mirror of
https://github.com/BerriAI/litellm.git
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[Feat] Agent Gateway - allow adding langgraph, bedrock agent core agents (#17802)
* fix: langgraph bridge streaming * add public/agents/fields * test_a2a_completion_bridge_non_streaming * TestA2AStreamingTransformation * AgentCredentialFieldMetadata * add new logo * refactor add agent * fix add dynamic fields * feat allow adding langgraph agent * add langgraph provider * stash * add AgentCreateInfo * agent_create_fields * fix fields * test_a2a_completion_bridge_bedrock_agentcore * test_a2a_completion_bridge_bedrock_agentcore * add public endpoints * fix a2a endpoints * fix dynamic fields
This commit is contained in:
Ishaan Jaff
GitHub
parent
ad5d94a82f
commit
4a7437ba5f
14 changed files with 918 additions and 68 deletions
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@ -2,6 +2,12 @@
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Handler for A2A to LiteLLM completion bridge.
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Routes A2A requests through litellm.acompletion based on custom_llm_provider.
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A2A Streaming Events (in order):
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1. Task event (kind: "task") - Initial task creation with status "submitted"
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2. Status update (kind: "status-update") - Status change to "working"
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3. Artifact update (kind: "artifact-update") - Content/artifact delivery
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4. Status update (kind: "status-update") - Final status "completed" with final=true
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"""
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from typing import Any, AsyncIterator, Dict, Optional
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@ -10,6 +16,7 @@ import litellm
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from litellm._logging import verbose_logger
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from litellm.a2a_protocol.litellm_completion_bridge.transformation import (
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A2ACompletionBridgeTransformation,
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A2AStreamingContext,
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)
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@ -50,7 +57,8 @@ class A2ACompletionBridgeHandler:
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model = litellm_params.get("model", "agent")
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# Build full model string if provider specified
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if custom_llm_provider:
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# Skip prepending if model already starts with the provider prefix
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if custom_llm_provider and not model.startswith(f"{custom_llm_provider}/"):
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full_model = f"{custom_llm_provider}/{model}"
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else:
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full_model = model
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@ -87,6 +95,12 @@ class A2ACompletionBridgeHandler:
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"""
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Handle streaming A2A request via litellm.acompletion with stream=True.
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Emits proper A2A streaming events:
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1. Task event (kind: "task") - Initial task with status "submitted"
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2. Status update (kind: "status-update") - Status "working"
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3. Artifact update (kind: "artifact-update") - Content delivery
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4. Status update (kind: "status-update") - Final "completed" status
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Args:
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request_id: A2A JSON-RPC request ID
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params: A2A MessageSendParams containing the message
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@ -94,11 +108,17 @@ class A2ACompletionBridgeHandler:
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api_base: API base URL from agent_card_params
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Yields:
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A2A streaming response chunks
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A2A streaming response events
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"""
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# Extract message from params
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message = params.get("message", {})
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# Create streaming context
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ctx = A2AStreamingContext(
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request_id=request_id,
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input_message=message,
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)
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# Transform A2A message to OpenAI format
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openai_messages = A2ACompletionBridgeTransformation.a2a_message_to_openai_messages(
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message
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@ -109,7 +129,8 @@ class A2ACompletionBridgeHandler:
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model = litellm_params.get("model", "agent")
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# Build full model string if provider specified
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if custom_llm_provider:
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# Skip prepending if model already starts with the provider prefix
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if custom_llm_provider and not model.startswith(f"{custom_llm_provider}/"):
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full_model = f"{custom_llm_provider}/{model}"
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else:
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full_model = model
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@ -118,6 +139,19 @@ class A2ACompletionBridgeHandler:
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f"A2A completion bridge streaming: model={full_model}, api_base={api_base}"
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)
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# 1. Emit initial task event (kind: "task", status: "submitted")
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task_event = A2ACompletionBridgeTransformation.create_task_event(ctx)
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yield task_event
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# 2. Emit status update (kind: "status-update", status: "working")
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working_event = A2ACompletionBridgeTransformation.create_status_update_event(
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ctx=ctx,
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state="working",
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final=False,
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message_text="Processing request...",
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)
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yield working_event
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# Call litellm.acompletion with streaming
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response = await litellm.acompletion(
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model=full_model,
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@ -126,27 +160,37 @@ class A2ACompletionBridgeHandler:
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stream=True,
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)
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# 3. Accumulate content and emit artifact update
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accumulated_text = ""
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chunk_count = 0
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async for chunk in response: # type: ignore[union-attr]
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chunk_count += 1
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a2a_chunk = A2ACompletionBridgeTransformation.openai_chunk_to_a2a_chunk(
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chunk=chunk,
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request_id=request_id,
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is_final=False,
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)
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if a2a_chunk:
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yield a2a_chunk
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# Send final chunk
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final_chunk = A2ACompletionBridgeTransformation.openai_chunk_to_a2a_chunk(
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chunk=None,
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request_id=request_id,
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is_final=True,
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# Extract delta content
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content = ""
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if chunk is not None and hasattr(chunk, "choices") and chunk.choices:
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choice = chunk.choices[0]
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if hasattr(choice, "delta") and choice.delta:
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content = choice.delta.content or ""
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if content:
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accumulated_text += content
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# Emit artifact update with accumulated content
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if accumulated_text:
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artifact_event = A2ACompletionBridgeTransformation.create_artifact_update_event(
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ctx=ctx,
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text=accumulated_text,
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)
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yield artifact_event
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# 4. Emit final status update (kind: "status-update", status: "completed", final: true)
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completed_event = A2ACompletionBridgeTransformation.create_status_update_event(
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ctx=ctx,
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state="completed",
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final=True,
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)
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if final_chunk:
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# Clear content for final chunk
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final_chunk["result"]["message"]["parts"][0]["text"] = ""
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yield final_chunk
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yield completed_event
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verbose_logger.info(
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f"A2A completion bridge streaming completed: request_id={request_id}, chunks={chunk_count}"
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@ -10,14 +10,36 @@ A2A Message Format:
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OpenAI Message Format:
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{"role": "user", "content": "Hello!"}
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A2A Streaming Events:
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- Task event (kind: "task") - Initial task creation with status "submitted"
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- Status update (kind: "status-update") - Status changes (working, completed)
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- Artifact update (kind: "artifact-update") - Content/artifact delivery
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"""
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from datetime import datetime, timezone
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from typing import Any, Dict, List, Optional
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from uuid import uuid4
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from litellm._logging import verbose_logger
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class A2AStreamingContext:
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"""
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Context holder for A2A streaming state.
