[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-langchain-langgraph-human-in-the-loop":3,"mdc-hrdfzy-key":37,"related-org-langchain-langgraph-human-in-the-loop":7005,"related-repo-langchain-langgraph-human-in-the-loop":7181},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":12,"stars":27,"repoUrl":28,"updatedAt":29,"license":30,"forks":31,"topics":32,"repo":33,"sourceUrl":35,"mdContent":36},"langgraph-human-in-the-loop","implement human-in-the-loop patterns in LangGraph","INVOKE THIS SKILL when implementing human-in-the-loop patterns, pausing for approval, or handling errors in LangGraph. Covers interrupt(), Command(resume=...), approval\u002Fvalidation workflows, and the 4-tier error handling strategy.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},"langchain","LangChain","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Flangchain.png","langchain-ai",[13,15,18,21,24],{"name":9,"slug":8,"type":14},"tag",{"name":16,"slug":17,"type":14},"LangGraph","langgraph",{"name":19,"slug":20,"type":14},"Agents","agents",{"name":22,"slug":23,"type":14},"Approvals","approvals",{"name":25,"slug":26,"type":14},"Workflow Automation","workflow-automation",877,"https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchain-skills","2026-04-06T18:26:33.547632",null,77,[],{"repoUrl":28,"stars":27,"forks":31,"topics":34,"description":30},[],"https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchain-skills\u002Ftree\u002FHEAD\u002Fconfig\u002Fskills\u002Flanggraph-human-in-the-loop","---\nname: langgraph-human-in-the-loop\ndescription: \"INVOKE THIS SKILL when implementing human-in-the-loop patterns, pausing for approval, or handling errors in LangGraph. Covers interrupt(), Command(resume=...), approval\u002Fvalidation workflows, and the 4-tier error handling strategy.\"\n---\n\n\u003Coverview>\nLangGraph's human-in-the-loop patterns let you pause graph execution, surface data to users, and resume with their input:\n\n- **`interrupt(value)`** — pauses execution, surfaces a value to the caller\n- **`Command(resume=value)`** — resumes execution, providing the value back to `interrupt()`\n- **Checkpointer** — required to save state while paused\n- **Thread ID** — required to identify which paused execution to resume\n\u003C\u002Foverview>\n\n---\n\n## Requirements\n\nThree things are required for interrupts to work:\n\n1. **Checkpointer** — compile with `checkpointer=InMemorySaver()` (dev) or `PostgresSaver` (prod)\n2. **Thread ID** — pass `{\"configurable\": {\"thread_id\": \"...\"}}` to every `invoke`\u002F`stream` call\n3. **JSON-serializable payload** — the value passed to `interrupt()` must be JSON-serializable\n\n---\n\n## Basic Interrupt + Resume\n\n`interrupt(value)` pauses the graph. The value surfaces in the result under `__interrupt__`. `Command(resume=value)` resumes — the resume value becomes the return value of `interrupt()`.\n\n**Critical**: when the graph resumes, the node restarts from the **beginning** — all code before `interrupt()` re-runs.\n\n\u003Cex-basic-interrupt-resume>\n\u003Cpython>\nPause execution for human review and resume with Command.\n\n```python\nfrom langgraph.types import interrupt, Command\nfrom langgraph.checkpoint.memory import InMemorySaver\nfrom langgraph.graph import StateGraph, START, END\nfrom typing_extensions import TypedDict\n\nclass State(TypedDict):\n approved: bool\n\ndef approval_node(state: State):\n # Pause and ask for approval\n approved = interrupt(\"Do you approve this action?\")\n # When resumed, Command(resume=...) returns that value here\n return {\"approved\": approved}\n\ncheckpointer = InMemorySaver()\ngraph = (\n StateGraph(State)\n .add_node(\"approval\", approval_node)\n .add_edge(START, \"approval\")\n .add_edge(\"approval\", END)\n .compile(checkpointer=checkpointer)\n)\n\nconfig = {\"configurable\": {\"thread_id\": \"thread-1\"}}\n\n# Initial run — hits interrupt and pauses\nresult = graph.invoke({\"approved\": False}, config)\nprint(result[\"__interrupt__\"])\n# [Interrupt(value='Do you approve this action?')]