chore: initial public snapshot for github upload

llm-gateway-competitors/litellm-wheel-src/litellm/a2a_protocol/__init__.py

@@ -0,0 +1,73 @@
+"""
+LiteLLM A2A - Wrapper for invoking A2A protocol agents.
+
+This module provides a thin wrapper around the official `a2a` SDK that:
+- Handles httpx client creation and agent card resolution
+- Adds LiteLLM logging via @client decorator
+- Matches the A2A SDK interface (SendMessageRequest, SendMessageResponse, etc.)
+
+Example usage (standalone functions with @client decorator):
+    ```python
+    from litellm.a2a_protocol import asend_message
+    from a2a.types import SendMessageRequest, MessageSendParams
+    from uuid import uuid4
+
+    request = SendMessageRequest(
+        id=str(uuid4()),
+        params=MessageSendParams(
+            message={
+                "role": "user",
+                "parts": [{"kind": "text", "text": "Hello!"}],
+                "messageId": uuid4().hex,
+            }
+        )
+    )
+    response = await asend_message(
+        base_url="http://localhost:10001",
+        request=request,
+    )
+    print(response.model_dump(mode='json', exclude_none=True))
+    ```
+
+Example usage (class-based):
+    ```python
+    from litellm.a2a_protocol import A2AClient
+
+    client = A2AClient(base_url="http://localhost:10001")
+    response = await client.send_message(request)
+    ```
+"""
+
+from litellm.a2a_protocol.client import A2AClient
+from litellm.a2a_protocol.exceptions import (
+    A2AAgentCardError,
+    A2AConnectionError,
+    A2AError,
+    A2ALocalhostURLError,
+)
+from litellm.a2a_protocol.main import (
+    aget_agent_card,
+    asend_message,
+    asend_message_streaming,
+    create_a2a_client,
+    send_message,
+)
+from litellm.types.agents import LiteLLMSendMessageResponse
+
+__all__ = [
+    # Client
+    "A2AClient",
+    # Functions
+    "asend_message",
+    "send_message",
+    "asend_message_streaming",
+    "aget_agent_card",
+    "create_a2a_client",
+    # Response types
+    "LiteLLMSendMessageResponse",
+    # Exceptions
+    "A2AError",
+    "A2AConnectionError",
+    "A2AAgentCardError",
+    "A2ALocalhostURLError",
+]

llm-gateway-competitors/litellm-wheel-src/litellm/a2a_protocol/card_resolver.py

@@ -0,0 +1,144 @@
+"""
+Custom A2A Card Resolver for LiteLLM.
+
+Extends the A2A SDK's card resolver to support multiple well-known paths.
+"""
+
+from typing import TYPE_CHECKING, Any, Dict, Optional
+
+from litellm._logging import verbose_logger
+from litellm.constants import LOCALHOST_URL_PATTERNS
+
+if TYPE_CHECKING:
+    from a2a.types import AgentCard
+
+# Runtime imports with availability check
+_A2ACardResolver: Any = None
+AGENT_CARD_WELL_KNOWN_PATH: str = "/.well-known/agent-card.json"
+PREV_AGENT_CARD_WELL_KNOWN_PATH: str = "/.well-known/agent.json"
+
+try:
+    from a2a.client import A2ACardResolver as _A2ACardResolver  # type: ignore[no-redef]
+    from a2a.utils.constants import (  # type: ignore[no-redef]
+        AGENT_CARD_WELL_KNOWN_PATH,
+        PREV_AGENT_CARD_WELL_KNOWN_PATH,
+    )
+except ImportError:
+    pass
+
+
+def is_localhost_or_internal_url(url: Optional[str]) -> bool:
+    """
+    Check if a URL is a localhost or internal URL.
+
+    This detects common development URLs that are accidentally left in
+    agent cards when deploying to production.
+
+    Args:
+        url: The URL to check
+
+    Returns:
+        True if the URL is localhost/internal
+    """
+    if not url:
+        return False
+
+    url_lower = url.lower()
+
+    return any(pattern in url_lower for pattern in LOCALHOST_URL_PATTERNS)
+
+
+def fix_agent_card_url(agent_card: "AgentCard", base_url: str) -> "AgentCard":
+    """
+    Fix the agent card URL if it contains a localhost/internal address.
+
+    Many A2A agents are deployed with agent cards that contain internal URLs
+    like "http://0.0.0.0:8001/" or "http://localhost:8000/". This function
+    replaces such URLs with the provided base_url.
+
+    Args:
+        agent_card: The agent card to fix
+        base_url: The base URL to use as replacement
+
+    Returns:
+        The agent card with the URL fixed if necessary
+    """
+    card_url = getattr(agent_card, "url", None)
+
+    if card_url and is_localhost_or_internal_url(card_url):
+        # Normalize base_url to ensure it ends with /
+        fixed_url = base_url.rstrip("/") + "/"
+        agent_card.url = fixed_url
+
+    return agent_card
+
+
+class LiteLLMA2ACardResolver(_A2ACardResolver):  # type: ignore[misc]
+    """
+    Custom A2A card resolver that supports multiple well-known paths.
+
+    Extends the base A2ACardResolver to try both:
+    - /.well-known/agent-card.json (standard)
+    - /.well-known/agent.json (previous/alternative)
+    """
+
+    async def get_agent_card(
+        self,
+        relative_card_path: Optional[str] = None,
+        http_kwargs: Optional[Dict[str, Any]] = None,
+    ) -> "AgentCard":
+        """
+        Fetch the agent card, trying multiple well-known paths.
+
+        First tries the standard path, then falls back to the previous path.
+
+        Args:
+            relative_card_path: Optional path to the agent card endpoint.
+                If None, tries both well-known paths.
+            http_kwargs: Optional dictionary of keyword arguments to pass to httpx.get
+
+        Returns:
+            AgentCard from the A2A agent
+
+        Raises:
+            A2AClientHTTPError or A2AClientJSONError if both paths fail
+        """
+        # If a specific path is provided, use the parent implementation
+        if relative_card_path is not None:
+            return await super().get_agent_card(
+                relative_card_path=relative_card_path,
+                http_kwargs=http_kwargs,
+            )
+
+        # Try both well-known paths
+        paths = [
+            AGENT_CARD_WELL_KNOWN_PATH,
+            PREV_AGENT_CARD_WELL_KNOWN_PATH,
+        ]
+
+        last_error = None
+        for path in paths:
+            try:
+                verbose_logger.debug(
+                    f"Attempting to fetch agent card from {self.base_url}{path}"
+                )
+                return await super().get_agent_card(
+                    relative_card_path=path,
+                    http_kwargs=http_kwargs,
+                )
+            except Exception as e:
+                verbose_logger.debug(
+                    f"Failed to fetch agent card from {self.base_url}{path}: {e}"
+                )
+                last_error = e
+                continue
+
+        # If we get here, all paths failed - re-raise the last error
+        if last_error is not None:
+            raise last_error
+
+        # This shouldn't happen, but just in case
+        raise Exception(
+            f"Failed to fetch agent card from {self.base_url}. "
+            f"Tried paths: {', '.join(paths)}"
+        )

llm-gateway-competitors/litellm-wheel-src/litellm/a2a_protocol/client.py

@@ -0,0 +1,109 @@
+"""
+LiteLLM A2A Client class.
+
+Provides a class-based interface for A2A agent invocation.
+"""
+
+from typing import TYPE_CHECKING, AsyncIterator, Dict, Optional
+
+from litellm.types.agents import LiteLLMSendMessageResponse
+
+if TYPE_CHECKING:
+    from a2a.client import A2AClient as A2AClientType
+    from a2a.types import (
+        AgentCard,
+        SendMessageRequest,
+        SendStreamingMessageRequest,
+        SendStreamingMessageResponse,
+    )
+
+
+class A2AClient:
+    """
+    LiteLLM wrapper for A2A agent invocation.
+
+    Creates the underlying A2A client once on first use and reuses it.
+
+    Example:
+        ```python
+        from litellm.a2a_protocol import A2AClient
+        from a2a.types import SendMessageRequest, MessageSendParams
+        from uuid import uuid4
+
+        client = A2AClient(base_url="http://localhost:10001")
+
+        request = SendMessageRequest(
+            id=str(uuid4()),
+            params=MessageSendParams(
+                message={
+                    "role": "user",
+                    "parts": [{"kind": "text", "text": "Hello!"}],
+                    "messageId": uuid4().hex,
+                }
+            )
+        )
+        response = await client.send_message(request)
+        ```
+    """
+
+    def __init__(
+        self,
+        base_url: str,
+        timeout: float = 60.0,
+        extra_headers: Optional[Dict[str, str]] = None,
+    ):
+        """
+        Initialize the A2A client wrapper.
+
+        Args:
+            base_url: The base URL of the A2A agent (e.g., "http://localhost:10001")
+            timeout: Request timeout in seconds (default: 60.0)
+            extra_headers: Optional additional headers to include in requests
+        """
+        self.base_url = base_url
+        self.timeout = timeout
+        self.extra_headers = extra_headers
+        self._a2a_client: Optional["A2AClientType"] = None
+
+    async def _get_client(self) -> "A2AClientType":
+        """Get or create the underlying A2A client."""
+        if self._a2a_client is None:
+            from litellm.a2a_protocol.main import create_a2a_client
+
+            self._a2a_client = await create_a2a_client(
+                base_url=self.base_url,
+                timeout=self.timeout,
+                extra_headers=self.extra_headers,
+            )
+        return self._a2a_client
+
+    async def get_agent_card(self) -> "AgentCard":
+        """Fetch the agent card from the server."""
+        from litellm.a2a_protocol.main import aget_agent_card
+
+        return await aget_agent_card(
+            base_url=self.base_url,
+            timeout=self.timeout,
+            extra_headers=self.extra_headers,
+        )
+
+    async def send_message(
+        self, request: "SendMessageRequest"
+    ) -> LiteLLMSendMessageResponse:
+        """Send a message to the A2A agent."""
+        from litellm.a2a_protocol.main import asend_message
+
+        a2a_client = await self._get_client()
+        return await asend_message(a2a_client=a2a_client, request=request)
+
+    async def send_message_streaming(
+        self, request: "SendStreamingMessageRequest"
+    ) -> AsyncIterator["SendStreamingMessageResponse"]:
+        """Send a streaming message to the A2A agent."""
+        from litellm.a2a_protocol.main import asend_message_streaming
+
+        a2a_client = await self._get_client()
+        async for chunk in asend_message_streaming(
+            a2a_client=a2a_client, request=request
+        ):
+            yield chunk

llm-gateway-competitors/litellm-wheel-src/litellm/a2a_protocol/cost_calculator.py

@@ -0,0 +1,107 @@
+"""
+Cost calculator for A2A (Agent-to-Agent) calls.
+
+Supports dynamic cost parameters that allow platform owners
+to define custom costs per agent query or per token.
+"""
+
+from typing import TYPE_CHECKING, Any, Optional
+
+if TYPE_CHECKING:
+    from litellm.litellm_core_utils.litellm_logging import (
+        Logging as LitellmLoggingObject,
+    )
+else:
+    LitellmLoggingObject = Any
+
+
+class A2ACostCalculator:
+    @staticmethod
+    def calculate_a2a_cost(
+        litellm_logging_obj: Optional[LitellmLoggingObject],
+    ) -> float:
+        """
+        Calculate the cost of an A2A send_message call.
+
+        Supports multiple cost parameters for platform owners:
+        - cost_per_query: Fixed cost per query
+        - input_cost_per_token + output_cost_per_token: Token-based pricing
+
+        Priority order:
+        1. response_cost - if set directly (backward compatibility)
+        2. cost_per_query - fixed cost per query
+        3. input_cost_per_token + output_cost_per_token - token-based cost
+        4. Default to 0.0
+
+        Args:
+            litellm_logging_obj: The LiteLLM logging object containing call details
+
+        Returns:
+            float: The cost of the A2A call
+        """
+        if litellm_logging_obj is None:
+            return 0.0
+
+        model_call_details = litellm_logging_obj.model_call_details
+
+        # Check if user set a custom response cost (backward compatibility)
+        response_cost = model_call_details.get("response_cost", None)
+        if response_cost is not None:
+            return float(response_cost)
+
+        # Get litellm_params for cost parameters
+        litellm_params = model_call_details.get("litellm_params", {}) or {}
+
+        # Check for cost_per_query (fixed cost per query)
+        if litellm_params.get("cost_per_query") is not None:
+            return float(litellm_params["cost_per_query"])
+
+        # Check for token-based pricing
+        input_cost_per_token = litellm_params.get("input_cost_per_token")
+        output_cost_per_token = litellm_params.get("output_cost_per_token")
+
+        if input_cost_per_token is not None or output_cost_per_token is not None:
+            return A2ACostCalculator._calculate_token_based_cost(
+                model_call_details=model_call_details,
+                input_cost_per_token=input_cost_per_token,
+                output_cost_per_token=output_cost_per_token,
+            )
+
+        # Default to 0.0 for A2A calls
+        return 0.0
+
+    @staticmethod
+    def _calculate_token_based_cost(
+        model_call_details: dict,
+        input_cost_per_token: Optional[float],
+        output_cost_per_token: Optional[float],
+    ) -> float:
+        """
+        Calculate cost based on token usage and per-token pricing.
+
+        Args:
+            model_call_details: The model call details containing usage
+            input_cost_per_token: Cost per input token (can be None, defaults to 0)
+            output_cost_per_token: Cost per output token (can be None, defaults to 0)
+
+        Returns:
+            float: The calculated cost
+        """
+        # Get usage from model_call_details
+        usage = model_call_details.get("usage")
+        if usage is None:
+            return 0.0
+
+        # Get token counts
+        prompt_tokens = getattr(usage, "prompt_tokens", 0) or 0
+        completion_tokens = getattr(usage, "completion_tokens", 0) or 0
+
+        # Calculate costs
+        input_cost = prompt_tokens * (
+            float(input_cost_per_token) if input_cost_per_token else 0.0
+        )
+        output_cost = completion_tokens * (
+            float(output_cost_per_token) if output_cost_per_token else 0.0
+        )
+
+        return input_cost + output_cost

llm-gateway-competitors/litellm-wheel-src/litellm/a2a_protocol/exception_mapping_utils.py