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Tracks task_id, context_id, and message accumulation.
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"""
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def __init__(self, request_id: str, input_message: Dict[str, Any]):
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self.request_id = request_id
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self.task_id = str(uuid4())
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self.context_id = str(uuid4())
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self.input_message = input_message
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self.accumulated_text = ""
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self.has_emitted_task = False
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self.has_emitted_working = False
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class A2ACompletionBridgeTransformation:
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"""
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Static methods for transforming between A2A and OpenAI message formats.
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@ -108,6 +130,114 @@ class A2ACompletionBridgeTransformation:
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return a2a_response
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@staticmethod
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def _get_timestamp() -> str:
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"""Get current timestamp in ISO format with timezone."""
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return datetime.now(timezone.utc).isoformat()
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@staticmethod
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def create_task_event(
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ctx: A2AStreamingContext,
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) -> Dict[str, Any]:
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"""
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Create the initial task event with status 'submitted'.
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This is the first event emitted in an A2A streaming response.
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"""
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return {
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"id": ctx.request_id,
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"jsonrpc": "2.0",
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"result": {
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"contextId": ctx.context_id,
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"history": [
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{
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"contextId": ctx.context_id,
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"kind": "message",
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"messageId": ctx.input_message.get("messageId", uuid4().hex),
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"parts": ctx.input_message.get("parts", []),
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"role": ctx.input_message.get("role", "user"),
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"taskId": ctx.task_id,
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}
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],
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"id": ctx.task_id,
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"kind": "task",
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"status": {
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"state": "submitted",
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},
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},
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}
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@staticmethod
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def create_status_update_event(
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ctx: A2AStreamingContext,
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state: str,
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final: bool = False,
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message_text: Optional[str] = None,
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) -> Dict[str, Any]:
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"""
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Create a status update event.
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Args:
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ctx: Streaming context
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state: Status state ('working', 'completed')
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final: Whether this is the final event
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message_text: Optional message text for 'working' status
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"""
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status: Dict[str, Any] = {
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"state": state,
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"timestamp": A2ACompletionBridgeTransformation._get_timestamp(),
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}
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# Add message for 'working' status
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if state == "working" and message_text:
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status["message"] = {
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"contextId": ctx.context_id,
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"kind": "message",
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"messageId": str(uuid4()),
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"parts": [{"kind": "text", "text": message_text}],
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"role": "agent",
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"taskId": ctx.task_id,
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}
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return {
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"id": ctx.request_id,
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"jsonrpc": "2.0",
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"result": {
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"contextId": ctx.context_id,
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"final": final,
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"kind": "status-update",
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"status": status,
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"taskId": ctx.task_id,
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},
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}
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@staticmethod
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def create_artifact_update_event(
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ctx: A2AStreamingContext,
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text: str,
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) -> Dict[str, Any]:
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"""
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Create an artifact update event with content.
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Args:
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ctx: Streaming context
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text: The text content for the artifact
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"""
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return {
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"id": ctx.request_id,
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"jsonrpc": "2.0",
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"result": {
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"artifact": {
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"artifactId": str(uuid4()),
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"name": "response",
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"parts": [{"kind": "text", "text": text}],
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},
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"contextId": ctx.context_id,
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"kind": "artifact-update",
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"taskId": ctx.task_id,
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},
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}
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@staticmethod
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def openai_chunk_to_a2a_chunk(
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chunk: Any,
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@ -117,6 +247,10 @@ class A2ACompletionBridgeTransformation:
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"""
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Transform a LiteLLM streaming chunk to A2A streaming format.
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NOTE: This method is deprecated for streaming. Use the event-based
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methods (create_task_event, create_status_update_event,
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create_artifact_update_event) instead for proper A2A streaming.
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Args:
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chunk: LiteLLM ModelResponse chunk
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request_id: Original A2A request ID
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@ -135,7 +269,7 @@ class A2ACompletionBridgeTransformation:
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if not content and not is_final:
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return None
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# Build A2A streaming chunk
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# Build A2A streaming chunk (legacy format)
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a2a_chunk = {
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"jsonrpc": "2.0",
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"id": request_id,
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@ -186,8 +186,7 @@ async def asend_message(
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if custom_llm_provider:
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if request is None:
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raise ValueError("request is required for completion bridge")
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if api_base is None:
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raise ValueError("api_base is required for completion bridge")
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# api_base is optional for providers that derive endpoint from model (e.g., bedrock/agentcore)
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verbose_logger.info(