\n\n# Resume with the human's response\nresult = graph.invoke(Command(resume=True), config)\nprint(result[\"approved\"]) # True\n```\n\u003C\u002Fpython>\n\u003Ctypescript>\nPause execution for human review and resume with Command.\n\n```typescript\nimport { interrupt, Command, MemorySaver, StateGraph, StateSchema, START, END } from \"@langchain\u002Flanggraph\";\nimport { z } from \"zod\";\n\nconst State = new StateSchema({\n approved: z.boolean().default(false),\n});\n\nconst approvalNode = async (state: typeof State.State) => {\n \u002F\u002F Pause and ask for approval\n const approved = interrupt(\"Do you approve this action?\");\n \u002F\u002F When resumed, Command({ resume }) returns that value here\n return { approved };\n};\n\nconst checkpointer = new MemorySaver();\nconst graph = new StateGraph(State)\n .addNode(\"approval\", approvalNode)\n .addEdge(START, \"approval\")\n .addEdge(\"approval\", END)\n .compile({ checkpointer });\n\nconst config = { configurable: { thread_id: \"thread-1\" } };\n\n\u002F\u002F Initial run — hits interrupt and pauses\nlet result = await graph.invoke({ approved: false }, config);\nconsole.log(result.__interrupt__);\n\u002F\u002F [{ value: 'Do you approve this action?', ... }]\n\n\u002F\u002F Resume with the human's response\nresult = await graph.invoke(new Command({ resume: true }), config);\nconsole.log(result.approved); \u002F\u002F true\n```\n\u003C\u002Ftypescript>\n\u003C\u002Fex-basic-interrupt-resume>\n\n---\n\n## Approval Workflow\n\nA common pattern: interrupt to show a draft, then route based on the human's decision.\n\n\u003Cex-approval-workflow>\n\u003Cpython>\nInterrupt for human review, then route to send or end based on the decision.\n\n```python\nfrom langgraph.types import interrupt, Command\nfrom langgraph.graph import StateGraph, START, END\nfrom typing import Literal\nfrom typing_extensions import TypedDict\n\nclass EmailAgentState(TypedDict):\n email_content: str\n draft_response: str\n classification: dict\n\ndef human_review(state: EmailAgentState) -> Command[Literal[\"send_reply\", \"__end__\"]]:\n \"\"\"Pause for human review using interrupt and route based on decision.\"\"\"\n classification = state.get(\"classification\", {})\n\n # interrupt() must come first — any code before it will re-run on resume\n human_decision = interrupt({\n \"email_id\": state.get(\"email_content\", \"\"),\n \"draft_response\": state.get(\"draft_response\", \"\"),\n \"urgency\": classification.get(\"urgency\"),\n \"action\": \"Please review and approve\u002Fedit this response\"\n })\n\n # Process the human's decision\n if human_decision.get(\"approved\"):\n return Command(\n update={\"draft_response\": human_decision.get(\"edited_response\", state.get(\"draft_response\", \"\"))},\n goto=\"send_reply\"\n )\n else:\n # Rejection — human will handle directly\n return Command(update={}, goto=END)\n```\n\u003C\u002Fpython>\n\u003Ctypescript>\nInterrupt for human review, then route to send or end based on the decision.\n\n```typescript\nimport { interrupt, Command, END, GraphNode } from \"@langchain\u002Flanggraph\";\n\nconst humanReview: GraphNode\u003Ctypeof EmailAgentState> = async (state) => {\n const classification = state.classification!;\n\n \u002F\u002F interrupt() must come first — any code before it will re-run on resume\n const humanDecision = interrupt({\n emailId: state.emailContent,\n draftResponse: state.responseText,\n urgency: classification.urgency,\n action: \"Please review and approve\u002Fedit this response\",\n });\n\n \u002F\u002F Process the human's decision\n if (humanDecision.approved) {\n return new Command({\n update: { responseText: humanDecision.editedResponse || state.responseText },\n goto: \"sendReply\",\n });\n } else {\n return new Command({ update: {}, goto: END });\n }\n};\n```\n\u003C\u002Ftypescript>\n\u003C\u002Fex-approval-workflow>\n\n---\n\n## Validation Loop\n\nUse `interrupt()` in a loop to validate human input and re-prompt if invalid.