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Found 80 Skills
Implements stateful agent graphs using LangGraph. Use when building graphs, adding nodes/edges, defining state schemas, implementing checkpointing, handling interrupts, or creating multi-agent systems with LangGraph.
LangGraph streaming patterns for real-time updates. Use when implementing progress indicators, token streaming, custom events, or real-time user feedback in workflows.
LangGraph state management patterns. Use when designing workflow state schemas, using TypedDict vs Pydantic, implementing accumulating state with Annotated operators, or managing shared state across nodes.
LangGraph-based agent framework for consistent tool calling with automatic tool loops. Use when you need reliable multi-step task execution with OpenAI-compatible providers (Z.AI/GLM-5, OpenRouter, Groq, DeepSeek, Ollama).
LangGraph supervisor-worker pattern. Use when building central coordinator agents that route to specialized workers, implementing round-robin or priority-based agent dispatch.
A comprehensive guide and reference for building agents using LangGraph 1.0, including ReAct agents, state graphs, and tool integrations.
Use when a migration is already known to stay on the LangGraph orchestration side, including stages, routing, checkpoints, interrupts, persistence, streaming, and subgraph boundaries.
Multi-agent systems with LangGraph - supervisor/swarm/handoff/router patterns, state coordination, Deep Agents, guardrails, testing, observability, deployment. Use when building multi-agent workflows, coordinating agents, or need cost-optimized orchestration. Uses Claude, DeepSeek, Gemini (no OpenAI).
LangGraph framework for building stateful, multi-agent AI applications with cyclical workflows, human-in-the-loop patterns, and persistent checkpointing.
Guides architectural decisions for LangGraph applications. Use when deciding between LangGraph vs alternatives, choosing state management strategies, designing multi-agent systems, or selecting persistence and streaming approaches.
LangGraph workflow patterns for state management, routing, parallel execution, supervisor-worker, tool calling, checkpointing, human-in-loop, streaming, subgraphs, and functional API. Use when building LangGraph pipelines, multi-agent systems, or AI workflows.
Design state schemas, implement reducers, configure persistence, and debug state issues for LangGraph applications. Use when users want to (1) design or define state schemas for LangGraph graphs, (2) implement reducer functions for state accumulation, (3) configure persistence with checkpointers (InMemorySaver/MemorySaver, SqliteSaver, PostgresSaver), (4) debug state update issues or unexpected state behavior, (5) migrate state schemas between versions, (6) validate state schema structure, (7) choose between TypedDict and MessagesState patterns, (8) implement custom reducers for lists, dicts, or sets, (9) use the Overwrite type to bypass reducers, (10) set up thread-based persistence for multi-turn conversations, or (11) inspect checkpoints for debugging.