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f"A2A using completion bridge: provider={custom_llm_provider}, api_base={api_base}"
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@ -334,8 +333,7 @@ async def asend_message_streaming(
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if custom_llm_provider:
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if request is None:
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raise ValueError("request is required for completion bridge")
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if api_base is None:
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raise ValueError("api_base is required for completion bridge")
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# api_base is optional for providers that derive endpoint from model (e.g., bedrock/agentcore)
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verbose_logger.info(
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f"A2A streaming using completion bridge: provider={custom_llm_provider}"
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@ -46,7 +46,7 @@ def _get_agent(agent_id: str):
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async def _handle_stream_message(
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api_base: str,
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api_base: Optional[str],
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request_id: str,
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params: dict,
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litellm_params: Optional[dict] = None,
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@ -213,13 +213,17 @@ async def invoke_agent_a2a(
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# Get backend URL and agent name
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agent_url = agent.agent_card_params.get("url")
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agent_name = agent.agent_card_params.get("name", agent_id)
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if not agent_url:
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return _jsonrpc_error(request_id, -32000, f"Agent '{agent_id}' has no URL configured", 500)
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verbose_proxy_logger.info(f"Proxying A2A request to agent '{agent_id}' at {agent_url}")
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# Get litellm_params (may include custom_llm_provider for completion bridge)
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litellm_params = agent.litellm_params or {}
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custom_llm_provider = litellm_params.get("custom_llm_provider")
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# URL is required unless using completion bridge with a provider that derives endpoint from model
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# (e.g., bedrock/agentcore derives endpoint from ARN in model string)
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if not agent_url and not custom_llm_provider:
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return _jsonrpc_error(request_id, -32000, f"Agent '{agent_id}' has no URL configured", 500)
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verbose_proxy_logger.info(f"Proxying A2A request to agent '{agent_id}' at {agent_url or 'completion-bridge'}")
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# Set up data dict for litellm processing
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body.update({
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@ -4,6 +4,10 @@ model_list:
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model: openai/gpt-4o-mini
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tpm: 1000
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# LangGraph models
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- model_name: langgraph/*
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litellm_params:
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model: langgraph/*
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litellm_settings:
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callbacks: ["dynamic_rate_limiter_v3"]
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76
litellm/proxy/public_endpoints/agent_create_fields.json
Normal file
76
litellm/proxy/public_endpoints/agent_create_fields.json
Normal file
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@ -0,0 +1,76 @@
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[
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{
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"agent_type": "a2a",
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"agent_type_display_name": "A2A Standard",
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"description": "Standard A2A protocol",
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"logo_url": "/assets/logos/a2a_agent.png",
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"credential_fields": [],
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"litellm_params_template": {}
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},
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{
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"agent_type": "langgraph",
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"agent_type_display_name": "LangGraph",
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"description": "Connect to LangGraph agents via the LangGraph Platform API",
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"logo_url": "/assets/logos/langgraph.png",
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"model_template": "langgraph/{assistant_id}",
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"credential_fields": [
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{
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"key": "assistant_id",
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"label": "Assistant ID",
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"placeholder": "agent",
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"tooltip": "The assistant/agent ID from your LangGraph deployment",
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"required": true,
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"field_type": "text",
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"default_value": "agent",
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"include_in_litellm_params": false
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},
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{
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"key": "api_base",
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"label": "LangGraph API Base",
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"placeholder": "http://localhost:2024",
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"tooltip": "The base URL for your LangGraph server (e.g., http://localhost:2024 or your deployed LangGraph Cloud URL)",
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"required": true,
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"field_type": "text",
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"default_value": "http://localhost:2024",
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"include_in_litellm_params": true
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},
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{
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"key": "api_key",
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"label": "LangGraph API Key",
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"placeholder": null,
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"tooltip": "API key for authenticating with your LangGraph server (optional for local development)",
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"required": false,
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"field_type": "password",
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"default_value": null,
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"include_in_litellm_params": true
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}
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],
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"litellm_params_template": {
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"custom_llm_provider": "langgraph"
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}
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},
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{
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"agent_type": "bedrock_agentcore",
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"agent_type_display_name": "Bedrock AgentCore",
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"description": "Connect to Amazon Bedrock AgentCore hosted agent runtimes",
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"logo_url": "/assets/logos/bedrock.svg",
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"inherit_credentials_from_provider": "Bedrock",
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"model_template": "bedrock/agentcore/{agent_runtime_arn}",
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"credential_fields": [
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{
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"key": "agent_runtime_arn",
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"label": "Agent Runtime ARN",
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"placeholder": "arn:aws:bedrock-agentcore:us-west-2:123456789012:runtime/my-agent-runtime",
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"tooltip": "The ARN of your Bedrock AgentCore runtime. Find this in your AWS Bedrock console under AgentCore.",
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"required": true,
|
||||
"field_type": "text",
|
||||
"default_value": null,
|
||||
"include_in_litellm_params": false
|
||||
}
|
||||
],
|
||||
"litellm_params_template": {
|
||||
"custom_llm_provider": "bedrock"
|
||||
}
|
||||
}
|
||||
]
|
||||
|
||||
|
|
@ -1,6 +1,6 @@
|
|||
from typing import List
|
||||
import os
|
||||
import json
|
||||
import os
|
||||
from typing import List
|
||||
|
||||
from fastapi import APIRouter, Depends, HTTPException
|
||||
|
||||
|
|
@ -12,6 +12,7 @@ from litellm.types.proxy.management_endpoints.model_management_endpoints import
|
|||
ModelGroupInfoProxy,
|
||||
)
|
||||
from litellm.types.proxy.public_endpoints.public_endpoints import (
|
||||
AgentCreateInfo,
|
||||
ProviderCreateInfo,
|
||||
PublicModelHubInfo,
|
||||
)
|
||||
|
|
@ -167,3 +168,52 @@ async def get_litellm_model_cost_map():
|
|||
status_code=500,
|
||||
detail=f"Internal Server Error ({str(e)})",
|
||||
)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/public/agents/fields",
|
||||
tags=["public", "[beta] Agents"],
|
||||
response_model=List[AgentCreateInfo],
|
||||
)
|
||||
async def get_agent_fields() -> List[AgentCreateInfo]:
|
||||
"""
|
||||
Return agent type metadata required by the dashboard create-agent flow.
|
||||
|
||||
If an agent has `inherit_credentials_from_provider`, the provider's credential
|
||||
fields are automatically appended to the agent's credential_fields.
