--- name: langgraph description: "LangGraph workflow skill. Use this skill when the user needs Expert in LangGraph - the production-grade framework for building and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off." version: "0.0.1" category: development tags: ["langgraph", "expert", "the", "production-grade", "framework", "for", "building", "development"] complexity: advanced risk: caution tools: ["codex-cli", "claude-code", "cursor", "gemini-cli", "opencode"] source: community author: "sickn33" date_added: "2026-04-15" date_updated: "2026-04-25" --- # LangGraph ## Overview This public intake copy packages `plugins/antigravity-awesome-skills-claude/skills/langgraph` from `https://github.com/sickn33/antigravity-awesome-skills` into the native Omni Skills editorial shape without hiding its origin. Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow. This intake keeps the copied upstream files intact and uses the `external_source` block in `metadata.json` plus `ORIGIN.md` as the provenance anchor for review. # LangGraph 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. Used in production at LinkedIn, Uber, and 400+ companies. This is LangChain's recommended approach for building agents. Role: LangGraph Agent Architect You are an expert in building production-grade AI agents with LangGraph. You understand that agents need explicit structure - graphs make the flow visible and debuggable. You design state carefully, use reducers appropriately, and always consider persistence for production. You know when cycles are needed and how to prevent infinite loops. ### Expertise - Graph topology design - State schema patterns - Conditional branching - Persistence strategies - Human-in-the-loop - Tool integration - Error handling and recovery Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Capabilities, Prerequisites, Scope, Ecosystem, Patterns, Collaboration. ## When to Use This Skill Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request. - User mentions or implies: langgraph - User mentions or implies: langchain agent - User mentions or implies: stateful agent - User mentions or implies: agent graph - User mentions or implies: react agent - User mentions or implies: agent workflow ## Operating Table | Situation | Start here | Why it matters | | --- | --- | --- | | First-time use | `metadata.json` | Confirms repository, branch, commit, and imported path through the `external_source` block before touching the copied workflow | | Provenance review | `ORIGIN.md` | Gives reviewers a plain-language audit trail for the imported source | | Workflow execution | `SKILL.md` | Starts with the smallest copied file that materially changes execution | | Supporting context | `SKILL.md` | Adds the next most relevant copied source file without loading the entire package | | Handoff decision | `## Related Skills` | Helps the operator switch to a stronger native skill when the task drifts | ## Workflow This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow. 1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task. 2. Read the overview and provenance files before loading any copied upstream support files. 3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request. 4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes. 5. Validate the result against the upstream expectations and the evidence you can point to in the copied files. 6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity. 7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify. ### Imported Workflow Notes #### Imported: Capabilities - Graph construction (StateGraph) - State management and reducers - Node and edge definitions - Conditional routing - Checkpointers and persistence - Human-in-the-loop patterns - Tool integration - Streaming and async execution ## Examples ### Example 1: Ask for the upstream workflow directly ```text Use @langgraph to handle . Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer. ``` **Explanation:** This is the safest starting point when the operator needs the imported workflow, but not the entire repository. ### Example 2: Ask for a provenance-grounded review ```text Review @langgraph against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why. ``` **Explanation:** Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection. ### Example 3: Narrow the copied support files before execution ```text Use @langgraph for . Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding. ``` **Explanation:** This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default. ### Example 4: Build a reviewer packet ```text Review @langgraph using the copied upstream files plus provenance, then summarize any gaps before merge. ``` **Explanation:** This is useful when the PR is waiting for human review and you want a repeatable audit packet. ## Best Practices Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution. - Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support. - Prefer the smallest useful set of support files so the workflow stays auditable and fast to review. - Keep provenance, source commit, and imported file paths visible in notes and PR descriptions. - Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate. - Treat generated examples as scaffolding; adapt them to the concrete task before execution. - Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant. ## Troubleshooting ### Problem: The operator skipped the imported context and answered too generically **Symptoms:** The result ignores the upstream workflow in `plugins/antigravity-awesome-skills-claude/skills/langgraph`, fails to mention provenance, or does not use any copied source files at all. **Solution:** Re-open `metadata.json`, `ORIGIN.md`, and the most relevant copied upstream files. Check the `external_source` block first, then restate the provenance before continuing. ### Problem: The imported workflow feels incomplete during review **Symptoms:** Reviewers can see the generated `SKILL.md`, but they cannot quickly tell which references, examples, or scripts matter for the current task. **Solution:** Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it. ### Problem: The task drifted into a different specialization **Symptoms:** The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. **Solution:** Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind. ## Related Skills - `@00-andruia-consultant` - Use when the work is better handled by that native specialization after this imported skill establishes context. - `@00-andruia-consultant-v2` - Use when the work is better handled by that native specialization after this imported skill establishes context. - `@10-andruia-skill-smith` - Use when the work is better handled by that native specialization after this imported skill establishes context. - `@10-andruia-skill-smith-v2` - Use when the work is better handled by that native specialization after this imported skill establishes context. ## Additional Resources Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding. | Resource family | What it gives the reviewer | Example path | | --- | --- | --- | | `references` | copied reference notes, guides, or background material from upstream | `references/n/a` | | `examples` | worked examples or reusable prompts copied from upstream | `examples/n/a` | | `scripts` | upstream helper scripts that change execution or validation | `scripts/n/a` | | `agents` | routing or delegation notes that are genuinely part of the imported package | `agents/n/a` | | `assets` | supporting assets or schemas copied from the source package | `assets/n/a` | ### Imported Reference Notes #### Imported: Prerequisites - 0: Python proficiency - 1: LLM API basics - 2: Async programming concepts - 3: Graph theory fundamentals - Required skills: Python 3.9+, langgraph package, LLM API access (OpenAI, Anthropic, etc.), Understanding of graph concepts #### Imported: Scope - 0: Python-only (TypeScript in early stages) - 1: Learning curve for graph concepts - 2: State management complexity - 3: Debugging can be challenging #### Imported: Ecosystem ### Primary - LangGraph - LangChain - LangSmith (observability) ### Common_integrations - OpenAI / Anthropic / Google - Tavily (search) - SQLite / PostgreSQL (persistence) - Redis (state store) ### Platforms - Python applications - FastAPI / Flask backends - Cloud deployments #### Imported: Patterns ### Basic Agent Graph Simple ReAct-style agent with tools **When to use**: Single agent with tool calling from typing import Annotated, TypedDict from langgraph.graph import StateGraph, START, END from langgraph.graph.message import add_messages from langgraph.prebuilt import ToolNode from langchain_openai import ChatOpenAI from langchain_core.tools import tool # 1. Define State class AgentState(TypedDict): messages: Annotated[list, add_messages] # add_messages reducer appends, doesn't overwrite # 2. Define Tools @tool def search(query: str) -> str: """Search the web for information.""" # Implementation here return f"Results for: {query}" @tool def calculator(expression: str) -> str: """Evaluate a math expression.""" return str(eval(expression)) tools = [search, calculator] # 3. Create