langchain-ai/langchain-oss-primer

<overview> **Always load this skill first.** This is the required starting point for any LangChain open source agent project — before choosing other skills, before writing code, before installing packages.

It answers three questions every project must resolve upfront:

1. Which framework? — LangChain, LangGraph, or Deep Agents
2. Which agent archetype? — maps your use case to the right API and patterns
3. What to install and which skills to load next

Load this skill first. Once you've decided on a framework and agent type, follow the "Next Skills" section at the bottom — it tells you exactly which skills to invoke next based on your choices.

</overview>

---

Step 1 — Pick Your Framework

The three frameworks are layered, not competing. Each builds on the one below:

┌─────────────────────────────────────────┐
│              Deep Agents                │  ← batteries included
│   (planning, memory, skills, files)     │
├─────────────────────────────────────────┤
│               LangGraph                 │  ← custom orchestration
│    (nodes, edges, state, persistence)   │
├─────────────────────────────────────────┤
│               LangChain                 │  ← foundation
│      (models, tools, prompts, RAG)      │
└─────────────────────────────────────────┘

<framework-decision>

Answer these questions in order:

| Question | Yes → | No → | |----------|-------|-------| | User needs or wants planning, persistent memory, complex task management, long-running tasks, out-of-the-box file management, on-demand skills, or built-in middleware, subagents, easy expansion capabilities? | Deep Agents | ↓ | | Needs custom control flow — specified loops, branching, deterministic parallel workers, or manually instrumented human-in-the-loop? | LangGraph | ↓ | | Single-purpose agent with a fixed set of tools? | LangChain (create_agent) | ↓ | | Simple prompt pipeline or retrieval chain with no agent loop? | LangChain (direct model / chain) | — |

Higher layers depend on lower ones only when necessary — you can mix them. A LangGraph graph can be a subagent inside Deep Agents; LangChain tools work inside both.

</framework-decision> <framework-profiles>

| | LangChain | LangGraph | Deep Agents | |---|-----------|-----------|-------------| | Control flow | Fixed (tool loop) | Custom (graph) | Managed (middleware) | | Middleware | Callbacks only | ✗ None | ✓ Explicit, configurable | | Planning | ✗ | Manual | ✓ TodoListMiddleware | | File management | ✗ | Manual | ✓ FilesystemMiddleware | | Persistent memory | ✗ | With checkpointer | ✓ MemoryMiddleware | | Subagent delegation | ✗ | Manual | ✓ SubAgentMiddleware | | On-demand skills | ✗ | ✗ | ✓ SkillsMiddleware | | Human-in-the-loop | ✗ | Manual interrupt | ✓ HumanInTheLoopMiddleware | | Custom graph edges | ✗ | ✓ Full control | Limited | | Setup complexity | Low | Medium | Low |

Middleware is a concept specific to Deep Agents (explicit middleware layer). LangGraph has no middleware — behavior is wired directly into nodes and edges. If a user asks for built-in hooks or automatic middleware, route to Deep Agents.

</framework-profiles>

---

Step 2 — Pick Your Agent Archetype

Once you've chosen a framework, match your use case to the right API and pattern.

<langchain-archetypes>

LangChain — use create_agent()

Best for single-purpose agents in a ReACT style with a fixed tool set. No built-in planning, memory management, or delegation.

| Archetype | Description | Key tools | |-----------|-------------|-----------| | QA / Chatbot | Answer questions, summarise, classify. One job, done well. | LLM + optional retrieval | | SQL Agent | Query a database, return structured results | SQLDatabase, create_agent | | Search Agent | Look up information, return findings | TavilySearchResults, DuckDuckGoSearch | | RAG Agent | Retrieve from a vector store, ground answers in documents | retriever tool + create_agent | | Data Analysis Agent | Load, transform, and summarise structured data | PythonREPL, pandas tools | | Tool-calling Agent | Call APIs, run code, or chain arbitrary tools | custom @tool functions |

All LangChain agents use create_agent(model, tools=[...]). Next skill: langchain-fundamentals.