|
||||
"""
|
||||
base_path = os.path.join(
|
||||
os.path.dirname(os.path.dirname(os.path.dirname(__file__))),
|
||||
"proxy",
|
||||
"public_endpoints",
|
||||
)
|
||||
|
||||
agent_create_fields_path = os.path.join(base_path, "agent_create_fields.json")
|
||||
provider_create_fields_path = os.path.join(base_path, "provider_create_fields.json")
|
||||
|
||||
with open(agent_create_fields_path, "r") as f:
|
||||
agent_create_fields = json.load(f)
|
||||
|
||||
with open(provider_create_fields_path, "r") as f:
|
||||
provider_create_fields = json.load(f)
|
||||
|
||||
# Build a lookup map for providers by name
|
||||
provider_map = {p["provider"]: p for p in provider_create_fields}
|
||||
|
||||
# Merge inherited credential fields
|
||||
for agent in agent_create_fields:
|
||||
inherit_from = agent.get("inherit_credentials_from_provider")
|
||||
if inherit_from and inherit_from in provider_map:
|
||||
provider = provider_map[inherit_from]
|
||||
# Copy provider fields and mark them for inclusion in litellm_params
|
||||
inherited_fields = []
|
||||
for field in provider.get("credential_fields", []):
|
||||
field_copy = field.copy()
|
||||
field_copy["include_in_litellm_params"] = True
|
||||
inherited_fields.append(field_copy)
|
||||
# Append provider credential fields after agent's own fields
|
||||
agent["credential_fields"] = agent.get("credential_fields", []) + inherited_fields
|
||||
# Remove the inherit field from response (not needed by frontend)
|
||||
agent.pop("inherit_credentials_from_provider", None)
|
||||
|
||||
return agent_create_fields
|
||||
|
|
|
|||
|
|
@ -27,3 +27,25 @@ class ProviderCreateInfo(BaseModel):
|
|||
litellm_provider: str
|
||||
credential_fields: List[ProviderCredentialField]
|
||||
default_model_placeholder: Optional[str] = None
|
||||
|
||||
|
||||
class AgentCredentialField(BaseModel):
|
||||
key: str
|
||||
label: str
|
||||
placeholder: Optional[str] = None
|
||||
tooltip: Optional[str] = None
|
||||
required: bool = False
|
||||
field_type: Literal["text", "password", "select", "upload", "textarea"] = "text"
|
||||
options: Optional[List[str]] = None
|
||||
default_value: Optional[str] = None
|
||||
include_in_litellm_params: Optional[bool] = None
|
||||
|
||||
|
||||
class AgentCreateInfo(BaseModel):
|
||||
agent_type: str
|
||||
agent_type_display_name: str
|
||||
description: Optional[str] = None
|
||||
logo_url: Optional[str] = None
|
||||
credential_fields: List[AgentCredentialField]
|
||||
litellm_params_template: Optional[Dict[str, str]] = None
|
||||
model_template: Optional[str] = None
|
||||
|
|
|
|||
|
|
@ -71,6 +71,12 @@ async def test_a2a_completion_bridge_non_streaming():
|
|||
async def test_a2a_completion_bridge_streaming():
|
||||
"""
|
||||
Test streaming A2A request via the completion bridge with LangGraph provider.
|
||||
|
||||
Validates proper A2A streaming format with 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
|
||||
"""
|
||||
from litellm.a2a_protocol import asend_message_streaming
|
||||
|
||||
|
|
@ -98,19 +104,100 @@ async def test_a2a_completion_bridge_streaming():
|
|||
chunks.append(chunk)
|
||||
print(f"Chunk: {chunk}")
|
||||
|
||||
# Validate we received chunks
|
||||
assert len(chunks) > 0
|
||||
# Validate we received proper A2A streaming events
|
||||
assert len(chunks) >= 4, f"Expected at least 4 chunks (task, working, artifact, completed), got {len(chunks)}"
|
||||
|
||||
# Validate chunk structure (chunks are dicts from bridge)
|
||||
# Validate chunk structure follows A2A spec
|
||||
for chunk in chunks:
|
||||
assert "jsonrpc" in chunk
|
||||
assert chunk["jsonrpc"] == "2.0"
|
||||
assert "id" in chunk
|
||||
assert "result" in chunk
|
||||
assert "message" in chunk["result"]
|
||||
message = chunk["result"]["message"]
|
||||
assert "role" in message
|
||||
assert message["role"] == "agent"
|
||||
assert "parts" in message
|
||||
|
||||
print(f"Received {len(chunks)} chunks")
|
||||
# Validate first chunk is task event
|
||||
task_chunk = chunks[0]
|
||||
assert task_chunk["result"]["kind"] == "task", "First chunk should be task event"
|
||||
assert task_chunk["result"]["status"]["state"] == "submitted"
|
||||
assert "contextId" in task_chunk["result"]
|
||||
assert "id" in task_chunk["result"] # task id
|
||||
assert "history" in task_chunk["result"]
|
||||
|
||||
# Validate second chunk is working status update
|
||||
working_chunk = chunks[1]
|
||||
assert working_chunk["result"]["kind"] == "status-update", "Second chunk should be status-update"
|
||||
assert working_chunk["result"]["status"]["state"] == "working"
|
||||
assert "taskId" in working_chunk["result"]
|
||||
assert "contextId" in working_chunk["result"]
|
||||
assert working_chunk["result"]["final"] is False
|
||||
|
||||
# Validate artifact update chunk
|
||||
artifact_chunk = chunks[2]
|
||||
assert artifact_chunk["result"]["kind"] == "artifact-update", "Third chunk should be artifact-update"
|
||||
assert "artifact" in artifact_chunk["result"]
|
||||
assert "artifactId" in artifact_chunk["result"]["artifact"]
|
||||
assert "parts" in artifact_chunk["result"]["artifact"]
|
||||
assert len(artifact_chunk["result"]["artifact"]["parts"]) > 0
|
||||
assert artifact_chunk["result"]["artifact"]["parts"][0]["kind"] == "text"
|
||||
|
||||
# Validate final chunk is completed status update
|
||||
final_chunk = chunks[-1]
|
||||
assert final_chunk["result"]["kind"] == "status-update", "Last chunk should be status-update"
|
||||
assert final_chunk["result"]["status"]["state"] == "completed"
|
||||
assert final_chunk["result"]["final"] is True
|
||||
|
||||
print(f"Received {len(chunks)} chunks with proper A2A streaming format")
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_a2a_completion_bridge_bedrock_agentcore():
|
||||
"""
|
||||
Test A2A request via the completion bridge with Bedrock AgentCore provider.
|
||||
|
||||
Uses the AgentCore runtime ARN to call a hosted agent.