@@ -0,0 +1,203 @@
+"""
+A2A Protocol Exception Mapping Utils.
+
+Maps A2A SDK exceptions to LiteLLM A2A exception types.
+"""
+
+from typing import TYPE_CHECKING, Any, Optional
+
+from litellm._logging import verbose_logger
+from litellm.a2a_protocol.card_resolver import (
+    fix_agent_card_url,
+    is_localhost_or_internal_url,
+)
+from litellm.a2a_protocol.exceptions import (
+    A2AAgentCardError,
+    A2AConnectionError,
+    A2AError,
+    A2ALocalhostURLError,
+)
+from litellm.constants import CONNECTION_ERROR_PATTERNS
+
+if TYPE_CHECKING:
+    from a2a.client import A2AClient as A2AClientType
+
+
+# Runtime import
+A2A_SDK_AVAILABLE = False
+try:
+    from a2a.client import A2AClient as _A2AClient  # type: ignore[no-redef]
+
+    A2A_SDK_AVAILABLE = True
+except ImportError:
+    _A2AClient = None  # type: ignore[assignment, misc]
+
+
+class A2AExceptionCheckers:
+    """
+    Helper class for checking various A2A error conditions.
+    """
+
+    @staticmethod
+    def is_connection_error(error_str: str) -> bool:
+        """
+        Check if an error string indicates a connection error.
+
+        Args:
+            error_str: The error string to check
+
+        Returns:
+            True if the error indicates a connection issue
+        """
+        if not isinstance(error_str, str):
+            return False
+
+        error_str_lower = error_str.lower()
+        return any(pattern in error_str_lower for pattern in CONNECTION_ERROR_PATTERNS)
+
+    @staticmethod
+    def is_localhost_url(url: Optional[str]) -> bool:
+        """
+        Check if a URL is a localhost/internal URL.
+
+        Args:
+            url: The URL to check
+
+        Returns:
+            True if the URL is localhost/internal
+        """
+        return is_localhost_or_internal_url(url)
+
+    @staticmethod
+    def is_agent_card_error(error_str: str) -> bool:
+        """
+        Check if an error string indicates an agent card error.
+
+        Args:
+            error_str: The error string to check
+
+        Returns:
+            True if the error is related to agent card fetching/parsing
+        """
+        if not isinstance(error_str, str):
+            return False
+
+        error_str_lower = error_str.lower()
+        agent_card_patterns = [
+            "agent card",
+            "agent-card",
+            ".well-known",
+            "card not found",
+            "invalid agent",
+        ]
+        return any(pattern in error_str_lower for pattern in agent_card_patterns)
+
+
+def map_a2a_exception(
+    original_exception: Exception,
+    card_url: Optional[str] = None,
+    api_base: Optional[str] = None,
+    model: Optional[str] = None,
+) -> Exception:
+    """
+    Map an A2A SDK exception to a LiteLLM A2A exception type.
+
+    Args:
+        original_exception: The original exception from the A2A SDK
+        card_url: The URL from the agent card (if available)
+        api_base: The original API base URL
+        model: The model/agent name
+
+    Returns:
+        A mapped LiteLLM A2A exception
+
+    Raises:
+        A2ALocalhostURLError: If the error is a connection error to a localhost URL
+        A2AConnectionError: If the error is a general connection error
+        A2AAgentCardError: If the error is related to agent card issues
+        A2AError: For other A2A-related errors
+    """
+    error_str = str(original_exception)
+
+    # Check for localhost URL connection error (special case - retryable)
+    if (
+        card_url
+        and api_base
+        and A2AExceptionCheckers.is_localhost_url(card_url)
+        and A2AExceptionCheckers.is_connection_error(error_str)
+    ):
+        raise A2ALocalhostURLError(
+            localhost_url=card_url,
+            base_url=api_base,
+            original_error=original_exception,
+            model=model,
+        )
+
+    # Check for agent card errors
+    if A2AExceptionCheckers.is_agent_card_error(error_str):
+        raise A2AAgentCardError(
+            message=error_str,
+            url=api_base,
+            model=model,
+        )
+
+    # Check for general connection errors
+    if A2AExceptionCheckers.is_connection_error(error_str):
+        raise A2AConnectionError(
+            message=error_str,
+            url=card_url or api_base,
+            model=model,
+        )
+
+    # Default: wrap in generic A2AError
+    raise A2AError(
+        message=error_str,
+        model=model,
+    )
+
+
+def handle_a2a_localhost_retry(
+    error: A2ALocalhostURLError,
+    agent_card: Any,
+    a2a_client: "A2AClientType",
+    is_streaming: bool = False,
+) -> "A2AClientType":
+    """
+    Handle A2ALocalhostURLError by fixing the URL and creating a new client.
+
+    This is called when we catch an A2ALocalhostURLError and want to retry
+    with the corrected URL.
+
+    Args:
+        error: The localhost URL error
+        agent_card: The agent card object to fix
+        a2a_client: The current A2A client
+        is_streaming: Whether this is a streaming request (for logging)
+
+    Returns:
+        A new A2A client with the fixed URL
+
+    Raises:
+        ImportError: If the A2A SDK is not installed
+    """
+    if not A2A_SDK_AVAILABLE or _A2AClient is None:
+        raise ImportError(
+            "A2A SDK is required for localhost retry handling. "
+            "Install it with: pip install a2a"
+        )
+
+    request_type = "streaming " if is_streaming else ""
+    verbose_logger.warning(
+        f"A2A {request_type}request to '{error.localhost_url}' failed: {error.original_error}. "
+        f"Agent card contains localhost/internal URL. "
+        f"Retrying with base_url '{error.base_url}'."
+    )
+
+    # Fix the agent card URL
+    fix_agent_card_url(agent_card, error.base_url)
+
+    # Create a new client with the fixed agent card (transport caches URL)
+    return _A2AClient(
+        httpx_client=a2a_client._transport.httpx_client,  # type: ignore[union-attr]
+        agent_card=agent_card,
+    )

llm-gateway-competitors/litellm-wheel-src/litellm/a2a_protocol/exceptions.py

@@ -0,0 +1,150 @@
+"""
+A2A Protocol Exceptions.
+
+Custom exception types for A2A protocol operations, following LiteLLM's exception pattern.
+"""
+
+from typing import Optional
+
+import httpx
+
+
+class A2AError(Exception):
+    """
+    Base exception for A2A protocol errors.
+
+    Follows the same pattern as LiteLLM's main exceptions.
+    """
+
+    def __init__(
+        self,
+        message: str,
+        status_code: int = 500,
+        llm_provider: str = "a2a_agent",
+        model: Optional[str] = None,
+        response: Optional[httpx.Response] = None,
+        litellm_debug_info: Optional[str] = None,
+        max_retries: Optional[int] = None,
+        num_retries: Optional[int] = None,
+    ):
+        self.status_code = status_code
+        self.message = f"litellm.A2AError: {message}"
+        self.llm_provider = llm_provider
+        self.model = model
+        self.litellm_debug_info = litellm_debug_info
+        self.max_retries = max_retries
+        self.num_retries = num_retries
+        self.response = response or httpx.Response(
+            status_code=self.status_code,
+            request=httpx.Request(method="POST", url="https://litellm.ai"),
+        )
+        super().__init__(self.message)
+
+    def __str__(self) -> str:
+        _message = self.message
+        if self.num_retries:
+            _message += f" LiteLLM Retried: {self.num_retries} times"
+        if self.max_retries:
+            _message += f", LiteLLM Max Retries: {self.max_retries}"
+        return _message
+
+    def __repr__(self) -> str:
+        return self.__str__()
+
+
+class A2AConnectionError(A2AError):
+    """
+    Raised when connection to an A2A agent fails.
+
+    This typically occurs when:
+    - The agent is unreachable
+    - The agent card contains a localhost/internal URL
+    - Network issues prevent connection
+    """
+
+    def __init__(
+        self,
+        message: str,
+        url: Optional[str] = None,
+        model: Optional[str] = None,
+        response: Optional[httpx.Response] = None,
+        litellm_debug_info: Optional[str] = None,
+        max_retries: Optional[int] = None,
+        num_retries: Optional[int] = None,
+    ):
+        self.url = url
+        super().__init__(
+            message=message,
+            status_code=503,
+            llm_provider="a2a_agent",
+            model=model,
+            response=response,
+            litellm_debug_info=litellm_debug_info,
+            max_retries=max_retries,
+            num_retries=num_retries,
+        )
+
+
+class A2AAgentCardError(A2AError):
+    """
+    Raised when there's an issue with the agent card.
+
+    This includes:
+    - Failed to fetch agent card
+    - Invalid agent card format
+    - Missing required fields
+    """
+
+    def __init__(
+        self,
+        message: str,
+        url: Optional[str] = None,
+        model: Optional[str] = None,
+        response: Optional[httpx.Response] = None,
+        litellm_debug_info: Optional[str] = None,
+    ):
+        self.url = url
+        super().__init__(
+            message=message,
+            status_code=404,
+            llm_provider="a2a_agent",
+            model=model,
+            response=response,
+            litellm_debug_info=litellm_debug_info,
+        )
+
+
+class A2ALocalhostURLError(A2AConnectionError):
+    """
+    Raised when an agent card contains a localhost/internal URL.
+
+    Many A2A agents are deployed with agent cards that contain internal URLs
+    like "http://0.0.0.0:8001/" or "http://localhost:8000/". This error
+    indicates that the URL needs to be corrected and the request should be retried.
+
+    Attributes:
+        localhost_url: The localhost/internal URL found in the agent card
+        base_url: The public base URL that should be used instead
+        original_error: The original connection error that was raised
+    """
+
+    def __init__(
+        self,
+        localhost_url: str,
+        base_url: str,
+        original_error: Optional[Exception] = None,
+        model: Optional[str] = None,
+    ):
+        self.localhost_url = localhost_url
+        self.base_url = base_url
+        self.original_error = original_error
+
+        message = (
+            f"Agent card contains localhost/internal URL '{localhost_url}'. "
+            f"Retrying with base URL '{base_url}'."
+        )
+        super().__init__(
+            message=message,
+            url=localhost_url,
+            model=model,
+        )

llm-gateway-competitors/litellm-wheel-src/litellm/a2a_protocol/litellm_completion_bridge/README.md

@@ -0,0 +1,74 @@
+# A2A to LiteLLM Completion Bridge
+
+Routes A2A protocol requests through `litellm.acompletion`, enabling any LiteLLM-supported provider to be invoked via A2A.
+
+## Flow
+
+```
+A2A Request → Transform → litellm.acompletion → Transform → A2A Response
+```
+
+## SDK Usage
+
+Use the existing `asend_message` and `asend_message_streaming` functions with `litellm_params`:
+
+```python
+from litellm.a2a_protocol import asend_message, asend_message_streaming
+from a2a.types import SendMessageRequest, SendStreamingMessageRequest, MessageSendParams
+from uuid import uuid4
+
+# Non-streaming
+request = SendMessageRequest(
+    id=str(uuid4()),
+    params=MessageSendParams(
+        message={"role": "user", "parts": [{"kind": "text", "text": "Hello!"}], "messageId": uuid4().hex}
+    )
+)
+response = await asend_message(
+    request=request,
+    api_base="http://localhost:2024",
+    litellm_params={"custom_llm_provider": "langgraph", "model": "agent"},
+)
+
+# Streaming
+stream_request = SendStreamingMessageRequest(
+    id=str(uuid4()),
+    params=MessageSendParams(
+        message={"role": "user", "parts": [{"kind": "text", "text": "Hello!"}], "messageId": uuid4().hex}
+    )
+)
+async for chunk in asend_message_streaming(
+    request=stream_request,
+    api_base="http://localhost:2024",
+    litellm_params={"custom_llm_provider": "langgraph", "model": "agent"},
+):
+    print(chunk)
+```
+
+## Proxy Usage
+
+Configure an agent with `custom_llm_provider` in `litellm_params`:
+
+```yaml
+agents:
+  - agent_name: my-langgraph-agent
+    agent_card_params:
+      name: "LangGraph Agent"
+      url: "http://localhost:2024"  # Used as api_base
+    litellm_params:
+      custom_llm_provider: langgraph
+      model: agent
+```
+
+When an A2A request hits `/a2a/{agent_id}/message/send`, the bridge:
+
+1. Detects `custom_llm_provider` in agent's `litellm_params`
+2. Transforms A2A message → OpenAI messages
+3. Calls `litellm.acompletion(model="langgraph/agent", api_base="http://localhost:2024")`
+4. Transforms response → A2A format
+
+## Classes
+
+- `A2ACompletionBridgeTransformation` - Static methods for message format conversion
+- `A2ACompletionBridgeHandler` - Static methods for handling requests (streaming/non-streaming)
+

llm-gateway-competitors/litellm-wheel-src/litellm/a2a_protocol/litellm_completion_bridge/__init__.py

@@ -0,0 +1,23 @@
+"""
+A2A to LiteLLM Completion Bridge.
+
+This module provides transformation between A2A protocol messages and
+LiteLLM completion API, enabling any LiteLLM-supported provider to be
+invoked via the A2A protocol.
+"""
+
+from litellm.a2a_protocol.litellm_completion_bridge.handler import (
+    A2ACompletionBridgeHandler,
+    handle_a2a_completion,
+    handle_a2a_completion_streaming,
+)
+from litellm.a2a_protocol.litellm_completion_bridge.transformation import (
+    A2ACompletionBridgeTransformation,
+)
+
+__all__ = [
+    "A2ACompletionBridgeTransformation",
+    "A2ACompletionBridgeHandler",
+    "handle_a2a_completion",
+    "handle_a2a_completion_streaming",
+]

llm-gateway-competitors/litellm-wheel-src/litellm/a2a_protocol/litellm_completion_bridge/handler.py

