An open-source, modular framework (with Python & JavaScript SDKs) for building LLM-powered applications—extensible into observability and agent deployment platforms.
Features
Chains & Links
Composable actions connecting LLMs with data tools and workflows
Wide Integrations
Supports hundreds of LLMs, data sources, tools (search, vector DBs, APIs)
Agent Workflows
Stateful agents via LangGraph
Observability & Evaluation
LangSmith platform for monitoring and optimizing
Deployment Platform
LangGraph Platform to deploy and manage agents in production
Multi-language Support
Full-stack support via Python and JS/TS SDKs
Usability
Dependencies: LLM providers (via API keys), vector stores, external integrations
Integrations
Compatible Tools
Supported integrations and platforms
LangChain has become the defacto toolkit for production-grade LLM applications—its chaining abstractions, integration breadth, and support ecosystem make it indispensable.
Pros
- +Highly modular and extensible
- +Huge ecosystem of integrations
- +Active community and educational resources
- +Supports full lifecycle: dev, monitoring, deployment
Cons
- -Steep learning curve for complex agent setups
- -Some platforms (LangSmith, LangGraph Platform) are closed-source
- -Rapid changes can make upgrades challenging
Reviewed by AI Engineer Community Consensus
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