“langchain”的搜索结果

Design AI applications with LangChain 1.x and LangGraph, libraries for coordinating language models and workflows. Build agents that use external tools, manage conversation history and saved state, process documents, and support tracing and testing.

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• Build an AI agent that calls external tools
• Manage conversation history and saved execution state
• Create document processing and retrieval workflows

Guides building chatbots and tool-using agents with LangChain. Covers conversation history, model provider integration, and RAG, which retrieves relevant documents to inform answers, plus tracing to inspect application behavior.

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• Build a chatbot that remembers conversation history
• Create document question answering with source references
• Build an agent that uses custom tools

帮助你使用FAISS实现大规模向量相似性搜索，涵盖索引选择、保存与加载及GPU使用，并调整搜索速度、准确率和内存占用。

• 实现大规模向量相似性搜索
• 按速度、准确率和内存选择索引
• 使用GPU批量搜索

Guide development of a LangChain or LangGraph project created with agents-cli. It explains tool additions, local runs, evaluation, and serving while identifying the required graph structure and commands that differ from ADK projects.

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• Adding tools to an existing agent
• Running and evaluating an agent locally
• Editing a graph while preserving its required interface

Build AI agents with LangChain v1 and LangGraph in Python. Use this skill whenever the user mentions LangChain, LangGraph, create_agent, init_chat_model, @tool, ToolRuntime, agent middleware, human-in-the-loop, structured output, MCP tools (langchain.mcp, MCPAdapter), RAG, multi-agent handoffs or subagents, checkpointers, Deep Agents (create_deep_agent) or Managed Deep Agents (mda, define_deep_agent). Also trigger for migrating from create_react_agent, LLMChain, AgentExecutor or langchain-mcp-adapters, stream_events, SummarizationMiddleware, and langchain-classic.

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Fetch execution traces, records of an agent’s actions, from LangSmith Studio to debug LangChain and LangGraph agents. Inspect errors, tool calls, memory operations, execution times, and token usage, or export records for further review.

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• Investigating an agent failure
• Checking tool selection and execution order
• Investigating memory storage and recall

Fetch execution traces, records of an agent’s actions, from LangSmith Studio to debug LangChain and LangGraph agents. Inspect errors, tool calls, memory operations, execution times, and token usage, or export records for further review.

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• Investigating an agent failure
• Checking tool selection and execution order
• Investigating memory storage and recall

Scaffold a minimal local LangChain agent in TypeScript by following the official quickstart. Use when the user wants to quickly build or try a LangChain agent locally.

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Scaffold a minimal local LangChain agent in Python by following the official quickstart. Use when the user wants to quickly build or try a LangChain agent locally.

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エージェント、メモリ、ツール統合パターンを備えたLangChainフレームワークを使用してLLMアプリケーションを設計します。LangChainアプリケーションの構築、AIエージェントの実装、または複雑なLLMワークフローの作成時に使用します。

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帮助将智能体的历史对话和资料存入Pinecone，检索相关内容并用于回答，构建跨会话长期记忆，按用户管理记忆，并试作基于语义的搜索。

• 构建跨会话保留的记忆
• 将检索到的信息用于回答
• 按用户分别管理记忆

Set up Langfuse to record LLM application activity, costs, and response times. Manage prompt versions, organize evaluation datasets, and compare results to investigate failures and guide improvements using recorded evidence.

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• Investigating failures in LLM applications
• Comparing prompt versions and evaluation scores
• Monitoring LLM costs and response times

Build AI applications with LangChain, including chatbots with conversation history and systems that search documents before answering. The skill provides examples for connecting models, calling tools, retrieving information, and tracing application runs.

中文简介正在准备中。当前显示英语简介。

• Building a chatbot that uses conversation history
• Creating a question-answering system for documents
• Developing an agent that calls external tools

Build AI agents that perform Solana operations such as transfers, token creation, NFT minting, and asset swaps. Provides plugin setup, LangChain and Vercel AI SDK integration, Claude Desktop connectivity through MCP, and autonomous execution patterns.

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• Build an agent for Solana transfers and balance checks
• Add token creation and NFT minting tools
• Connect Solana operations to Claude Desktop through MCP

INVOKE FIRST for any LangChain / LangGraph / Deep Agents agent building project before consulting other skills or writing any agent code. Required starting point for up to date info on framework selection (LangChain vs LangGraph vs Deep Agents vs hybrid composition), agent patterns, install, environment setup, and which skill to load next.

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Create LangChain agents with create_agent, define tools, and use middleware for human-in-the-loop and error handling.

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INVOKE THIS SKILL when setting up a new project or when asked about package versions, installation, or dependency management for LangChain, LangGraph, LangSmith, or Deep Agents. Covers required packages, minimum versions, environment requirements, versioning best practices, and common community tool packages for both Python and TypeScript.

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LangChain 1.0 使用指南。提供 Agent、Tool、Memory、Middleware 等核心概念的快速参考。当用户需要创建 AI Agent、集成 LangChain、或解决 LangChain 相关问题时激活。

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使用Pinecone构建按内容含义查找信息的搜索基础设施，支持数据写入、更新与删除、条件筛选，以及语义搜索与关键词搜索的组合。

• 构建以文档为依据的AI检索系统
• 结合语义搜索与关键词搜索
• 按类别或价格筛选搜索结果

使用Chroma构建按语义相似度检索文档的系统，保存和更新正文及附加信息，按条件筛选结果，并用于开发结合检索资料的AI应用原型。

• 按语义相似度搜索文档
• 试作基于检索资料的AI回答
• 将文档数据保存在本地

Build AI agents with LangGraph by connecting processing steps into a graph. Define shared state, conditional paths, tool calls, saved checkpoints, and human review points to create workflows that retain information as they run.

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• Building an agent that calls tools in a loop
• Sharing accumulated findings between multiple agents
• Routing requests to different processing paths

Implement, update, test, or document TypeScript provider packages, the adapters connecting supported services and frameworks. This skill focuses on work under ts/packages/providers and directs the agent to read the provider workflow before making changes.

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• Implement a TypeScript provider
• Modify a service or framework adapter
• Test provider-specific changes

Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.

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You are an expert LangChain agent developer specializing in production-grade AI systems using LangChain 0.1+ and LangGraph.

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