\n\n\u003Cex-validation-loop>\n\u003Cpython>\nValidate human input in a loop, re-prompting until valid.\n\n```python\nfrom langgraph.types import interrupt\n\ndef get_age_node(state):\n prompt = \"What is your age?\"\n\n while True:\n answer = interrupt(prompt)\n\n # Validate the input\n if isinstance(answer, int) and answer > 0:\n break\n else:\n # Invalid input — ask again with a more specific prompt\n prompt = f\"'{answer}' is not a valid age. Please enter a positive number.\"\n\n return {\"age\": answer}\n```\n\nEach `Command(resume=...)` call provides the next answer. If invalid, the loop re-interrupts with a clearer message.\n\n```python\nconfig = {\"configurable\": {\"thread_id\": \"form-1\"}}\nfirst = graph.invoke({\"age\": None}, config)\n# __interrupt__: \"What is your age?\"\n\nretry = graph.invoke(Command(resume=\"thirty\"), config)\n# __interrupt__: \"'thirty' is not a valid age...\"\n\nfinal = graph.invoke(Command(resume=30), config)\nprint(final[\"age\"]) # 30\n```\n\u003C\u002Fpython>\n\u003Ctypescript>\nValidate human input in a loop, re-prompting until valid.\n\n```typescript\nimport { interrupt } from \"@langchain\u002Flanggraph\";\n\nconst getAgeNode = (state: typeof State.State) => {\n let prompt = \"What is your age?\";\n\n while (true) {\n const answer = interrupt(prompt);\n\n \u002F\u002F Validate the input\n if (typeof answer === \"number\" && answer > 0) {\n return { age: answer };\n } else {\n \u002F\u002F Invalid input — ask again with a more specific prompt\n prompt = `'${answer}' is not a valid age. Please enter a positive number.`;\n }\n }\n};\n```\n\u003C\u002Ftypescript>\n\u003C\u002Fex-validation-loop>\n\n---\n\n## Multiple Interrupts\n\nWhen parallel branches each call `interrupt()`, resume all of them in a single invocation by mapping each interrupt ID to its resume value.\n\n\u003Cex-multiple-interrupts>\n\u003Cpython>\nResume multiple parallel interrupts by mapping interrupt IDs to values.\n\n```python\nfrom typing import Annotated, TypedDict\nimport operator\nfrom langgraph.checkpoint.memory import InMemorySaver\nfrom langgraph.graph import START, END, StateGraph\nfrom langgraph.types import Command, interrupt\n\nclass State(TypedDict):\n vals: Annotated[list[str], operator.add]\n\ndef node_a(state):\n answer = interrupt(\"question_a\")\n return {\"vals\": [f\"a:{answer}\"]}\n\ndef node_b(state):\n answer = interrupt(\"question_b\")\n return {\"vals\": [f\"b:{answer}\"]}\n\ngraph = (\n StateGraph(State)\n .add_node(\"a\", node_a)\n .add_node(\"b\", node_b)\n .add_edge(START, \"a\")\n .add_edge(START, \"b\")\n .add_edge(\"a\", END)\n .add_edge(\"b\", END)\n .compile(checkpointer=InMemorySaver())\n)\n\nconfig = {\"configurable\": {\"thread_id\": \"1\"}}\n\n# Both parallel nodes hit interrupt() and pause\nresult = graph.invoke({\"vals\": []}, config)\n# result[\"__interrupt__\"] contains both Interrupt objects with IDs\n\n# Resume all pending interrupts at once using a map of id -> value\nresume_map = {\n i.id: f\"answer for {i.value}\"\n for i in result[\"__interrupt__\"]\n}\nresult = graph.invoke(Command(resume=resume_map), config)\n# result[\"vals\"] = [\"a:answer for question_a\", \"b:answer for question_b\"]\n```\n\u003C\u002Fpython>\n\u003Ctypescript>\nResume multiple parallel interrupts by mapping interrupt IDs to values.\n\n```typescript\nimport { Command, END, MemorySaver, START, StateGraph, interrupt, isInterrupted, INTERRUPT, Annotation } from \"@langchain\u002Flanggraph\";\n\nconst State = Annotation.Root({\n vals: Annotation\u003Cstring[]>({\n reducer: (left, right) => left.concat(Array.isArray(right) ? right : [right]),\n default: () => [],\n }),\n});\n\nfunction nodeA(_state: typeof State.State) {\n const answer = interrupt(\"question_a\") as string;\n return { vals: [`a:${answer}`] };\n}\n\nfunction nodeB(_state: typeof State.State) {\n const answer = interrupt(\"question_b\") as string;\n return { vals: [`b:${answer}`] };\n}\n\nconst graph = new StateGraph(State)\n .addNode(\"a\", nodeA)\n .addNode(\"b\", nodeB)\n .addEdge(START, \"a\")\n .addEdge(START, \"b\")\n .addEdge(\"a\", END)\n .addEdge(\"b\", END)\n .compile({ checkpointer: new MemorySaver() });\n\nconst config = { configurable: { thread_id: \"1\" } };\n\nconst interruptedResult = await graph.invoke({ vals: [] }, config);\n\n\u002F\u002F Resume all pending interrupts at once\nconst resumeMap: Record\u003Cstring, string> = {};\nif (isInterrupted(interruptedResult)) {\n for (const i of interruptedResult[INTERRUPT]) {\n if (i.id != null) {\n resumeMap[i.id] = `answer for ${i.value}`;\n }\n }\n}\nconst result = await graph.invoke(new Command({ resume: resumeMap }), config);\n\u002F\u002F result.vals = [\"a:answer for question_a\", \"b:answer for question_b\"]\n```\n\u003C\u002Ftypescript>\n\u003C\u002Fex-multiple-interrupts>\n\nUser-fixable errors use `interrupt()` to pause and collect missing data — that's the pattern covered by this skill. For the full 4-tier error handling strategy (RetryPolicy, Command error loops, etc.), see the **fundamentals** skill.\n\n---\n\n## Side Effects Before Interrupt Must Be Idempotent\n\nWhen the graph resumes, the node restarts from the **beginning** — ALL code before `interrupt()` re-runs. In subgraphs, BOTH the parent node and the subgraph node re-execute.\n\n\u003Cidempotency-rules>\n\n**Do:**\n- Use **upsert** (not insert) operations before `interrupt()`\n- Use **check-before-create** patterns\n- Place side effects **after** `interrupt()` when possible\n- Separate side effects into their own nodes\n\n**Don't:**\n- Create new records before `interrupt()` — duplicates on each resume\n- Append to lists before `interrupt()` — duplicate entries on each resume\n\n\u003C\u002Fidempotency-rules>\n\n\u003Cex-idempotent-patterns>\n\u003Cpython>\nIdempotent operations before interrupt vs non-idempotent (wrong).\n\n```python\n# GOOD: Upsert is idempotent — safe before interrupt\ndef node_a(state: State):\n db.upsert_user(user_id=state[\"user_id\"], status=\"pending_approval\")\n approved = interrupt(\"Approve this change?\")\n return {\"approved\": approved}\n\n# GOOD: Side effect AFTER interrupt — only runs once\ndef node_a(state: State):\n approved = interrupt(\"Approve this change?\")\n if approved:\n db.create_audit_log(user_id=state[\"user_id\"], action=\"approved\")\n return {\"approved\": approved}\n\n# BAD: Insert creates duplicates on each resume!\ndef node_a(state: State):\n audit_id = db.create_audit_log({ # Runs again on resume!\n \"user_id\": state[\"user_id\"],\n \"action\": \"pending_approval\",\n })\n approved = interrupt(\"Approve this change?\")\n return {\"approved\": approved}\n```\n\u003C\u002Fpython>\n\u003Ctypescript>\nIdempotent operations before interrupt vs non-idempotent (wrong).\n\n```typescript\n\u002F\u002F GOOD: Upsert is idempotent — safe before interrupt\nconst nodeA = async (state: typeof State.State) => {\n await db.upsertUser({ userId: state.userId, status: \"pending_approval\" });\n const approved = interrupt(\"Approve this change?\");\n return { approved };\n};\n\n\u002F\u002F GOOD: Side effect AFTER interrupt — only runs once\nconst nodeA = async (state: typeof State.State) => {\n const approved = interrupt(\"Approve this change?\");\n if (approved) {\n await db.createAuditLog({ userId: state.userId, action: \"approved\" });\n }\n return { approved };\n};\n\n\u002F\u002F BAD: Insert creates duplicates on each resume!\nconst nodeA = async (state: typeof State.State) => {\n await db.createAuditLog({ \u002F\u002F Runs again on resume!\n userId: state.userId,\n action: \"pending_approval\",\n });\n const approved = interrupt(\"Approve this change?\");\n return { approved };\n};\n```\n\u003C\u002Ftypescript>\n\u003C\u002Fex-idempotent-patterns>\n\n\u003Csubgraph-interrupt-re-execution>\n\n### Subgraph re-execution on resume\n\nWhen a subgraph contains an `interrupt()`, resuming re-executes BOTH the parent node (that invoked the subgraph) AND the subgraph node (that called `interrupt()`):\n\n\u003Cpython>\n\n```python\ndef node_in_parent_graph(state: State):\n some_code() # \u003C-- Re-executes on resume\n subgraph_result = subgraph.invoke(some_input)\n # ...