[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-langchain-langchain-fundamentals":3,"mdc-3bvesk-key":34,"related-repo-langchain-langchain-fundamentals":5803,"related-org-langchain-langchain-fundamentals":5907},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":12,"stars":24,"repoUrl":25,"updatedAt":26,"license":27,"forks":28,"topics":29,"repo":30,"sourceUrl":32,"mdContent":33},"langchain-fundamentals","build LangChain agents and tools","Create LangChain agents with create_agent, define tools, and use middleware for human-in-the-loop and error handling.",{"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",[13,17,18,21],{"name":14,"slug":15,"type":16},"LLM","llm","tag",{"name":9,"slug":8,"type":16},{"name":19,"slug":20,"type":16},"Agents","agents",{"name":22,"slug":23,"type":16},"API Development","api-development",877,"https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchain-skills","2026-04-06T18:26:29.746246",null,77,[],{"repoUrl":25,"stars":24,"forks":28,"topics":31,"description":27},[],"https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchain-skills\u002Ftree\u002FHEAD\u002Fconfig\u002Fskills\u002Flangchain-fundamentals","---\nname: langchain-fundamentals\ndescription: Create LangChain agents with create_agent, define tools, and use middleware for human-in-the-loop and error handling.\n---\n\n\u003Coneliner>\nBuild production agents using `create_agent()`, middleware patterns, and the `@tool` decorator \u002F `tool()` function. When creating LangChain agents, you MUST use create_agent(), with middleware for custom flows. All other alternatives are outdated.\n\u003C\u002Foneliner>\n\n\u003Ccreate_agent>\n## Creating Agents with create_agent\n\n`create_agent()` is the recommended way to build agents. It handles the agent loop, tool execution, and state management.\n\n### Agent Configuration Options\n\n| Parameter | Purpose | Example |\n|-----------|---------|---------|\n| `model` | LLM to use | `\"anthropic:claude-sonnet-4-5\"` or model instance |\n| `tools` | List of tools | `[search, calculator]` |\n| `system_prompt` \u002F `systemPrompt` | Agent instructions | `\"You are a helpful assistant\"` |\n| `checkpointer` | State persistence | `MemorySaver()` |\n| `middleware` | Processing hooks | `[HumanInTheLoopMiddleware]` (Python) \u002F `[humanInTheLoopMiddleware({...})]` (TypeScript) |\n\u003C\u002Fcreate_agent>\n\n\u003Cex-basic-agent>\n\u003Cpython>\n\n```python\nfrom langchain.agents import create_agent\nfrom langchain_core.tools import tool\n\n@tool\ndef get_weather(location: str) -> str:\n \"\"\"Get current weather for a location.\n\n Args:\n location: City name\n \"\"\"\n return f\"Weather in {location}: Sunny, 72F\"\n\nagent = create_agent(\n model=\"anthropic:claude-sonnet-4-5\",\n tools=[get_weather],\n system_prompt=\"You are a helpful assistant.\"\n)\n\nresult = agent.invoke({\n \"messages\": [{\"role\": \"user\", \"content\": \"What's the weather in Paris?\"}]\n})\nprint(result[\"messages\"][-1].content)\n```\n\u003C\u002Fpython>\n\u003Ctypescript>\n\n```typescript\nimport { createAgent } from \"langchain\";\nimport { tool } from \"@langchain\u002Fcore\u002Ftools\";\nimport { z } from \"zod\";\n\nconst getWeather = tool(\n async ({ location }) => `Weather in ${location}: Sunny, 72F`,\n {\n name: \"get_weather\",\n description: \"Get current weather for a location.\",\n schema: z.object({ location: z.string().describe(\"City name\") }),\n }\n);\n\nconst agent = createAgent({\n model: \"anthropic:claude-sonnet-4-5\",\n tools: [getWeather],\n systemPrompt: \"You are a helpful assistant.