|
||||
"""
|
||||
from litellm.a2a_protocol import asend_message_streaming
|
||||
|
||||
litellm._turn_on_debug()
|
||||
|
||||
# Bedrock AgentCore ARN (streaming-capable runtime)
|
||||
agentcore_arn = "arn:aws:bedrock-agentcore:us-west-2:888602223428:runtime/hosted_agent_r9jvp-3ySZuRHjLC"
|
||||
|
||||
send_message_payload = {
|
||||
"message": {
|
||||
"role": "user",
|
||||
"parts": [{"kind": "text", "text": "Explain machine learning in simple terms"}],
|
||||
"messageId": uuid4().hex,
|
||||
}
|
||||
}
|
||||
|
||||
request = SendStreamingMessageRequest(
|
||||
id=str(uuid4()),
|
||||
params=MessageSendParams(**send_message_payload), # type: ignore
|
||||
)
|
||||
|
||||
chunks = []
|
||||
async for chunk in asend_message_streaming(
|
||||
request=request,
|
||||
api_base=None, # Not needed for Bedrock AgentCore
|
||||
litellm_params={
|
||||
"custom_llm_provider": "bedrock",
|
||||
"model": f"bedrock/agentcore/{agentcore_arn}",
|
||||
},
|
||||
):
|
||||
chunks.append(chunk)
|
||||
print(f"Chunk: {chunk}")
|
||||
|
||||
# Validate we received proper A2A streaming events
|
||||
assert len(chunks) >= 4, f"Expected at least 4 chunks, got {len(chunks)}"
|
||||
|
||||
# Validate first chunk is task event
|
||||
assert chunks[0]["result"]["kind"] == "task"
|
||||
assert chunks[0]["result"]["status"]["state"] == "submitted"
|
||||
|
||||
# Validate final chunk is completed status
|
||||
assert chunks[-1]["result"]["kind"] == "status-update"
|
||||
assert chunks[-1]["result"]["status"]["state"] == "completed"
|
||||
assert chunks[-1]["result"]["final"] is True
|
||||
|
||||
print(f"Received {len(chunks)} chunks from Bedrock AgentCore")
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,159 @@
|
|||
"""
|
||||
Test A2A completion bridge streaming transformation to proper A2A format.
|
||||
|
||||
Tests that the completion bridge 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
|
||||
"""
|
||||
|
||||
from unittest.mock import AsyncMock, MagicMock, patch
|
||||
|
||||
import pytest
|
||||
|
||||
|
||||
class TestA2AStreamingTransformation:
|
||||
"""Test the A2A streaming transformation creates proper events."""
|
||||
|
||||
def test_create_task_event(self):
|
||||
"""Test that create_task_event produces proper A2A task event structure."""
|
||||
from litellm.a2a_protocol.litellm_completion_bridge.transformation import (
|
||||
A2ACompletionBridgeTransformation,
|
||||
A2AStreamingContext,
|
||||
)
|
||||
|
||||
input_message = {
|
||||
"role": "user",
|
||||
"parts": [{"kind": "text", "text": "Hello"}],
|
||||
"messageId": "msg-123",
|
||||
}
|
||||
ctx = A2AStreamingContext(request_id="req-456", input_message=input_message)
|
||||
|
||||
event = A2ACompletionBridgeTransformation.create_task_event(ctx)
|
||||
|
||||
# Validate structure
|
||||
assert event["jsonrpc"] == "2.0"
|
||||
assert event["id"] == "req-456"
|
||||
assert event["result"]["kind"] == "task"
|
||||
assert event["result"]["status"]["state"] == "submitted"
|
||||
assert "contextId" in event["result"]
|
||||
assert "id" in event["result"] # task id
|
||||
assert "history" in event["result"]
|
||||
assert len(event["result"]["history"]) == 1
|
||||
assert event["result"]["history"][0]["role"] == "user"
|
||||
|
||||
def test_create_status_update_working(self):
|
||||
"""Test that create_status_update_event produces proper working status."""
|
||||
from litellm.a2a_protocol.litellm_completion_bridge.transformation import (
|
||||
A2ACompletionBridgeTransformation,
|
||||
A2AStreamingContext,
|
||||
)
|
||||
|
||||
ctx = A2AStreamingContext(
|
||||
request_id="req-456",
|
||||
input_message={"role": "user", "parts": []},
|
||||
)
|
||||
|
||||
event = A2ACompletionBridgeTransformation.create_status_update_event(
|
||||
ctx=ctx,
|
||||
state="working",
|
||||
final=False,
|
||||
message_text="Processing...",
|
||||
)
|
||||
|
||||
assert event["result"]["kind"] == "status-update"
|
||||
assert event["result"]["status"]["state"] == "working"
|
||||
assert event["result"]["final"] is False
|
||||
assert "taskId" in event["result"]
|
||||
assert "contextId" in event["result"]
|
||||
assert "timestamp" in event["result"]["status"]
|
||||
|
||||
def test_create_artifact_update(self):
|
||||
"""Test that create_artifact_update_event produces proper artifact event."""
|
||||
from litellm.a2a_protocol.litellm_completion_bridge.transformation import (
|
||||
A2ACompletionBridgeTransformation,
|
||||
A2AStreamingContext,
|
||||
)
|
||||
|
||||
ctx = A2AStreamingContext(
|
||||
request_id="req-456",
|
||||
input_message={"role": "user", "parts": []},
|
||||
)
|
||||
|
||||
event = A2ACompletionBridgeTransformation.create_artifact_update_event(
|
||||
ctx=ctx,
|
||||
text="Hello, I am an AI assistant.",
|
||||
)
|
||||
|
||||
assert event["result"]["kind"] == "artifact-update"
|
||||
assert "artifact" in event["result"]
|
||||
assert "artifactId" in event["result"]["artifact"]
|
||||
assert event["result"]["artifact"]["name"] == "response"
|
||||
assert event["result"]["artifact"]["parts"][0]["kind"] == "text"
|
||||
assert event["result"]["artifact"]["parts"][0]["text"] == "Hello, I am an AI assistant."