LLM with tools llm = ChatOpenAI(model="gpt-4o").bind_tools(tools) # 4. Define Nodes def agent(state: AgentState) -> dict: """The agent node - calls LLM.""" response = llm.invoke(state["messages"]) return {"messages": [response]} # Tool node handles tool execution tool_node = ToolNode(tools) # 5. Define Routing def should_continue(state: AgentState) -> str: """Route based on whether tools were called.""" last_message = state["messages"][-1] if last_message.tool_calls: return "tools" return END # 6. Build Graph graph = StateGraph(AgentState) # Add nodes graph.add_node("agent", agent) graph.add_node("tools", tool_node) # Add edges graph.add_edge(START, "agent") graph.add_conditional_edges("agent", should_continue, ["tools", END]) graph.add_edge("tools", "agent") # Loop back # Compile app = graph.compile() # 7. Run result = app.invoke({ "messages": [("user", "What is 25 * 4?")] }) ### State with Reducers Complex state management with custom reducers **When to use**: Multiple agents updating shared state from typing import Annotated, TypedDict from operator import add from langgraph.graph import StateGraph # Custom reducer for merging dictionaries def merge_dicts(left: dict, right: dict) -> dict: return {**left, **right} # State with multiple reducers class ResearchState(TypedDict): # Messages append (don't overwrite) messages: Annotated[list, add_messages] # Research findings merge findings: Annotated[dict, merge_dicts] # Sources accumulate sources: Annotated[list[str], add] # Current step (overwrites - no reducer) current_step: str # Error count (custom reducer) errors: Annotated[int, lambda a, b: a + b] # Nodes return partial state updates def researcher(state: ResearchState) -> dict: # Only return fields being updated return { "findings": {"topic_a": "New finding"}, "sources": ["source1.com"], "current_step": "researching" } def writer(state: ResearchState) -> dict: # Access accumulated state all_findings = state["findings"] all_sources = state["sources"] return { "messages": [("assistant", f"Report based on {len(all_sources)} sources")], "current_step": "writing" } # Build graph graph = StateGraph(ResearchState) graph.add_node("researcher", researcher) graph.add_node("writer", writer) # ... add edges ### Conditional Branching Route to different paths based on state **When to use**: Multiple possible workflows from langgraph.graph import StateGraph, START, END class RouterState(TypedDict): query: str query_type: str result: str def classifier(state: RouterState) -> dict: """Classify the query type.""" query = state["query"].lower() if "code" in query or "program" in query: return {"query_type": "coding"} elif "search" in query or "find" in query: return {"query_type": "search"} else: return {"query_type": "chat"} def coding_agent(state: RouterState) -> dict: return {"result": "Here's your code..."} def search_agent(state: RouterState) -> dict: return {"result": "Search results..."} def chat_agent(state: RouterState) -> dict: return {"result": "Let me help..."} # Routing function def route_query(state: RouterState) -> str: """Route to appropriate agent.""" query_type = state["query_type"] return query_type # Returns node name # Build graph graph = StateGraph(RouterState) graph.add_node("classifier", classifier) graph.add_node("coding", coding_agent) graph.add_node("search", search_agent) graph.add_node("chat", chat_agent) graph.add_edge(START, "classifier") # Conditional edges from classifier graph.add_conditional_edges( "classifier", route_query, { "coding": "coding", "search": "search", "chat": "chat" } ) # All agents lead to END graph.add_edge("coding", END) graph.add_edge("search", END) graph.add_edge("chat", END) app = graph.compile() ### Persistence with Checkpointer Save and resume agent state **When to use**: Multi-turn conversations, long-running agents from langgraph.graph import StateGraph from langgraph.checkpoint.sqlite import SqliteSaver from langgraph.checkpoint.postgres import PostgresSaver # SQLite for development memory = SqliteSaver.from_conn_string(":memory:") # Or persistent file memory = SqliteSaver.from_conn_string("agent_state.db") # PostgreSQL for production # memory = PostgresSaver.from_conn_string(DATABASE_URL) # Compile with checkpointer app = graph.compile(checkpointer=memory) # Run with thread_id for conversation continuity config = {"configurable": {"thread_id": "user-123-session-1"}} # First message result1 = app.invoke( {"messages": [("user", "My name is Alice")]}, config=config ) # Second message - agent remembers context result2 = app.invoke( {"messages": [("user", "What's my name?")]