\",\n});\n\nconst result = await agent.invoke({\n messages: [{ role: \"user\", content: \"What's the weather in Paris?\" }],\n});\nconsole.log(result.messages[result.messages.length - 1].content);\n```\n\u003C\u002Ftypescript>\n\u003C\u002Fex-basic-agent>\n\n\u003Cex-agent-with-persistence>\n\u003Cpython>\nAdd MemorySaver checkpointer to maintain conversation state across invocations.\n\n```python\nfrom langchain.agents import create_agent\nfrom langgraph.checkpoint.memory import MemorySaver\n\ncheckpointer = MemorySaver()\n\nagent = create_agent(\n model=\"anthropic:claude-sonnet-4-5\",\n tools=[search],\n checkpointer=checkpointer,\n)\n\nconfig = {\"configurable\": {\"thread_id\": \"user-123\"}}\nagent.invoke({\"messages\": [{\"role\": \"user\", \"content\": \"My name is Alice\"}]}, config=config)\nresult = agent.invoke({\"messages\": [{\"role\": \"user\", \"content\": \"What's my name?\"}]}, config=config)\n# Agent remembers: \"Your name is Alice\"\n```\n\u003C\u002Fpython>\n\u003Ctypescript>\nAdd MemorySaver checkpointer to maintain conversation state across invocations.\n\n```typescript\nimport { createAgent } from \"langchain\";\nimport { MemorySaver } from \"@langchain\u002Flanggraph\";\n\nconst checkpointer = new MemorySaver();\n\nconst agent = createAgent({\n model: \"anthropic:claude-sonnet-4-5\",\n tools: [search],\n checkpointer,\n});\n\nconst config = { configurable: { thread_id: \"user-123\" } };\nawait agent.invoke({ messages: [{ role: \"user\", content: \"My name is Alice\" }] }, config);\nconst result = await agent.invoke({ messages: [{ role: \"user\", content: \"What's my name?\" }] }, config);\n\u002F\u002F Agent remembers: \"Your name is Alice\"\n```\n\u003C\u002Ftypescript>\n\u003C\u002Fex-agent-with-persistence>\n\n\u003Ctools>\n## Defining Tools\n\nTools are functions that agents can call. Use the `@tool` decorator (Python) or `tool()` function (TypeScript).\n\u003C\u002Ftools>\n\n\u003Cex-basic-tool>\n\u003Cpython>\n\n```python\nfrom langchain_core.tools import tool\n\n@tool\ndef add(a: float, b: float) -> float:\n \"\"\"Add two numbers.\n\n Args:\n a: First number\n b: Second number\n \"\"\"\n return a + b\n```\n\u003C\u002Fpython>\n\u003Ctypescript>\n\n```typescript\nimport { tool } from \"@langchain\u002Fcore\u002Ftools\";\nimport { z } from \"zod\";\n\nconst add = tool(\n async ({ a, b }) => a + b,\n {\n name: \"add\",\n description: \"Add two numbers.\",\n schema: z.object({\n a: z.number().describe(\"First number\"),\n b: z.number().describe(\"Second number\"),\n }),\n }\n);\n```\n\u003C\u002Ftypescript>\n\u003C\u002Fex-basic-tool>\n\n\u003Cmiddleware>\n## Middleware for Agent Control\n\nMiddleware intercepts the agent loop to add human approval, error handling, logging, and more. A deep understanding of middleware is essential for production agents — use `HumanInTheLoopMiddleware` (Python) \u002F `humanInTheLoopMiddleware` (TypeScript) for approval workflows, and `@wrap_tool_call` (Python) \u002F `createMiddleware` (TypeScript) for custom hooks.\n\nKey imports:\n\n```python\nfrom langchain.agents.middleware import HumanInTheLoopMiddleware, wrap_tool_call\n```\n\n```typescript\nimport { humanInTheLoopMiddleware, createMiddleware } from \"langchain\";\n```\n\nKey patterns:\n- **HITL**: `middleware=[HumanInTheLoopMiddleware(interrupt_on={\"dangerous_tool\": True})]` — requires `checkpointer` + `thread_id`\n- **Resume after interrupt**: `agent.invoke(Command(resume={\"decisions\": [{\"type\": \"approve\"}]}), config=config)`\n- **Custom middleware**: `@wrap_tool_call` decorator (Python) or `createMiddleware({ wrapToolCall: ... })` (TypeScript)\n\u003C\u002Fmiddleware>\n\n\u003Cstructured_output>\n## Structured Output\n\nGet typed, validated responses from agents using `response_format` or `with_structured_output()`.