\n\ndef node_in_subgraph(state: State):\n some_other_code() # \u003C-- Also re-executes on resume\n result = interrupt(\"What's your name?\")\n # ...\n```\n\u003C\u002Fpython>\n\u003Ctypescript>\n\n```typescript\nasync function nodeInParentGraph(state: State) {\n someCode(); \u002F\u002F \u003C-- Re-executes on resume\n const subgraphResult = await subgraph.invoke(someInput);\n \u002F\u002F ...\n}\n\nasync function nodeInSubgraph(state: State) {\n someOtherCode(); \u002F\u002F \u003C-- Also re-executes on resume\n const result = interrupt(\"What's your name?\");\n \u002F\u002F ...\n}\n```\n\u003C\u002Ftypescript>\n\u003C\u002Fsubgraph-interrupt-re-execution>\n\n---\n\n## Command(resume) Warning\n\n`Command(resume=...)` is the **only** Command pattern intended as input to `invoke()`\u002F`stream()`. Do NOT pass `Command(update=...)` as input — it resumes from the latest checkpoint and the graph appears stuck. See the fundamentals skill for the full antipattern explanation.\n\n---\n\n## Fixes\n\n\u003Cfix-checkpointer-required-for-interrupts>\n\u003Cpython>\nCheckpointer required for interrupt functionality.\n\n```python\n# WRONG\ngraph = builder.compile()\n\n# CORRECT\ngraph = builder.compile(checkpointer=InMemorySaver())\n```\n\u003C\u002Fpython>\n\u003Ctypescript>\nCheckpointer required for interrupt functionality.\n\n```typescript\n\u002F\u002F WRONG\nconst graph = builder.compile();\n\n\u002F\u002F CORRECT\nconst graph = builder.compile({ checkpointer: new MemorySaver() });\n```\n\u003C\u002Ftypescript>\n\u003C\u002Ffix-checkpointer-required-for-interrupts>\n\n\u003Cfix-resume-with-command>\n\u003Cpython>\nUse Command to resume from an interrupt (regular dict restarts graph).\n\n```python\n# WRONG\ngraph.invoke({\"resume_data\": \"approve\"}, config)\n\n# CORRECT\ngraph.invoke(Command(resume=\"approve\"), config)\n```\n\u003C\u002Fpython>\n\u003Ctypescript>\nUse Command to resume from an interrupt (regular object restarts graph).\n\n```typescript\n\u002F\u002F WRONG\nawait graph.invoke({ resumeData: \"approve\" }, config);\n\n\u002F\u002F CORRECT\nawait graph.invoke(new Command({ resume: \"approve\" }), config);\n```\n\u003C\u002Ftypescript>\n\u003C\u002Ffix-resume-with-command>\n\n\u003Cboundaries>\n### What You Should NOT Do\n\n- Use interrupts without a checkpointer — will fail\n- Resume without the same thread_id — creates a new thread instead of resuming\n- Pass `Command(update=...)` as invoke input — graph appears stuck (use plain dict)\n- Perform non-idempotent side effects before `interrupt()` — creates duplicates on resume\n- Assume code before `interrupt()` only runs once — it re-runs every 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analysis and size estimation","Perform a market analysis for a product category or segment. Trigger on: market analysis, market size, TAM SAM SOM, market opportunity, industry analysis.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[7013,7016,7019,7022],{"name":7014,"slug":7015,"type":14},"Marketing","marketing",{"name":7017,"slug":7018,"type":14},"Research","research",{"name":7020,"slug":7021,"type":14},"Sales","sales",{"name":7023,"slug":7024,"type":14},"Strategy","strategy",26592,"https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Fdeepagents","2026-04-18T04:46:54.557115",{"slug":7029,"name":7029,"fn":7030,"description":7031,"org":7032,"tags":7033,"stars":7025,"repoUrl":7026,"updatedAt":7038},"arxiv-search","search arXiv for academic research papers","Searches arXiv for preprints and academic papers, retrieves abstracts, and filters by topic. Use when the user asks to find research papers, search arXiv, look up preprints, find academic articles in physics, math, CS, biology, statistics, or related fields.