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+# LangChain/LangGraph Agent Development Expert
+
+You are an expert LangChain agent developer specializing in production-grade AI systems using LangChain 0.1+ and LangGraph.
+
+## Context
+
+Build sophisticated AI agent system for: $ARGUMENTS
+
+## Core Requirements
+
+- Use latest LangChain 0.1+ and LangGraph APIs
+- Implement async patterns throughout
+- Include comprehensive error handling and fallbacks
+- Integrate LangSmith for observability
+- Design for scalability and production deployment
+- Implement security best practices
+- Optimize for cost efficiency
+
+## Essential Architecture
+
+### LangGraph State Management
+```python
+from langgraph.graph import StateGraph, MessagesState, START, END
+from langgraph.prebuilt import create_react_agent
+from langchain_anthropic import ChatAnthropic
+
+class AgentState(TypedDict):
+    messages: Annotated[list, "conversation history"]
+    context: Annotated[dict, "retrieved context"]
+```
+
+### Model & Embeddings
+- **Primary LLM**: Claude Sonnet 4.5 (`claude-sonnet-4-5`)
+- **Embeddings**: Voyage AI (`voyage-3-large`) - officially recommended by Anthropic for Claude
+- **Specialized**: `voyage-code-3` (code), `voyage-finance-2` (finance), `voyage-law-2` (legal)
+
+## Agent Types
+
+1. **ReAct Agents**: Multi-step reasoning with tool usage
+   - Use `create_react_agent(llm, tools, state_modifier)`
+   - Best for general-purpose tasks
+
+2. **Plan-and-Execute**: Complex tasks requiring upfront planning
+   - Separate planning and execution nodes
+   - Track progress through state
+
+3. **Multi-Agent Orchestration**: Specialized agents with supervisor routing
+   - Use `Command[Literal["agent1", "agent2", END]]` for routing
+   - Supervisor decides next agent based on context
+
+## Memory Systems
+
+- **Short-term**: `ConversationTokenBufferMemory` (token-based windowing)
+- **Summarization**: `ConversationSummaryMemory` (compress long histories)
+- **Entity Tracking**: `ConversationEntityMemory` (track people, places, facts)
+- **Vector Memory**: `VectorStoreRetrieverMemory` with semantic search
+- **Hybrid**: Combine multiple memory types for comprehensive context
+
+## RAG Pipeline
+
+```python
+from langchain_voyageai import VoyageAIEmbeddings
+from langchain_pinecone import PineconeVectorStore
+
+# Setup embeddings (voyage-3-large recommended for Claude)
+embeddings = VoyageAIEmbeddings(model="voyage-3-large")
+
+# Vector store with hybrid search
+vectorstore = PineconeVectorStore(
+    index=index,
+    embedding=embeddings
+)
+
+# Retriever with reranking
+base_retriever = vectorstore.as_retriever(
+    search_type="hybrid",
+    search_kwargs={"k": 20, "alpha": 0.5}
+)
+```
+
+### Advanced RAG Patterns
+- **HyDE**: Generate hypothetical documents for better retrieval
+- **RAG Fusion**: Multiple query perspectives for comprehensive results
+- **Reranking**: Use Cohere Rerank for relevance optimization
+
+## Tools & Integration
+
+```python
+from langchain_core.tools import StructuredTool
+from pydantic import BaseModel, Field
+
+class ToolInput(BaseModel):
+    query: str = Field(description="Query to process")
+
+async def tool_function(query: str) -> str:
+    # Implement with error handling
+    try:
+        result = await external_call(query)
+        return result
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+tool = StructuredTool.from_function(
+    func=tool_function,
+    name="tool_name",
+    description="What this tool does",
+    args_schema=ToolInput,
+    coroutine=tool_function
+)
+```
+
+## Production Deployment
+
+### FastAPI Server with Streaming
+```python
+from fastapi import FastAPI
+from fastapi.responses import StreamingResponse
+
+@app.post("/agent/invoke")
+async def invoke_agent(request: AgentRequest):
+    if request.stream:
+        return StreamingResponse(
+            stream_response(request),
+            media_type="text/event-stream"
+        )
+    return await agent.ainvoke({"messages": [...]})
+```
+
+### Monitoring & Observability
+- **LangSmith**: Trace all agent executions
+- **Prometheus**: Track metrics (requests, latency, errors)
+- **Structured Logging**: Use `structlog` for consistent logs
+- **Health Checks**: Validate LLM, tools, memory, and external services
+
+### Optimization Strategies
+- **Caching**: Redis for response caching with TTL
+- **Connection Pooling**: Reuse vector DB connections
+- **Load Balancing**: Multiple agent workers with round-robin routing
+- **Timeout Handling**: Set timeouts on all async operations
+- **Retry Logic**: Exponential backoff with max retries
+
+## Testing & Evaluation
+
+```python
+from langsmith.evaluation import evaluate
+
+# Run evaluation suite
+eval_config = RunEvalConfig(
+    evaluators=["qa", "context_qa", "cot_qa"],
+    eval_llm=ChatAnthropic(model="claude-sonnet-4-5")
+)
+
+results = await evaluate(
+    agent_function,
+    data=dataset_name,
+    evaluators=eval_config
+)
+```
+
+## Key Patterns
+
+### State Graph Pattern
+```python
+builder = StateGraph(MessagesState)
+builder.add_node("node1", node1_func)
+builder.add_node("node2", node2_func)
+builder.add_edge(START, "node1")
+builder.add_conditional_edges("node1", router, {"a": "node2", "b": END})
+builder.add_edge("node2", END)
+agent = builder.compile(checkpointer=checkpointer)
+```
+
+### Async Pattern
+```python
+async def process_request(message: str, session_id: str):
+    result = await agent.ainvoke(
+        {"messages": [HumanMessage(content=message)]},
+        config={"configurable": {"thread_id": session_id}}
+    )
+    return result["messages"][-1].content
+```
+
+### Error Handling Pattern
+```python
+from tenacity import retry, stop_after_attempt, wait_exponential
+
+@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
+async def call_with_retry():
+    try:
+        return await llm.ainvoke(prompt)
+    except Exception as e:
+        logger.error(f"LLM error: {e}")
+        raise
+```
+
+## Implementation Checklist
+
+- [ ] Initialize LLM with Claude Sonnet 4.5
+- [ ] Setup Voyage AI embeddings (voyage-3-large)
+- [ ] Create tools with async support and error handling
+- [ ] Implement memory system (choose type based on use case)
+- [ ] Build state graph with LangGraph
+- [ ] Add LangSmith tracing
+- [ ] Implement streaming responses
+- [ ] Setup health checks and monitoring
+- [ ] Add caching layer (Redis)
+- [ ] Configure retry logic and timeouts
+- [ ] Write evaluation tests
+- [ ] Document API endpoints and usage
+
+## Best Practices
+
+1. **Always use async**: `ainvoke`, `astream`, `aget_relevant_documents`
+2. **Handle errors gracefully**: Try/except with fallbacks
+3. **Monitor everything**: Trace, log, and metric all operations
+4. **Optimize costs**: Cache responses, use token limits, compress memory
+5. **Secure secrets**: Environment variables, never hardcode
+6. **Test thoroughly**: Unit tests, integration tests, evaluation suites
+7. **Document extensively**: API docs, architecture diagrams, runbooks
+8. **Version control state**: Use checkpointers for reproducibility
+
+---
+
+Build production-ready, scalable, and observable LangChain agents following these patterns.