\n\n\u003Cpython>\n\n```python\nfrom langchain.agents import create_agent\nfrom pydantic import BaseModel, Field\n\nclass ContactInfo(BaseModel):\n name: str\n email: str\n phone: str = Field(description=\"Phone number with area code\")\n\n# Option 1: Agent with structured output\nagent = create_agent(model=\"gpt-4.1\", tools=[search], response_format=ContactInfo)\nresult = agent.invoke({\"messages\": [{\"role\": \"user\", \"content\": \"Find contact for John\"}]})\nprint(result[\"structured_response\"]) # ContactInfo(name='John', ...)\n\n# Option 2: Model-level structured output (no agent needed)\nfrom langchain_openai import ChatOpenAI\nmodel = ChatOpenAI(model=\"gpt-4.1\")\nstructured_model = model.with_structured_output(ContactInfo)\nresponse = structured_model.invoke(\"Extract: John, john@example.com, 555-1234\")\n# ContactInfo(name='John', email='john@example.com', phone='555-1234')\n```\n\u003C\u002Fpython>\n\u003Ctypescript>\n\n```typescript\nimport { ChatOpenAI } from \"@langchain\u002Fopenai\";\nimport { z } from \"zod\";\n\nconst ContactInfo = z.object({\n name: z.string(),\n email: z.string().email(),\n phone: z.string().describe(\"Phone number with area code\"),\n});\n\n\u002F\u002F Model-level structured output\nconst model = new ChatOpenAI({ model: \"gpt-4.1\" });\nconst structuredModel = model.withStructuredOutput(ContactInfo);\nconst response = await structuredModel.invoke(\"Extract: John, john@example.com, 555-1234\");\n\u002F\u002F { name: 'John', email: 'john@example.com', phone: '555-1234' }\n```\n\u003C\u002Ftypescript>\n\u003C\u002Fstructured_output>\n\n\u003Cmodel_config>\n## Model Configuration\n\n`create_agent` accepts model strings (`\"anthropic:claude-sonnet-4-5\"`, `\"openai:gpt-4.1\"`) or model instances for custom settings:\n\n```python\nfrom langchain_anthropic import ChatAnthropic\nagent = create_agent(model=ChatAnthropic(model=\"claude-sonnet-4-5\", temperature=0), tools=[...])\n```\n\u003C\u002Fmodel_config>\n\n\n\u003Cfix-missing-tool-description>\n\u003Cpython>\nClear descriptions help the agent know when to use each tool.\n\n```python\n# WRONG: Vague or missing description\n@tool\ndef bad_tool(input: str) -> str:\n \"\"\"Does stuff.\"\"\"\n return \"result\"\n\n# CORRECT: Clear, specific description with Args\n@tool\ndef search(query: str) -> str:\n \"\"\"Search the web for current information about a topic.\n\n Use this when you need recent data or facts.\n\n Args:\n query: The search query (2-10 words recommended)\n \"\"\"\n return web_search(query)\n```\n\u003C\u002Fpython>\n\u003Ctypescript>\nClear descriptions help the agent know when to use each tool.\n\n```typescript\n\u002F\u002F WRONG: Vague description\nconst badTool = tool(async ({ input }) => \"result\", {\n name: \"bad_tool\",\n description: \"Does stuff.\", \u002F\u002F Too vague!\n schema: z.object({ input: z.string() }),\n});\n\n\u002F\u002F CORRECT: Clear, specific description\nconst search = tool(async ({ query }) => webSearch(query), {\n name: \"search\",\n description: \"Search the web for current information about a topic. Use this when you need recent data or facts.\",\n schema: z.object({\n query: z.string().describe(\"The search query (2-10 words recommended)\"),\n }),\n});\n```\n\u003C\u002Ftypescript>\n\u003C\u002Ffix-missing-tool-description>\n\n\u003Cfix-no-checkpointer>\n\u003Cpython>\nAdd checkpointer and thread_id for conversation memory across invocations.