@@ -0,0 +1,299 @@
+"""
+Handler for A2A to LiteLLM completion bridge.
+
+Routes A2A requests through litellm.acompletion based on custom_llm_provider.
+
+A2A Streaming Events (in order):
+1. Task event (kind: "task") - Initial task creation with status "submitted"
+2. Status update (kind: "status-update") - Status change to "working"
+3. Artifact update (kind: "artifact-update") - Content/artifact delivery
+4. Status update (kind: "status-update") - Final status "completed" with final=true
+"""
+
+from typing import Any, AsyncIterator, Dict, Optional
+
+import litellm
+from litellm._logging import verbose_logger
+from litellm.a2a_protocol.litellm_completion_bridge.transformation import (
+    A2ACompletionBridgeTransformation,
+    A2AStreamingContext,
+)
+from litellm.a2a_protocol.providers.config_manager import A2AProviderConfigManager
+
+
+class A2ACompletionBridgeHandler:
+    """
+    Static methods for handling A2A requests via LiteLLM completion.
+    """
+
+    @staticmethod
+    async def handle_non_streaming(
+        request_id: str,
+        params: Dict[str, Any],
+        litellm_params: Dict[str, Any],
+        api_base: Optional[str] = None,
+    ) -> Dict[str, Any]:
+        """
+        Handle non-streaming A2A request via litellm.acompletion.
+
+        Args:
+            request_id: A2A JSON-RPC request ID
+            params: A2A MessageSendParams containing the message
+            litellm_params: Agent's litellm_params (custom_llm_provider, model, etc.)
+            api_base: API base URL from agent_card_params
+
+        Returns:
+            A2A SendMessageResponse dict
+        """
+        # Get provider config for custom_llm_provider
+        custom_llm_provider = litellm_params.get("custom_llm_provider")
+        a2a_provider_config = A2AProviderConfigManager.get_provider_config(
+            custom_llm_provider=custom_llm_provider
+        )
+
+        # If provider config exists, use it
+        if a2a_provider_config is not None:
+            if api_base is None:
+                raise ValueError(f"api_base is required for {custom_llm_provider}")
+
+            verbose_logger.info(f"A2A: Using provider config for {custom_llm_provider}")
+
+            response_data = await a2a_provider_config.handle_non_streaming(
+                request_id=request_id,
+                params=params,
+                api_base=api_base,
+            )
+
+            return response_data
+
+        # Extract message from params
+        message = params.get("message", {})
+
+        # Transform A2A message to OpenAI format
+        openai_messages = (
+            A2ACompletionBridgeTransformation.a2a_message_to_openai_messages(message)
+        )
+
+        # Get completion params
+        custom_llm_provider = litellm_params.get("custom_llm_provider")
+        model = litellm_params.get("model", "agent")
+
+        # Build full model string if provider specified
+        # Skip prepending if model already starts with the provider prefix
+        if custom_llm_provider and not model.startswith(f"{custom_llm_provider}/"):
+            full_model = f"{custom_llm_provider}/{model}"
+        else:
+            full_model = model
+
+        verbose_logger.info(
+            f"A2A completion bridge: model={full_model}, api_base={api_base}"
+        )
+
+        # Build completion params dict
+        completion_params = {
+            "model": full_model,
+            "messages": openai_messages,
+            "api_base": api_base,
+            "stream": False,
+        }
+        # Add litellm_params (contains api_key, client_id, client_secret, tenant_id, etc.)
+        litellm_params_to_add = {
+            k: v
+            for k, v in litellm_params.items()
+            if k not in ("model", "custom_llm_provider")
+        }
+        completion_params.update(litellm_params_to_add)
+
+        # Call litellm.acompletion
+        response = await litellm.acompletion(**completion_params)
+
+        # Transform response to A2A format
+        a2a_response = (
+            A2ACompletionBridgeTransformation.openai_response_to_a2a_response(
+                response=response,
+                request_id=request_id,
+            )
+        )
+
+        verbose_logger.info(f"A2A completion bridge completed: request_id={request_id}")
+
+        return a2a_response
+
+    @staticmethod
+    async def handle_streaming(
+        request_id: str,
+        params: Dict[str, Any],
+        litellm_params: Dict[str, Any],
+        api_base: Optional[str] = None,
+    ) -> AsyncIterator[Dict[str, Any]]:
+        """
+        Handle streaming A2A request via litellm.acompletion with stream=True.
+
+        Emits proper A2A streaming events:
+        1. Task event (kind: "task") - Initial task with status "submitted"
+        2. Status update (kind: "status-update") - Status "working"
+        3. Artifact update (kind: "artifact-update") - Content delivery
+        4. Status update (kind: "status-update") - Final "completed" status
+
+        Args:
+            request_id: A2A JSON-RPC request ID
+            params: A2A MessageSendParams containing the message
+            litellm_params: Agent's litellm_params (custom_llm_provider, model, etc.)
+            api_base: API base URL from agent_card_params
+
+        Yields:
+            A2A streaming response events
+        """
+        # Get provider config for custom_llm_provider
+        custom_llm_provider = litellm_params.get("custom_llm_provider")
+        a2a_provider_config = A2AProviderConfigManager.get_provider_config(
+            custom_llm_provider=custom_llm_provider
+        )
+
+        # If provider config exists, use it
+        if a2a_provider_config is not None:
+            if api_base is None:
+                raise ValueError(f"api_base is required for {custom_llm_provider}")
+
+            verbose_logger.info(
+                f"A2A: Using provider config for {custom_llm_provider} (streaming)"
+            )
+
+            async for chunk in a2a_provider_config.handle_streaming(
+                request_id=request_id,
+                params=params,
+                api_base=api_base,
+            ):
+                yield chunk
+
+            return
+
+        # Extract message from params
+        message = params.get("message", {})
+
+        # Create streaming context
+        ctx = A2AStreamingContext(
+            request_id=request_id,
+            input_message=message,
+        )
+
+        # Transform A2A message to OpenAI format
+        openai_messages = (
+            A2ACompletionBridgeTransformation.a2a_message_to_openai_messages(message)
+        )
+
+        # Get completion params
+        custom_llm_provider = litellm_params.get("custom_llm_provider")
+        model = litellm_params.get("model", "agent")
+
+        # Build full model string if provider specified
+        # Skip prepending if model already starts with the provider prefix
+        if custom_llm_provider and not model.startswith(f"{custom_llm_provider}/"):
+            full_model = f"{custom_llm_provider}/{model}"
+        else:
+            full_model = model
+
+        verbose_logger.info(
+            f"A2A completion bridge streaming: model={full_model}, api_base={api_base}"
+        )
+
+        # Build completion params dict
+        completion_params = {
+            "model": full_model,
+            "messages": openai_messages,
+            "api_base": api_base,
+            "stream": True,
+        }
+        # Add litellm_params (contains api_key, client_id, client_secret, tenant_id, etc.)
+        litellm_params_to_add = {
+            k: v
+            for k, v in litellm_params.items()
+            if k not in ("model", "custom_llm_provider")
+        }
+        completion_params.update(litellm_params_to_add)
+
+        # 1. Emit initial task event (kind: "task", status: "submitted")
+        task_event = A2ACompletionBridgeTransformation.create_task_event(ctx)
+        yield task_event
+
+        # 2. Emit status update (kind: "status-update", status: "working")
+        working_event = A2ACompletionBridgeTransformation.create_status_update_event(
+            ctx=ctx,
+            state="working",
+            final=False,
+            message_text="Processing request...",
+        )
+        yield working_event
+
+        # Call litellm.acompletion with streaming
+        response = await litellm.acompletion(**completion_params)
+
+        # 3. Accumulate content and emit artifact update
+        accumulated_text = ""
+        chunk_count = 0
+        async for chunk in response:  # type: ignore[union-attr]
+            chunk_count += 1
+
+            # Extract delta content
+            content = ""
+            if chunk is not None and hasattr(chunk, "choices") and chunk.choices:
+                choice = chunk.choices[0]
+                if hasattr(choice, "delta") and choice.delta:
+                    content = choice.delta.content or ""
+
+            if content:
+                accumulated_text += content
+
+        # Emit artifact update with accumulated content
+        if accumulated_text:
+            artifact_event = (
+                A2ACompletionBridgeTransformation.create_artifact_update_event(
+                    ctx=ctx,
+                    text=accumulated_text,
+                )
+            )
+            yield artifact_event
+
+        # 4. Emit final status update (kind: "status-update", status: "completed", final: true)
+        completed_event = A2ACompletionBridgeTransformation.create_status_update_event(
+            ctx=ctx,
+            state="completed",
+            final=True,
+        )
+        yield completed_event
+
+        verbose_logger.info(
+            f"A2A completion bridge streaming completed: request_id={request_id}, chunks={chunk_count}"
+        )
+
+
+# Convenience functions that delegate to the class methods
+async def handle_a2a_completion(
+    request_id: str,
+    params: Dict[str, Any],
+    litellm_params: Dict[str, Any],
+    api_base: Optional[str] = None,
+) -> Dict[str, Any]:
+    """Convenience function for non-streaming A2A completion."""
+    return await A2ACompletionBridgeHandler.handle_non_streaming(
+        request_id=request_id,
+        params=params,
+        litellm_params=litellm_params,
+        api_base=api_base,
+    )
+
+
+async def handle_a2a_completion_streaming(
+    request_id: str,
+    params: Dict[str, Any],
+    litellm_params: Dict[str, Any],
+    api_base: Optional[str] = None,
+) -> AsyncIterator[Dict[str, Any]]:
+    """Convenience function for streaming A2A completion."""
+    async for chunk in A2ACompletionBridgeHandler.handle_streaming(
+        request_id=request_id,
+        params=params,
+        litellm_params=litellm_params,
+        api_base=api_base,
+    ):
+        yield chunk

llm-gateway-competitors/litellm-wheel-src/litellm/a2a_protocol/litellm_completion_bridge/transformation.py

@@ -0,0 +1,284 @@
+"""
+Transformation utilities for A2A <-> OpenAI message format conversion.
+
+A2A Message Format:
+{
+    "role": "user",
+    "parts": [{"kind": "text", "text": "Hello!"}],
+    "messageId": "abc123"
+}
+
+OpenAI Message Format:
+{"role": "user", "content": "Hello!"}
+
+A2A Streaming Events:
+- Task event (kind: "task") - Initial task creation with status "submitted"
+- Status update (kind: "status-update") - Status changes (working, completed)
+- Artifact update (kind: "artifact-update") - Content/artifact delivery
+"""
+
+from datetime import datetime, timezone
+from typing import Any, Dict, List, Optional
+from uuid import uuid4
+
+from litellm._logging import verbose_logger
+
+
+class A2AStreamingContext:
+    """
+    Context holder for A2A streaming state.
+    Tracks task_id, context_id, and message accumulation.
+    """
+
+    def __init__(self, request_id: str, input_message: Dict[str, Any]):
+        self.request_id = request_id
+        self.task_id = str(uuid4())
+        self.context_id = str(uuid4())
+        self.input_message = input_message
+        self.accumulated_text = ""
+        self.has_emitted_task = False
+        self.has_emitted_working = False
+
+
+class A2ACompletionBridgeTransformation:
+    """
+    Static methods for transforming between A2A and OpenAI message formats.
+    """
+
+    @staticmethod
+    def a2a_message_to_openai_messages(
+        a2a_message: Dict[str, Any],
+    ) -> List[Dict[str, str]]:
+        """
+        Transform an A2A message to OpenAI message format.
+
+        Args:
+            a2a_message: A2A message with role, parts, and messageId
+
+        Returns:
+            List of OpenAI-format messages
+        """
+        role = a2a_message.get("role", "user")
+        parts = a2a_message.get("parts", [])
+
+        # Map A2A roles to OpenAI roles
+        openai_role = role
+        if role == "user":
+            openai_role = "user"
+        elif role == "assistant":
+            openai_role = "assistant"
+        elif role == "system":
+            openai_role = "system"
+
+        # Extract text content from parts
+        content_parts = []
+        for part in parts:
+            kind = part.get("kind", "")
+            if kind == "text":
+                text = part.get("text", "")
+                content_parts.append(text)
+
+        content = "\n".join(content_parts) if content_parts else ""
+
+        verbose_logger.debug(
+            f"A2A -> OpenAI transform: role={role} -> {openai_role}, content_length={len(content)}"
+        )
+
+        return [{"role": openai_role, "content": content}]
+
+    @staticmethod
+    def openai_response_to_a2a_response(
+        response: Any,
+        request_id: Optional[str] = None,
+    ) -> Dict[str, Any]:
+        """
+        Transform a LiteLLM ModelResponse to A2A SendMessageResponse format.
+
+        Args:
+            response: LiteLLM ModelResponse object
+            request_id: Original A2A request ID
+
+        Returns:
+            A2A SendMessageResponse dict
+        """
+        # Extract content from response
+        content = ""
+        if hasattr(response, "choices") and response.choices:
+            choice = response.choices[0]
+            if hasattr(choice, "message") and choice.message:
+                content = choice.message.content or ""
+
+        # Build A2A message
+        a2a_message = {
+            "role": "agent",
+            "parts": [{"kind": "text", "text": content}],
+            "messageId": uuid4().hex,
+        }
+
+        # Build A2A response
+        a2a_response = {
+            "jsonrpc": "2.0",
+            "id": request_id,
+            "result": {
+                "message": a2a_message,
+            },
+        }
+
+        verbose_logger.debug(f"OpenAI -> A2A transform: content_length={len(content)}")
+
+        return a2a_response
+
+    @staticmethod
+    def _get_timestamp() -> str:
+        """Get current timestamp in ISO format with timezone."""
+        return datetime.now(timezone.utc).isoformat()
+
+    @staticmethod
+    def create_task_event(
+        ctx: A2AStreamingContext,
+    ) -> Dict[str, Any]:
+        """
+        Create the initial task event with status 'submitted'.
+
+        This is the first event emitted in an A2A streaming response.
+        """
+        return {
+            "id": ctx.request_id,
+            "jsonrpc": "2.0",
+            "result": {
+                "contextId": ctx.context_id,
+                "history": [
+                    {
+                        "contextId": ctx.context_id,
+                        "kind": "message",
+                        "messageId": ctx.input_message.get("messageId", uuid4().hex),
+                        "parts": ctx.input_message.get("parts", []),
+                        "role": ctx.input_message.get("role", "user"),
+                        "taskId": ctx.task_id,
+                    }
+                ],
+                "id": ctx.task_id,
+                "kind": "task",
+                "status": {
+                    "state": "submitted",
+                },
+            },
+        }
+
+    @staticmethod
+    def create_status_update_event(
+        ctx: A2AStreamingContext,
+        state: str,
+        final: bool = False,
+        message_text: Optional[str] = None,
+    ) -> Dict[str, Any]:
+        """
+        Create a status update event.
+
+        Args:
+            ctx: Streaming context
+            state: Status state ('working', 'completed')
+            final: Whether this is the final event
+            message_text: Optional message text for 'working' status
+        """
+        status: Dict[str, Any] = {
+            "state": state,
+            "timestamp": A2ACompletionBridgeTransformation._get_timestamp(),
+        }
+
+        # Add message for 'working' status
+        if state == "working" and message_text:
+            status["message"] = {
+                "contextId": ctx.context_id,
+                "kind": "message",
+                "messageId": str(uuid4()),
+                "parts": [{"kind": "text", "text": message_text}],
+                "role": "agent",
+                "taskId": ctx.task_id,
+            }
+
+        return {
+            "id": ctx.request_id,
+            "jsonrpc": "2.0",
+            "result": {
+                "contextId": ctx.context_id,
+                "final": final,
+                "kind": "status-update",
+                "status": status,
+                "taskId": ctx.task_id,
+            },
+        }
+
+    @staticmethod
+    def create_artifact_update_event(
+        ctx: A2AStreamingContext,
+        text: str,
+    ) -> Dict[str, Any]:
+        """
+        Create an artifact update event with content.
+
+        Args:
+            ctx: Streaming context
+            text: The text content for the artifact
+        """
+        return {
+            "id": ctx.request_id,
+            "jsonrpc": "2.0",
+            "result": {
+                "artifact": {
+                    "artifactId": str(uuid4()),
+                    "name": "response",
+                    "parts": [{"kind": "text", "text": text}],
+                },
+                "contextId": ctx.context_id,
+                "kind": "artifact-update",
+                "taskId": ctx.task_id,
+            },
+        }
+
+    @staticmethod
+    def openai_chunk_to_a2a_chunk(
+        chunk: Any,
+        request_id: Optional[str] = None,
+        is_final: bool = False,
+    ) -> Optional[Dict[str, Any]]:
+        """
+        Transform a LiteLLM streaming chunk to A2A streaming format.
+
+        NOTE: This method is deprecated for streaming. Use the event-based
+        methods (create_task_event, create_status_update_event,
+        create_artifact_update_event) instead for proper A2A streaming.
+
+        Args:
+            chunk: LiteLLM ModelResponse chunk
+            request_id: Original A2A request ID
+            is_final: Whether this is the final chunk
+
+        Returns:
+            A2A streaming chunk dict or None if no content
+        """
+        # Extract delta content
+        content = ""
+        if chunk is not None and hasattr(chunk, "choices") and chunk.choices:
+            choice = chunk.choices[0]
+            if hasattr(choice, "delta") and choice.delta:
+                content = choice.delta.content or ""
+
+        if not content and not is_final:
+            return None
+
+        # Build A2A streaming chunk (legacy format)
+        a2a_chunk = {
+            "jsonrpc": "2.0",
+            "id": request_id,
+            "result": {
+                "message": {
+                    "role": "agent",
+                    "parts": [{"kind": "text", "text": content}],
+                    "messageId": uuid4().hex,
+                },
+                "final": is_final,
+            },
+        }
+
+        return a2a_chunk

