The industry-standard framework for building context-aware, reasoning applications with Large Language Models.
LangChain is a modular, open-source orchestration framework designed to bridge the gap between Large Language Models (LLMs) and external data sources. In the 2026 market landscape, LangChain has evolved from a simple chaining library into a robust ecosystem comprising LangGraph (for stateful multi-agent workflows), LangSmith (for enterprise-grade observability and evaluation), and LangServe (for REST API deployment). Its architecture relies on the LangChain Expression Language (LCEL), which provides a declarative way to compose chains, enabling features like streaming, async support, and optimized parallel execution. The framework excels at Retrieval-Augmented Generation (RAG) by providing a unified interface for over 700 integrations, including vector databases, document loaders, and embedding models. By standardizing the way developers manage memory, prompt templates, and output parsing, LangChain has become the foundational layer for 2026 AI-native enterprise stacks. Its strategic shift toward LangGraph addresses the industry's move from linear pipelines to complex, iterative agentic architectures, allowing developers to build self-correcting loops and persistent human-in-the-loop interactions with minimal overhead.
LangChain is a modular, open-source orchestration framework designed to bridge the gap between Large Language Models (LLMs) and external data sources.
Explore all tools that specialize in retrieval-augmented generation (rag). This domain focus ensures LangChain delivers optimized results for this specific requirement.
Explore all tools that specialize in autonomous agent development. This domain focus ensures LangChain delivers optimized results for this specific requirement.
Explore all tools that specialize in context-aware chatbots. This domain focus ensures LangChain delivers optimized results for this specific requirement.
Explore all tools that specialize in automated data extraction. This domain focus ensures LangChain delivers optimized results for this specific requirement.
Explore all tools that specialize in code generation pipelines. This domain focus ensures LangChain delivers optimized results for this specific requirement.
A state management layer built on top of LangChain that allows agents to 'remember' context across multiple sessions using a checkpointer system.
A declarative language for composing chains that provides built-in support for streaming, batching, and async operations.
Standardized CRUD operations across 50+ vector databases, allowing hot-swapping of DB providers without changing application logic.
Reduces LLM costs by caching responses based on semantic similarity rather than exact string matches.
An internal mechanism that allows the LLM to write its own metadata filters for vector database queries.
Automated backtesting of prompt changes against historical datasets with heuristic and LLM-as-a-judge scoring.
Integrated handling of image, audio, and video inputs within standard LCEL chains.
Install the core package via 'pip install langchain' or 'npm install langchain'.
Configure environment variables for LLM providers (e.g., OPENAI_API_KEY, ANTHROPIC_API_KEY).
Initialize a ChatModel instance with specific temperature and model parameters.
Define a PromptTemplate using placeholders for dynamic context injection.
Select and initialize a VectorStore (e.g., Pinecone, Chroma) for document retrieval.
Construct a chain using LCEL (LangChain Expression Language) to link retrieval and generation.
Implement LangGraph for stateful agent logic if loops or persistence are required.
Enable LangSmith tracing by setting LANGCHAIN_TRACING_V2=true for debugging.
Deploy the logic as a REST API endpoint using LangServe and FastAPI.
Set up monitoring alerts in LangSmith to track token usage and latency in production.
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