agent-framework/python/CODING_STANDARD.md

Coding Standards

This document describes the coding standards and conventions for the Agent Framework project.

Code Style and Formatting

We use ruff for both linting and formatting with the following configuration:

• Line length: 120 characters
• Target Python version: 3.10+
• Google-style docstrings: All public functions, classes, and modules should have docstrings following Google conventions

Type Annotations

Future Annotations

Note: This convention is being adopted. See #3578 for progress.

Use from __future__ import annotations at the top of files to enable postponed evaluation of annotations. This prevents the need for string-based type hints for forward references:

# ✅ Preferred - use future annotations
from __future__ import annotations

class Agent:
    def create_child(self) -> Agent:  # No quotes needed
        ...

# ❌ Avoid - string-based type hints
class Agent:
    def create_child(self) -> "Agent":  # Requires quotes without future annotations
        ...

TypeVar Naming Convention

Note: This convention is being adopted. See #3594 for progress.

Use the suffix T for TypeVar names instead of a prefix:

# ✅ Preferred - suffix T
ChatResponseT = TypeVar("ChatResponseT", bound=ChatResponse)
AgentT = TypeVar("AgentT", bound=Agent)

# ❌ Avoid - prefix T
TChatResponse = TypeVar("TChatResponse", bound=ChatResponse)
TAgent = TypeVar("TAgent", bound=Agent)

Mapping Types

Note: This convention is being adopted. See #3577 for progress.

Use Mapping instead of MutableMapping for input parameters when mutation is not required:

# ✅ Preferred - Mapping for read-only access
def process_config(config: Mapping[str, Any]) -> None:
    ...

# ❌ Avoid - MutableMapping when mutation isn't needed
def process_config(config: MutableMapping[str, Any]) -> None:
    ...

Function Parameter Guidelines

To make the code easier to use and maintain:

• Positional parameters: Only use for up to 3 fully expected parameters (this is not a hard rule, but a guideline there are instances where this does make sense to exceed)
• Keyword-only parameters: Arguments after * in function signatures are keyword-only; prefer these for optional parameters
• Avoid additional imports: Do not require the user to import additional modules to use the function, so provide string based overrides when applicable, for instance:

def create_agent(name: str, tool_mode: ChatToolMode) -> Agent:
    # Implementation here

Should be:

def create_agent(name: str, tool_mode: Literal['auto', 'required', 'none'] | ChatToolMode) -> Agent:
    # Implementation here
    if isinstance(tool_mode, str):
        tool_mode = ChatToolMode(tool_mode)

• Avoid shadowing built-ins: Do not use parameter names that shadow Python built-ins (e.g., use next_handler instead of next). See #3583 for progress.

Using **kwargs

Note: This convention is being adopted. See #3642 for progress.

Avoid **kwargs unless absolutely necessary. It should only be used as an escape route, not for well-known flows of data:

• Prefer named parameters: If there are known extra arguments being passed, use explicit named parameters instead of kwargs
• Subclassing support: kwargs is acceptable in methods that are part of classes designed for subclassing, allowing subclass-defined kwargs to pass through without issues. In this case, clearly document that kwargs exists for subclass extensibility and not for passing arbitrary data
• Remove when possible: In other cases, removing kwargs is likely better than keeping it
• Separate kwargs by purpose: When combining kwargs for multiple purposes, use specific parameters like client_kwargs: dict[str, Any] instead of mixing everything in **kwargs
• Always document: If kwargs must be used, always document how it's used, either by referencing external documentation or explaining its purpose

Method Naming Inside Connectors

When naming methods inside connectors, we have a loose preference for using the following conventions:

• Use _prepare_<object>_for_<purpose> as a prefix for methods that prepare data for sending to the external service.
• Use _parse_<object>_from_<source> as a prefix for methods that process data received from the external service.

This is not a strict rule, but a guideline to help maintain consistency across the codebase.

Implementation Decisions

Asynchronous Programming

It's important to note that most of this library is written with asynchronous in mind. The developer should always assume everything is asynchronous. One can use the function signature with either async def or def to understand if something is asynchronous or not.

Attributes vs Inheritance

Prefer attributes over inheritance when parameters are mostly the same:

# ✅ Preferred - using attributes
from agent_framework import ChatMessage

user_msg = ChatMessage("user", ["Hello, world!"])
asst_msg = ChatMessage("assistant", ["Hello, world!"])

