LangChain Expert Skill
LangChain is an MIT-licensed Python framework for building LLM-powered applications. Since v1.0 (October 2025), it provides a layered architecture: high-level chain composition via LCEL (LangChain Expression Language), agent creation via create_agent (running on the LangGraph runtime underneath), and production observability via LangSmith. With 1000+ integrations and 100K+ GitHub stars, it is the most widely adopted LLM orchestration framework.
Key v1.0 change: All new LangChain agents run on the LangGraph runtime. AgentExecutor is in maintenance mode until December 2026. Use create_agent for new agents. Drop to LangGraph directly when you need full state-machine control.
⚠️ CRITICAL: Do NOT use AgentExecutor for new code. It is in maintenance mode until December 2026. Use create_agent(model, tools, prompt) instead — it generates a LangGraph state machine with streaming, persistence, and observability out of the box.
Core Principles
These principles govern every decision when building with LangChain. Read them before proceeding to the reference guides.
- LCEL is the composition primitive. The pipe operator (
|) chains Runnables. Every component — prompt, model, parser, retriever — implements the Runnable interface. Build everything in LCEL.
- Agents run on LangGraph. Since v1.0,
create_agent generates a LangGraph state machine underneath. You get streaming, persistence, and observability without writing graph code. Drop to LangGraph when you need branching, cycles, or human-in-the-loop.
- RAG is a chain, not a framework.
retriever | prompt | model | parser is the canonical RAG pattern. Document loaders, splitters, and vector stores are all interchangeable components.
- LangSmith is production observability. Enable tracing at startup. 89% of production teams use observability — without it, debugging agent behavior is guesswork.
- The ecosystem is the moat. 1000+ integrations mean model providers, vector stores, and tools are swappable with one line. Build against the interface, not the implementation.
Where to Start
| You already have... |
Start here |
| Nothing — blank project |
Install LangChain, build a basic LCEL chain |
| Documents to query |
Build a RAG chain (load, split, embed, retrieve, generate) |
| A need for agentic behavior |
Use create_agent with tools |
| Existing AgentExecutor code |
Migrate to create_agent — see references/agent-patterns.md |
| A production deployment |
Add LangSmith tracing + LangServe deployment |
| Comparing frameworks |
See the Framework Routing Guide |
Pipeline Mode
| Mode |
When |
Phases to run |
Skip |
| Quick |
Single chain, exploration |
prompt → model → parser |
Retrieval, agents, production hardening |
| RAG |
Document Q&A |
load → split → embed → retrieve → generate |
Agent orchestration, deployment |
| Agent |
Tool-using agents |
create_agent + tools + LangGraph runtime |
If simple chain suffices |
| Production |
Shipping to users |
RAG/Agent + LangSmith + LangServe |
Nothing |
Quick Reference
| Task |
Approach |
Reference |
| Basic chain |
prompt | model | parser |
references/lcel-reference.md |
| RAG pipeline |
retriever | prompt | model | parser |
references/rag-strategies.md |
| Create agent |
create_agent(model, tools, prompt) |
references/agent-patterns.md |
| Tool definition |
@tool decorator |
references/agent-patterns.md |
| Multi-agent |
LangGraph supervisor pattern |
references/agent-patterns.md |
| Observability |
Set LANGCHAIN_TRACING_V2=true |
references/production-deployment.md |
| Deployment |
LangServe or LangSmith Deployment |
references/production-deployment.md |
| Vector store |
One-line swap (Chroma, Pinecone, pgvector) |
references/integration-ecosystem.md |
When to Use This Skill
Load this skill any time you are:
- Building LCEL chains for LLM-powered applications
- Implementing RAG pipelines over enterprise or personal data
- Creating agents with tool-calling and multi-step reasoning
- Deploying LLM applications to production with observability
- Comparing LangChain with LlamaIndex, Haystack, or raw API calls
Framework Routing Guide
This skill is part of a portfolio of framework skills. When deciding which fits:
| Scenario |
Reach for |
Why |
| I have chains to compose |
LangChain |
LCEL is the cleanest pipe-based composition model |
| I have documents to query |
LlamaIndex |
Data ingestion and retrieval are first-class primitives |
| I have agents to orchestrate |
LangGraph |
State-machine semantics, subgraphs, human-in-the-loop |
| I have a tool to wrap as an agent |
PydanticAI |
Type-safe agent definitions with dependency injection |
| I have search pipelines |
Haystack |
Pipeline model is more mature for search workloads |
| Fast prototype of any kind |
LangChain |
Fastest path from zero to working chain |
Reference Files
| Reference |
Load when |
File |
| LCEL Reference |
Building chains with the pipe operator |
references/lcel-reference.md |
| Architecture |
Understanding package structure, Runnable, v1.0 |
references/architecture.md |
| RAG Strategies |
Building RAG pipelines |
references/rag-strategies.md |
| Agent Patterns |
Creating agents with tools and multi-agent |
references/agent-patterns.md |
| Production & Deployment |
LangServe, LangSmith, deployment |
references/production-deployment.md |
| Integration Ecosystem |
Model providers, vector stores, tools |
references/integration-ecosystem.md |