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_handle_streaming_emits_proper_events():
|
||||
"""Test that handle_streaming emits events in correct order with proper structure."""
|
||||
from litellm.a2a_protocol.litellm_completion_bridge.handler import (
|
||||
A2ACompletionBridgeHandler,
|
||||
)
|
||||
|
||||
# Mock litellm.acompletion to return a streaming response
|
||||
mock_chunk1 = MagicMock()
|
||||
mock_chunk1.choices = [MagicMock()]
|
||||
mock_chunk1.choices[0].delta = MagicMock()
|
||||
mock_chunk1.choices[0].delta.content = "Hello"
|
||||
|
||||
mock_chunk2 = MagicMock()
|
||||
mock_chunk2.choices = [MagicMock()]
|
||||
mock_chunk2.choices[0].delta = MagicMock()
|
||||
mock_chunk2.choices[0].delta.content = " world"
|
||||
|
||||
async def mock_streaming_response():
|
||||
yield mock_chunk1
|
||||
yield mock_chunk2
|
||||
|
||||
with patch("litellm.acompletion", new_callable=AsyncMock) as mock_acompletion:
|
||||
mock_acompletion.return_value = mock_streaming_response()
|
||||
|
||||
params = {
|
||||
"message": {
|
||||
"role": "user",
|
||||
"parts": [{"kind": "text", "text": "Hi"}],
|
||||
"messageId": "msg-123",
|
||||
}
|
||||
}
|
||||
|
||||
events = []
|
||||
async for event in A2ACompletionBridgeHandler.handle_streaming(
|
||||
request_id="req-456",
|
||||
params=params,
|
||||
litellm_params={"custom_llm_provider": "langgraph", "model": "agent"},
|
||||
api_base="http://localhost:2024",
|
||||
):
|
||||
events.append(event)
|
||||
|
||||
# Should have 4 events: task, working, artifact, completed
|
||||
assert len(events) == 4
|
||||
|
||||
# Event 1: task submitted
|
||||
assert events[0]["result"]["kind"] == "task"
|
||||
assert events[0]["result"]["status"]["state"] == "submitted"
|
||||
|
||||
# Event 2: status working
|
||||
assert events[1]["result"]["kind"] == "status-update"
|
||||
assert events[1]["result"]["status"]["state"] == "working"
|
||||
assert events[1]["result"]["final"] is False
|
||||
|
||||
# Event 3: artifact update with accumulated content
|
||||
assert events[2]["result"]["kind"] == "artifact-update"
|
||||
assert events[2]["result"]["artifact"]["parts"][0]["text"] == "Hello world"
|
||||
|
||||
# Event 4: status completed
|
||||
assert events[3]["result"]["kind"] == "status-update"
|
||||
assert events[3]["result"]["status"]["state"] == "completed"
|
||||
assert events[3]["result"]["final"] is True
|
||||
|
||||
BIN
ui/litellm-dashboard/public/assets/logos/langgraph.png
Normal file
BIN
ui/litellm-dashboard/public/assets/logos/langgraph.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 5.4 KiB |
|
|
@ -1,7 +1,9 @@
|
|||
import React, { useState } from "react";
|
||||
import { Modal, Form, Button as AntButton, message } from "antd";
|
||||
import { createAgentCall } from "../networking";
|
||||
import React, { useState, useEffect } from "react";
|
||||
import { Modal, Form, message, Select } from "antd";
|
||||
import { Button } from "@tremor/react";
|
||||
import { createAgentCall, getAgentCreateMetadata, AgentCreateInfo } from "../networking";
|
||||
import AgentFormFields from "./agent_form_fields";
|
||||
import DynamicAgentFormFields, { buildDynamicAgentData } from "./dynamic_agent_form_fields";
|
||||
import { getDefaultFormValues, buildAgentDataFromForm } from "./agent_config";
|
||||
|
||||
interface AddAgentFormProps {
|
||||
|
|
@ -19,6 +21,29 @@ const AddAgentForm: React.FC<AddAgentFormProps> = ({
|
|||
}) => {
|
||||
const [form] = Form.useForm();
|
||||
const [isSubmitting, setIsSubmitting] = useState(false);
|
||||
const [agentType, setAgentType] = useState<string>("a2a");
|
||||
const [agentTypeMetadata, setAgentTypeMetadata] = useState<AgentCreateInfo[]>([]);
|
||||
const [loadingMetadata, setLoadingMetadata] = useState(false);
|
||||
|
||||
// Fetch agent type metadata on mount
|
||||
useEffect(() => {
|
||||
const fetchMetadata = async () => {
|
||||
setLoadingMetadata(true);
|
||||
try {
|
||||
const metadata = await getAgentCreateMetadata();
|
||||
setAgentTypeMetadata(metadata);
|
||||
} catch (error) {
|
||||
console.error("Error fetching agent metadata:", error);
|
||||
} finally {
|
||||
setLoadingMetadata(false);
|
||||
}
|
||||
};
|
||||
fetchMetadata();
|
||||
}, []);
|
||||
|
||||
const selectedAgentTypeInfo = agentTypeMetadata.find(
|
||||
(info) => info.agent_type === agentType
|
||||
);
|
||||
|
||||
const handleSubmit = async (values: any) => {
|
||||
if (!accessToken) {
|
||||
|
|
@ -28,10 +53,18 @@ const AddAgentForm: React.FC<AddAgentFormProps> = ({
|
|||
|
||||
setIsSubmitting(true);
|
||||
try {
|
||||
const agentData = buildAgentDataFromForm(values);
|
||||
let agentData: any;
|
||||
|
||||
if (agentType === "a2a") {
|
||||
agentData = buildAgentDataFromForm(values);
|
||||
} else if (selectedAgentTypeInfo) {
|
||||