\n\n```python\n# WRONG: No persistence - agent forgets between calls\nagent = create_agent(model=\"anthropic:claude-sonnet-4-5\", tools=[search])\nagent.invoke({\"messages\": [{\"role\": \"user\", \"content\": \"I'm Bob\"}]})\nagent.invoke({\"messages\": [{\"role\": \"user\", \"content\": \"What's my name?\"}]})\n# Agent doesn't remember!\n\n# CORRECT: Add checkpointer and thread_id\nfrom langgraph.checkpoint.memory import MemorySaver\n\nagent = create_agent(\n model=\"anthropic:claude-sonnet-4-5\",\n tools=[search],\n checkpointer=MemorySaver(),\n)\nconfig = {\"configurable\": {\"thread_id\": \"session-1\"}}\nagent.invoke({\"messages\": [{\"role\": \"user\", \"content\": \"I'm Bob\"}]}, config=config)\nagent.invoke({\"messages\": [{\"role\": \"user\", \"content\": \"What's my name?\"}]}, config=config)\n# Agent remembers: \"Your name is Bob\"\n```\n\u003C\u002Fpython>\n\u003Ctypescript>\nAdd checkpointer and thread_id for conversation memory across invocations.\n\n```typescript\n\u002F\u002F WRONG: No persistence\nconst agent = createAgent({ model: \"anthropic:claude-sonnet-4-5\", tools: [search] });\nawait agent.invoke({ messages: [{ role: \"user\", content: \"I'm Bob\" }] });\nawait agent.invoke({ messages: [{ role: \"user\", content: \"What's my name?\" }] });\n\u002F\u002F Agent doesn't remember!\n\n\u002F\u002F CORRECT: Add checkpointer and thread_id\nimport { MemorySaver } from \"@langchain\u002Flanggraph\";\n\nconst agent = createAgent({\n model: \"anthropic:claude-sonnet-4-5\",\n tools: [search],\n checkpointer: new MemorySaver(),\n});\nconst config = { configurable: { thread_id: \"session-1\" } };\nawait agent.invoke({ messages: [{ role: \"user\", content: \"I'm Bob\" }] }, config);\nawait agent.invoke({ messages: [{ role: \"user\", content: \"What's my name?\" }] }, config);\n\u002F\u002F Agent remembers: \"Your name is Bob\"\n```\n\u003C\u002Ftypescript>\n\u003C\u002Ffix-no-checkpointer>\n\n\u003Cfix-infinite-loop>\n\u003Cpython>\nSet recursion_limit in the invoke config to prevent runaway agent loops.\n\n```python\n# WRONG: No iteration limit - could loop forever\nresult = agent.invoke({\"messages\": [(\"user\", \"Do research\")]})\n\n# CORRECT: Set recursion_limit in config\nresult = agent.invoke(\n {\"messages\": [(\"user\", \"Do research\")]},\n config={\"recursion_limit\": 10}, # Stop after 10 steps\n)\n```\n\u003C\u002Fpython>\n\u003Ctypescript>\nSet recursionLimit in the invoke config to prevent runaway agent loops.\n\n```typescript\n\u002F\u002F WRONG: No iteration limit\nconst result = await agent.invoke({ messages: [[\"user\", \"Do research\"]] });\n\n\u002F\u002F CORRECT: Set recursionLimit in config\nconst result = await agent.invoke(\n { messages: [[\"user\", \"Do research\"]] },\n { recursionLimit: 10 }, \u002F\u002F Stop after 10 steps\n);\n```\n\u003C\u002Ftypescript>\n\u003C\u002Ffix-infinite-loop>\n\n\u003Cfix-accessing-result-wrong>\n\u003Cpython>\nAccess the messages array from the result, not result.content directly.\n\n```python\n# WRONG: Trying to access result.content directly\nresult = agent.invoke({\"messages\": [{\"role\": \"user\", \"content\": \"Hello\"}]})\nprint(result.content) # AttributeError!\n\n# CORRECT: Access messages from result dict\nresult = agent.invoke({\"messages\": [{\"role\": \"user\", \"content\": \"Hello\"}]})\nprint(result[\"messages\"][-1].content) # Last message content\n```\n\u003C\u002Fpython>\n\u003Ctypescript>\nAccess the messages array from the result, not result.content directly.