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[7034,7035],{"name":7017,"slug":7018,"type":14},{"name":7036,"slug":7037,"type":14},"Search","search","2026-05-13T06:11:01.203061",{"slug":7040,"name":7040,"fn":7041,"description":7042,"org":7043,"tags":7044,"stars":7025,"repoUrl":7026,"updatedAt":7055},"blog-post","write SEO-optimized blog posts","Write long-form blog posts with SEO optimization and clear structure.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[7045,7048,7049,7052],{"name":7046,"slug":7047,"type":14},"Content Creation","content-creation",{"name":7014,"slug":7015,"type":14},{"name":7050,"slug":7051,"type":14},"SEO","seo",{"name":7053,"slug":7054,"type":14},"Writing","writing","2026-04-15T05:00:54.149813",{"slug":7057,"name":7057,"fn":7058,"description":7059,"org":7060,"tags":7061,"stars":7025,"repoUrl":7026,"updatedAt":7068},"competitor-analysis","analyze competitors and market positioning","Analyze competitors in a given market segment. Trigger on: competitive landscape, competitor analysis, market comparison, competitive positioning.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[7062,7065,7066,7067],{"name":7063,"slug":7064,"type":14},"Competitive Intelligence","competitive-intelligence",{"name":7014,"slug":7015,"type":14},{"name":7017,"slug":7018,"type":14},{"name":7023,"slug":7024,"type":14},"2026-04-18T04:46:55.79306",{"slug":7070,"name":7070,"fn":7071,"description":7072,"org":7073,"tags":7074,"stars":7025,"repoUrl":7026,"updatedAt":7083},"deepagents-thread-inspector","inspect local Deep Agents conversation threads","Inspect and explain conversations in the local Deep Agents Code SQLite session store. Use as a fallback when LangSmith trace tooling is unavailable, for offline or untraced sessions, or when asked to identify or summarize a local dcode thread, inspect checkpoint metadata, list recent local threads, or parse ~\u002F.deepagents\u002F.state\u002Fsessions.db and a thread UUID or prefix.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[7075,7076,7079,7080],{"name":19,"slug":20,"type":14},{"name":7077,"slug":7078,"type":14},"Debugging","debugging",{"name":9,"slug":8,"type":14},{"name":7081,"slug":7082,"type":14},"SQLite","sqlite","2026-07-24T06:08:57.102689",{"slug":7085,"name":7085,"fn":7086,"description":7087,"org":7088,"tags":7089,"stars":7025,"repoUrl":7026,"updatedAt":7098},"langgraph-docs","build stateful agents with LangGraph","Fetches and references LangGraph Python documentation to build stateful agents, create multi-agent workflows, and implement human-in-the-loop patterns. Use when the user asks about LangGraph, graph agents, state machines, agent orchestration, LangGraph API, or needs LangGraph implementation guidance.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[7090,7091,7094,7095],{"name":19,"slug":20,"type":14},{"name":7092,"slug":7093,"type":14},"Documentation","documentation",{"name":16,"slug":17,"type":14},{"name":7096,"slug":7097,"type":14},"Multi-Agent","multi-agent","2026-05-13T06:11:03.650877",{"slug":7100,"name":7100,"fn":7101,"description":7102,"org":7103,"tags":7104,"stars":7025,"repoUrl":7026,"updatedAt":7113},"remember","capture knowledge into persistent memory","Review the current conversation and capture valuable knowledge — best practices, coding conventions, architecture decisions, workflows, and user feedback — into persistent memory (AGENTS.md) or reusable skills. Use when the user says: (1) remember this, (2) save what we learned, (3) update memory, (4) capture learnings.