agentData = buildDynamicAgentData(values, selectedAgentTypeInfo);
|
||||
}
|
||||
|
||||
await createAgentCall(accessToken, agentData);
|
||||
message.success("Agent created successfully");
|
||||
form.resetFields();
|
||||
setAgentType("a2a");
|
||||
onSuccess();
|
||||
onClose();
|
||||
} catch (error) {
|
||||
|
|
@ -44,42 +77,114 @@ const AddAgentForm: React.FC<AddAgentFormProps> = ({
|
|||
|
||||
const handleCancel = () => {
|
||||
form.resetFields();
|
||||
setAgentType("a2a");
|
||||
onClose();
|
||||
};
|
||||
|
||||
const handleAgentTypeChange = (value: string) => {
|
||||
setAgentType(value);
|
||||
form.resetFields();
|
||||
};
|
||||
|
||||
// Get the logo for the selected agent type for the header
|
||||
const selectedLogo = selectedAgentTypeInfo?.logo_url || agentTypeMetadata.find(a => a.agent_type === "a2a")?.logo_url;
|
||||
|
||||
return (
|
||||
<Modal
|
||||
title="Add New Agent"
|
||||
title={
|
||||
<div className="flex items-center space-x-3 pb-4 border-b border-gray-100">
|
||||
{selectedLogo && (
|
||||
<img
|
||||
src={selectedLogo}
|
||||
alt="Agent"
|
||||
className="w-6 h-6 object-contain"
|
||||
/>
|
||||
)}
|
||||
<h2 className="text-xl font-semibold text-gray-900">Add New Agent</h2>
|
||||
</div>
|
||||
}
|
||||
open={visible}
|
||||
onCancel={handleCancel}
|
||||
footer={null}
|
||||
width={800}
|
||||
width={900}
|
||||
className="top-8"
|
||||
styles={{
|
||||
body: { padding: "24px" },
|
||||
header: { padding: "24px 24px 0 24px", border: "none" },
|
||||
}}
|
||||
>
|
||||
<Form
|
||||
form={form}
|
||||
layout="vertical"
|
||||
onFinish={handleSubmit}
|
||||
initialValues={getDefaultFormValues()}
|
||||
>
|
||||
<AgentFormFields showAgentName={true} />
|
||||
|
||||
<Form.Item>
|
||||
<div style={{ display: "flex", justifyContent: "flex-end", gap: "8px" }}>
|
||||
<AntButton onClick={handleCancel}>
|
||||
Cancel
|
||||
</AntButton>
|
||||
<AntButton
|
||||
htmlType="submit"
|
||||
loading={isSubmitting}
|
||||
<div className="mt-4">
|
||||
<Form
|
||||
form={form}
|
||||
layout="vertical"
|
||||
onFinish={handleSubmit}
|
||||
initialValues={agentType === "a2a" ? getDefaultFormValues() : {}}
|
||||
className="space-y-4"
|
||||
>
|
||||
{/* Agent Type Selection */}
|
||||
<Form.Item
|
||||
label={<span className="text-sm font-medium text-gray-700">Agent Type</span>}
|
||||
required
|
||||
tooltip="Select the type of agent you want to create"
|
||||
>
|
||||
<Select
|
||||
value={agentType}
|
||||
onChange={handleAgentTypeChange}
|
||||
size="large"
|
||||
style={{ width: "100%" }}
|
||||
optionLabelProp="label"
|
||||
>
|
||||
Create Agent
|
||||
</AntButton>
|
||||
{agentTypeMetadata.map((info) => (
|
||||
<Select.Option
|
||||
key={info.agent_type}
|
||||
value={info.agent_type}
|
||||
label={
|
||||
<div className="flex items-center gap-2">
|
||||
<img src={info.logo_url || ""} alt="" className="w-4 h-4 object-contain" />
|
||||
<span>{info.agent_type_display_name}</span>
|
||||
</div>
|
||||
}
|
||||
>
|
||||
<div className="flex items-center gap-3 py-1">
|
||||
<img
|
||||
src={info.logo_url || ""}
|
||||
alt={info.agent_type_display_name}
|
||||
className="w-5 h-5 object-contain"
|
||||
/>
|
||||
<div>
|
||||
<div className="font-medium">{info.agent_type_display_name}</div>
|
||||
{info.description && (
|
||||
<div className="text-xs text-gray-500">{info.description}</div>
|
||||
)}
|
||||
</div>
|
||||
</div>
|
||||
</Select.Option>
|
||||
))}
|
||||
</Select>
|
||||
</Form.Item>
|
||||
|
||||
{/* Conditional Form Fields */}
|
||||
<div className="mt-6">
|
||||
{agentType === "a2a" ? (
|
||||
<AgentFormFields showAgentName={true} />
|
||||
) : selectedAgentTypeInfo ? (
|
||||
<DynamicAgentFormFields agentTypeInfo={selectedAgentTypeInfo} />
|
||||
) : null}
|
||||
</div>
|
||||
</Form.Item>
|
||||
</Form>
|
||||
|
||||
{/* Footer Buttons */}
|
||||
<div className="flex items-center justify-end space-x-3 pt-6 border-t border-gray-100 mt-6">
|
||||
<Button variant="secondary" onClick={handleCancel}>
|
||||
Cancel
|
||||
</Button>
|
||||
<Button variant="primary" loading={isSubmitting}>
|
||||
{isSubmitting ? "Creating..." : "Create Agent"}
|
||||
</Button>
|
||||
</div>
|
||||
</Form>
|
||||
</div>
|
||||
</Modal>
|
||||
);
|
||||
};
|
||||
|
||||
export default AddAgentForm;
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,125 @@
|
|||
import React from "react";
|
||||
import { Form, Input, Select } from "antd";
|
||||
import { AgentCreateInfo, AgentCredentialFieldMetadata } from "../networking";
|
||||
|
||||
interface DynamicAgentFormFieldsProps {
|
||||
agentTypeInfo: AgentCreateInfo;
|
||||
}
|
||||
|
||||
/**
|
||||
* Form fields for dynamic agent types (e.g., LangGraph).
|
||||
* Renders common fields (agent name, display name, description) plus
|
||||
* credential fields defined by the agent type metadata.