| FAQ & Troubleshooting |
Common errors and fixes |
references/faq-and-troubleshooting.md |
| Callbacks System |
Custom logging, monitoring, agent auditing |
references/callbacks.md |
| Validation Audit |
Research validation of all API claims |
references/validation-audit.md |
Template Files
| Template |
When to use |
File |
| Basic Chain |
Single prompt→model→parser chain |
templates/basic-chain.py |
| RAG Pipeline |
Document Q&A with retrieval |
templates/rag-pipeline.py |
| Agent with Tools |
Tool-using agent with LangGraph runtime |
templates/agent-with-tools.py |
| Production Deploy |
LangServe deployment with LangSmith |
templates/production-deploy.py |
Scripts
| Script |
Purpose |
File |
| check-setup |
Verify LangChain installation |
scripts/check-setup.py |
Troubleshooting Guide
| Symptom |
Likely cause |
Fix |
Reference |
| Chain returns nothing |
Output parser not connected |
Add .pipe(StrOutputParser()) or equivalent |
references/lcel-reference.md |
| Agent not calling tools |
Tool schema mismatch |
Check tool has docstring and type hints |
references/agent-patterns.md |
| LangSmith traces missing |
LANGCHAIN_TRACING_V2 not set |
Set env var before any chain execution |
references/production-deployment.md |
| Deprecation warning |
Using AgentExecutor |
Migrate to create_agent (LangGraph runtime) |
references/agent-patterns.md |
| Model not found |
Integration package missing |
Install langchain-openai, langchain-anthropic, etc. |
references/integration-ecosystem.md |
| Streaming not working |
LCEL chain not streaming-native |
Ensure all components implement stream() |
references/lcel-reference.md |
| Vector store connection fails |
Wrong credentials or missing package |
Install langchain-community + provider package |
references/integration-ecosystem.md |
When NOT to Use LangChain
- Single-model, single-prompt application — raw API calls are simpler and more debuggable
- Maximum transparency needed — LangGraph (which LangChain uses underneath) provides more visibility
- Pure multi-agent state machines — LangGraph directly is the correct tool, not the high-level API
- Stateless microservice with no LLM orchestration — LangChain adds overhead without benefit
1---2name: langchain3description: Expert skill for building LLM applications with LangChain — LCEL chains, RAG pipelines, agent orchestration, LangGraph integration, LangSmith observability, and production deployment via LangServe. Use when working with LangChain or comparing LLM application frameworks.4license: MIT5---67# LangChain Expert Skill89LangChain is an MIT-licensed Python framework for building LLM-powered applications. Since v1.0 (October 2025), it provides a layered architecture: high-level chain composition via LCEL (LangChain Expression Language), agent creation via `create_agent` (running on the LangGraph runtime underneath), and production observability via LangSmith. With 1000+ integrations and 100K+ GitHub stars, it is the most widely adopted LLM orchestration framework.1011**Key v1.0 change:** All new LangChain agents run on the LangGraph runtime. AgentExecutor is in maintenance mode until December 2026. Use `create_agent` for new agents. Drop to LangGraph directly when you need full state-machine control.1213> **⚠️ CRITICAL: Do NOT use AgentExecutor for new code.** It is in maintenance mode until December 2026. Use `create_agent(model, tools, prompt)` instead — it generates a LangGraph state machine with streaming, persistence, and observability out of the box.1415## Core Principles1617> These principles govern every decision when building with LangChain. Read them before proceeding to the reference guides.18191. **LCEL is the composition primitive.** The pipe operator (`|`) chains Runnables. Every component — prompt, model, parser, retriever — implements the Runnable interface. Build everything in LCEL.202. **Agents run on LangGraph.** Since v1.0, `create_agent` generates a LangGraph state machine underneath. You get streaming, persistence, and observability without writing graph code. Drop to LangGraph when you need branching, cycles, or human-in-the-loop.213. **RAG is a chain, not a framework.** `retriever | prompt | model | parser` is the canonical RAG pattern. Document loaders, splitters, and vector stores are all interchangeable components.224. **LangSmith is production observability.** Enable tracing at startup. 89% of production teams use observability — without it, debugging agent behavior is guesswork.235. **The ecosystem is the moat.** 1000+ integrations mean model providers, vector stores, and tools are swappable with one line. Build against the interface, not the implementation.2425## Where to Start2627| You already have... | Start here |28|---|---|29| Nothing — blank project | Install LangChain, build a basic LCEL chain |30| Documents to query | Build a RAG chain (load, split, embed, retrieve, generate) |31| A need for agentic behavior | Use `create_agent` with tools |32| Existing AgentExecutor code | Migrate to `create_agent` — see `references/agent-patterns.md` |33| A production deployment | Add LangSmith tracing + LangServe deployment |34| Comparing frameworks | See the Framework Routing Guide |3536## Pipeline Mode3738| Mode | When | Phases to run | Skip |39|------|------|---------------|------|40| **Quick** | Single chain, exploration | prompt → model → parser | Retrieval, agents, production hardening |41| **RAG** | Document