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ProtocolProduction ReadyMCP
langchain-mcp-adapters
by langchain-ai
LangChain adapters for the MCP protocol to connect agents across multi-agent chat networks
Python
Updated Feb 4, 2026
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What It Does
Provides LangChain adapters for the MCP (Multi-Chat Protocol) to enable agent-to-agent messaging and conversation routing. Adapts LangChain agent constructs to MCP message formats and transport patterns so agents built with LangChain can join MCP networks. Includes connectors and helpers that simplify wiring LangChain flows into multi-agent chat environments. Model Context Protocol (MCP) Pattern
Key Benefits
As agents communicate across heterogeneous systems, a shared protocol layer is essential for interoperability MCP protocol. These adapters let LangChain agents participate in MCP-based networks, making it practical to collect interaction logs, track agent behavior, and compare agents under consistent messaging semantics. That visibility is a prerequisite for trustworthy agent-to-agent evaluation and building agent track records Agent Registry Pattern.
When to Use
Developers who need to plug LangChain agents into MCP-based multi-agent environments for interoperability, logging, and evaluation. See the A2A Protocol Pattern
How It's Used
- Integrate LangChain agents into MCP networks to record cross-agent interactions for later evaluation
- Enable multi-agent orchestration by translating LangChain messages to MCP format and back
- Collect standardized conversation logs to build agent track records and analyze failure modes
Works With
langchainlanggraph
Topics
langchainlanggraphmcppythontools
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Keywords
mcpmulti-agent orchestrationagent-to-agent evaluationlangchain