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[7105,7106,7107,7110],{"name":19,"slug":20,"type":14},{"name":7092,"slug":7093,"type":14},{"name":7108,"slug":7109,"type":14},"Knowledge Management","knowledge-management",{"name":7111,"slug":7112,"type":14},"Memory","memory","2026-05-13T06:10:58.510037",{"slug":7115,"name":7115,"fn":7116,"description":7117,"org":7118,"tags":7119,"stars":7025,"repoUrl":7026,"updatedAt":7127},"skill-creator","create agent skills and tool integrations","Guide for creating effective skills that extend agent capabilities with specialized knowledge, workflows, or tool integrations. Use this skill when the user asks to: (1) create a new skill, (2) make a skill, (3) build a skill, (4) set up a skill, (5) initialize a skill, (6) scaffold a skill, (7) update or modify an existing skill, (8) validate a skill, (9) learn about skill structure, (10) understand how skills work, or (11) get guidance on skill design patterns. Trigger on phrases like \"create a skill\", \"new skill\", \"make a skill\", \"skill for X\", \"how do I create a skill\", or \"help me build a skill\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[7120,7121,7124],{"name":19,"slug":20,"type":14},{"name":7122,"slug":7123,"type":14},"Engineering","engineering",{"name":7125,"slug":7126,"type":14},"Plugin Development","plugin-development","2026-05-13T06:10:59.88449",{"slug":7129,"name":7129,"fn":7130,"description":7131,"org":7132,"tags":7133,"stars":7025,"repoUrl":7026,"updatedAt":7137},"social-media","create optimized social media posts","Create social media posts optimized for engagement across platforms.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[7134,7135,7136],{"name":7046,"slug":7047,"type":14},{"name":7014,"slug":7015,"type":14},{"name":7053,"slug":7054,"type":14},"2026-04-15T05:00:55.37452",{"slug":7139,"name":7139,"fn":7140,"description":7141,"org":7142,"tags":7143,"stars":7025,"repoUrl":7026,"updatedAt":7148},"web-research","conduct and synthesize web research","Searches multiple web sources, synthesizes findings, and produces cited research reports using delegated subagents. Use when the user asks to research a topic online, search the web, look something up, find current information, compare options, or produce a research report.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[7144,7145,7146,7147],{"name":19,"slug":20,"type":14},{"name":7096,"slug":7097,"type":14},{"name":7017,"slug":7018,"type":14},{"name":7036,"slug":7037,"type":14},"2026-05-13T06:11:04.930044",{"slug":7150,"name":7150,"fn":7151,"description":7152,"org":7153,"tags":7154,"stars":7165,"repoUrl":7166,"updatedAt":7167},"mermaid-diagrams","embed Mermaid diagrams in documentation","Embed Mermaid diagrams in generated wiki pages. Use whenever documenting a runtime or request flow, a call sequence, a state machine or lifecycle, a data model or entity relationships, or non-trivial control flow, since these are clearer as a diagram than as prose. Also use when an update run touches a page that already contains a mermaid fence, or a page that contains a text fence a previous run degraded.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[7155,7158,7159,7162],{"name":7156,"slug":7157,"type":14},"Diagrams","diagrams",{"name":7092,"slug":7093,"type":14},{"name":7160,"slug":7161,"type":14},"Markdown","markdown",{"name":7163,"slug":7164,"type":14},"Technical Writing","technical-writing",12181,"https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Fopenwiki","2026-07-24T06:09:01.089597",{"slug":7169,"name":7169,"fn":7170,"description":7171,"org":7172,"tags":7173,"stars":7165,"repoUrl":7166,"updatedAt":7180},"write-connector","implement OpenWiki source connectors","Add a new built-in OpenWiki source connector. Use when a user asks to create or implement an OpenWiki connector.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[7174,7177],{"name":7175,"slug":7176,"type":14},"API Development","api-development",{"name":7178,"slug":7179,"type":14},"Integrations","integrations","2026-07-18T05:48:23.961804",{"items":7182,"total":484},[7183,7198,7211,7223,7234,7245,7259],{"slug":7184,"name":7184,"fn":7185,"description":7186,"org":7187,"tags":7188,"stars":27,"repoUrl":28,"updatedAt":7197},"deep-agents-core","build applications with LangChain Deep Agents","INVOKE THIS SKILL when building ANY Deep Agents application. Covers create_deep_agent(), harness architecture, SKILL.md format, and configuration options.