# ❌ Not preferred - unnecessary inheritance
from agent_framework import UserMessage, AssistantMessage

user_msg = UserMessage(content="Hello, world!")
asst_msg = AssistantMessage(content="Hello, world!")

Logging

Use the centralized logging system:

from agent_framework import get_logger

# For main package
logger = get_logger()

# For subpackages
logger = get_logger('agent_framework.azure')

Do not use direct logging module imports:

# ❌ Avoid this
import logging
logger = logging.getLogger(__name__)

Import Structure

The package follows a flat import structure:

• Core: Import directly from agent_framework

  from agent_framework import ChatAgent, tool
  

• Components: Import from agent_framework.<component>

  from agent_framework.observability import enable_instrumentation, configure_otel_providers
  

• Connectors: Import from agent_framework.<vendor/platform>

  from agent_framework.openai import OpenAIChatClient
  from agent_framework.azure import AzureOpenAIChatClient
  

Package Structure

The project uses a monorepo structure with separate packages for each connector/extension:

python/
├── pyproject.toml              # Root package (agent-framework) depends on agent-framework-core[all]
├── samples/                    # Sample code and examples
├── packages/
│   ├── core/                   # agent-framework-core - Core abstractions and implementations
│   │   ├── pyproject.toml      # Defines [all] extra that includes all connector packages
│   │   ├── tests/              # Tests for core package
│   │   └── agent_framework/
│   │       ├── __init__.py     # Public API exports
│   │       ├── _agents.py      # Agent implementations
│   │       ├── _clients.py     # Chat client protocols and base classes
│   │       ├── _tools.py       # Tool definitions
│   │       ├── _types.py       # Type definitions
│   │       ├── _logging.py     # Logging utilities
│   │       │
│   │       │   # Provider folders - lazy load from connector packages
│   │       ├── openai/         # OpenAI clients (built into core)
│   │       ├── azure/          # Lazy loads from azure-ai, azure-ai-search, azurefunctions
│   │       ├── anthropic/      # Lazy loads from agent-framework-anthropic
│   │       ├── ollama/         # Lazy loads from agent-framework-ollama
│   │       ├── a2a/            # Lazy loads from agent-framework-a2a
│   │       ├── ag_ui/          # Lazy loads from agent-framework-ag-ui
│   │       ├── chatkit/        # Lazy loads from agent-framework-chatkit
│   │       ├── declarative/    # Lazy loads from agent-framework-declarative
│   │       ├── devui/          # Lazy loads from agent-framework-devui
│   │       ├── mem0/           # Lazy loads from agent-framework-mem0
│   │       └── redis/          # Lazy loads from agent-framework-redis
│   │
│   ├── azure-ai/               # agent-framework-azure-ai
│   │   ├── pyproject.toml
│   │   ├── tests/
│   │   └── agent_framework_azure_ai/
│   │       ├── __init__.py     # Public exports
│   │       ├── _chat_client.py # AzureAIClient implementation
│   │       ├── _client.py      # AzureAIAgentClient implementation
│   │       ├── _shared.py      # AzureAISettings and shared utilities
│   │       └── py.typed        # PEP 561 marker
│   ├── anthropic/              # agent-framework-anthropic
│   ├── bedrock/                # agent-framework-bedrock
│   ├── ollama/                 # agent-framework-ollama
│   └── ...                     # Other connector packages

Lazy Loading Pattern

Provider folders in the core package use __getattr__ to lazy load classes from their respective connector packages. This allows users to import from a consistent location while only loading dependencies when needed:

# In agent_framework/azure/__init__.py
_IMPORTS: dict[str, tuple[str, str]] = {
    "AzureAIAgentClient": ("agent_framework_azure_ai", "agent-framework-azure-ai"),
    # ...
}

def __getattr__(name: str) -> Any:
    if name in _IMPORTS:
        import_path, package_name = _IMPORTS[name]
        try:
            return getattr(importlib.import_module(import_path), name)
        except ModuleNotFoundError as exc:
            raise ModuleNotFoundError(
                f"The package {package_name} is required to use `{name}`. "
                f"Install it with: pip install {package_name}"
            ) from exc

Adding a New Connector Package

Important: Do not create a new package unless there is an issue that has been reviewed and approved by the core team.