llm-gateway-competitors/litellm-wheel-src/litellm/a2a_protocol/main.py

@@ -0,0 +1,744 @@
+"""
+LiteLLM A2A SDK functions.
+
+Provides standalone functions with @client decorator for LiteLLM logging integration.
+"""
+
+import asyncio
+import datetime
+import uuid
+from typing import TYPE_CHECKING, Any, AsyncIterator, Coroutine, Dict, Optional, Union
+
+import litellm
+from litellm._logging import verbose_logger, verbose_proxy_logger
+from litellm.a2a_protocol.streaming_iterator import A2AStreamingIterator
+from litellm.a2a_protocol.utils import A2ARequestUtils
+from litellm.constants import DEFAULT_A2A_AGENT_TIMEOUT
+from litellm.litellm_core_utils.litellm_logging import Logging
+from litellm.llms.custom_httpx.http_handler import (
+    get_async_httpx_client,
+    httpxSpecialProvider,
+)
+from litellm.types.agents import LiteLLMSendMessageResponse
+from litellm.utils import client
+
+if TYPE_CHECKING:
+    from a2a.client import A2AClient as A2AClientType
+    from a2a.types import AgentCard, SendMessageRequest, SendStreamingMessageRequest
+
+# Runtime imports with availability check
+A2A_SDK_AVAILABLE = False
+A2ACardResolver: Any = None
+_A2AClient: Any = None
+
+try:
+    from a2a.client import A2AClient as _A2AClient  # type: ignore[no-redef]
+
+    A2A_SDK_AVAILABLE = True
+except ImportError:
+    pass
+
+# Import our custom card resolver that supports multiple well-known paths
+from litellm.a2a_protocol.card_resolver import LiteLLMA2ACardResolver
+from litellm.a2a_protocol.exception_mapping_utils import (
+    handle_a2a_localhost_retry,
+    map_a2a_exception,
+)
+from litellm.a2a_protocol.exceptions import A2ALocalhostURLError
+
+# Use our custom resolver instead of the default A2A SDK resolver
+A2ACardResolver = LiteLLMA2ACardResolver
+
+
+def _set_usage_on_logging_obj(
+    kwargs: Dict[str, Any],
+    prompt_tokens: int,
+    completion_tokens: int,
+) -> None:
+    """
+    Set usage on litellm_logging_obj for standard logging payload.
+
+    Args:
+        kwargs: The kwargs dict containing litellm_logging_obj
+        prompt_tokens: Number of input tokens
+        completion_tokens: Number of output tokens
+    """
+    litellm_logging_obj = kwargs.get("litellm_logging_obj")
+    if litellm_logging_obj is not None:
+        usage = litellm.Usage(
+            prompt_tokens=prompt_tokens,
+            completion_tokens=completion_tokens,
+            total_tokens=prompt_tokens + completion_tokens,
+        )
+        litellm_logging_obj.model_call_details["usage"] = usage
+
+
+def _set_agent_id_on_logging_obj(
+    kwargs: Dict[str, Any],
+    agent_id: Optional[str],
+) -> None:
+    """
+    Set agent_id on litellm_logging_obj for SpendLogs tracking.
+
+    Args:
+        kwargs: The kwargs dict containing litellm_logging_obj
+        agent_id: The A2A agent ID
+    """
+    if agent_id is None:
+        return
+
+    litellm_logging_obj = kwargs.get("litellm_logging_obj")
+    if litellm_logging_obj is not None:
+        # Set agent_id directly on model_call_details (same pattern as custom_llm_provider)
+        litellm_logging_obj.model_call_details["agent_id"] = agent_id
+
+
+def _get_a2a_model_info(a2a_client: Any, kwargs: Dict[str, Any]) -> str:
+    """
+    Extract agent info and set model/custom_llm_provider for cost tracking.
+
+    Sets model info on the litellm_logging_obj if available.
+    Returns the agent name for logging.
+    """
+    agent_name = "unknown"
+
+    # Try to get agent card from our stored attribute first, then fallback to SDK attribute
+    agent_card = getattr(a2a_client, "_litellm_agent_card", None)
+    if agent_card is None:
+        agent_card = getattr(a2a_client, "agent_card", None)
+
+    if agent_card is not None:
+        agent_name = getattr(agent_card, "name", "unknown") or "unknown"
+
+    # Build model string
+    model = f"a2a_agent/{agent_name}"
+    custom_llm_provider = "a2a_agent"
+
+    # Set on litellm_logging_obj if available (for standard logging payload)
+    litellm_logging_obj = kwargs.get("litellm_logging_obj")
+    if litellm_logging_obj is not None:
+        litellm_logging_obj.model = model
+        litellm_logging_obj.custom_llm_provider = custom_llm_provider
+        litellm_logging_obj.model_call_details["model"] = model
+        litellm_logging_obj.model_call_details[
+            "custom_llm_provider"
+        ] = custom_llm_provider
+
+    return agent_name
+
+
+async def _send_message_via_completion_bridge(
+    request: "SendMessageRequest",
+    custom_llm_provider: str,
+    api_base: Optional[str],
+    litellm_params: Dict[str, Any],
+) -> LiteLLMSendMessageResponse:
+    """
+    Route a send_message through the LiteLLM completion bridge (e.g. LangGraph, Bedrock AgentCore).
+
+    Requires request; api_base is optional for providers that derive endpoint from model.
+    """
+    verbose_logger.info(
+        f"A2A using completion bridge: provider={custom_llm_provider}, api_base={api_base}"
+    )
+
+    from litellm.a2a_protocol.litellm_completion_bridge.handler import (
+        A2ACompletionBridgeHandler,
+    )
+
+    params = (
+        request.params.model_dump(mode="json")
+        if hasattr(request.params, "model_dump")
+        else dict(request.params)
+    )
+
+    response_dict = await A2ACompletionBridgeHandler.handle_non_streaming(
+        request_id=str(request.id),
+        params=params,
+        litellm_params=litellm_params,
+        api_base=api_base,
+    )
+
+    return LiteLLMSendMessageResponse.from_dict(response_dict)
+
+
+async def _execute_a2a_send_with_retry(
+    a2a_client: Any,
+    request: Any,
+    agent_card: Any,
+    card_url: Optional[str],
+    api_base: Optional[str],
+    agent_name: Optional[str],
+) -> Any:
+    """Send an A2A message with retry logic for localhost URL errors."""
+    a2a_response = None
+    for _ in range(2):  # max 2 attempts: original + 1 retry
+        try:
+            a2a_response = await a2a_client.send_message(request)
+            break  # success, exit retry loop
+        except A2ALocalhostURLError as e:
+            a2a_client = handle_a2a_localhost_retry(
+                error=e,
+                agent_card=agent_card,
+                a2a_client=a2a_client,
+                is_streaming=False,
+            )
+            card_url = agent_card.url if agent_card else None
+        except Exception as e:
+            try:
+                map_a2a_exception(e, card_url, api_base, model=agent_name)
+            except A2ALocalhostURLError as localhost_err:
+                a2a_client = handle_a2a_localhost_retry(
+                    error=localhost_err,
+                    agent_card=agent_card,
+                    a2a_client=a2a_client,
+                    is_streaming=False,
+                )
+                card_url = agent_card.url if agent_card else None
+                continue
+            except Exception:
+                raise
+    if a2a_response is None:
+        raise RuntimeError(
+            "A2A send_message failed: no response received after retry attempts."
+        )
+    return a2a_response
+
+
+@client
+async def asend_message(
+    a2a_client: Optional["A2AClientType"] = None,
+    request: Optional["SendMessageRequest"] = None,
+    api_base: Optional[str] = None,
+    litellm_params: Optional[Dict[str, Any]] = None,
+    agent_id: Optional[str] = None,
+    agent_extra_headers: Optional[Dict[str, str]] = None,
+    **kwargs: Any,
+) -> LiteLLMSendMessageResponse:
+    """
+    Async: Send a message to an A2A agent.
+
+    Uses the @client decorator for LiteLLM logging and tracking.
+    If litellm_params contains custom_llm_provider, routes through the completion bridge.
+
+    Args:
+        a2a_client: An initialized a2a.client.A2AClient instance (optional if using completion bridge)
+        request: SendMessageRequest from a2a.types (optional if using completion bridge with api_base)
+        api_base: API base URL (required for completion bridge, optional for standard A2A)
+        litellm_params: Optional dict with custom_llm_provider, model, etc. for completion bridge
+        agent_id: Optional agent ID for tracking in SpendLogs
+        **kwargs: Additional arguments passed to the client decorator
+
+    Returns:
+        LiteLLMSendMessageResponse (wraps a2a SendMessageResponse with _hidden_params)
+
+    Example (standard A2A):
+        ```python
+        from litellm.a2a_protocol import asend_message, create_a2a_client
+        from a2a.types import SendMessageRequest, MessageSendParams
+        from uuid import uuid4
+
+        a2a_client = await create_a2a_client(base_url="http://localhost:10001")
+        request = SendMessageRequest(
+            id=str(uuid4()),
+            params=MessageSendParams(
+                message={"role": "user", "parts": [{"kind": "text", "text": "Hello!"}], "messageId": uuid4().hex}
+            )
+        )
+        response = await asend_message(a2a_client=a2a_client, request=request)
+        ```
+
+    Example (completion bridge with LangGraph):
+        ```python
+        from litellm.a2a_protocol import asend_message
+        from a2a.types import SendMessageRequest, MessageSendParams
+        from uuid import uuid4
+
+        request = SendMessageRequest(
+            id=str(uuid4()),
+            params=MessageSendParams(
+                message={"role": "user", "parts": [{"kind": "text", "text": "Hello!"}], "messageId": uuid4().hex}
+            )
+        )
+        response = await asend_message(
+            request=request,
+            api_base="http://localhost:2024",
+            litellm_params={"custom_llm_provider": "langgraph", "model": "agent"},
+        )
+        ```
+    """
+    litellm_params = litellm_params or {}
+    logging_obj = kwargs.get("litellm_logging_obj")
+    trace_id = getattr(logging_obj, "litellm_trace_id", None) if logging_obj else None
+    custom_llm_provider = litellm_params.get("custom_llm_provider")
+
+    # Route through completion bridge if custom_llm_provider is set
+    if custom_llm_provider:
+        if request is None:
+            raise ValueError("request is required for completion bridge")
+        return await _send_message_via_completion_bridge(
+            request=request,
+            custom_llm_provider=custom_llm_provider,
+            api_base=api_base,
+            litellm_params=litellm_params,
+        )
+
+    # Standard A2A client flow
+    if request is None:
+        raise ValueError("request is required")
+
+    # Create A2A client if not provided but api_base is available
+    if a2a_client is None:
+        if api_base is None:
+            raise ValueError(
+                "Either a2a_client or api_base is required for standard A2A flow"
+            )
+        trace_id = trace_id or str(uuid.uuid4())
+        extra_headers: Dict[str, str] = {"X-LiteLLM-Trace-Id": trace_id}
+        if agent_id:
+            extra_headers["X-LiteLLM-Agent-Id"] = agent_id
+        # Overlay agent-level headers (agent headers take precedence over LiteLLM internal ones)
+        if agent_extra_headers:
+            extra_headers.update(agent_extra_headers)
+        a2a_client = await create_a2a_client(
+            base_url=api_base, extra_headers=extra_headers
+        )
+
+    # Type assertion: a2a_client is guaranteed to be non-None here
+    assert a2a_client is not None
+
+    agent_name = _get_a2a_model_info(a2a_client, kwargs)
+
+    verbose_logger.info(f"A2A send_message request_id={request.id}, agent={agent_name}")
+
+    # Get agent card URL for localhost retry logic
+    agent_card = getattr(a2a_client, "_litellm_agent_card", None) or getattr(
+        a2a_client, "agent_card", None
+    )
+    card_url = getattr(agent_card, "url", None) if agent_card else None
+
+    context_id = trace_id or str(uuid.uuid4())
+    message = request.params.message
+    if isinstance(message, dict):
+        if message.get("context_id") is None:
+            message["context_id"] = context_id
+    else:
+        if getattr(message, "context_id", None) is None:
+            message.context_id = context_id
+
+    a2a_response = await _execute_a2a_send_with_retry(
+        a2a_client=a2a_client,
+        request=request,
+        agent_card=agent_card,
+        card_url=card_url,
+        api_base=api_base,
+        agent_name=agent_name,
+    )
+
+    verbose_logger.info(f"A2A send_message completed, request_id={request.id}")
+
+    # Wrap in LiteLLM response type for _hidden_params support
+    response = LiteLLMSendMessageResponse.from_a2a_response(a2a_response)
+
+    # Calculate token usage from request and response
+    response_dict = a2a_response.model_dump(mode="json", exclude_none=True)
+    (
+        prompt_tokens,
+        completion_tokens,
+        _,
+    ) = A2ARequestUtils.calculate_usage_from_request_response(
+        request=request,
+        response_dict=response_dict,
+    )
+
+    # Set usage on logging obj for standard logging payload
+    _set_usage_on_logging_obj(
+        kwargs=kwargs,
+        prompt_tokens=prompt_tokens,
+        completion_tokens=completion_tokens,
+    )
+
+    # Set agent_id on logging obj for SpendLogs tracking
+    _set_agent_id_on_logging_obj(kwargs=kwargs, agent_id=agent_id)
+
+    return response
+
+
+@client
+def send_message(
+    a2a_client: "A2AClientType",
+    request: "SendMessageRequest",
+    **kwargs: Any,
+) -> Union[LiteLLMSendMessageResponse, Coroutine[Any, Any, LiteLLMSendMessageResponse]]:
+    """
+    Sync: Send a message to an A2A agent.
+
+    Uses the @client decorator for LiteLLM logging and tracking.
+
+    Args:
+        a2a_client: An initialized a2a.client.A2AClient instance
+        request: SendMessageRequest from a2a.types
+        **kwargs: Additional arguments passed to the client decorator
+
+    Returns:
+        LiteLLMSendMessageResponse (wraps a2a SendMessageResponse with _hidden_params)
+    """
+    try:
+        loop = asyncio.get_running_loop()
+    except RuntimeError:
+        loop = None
+
+    if loop is not None:
+        return asend_message(a2a_client=a2a_client, request=request, **kwargs)
+    else:
+        return asyncio.run(
+            asend_message(a2a_client=a2a_client, request=request, **kwargs)
+        )
+
+
+def _build_streaming_logging_obj(
+    request: "SendStreamingMessageRequest",
+    agent_name: str,
+    agent_id: Optional[str],
+    litellm_params: Optional[Dict[str, Any]],
+    metadata: Optional[Dict[str, Any]],
+    proxy_server_request: Optional[Dict[str, Any]],
+) -> Logging:
+    """Build logging object for streaming A2A requests."""
+    start_time = datetime.datetime.now()
+    model = f"a2a_agent/{agent_name}"
+
+    logging_obj = Logging(
+        model=model,
+        messages=[{"role": "user", "content": "streaming-request"}],
+        stream=False,
+        call_type="asend_message_streaming",
+        start_time=start_time,
+        litellm_call_id=str(request.id),
+        function_id=str(request.id),
+    )
+    logging_obj.model = model
+    logging_obj.custom_llm_provider = "a2a_agent"
+    logging_obj.model_call_details["model"] = model
+    logging_obj.model_call_details["custom_llm_provider"] = "a2a_agent"
+    if agent_id:
+        logging_obj.model_call_details["agent_id"] = agent_id
+
+    _litellm_params = litellm_params.copy() if litellm_params else {}
+    if metadata:
+        _litellm_params["metadata"] = metadata
+    if proxy_server_request:
+        _litellm_params["proxy_server_request"] = proxy_server_request
+
+    logging_obj.litellm_params = _litellm_params
+    logging_obj.optional_params = _litellm_params
+    logging_obj.model_call_details["litellm_params"] = _litellm_params
+    logging_obj.model_call_details["metadata"] = metadata or {}
+
+    return logging_obj
+
+
+async def asend_message_streaming(  # noqa: PLR0915
+    a2a_client: Optional["A2AClientType"] = None,
+    request: Optional["SendStreamingMessageRequest"] = None,
+    api_base: Optional[str] = None,
+    litellm_params: Optional[Dict[str, Any]] = None,
+    agent_id: Optional[str] = None,
+    metadata: Optional[Dict[str, Any]] = None,
+    proxy_server_request: Optional[Dict[str, Any]] = None,
+    agent_extra_headers: Optional[Dict[str, str]] = None,
+) -> AsyncIterator[Any]:
+    """
+    Async: Send a streaming message to an A2A agent.
+
+    If litellm_params contains custom_llm_provider, routes through the completion bridge.
+
+    Args:
+        a2a_client: An initialized a2a.client.A2AClient instance (optional if using completion bridge)
+        request: SendStreamingMessageRequest from a2a.types
+        api_base: API base URL (required for completion bridge)
+        litellm_params: Optional dict with custom_llm_provider, model, etc. for completion bridge
+        agent_id: Optional agent ID for tracking in SpendLogs
+        metadata: Optional metadata dict (contains user_api_key, user_id, team_id, etc.)
+        proxy_server_request: Optional proxy server request data
+
+    Yields:
+        SendStreamingMessageResponse chunks from the agent
+
+    Example (completion bridge with LangGraph):
+        ```python
+        from litellm.a2a_protocol import asend_message_streaming
+        from a2a.types import SendStreamingMessageRequest, MessageSendParams
+        from uuid import uuid4
+
+        request = SendStreamingMessageRequest(
+            id=str(uuid4()),
+            params=MessageSendParams(
+                message={"role": "user", "parts": [{"kind": "text", "text": "Hello!"}], "messageId": uuid4().hex}
+            )
+        )
+        async for chunk in asend_message_streaming(
+            request=request,
+            api_base="http://localhost:2024",
+            litellm_params={"custom_llm_provider": "langgraph", "model": "agent"},
+        ):
+            print(chunk)
+        ```
+    """
+    litellm_params = litellm_params or {}
+    custom_llm_provider = litellm_params.get("custom_llm_provider")
+
+    # Route through completion bridge if custom_llm_provider is set
+    if custom_llm_provider:
+        if request is None:
+            raise ValueError("request is required for completion bridge")
+        # api_base is optional for providers that derive endpoint from model (e.g., bedrock/agentcore)
+
+        verbose_logger.info(
+            f"A2A streaming using completion bridge: provider={custom_llm_provider}"
+        )
+
+        from litellm.a2a_protocol.litellm_completion_bridge.handler import (
+            A2ACompletionBridgeHandler,
+        )
+
+        # Extract params from request
+        params = (
+            request.params.model_dump(mode="json")
+            if hasattr(request.params, "model_dump")
+            else dict(request.params)
+        )
+
+        async for chunk in A2ACompletionBridgeHandler.handle_streaming(
+            request_id=str(request.id),
+            params=params,
+            litellm_params=litellm_params,
+            api_base=api_base,
+        ):
+            yield chunk
+        return
+
+    # Standard A2A client flow
+    if request is None:
+        raise ValueError("request is required")
+
+    # Create A2A client if not provided but api_base is available
+    if a2a_client is None:
+        if api_base is None:
+            raise ValueError(
+                "Either a2a_client or api_base is required for standard A2A flow"
+            )
+        # Mirror the non-streaming path: always include trace and agent-id headers
+        streaming_extra_headers: Dict[str, str] = {
+            "X-LiteLLM-Trace-Id": str(request.id),
+        }
+        if agent_id:
+            streaming_extra_headers["X-LiteLLM-Agent-Id"] = agent_id
+        if agent_extra_headers:
+            streaming_extra_headers.update(agent_extra_headers)
+        a2a_client = await create_a2a_client(
+            base_url=api_base, extra_headers=streaming_extra_headers
+        )
+
+    # Type assertion: a2a_client is guaranteed to be non-None here
+    assert a2a_client is not None
+
+    verbose_logger.info(f"A2A send_message_streaming request_id={request.id}")
+
+    # Build logging object for streaming completion callbacks
+    agent_card = getattr(a2a_client, "_litellm_agent_card", None) or getattr(
+        a2a_client, "agent_card", None
+    )
+    card_url = getattr(agent_card, "url", None) if agent_card else None
+    agent_name = getattr(agent_card, "name", "unknown") if agent_card else "unknown"
+
+    logging_obj = _build_streaming_logging_obj(
+        request=request,
+        agent_name=agent_name,
+        agent_id=agent_id,
+        litellm_params=litellm_params,
+        metadata=metadata,
+        proxy_server_request=proxy_server_request,
+    )
+
+    # Retry loop: if connection fails due to localhost URL in agent card, retry with fixed URL
+    # Connection errors in streaming typically occur on first chunk iteration
+    first_chunk = True
+    for attempt in range(2):  # max 2 attempts: original + 1 retry
+        stream = a2a_client.send_message_streaming(request)
+        iterator = A2AStreamingIterator(
+            stream=stream,
+            request=request,
+            logging_obj=logging_obj,
+            agent_name=agent_name,
+        )
+
+        try:
+            first_chunk = True
+            async for chunk in iterator:
+                if first_chunk:
+                    first_chunk = False  # connection succeeded
+                yield chunk
+            return  # stream completed successfully
+        except A2ALocalhostURLError as e:
+            # Only retry on first chunk, not mid-stream
+            if first_chunk and attempt == 0:
+                a2a_client = handle_a2a_localhost_retry(
+                    error=e,
+                    agent_card=agent_card,
+                    a2a_client=a2a_client,
+                    is_streaming=True,
+                )
+                card_url = agent_card.url if agent_card else None
+            else:
+                raise
+        except Exception as e:
+            # Only map exception on first chunk
+            if first_chunk and attempt == 0:
+                try:
+                    map_a2a_exception(e, card_url, api_base, model=agent_name)
+                except A2ALocalhostURLError as localhost_err:
+                    # Localhost URL error - fix and retry
+                    a2a_client = handle_a2a_localhost_retry(
+                        error=localhost_err,
+                        agent_card=agent_card,
+                        a2a_client=a2a_client,
+                        is_streaming=True,
+                    )
+                    card_url = agent_card.url if agent_card else None
+                    continue
+                except Exception:
+                    # Re-raise the mapped exception
+                    raise
+            raise
+
+
+async def create_a2a_client(
+    base_url: str,
+    timeout: float = 60.0,
+    extra_headers: Optional[Dict[str, str]] = None,
+) -> "A2AClientType":
+    """
+    Create an A2A client for the given agent URL.
+
+    This resolves the agent card and returns a ready-to-use A2A client.
+    The client can be reused for multiple requests.
+
+    Args:
+        base_url: The base URL of the A2A agent (e.g., "http://localhost:10001")
+        timeout: Request timeout in seconds (default: 60.0)
+        extra_headers: Optional additional headers to include in requests
+
+    Returns:
+        An initialized a2a.client.A2AClient instance
+
+    Example:
+        ```python
+        from litellm.a2a_protocol import create_a2a_client, asend_message
+
+        # Create client once
+        client = await create_a2a_client(base_url="http://localhost:10001")
+
+        # Reuse for multiple requests
+        response1 = await asend_message(a2a_client=client, request=request1)
+        response2 = await asend_message(a2a_client=client, request=request2)
+        ```
+    """
+    if not A2A_SDK_AVAILABLE:
+        raise ImportError(
+            "The 'a2a' package is required for A2A agent invocation. "
+            "Install it with: pip install a2a-sdk"
+        )
+
+    verbose_logger.info(f"Creating A2A client for {base_url}")
+
+    # Use get_async_httpx_client with per-agent params so that different agents
+    # (with different extra_headers) get separate cached clients.  The params
+    # dict is hashed into the cache key, keeping agent auth isolated while
+    # still reusing connections within the same agent.
+    #
+    # Only pass params that AsyncHTTPHandler.__init__ accepts (e.g. timeout).
+    # Use "disable_aiohttp_transport" key for cache-key-only data (it's
+    # filtered out before reaching the constructor).
+    _client_params: dict = {"timeout": timeout}
+    if extra_headers:
+        # Encode headers into a cache-key-only param so each unique header
+        # set produces a distinct cache key.
+        _client_params["disable_aiohttp_transport"] = str(sorted(extra_headers.items()))
+    _async_handler = get_async_httpx_client(
+        llm_provider=httpxSpecialProvider.A2AProvider,
+        params=_client_params,
+    )
+    httpx_client = _async_handler.client
+    if extra_headers:
+        httpx_client.headers.update(extra_headers)
+        verbose_proxy_logger.debug(
+            f"A2A client created with extra_headers={list(extra_headers.keys())}"
+        )
+
+    # Resolve agent card
+    resolver = A2ACardResolver(
+        httpx_client=httpx_client,
+        base_url=base_url,
+    )
+    agent_card = await resolver.get_agent_card()
+
+    verbose_logger.debug(
+        f"Resolved agent card: {agent_card.name if hasattr(agent_card, 'name') else 'unknown'}"
+    )
+
+    # Create A2A client
+    a2a_client = _A2AClient(
+        httpx_client=httpx_client,
+        agent_card=agent_card,
+    )
+
+    # Store agent_card on client for later retrieval (SDK doesn't expose it)
+    a2a_client._litellm_agent_card = agent_card  # type: ignore[attr-defined]
+
+    verbose_logger.info(f"A2A client created for {base_url}")
+
+    return a2a_client
+
+
+async def aget_agent_card(
+    base_url: str,
+    timeout: float = DEFAULT_A2A_AGENT_TIMEOUT,
+    extra_headers: Optional[Dict[str, str]] = None,
+) -> "AgentCard":
+    """
+    Fetch the agent card from an A2A agent.
+
+    Args:
+        base_url: The base URL of the A2A agent (e.g., "http://localhost:10001")
+        timeout: Request timeout in seconds (default: 60.0)
+        extra_headers: Optional additional headers to include in requests
+
+    Returns:
+        AgentCard from the A2A agent
+    """
+    if not A2A_SDK_AVAILABLE:
+        raise ImportError(
+            "The 'a2a' package is required for A2A agent invocation. "
+            "Install it with: pip install a2a-sdk"
+        )
+
+    verbose_logger.info(f"Fetching agent card from {base_url}")
+
+    # Use LiteLLM's cached httpx client
+    http_handler = get_async_httpx_client(
+        llm_provider=httpxSpecialProvider.A2A,
+        params={"timeout": timeout},
+    )
+    httpx_client = http_handler.client
+
+    resolver = A2ACardResolver(
+        httpx_client=httpx_client,
+        base_url=base_url,
+    )
+    agent_card = await resolver.get_agent_card()
+
+    verbose_logger.info(
+        f"Fetched agent card: {agent_card.name if hasattr(agent_card, 'name') else 'unknown'}"
+    )
+    return agent_card

