chore: initial public snapshot for github upload

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

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+# 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

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+"""
+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

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+"""
+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

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+"""
+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