\n\n```typescript\n\u002F\u002F WRONG: Trying to access result.content directly\nconst result = await agent.invoke({ messages: [{ role: \"user\", content: \"Hello\" }] });\nconsole.log(result.content); \u002F\u002F undefined!\n\n\u002F\u002F CORRECT: Access messages from result object\nconst result = await agent.invoke({ messages: [{ role: \"user\", content: \"Hello\" }] });\nconsole.log(result.messages[result.messages.length - 1].content); \u002F\u002F Last message content\n```\n\u003C\u002Ftypescript>\n\u003C\u002Ffix-accessing-result-wrong>\n",{"data":35,"body":36},{"name":4,"description":6},{"type":37,"children":38},"root",[39,47,53,60,72,79,276,1291,1972,5797],{"type":40,"tag":41,"props":42,"children":43},"element","oneliner",{},[44],{"type":45,"value":46},"text","\nBuild production agents using `create_agent()`, middleware patterns, and the `@tool` decorator \u002F `tool()` function. When creating LangChain agents, you MUST use create_agent(), with middleware for custom flows. All other alternatives are outdated.\n",{"type":40,"tag":48,"props":49,"children":50},"p",{},[51],{"type":45,"value":52},"\u003Ccreate_agent>",{"type":40,"tag":54,"props":55,"children":57},"h2",{"id":56},"creating-agents-with-create_agent",[58],{"type":45,"value":59},"Creating Agents with create_agent",{"type":40,"tag":48,"props":61,"children":62},{},[63,70],{"type":40,"tag":64,"props":65,"children":67},"code",{"className":66},[],[68],{"type":45,"value":69},"create_agent()",{"type":45,"value":71}," is the recommended way to build agents. It handles the agent loop, tool execution, and state management.",{"type":40,"tag":73,"props":74,"children":76},"h3",{"id":75},"agent-configuration-options",[77],{"type":45,"value":78},"Agent Configuration 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shiki-themes material-theme-lighter material-theme material-theme-palenight","from langchain.agents import create_agent\nfrom langchain_core.tools import tool\n\n@tool\ndef get_weather(location: str) -> str:\n \"\"\"Get current weather for a location.\n\n Args:\n location: City name\n \"\"\"\n return f\"Weather in {location}: Sunny, 72F\"\n\nagent = create_agent(\n model=\"anthropic:claude-sonnet-4-5\",\n tools=[get_weather],\n system_prompt=\"You are a helpful assistant.\"\n)\n\nresult = agent.invoke({\n \"messages\": [{\"role\": \"user\", \"content\": \"What's the weather in Paris?\"}]\n})\nprint(result[\"messages\"][-1].content)\n","",[291],{"type":40,"tag":64,"props":292,"children":293},{"__ignoreMap":289},[294,305,314,324,333,342,351,359,368,377,386,395,403,412,421,430,439,448,456,465,474,483],{"type":40,"tag":295,"props":296,"children":299},"span",{"class":297,"line":298},"line",1,[300],{"type":40,"tag":295,"props":301,"children":302},{},[303],{"type":45,"value":304},"from 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Covers create_deep_agent(), harness architecture, SKILL.md format, and configuration options.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[5811,5812,5815,5816],{"name":19,"slug":20,"type":16},{"name":5813,"slug":5814,"type":16},"Architecture","architecture",{"name":9,"slug":8,"type":16},{"name":14,"slug":15,"type":16},"2026-04-06T18:26:24.822592",{"slug":5819,"name":5819,"fn":5820,"description":5821,"org":5822,"tags":5823,"stars":24,"repoUrl":25,"updatedAt":5832},"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},[5824,5825,5826,5829],{"name":19,"slug":20,"type":16},{"name":9,"slug":8,"type":16},{"name":5827,"slug":5828,"type":16},"Memory","memory",{"name":5830,"slug":5831,"type":16},"Storage","storage","2026-04-06T18:26:26.065654",{"slug":5834,"name":5834,"fn":5835,"description":5836,"org":5837,"tags":5838,"stars":24,"repoUrl":25,"updatedAt":5850},"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},[5839,5840,5843,5844,5847],{"name":19,"slug":20,"type":16},{"name":5841,"slug":5842,"type":16},"Approvals","approvals",{"name":9,"slug":8,"type":16},{"name":5845,"slug":5846,"type":16},"Multi-Agent","multi-agent",{"name":5848,"slug":5849,"type":16},"Workflow