LangChain
🛠️ Developer Toolsfree
★4.2
Framework for building LLM applications
frameworkllmdevelopment
Try LangChain →Use Cases
- •Build retrieval-augmented generation (RAG) applications that answer questions from your documents
- •Create multi-agent workflows where specialized AI agents collaborate on complex tasks
- •Develop production LLM applications with observability, tracing, and evaluation via LangSmith
Integrations
OpenAIAnthropicGoogle Vertex AIPineconeChromaWeaviateRedisHugging Face
Pros
- +Most comprehensive open-source framework for building LLM applications with extensive abstractions
- +LangSmith provides powerful observability, evaluation, and debugging for production AI systems
- +Huge ecosystem of integrations for LLMs, vector stores, tools, and memory providers
Cons
- -Frequent breaking changes and rapid API evolution create maintenance burden
- -Heavy abstraction layers add complexity and can obscure what is actually happening under the hood
- -LangSmith tracing costs can add up significantly at high volumes in production
Quick Start
1. Install LangChain: pip install langchain langchain-openai
2. Set your LLM API key as an environment variable (e.g., OPENAI_API_KEY)
3. Create a simple chain: from langchain_openai import ChatOpenAI; llm = ChatOpenAI(); llm.invoke('Hello')
4. For RAG, load documents, split into chunks, embed into a vector store, and create a retrieval chain
5. Sign up at smith.langchain.com for LangSmith tracing to monitor and debug your chains in production
Pricing
LangChain framework: Free and open source (MIT license). LangSmith Developer: Free — 1 seat, 5,000 traces/mo. LangSmith Plus: $39/seat/mo — unlimited seats, 10,000 traces/mo, 1 free deployment. LangSmith Enterprise: Custom pricing — SSO, advanced security, dedicated support. Additional traces: $2.50 per 1,000 (base) or $5.00 per 1,000 (extended 400-day retention).