Q&A | load → split → embed → retrieve → generate | Agent orchestration, deployment |42| **Agent** | Tool-using agents | create_agent + tools + LangGraph runtime | If simple chain suffices |43| **Production** | Shipping to users | RAG/Agent + LangSmith + LangServe | Nothing |4445## Quick Reference4647| Task | Approach | Reference |48|------|----------|-----------|49| Basic chain | `prompt \| model \| parser` | `references/lcel-reference.md` |50| RAG pipeline | `retriever \| prompt \| model \| parser` | `references/rag-strategies.md` |51| Create agent | `create_agent(model, tools, prompt)` | `references/agent-patterns.md` |52| Tool definition | `@tool` decorator | `references/agent-patterns.md` |53| Multi-agent | LangGraph supervisor pattern | `references/agent-patterns.md` |54| Observability | Set LANGCHAIN_TRACING_V2=true | `references/production-deployment.md` |55| Deployment | LangServe or LangSmith Deployment | `references/production-deployment.md` |56| Vector store | One-line swap (Chroma, Pinecone, pgvector) | `references/integration-ecosystem.md` |5758## When to Use This Skill5960Load this skill any time you are:61- Building LCEL chains for LLM-powered applications62- Implementing RAG pipelines over enterprise or personal data63- Creating agents with tool-calling and multi-step reasoning64- Deploying LLM applications to production with observability65- Comparing LangChain with LlamaIndex, Haystack, or raw API calls6667## Framework Routing Guide6869This skill is part of a portfolio of framework skills. When deciding which fits:7071| Scenario | Reach for | Why |72|----------|-----------|-----|73| I have chains to compose | **LangChain** | LCEL is the cleanest pipe-based composition model |74| I have documents to query | **LlamaIndex** | Data ingestion and retrieval are first-class primitives |75| I have agents to orchestrate | **LangGraph** | State-machine semantics, subgraphs, human-in-the-loop |76| I have a tool to wrap as an agent | **PydanticAI** | Type-safe agent definitions with dependency injection |77| I have search pipelines | **Haystack** | Pipeline model is more mature for search workloads |78| Fast prototype of any kind | **LangChain** | Fastest path from zero to working chain |7980## Reference Files8182| Reference | Load when | File |83|-----------|-----------|------|84| LCEL Reference | Building chains with the pipe operator | `references/lcel-reference.md` |85| Architecture | Understanding package structure, Runnable, v1.0 | `references/architecture.md` |86| RAG Strategies | Building RAG pipelines | `references/rag-strategies.md` |87| Agent Patterns | Creating agents with tools and multi-agent | `references/agent-patterns.md` |88| Production & Deployment | LangServe, LangSmith, deployment | `references/production-deployment.md` |89| Integration Ecosystem | Model providers, vector stores, tools | `references/integration-ecosystem.md` |90| FAQ & Troubleshooting | Common errors and fixes | `references/faq-and-troubleshooting.md` |91| Callbacks System | Custom logging, monitoring, agent auditing | `references/callbacks.md` |92| Validation Audit | Research validation of all API claims | `references/validation-audit.md` |9394## Template Files9596| Template | When to use | File |97|----------|-------------|------|98| Basic Chain | Single prompt→model→parser chain | `templates/basic-chain.py` |99| RAG Pipeline | Document Q&A with retrieval | `templates/rag-pipeline.py` |100| Agent with Tools | Tool-using agent with LangGraph runtime | `templates/agent-with-tools.py` |101| Production Deploy | LangServe deployment with LangSmith | `templates/production-deploy.py` |102103## Scripts104105| Script | Purpose | File |106|--------|---------|------|107| check-setup | Verify LangChain installation | `scripts/check-setup.py` |108109## Troubleshooting Guide110111| Symptom | Likely cause | Fix | Reference |112|---------|-------------|-----|-----------|113| Chain returns nothing | Output parser not connected | Add `.pipe(StrOutputParser())` or equivalent | `references/lcel-reference.md` |114| Agent not calling tools | Tool schema mismatch | Check tool has docstring and type hints | `references/agent-patterns.md` |115| LangSmith traces missing | LANGCHAIN_TRACING_V2 not set | Set env var before any chain execution | `references/production-deployment.md` |116| Deprecation warning | Using AgentExecutor | Migrate to `create_agent` (LangGraph runtime) | `references/agent-patterns.md` |117| Model not found | Integration package missing | Install `langchain-openai`, `langchain-anthropic`, etc. | `references/integration-ecosystem.md` |118| Streaming not working | LCEL chain not streaming-native | Ensure all components implement `stream()` | `references/lcel-reference.md` |119| Vector store connection fails | Wrong credentials or missing package | Install `langchain-community` + provider package | `references/integration-ecosystem.md` |120121## When NOT to Use LangChain122123- Single-model, single-prompt application — raw API calls are simpler and more debuggable124- Maximum transparency needed — LangGraph (which LangChain uses underneath) provides more visibility125- Pure multi-agent state machines — LangGraph directly is the correct tool, not the high-level API126- Stateless microservice with no LLM orchestration — LangChain adds overhead without benefit
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