Initial Release (Preview Phase)

For the first release of a new connector package:

1. Create a new directory under packages/ (e.g., packages/my-connector/)
2. Add the package to tool.uv.sources in the root pyproject.toml
3. Include samples inside the package itself (e.g., packages/my-connector/samples/)
4. Do NOT add the package to the [all] extra in packages/core/pyproject.toml
5. Do NOT create lazy loading in core yet

Promotion to Stable

After the package has been released and gained a measure of confidence:

1. Move samples from the package to the root samples/ folder
2. Add the package to the [all] extra in packages/core/pyproject.toml
3. Create a provider folder in agent_framework/ with lazy loading __init__.py

Versioning and Core Dependency

All non-core packages declare a lower bound on agent-framework-core (e.g., "agent-framework-core>=1.0.0b260130"). Follow these rules when bumping versions:

• Core version changes: When agent-framework-core is updated with breaking or significant changes and its version is bumped, update the agent-framework-core>=... lower bound in every other package's pyproject.toml to match the new core version.
• Non-core version changes: Non-core packages (connectors, extensions) can have their own versions incremented independently while keeping the existing core lower bound pinned. Only raise the core lower bound if the non-core package actually depends on new core APIs.

Installation Options

Connectors are distributed as separate packages and are not imported by default in the core package. Users install the specific connectors they need:

# Install core only
pip install agent-framework-core

# Install core with all connectors
pip install agent-framework-core[all]
# or (equivalently):
pip install agent-framework

# Install specific connector (pulls in core as dependency)
pip install agent-framework-azure-ai

Documentation

Each file should have a single first line containing: # Copyright (c) Microsoft. All rights reserved.

We follow the Google Docstring style guide for functions and methods. They are currently not checked for private functions (functions starting with '_').

They should contain:

• Single line explaining what the function does, ending with a period.
• If necessary to further explain the logic a newline follows the first line and then the explanation is given.
• The following three sections are optional, and if used should be separated by a single empty line.
• Arguments are then specified after a header called Args:, with each argument being specified in the following format:
  • arg_name: Explanation of the argument.
    • if a longer explanation is needed for a argument, it should be placed on the next line, indented by 4 spaces.
    • Type and default values do not have to be specified, they will be pulled from the definition.
• Returns are specified after a header called Returns: or Yields:, with the return type and explanation of the return value.
• Keyword arguments are specified after a header called Keyword Args:, with each argument being specified in the same format as Args:.
• A header for exceptions can be added, called Raises:, following these guidelines:
  • Always document Agent Framework specific exceptions (e.g., AgentInitializationError, AgentExecutionException)
  • Only document standard Python exceptions (TypeError, ValueError, KeyError, etc.) when the condition is non-obvious or provides value to API users
  • Format: ExceptionType: Explanation of the exception.
  • If a longer explanation is needed, it should be placed on the next line, indented by 4 spaces.
• Code examples can be added using the Examples: header followed by .. code-block:: python directive.

Putting them all together, gives you at minimum this:

def equal(arg1: str, arg2: str) -> bool:
    """Compares two strings and returns True if they are the same."""
    ...

Or a complete version of this:

def equal(arg1: str, arg2: str) -> bool:
    """Compares two strings and returns True if they are the same.

    Here is extra explanation of the logic involved.

    Args:
        arg1: The first string to compare.
        arg2: The second string to compare.

    Returns:
        True if the strings are the same, False otherwise.
    """

A more complete example with keyword arguments and code samples:

def create_client(
    model_id: str | None = None,
    *,
    timeout: float | None = None,
    env_file_path: str | None = None,
    **kwargs: Any,
) -> Client:
    """Create a new client with the specified configuration.

    Args:
        model_id: The model ID to use. If not provided,
            it will be loaded from settings.

    Keyword Args:
        timeout: Optional timeout for requests.
        env_file_path: If provided, settings are read from this file.
        kwargs: Additional keyword arguments passed to the underlying client.

    Returns:
        A configured client instance.

    Raises:
        ValueError: If the model_id is invalid.

    Examples:

        .. code-block:: python

            # Create a client with default settings:
            client = create_client(model_id="gpt-4o")

            # Or load from environment:
            client = create_client(env_file_path=".env")
    """
    ...

Use Google-style docstrings for all public APIs:

def create_agent(name: str, chat_client: ChatClientProtocol) -> Agent:
    """Create a new agent with the specified configuration.

    Args:
        name: The name of the agent.
        chat_client: The chat client to use for communication.

    Returns:
        True if the strings are the same, False otherwise.

    Raises:
        ValueError: If one of the strings is empty.
    """
    ...

