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Open-Source AI Agent Framework
The leading open-source framework for building LLM-powered applications. MIT-licensed with 52M+ weekly downloads, 3 agent architectures, and the LangSmith observability platform for production deployment.
Chains
Composable sequences of LLM calls, prompts, and tools via LCEL pipeline syntax
Agents
Autonomous reasoning with create_agent, LangGraph, and Deep Agents architectures
LangGraph
Stateful, multi-actor orchestration with cycles, persistence, and human-in-the-loop
Memory
Conversation buffers, summaries, and vector-backed long-term memory for agents
LangSmith Platform
Observability, evaluation, prompt management, and deployment for production LLM applications
LangChain is an open-source framework created by Harrison Chase for building applications powered by large language models. Launched in October 2022, it provides composable abstractions for chains, agents, tools, and memory, making it the most widely adopted LLM application framework. The ecosystem includes the core library, LangGraph for stateful agent orchestration, and LangSmith for production observability and evaluation.
LangChain Expression Language (LCEL) enables declarative composition of prompts, LLMs, retrievers, and tools into pipelines. The Runnable protocol and pipeline operator provide sync, async, batch, and streaming execution automatically. With 1,000+ integrations, teams can swap models and providers without rewriting application logic.
LangGraph extends LangChain with stateful, multi-actor agent workflows that support cycles, persistence, and human-in-the-loop checkpoints. Unlike simple chain execution, LangGraph models agent interactions as directed graphs with conditional branching, enabling complex reasoning patterns that maintain state across turns and sessions.
LangSmith provides the production layer: tracing, evaluation, prompt management, and deployment. It captures every LLM call, chain step, and agent decision for debugging and optimization. Free tier available for development; paid tiers for production workloads with team collaboration, dataset management, and automated evaluation pipelines.
52M+
Weekly Downloads
MIT
Open-Source License
3
Agent Architectures
1,000+
Integrations
In-depth coverage of LangChain's agent framework, pricing model, tutorial walkthroughs, and head-to-head framework comparisons. Chain architecture analyzed with verified benchmarks and honest trade-offs.
The open-source LLM application framework explained: chains, agents, tools, memory, and why 52 million weekly downloads make it the default choice for AI developers.
Step-by-step guide to installing LangChain, composing chains with LCEL, adding retrieval, building agents, and deploying with LangSmith tracing enabled.
The core framework is free and MIT-licensed. LangSmith tiers from Developer (free) through Enterprise. What you pay for, what stays free, and where the costs actually land.
Modular chains vs. role-based teams. Architecture, learning curve, agent patterns, and which framework fits your orchestration needs.
Sequential chains vs. stateful graph orchestration. When to use base LangChain, when to upgrade to LangGraph, and how the two fit together in production.
Explore competing agent frameworks, enterprise AI platforms, and the broader AI Tools Hub.
CrewAI Hub
Multi-agent orchestration framework and direct LangChain competitor.
Anthropic Claude Hub
Safety-first frontier model with native tool use, commonly used as a LangChain LLM provider.
ChatGPT Hub
OpenAI's consumer platform and the most common LLM backend for LangChain applications.
Google Gemini Hub
Multimodal models with LangChain integration via langchain-google-genai provider.
AI Tools Hub
65+ articles across 11 vendors. Breakdowns, comparisons, and guides.
AI Governance
Responsible AI, EU AI Act, and compliance frameworks for LLM application deployments.
Important context for responsible AI adoption
LangChain's open-source framework runs locally and sends no data to LangChain servers by default. When chains or agents invoke external LLM APIs (OpenAI, Anthropic, Google, etc.), data is subject to each provider's terms of service and data processing agreements. LangSmith tracing, when enabled, transmits trace data to LangChain Inc.'s cloud infrastructure. Enterprise LangSmith deployments offer self-hosted options. Review the privacy policies of LangSmith and any LLM provider your applications call before processing confidential or personally identifiable information.
LLM-powered applications built with LangChain automate complex workflows but should not replace human expertise or judgment in critical decisions. Chains and agents execute tasks based on LLM outputs, which can produce plausible but incorrect results. If you are experiencing distress:
AI systems can produce plausible-sounding but incorrect guidance. For mental health, medical, legal, or financial decisions, always consult a qualified professional.
See the NIST AI Risk Management Framework for structured guidance on AI risk assessment.
Under GDPR (EU) and CCPA (California), you have the right to access, correct, and delete your personal data. LangChain's open-source framework gives developers direct control over all data their applications process. LangSmith's hosted platform operates under LangChain Inc.'s data processing terms.
The EU AI Act classifies general-purpose AI models above certain capability thresholds under transparency and risk obligations. LLM applications deployed within the EU are subject to these provisions, with compliance responsibilities falling on the deploying organization under the EU AI Act's provider liability framework.
This publication is editorially independent. AI tool coverage reflects independent research, verified benchmarks, and editorial judgment. Where affiliate links are present, they are clearly disclosed and do not influence conclusions.