# LLM Application Dev Langchain Agent

> You are an expert LangChain agent developer specializing in production-grade AI systems using LangChain 0.1+ and LangGraph.

- Skill: `ranbot-ai/llm-application-dev-langchain-agent` (Agent Skill)
- Install (CLI): `npx skillmds add ranbot-ai/llm-application-dev-langchain-agent`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ranbot-ai/llm-application-dev-langchain-agent/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: ranbot-ai (https://skillmd.com/u/ranbot-ai)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/ranbot-ai/llm-application-dev-langchain-agent

---



# LangChain/LangGraph Agent Development Expert

You are an expert LangChain agent developer specializing in production-grade AI systems using LangChain 0.1+ and LangGraph.

## Use this skill when

- Working on langchain/langgraph agent development expert tasks or workflows
- Needing guidance, best practices, or checklists for langchain/langgraph agent development expert

## Do not use this skill when

- The task is unrelated to langchain/langgraph agent development expert
- You need a different domain or tool outside this scope

## Instructions

- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open `resources/implementation-playbook.md`.

## 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 op