}, config=config ) # Agent knows name is Alice! # Get conversation history state = app.get_state(config) print(state.values["messages"]) # List all checkpoints for checkpoint in app.get_state_history(config): print(checkpoint.config, checkpoint.values) ### Human-in-the-Loop Pause for human approval before actions **When to use**: Sensitive operations, review before execution from langgraph.graph import StateGraph, START, END class ApprovalState(TypedDict): messages: Annotated[list, add_messages] pending_action: dict | None approved: bool def agent(state: ApprovalState) -> dict: # Agent decides on action action = {"type": "send_email", "to": "user@example.com"} return { "pending_action": action, "messages": [("assistant", f"I want to: {action}")] } def execute_action(state: ApprovalState) -> dict: action = state["pending_action"] # Execute the approved action result = f"Executed: {action['type']}" return { "messages": [("assistant", result)], "pending_action": None } def should_execute(state: ApprovalState) -> str: if state.get("approved"): return "execute" return END # Wait for approval # Build graph graph = StateGraph(ApprovalState) graph.add_node("agent", agent) graph.add_node("execute", execute_action) graph.add_edge(START, "agent") graph.add_conditional_edges("agent", should_execute, ["execute", END]) graph.add_edge("execute", END) # Compile with interrupt_before for human review app = graph.compile( checkpointer=memory, interrupt_before=["execute"] # Pause before execution ) # Run until interrupt config = {"configurable": {"thread_id": "approval-flow"}} result = app.invoke({"messages": [("user", "Send report")]}, config) # Agent paused - get pending state state = app.get_state(config) pending = state.values["pending_action"] print(f"Pending: {pending}") # Human reviews # Human approves - update state and continue app.update_state(config, {"approved": True}) result = app.invoke(None, config) # Resume ### Parallel Execution (Map-Reduce) Run multiple branches in parallel **When to use**: Parallel research, batch processing from langgraph.graph import StateGraph, START, END, Send from langgraph.constants import Send class ParallelState(TypedDict): topics: list[str] results: Annotated[list[str], add] summary: str def research_topic(state: dict) -> dict: """Research a single topic.""" topic = state["topic"] result = f"Research on {topic}..." return {"results": [result]} def summarize(state: ParallelState) -> dict: """Combine all research results.""" all_results = state["results"] summary = f"Summary of {len(all_results)} topics" return {"summary": summary} def fanout_topics(state: ParallelState) -> list[Send]: """Create parallel tasks for each topic.""" return [ Send("research", {"topic": topic}) for topic in state["topics"] ] # Build graph graph = StateGraph(ParallelState) graph.add_node("research", research_topic) graph.add_node("summarize", summarize) # Fan out to parallel research graph.add_conditional_edges(START, fanout_topics, ["research"]) # All research nodes lead to summarize graph.add_edge("research", "summarize") graph.add_edge("summarize", END) app = graph.compile() result = app.invoke({ "topics": ["AI", "Climate", "Space"], "results": [] }) # Research runs in parallel, then summarizes #### Imported: Collaboration ### Delegation Triggers - crewai|role-based|crew -> crewai (Need role-based multi-agent approach) - observability|tracing|langsmith -> langfuse (Need LLM observability) - structured output|json schema -> structured-output (Need structured LLM responses) - evaluate|benchmark|test agent -> agent-evaluation (Need to evaluate agent performance) ### Production Agent Stack Skills: langgraph, langfuse, structured-output Workflow: ``` 1. Design agent graph with LangGraph 2. Add structured outputs for tool responses 3. Integrate Langfuse for observability 4. Test and monitor in production ``` ### Multi-Agent System Skills: langgraph, crewai, agent-communication Workflow: ``` 1. Design agent roles (CrewAI patterns) 2. Implement as LangGraph with subgraphs 3. Add inter-agent communication 4. Orchestrate with supervisor pattern ``` ### Evaluated Agent Skills: langgraph, agent-evaluation, langfuse Workflow: ``` 1. Build agent with LangGraph 2. Create evaluation suite 3. Monitor with Langfuse 4. Iterate based on metrics ``` #### Imported: Limitations - Use this skill only when the task clearly matches the scope described above. - Do not treat the output as a substitute for environment-specific validation, testing, or expert review. - Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.