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, environ

<overview LangChain Inc. maintains three layered open source tools for building agents, plus LangSmith for observability. The stack, top down: Deep Agents (top layer, harness ) — batteries included toolkit built on LangChain + LangGraph. Ships with planning, file management, subagent spawning, and memory out of the box. LangGraph (middle layer, runtime ) — low level orchestration for durable execution, custom control flow, and stateful workflows. LangChain agents run on top of LangGraph. LangChain (bottom layer, framework ) — abstractions for models, tools, and the agent loop. Provider agnostic, easiest to start with. LangSmith (cross cutting) — observability and evaluation platform. Framework agnostic; always recommended alongside any of the above. Higher layers depend on lower ones, but you don't need to use lower layers directly. Deep Agents gives you LangGraph's durable execution without writing graph code. LangChain gives you models and tools without managing graph edges. </overview Step 1 — Choose Your Tool <decision table Evaluate these conditions in order and stop at the first match: 1. If the task needs planning, file management across a long session, persistent memory, subagent delegation, or on demand skills → Deep Agents 2. Else, if the task needs custom control flow (deterministic loops, branching logic) → LangGraph 3. Else, if it's a single purpose agent with a fixed set of tools → LangChain ( create agent function) 4. Else, if it's a pure model call, retrieval pipeline, or simple prompt chain with no agent loop → LangChain (direct model / chain) This is your layer . BUT you are not done: later in Step 4, you MUST load the layer specific skill before writing any agent code. </decision table Tool Profiles <langchain profile LangChain — agent framework Best for: Single purpose agents with a fixed tool set RAG pipelines and document Q&A Model calls, prompt templates, structured output Not ideal when: The agent needs to plan across many steps or manage large context Control flow is conditional, iterative, or parallel State must persist across sessions All LangChain agents use create agent(model, tools=[...]) . </langchain profile <langgraph profile LangGraph — agent runtime Best for: Custom control flow — deterministic loops, reflection cycles, parallel fan out Complex workflows combining deterministic and agentic steps Human in the loop with precise interrupt and resume points State that must survive failures or span long sessions Not ideal when: You want planning, file management, and subagent delegation out of the box (use Deep Agents instead) The workflow is simple enough for a straight tool loop All LangGraph graphs use StateGraph(State) with explicit nodes, edges, and conditional edges. </langgraph profile <deep agents profile Deep Agents — agent harness Best for: Long running tasks that require planning and decomposition Agents that read, write, and manage files across a session Delegating subtasks to specialized subagents Persistent memory across sessions Loading domain specific skills on demand Not ideal when: The task is simple enough for a single purpose agent You need precise hand crafted control over every graph edge (use LangGraph directly) All Deep Agents use create deep agent(model, tools=[...]) . </deep agents profile Mixing Layers <mixing layers The tools are layered, so they can be combined in the same project. Common patterns: Deep Agents orchestrator → LangGraph subagent — when the main agent needs planning and memory but one subtask requires a deterministic graph. LangGraph graph wrapped as a tool or subagent — when a specialized pipeline (e.g. RAG, reflection loop) is called by a broader agent. A compiled LangGraph graph can be registered as a named subagent inside Deep Agents — the orchestrator delegates to it via the task tool without knowing its internal structure. LangChain tools and retrievers work freely inside both LangGraph nodes and Deep Agents tools. </mixing layers Step 2 — Set Environment Variables Always set these for observability. These are the current LangSmith env var names. Copy them as is. OLDER NAMES NO LONGER WORK. <environment variables LANGSMITH API KEY=<your key LANGSMITH TRACING=true LANGSMITH PROJECT=<project name </environment variables Model provider and tool specific keys ( ANTHROPIC API KEY , OPENAI API KEY , TAVILY API KEY , etc.) depend on your stack — set them as needed. Step 3 — How the Docs Work <docs All documentation lives at docs.langchain.com , organized into two top level sections: OSS — LangChain, LangGraph, Deep Agents. Python ( /oss/python/ ) and TypeScript ( /oss/javascript/ ) trees in parallel. LangSmith — observability, evaluation, deployment, prompt engineering. Each product has its own page tree: overview → quickstart → how to guides → reference. Canonical landing pages Start here rather than tree searching from root (swap python → javascript for TypeScript): LangChain — /oss/python/langchain/overview LangGraph — /oss/python/langgraph/overview Deep Agents — /oss/python/deepagents/overview LangSmith — /langsmith/home (no language split) Accessing docs in an agent context If the LangChain Docs MCP server is connected ( mcp docs langchain tools are available), query it directly: If the MCP server is not available , use the llms.txt index: 1. Fetch https://docs.langchain.com/llms.txt — structured list of all pages with descriptions 2. Identify the 2–4 most relevant pages for the question 3. Fetch those pages directly for accurate, up to date content Always prefer fetching live docs over relying on training data knowledge — these libraries evolve fast and APIs change often. </docs Step 4 — Load the Right Skill Next If the user only wants a minimal local working agent (new project, stub tool, provider key), load the matching quickstart first: LangChain → langchain python quickstart or langchain typescript quickstart LangGraph → langgraph python quickstart or langgraph typescript quickstart Deep Agents → deepagents python quickstart or deepagents typescript quickstart Otherwise load the skill below that matches your layer from Step 1. This is required — the layer specific skill carries the current API; the primer alone does not. <next skills LangChain langchain fundamentals — building any LangChain agent langchain rag — adding RAG / vector store retrieval langchain middleware — structured output with Pydantic langchain dependencies — package versions, installs, or dependency management questions LangGraph langgraph fundamentals — any LangGraph graph langgraph human in the loop — human in the loop or approval workflows langgraph persistence — state that must survive restarts, or cross thread memory Deep Agents Always load deep agents core first. Then, as needed: deep agents orchestration — subagent delegation or orchestration deep agents memory — cross session persistent memory </next skills

ecosystem-primer