'langgraph' 검색 결과

Subgraph composition and modular workflow design for LangGraph

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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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INVOKE THIS SKILL when routing a LangGraph agent with a decision model (TypeSafe Jev, SemIf) instead of an LLM, or when auditing an existing agent for LLM calls that only produce a routing decision. Covers langchain-typesafe Noul/Choice/Score, reading answers correctly, threshold design, and LangSmith Gateway wiring.

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INVOKE THIS SKILL when writing ANY LangGraph code. Covers StateGraph, state schemas, nodes, edges, Command, Send, invoke, streaming, and error handling.

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INVOKE THIS SKILL when implementing human-in-the-loop patterns, pausing for approval, or handling errors in LangGraph. Covers interrupt(), Command(resume=...), approval/validation workflows, and the 4-tier error handling strategy.

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INVOKE THIS SKILL when your LangGraph needs to persist state, remember conversations, travel through history, or configure subgraph checkpointer scoping. Covers checkpointers, thread_id, time travel, Store, and subgraph persistence modes.

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Neutral, framework-agnostic decision tree for project kickoff: "which agent / RAG / LLM framework should I reach for?" Synthesizes the ecosystem sections of 7 landmark-project SOPs (LangGraph, LlamaIndex, DSPy, CrewAI, vLLM, Aider, Dify) into one layered rubric. Core stance: frameworks are LAYERS, not competitors — a real project usually combines DSPy (compile) + LlamaIndex (retrieve) + LangGraph (orchestrate) + vLLM (serve), and you choose ONE per layer, not one to rule all. Use when starting any LLM/agent/RAG project, or whenever the "which framework?" question is asked. Deliberately neutral — unlike vendor docs and the LangChain-biased `framework-selection` on skill.sh, this skill has no horse in the race.

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Use this skill for requests related to LangGraph in order to fetch relevant documentation to provide accurate, up-to-date guidance.

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Build original LangGraph agents for Warden Protocol and prepare them for publishing in Warden Studio. Use this skill when users want to: (1) Create new Warden agents (not community examples), (2) Build LangGraph-based crypto/Web3 agents, (3) Deploy agents via LangSmith Deployments or custom infra, (4) Participate in the Warden Agent Builder Incentive Programme (open to OpenClaw agents), or (5) Integrate with Warden Studio for Agent Hub publishing.

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Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability. This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% by step 10. Build for reliability first, autonomy second. 2025 lesson: The winners are constrained, domain-specific agents with clear boundaries, not "autonomous everything." Treat AI outputs as proposals, not truth. Use when "autonomous agent, autogpt, babyagi, self-prompting, goal decomposition, react pattern, agent loop, self-correcting agent, reflection agent, langgraph, agentic ai, agent planning, autonomous, agents, langgraph, react, planning, reflection, guardrails, reliability, checkpointing" mentioned.

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Comprehensive AI/ML development guide for LangChain, LangGraph, and ML model integration in FastAPI. Use when building LLM applications, agents, RAG systems, sentiment analysis, aspect-based analysis, chain orchestration, prompt engineering, vector stores, embeddings, or integrating ML models with FastAPI endpoints. Covers LangChain patterns, LangGraph state machines, model deployment, API integration, streaming, error handling, and best practices.

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Give LangGraph agents memory management and search tools so they can store, retrieve, and update durable facts across sessions.

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Makers 开发专家团 AI Agent 工程师 Skill — Claude Agent SDK、 OpenAI Agents SDK、LangGraph、DeepAgents、CrewAI 框架集成， SSE 流式响应、conversation store、EdgeOne Makers Agent Runtime。 触发词：AI Agent、聊天机器人、LLM 应用、多 Agent 系统、 Claude SDK、OpenAI Agents、LangGraph、CrewAI、SSE 流式、 智能助手、AI 推理端点。

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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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AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.

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Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern.

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Expert en orchestration d'agents autonomes (CrewAI, AutoGen, LangGraph, swarm patterns, planning)

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Autonomous Agents Specialist IA — Expert en agents autonomes (AutoGPT, CrewAI, LangGraph, planification multi-étapes, tool use)

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Agent Orchestrator IA — Expert en orchestration multi-agent (CrewAI, AutoGen, LangGraph, task decomposition, coordination)

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Sanguo event review loop with LangGraph Studio visualization. Use for: 三國事件審核、generic event candidates、battle candidates、event review MCQ、accepted/rejected events、promote events、LangGraph Studio、human-in-the-loop、事件候選可視化、chunk -> extract -> validate -> review -> publish。

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Design systems where multiple agents work with separate task information. Choose coordination structures, define handoffs and shared state, and plan parallel work while checking whether communication costs and failure risks justify using more than one agent.

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• Split large tasks across separate agent contexts
• Run independent research tasks in parallel
• Define handoffs between specialized agents

Inspect imports and agent patterns in existing Python code to identify its framework and recommend an Omnigent executor, the component that runs the agent. Explain unsupported frameworks and suggest available next steps.

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• Identify the framework used by existing Python agent code
• Choose an executor for Claude SDK or OpenAI Agents SDK code
• Check whether a framework has native support

Use when working in this project — adding tools, editing the agent, running, evaluating, serving or deploying it — or when guidance mentions ADK, LlmAgent, google.adk, adk web, or an ADK runner. This project is LangChain/LangGraph scaffolded by agents-cli, so ADK-specific instructions do not apply.

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Make a custom AI agent — Python or TypeScript/JavaScript, on a framework or hand-built — report what it did to Failproof AI, and run your own evaluator worker (the "eval pod") that scores those runs. Reach for it on vague phrasing too: "add observability to my agent", "why isn't my agent showing up?", "run an LLM judge on our own infra". Trigger when the user wants to: • plan an integration — which points in the agent loop to record; • instrument — add `failproofai-sdk` (Python) or `@failproofai/sdk` (Node, Bun, Deno, Next.js): turn on an adapter (LangChain/LangGraph, CrewAI, LlamaIndex, Pydantic AI, Vercel AI SDK, Mastra) or wire a hand-built loop; • verify — confirm events are written, or debug an integration that produces nothing; • evaluate — write, deploy or debug an Evaluator worker in Python or TypeScript. NOT for reading telemetry or scores that already landed (that's `fp-cloud-cli`), or deciding what is worth evaluating (that's `failproofai-eval-brainstorm`).

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