llm-gateway-competitors/litellm-wheel-src/litellm/a2a_protocol/providers/__init__.py

@@ -0,0 +1,10 @@
+"""
+A2A Protocol Providers.
+
+This module contains provider-specific implementations for the A2A protocol.
+"""
+
+from litellm.a2a_protocol.providers.base import BaseA2AProviderConfig
+from litellm.a2a_protocol.providers.config_manager import A2AProviderConfigManager
+
+__all__ = ["BaseA2AProviderConfig", "A2AProviderConfigManager"]

llm-gateway-competitors/litellm-wheel-src/litellm/a2a_protocol/providers/base.py

@@ -0,0 +1,62 @@
+"""
+Base configuration for A2A protocol providers.
+"""
+
+from abc import ABC, abstractmethod
+from typing import Any, AsyncIterator, Dict
+
+
+class BaseA2AProviderConfig(ABC):
+    """
+    Base configuration class for A2A protocol providers.
+
+    Each provider should implement this interface to define how to handle
+    A2A requests for their specific agent type.
+    """
+
+    @abstractmethod
+    async def handle_non_streaming(
+        self,
+        request_id: str,
+        params: Dict[str, Any],
+        api_base: str,
+        **kwargs,
+    ) -> Dict[str, Any]:
+        """
+        Handle non-streaming A2A request.
+
+        Args:
+            request_id: A2A JSON-RPC request ID
+            params: A2A MessageSendParams containing the message
+            api_base: Base URL of the agent
+            **kwargs: Additional provider-specific parameters
+
+        Returns:
+            A2A SendMessageResponse dict
+        """
+        pass
+
+    @abstractmethod
+    async def handle_streaming(
+        self,
+        request_id: str,
+        params: Dict[str, Any],
+        api_base: str,
+        **kwargs,
+    ) -> AsyncIterator[Dict[str, Any]]:
+        """
+        Handle streaming A2A request.
+
+        Args:
+            request_id: A2A JSON-RPC request ID
+            params: A2A MessageSendParams containing the message
+            api_base: Base URL of the agent
+            **kwargs: Additional provider-specific parameters
+
+        Yields:
+            A2A streaming response events
+        """
+        # This is an abstract method - subclasses must implement
+        # The yield is here to make this a generator function
+        if False:  # pragma: no cover
+            yield {}

llm-gateway-competitors/litellm-wheel-src/litellm/a2a_protocol/providers/config_manager.py

@@ -0,0 +1,47 @@
+"""
+A2A Provider Config Manager.
+
+Manages provider-specific configurations for A2A protocol.
+"""
+
+from typing import Optional
+
+from litellm.a2a_protocol.providers.base import BaseA2AProviderConfig
+
+
+class A2AProviderConfigManager:
+    """
+    Manager for A2A provider configurations.
+
+    Similar to ProviderConfigManager in litellm.utils but specifically for A2A providers.
+    """
+
+    @staticmethod
+    def get_provider_config(
+        custom_llm_provider: Optional[str],
+    ) -> Optional[BaseA2AProviderConfig]:
+        """
+        Get the provider configuration for a given custom_llm_provider.
+
+        Args:
+            custom_llm_provider: The provider identifier (e.g., "pydantic_ai_agents")
+
+        Returns:
+            Provider configuration instance or None if not found
+        """
+        if custom_llm_provider is None:
+            return None
+
+        if custom_llm_provider == "pydantic_ai_agents":
+            from litellm.a2a_protocol.providers.pydantic_ai_agents.config import (
+                PydanticAIProviderConfig,
+            )
+
+            return PydanticAIProviderConfig()
+
+        # Add more providers here as needed
+        # elif custom_llm_provider == "another_provider":
+        #     from litellm.a2a_protocol.providers.another_provider.config import AnotherProviderConfig
+        #     return AnotherProviderConfig()
+
+        return None

llm-gateway-competitors/litellm-wheel-src/litellm/a2a_protocol/providers/litellm_completion/README.md

@@ -0,0 +1,74 @@
+# A2A to LiteLLM Completion Bridge
+
+Routes A2A protocol requests through `litellm.acompletion`, enabling any LiteLLM-supported provider to be invoked via A2A.
+
+## Flow
+
+```
+A2A Request → Transform → litellm.acompletion → Transform → A2A Response
+```
+
+## SDK Usage
+
+Use the existing `asend_message` and `asend_message_streaming` functions with `litellm_params`:
+
+```python
+from litellm.a2a_protocol import asend_message, asend_message_streaming
+from a2a.types import SendMessageRequest, SendStreamingMessageRequest, MessageSendParams
+from uuid import uuid4
+
+# Non-streaming
+request = SendMessageRequest(
+    id=str(uuid4()),
+    params=MessageSendParams(
+        message={"role": "user", "parts": [{"kind": "text", "text": "Hello!"}], "messageId": uuid4().hex}
+    )
+)
+response = await asend_message(
+    request=request,
+    api_base="http://localhost:2024",
+    litellm_params={"custom_llm_provider": "langgraph", "model": "agent"},
+)
+
+# Streaming
+stream_request = SendStreamingMessageRequest(
+    id=str(uuid4()),
+    params=MessageSendParams(
+        message={"role": "user", "parts": [{"kind": "text", "text": "Hello!"}], "messageId": uuid4().hex}
+    )
+)
+async for chunk in asend_message_streaming(
+    request=stream_request,
+    api_base="http://localhost:2024",
+    litellm_params={"custom_llm_provider": "langgraph", "model": "agent"},
+):
+    print(chunk)
+```
+
+## Proxy Usage
+
+Configure an agent with `custom_llm_provider` in `litellm_params`:
+
+```yaml
+agents:
+  - agent_name: my-langgraph-agent
+    agent_card_params:
+      name: "LangGraph Agent"
+      url: "http://localhost:2024"  # Used as api_base
+    litellm_params:
+      custom_llm_provider: langgraph
+      model: agent
+```
+
+When an A2A request hits `/a2a/{agent_id}/message/send`, the bridge:
+
+1. Detects `custom_llm_provider` in agent's `litellm_params`
+2. Transforms A2A message → OpenAI messages
+3. Calls `litellm.acompletion(model="langgraph/agent", api_base="http://localhost:2024")`
+4. Transforms response → A2A format
+
+## Classes
+
+- `A2ACompletionBridgeTransformation` - Static methods for message format conversion
+- `A2ACompletionBridgeHandler` - Static methods for handling requests (streaming/non-streaming)
+

llm-gateway-competitors/litellm-wheel-src/litellm/a2a_protocol/providers/litellm_completion/__init__.py

@@ -0,0 +1,5 @@
+"""
+LiteLLM Completion bridge provider for A2A protocol.
+
+Routes A2A requests through litellm.acompletion based on custom_llm_provider.
+"""

llm-gateway-competitors/litellm-wheel-src/litellm/a2a_protocol/providers/litellm_completion/handler.py