|
||||
*/
|
||||
const DynamicAgentFormFields: React.FC<DynamicAgentFormFieldsProps> = ({
|
||||
agentTypeInfo,
|
||||
}) => {
|
||||
return (
|
||||
<>
|
||||
<Form.Item
|
||||
label="Agent Name"
|
||||
name="agent_name"
|
||||
rules={[{ required: true, message: "Please enter a unique agent name" }]}
|
||||
tooltip="Unique identifier for the agent"
|
||||
>
|
||||
<Input placeholder="e.g., my-langgraph-agent" />
|
||||
</Form.Item>
|
||||
|
||||
<Form.Item
|
||||
label="Description"
|
||||
name="description"
|
||||
tooltip="Brief description of what this agent does"
|
||||
>
|
||||
<Input.TextArea rows={2} placeholder="Describe what this agent does..." />
|
||||
</Form.Item>
|
||||
|
||||
{agentTypeInfo.credential_fields.map((field: AgentCredentialFieldMetadata) => (
|
||||
<Form.Item
|
||||
key={field.key}
|
||||
label={field.label}
|
||||
name={field.key}
|
||||
rules={field.required ? [{ required: true, message: `Please enter ${field.label}` }] : undefined}
|
||||
tooltip={field.tooltip}
|
||||
initialValue={field.default_value}
|
||||
>
|
||||
{field.field_type === "password" ? (
|
||||
<Input.Password placeholder={field.placeholder || ""} />
|
||||
) : field.field_type === "textarea" ? (
|
||||
<Input.TextArea rows={3} placeholder={field.placeholder || ""} />
|
||||
) : field.field_type === "select" && field.options ? (
|
||||
<Select placeholder={field.placeholder || ""}>
|
||||
{field.options.map((opt) => (
|
||||
<Select.Option key={opt} value={opt}>
|
||||
{opt}
|
||||
</Select.Option>
|
||||
))}
|
||||
</Select>
|
||||
) : (
|
||||
<Input placeholder={field.placeholder || ""} />
|
||||
)}
|
||||
</Form.Item>
|
||||
))}
|
||||
</>
|
||||
);
|
||||
};
|
||||
|
||||
/**
|
||||
* Builds agent data from form values for dynamic agent types.
|
||||
* Uses configuration from agentTypeInfo to determine which fields to include.
|
||||
*/
|
||||
export const buildDynamicAgentData = (
|
||||
values: any,
|
||||
agentTypeInfo: AgentCreateInfo
|
||||
) => {
|
||||
// Build litellm_params from template
|
||||
const litellmParams: Record<string, any> = {
|
||||
...(agentTypeInfo.litellm_params_template || {}),
|
||||
};
|
||||
|
||||
// Add credential fields marked with include_in_litellm_params
|
||||
for (const field of agentTypeInfo.credential_fields) {
|
||||
const value = values[field.key];
|
||||
if (value && field.include_in_litellm_params !== false) {
|
||||
litellmParams[field.key] = value;
|
||||
}
|
||||
}
|
||||
|
||||
// Apply model_template if defined (e.g., "bedrock/agentcore/{agent_runtime_arn}")
|
||||
if (agentTypeInfo.model_template) {
|
||||
let model = agentTypeInfo.model_template;
|
||||
// Replace {field_key} placeholders with actual values
|
||||
for (const field of agentTypeInfo.credential_fields) {
|
||||
const placeholder = `{${field.key}}`;
|
||||
if (model.includes(placeholder) && values[field.key]) {
|
||||
model = model.replace(placeholder, values[field.key]);
|
||||
}
|
||||
}
|
||||
litellmParams.model = model;
|
||||
}
|
||||
|
||||
return {
|
||||
agent_name: values.agent_name,
|
||||
agent_card_params: {
|
||||
protocolVersion: "1.0",
|
||||
name: values.display_name || values.agent_name,
|
||||
description: values.description || `${agentTypeInfo.agent_type_display_name} agent`,
|
||||
url: values.api_base || "",
|
||||
version: "1.0.0",
|
||||
defaultInputModes: ["text"],
|
||||
defaultOutputModes: ["text"],
|
||||
capabilities: {
|
||||
streaming: true,
|
||||
},
|
||||
skills: [{
|
||||
id: "chat",
|
||||
name: "Chat",
|
||||
description: "General chat capability",
|
||||
tags: ["chat", "conversation"],
|
||||
}],
|
||||
},
|
||||
litellm_params: litellmParams,
|
||||
};
|
||||
};
|
||||
|
||||
export default DynamicAgentFormFields;
|
||||
|
||||
|
|
@ -195,6 +195,28 @@ export interface ProviderCreateInfo {
|
|||
credential_fields: ProviderCredentialFieldMetadata[];
|
||||
}
|
||||
|
||||
export interface AgentCredentialFieldMetadata {
|
||||
key: string;
|
||||
label: string;
|
||||
placeholder?: string | null;
|
||||
tooltip?: string | null;
|
||||
required?: boolean;
|
||||
field_type?: "text" | "password" | "select" | "upload" | "textarea";
|
||||
options?: string[] | null;
|
||||
default_value?: string | null;
|
||||
include_in_litellm_params?: boolean;
|
||||
}
|
||||
|
||||
export interface AgentCreateInfo {
|
||||
agent_type: string;
|
||||
agent_type_display_name: string;
|
||||
description?: string | null;
|
||||
logo_url?: string | null;
|
||||
credential_fields: AgentCredentialFieldMetadata[];
|
||||
litellm_params_template?: Record<string, string> | null;
|
||||
model_template?: string | null;
|
||||
}
|
||||
|
||||
export interface PublicModelHubInfo {
|
||||
docs_title: string;
|
||||
custom_docs_description: string | null;
|
||||
|
|
@ -255,6 +277,26 @@ export const getProviderCreateMetadata = async (): Promise<ProviderCreateInfo[]>
|
|||
return jsonData;
|
||||
};
|
||||
|
||||
export const getAgentCreateMetadata = async (): Promise<AgentCreateInfo[]> => {
|
||||
/**
|
||||
* Fetch agent type metadata from the proxy's public endpoint.
|
||||
* This is used by the UI to dynamically render agent-specific credential fields.
|
||||
*/
|
||||
const url = proxyBaseUrl ? `${proxyBaseUrl}/public/agents/fields` : `/public/agents/fields`;
|
||||
const response = await fetch(url, {
|
||||
method: "GET",
|
||||
});
|
||||
|
||||
if (!response.ok) {
|
||||
const errorText = await response.text();
|
||||
console.error("Failed to fetch agent create metadata:", response.status, errorText);
|
||||
throw new Error("Failed to load agent configuration");
|
||||
}
|
||||
|
||||
const jsonData: AgentCreateInfo[] = await response.json();
|
||||
return jsonData;
|
||||
};
|
||||
|
||||
// Global variable for the header name
|
||||
let globalLitellmHeaderName: string = "Authorization";
|
||||
const MCP_AUTH_HEADER: string = "x-mcp-auth";
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue