Reset runner state for next task + task_runner.reinit() + + # STEP 8: Finalize and report aggregate results + if result_fp is not None: + result_fp.close() + + # Calculate overall benchmark performance + all_accuracy = sum(all_rewards) / len(all_rewards) if all_rewards else 0.0 + + # Report final statistics with colored formatting + _logger.info("Final Results:") + _logger.info(f"All tasks accuracy: {all_accuracy:.2f} ({int(sum(all_rewards))}/{len(tasks)})") + + +if __name__ == "__main__": + """Command-line interface for tau2 benchmark execution. + + Provides flexible execution modes: + + - Full benchmark: Runs all tasks and generates timestamped results file + - Debug mode: Single task execution with verbose logging for development + - Environment patching: Optional compatibility layer for tau2-bench integration + + Usage Examples: + # Full benchmark with default models + python run_benchmark.py + + # Custom models + python run_benchmark.py --assistant gpt-4o --user gpt-4o-mini + + # Debug specific task + python run_benchmark.py --debug-task-id task_123 + + # Disable environment patching for testing + python run_benchmark.py --disable-env-patch + """ + + parser = argparse.ArgumentParser(description="Run tau2-agent-framework model test") + + # Model configuration arguments + parser.add_argument("--assistant", type=str, default="gpt-4.1", help="Assistant model id, e.g., gpt-4.1-mini") + parser.add_argument("--user", type=str, default="gpt-4.1", help="User model id") + + # Execution mode arguments + parser.add_argument( + "--debug-task-id", type=str, default=None, help="Debug a specific task ID (disables result file creation)" + ) + parser.add_argument("--max-steps", type=int, default=100, help="Maximum number of steps to run") + + # Environment configuration arguments + parser.add_argument("--disable-env-patch", action="store_true", help="Disable patching tau2-bench environment") + + args = parser.parse_args() + + # Apply environment patch for tau2-bench compatibility + # This modifies tau2's environment to be more flexible with tool call validation + if not args.disable_env_patch: + patch_env_set_state() + + # Execute benchmark with configured parameters + asyncio.run( + run_benchmark( + assistant_model=args.assistant, + user_model=args.user, + debug_task_id=args.debug_task_id, + max_steps=args.max_steps, + ) + ) diff --git a/python/packages/lab/tau2/tests/__init__.py b/python/packages/lab/tau2/tests/__init__.py new file mode 100644 index 0000000000..2a50eae894 --- /dev/null +++ b/python/packages/lab/tau2/tests/__init__.py @@ -0,0 +1 @@ +# Copyright (c) Microsoft. All rights reserved. diff --git a/python/packages/lab/tau2/tests/purge_data.py b/python/packages/lab/tau2/tests/purge_data.py new file mode 100644 index 0000000000..612caabf07 --- /dev/null +++ b/python/packages/lab/tau2/tests/purge_data.py @@ -0,0 +1,20 @@ +# Copyright (c) Microsoft. All rights reserved. + +import shutil +from pathlib import Path + + +def purge_tau2_data(): + """Purge tau2 data directory if it exists.""" + + data_dir = Path.cwd() / "data" + + if data_dir.exists(): + shutil.rmtree(data_dir) + print(f"Data directory at {data_dir} has been purged.") + else: + print("Data directory not found. Skipping purge.") + + +if __name__ == "__main__": + purge_tau2_data() diff --git a/python/packages/lab/tau2/tests/setup_data.py b/python/packages/lab/tau2/tests/setup_data.py new file mode 100644 index 0000000000..70be4c0aa1 --- /dev/null +++ b/python/packages/lab/tau2/tests/setup_data.py @@ -0,0 +1,62 @@ +# Copyright (c) Microsoft. All rights reserved. + +import shutil +import subprocess +from pathlib import Path + + +def setup_tau2_data(): + """Set up tau2 data directory by cloning repository if needed.""" + + # Get project directory (parent of tests directory) + data_dir = Path.cwd() / "data" + print(data_dir) + + print("Setting up tau2 data directory...") + + # Check if data directory already exists + if data_dir.exists(): + print(f"Data directory already exists at {data_dir}") + else: + print("Data directory not found. Cloning tau2-bench repository...") + + try: + # Clone the repository + print("Cloning https://github.com/sierra-research/tau2-bench.git...") + subprocess.run( + ["git", "clone", "https://github.com/sierra-research/tau2-bench.git"], + check=True, + capture_output=True, + text=True, + ) + + # Move data directory + print("Moving data directory...") + tau2_bench_dir = Path.cwd() / "tau2-bench" + tau2_data_dir = tau2_bench_dir / "data" + + if tau2_data_dir.exists(): + shutil.move(str(tau2_data_dir), str(data_dir)) + else: + raise FileNotFoundError(f"Data directory not found in cloned repository: {tau2_data_dir}") + + # Clean up cloned repository + print("Cleaning up cloned repository...") + shutil.rmtree(tau2_bench_dir) + + print("Data directory setup completed successfully!") + + except subprocess.CalledProcessError as e: + print(f"ERROR: Failed to clone repository: {e}") + raise + except Exception as e: + print(f"ERROR: Failed to set up data directory: {e}") + raise + + print(f"TAU2_DATA_DIR should be set to: {data_dir}") + + return str(data_dir) + + +if __name__ == "__main__": + setup_tau2_data() diff --git a/python/packages/lab/tau2/tests/test_message_utils.py b/python/packages/lab/tau2/tests/test_message_utils.py new file mode 100644 index 0000000000..d86d16085d --- /dev/null +++ b/python/packages/lab/tau2/tests/test_message_utils.py @@ -0,0 +1,265 @@ +# Copyright (c) Microsoft. All rights reserved. + +from unittest.mock import patch + +from agent_framework._types import ChatMessage, Role, TextContent, FunctionCallContent, FunctionResultContent +from agent_framework_lab_tau2._message_utils import flip_messages, log_messages + + +def test_flip_messages_user_to_assistant(): + """Test flipping user message to assistant.""" + messages = [ + ChatMessage( + role=Role.USER, contents=[TextContent(text="Hello assistant")], author_name="User1", message_id="msg_001" + ) + ] + + flipped = flip_messages(messages) + + assert len(flipped) == 1 + assert flipped[0].role == Role.ASSISTANT + assert flipped[0].text == "Hello assistant" + assert flipped[0].author_name == "User1" + assert flipped[0].message_id == "msg_001" + + +def test_flip_messages_assistant_to_user(): + """Test flipping assistant message to user.""" + messages = [ + ChatMessage( + role=Role.ASSISTANT, + contents=[TextContent(text="Hello user")], + author_name="Assistant1", + message_id="msg_002", + ) + ] + + flipped = flip_messages(messages) + + assert len(flipped) == 1 + assert flipped[0].role == Role.USER + assert flipped[0].text == "Hello user" + assert flipped[0].author_name == "Assistant1" + assert flipped[0].message_id == "msg_002" + + +def test_flip_messages_assistant_with_function_calls_filtered(): + """Test that function calls are filtered out when flipping assistant to user.""" + function_call = FunctionCallContent(call_id="call_123", name="test_function", arguments={"param": "value"}) + + messages = [ + ChatMessage( + role=Role.ASSISTANT, + contents=[TextContent(text="I'll call a function"), function_call, TextContent(text="After the call")], + message_id="msg_003", + ) + ] + + flipped = flip_messages(messages) + + assert len(flipped) == 1 + assert flipped[0].role == Role.USER + # Function call should be filtered out + assert len(flipped[0].contents) == 2 + assert all(content.type == "text" for content in flipped[0].contents) + assert "I'll call a function" in flipped[0].text + assert "After the call" in flipped[0].text + + +def test_flip_messages_assistant_with_only_function_calls_skipped(): + """Test that assistant messages with only function calls are skipped.""" + function_call = FunctionCallContent(call_id="call_456", name="another_function", arguments={"key": "value"}) + + messages = [ + ChatMessage(role=Role.ASSISTANT, contents=[function_call], message_id="msg_004") # Only function call, no text + ] + + flipped = flip_messages(messages) + + # Should be empty since the message had no text content after filtering + assert len(flipped) == 0 + + +def test_flip_messages_tool_messages_skipped(): + """Test that tool messages are skipped.""" + function_result = FunctionResultContent(call_id="call_789", result={"success": True}) + + messages = [ChatMessage(role=Role.TOOL, contents=[function_result])] + + flipped = flip_messages(messages) + + # Tool messages should be skipped + assert len(flipped) == 0 + + +def test_flip_messages_system_messages_preserved(): + """Test that system messages are preserved as-is.""" + messages = [ChatMessage(role=Role.SYSTEM, contents=[TextContent(text="System instruction")], message_id="sys_001")] + + flipped = flip_messages(messages) + + assert len(flipped) == 1 + assert flipped[0].role == Role.SYSTEM + assert flipped[0].text == "System instruction" + assert flipped[0].message_id == "sys_001" + + +def test_flip_messages_mixed_conversation(): + """Test flipping a mixed conversation.""" + function_call = FunctionCallContent(call_id="call_mixed", name="mixed_function", arguments={}) + + function_result = FunctionResultContent(call_id="call_mixed", result="function result") + + messages = [ + ChatMessage(role=Role.SYSTEM, contents=[TextContent(text="System prompt")]), + ChatMessage(role=Role.USER, contents=[TextContent(text="User question")]), + ChatMessage(role=Role.ASSISTANT, contents=[TextContent(text="Assistant response"), function_call]), + ChatMessage(role=Role.TOOL, contents=[function_result]), + ChatMessage(role=Role.ASSISTANT, contents=[TextContent(text="Final response")]), + ] + + flipped = flip_messages(messages) + + # Should have: system (unchanged), assistant (from user), user (from assistant, filtered), assistant (from final assistant) + assert len(flipped) == 4 + + # Check each flipped message + assert flipped[0].role == Role.SYSTEM + assert flipped[0].text == "System prompt" + + assert flipped[1].role == Role.ASSISTANT + assert flipped[1].text == "User question" + + assert flipped[2].role == Role.USER + assert flipped[2].text == "Assistant response" # Function call filtered out + + # Tool message skipped + + assert flipped[3].role == Role.USER + assert flipped[3].text == "Final response" + + +def test_flip_messages_empty_list(): + """Test flipping empty message list.""" + messages = [] + flipped = flip_messages(messages) + assert len(flipped) == 0 + + +def test_flip_messages_preserves_metadata(): + """Test that message metadata is preserved during flipping.""" + messages = [ + ChatMessage( + role=Role.USER, contents=[TextContent(text="Test message")], author_name="TestUser", message_id="test_123" + ) + ] + + flipped = flip_messages(messages) + + assert len(flipped) == 1 + assert flipped[0].author_name == "TestUser" + assert flipped[0].message_id == "test_123" + + +@patch("agent_framework_lab_tau2._message_utils.logger") +def test_log_messages_text_content(mock_logger): + """Test logging messages with text content.""" + messages = [ + ChatMessage(role=Role.USER, contents=[TextContent(text="Hello")]), + ChatMessage(role=Role.ASSISTANT, contents=[TextContent(text="Hi there!")]), + ] + + log_messages(messages) + + # Should have called logger.info for each message + assert mock_logger.opt.return_value.info.call_count == 2 + + +@patch("agent_framework_lab_tau2._message_utils.logger") +def test_log_messages_function_call(mock_logger): + """Test logging messages with function calls.""" + function_call = FunctionCallContent(call_id="call_log", name="log_function", arguments={"param": "value"}) + + messages = [ChatMessage(role=Role.ASSISTANT, contents=[function_call])] + + log_messages(messages) + + # Should log the function call + mock_logger.opt.return_value.info.assert_called() + call_args = mock_logger.opt.return_value.info.call_args[0][0] + assert "TOOL_CALL" in call_args + assert "log_function" in call_args + + +@patch("agent_framework_lab_tau2._message_utils.logger") +def test_log_messages_function_result(mock_logger): + """Test logging messages with function results.""" + function_result = FunctionResultContent(call_id="call_result", result="success") + + messages = [ChatMessage(role=Role.TOOL, contents=[function_result])] + + log_messages(messages) + + # Should log the function result + mock_logger.opt.return_value.info.assert_called() + call_args = mock_logger.opt.return_value.info.call_args[0][0] + assert "TOOL_RESULT" in call_args + + +@patch("agent_framework_lab_tau2._message_utils.logger") +def test_log_messages_different_roles(mock_logger): + """Test logging messages with different roles get different colors.""" + messages = [ + ChatMessage(role=Role.SYSTEM, contents=[TextContent(text="System")]), + ChatMessage(role=Role.USER, contents=[TextContent(text="User")]), + ChatMessage(role=Role.ASSISTANT, contents=[TextContent(text="Assistant")]), + ChatMessage(role=Role.TOOL, contents=[TextContent(text="Tool")]), + ] + + log_messages(messages) + + # Should have called logger for each message + assert mock_logger.opt.return_value.info.call_count == 4 + + # Check that different color tags are used + calls = mock_logger.opt.return_value.info.call_args_list + system_call = calls[0][0][0] + user_call = calls[1][0][0] + assistant_call = calls[2][0][0] + tool_call = calls[3][0][0] + + assert "cyan" in system_call