@@ -0,0 +1,301 @@
+"""
+Handler for A2A to LiteLLM completion bridge.
+
+Routes A2A requests through litellm.acompletion based on custom_llm_provider.
+
+A2A Streaming Events (in order):
+1. Task event (kind: "task") - Initial task creation with status "submitted"
+2. Status update (kind: "status-update") - Status change to "working"
+3. Artifact update (kind: "artifact-update") - Content/artifact delivery
+4. Status update (kind: "status-update") - Final status "completed" with final=true
+"""
+
+from typing import Any, AsyncIterator, Dict, Optional
+
+import litellm
+from litellm._logging import verbose_logger
+from litellm.a2a_protocol.litellm_completion_bridge.pydantic_ai_transformation import (
+    PydanticAITransformation,
+)
+from litellm.a2a_protocol.litellm_completion_bridge.transformation import (
+    A2ACompletionBridgeTransformation,
+    A2AStreamingContext,
+)
+
+
+class A2ACompletionBridgeHandler:
+    """
+    Static methods for handling A2A requests via LiteLLM completion.
+    """
+
+    @staticmethod
+    async def handle_non_streaming(
+        request_id: str,
+        params: Dict[str, Any],
+        litellm_params: Dict[str, Any],
+        api_base: Optional[str] = None,
+    ) -> Dict[str, Any]:
+        """
+        Handle non-streaming A2A request via litellm.acompletion.
+
+        Args:
+            request_id: A2A JSON-RPC request ID
+            params: A2A MessageSendParams containing the message
+            litellm_params: Agent's litellm_params (custom_llm_provider, model, etc.)
+            api_base: API base URL from agent_card_params
+
+        Returns:
+            A2A SendMessageResponse dict
+        """
+        # Check if this is a Pydantic AI agent request
+        custom_llm_provider = litellm_params.get("custom_llm_provider")
+        if custom_llm_provider == "pydantic_ai_agents":
+            if api_base is None:
+                raise ValueError("api_base is required for Pydantic AI agents")
+
+            verbose_logger.info(
+                f"Pydantic AI: Routing to Pydantic AI agent at {api_base}"
+            )
+
+            # Send request directly to Pydantic AI agent
+            response_data = await PydanticAITransformation.send_non_streaming_request(
+                api_base=api_base,
+                request_id=request_id,
+                params=params,
+            )
+
+            return response_data
+
+        # Extract message from params
+        message = params.get("message", {})
+
+        # Transform A2A message to OpenAI format
+        openai_messages = (
+            A2ACompletionBridgeTransformation.a2a_message_to_openai_messages(message)
+        )
+
+        # Get completion params
+        custom_llm_provider = litellm_params.get("custom_llm_provider")
+        model = litellm_params.get("model", "agent")
+
+        # Build full model string if provider specified
+        # Skip prepending if model already starts with the provider prefix
+        if custom_llm_provider and not model.startswith(f"{custom_llm_provider}/"):
+            full_model = f"{custom_llm_provider}/{model}"
+        else:
+            full_model = model
+
+        verbose_logger.info(
+            f"A2A completion bridge: model={full_model}, api_base={api_base}"
+        )
+
+        # Build completion params dict
+        completion_params = {
+            "model": full_model,
+            "messages": openai_messages,
+            "api_base": api_base,
+            "stream": False,
+        }
+        # Add litellm_params (contains api_key, client_id, client_secret, tenant_id, etc.)
+        litellm_params_to_add = {
+            k: v
+            for k, v in litellm_params.items()
+            if k not in ("model", "custom_llm_provider")
+        }
+        completion_params.update(litellm_params_to_add)
+
+        # Call litellm.acompletion
+        response = await litellm.acompletion(**completion_params)
+
+        # Transform response to A2A format
+        a2a_response = (
+            A2ACompletionBridgeTransformation.openai_response_to_a2a_response(
+                response=response,
+                request_id=request_id,
+            )
+        )
+
+        verbose_logger.info(f"A2A completion bridge completed: request_id={request_id}")
+
+        return a2a_response
+
+    @staticmethod
+    async def handle_streaming(
+        request_id: str,
+        params: Dict[str, Any],
+        litellm_params: Dict[str, Any],
+        api_base: Optional[str] = None,
+    ) -> AsyncIterator[Dict[str, Any]]:
+        """
+        Handle streaming A2A request via litellm.acompletion with stream=True.
+
+        Emits proper A2A streaming events:
+        1. Task event (kind: "task") - Initial task with status "submitted"
+        2. Status update (kind: "status-update") - Status "working"
+        3. Artifact update (kind: "artifact-update") - Content delivery
+        4. Status update (kind: "status-update") - Final "completed" status
+
+        Args:
+            request_id: A2A JSON-RPC request ID
+            params: A2A MessageSendParams containing the message
+            litellm_params: Agent's litellm_params (custom_llm_provider, model, etc.)
+            api_base: API base URL from agent_card_params
+
+        Yields:
+            A2A streaming response events
+        """
+        # Check if this is a Pydantic AI agent request
+        custom_llm_provider = litellm_params.get("custom_llm_provider")
+        if custom_llm_provider == "pydantic_ai_agents":
+            if api_base is None:
+                raise ValueError("api_base is required for Pydantic AI agents")
+
+            verbose_logger.info(
+                f"Pydantic AI: Faking streaming for Pydantic AI agent at {api_base}"
+            )
+
+            # Get non-streaming response first
+            response_data = await PydanticAITransformation.send_non_streaming_request(
+                api_base=api_base,
+                request_id=request_id,
+                params=params,
+            )
+
+            # Convert to fake streaming
+            async for chunk in PydanticAITransformation.fake_streaming_from_response(
+                response_data=response_data,
+                request_id=request_id,
+            ):
+                yield chunk
+
+            return
+
+        # Extract message from params
+        message = params.get("message", {})
+
+        # Create streaming context
+        ctx = A2AStreamingContext(
+            request_id=request_id,
+            input_message=message,
+        )
+
+        # Transform A2A message to OpenAI format
+        openai_messages = (
+            A2ACompletionBridgeTransformation.a2a_message_to_openai_messages(message)
+        )
+
+        # Get completion params
+        custom_llm_provider = litellm_params.get("custom_llm_provider")
+        model = litellm_params.get("model", "agent")
+
+        # Build full model string if provider specified
+        # Skip prepending if model already starts with the provider prefix
+        if custom_llm_provider and not model.startswith(f"{custom_llm_provider}/"):
+            full_model = f"{custom_llm_provider}/{model}"
+        else:
+            full_model = model
+
+        verbose_logger.info(
+            f"A2A completion bridge streaming: model={full_model}, api_base={api_base}"
+        )
+
+        # Build completion params dict
+        completion_params = {
+            "model": full_model,
+            "messages": openai_messages,
+            "api_base": api_base,
+            "stream": True,
+        }
+        # Add litellm_params (contains api_key, client_id, client_secret, tenant_id, etc.)
+        litellm_params_to_add = {
+            k: v
+            for k, v in litellm_params.items()
+            if k not in ("model", "custom_llm_provider")
+        }
+        completion_params.update(litellm_params_to_add)
+
+        # 1. Emit initial task event (kind: "task", status: "submitted")
+        task_event = A2ACompletionBridgeTransformation.create_task_event(ctx)
+        yield task_event
+
+        # 2. Emit status update (kind: "status-update", status: "working")
+        working_event = A2ACompletionBridgeTransformation.create_status_update_event(
+            ctx=ctx,
+            state="working",
+            final=False,
+            message_text="Processing request...",
+        )
+        yield working_event
+
+        # Call litellm.acompletion with streaming
+        response = await litellm.acompletion(**completion_params)
+
+        # 3. Accumulate content and emit artifact update
+        accumulated_text = ""
+        chunk_count = 0
+        async for chunk in response:  # type: ignore[union-attr]
+            chunk_count += 1
+
+            # Extract delta content
+            content = ""
+            if chunk is not None and hasattr(chunk, "choices") and chunk.choices:
+                choice = chunk.choices[0]
+                if hasattr(choice, "delta") and choice.delta:
+                    content = choice.delta.content or ""
+
+            if content:
+                accumulated_text += content
+
+        # Emit artifact update with accumulated content
+        if accumulated_text:
+            artifact_event = (
+                A2ACompletionBridgeTransformation.create_artifact_update_event(
+                    ctx=ctx,
+                    text=accumulated_text,
+                )
+            )
+            yield artifact_event
+
+        # 4. Emit final status update (kind: "status-update", status: "completed", final: true)
+        completed_event = A2ACompletionBridgeTransformation.create_status_update_event(
+            ctx=ctx,
+            state="completed",
+            final=True,
+        )
+        yield completed_event
+
+        verbose_logger.info(
+            f"A2A completion bridge streaming completed: request_id={request_id}, chunks={chunk_count}"
+        )
+
+
+# Convenience functions that delegate to the class methods
+async def handle_a2a_completion(
+    request_id: str,
+    params: Dict[str, Any],
+    litellm_params: Dict[str, Any],
+    api_base: Optional[str] = None,
+) -> Dict[str, Any]:
+    """Convenience function for non-streaming A2A completion."""
+    return await A2ACompletionBridgeHandler.handle_non_streaming(
+        request_id=request_id,
+        params=params,
+        litellm_params=litellm_params,
+        api_base=api_base,
+    )
+
+
+async def handle_a2a_completion_streaming(
+    request_id: str,
+    params: Dict[str, Any],
+    litellm_params: Dict[str, Any],
+    api_base: Optional[str] = None,
+) -> AsyncIterator[Dict[str, Any]]:
+    """Convenience function for streaming A2A completion."""
+    async for chunk in A2ACompletionBridgeHandler.handle_streaming(
+        request_id=request_id,
+        params=params,
+        litellm_params=litellm_params,
+        api_base=api_base,
+    ):
+        yield chunk

llm-gateway-competitors/litellm-wheel-src/litellm/a2a_protocol/providers/litellm_completion/transformation.py

@@ -0,0 +1,284 @@
+"""
+Transformation utilities for A2A <-> OpenAI message format conversion.
+
+A2A Message Format:
+{
+    "role": "user",
+    "parts": [{"kind": "text", "text": "Hello!"}],
+    "messageId": "abc123"
+}
+
+OpenAI Message Format:
+{"role": "user", "content": "Hello!"}
+
+A2A Streaming Events:
+- Task event (kind: "task") - Initial task creation with status "submitted"
+- Status update (kind: "status-update") - Status changes (working, completed)
+- Artifact update (kind: "artifact-update") - Content/artifact delivery
+"""
+
+from datetime import datetime, timezone
+from typing import Any, Dict, List, Optional
+from uuid import uuid4
+
+from litellm._logging import verbose_logger
+
+
+class A2AStreamingContext:
+    """
+    Context holder for A2A streaming state.
+    Tracks task_id, context_id, and message accumulation.
+    """
+
+    def __init__(self, request_id: str, input_message: Dict[str, Any]):
+        self.request_id = request_id
+        self.task_id = str(uuid4())
+        self.context_id = str(uuid4())
+        self.input_message = input_message
+        self.accumulated_text = ""
+        self.has_emitted_task = False
+        self.has_emitted_working = False
+
+
+class A2ACompletionBridgeTransformation:
+    """
+    Static methods for transforming between A2A and OpenAI message formats.
+    """
+
+    @staticmethod
+    def a2a_message_to_openai_messages(
+        a2a_message: Dict[str, Any],
+    ) -> List[Dict[str, str]]:
+        """
+        Transform an A2A message to OpenAI message format.
+
+        Args:
+            a2a_message: A2A message with role, parts, and messageId
+
+        Returns:
+            List of OpenAI-format messages
+        """
+        role = a2a_message.get("role", "user")
+        parts = a2a_message.get("parts", [])
+
+        # Map A2A roles to OpenAI roles
+        openai_role = role
+        if role == "user":
+            openai_role = "user"
+        elif role == "assistant":
+            openai_role = "assistant"
+        elif role == "system":
+            openai_role = "system"
+
+        # Extract text content from parts
+        content_parts = []
+        for part in parts:
+            kind = part.get("kind", "")
+            if kind == "text":
+                text = part.get("text", "")
+                content_parts.append(text)
+
+        content = "\n".join(content_parts) if content_parts else ""
+
+        verbose_logger.debug(
+            f"A2A -> OpenAI transform: role={role} -> {openai_role}, content_length={len(content)}"
+        )
+
+        return [{"role": openai_role, "content": content}]
+
+    @staticmethod
+    def openai_response_to_a2a_response(
+        response: Any,
+        request_id: Optional[str] = None,
+    ) -> Dict[str, Any]:
+        """
+        Transform a LiteLLM ModelResponse to A2A SendMessageResponse format.
+
+        Args:
+            response: LiteLLM ModelResponse object
+            request_id: Original A2A request ID
+
+        Returns:
+            A2A SendMessageResponse dict
+        """
+        # Extract content from response
+        content = ""
+        if hasattr(response, "choices") and response.choices:
+            choice = response.choices[0]
+            if hasattr(choice, "message") and choice.message:
+                content = choice.message.content or ""
+
+        # Build A2A message
+        a2a_message = {
+            "role": "agent",
+            "parts": [{"kind": "text", "text": content}],
+            "messageId": uuid4().hex,
+        }
+
+        # Build A2A response
+        a2a_response = {
+            "jsonrpc": "2.0",
+            "id": request_id,
+            "result": {
+                "message": a2a_message,
+            },
+        }
+
+        verbose_logger.debug(f"OpenAI -> A2A transform: content_length={len(content)}")
+
+        return a2a_response
+
+    @staticmethod
+    def _get_timestamp() -> str:
+        """Get current timestamp in ISO format with timezone."""
+        return datetime.now(timezone.utc).isoformat()
+
+    @staticmethod
+    def create_task_event(
+        ctx: A2AStreamingContext,
+    ) -> Dict[str, Any]:
+        """
+        Create the initial task event with status 'submitted'.
+
+        This is the first event emitted in an A2A streaming response.
+        """
+        return {
+            "id": ctx.request_id,
+            "jsonrpc": "2.0",
+            "result": {
+                "contextId": ctx.context_id,
+                "history": [
+                    {
+                        "contextId": ctx.context_id,
+                        "kind": "message",
+                        "messageId": ctx.input_message.get("messageId", uuid4().hex),
+                        "parts": ctx.input_message.get("parts", []),
+                        "role": ctx.input_message.get("role", "user"),
+                        "taskId": ctx.task_id,
+                    }
+                ],
+                "id": ctx.task_id,
+                "kind": "task",
+                "status": {
+                    "state": "submitted",
+                },
+            },
+        }
+
+    @staticmethod
+    def create_status_update_event(
+        ctx: A2AStreamingContext,
+        state: str,
+        final: bool = False,
+        message_text: Optional[str] = None,
+    ) -> Dict[str, Any]:
+        """
+        Create a status update event.
+
+        Args:
+            ctx: Streaming context
+            state: Status state ('working', 'completed')
+            final: Whether this is the final event
+            message_text: Optional message text for 'working' status
+        """
+        status: Dict[str, Any] = {
+            "state": state,
+            "timestamp": A2ACompletionBridgeTransformation._get_timestamp(),
+        }
+
+        # Add message for 'working' status
+        if state == "working" and message_text:
+            status["message"] = {
+                "contextId": ctx.context_id,
+                "kind": "message",
+                "messageId": str(uuid4()),
+                "parts": [{"kind": "text", "text": message_text}],
+                "role": "agent",
+                "taskId": ctx.task_id,
+            }
+
+        return {
+            "id": ctx.request_id,
+            "jsonrpc": "2.0",
+            "result": {
+                "contextId": ctx.context_id,
+                "final": final,
+                "kind": "status-update",
+                "status": status,
+                "taskId": ctx.task_id,
+            },
+        }
+
+    @staticmethod
+    def create_artifact_update_event(
+        ctx: A2AStreamingContext,
+        text: str,
+    ) -> Dict[str, Any]:
+        """
+        Create an artifact update event with content.
+
+        Args:
+            ctx: Streaming context
+            text: The text content for the artifact
+        """
+        return {
+            "id": ctx.request_id,
+            "jsonrpc": "2.0",
+            "result": {
+                "artifact": {
+                    "artifactId": str(uuid4()),
+                    "name": "response",
+                    "parts": [{"kind": "text", "text": text}],
+                },
+                "contextId": ctx.context_id,
+                "kind": "artifact-update",
+                "taskId": ctx.task_id,
+            },
+        }
+
+    @staticmethod
+    def openai_chunk_to_a2a_chunk(
+        chunk: Any,
+        request_id: Optional[str] = None,
+        is_final: bool = False,
+    ) -> Optional[Dict[str, Any]]:
+        """
+        Transform a LiteLLM streaming chunk to A2A streaming format.
+
+        NOTE: This method is deprecated for streaming. Use the event-based
+        methods (create_task_event, create_status_update_event,
+        create_artifact_update_event) instead for proper A2A streaming.
+
+        Args:
+            chunk: LiteLLM ModelResponse chunk
+            request_id: Original A2A request ID
+            is_final: Whether this is the final chunk
+
+        Returns:
+            A2A streaming chunk dict or None if no content
+        """
+        # Extract delta content
+        content = ""
+        if chunk is not None and hasattr(chunk, "choices") and chunk.choices:
+            choice = chunk.choices[0]
+            if hasattr(choice, "delta") and choice.delta:
+                content = choice.delta.content or ""
+
+        if not content and not is_final:
+            return None
+
+        # Build A2A streaming chunk (legacy format)
+        a2a_chunk = {
+            "jsonrpc": "2.0",
+            "id": request_id,
+            "result": {
+                "message": {
+                    "role": "agent",
+                    "parts": [{"kind": "text", "text": content}],
+                    "messageId": uuid4().hex,
+                },
+                "final": is_final,
+            },
+        }
+
+        return a2a_chunk

llm-gateway-competitors/litellm-wheel-src/litellm/a2a_protocol/providers/pydantic_ai_agents/__init__.py