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[7189,7190,7193,7194],{"name":19,"slug":20,"type":14},{"name":7191,"slug":7192,"type":14},"Architecture","architecture",{"name":9,"slug":8,"type":14},{"name":7195,"slug":7196,"type":14},"LLM","llm","2026-04-06T18:26:24.822592",{"slug":7199,"name":7199,"fn":7200,"description":7201,"org":7202,"tags":7203,"stars":27,"repoUrl":28,"updatedAt":7210},"deep-agents-memory","implement memory and persistence for Deep Agents","INVOKE THIS SKILL when your Deep Agent needs memory, persistence, or filesystem access. Covers StateBackend (ephemeral), StoreBackend (persistent), FilesystemMiddleware, and CompositeBackend for routing.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[7204,7205,7206,7207],{"name":19,"slug":20,"type":14},{"name":9,"slug":8,"type":14},{"name":7111,"slug":7112,"type":14},{"name":7208,"slug":7209,"type":14},"Storage","storage","2026-04-06T18:26:26.065654",{"slug":7212,"name":7212,"fn":7213,"description":7214,"org":7215,"tags":7216,"stars":27,"repoUrl":28,"updatedAt":7222},"deep-agents-orchestration","orchestrate subagents and tasks in Deep Agents","INVOKE THIS SKILL when using subagents, task planning, or human approval in Deep Agents. Covers SubAgentMiddleware, TodoList for planning, and HITL interrupts.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[7217,7218,7219,7220,7221],{"name":19,"slug":20,"type":14},{"name":22,"slug":23,"type":14},{"name":9,"slug":8,"type":14},{"name":7096,"slug":7097,"type":14},{"name":25,"slug":26,"type":14},"2026-04-06T18:26:32.280463",{"slug":7224,"name":7224,"fn":7225,"description":7226,"org":7227,"tags":7228,"stars":27,"repoUrl":28,"updatedAt":7233},"deepagents-python-quickstart","scaffold local Deep Agents","Scaffold a minimal local Deep Agent in Python by following the official quickstart, using provider-native web search instead of Tavily. Use when the user wants to quickly build or try a Deep Agent locally.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[7229,7230,7231],{"name":19,"slug":20,"type":14},{"name":9,"slug":8,"type":14},{"name":7232,"slug":277,"type":14},"Python","2026-07-24T06:09:00.291108",{"slug":7235,"name":7235,"fn":7236,"description":7237,"org":7238,"tags":7239,"stars":27,"repoUrl":28,"updatedAt":7244},"deepagents-typescript-quickstart","build Deep Agents in TypeScript","Scaffold a minimal local Deep Agent in TypeScript by following the official quickstart, using provider-native web search instead of Tavily. Use when the user wants to quickly build or try a Deep Agent locally.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[7240,7241,7242],{"name":19,"slug":20,"type":14},{"name":9,"slug":8,"type":14},{"name":7243,"slug":588,"type":14},"TypeScript","2026-07-24T06:09:00.673714",{"slug":7246,"name":7246,"fn":7247,"description":7248,"org":7249,"tags":7250,"stars":27,"repoUrl":28,"updatedAt":7258},"ecosystem-primer","select frameworks for LangChain and LangGraph agents","INVOKE FIRST for any LangChain \u002F LangGraph \u002F 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.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[7251,7252,7255,7256,7257],{"name":19,"slug":20,"type":14},{"name":7253,"slug":7254,"type":14},"AI Infrastructure","ai-infrastructure",{"name":7191,"slug":7192,"type":14},{"name":9,"slug":8,"type":14},{"name":16,"slug":17,"type":14},"2026-07-24T05:42:48.348966",{"slug":7260,"name":7260,"fn":7261,"description":7262,"org":7263,"tags":7264,"stars":27,"repoUrl":28,"updatedAt":7276},"eval-engineering","create and audit Harbor agent evals","Iteratively inspect an agent repository and optional user-provided traces, interview the user, and create, run, and audit Harbor evals one at a time. Use for agent evals, Harbor tasks, benchmark cases, verifier design, or controlled agent environments.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[7265,7266,7269,7272,7273],{"name":19,"slug":20,"type":14},{"name":7267,"slug":7268,"type":14},"Code Analysis","code-analysis",{"name":7270,"slug":7271,"type":14},"Evals","evals",{"name":9,"slug":8,"type":14},{"name":7274,"slug":7275,"type":14},"Testing","testing","2026-07-31T05:53:29.552458"]