or "SYSTEM" in system_call + assert "green" in user_call or "USER" in user_call + assert "blue" in assistant_call or "ASSISTANT" in assistant_call + assert "yellow" in tool_call or "TOOL" in tool_call + + +@patch("agent_framework_lab_tau2._message_utils.logger") +def test_log_messages_escapes_html(mock_logger): + """Test that HTML-like characters are properly escaped in log output.""" + messages = [ChatMessage(role=Role.USER, contents=[TextContent(text="Message with content")])] + + log_messages(messages) + + mock_logger.opt.return_value.info.assert_called() + call_args = mock_logger.opt.return_value.info.call_args[0][0] + # Should escape < characters + assert "\\" in call_args or "<tag>" in call_args + + +@patch("agent_framework_lab_tau2._message_utils.logger") +def test_log_messages_mixed_content_types(mock_logger): + """Test logging messages with mixed content types.""" + function_call = FunctionCallContent(call_id="mixed_call", name="mixed_function", arguments={"key": "value"}) + + messages = [ + ChatMessage( + role=Role.ASSISTANT, + contents=[TextContent(text="I'll call a function"), function_call, TextContent(text="Done!")], + ) + ] + + log_messages(messages) + + # Should log multiple times for different content types + assert mock_logger.opt.return_value.info.call_count == 3 diff --git a/python/packages/lab/tau2/tests/test_sliding_window.py b/python/packages/lab/tau2/tests/test_sliding_window.py new file mode 100644 index 0000000000..4ab5d9c04a --- /dev/null +++ b/python/packages/lab/tau2/tests/test_sliding_window.py @@ -0,0 +1,267 @@ +# Copyright (c) Microsoft. All rights reserved. + +"""Tests for sliding window message list.""" + +import pytest +from unittest.mock import patch + +from agent_framework._types import ChatMessage, Role, TextContent, FunctionCallContent, FunctionResultContent +from agent_framework_lab_tau2._sliding_window import SlidingWindowChatMessageList + + +def test_initialization_empty(): + """Test initializing with no messages.""" + sliding_window = SlidingWindowChatMessageList(max_tokens=1000) + + assert sliding_window.max_tokens == 1000 + assert sliding_window.system_message is None + assert sliding_window.tool_definitions is None + assert len(sliding_window._messages) == 0 + assert len(sliding_window._truncated_messages) == 0 + + +def test_initialization_with_parameters(): + """Test initializing with system message and tool definitions.""" + system_msg = "You are a helpful assistant" + tool_defs = [{"name": "test_tool", "description": "A test tool"}] + + sliding_window = SlidingWindowChatMessageList( + max_tokens=2000, system_message=system_msg, tool_definitions=tool_defs + ) + + assert sliding_window.max_tokens == 2000 + assert sliding_window.system_message == system_msg + assert sliding_window.tool_definitions == tool_defs + + +def test_initialization_with_messages(): + """Test initializing with existing messages.""" + messages = [ + ChatMessage(role=Role.USER, contents=[TextContent(text="Hello")]), + ChatMessage(role=Role.ASSISTANT, contents=[TextContent(text="Hi there!")]), + ] + + sliding_window = SlidingWindowChatMessageList(messages=messages, max_tokens=1000) + + assert len(sliding_window._messages) == 2 + assert len(sliding_window._truncated_messages) == 2 + + +@pytest.mark.asyncio +async def test_add_messages_simple(): + """Test adding messages without truncation.""" + sliding_window = SlidingWindowChatMessageList(max_tokens=10000) # Large limit + + new_messages = [ + ChatMessage(role=Role.USER, contents=[TextContent(text="What's the weather?")]), + ChatMessage(role=Role.ASSISTANT, contents=[TextContent(text="I can help with that.")]), + ] + + await sliding_window.add_messages(new_messages) + + messages = await sliding_window.list_messages() + assert len(messages) == 2 + assert messages[0].text == "What's the weather?" + assert messages[1].text == "I can help with that." + + +@pytest.mark.asyncio +async def test_list_all_messages_vs_list_messages(): + """Test difference between list_all_messages and list_messages.""" + sliding_window = SlidingWindowChatMessageList(max_tokens=50) # Small limit to force truncation + + # Add many messages to trigger truncation + messages = [ + ChatMessage(role=Role.USER, contents=[TextContent(text=f"Message {i} with some content")]) for i in range(10) + ] + + await sliding_window.add_messages(messages) + + truncated_messages = await sliding_window.list_messages() + all_messages = await sliding_window.list_all_messages() + + # All messages should contain everything + assert len(all_messages) == 10 + + # Truncated messages should be fewer due to token limit + assert len(truncated_messages) < len(all_messages) + + +def test_get_token_count_basic(): + """Test basic token counting.""" + sliding_window = SlidingWindowChatMessageList(max_tokens=1000) + sliding_window._truncated_messages = [ChatMessage(role=Role.USER, contents=[TextContent(text="Hello")])] + + token_count = sliding_window.get_token_count() + + # Should be more than 0 (exact count depends on encoding) + assert token_count > 0 + + +def test_get_token_count_with_system_message(): + """Test token counting includes system message.""" + system_msg = "You are a helpful assistant" + sliding_window = SlidingWindowChatMessageList(max_tokens=1000, system_message=system_msg) + + # Without messages + token_count_empty = sliding_window.get_token_count() + + # Add a message + sliding_window._truncated_messages = [ChatMessage(role=Role.USER, contents=[TextContent(text="Hello")])] + token_count_with_message = sliding_window.get_token_count() + + # With message should be more tokens + assert token_count_with_message > token_count_empty + assert token_count_empty > 0 # System message contributes tokens + + +def test_get_token_count_function_call(): + """Test token counting with function calls.""" + function_call = FunctionCallContent(call_id="call_123", name="test_function", arguments={"param": "value"}) + + sliding_window = SlidingWindowChatMessageList(max_tokens=1000) + sliding_window._truncated_messages = [ChatMessage(role=Role.ASSISTANT, contents=[function_call])] + + token_count = sliding_window.get_token_count() + assert token_count > 0 + + +def test_get_token_count_function_result(): + """Test token counting with function results.""" + function_result = FunctionResultContent(call_id="call_123", result={"success": True, "data": "result"}) + + sliding_window = SlidingWindowChatMessageList(max_tokens=1000) + sliding_window._truncated_messages = [ChatMessage(role=Role.TOOL, contents=[function_result])] + + token_count = sliding_window.get_token_count() + assert token_count > 0 + + +@patch("agent_framework_lab_tau2._sliding_window.logger") +def test_truncate_messages_removes_old_messages(mock_logger): + """Test that truncation removes old messages when token limit exceeded.""" + sliding_window = SlidingWindowChatMessageList(max_tokens=20) # Very small limit + + # Create messages that will exceed the limit + messages = [ + ChatMessage( + role=Role.USER, + contents=[TextContent(text="This is a very long message that should exceed the token limit")], + ), + ChatMessage( + role=Role.ASSISTANT, + contents=[TextContent(text="This is another very long message that should also exceed the token limit")], + ), + ChatMessage(role=Role.USER, contents=[TextContent(text="Short msg")]), + ] + + sliding_window._truncated_messages = messages.copy() + sliding_window.truncate_messages() + + # Should have fewer messages after truncation + assert len(sliding_window._truncated_messages) < len(messages) + + # Should have logged warnings + assert mock_logger.warning.called + + +@patch("agent_framework_lab_tau2._sliding_window.logger") +def test_truncate_messages_removes_leading_tool_messages(mock_logger): + """Test that truncation removes leading tool messages.""" + sliding_window = SlidingWindowChatMessageList(max_tokens=10000) # Large limit + + # Create messages starting with tool message + tool_message = ChatMessage(role=Role.TOOL, contents=[FunctionResultContent(call_id="call_123", result="result")]) + user_message = ChatMessage(role=Role.USER, contents=[TextContent(text="Hello")]) + + sliding_window._truncated_messages = [tool_message, user_message] + sliding_window.truncate_messages() + + # Tool message should be removed from the beginning + assert len(sliding_window._truncated_messages) == 1 + assert sliding_window._truncated_messages[0].role == Role.USER + + # Should have logged warning about removing tool message + mock_logger.warning.assert_called() + + +def test_estimate_any_object_token_count_dict(): + """Test token counting for dictionary objects.""" + sliding_window = SlidingWindowChatMessageList(max_tokens=1000) + + test_dict = {"key": "value", "number": 42} + token_count = sliding_window.estimate_any_object_token_count(test_dict) + + assert token_count > 0 + + +def test_estimate_any_object_token_count_string(): + """Test token counting for string objects.""" + sliding_window = SlidingWindowChatMessageList(max_tokens=1000) + + test_string = "This is a test string" + token_count = sliding_window.estimate_any_object_token_count(test_string) + + assert token_count > 0 + + +def test_estimate_any_object_token_count_non_serializable(): + """Test token counting for non-JSON-serializable objects.""" + sliding_window = SlidingWindowChatMessageList(max_tokens=1000) + + # Create an object that can't be JSON serialized + class CustomObject: + def __str__(self): + return "CustomObject instance" + + custom_obj = CustomObject() + token_count = sliding_window.estimate_any_object_token_count(custom_obj) + + # Should fall back to string representation + assert token_count > 0 + + +@pytest.mark.asyncio +async def test_real_world_scenario(): + """Test a realistic conversation scenario.""" + sliding_window = SlidingWindowChatMessageList( + max_tokens=30, system_message="You are a helpful assistant" # Moderate limit + ) + + # Simulate a conversation + conversation = [ + ChatMessage(role=Role.USER, contents=[TextContent(text="Hello, how are you?")]), + ChatMessage( + role=Role.ASSISTANT, contents=[TextContent(text="I'm doing well, thank you! How can I help you today?")] + ), + ChatMessage(role=Role.USER, contents=[TextContent(text="Can you tell me about the weather?")]), + ChatMessage( + role=Role.ASSISTANT, + contents=[ + TextContent( + text="I'd be happy to help with weather information, but I don't have access to current weather data." + ) + ], + ), + ChatMessage(role=Role.USER, contents=[TextContent(text="What about telling me a joke instead?")]), + ChatMessage( + role=Role.ASSISTANT, + contents=[TextContent(text="Sure! Why don't scientists trust atoms? Because they make up everything!")], + ), + ] + + await sliding_window.add_messages(conversation) + + current_messages = await sliding_window.list_messages() + all_messages = await sliding_window.list_all_messages() + + # All messages should be preserved + assert len(all_messages) == 6 + + # Current messages might be truncated + assert len(current_messages) <= 6 + + # Token count should be within or close to limit + token_count = sliding_window.get_token_count() + # Allow some margin since truncation happens when exceeded + assert token_count <= sliding_window.max_tokens * 1.1 diff --git a/python/packages/lab/tau2/tests/test_tau2_utils.py b/python/packages/lab/tau2/tests/test_tau2_utils.py new file mode 100644 index 0000000000..b942baba22 --- /dev/null +++ b/python/packages/lab/tau2/tests/test_tau2_utils.py @@ -0,0 +1,189 @@ +# Copyright (c) Microsoft. All rights reserved. + +"""Tests for tau2 utils module.""" + +from typing import Any, cast +from pydantic import BaseModel + +from agent_framework._tools import AIFunction +from agent_framework._types import ChatMessage, Role, TextContent, FunctionCallContent, FunctionResultContent +from agent_framework_lab_tau2._tau2_utils import ( + convert_tau2_tool_to_ai_function, + convert_agent_framework_messages_to_tau2_messages, +) +from tau2.data_model.message import SystemMessage, UserMessage, AssistantMessage, ToolMessage, ToolCall +from tau2.domains.airline.environment import get_environment + + +def test_convert_tau2_tool_to_ai_function_basic(): + """Test basic conversion from tau2 tool to AIFunction.""" + # Get real tools from tau2 environment + env = get_environment() + tools = env.get_tools() + + # Use the first available tool for testing + assert len(tools) > 0, "No tools available in environment" + tau2_tool = tools[0] + + # Convert the tool + ai_function = convert_tau2_tool_to_ai_function(tau2_tool) + + # Verify the conversion + assert isinstance(ai_function, AIFunction) + assert ai_function.name == tau2_tool.name + assert ai_function.description == tau2_tool._get_description() + assert ai_function.input_model == tau2_tool.params + + # Test that the function is callable (we won't call it with real params to avoid side effects) + assert callable(ai_function.func) + + +def test_convert_tau2_tool_to_ai_function_multiple_tools(): + """Test conversion with multiple tau2 tools.""" + # Get real tools from tau2 environment + env = get_environment() + tools = env.get_tools() + + # Convert multiple tools + ai_functions = [convert_tau2_tool_to_ai_function(tool) for tool in tools[:3]] # Test first 3 tools + + # Verify all conversions + for ai_function, tau2_tool in zip(ai_functions, tools[:3]): + assert isinstance(ai_function, AIFunction) + assert ai_function.name == tau2_tool.name + assert ai_function.description == tau2_tool._get_description() + assert ai_function.input_model == tau2_tool.params + assert callable(ai_function.func) + + +def test_convert_agent_framework_messages_to_tau2_messages_system(): + """Test converting system message.""" + messages = [ChatMessage(role=Role.SYSTEM, contents=[TextContent(text="System instruction")])] + + tau2_messages = convert_agent_framework_messages_to_tau2_messages(messages) + + assert len(tau2_messages) == 1 + assert isinstance(tau2_messages[0], SystemMessage) + assert tau2_messages[0].role == "system" + assert tau2_messages[0].content == "System instruction" + + +def test_convert_agent_framework_messages_to_tau2_messages_user(): + """Test converting user message.""" + messages = [ChatMessage(role=Role.USER, contents=[TextContent(text="Hello assistant")])] + + tau2_messages = convert_agent_framework_messages_to_tau2_messages(messages) + + assert len(tau2_messages) == 1 + assert isinstance(tau2_messages[0], UserMessage) + assert tau2_messages[0].role == "user" + assert tau2_messages[0].content == "Hello assistant" + assert tau2_messages[0].tool_calls is None + + +def test_convert_agent_framework_messages_to_tau2_messages_assistant(): + """Test converting assistant message.""" + messages = [ChatMessage(role=Role.ASSISTANT, contents=[TextContent(text="Hello user")])] + + tau2_messages = convert_agent_framework_messages_to_tau2_messages(messages) + + assert len(tau2_messages) == 1 + assert isinstance(tau2_messages[0], AssistantMessage) + assert tau2_messages[0].role == "assistant" + assert tau2_messages[0].content == "Hello user" + assert tau2_messages[0].tool_calls is None + + +def test_convert_agent_framework_messages_to_tau2_messages_with_function_call(): + """Test converting message with function call.""" + function_call = FunctionCallContent(call_id="call_123", name="test_function", arguments={"param": "value"}) + + messages = [ChatMessage(role=Role.ASSISTANT, contents=[TextContent(text="I'll call a function"), function_call])] + + tau2_messages = convert_agent_framework_messages_to_tau2_messages(messages) + + assert len(tau2_messages) == 1 + assert isinstance(tau2_messages[0], AssistantMessage) + assert tau2_messages[0].content == "I'll call a function" + assert tau2_messages[0].tool_calls is not None + assert len(tau2_messages[0].tool_calls) == 1 + + tool_call = tau2_messages[0].tool_calls[0] + assert isinstance(tool_call, ToolCall) + assert tool_call.id == "call_123" + assert tool_call.name == "test_function" + assert tool_call.arguments == {"param": "value"} + assert tool_call.requestor == "assistant" + + +def test_convert_agent_framework_messages_to_tau2_messages_with_function_result(): + """Test converting message with function result.""" + function_result = FunctionResultContent(call_id="call_123", result={"success": True, "data": "result data"}) + + messages = [ChatMessage(role=Role.TOOL, contents=[function_result])] + + tau2_messages = convert_agent_framework_messages_to_tau2_messages(messages) + + assert len(tau2_messages) == 1 + assert isinstance(tau2_messages[0], ToolMessage) + assert tau2_messages[0].id == "call_123" + assert tau2_messages[0].role == "tool" + assert tau2_messages[0].content is not None + assert '{"success": true, "data": "result data"}' in tau2_messages[0].content + assert tau2_messages[0].requestor == "assistant" + assert tau2_messages[0].error is False + + +def test_convert_agent_framework_messages_to_tau2_messages_with_error(): + """Test converting function result with error.""" + function_result = FunctionResultContent( + call_id="call_456", result="Error occurred", exception=Exception("Test error") + ) + + messages = [ChatMessage(role=Role.TOOL, contents=[function_result])] + + tau2_messages = convert_agent_framework_messages_to_tau2_messages(messages) + + assert len(tau2_messages) == 1 + assert isinstance(tau2_messages[0], ToolMessage) + assert tau2_messages[0].error is True + + +def test_convert_agent_framework_messages_to_tau2_messages_multiple_text_contents(): + """Test converting message with multiple text contents.""" + messages = [ChatMessage(role=Role.USER, contents=[TextContent(text="First part"), TextContent(text="Second part")])] + + tau2_messages = convert_agent_framework_messages_to_tau2_messages(messages) + + assert len(tau2_messages) == 1 + assert isinstance(tau2_messages[0], UserMessage) + assert tau2_messages[0].content == "First part Second part" + + +def test_convert_agent_framework_messages_to_tau2_messages_complex_scenario(): + """Test converting complex scenario with multiple message types.""" + function_call = FunctionCallContent(call_id="call_789", name="complex_tool", arguments='{"key": "value"}') + + function_result = FunctionResultContent(call_id="call_789", result={"output": "tool result"}) + + messages = [ + ChatMessage(role=Role.SYSTEM, contents=[TextContent(text="System prompt")]), + ChatMessage(role=Role.USER, contents=[TextContent(text="User request")]), + ChatMessage(role=Role.ASSISTANT, contents=[TextContent(text="I'll help you"), function_call]), + ChatMessage(role=Role.TOOL, contents=[function_result]), + ChatMessage(role=Role.ASSISTANT, contents=[TextContent(text="Based on the result...")]), + ] + + tau2_messages = convert_agent_framework_messages_to_tau2_messages(messages) + + assert len(tau2_messages) == 5 + assert isinstance(tau2_messages[0], SystemMessage) + assert isinstance(tau2_messages[1], UserMessage) + assert isinstance(tau2_messages[2], AssistantMessage) + assert isinstance(tau2_messages[3], ToolMessage) + assert isinstance(tau2_messages[4], AssistantMessage) + + # Check the assistant message with tool call + assert tau2_messages[2].tool_calls is not None + assert len(tau2_messages[2].tool_calls) == 1 + assert tau2_messages[2].tool_calls[0].name == "complex_tool" diff --git a/python/pyproject.toml b/python/pyproject.toml index 291a5613a5..cdb8bde07b 100644 --- a/python/pyproject.toml +++ b/python/pyproject.toml @@ -20,6 +20,7 @@ dev = [ "pytest>=8.4.1", "pytest-asyncio>=1.0.0", "pytest-cov>=6.2.1", + "pytest-env>=1.1.5", "pytest-xdist[psutil]>=3.8.0", "pytest-timeout>=2.3.1", "mypy>=1.16.1", @@ -164,7 +165,7 @@ exclude_dirs = ["tests", "./run_tasks_in_packages_if_exists.py", "./check_md_cod executor.type = "uv" [tool.poe.tasks] -markdown-code-lint = """uv run python check_md_code_blocks.py README.md ./packages/**/README.md ./samples/**/*.md --exclude cookiecutter-agent-framework-lab""" +markdown-code-lint = """uv run python check_md_code_blocks.py README.md ./packages/**/README.md ./samples/**/*.md --exclude cookiecutter-agent-framework-lab --exclude tau2""" samples-code-check = """pyright ./samples""" docs-install = "uv sync --all-packages --all-extras --dev -U --prerelease=if-necessary-or-explicit --group=docs" docs-clean = "rm -rf docs/build" diff --git a/python/uv.lock b/python/uv.lock index 6744f07251..0977b8e326 100644 --- a/python/uv.lock +++ b/python/uv.lock @@ -29,11 +29,21 @@ members = [ "agent-framework-foundry", "agent-framework-lab-gaia", "agent-framework-lab-lightning", + "agent-framework-lab-tau2", "agent-framework-mem0", "agent-framework-project", "agent-framework-runtime", ] +[[package]] +name = "addict" +version = "2.4.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/85/ef/fd7649da8af11d93979831e8f1f8097e85e82d5bfeabc8c68b39175d8e75/addict-2.4.0.tar.gz", hash = "sha256:b3b2210e0e067a281f5646c8c5db92e99b7231ea8b0eb5f74dbdf9e259d4e494", size = 9186, upload-time = "2020-11-21T16:21:31.416Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/6a/00/b08f23b7d7e1e14ce01419a467b583edbb93c6cdb8654e54a9cc579cd61f/addict-2.4.0-py3-none-any.whl", hash = "sha256:249bb56bbfd3cdc2a004ea0ff4c2b6ddc84d53bc2194761636eb314d5cfa5dfc", size = 3832, upload-time = "2020-11-21T16:21:29.588Z" }, +] + [[package]] name = "agent-framework" version = "0.1.0b1" @@ -189,6 +199,27 @@ requires-dist = [ { name = "pydantic", specifier = ">=2.0.0" }, ] +[[package]] +name = "agent-framework-lab-tau2" +version = "0.1.0b1" +source = { editable = "packages/lab/tau2" } +dependencies = [ + { name = "agent-framework", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "loguru", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "pydantic", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "tau2", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" }, + { name = "tiktoken", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" }, +] + +[package.metadata] +requires-dist = [ + { name = "agent-framework", editable = "packages/main" }, + { name = "loguru", specifier = ">=0.7.3" }, + { name = "pydantic", 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