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ToolProduction Ready
langchain
by langchain-ai
Framework for building and orchestrating production-ready LLM agents
Python
Updated Feb 11, 2026
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What It Does
Provides a Python framework to build, orchestrate, and run LLM-based agents and chains. Offers pluggable agent classes, tools, memory, and connector integrations so you can compose conversational and goal-directed workflows. Distinctive features include flexible agent templates, retriever-augmented generation (RAG) support, and a large ecosystem of integrations and orchestration primitives.
Key Benefits
As agents become more autonomous and start delegating work, teams need a consistent way to build, observe, and evaluate agent behaviors. LangChain standardizes agent patterns and connectors, making it easier to instrument workflows for evaluation and to gather signals like tool usage and decision traces. That visibility is a necessary foundation for multi-agent trust, A2A evaluation, and building agent track records across deployments.
When to Use
Teams building production or experimental agent systems that need flexible agents, RAG, and broad model/tool integrations.
How It's Used
- Compose conversational agents that call external tools and knowledge retrievers
- Build pipelines that capture tool calls and decision traces for later evaluation
- Prototype multi-agent workflows with different agent types and memory strategies
Works With
openaianthropichuggingfacellamaindexpydanticgemini
Topics
agentsaiai-agentsanthropicchatgptdeepagentsenterpriseframeworkgeminigenerative-ai+9 more
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Keywords
multi-agent orchestrationagent-evaluationragllm-agents