Automation","workflow-automation","2026-04-06T18:26:32.280463",{"slug":5852,"name":5852,"fn":5853,"description":5854,"org":5855,"tags":5856,"stars":24,"repoUrl":25,"updatedAt":5861},"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},[5857,5858,5859],{"name":19,"slug":20,"type":16},{"name":9,"slug":8,"type":16},{"name":5860,"slug":281,"type":16},"Python","2026-07-24T06:09:00.291108",{"slug":5863,"name":5863,"fn":5864,"description":5865,"org":5866,"tags":5867,"stars":24,"repoUrl":25,"updatedAt":5872},"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},[5868,5869,5870],{"name":19,"slug":20,"type":16},{"name":9,"slug":8,"type":16},{"name":5871,"slug":493,"type":16},"TypeScript","2026-07-24T06:09:00.673714",{"slug":5874,"name":5874,"fn":5875,"description":5876,"org":5877,"tags":5878,"stars":24,"repoUrl":25,"updatedAt":5888},"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},[5879,5880,5883,5884,5885],{"name":19,"slug":20,"type":16},{"name":5881,"slug":5882,"type":16},"AI Infrastructure","ai-infrastructure",{"name":5813,"slug":5814,"type":16},{"name":9,"slug":8,"type":16},{"name":5886,"slug":5887,"type":16},"LangGraph","langgraph","2026-07-24T05:42:48.348966",{"slug":5890,"name":5890,"fn":5891,"description":5892,"org":5893,"tags":5894,"stars":24,"repoUrl":25,"updatedAt":5906},"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},[5895,5896,5899,5902,5903],{"name":19,"slug":20,"type":16},{"name":5897,"slug":5898,"type":16},"Code Analysis","code-analysis",{"name":5900,"slug":5901,"type":16},"Evals","evals",{"name":9,"slug":8,"type":16},{"name":5904,"slug":5905,"type":16},"Testing","testing","2026-07-31T05:53:29.552458",{"items":5908,"total":6076},[5909,5930,5940,5957,5970,5985,5998,6011,6025,6035,6046,6065],{"slug":5910,"name":5910,"fn":5911,"description":5912,"org":5913,"tags":5914,"stars":5927,"repoUrl":5928,"updatedAt":5929},"analyze-market","perform market 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},[5915,5918,5921,5924],{"name":5916,"slug":5917,"type":16},"Marketing","marketing",{"name":5919,"slug":5920,"type":16},"Research","research",{"name":5922,"slug":5923,"type":16},"Sales","sales",{"name":5925,"slug":5926,"type":16},"Strategy","strategy",26592,"https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Fdeepagents","2026-04-18T04:46:54.557115",{"slug":5931,"name":5931,"fn":5932,"description":5933,"org":5934,"tags":5935,"stars":5927,"repoUrl":5928,"updatedAt":5939},"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},[5936,5937],{"name":5919,"slug":5920,"type":16},{"name":5938,"slug":3910,"type":16},"Search","2026-05-13T06:11:01.203061",{"slug":5941,"name":5941,"fn":5942,"description":5943,"org":5944,"tags":5945,"stars":5927,"repoUrl":5928,"updatedAt":5956},"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},[5946,5949,5950,5953],{"name":5947,"slug":5948,"type":16},"Content Creation","content-creation",{"name":5916,"slug":5917,"type":16},{"name":5951,"slug":5952,"type":16},"SEO","seo",{"name":5954,"slug":5955,"type":16},"Writing","writing","2026-04-15T05:00:54.149813",{"slug":5958,"name":5958,"fn":5959,"description":5960,"org":5961,"tags":5962,"stars":5927,"repoUrl":5928,"updatedAt":5969},"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},[5963,5966,5967,5968],{"name":5964,"slug":5965,"type":16},"Competitive