@@ -0,0 +1,16 @@
+"""
+Pydantic AI agent provider for A2A protocol.
+
+Pydantic AI agents follow A2A protocol but don't support streaming natively.
+This provider handles fake streaming by converting non-streaming responses into streaming chunks.
+"""
+
+from litellm.a2a_protocol.providers.pydantic_ai_agents.config import (
+    PydanticAIProviderConfig,
+)
+from litellm.a2a_protocol.providers.pydantic_ai_agents.handler import PydanticAIHandler
+from litellm.a2a_protocol.providers.pydantic_ai_agents.transformation import (
+    PydanticAITransformation,
+)
+
+__all__ = ["PydanticAIHandler", "PydanticAITransformation", "PydanticAIProviderConfig"]

llm-gateway-competitors/litellm-wheel-src/litellm/a2a_protocol/providers/pydantic_ai_agents/config.py

@@ -0,0 +1,50 @@
+"""
+Pydantic AI provider configuration.
+"""
+
+from typing import Any, AsyncIterator, Dict
+
+from litellm.a2a_protocol.providers.base import BaseA2AProviderConfig
+from litellm.a2a_protocol.providers.pydantic_ai_agents.handler import PydanticAIHandler
+
+
+class PydanticAIProviderConfig(BaseA2AProviderConfig):
+    """
+    Provider configuration for Pydantic AI agents.
+
+    Pydantic AI agents follow A2A protocol but don't support streaming natively.
+    This config provides fake streaming by converting non-streaming responses into streaming chunks.
+    """
+
+    async def handle_non_streaming(
+        self,
+        request_id: str,
+        params: Dict[str, Any],
+        api_base: str,
+        **kwargs,
+    ) -> Dict[str, Any]:
+        """Handle non-streaming request to Pydantic AI agent."""
+        return await PydanticAIHandler.handle_non_streaming(
+            request_id=request_id,
+            params=params,
+            api_base=api_base,
+            timeout=kwargs.get("timeout", 60.0),
+        )
+
+    async def handle_streaming(
+        self,
+        request_id: str,
+        params: Dict[str, Any],
+        api_base: str,
+        **kwargs,
+    ) -> AsyncIterator[Dict[str, Any]]:
+        """Handle streaming request with fake streaming."""
+        async for chunk in PydanticAIHandler.handle_streaming(
+            request_id=request_id,
+            params=params,
+            api_base=api_base,
+            timeout=kwargs.get("timeout", 60.0),
+            chunk_size=kwargs.get("chunk_size", 50),
+            delay_ms=kwargs.get("delay_ms", 10),
+        ):
+            yield chunk

llm-gateway-competitors/litellm-wheel-src/litellm/a2a_protocol/providers/pydantic_ai_agents/handler.py

@@ -0,0 +1,102 @@
+"""
+Handler for Pydantic AI agents.
+
+Pydantic AI agents follow A2A protocol but don't support streaming natively.
+This handler provides fake streaming by converting non-streaming responses into streaming chunks.
+"""
+
+from typing import Any, AsyncIterator, Dict
+
+from litellm._logging import verbose_logger
+from litellm.a2a_protocol.providers.pydantic_ai_agents.transformation import (
+    PydanticAITransformation,
+)
+
+
+class PydanticAIHandler:
+    """
+    Handler for Pydantic AI agent requests.
+
+    Provides:
+    - Direct non-streaming requests to Pydantic AI agents
+    - Fake streaming by converting non-streaming responses into streaming chunks
+    """
+
+    @staticmethod
+    async def handle_non_streaming(
+        request_id: str,
+        params: Dict[str, Any],
+        api_base: str,
+        timeout: float = 60.0,
+    ) -> Dict[str, Any]:
+        """
+        Handle non-streaming request to Pydantic AI agent.
+
+        Args:
+            request_id: A2A JSON-RPC request ID
+            params: A2A MessageSendParams containing the message
+            api_base: Base URL of the Pydantic AI agent
+            timeout: Request timeout in seconds
+
+        Returns:
+            A2A SendMessageResponse dict
+        """
+        verbose_logger.info(f"Pydantic AI: Routing to Pydantic AI agent at {api_base}")
+
+        # Send request directly to Pydantic AI agent
+        response_data = await PydanticAITransformation.send_non_streaming_request(
+            api_base=api_base,
+            request_id=request_id,
+            params=params,
+            timeout=timeout,
+        )
+
+        return response_data
+
+    @staticmethod
+    async def handle_streaming(
+        request_id: str,
+        params: Dict[str, Any],
+        api_base: str,
+        timeout: float = 60.0,
+        chunk_size: int = 50,
+        delay_ms: int = 10,
+    ) -> AsyncIterator[Dict[str, Any]]:
+        """
+        Handle streaming request to Pydantic AI agent with fake streaming.
+
+        Since Pydantic AI agents don't support streaming natively, this method:
+        1. Makes a non-streaming request
+        2. Converts the response into streaming chunks
+
+        Args:
+            request_id: A2A JSON-RPC request ID
+            params: A2A MessageSendParams containing the message
+            api_base: Base URL of the Pydantic AI agent
+            timeout: Request timeout in seconds
+            chunk_size: Number of characters per chunk
+            delay_ms: Delay between chunks in milliseconds
+
+        Yields:
+            A2A streaming response events
+        """
+        verbose_logger.info(
+            f"Pydantic AI: Faking streaming for Pydantic AI agent at {api_base}"
+        )
+
+        # Get raw task response first (not the transformed A2A format)
+        raw_response = await PydanticAITransformation.send_and_get_raw_response(
+            api_base=api_base,
+            request_id=request_id,
+            params=params,
+            timeout=timeout,
+        )
+
+        # Convert raw task response to fake streaming chunks
+        async for chunk in PydanticAITransformation.fake_streaming_from_response(
+            response_data=raw_response,
+            request_id=request_id,
+            chunk_size=chunk_size,
+            delay_ms=delay_ms,
+        ):
+            yield chunk

llm-gateway-competitors/litellm-wheel-src/litellm/a2a_protocol/providers/pydantic_ai_agents/transformation.py

@@ -0,0 +1,530 @@
+"""
+Transformation layer for Pydantic AI agents.
+
+Pydantic AI agents follow A2A protocol but don't support streaming.
+This module provides fake streaming by converting non-streaming responses into streaming chunks.
+"""
+
+import asyncio
+from typing import Any, AsyncIterator, Dict, cast
+from uuid import uuid4
+
+from litellm._logging import verbose_logger
+from litellm.llms.custom_httpx.http_handler import (
+    AsyncHTTPHandler,
+    get_async_httpx_client,
+)
+
+
+class PydanticAITransformation:
+    """
+    Transformation layer for Pydantic AI agents.
+
+    Handles:
+    - Direct A2A requests to Pydantic AI endpoints
+    - Polling for task completion (since Pydantic AI doesn't support streaming)
+    - Fake streaming by chunking non-streaming responses
+    """
+
+    @staticmethod
+    def _remove_none_values(obj: Any) -> Any:
+        """
+        Recursively remove None values from a dict/list structure.
+
+        FastA2A/Pydantic AI servers don't accept None values for optional fields -
+        they expect those fields to be omitted entirely.
+
+        Args:
+            obj: Dict, list, or other value to clean
+
+        Returns:
+            Cleaned object with None values removed
+        """
+        if isinstance(obj, dict):
+            return {
+                k: PydanticAITransformation._remove_none_values(v)
+                for k, v in obj.items()
+                if v is not None
+            }
+        elif isinstance(obj, list):
+            return [
+                PydanticAITransformation._remove_none_values(item)
+                for item in obj
+                if item is not None
+            ]
+        else:
+            return obj
+
+    @staticmethod
+    def _params_to_dict(params: Any) -> Dict[str, Any]:
+        """
+        Convert params to a dict, handling Pydantic models.
+
+        Args:
+            params: Dict or Pydantic model
+
+        Returns:
+            Dict representation of params
+        """
+        if hasattr(params, "model_dump"):
+            # Pydantic v2 model
+            return params.model_dump(mode="python", exclude_none=True)
+        elif hasattr(params, "dict"):
+            # Pydantic v1 model
+            return params.dict(exclude_none=True)
+        elif isinstance(params, dict):
+            return params
+        else:
+            # Try to convert to dict
+            return dict(params)
+
+    @staticmethod
+    async def _poll_for_completion(
+        client: AsyncHTTPHandler,
+        endpoint: str,
+        task_id: str,
+        request_id: str,
+        max_attempts: int = 30,
+        poll_interval: float = 0.5,
+    ) -> Dict[str, Any]:
+        """
+        Poll for task completion using tasks/get method.
+
+        Args:
+            client: HTTPX async client
+            endpoint: API endpoint URL
+            task_id: Task ID to poll for
+            request_id: JSON-RPC request ID
+            max_attempts: Maximum polling attempts
+            poll_interval: Seconds between poll attempts
+
+        Returns:
+            Completed task response
+        """
+        for attempt in range(max_attempts):
+            poll_request = {
+                "jsonrpc": "2.0",
+                "id": f"{request_id}-poll-{attempt}",
+                "method": "tasks/get",
+                "params": {"id": task_id},
+            }
+
+            response = await client.post(
+                endpoint,
+                json=poll_request,
+                headers={"Content-Type": "application/json"},
+            )
+            response.raise_for_status()
+            poll_data = response.json()
+
+            result = poll_data.get("result", {})
+            status = result.get("status", {})
+            state = status.get("state", "")
+
+            verbose_logger.debug(
+                f"Pydantic AI: Poll attempt {attempt + 1}/{max_attempts}, state={state}"
+            )
+
+            if state == "completed":
+                return poll_data
+            elif state in ("failed", "canceled"):
+                raise Exception(f"Task {task_id} ended with state: {state}")
+
+            await asyncio.sleep(poll_interval)
+
+        raise TimeoutError(
+            f"Task {task_id} did not complete within {max_attempts * poll_interval} seconds"
+        )
+
+    @staticmethod
+    async def _send_and_poll_raw(
+        api_base: str,
+        request_id: str,
+        params: Any,
+        timeout: float = 60.0,
+    ) -> Dict[str, Any]:
+        """
+        Send a request to Pydantic AI agent and return the raw task response.
+
+        This is an internal method used by both non-streaming and streaming handlers.
+        Returns the raw Pydantic AI task format with history/artifacts.
+
+        Args:
+            api_base: Base URL of the Pydantic AI agent
+            request_id: A2A JSON-RPC request ID
+            params: A2A MessageSendParams containing the message
+            timeout: Request timeout in seconds
+
+        Returns:
+            Raw Pydantic AI task response (with history/artifacts)
+        """
+        # Convert params to dict if it's a Pydantic model
+        params_dict = PydanticAITransformation._params_to_dict(params)
+
+        # Remove None values - FastA2A doesn't accept null for optional fields
+        params_dict = PydanticAITransformation._remove_none_values(params_dict)
+
+        # Ensure the message has 'kind': 'message' as required by FastA2A/Pydantic AI
+        if "message" in params_dict:
+            params_dict["message"]["kind"] = "message"
+
+        # Build A2A JSON-RPC request using message/send method for FastA2A compatibility
+        a2a_request = {
+            "jsonrpc": "2.0",
+            "id": request_id,
+            "method": "message/send",
+            "params": params_dict,
+        }
+
+        # FastA2A uses root endpoint (/) not /messages
+        endpoint = api_base.rstrip("/")
+
+        verbose_logger.info(f"Pydantic AI: Sending non-streaming request to {endpoint}")
+
+        # Send request to Pydantic AI agent using shared async HTTP client
+        client = get_async_httpx_client(
+            llm_provider=cast(Any, "pydantic_ai_agent"),
+            params={"timeout": timeout},
+        )
+        response = await client.post(
+            endpoint,
+            json=a2a_request,
+            headers={"Content-Type": "application/json"},
+        )
+        response.raise_for_status()
+        response_data = response.json()
+
+        # Check if task is already completed
+        result = response_data.get("result", {})
+        status = result.get("status", {})
+        state = status.get("state", "")
+
+        if state != "completed":
+            # Need to poll for completion
+            task_id = result.get("id")
+            if task_id:
+                verbose_logger.info(
+                    f"Pydantic AI: Task {task_id} submitted, polling for completion..."
+                )
+                response_data = await PydanticAITransformation._poll_for_completion(
+                    client=client,
+                    endpoint=endpoint,
+                    task_id=task_id,
+                    request_id=request_id,
+                )
+
+        verbose_logger.info(
+            f"Pydantic AI: Received completed response for request_id={request_id}"
+        )
+
+        return response_data
+
+    @staticmethod
+    async def send_non_streaming_request(
+        api_base: str,
+        request_id: str,
+        params: Any,
+        timeout: float = 60.0,
+    ) -> Dict[str, Any]:
+        """
+        Send a non-streaming A2A request to Pydantic AI agent and wait for completion.
+
+        Args:
+            api_base: Base URL of the Pydantic AI agent (e.g., "http://localhost:9999")
+            request_id: A2A JSON-RPC request ID
+            params: A2A MessageSendParams containing the message (dict or Pydantic model)
+            timeout: Request timeout in seconds
+
+        Returns:
+            Standard A2A non-streaming response format with message
+        """
+        # Get raw task response
+        raw_response = await PydanticAITransformation._send_and_poll_raw(
+            api_base=api_base,
+            request_id=request_id,
+            params=params,
+            timeout=timeout,
+        )
+
+        # Transform to standard A2A non-streaming format
+        return PydanticAITransformation._transform_to_a2a_response(
+            response_data=raw_response,
+            request_id=request_id,
+        )
+
+    @staticmethod
+    async def send_and_get_raw_response(
+        api_base: str,
+        request_id: str,
+        params: Any,
+        timeout: float = 60.0,
+    ) -> Dict[str, Any]:
+        """
+        Send a request to Pydantic AI agent and return the raw task response.
+
+        Used by streaming handler to get raw response for fake streaming.
+
+        Args:
+            api_base: Base URL of the Pydantic AI agent
+            request_id: A2A JSON-RPC request ID
+            params: A2A MessageSendParams containing the message
+            timeout: Request timeout in seconds
+
+        Returns:
+            Raw Pydantic AI task response (with history/artifacts)
+        """
+        return await PydanticAITransformation._send_and_poll_raw(
+            api_base=api_base,
+            request_id=request_id,
+            params=params,
+            timeout=timeout,
+        )
+
+    @staticmethod
+    def _transform_to_a2a_response(
+        response_data: Dict[str, Any],
+        request_id: str,
+    ) -> Dict[str, Any]:
+        """
+        Transform Pydantic AI task response to standard A2A non-streaming format.
+
+        Pydantic AI returns a task with history/artifacts, but the standard A2A
+        non-streaming format expects:
+        {
+            "jsonrpc": "2.0",
+            "id": "...",
+            "result": {
+                "message": {
+                    "role": "agent",
+                    "parts": [{"kind": "text", "text": "..."}],
+                    "messageId": "..."
+                }
+            }
+        }
+
+        Args:
+            response_data: Pydantic AI task response
+            request_id: Original request ID
+
+        Returns:
+            Standard A2A non-streaming response format
+        """
+        # Extract the agent response text
+        full_text, message_id, parts = PydanticAITransformation._extract_response_text(
+            response_data
+        )
+
+        # Build standard A2A message
+        a2a_message = {
+            "role": "agent",
+            "parts": parts if parts else [{"kind": "text", "text": full_text}],
+            "messageId": message_id,
+        }
+
+        # Return standard A2A non-streaming format
+        return {
+            "jsonrpc": "2.0",
+            "id": request_id,
+            "result": {
+                "message": a2a_message,
+            },
+        }
+
+    @staticmethod
+    def _extract_response_text(response_data: Dict[str, Any]) -> tuple[str, str, list]:
+        """
+        Extract response text from completed task response.
+
+        Pydantic AI returns completed tasks with:
+        - history: list of messages (user and agent)
+        - artifacts: list of result artifacts
+
+        Args:
+            response_data: Completed task response
+
+        Returns:
+            Tuple of (full_text, message_id, parts)
+        """
+        result = response_data.get("result", {})
+
+        # Try to extract from artifacts first (preferred for results)
+        artifacts = result.get("artifacts", [])
+        if artifacts:
+            for artifact in artifacts:
+                parts = artifact.get("parts", [])
+                for part in parts:
+                    if part.get("kind") == "text":
+                        text = part.get("text", "")
+                        if text:
+                            return text, str(uuid4()), parts
+
+        # Fall back to history - get the last agent message
+        history = result.get("history", [])
+        for msg in reversed(history):
+            if msg.get("role") == "agent":
+                parts = msg.get("parts", [])
+                message_id = msg.get("messageId", str(uuid4()))
+                full_text = ""
+                for part in parts:
+                    if part.get("kind") == "text":
+                        full_text += part.get("text", "")
+                if full_text:
+                    return full_text, message_id, parts
+
+        # Fall back to message field (original format)
+        message = result.get("message", {})
+        if message:
+            parts = message.get("parts", [])
+            message_id = message.get("messageId", str(uuid4()))
+            full_text = ""
+            for part in parts:
+                if part.get("kind") == "text":
+                    full_text += part.get("text", "")
+            return full_text, message_id, parts
+
+        return "", str(uuid4()), []
+
+    @staticmethod
+    async def fake_streaming_from_response(
+        response_data: Dict[str, Any],
+        request_id: str,
+        chunk_size: int = 50,
+        delay_ms: int = 10,
+    ) -> AsyncIterator[Dict[str, Any]]:
+        """
+        Convert a non-streaming A2A response into fake streaming chunks.
+
+        Emits proper A2A streaming events:
+        1. Task event (kind: "task") - Initial task with status "submitted"
+        2. Status update (kind: "status-update") - Status "working"
+        3. Artifact update chunks (kind: "artifact-update") - Content delivery in chunks
+        4. Status update (kind: "status-update") - Final "completed" status
+
+        Args:
+            response_data: Non-streaming A2A response dict (completed task)
+            request_id: A2A JSON-RPC request ID
+            chunk_size: Number of characters per chunk (default: 50)
+            delay_ms: Delay between chunks in milliseconds (default: 10)
+
+        Yields:
+            A2A streaming response events
+        """
+        # Extract the response text from completed task
+        full_text, message_id, parts = PydanticAITransformation._extract_response_text(
+            response_data
+        )
+
+        # Extract input message from raw response for history
+        result = response_data.get("result", {})
+        history = result.get("history", [])
+        input_message = {}
+        for msg in history:
+            if msg.get("role") == "user":
+                input_message = msg
+                break
+
+        # Generate IDs for streaming events
+        task_id = str(uuid4())
+        context_id = str(uuid4())
+        artifact_id = str(uuid4())
+        input_message_id = input_message.get("messageId", str(uuid4()))
+
+        # 1. Emit initial task event (kind: "task", status: "submitted")
+        # Format matches A2ACompletionBridgeTransformation.create_task_event
+        task_event = {
+            "jsonrpc": "2.0",
+            "id": request_id,
+            "result": {
+                "contextId": context_id,
+                "history": [
+                    {
+                        "contextId": context_id,
+                        "kind": "message",
+                        "messageId": input_message_id,
+                        "parts": input_message.get(
+                            "parts", [{"kind": "text", "text": ""}]
+                        ),
+                        "role": "user",
+                        "taskId": task_id,
+                    }
+                ],
+                "id": task_id,
+                "kind": "task",
+                "status": {
+                    "state": "submitted",
+                },
+            },
+        }
+        yield task_event
+
+        # 2. Emit status update (kind: "status-update", status: "working")
+        # Format matches A2ACompletionBridgeTransformation.create_status_update_event
+        working_event = {
+            "jsonrpc": "2.0",
+            "id": request_id,
+            "result": {
+                "contextId": context_id,
+                "final": False,
+                "kind": "status-update",
+                "status": {
+                    "state": "working",
+                },
+                "taskId": task_id,
+            },
+        }
+        yield working_event
+
+        # Small delay to simulate processing
+        await asyncio.sleep(delay_ms / 1000.0)
+
+        # 3. Emit artifact update chunks (kind: "artifact-update")
+        # Format matches A2ACompletionBridgeTransformation.create_artifact_update_event
+        if full_text:
+            # Split text into chunks
+            for i in range(0, len(full_text), chunk_size):
+                chunk_text = full_text[i : i + chunk_size]
+                is_last_chunk = (i + chunk_size) >= len(full_text)
+
+                artifact_event = {
+                    "jsonrpc": "2.0",
+                    "id": request_id,
+                    "result": {
+                        "contextId": context_id,
+                        "kind": "artifact-update",
+                        "taskId": task_id,
+                        "artifact": {
+                            "artifactId": artifact_id,
+                            "parts": [
+                                {
+                                    "kind": "text",
+                                    "text": chunk_text,
+                                }
+                            ],
+                        },
+                    },
+                }
+                yield artifact_event
+
+                # Add delay between chunks (except for last chunk)
+                if not is_last_chunk:
+                    await asyncio.sleep(delay_ms / 1000.0)
+
+        # 4. Emit final status update (kind: "status-update", status: "completed", final: true)
+        completed_event = {
+            "jsonrpc": "2.0",
+            "id": request_id,
+            "result": {
+                "contextId": context_id,
+                "final": True,
+                "kind": "status-update",
+                "status": {
+                    "state": "completed",
+                },
+                "taskId": task_id,
+            },
+        }
+        yield completed_event
+
+        verbose_logger.info(
+            f"Pydantic AI: Fake streaming completed for request_id={request_id}"
+        )

