How does this skill help with LangChain 1.0?

It provides specialized guidance and implementation patterns for the latest LangChain 1.0 features, specifically focusing on the new LangGraph-based agent architecture.

Does it support custom tool integration?

Absolutely. It provides standardized templates for using the @tool decorator and accessing ToolRuntime for advanced context-aware tool logic.

Can I manage persistent chat history with this skill?

Yes, the skill includes detailed patterns for using InMemorySaver and PostgresSaver to handle thread-based memory and long-term session persistence.

Can I enforce structured outputs from my agents?

Yes, the skill includes examples for using ToolStrategy with Pydantic or dataclasses to ensure your agent returns validated, structured data.

LangChain Agent Builder

Name: LangChain Agent Builder
Author: NanmiCoder

byNanmiCoder

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데이터 과학 및 ML

Streamlines the development of AI agents and LLM applications using the LangChain 1.0 framework and LangGraph.

소개

The LangChain Use skill provides a comprehensive toolkit for developers building agentic workflows with LangChain 1.0. It offers standardized patterns for creating agents, defining specialized tools with runtime context, and managing both short-term and long-term memory. By implementing core concepts like middleware for model interception and structured output strategies, this skill enables the rapid construction of production-ready AI applications that are robust, stateful, and easily extensible via the LangGraph ecosystem.

주요 기능

Standardized ReAct agent creation using the create_agent abstraction
Support for structured output formats using Pydantic and dataclasses
Comprehensive memory management using checkpointers and persistent stores
23 GitHub stars
Advanced tool definition with ToolRuntime for state and context access
Middleware hooks for pre/post-model processing and dynamic prompting

사용 사례

Implementing structured RAG pipelines with custom security guardrails and PII filtering
Building conversational AI agents with persistent multi-session memory
Developing complex multi-tool systems for automated data retrieval and processing

소개

주요 기능

Standardized ReAct agent creation using the create_agent abstraction
Support for structured output formats using Pydantic and dataclasses
Comprehensive memory management using checkpointers and persistent stores
23 GitHub stars
Advanced tool definition with ToolRuntime for state and context access
Middleware hooks for pre/post-model processing and dynamic prompting

사용 사례

Implementing structured RAG pipelines with custom security guardrails and PII filtering
Building conversational AI agents with persistent multi-session memory
Developing complex multi-tool systems for automated data retrieval and processing