Intelligence","competitive-intelligence",{"name":5916,"slug":5917,"type":16},{"name":5919,"slug":5920,"type":16},{"name":5925,"slug":5926,"type":16},"2026-04-18T04:46:55.79306",{"slug":5971,"name":5971,"fn":5972,"description":5973,"org":5974,"tags":5975,"stars":5927,"repoUrl":5928,"updatedAt":5984},"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},[5976,5977,5980,5981],{"name":19,"slug":20,"type":16},{"name":5978,"slug":5979,"type":16},"Debugging","debugging",{"name":9,"slug":8,"type":16},{"name":5982,"slug":5983,"type":16},"SQLite","sqlite","2026-07-24T06:08:57.102689",{"slug":5986,"name":5986,"fn":5987,"description":5988,"org":5989,"tags":5990,"stars":5927,"repoUrl":5928,"updatedAt":5997},"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},[5991,5992,5995,5996],{"name":19,"slug":20,"type":16},{"name":5993,"slug":5994,"type":16},"Documentation","documentation",{"name":5886,"slug":5887,"type":16},{"name":5845,"slug":5846,"type":16},"2026-05-13T06:11:03.650877",{"slug":5999,"name":5999,"fn":6000,"description":6001,"org":6002,"tags":6003,"stars":5927,"repoUrl":5928,"updatedAt":6010},"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},[6004,6005,6006,6009],{"name":19,"slug":20,"type":16},{"name":5993,"slug":5994,"type":16},{"name":6007,"slug":6008,"type":16},"Knowledge Management","knowledge-management",{"name":5827,"slug":5828,"type":16},"2026-05-13T06:10:58.510037",{"slug":6012,"name":6012,"fn":6013,"description":6014,"org":6015,"tags":6016,"stars":5927,"repoUrl":5928,"updatedAt":6024},"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},[6017,6018,6021],{"name":19,"slug":20,"type":16},{"name":6019,"slug":6020,"type":16},"Engineering","engineering",{"name":6022,"slug":6023,"type":16},"Plugin Development","plugin-development","2026-05-13T06:10:59.88449",{"slug":6026,"name":6026,"fn":6027,"description":6028,"org":6029,"tags":6030,"stars":5927,"repoUrl":5928,"updatedAt":6034},"social-media","create optimized social media posts","Create social media posts optimized for engagement across platforms.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[6031,6032,6033],{"name":5947,"slug":5948,"type":16},{"name":5916,"slug":5917,"type":16},{"name":5954,"slug":5955,"type":16},"2026-04-15T05:00:55.37452",{"slug":6036,"name":6036,"fn":6037,"description":6038,"org":6039,"tags":6040,"stars":5927,"repoUrl":5928,"updatedAt":6045},"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},[6041,6042,6043,6044],{"name":19,"slug":20,"type":16},{"name":5845,"slug":5846,"type":16},{"name":5919,"slug":5920,"type":16},{"name":5938,"slug":3910,"type":16},"2026-05-13T06:11:04.930044",{"slug":6047,"name":6047,"fn":6048,"description":6049,"org":6050,"tags":6051,"stars":6062,"repoUrl":6063,"updatedAt":6064},"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},[6052,6055,6056,6059],{"name":6053,"slug":6054,"type":16},"Diagrams","diagrams",{"name":5993,"slug":5994,"type":16},{"name":6057,"slug":6058,"type":16},"Markdown","markdown",{"name":6060,"slug":6061,"type":16},"Technical Writing","technical-writing",12181,"https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Fopenwiki","2026-07-24T06:09:01.089597",{"slug":6066,"name":6066,"fn":6067,"description":6068,"org":6069,"tags":6070,"stars":6062,"repoUrl":6063,"updatedAt":6075},"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},[6071,6072],{"name":22,"slug":23,"type":16},{"name":6073,"slug":6074,"type":16},"Integrations","integrations","2026-07-18T05:48:23.961804",41]