If in doubt, use the link above to read much more considerations of what to do and when, or use common sense.

Public API and Exports

Explicit Exports

Note: This convention is being adopted. See #3605 for progress.

Define __all__ in each module to explicitly declare the public API. Avoid using from module import * in __init__.py files as it can impact performance and makes the public API unclear:

# ✅ Preferred - explicit __all__ and imports
__all__ = ["ChatAgent", "ChatMessage", "ChatResponse"]

from ._agents import ChatAgent
from ._types import ChatMessage, ChatResponse

# ❌ Avoid - star imports
from ._agents import *
from ._types import *

Performance considerations

Cache Expensive Computations

Think about caching where appropriate. Cache the results of expensive operations that are called repeatedly with the same inputs:

# ✅ Preferred - cache expensive computations
class FunctionTool:
    def __init__(self, ...):
        self._cached_parameters: dict[str, Any] | None = None

    def parameters(self) -> dict[str, Any]:
        """Return the JSON schema for the function's parameters.

        The result is cached after the first call for performance.
        """
        if self._cached_parameters is None:
            self._cached_parameters = self.input_model.model_json_schema()
        return self._cached_parameters

# ❌ Avoid - recalculating every time
def parameters(self) -> dict[str, Any]:
    return self.input_model.model_json_schema()

Prefer Attribute Access Over isinstance()

When checking types in hot paths, prefer checking a type attribute (fast string comparison) over isinstance() (slower due to method resolution order traversal):

# ✅ Preferred - use match/case with type attribute (faster)
match content.type:
    case "function_call":
        # handle function call
    case "usage":
        # handle usage
    case _:
        # handle other types

# ❌ Avoid in hot paths - isinstance() is slower
if isinstance(content, FunctionCallContent):
    # handle function call
elif isinstance(content, UsageContent):
    # handle usage

For inline conditionals:

# ✅ Preferred - type attribute comparison
result = value if content.type == "function_call" else other

# ❌ Avoid - isinstance() in hot paths
result = value if isinstance(content, FunctionCallContent) else other

Avoid Redundant Serialization

When the same data needs to be used in multiple places, compute it once and reuse it:

# ✅ Preferred - reuse computed representation
otel_message = _to_otel_message(message)
otel_messages.append(otel_message)
logger.info(otel_message, extra={...})

# ❌ Avoid - computing the same thing twice
otel_messages.append(_to_otel_message(message)) # this already serializes
message_data = message.to_dict(exclude_none=True)  # and this does so again!
logger.info(message_data, extra={...})

Test Organization

Test Directory Structure

Test folders require specific organization to avoid pytest conflicts when running tests across packages:

1. No __init__.py in test folders: Test directories should NOT contain __init__.py files. This can cause import conflicts when pytest collects tests across multiple packages.

2. File naming: Files starting with test_ are treated as test files by pytest. Do not use this prefix for helper modules or utilities. If you need shared test utilities, put them in conftest.py or a file with a different name pattern (e.g., helpers.py, fixtures.py).

3. Package-specific conftest location: The tests/conftest.py path is reserved for the core package (packages/core/tests/conftest.py). Other packages must place their tests in a uniquely-named subdirectory:

# ✅ Correct structure for non-core packages
packages/devui/
├── tests/
│   └── devui/           # Unique subdirectory matching package name
│       ├── conftest.py  # Package-specific fixtures
│       ├── test_server.py
│       └── test_mapper.py

packages/anthropic/
├── tests/
│   └── anthropic/       # Unique subdirectory
│       ├── conftest.py
│       └── test_client.py

# ❌ Incorrect - will conflict with core package
packages/devui/
├── tests/
│   ├── conftest.py      # Conflicts when running all tests
│   ├── test_server.py
│   └── test_helpers.py  # Bad name - looks like a test file

# ✅ Core package can use tests/ directly
packages/core/
├── tests/
│   ├── conftest.py      # Core's conftest.py
│   ├── core/
│   │   └── test_agents.py
│   └── openai/
│       └── test_client.py

4. Keep the tests/ folder: Even when using a subdirectory, keep the tests/ folder at the package root. Some test discovery commands and tooling rely on this convention.

Fixture Guidelines

• Use conftest.py for shared fixtures within a test directory
• Factory functions with parameters should be regular functions, not fixtures (fixtures can't accept arguments)
• Import factory functions explicitly: from conftest import create_test_request
• Fixtures should use simple names that describe what they provide: mapper, test_request, mock_client