LangChain

Vector Databases & Memory

Code Development

Open-source framework for building LLM applications with memory management, conversation buffers, and retrieval chains. Integrates with every major vector database and LLM provider. Includes LangSmith for debugging and monitoring agent memory.

Why Use LangChain

De facto standard for agent memory management in Python and TypeScript. Pre-built components for episodic memory, conversation buffers, and entity tracking. Abstracts away complexity of integrating multiple tools and memory stores. Active community with 80K+ GitHub stars. LangSmith provides observability into memory retrieval and agent decision-making.

Use Cases for Builders

Practical ways to use LangChain in your workflow

Implement conversation memory with automatic summarization
Build RAG applications with retrieval chain abstractions
Create multi-session agents that remember user preferences
Debug memory retrieval issues with LangSmith tracing
Combine episodic and semantic memory in hybrid systems

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LLM observability and evaluation platform from LangChain. Debug, test, and monitor AI agents and chains. Trace every step of agent execution, evaluate outputs with custom criteria, and track performance over time. Essential for production AI applications.

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