llm-gateway-competitors/litellm-wheel-src/litellm/a2a_protocol/streaming_iterator.py

@@ -0,0 +1,184 @@
+"""
+A2A Streaming Iterator with token tracking and logging support.
+"""
+
+import asyncio
+from datetime import datetime
+from typing import TYPE_CHECKING, Any, AsyncIterator, Dict, List, Optional
+
+import litellm
+from litellm._logging import verbose_logger
+from litellm.a2a_protocol.cost_calculator import A2ACostCalculator
+from litellm.a2a_protocol.utils import A2ARequestUtils
+from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.litellm_core_utils.thread_pool_executor import executor
+
+if TYPE_CHECKING:
+    from a2a.types import SendStreamingMessageRequest, SendStreamingMessageResponse
+
+
+class A2AStreamingIterator:
+    """
+    Async iterator for A2A streaming responses with token tracking.
+
+    Collects chunks, extracts text, and logs usage on completion.
+    """
+
+    def __init__(
+        self,
+        stream: AsyncIterator["SendStreamingMessageResponse"],
+        request: "SendStreamingMessageRequest",
+        logging_obj: LiteLLMLoggingObj,
+        agent_name: str = "unknown",
+    ):
+        self.stream = stream
+        self.request = request
+        self.logging_obj = logging_obj
+        self.agent_name = agent_name
+        self.start_time = datetime.now()
+
+        # Collect chunks for token counting
+        self.chunks: List[Any] = []
+        self.collected_text_parts: List[str] = []
+        self.final_chunk: Optional[Any] = None
+
+    def __aiter__(self):
+        return self
+
+    async def __anext__(self) -> "SendStreamingMessageResponse":
+        try:
+            chunk = await self.stream.__anext__()
+
+            # Store chunk
+            self.chunks.append(chunk)
+
+            # Extract text from chunk for token counting
+            self._collect_text_from_chunk(chunk)
+
+            # Check if this is the final chunk (completed status)
+            if self._is_completed_chunk(chunk):
+                self.final_chunk = chunk
+
+            return chunk
+
+        except StopAsyncIteration:
+            # Stream ended - handle logging
+            if self.final_chunk is None and self.chunks:
+                self.final_chunk = self.chunks[-1]
+            await self._handle_stream_complete()
+            raise
+
+    def _collect_text_from_chunk(self, chunk: Any) -> None:
+        """Extract text from a streaming chunk and add to collected parts."""
+        try:
+            chunk_dict = (
+                chunk.model_dump(mode="json", exclude_none=True)
+                if hasattr(chunk, "model_dump")
+                else {}
+            )
+            text = A2ARequestUtils.extract_text_from_response(chunk_dict)
+            if text:
+                self.collected_text_parts.append(text)
+        except Exception:
+            verbose_logger.debug("Failed to extract text from A2A streaming chunk")
+
+    def _is_completed_chunk(self, chunk: Any) -> bool:
+        """Check if chunk indicates stream completion."""
+        try:
+            chunk_dict = (
+                chunk.model_dump(mode="json", exclude_none=True)
+                if hasattr(chunk, "model_dump")
+                else {}
+            )
+            result = chunk_dict.get("result", {})
+            if isinstance(result, dict):
+                status = result.get("status", {})
+                if isinstance(status, dict):
+                    return status.get("state") == "completed"
+        except Exception:
+            pass
+        return False
+
+    async def _handle_stream_complete(self) -> None:
+        """Handle logging and token counting when stream completes."""
+        try:
+            end_time = datetime.now()
+
+            # Calculate tokens from collected text
+            input_message = A2ARequestUtils.get_input_message_from_request(self.request)
+            input_text = A2ARequestUtils.extract_text_from_message(input_message)
+            prompt_tokens = A2ARequestUtils.count_tokens(input_text)
+
+            # Use the last (most complete) text from chunks
+            output_text = (
+                self.collected_text_parts[-1] if self.collected_text_parts else ""
+            )
+            completion_tokens = A2ARequestUtils.count_tokens(output_text)
+
+            total_tokens = prompt_tokens + completion_tokens
+
+            # Create usage object
+            usage = litellm.Usage(
+                prompt_tokens=prompt_tokens,
+                completion_tokens=completion_tokens,
+                total_tokens=total_tokens,
+            )
+
+            # Set usage on logging obj
+            self.logging_obj.model_call_details["usage"] = usage
+            # Mark stream flag for downstream callbacks
+            self.logging_obj.model_call_details["stream"] = False
+
+            # Calculate cost using A2ACostCalculator
+            response_cost = A2ACostCalculator.calculate_a2a_cost(self.logging_obj)
+            self.logging_obj.model_call_details["response_cost"] = response_cost
+
+            # Build result for logging
+            result = self._build_logging_result(usage)
+
+            # Call success handlers - they will build standard_logging_object
+            asyncio.create_task(
+                self.logging_obj.async_success_handler(
+                    result=result,
+                    start_time=self.start_time,
+                    end_time=end_time,
+                    cache_hit=None,
+                )
+            )
+
+            executor.submit(
+                self.logging_obj.success_handler,
+                result=result,
+                cache_hit=None,
+                start_time=self.start_time,
+                end_time=end_time,
+            )
+
+            verbose_logger.info(
+                f"A2A streaming completed: prompt_tokens={prompt_tokens}, "
+                f"completion_tokens={completion_tokens}, total_tokens={total_tokens}, "
+                f"response_cost={response_cost}"
+            )
+
+        except Exception as e:
+            verbose_logger.debug(f"Error in A2A streaming completion handler: {e}")
+
+    def _build_logging_result(self, usage: litellm.Usage) -> Dict[str, Any]:
+        """Build a result dict for logging."""
+        result: Dict[str, Any] = {
+            "id": getattr(self.request, "id", "unknown"),
+            "jsonrpc": "2.0",
+            "usage": usage.model_dump()
+            if hasattr(usage, "model_dump")
+            else dict(usage),
+        }
+
+        # Add final chunk result if available
+        if self.final_chunk:
+            try:
+                chunk_dict = self.final_chunk.model_dump(mode="json", exclude_none=True)
+                result["result"] = chunk_dict.get("result", {})
+            except Exception:
+                pass
+
+        return result

llm-gateway-competitors/litellm-wheel-src/litellm/a2a_protocol/utils.py

@@ -0,0 +1,138 @@
+"""
+Utility functions for A2A protocol.
+"""
+
+from typing import TYPE_CHECKING, Any, Dict, List, Tuple, Union
+
+import litellm
+from litellm._logging import verbose_logger
+
+if TYPE_CHECKING:
+    from a2a.types import SendMessageRequest, SendStreamingMessageRequest
+
+
+class A2ARequestUtils:
+    """Utility class for A2A request/response processing."""
+
+    @staticmethod
+    def extract_text_from_message(message: Any) -> str:
+        """
+        Extract text content from A2A message parts.
+
+        Args:
+            message: A2A message dict or object with 'parts' containing text parts
+
+        Returns:
+            Concatenated text from all text parts
+        """
+        if message is None:
+            return ""
+
+        # Handle both dict and object access
+        if isinstance(message, dict):
+            parts = message.get("parts", [])
+        else:
+            parts = getattr(message, "parts", []) or []
+
+        text_parts: List[str] = []
+        for part in parts:
+            if isinstance(part, dict):
+                if part.get("kind") == "text":
+                    text_parts.append(part.get("text", ""))
+            else:
+                if getattr(part, "kind", None) == "text":
+                    text_parts.append(getattr(part, "text", ""))
+
+        return " ".join(text_parts)
+
+    @staticmethod
+    def extract_text_from_response(response_dict: Dict[str, Any]) -> str:
+        """
+        Extract text content from A2A response result.
+
+        Args:
+            response_dict: A2A response dict with 'result' containing message
+
+        Returns:
+            Text from response message parts
+        """
+        result = response_dict.get("result", {})
+        if not isinstance(result, dict):
+            return ""
+
+        message = result.get("message", {})
+        return A2ARequestUtils.extract_text_from_message(message)
+
+    @staticmethod
+    def get_input_message_from_request(
+        request: "Union[SendMessageRequest, SendStreamingMessageRequest]",
+    ) -> Any:
+        """
+        Extract the input message from an A2A request.
+
+        Args:
+            request: The A2A SendMessageRequest or SendStreamingMessageRequest
+
+        Returns:
+            The message object/dict or None
+        """
+        params = getattr(request, "params", None)
+        if params is None:
+            return None
+        return getattr(params, "message", None)
+
+    @staticmethod
+    def count_tokens(text: str) -> int:
+        """
+        Count tokens in text using litellm.token_counter.
+
+        Args:
+            text: Text to count tokens for
+
+        Returns:
+            Token count, or 0 if counting fails
+        """
+        if not text:
+            return 0
+        try:
+            return litellm.token_counter(text=text)
+        except Exception:
+            verbose_logger.debug("Failed to count tokens")
+            return 0
+
+    @staticmethod
+    def calculate_usage_from_request_response(
+        request: "Union[SendMessageRequest, SendStreamingMessageRequest]",
+        response_dict: Dict[str, Any],
+    ) -> Tuple[int, int, int]:
+        """
+        Calculate token usage from A2A request and response.
+
+        Args:
+            request: The A2A SendMessageRequest or SendStreamingMessageRequest
+            response_dict: The A2A response as a dict
+
+        Returns:
+            Tuple of (prompt_tokens, completion_tokens, total_tokens)
+        """
+        # Count input tokens
+        input_message = A2ARequestUtils.get_input_message_from_request(request)
+        input_text = A2ARequestUtils.extract_text_from_message(input_message)
+        prompt_tokens = A2ARequestUtils.count_tokens(input_text)
+
+        # Count output tokens
+        output_text = A2ARequestUtils.extract_text_from_response(response_dict)
+        completion_tokens = A2ARequestUtils.count_tokens(output_text)
+
+        total_tokens = prompt_tokens + completion_tokens
+
+        return prompt_tokens, completion_tokens, total_tokens
+
+
+# Backwards compatibility aliases
+def extract_text_from_a2a_message(message: Any) -> str:
+    return A2ARequestUtils.extract_text_from_message(message)
+
+
+def extract_text_from_a2a_response(response_dict: Dict[str, Any]) -> str:
+    return A2ARequestUtils.extract_text_from_response(response_dict)