</langchain-archetypes> <langgraph-archetypes>

LangGraph — use StateGraph

Best when you need explicit, deterministic control flow.

| Archetype | Description | Key pattern | |-----------|-------------|-------------| | Deterministic Parallel Workflows | Fan out to multiple nodes, collect results, merge | parallel edges → aggregation node | not offloaded to agent decisionmaking | Multi-stage Pipeline | Extract → Transform → Load with typed state | TypedDict state + sequential nodes | | Branching Classifier | Route inputs to different handlers based on content | conditional edges + classifer node | | Reflection Loop | Generate → Critique → Revise cycle with explicit exit | cycle edges + iteration counter | | Custom HITL | Complex human-in-the-loop with structured review and conditional edges | interrupt_before/interrupt_after + Command resume |

LangGraph agents use StateGraph(State) with explicit add_node, add_edge, add_conditional_edges. Next skill: langgraph-fundamentals.

</langgraph-archetypes> <deep-agents-archetypes>

Deep Agents — use create_deep_agent()

Best when the agent needs to manage its own work: planning tasks, remembering users across sessions, delegating to specialists, or managing files autonomously.

| Archetype | Description | Why Deep Agents | |-----------|-------------|-----------------| | Research Assistant | Receives an open-ended research brief, breaks it into subtasks, delegates to specialist subagents, writes up findings | Needs SubAgentMiddleware for delegation + TodoListMiddleware for planning | | Personal Assistant | Remembers user preferences, ongoing projects, and context across multiple sessions | Needs MemoryMiddleware (Store) for cross-session persistence | | Coding Assistant | Reads codebases, writes files, plans refactors across many steps, optionally asks for approval before writes | Needs FilesystemMiddleware + TodoListMiddleware + optional HITL | | Orchestrator | Top-level agent that routes work to 2+ specialized subagents (researcher, coder, writer…) | Needs SubAgentMiddleware with custom subagent configs | | Long-running Task Agent | Multi-hour or multi-day workflows where state must survive restarts | Needs checkpointer + MemoryMiddleware | | On-demand Skills Agent | Agent that loads different skill sets depending on what the user asks | Needs SkillsMiddleware + FilesystemBackend | | Multi Agent Architecture | Agent that spawns or has access to subagents for isolated tasks |

All Deep Agents use create_deep_agent(model, tools=[...], ...). Next skill: deep-agents-core — load it immediately after deciding on Deep Agents.

<deep-agents-middleware>

Deep Agents built-in middleware

Six components pre-wired out of the box. First three are always active; the rest are opt-in:

| Middleware | Always on? | What it gives the agent | |------------|-----------|--------------------------| | TodoListMiddleware | ✓ | write_todos tool — tracks multi-step task plans | | FilesystemMiddleware | ✓ | ls, read_file, write_file, edit_file, glob, grep | | SubAgentMiddleware | ✓ | task tool — delegates subtasks to named subagents | | SkillsMiddleware | Opt-in | Loads SKILL.md files on demand from a skills directory | | MemoryMiddleware | Opt-in | Long-term memory across sessions via a Store instance | | HumanInTheLoopMiddleware | Opt-in | Pauses execution and requests human approval before specified tool calls |

You configure middleware — you don't implement it.

</deep-agents-middleware> <mixing-note> You can combine layers in the same project. The most common pattern: Deep Agents as the top-level orchestrator, with a compiled LangGraph graph registered as a specialized subagent. LangChain tools and chains are usable at every level. </mixing-note> </deep-agents-archetypes>

---

Step 3 — Set Up Your Dependencies

Environment requirements

| | Python | TypeScript / Node | |---|--------|-------------------| | Runtime | Python 3.10+ | Node.js 20+ | | LangChain | 1.0+ (LTS) | 1.0+ (LTS) | | LangSmith SDK | >= 0.3.0 | >= 0.3.0 |

Always use LangChain 1.0+. LangChain 0.3 is maintenance-only until December 2026 — do not start new projects on it.

---

Core packages — always required

<python-core> **Python**

| Package | Role | Version | |---------|------|---------| | langchain | Agents, chains, retrieval | >=1.0,<2.0 | | langchain-core | Base types & interfaces | >=1.0,<2.0 | | langsmith | Tracing, evaluation, datasets | >=0.3.0 | </python-core>

<typescript-core> **TypeScript**

| Package | Role | Version | |---------|------|---------| | @langchain/core | Base types & interfaces (peer dep — install explicitly) | ^1.0.0 | | langchain | Agents, chains, retrieval | ^1.0.0 | | langsmith | Tracing, evaluation, datasets | ^0.3.0 | </typescript-core>

---

Orchestration — add based on your framework choice

<orchestration-packages>

| Framework | Python | TypeScript | |-----------|--------|------------| | LangGraph | langgraph>=1.0,<2.0 | @langchain/langgraph ^1.0.0 | | Deep Agents | deepagents (depends on LangGraph; installs it as a transitive dep) | deepagents |

</orchestration-packages>

---

Model providers — pick the one(s) you use

<provider-packages>

| Provider | Python | TypeScript | |----------|--------|------------| | OpenAI | langchain-openai | @langchain/openai | | Anthropic | langchain-anthropic | @langchain/anthropic | | Google Gemini | langchain-google-genai | @langchain/google-genai | | Mistral | langchain-mistralai | @langchain/mistralai | | Groq | langchain-groq | @langchain/groq | | Cohere | langchain-cohere | @langchain/cohere | | AWS Bedrock | langchain-aws | @langchain/aws | | Azure AI | langchain-azure-ai | @langchain/azure-openai | | Ollama (local) | langchain-ollama | @langchain/ollama | | Hugging Face | langchain-huggingface | — | | Fireworks AI | langchain-fireworks | — | | Together AI | langchain-together | — |

</provider-packages>

---

Common tools & retrieval — add as needed

<tool-packages>

| Package | Adds | Notes | |---------|------|-------| | langchain-tavily / @langchain/tavily | Tavily web search | Keep at latest; frequently updated for compatibility | | langchain-text-splitters | Text chunking | Semver; keep current | | langchain-chroma / @langchain/community | Chroma vector store | Dedicated integration package; keep at latest | | langchain-pinecone / @langchain/pinecone | Pinecone vector store | Dedicated integration package; keep at latest | | langchain-qdrant / @langchain/qdrant | Qdrant vector store | Dedicated integration package; keep at latest | | faiss-cpu | FAISS vector store (Python only, local) | Via langchain-community | | langchain-community / @langchain/community | 1000+ integrations fallback | Python: NOT semver — pin to minor series | | langsmith[pytest] | pytest plugin | Requires langsmith>=0.3.4 |

Prefer dedicated integration packages over langchain-community when one exists — they are independently versioned and more stable.

</tool-packages>

---

Dependency templates

<ex-langchain-python> <python> LangChain agent — provider-agnostic starting point. ``` # requirements.txt langchain>=1.0,<2.0 langchain-core>=1.0,<2.0 langsmith>=0.3.0

Add your model provider:

langchain-openai | langchain-anthropic | langchain-google-genai | ...

Add tools/retrieval as needed:

langchain-tavily | langchain-chroma | langchain-text-splitters | ...

</python>
</ex-langchain-python>

<ex-langgraph-python>
<python>
LangGraph project — provider-agnostic starting point.

requirements.txt

langchain>=1.0,<2.0 langchain-core>=1.0,<2.0 langgraph>=1.0,<2.0 langsmith>=0.3.0

Add your model provider:

langchain-openai | langchain-anthropic | langchain-google-genai | ...

</python>
</ex-langgraph-python>

<ex-langgraph-typescript>
<typescript>
LangGraph project — provider-agnostic starting point.
```json
{
  "dependencies": {
    "@langchain/core": "^1.0.0",
    "langchain": "^1.0.0",
    "@langchain/langgraph": "^1.0.0",
    "langsmith": "^0.3.0"
  }
}

</typescript> </ex-langgraph-typescript> <ex-deepagents-python> <python> Deep Agents project — provider-agnostic starting point. ``` # requirements.txt deepagents langchain>=1.0,<2.0 langchain-core>=1.0,<2.0 langsmith>=0.3.0

Add your model provider:

langchain-openai | langchain-anthropic | langchain-google-genai | ...

</python>
</ex-deepagents-python>

<ex-deepagents-typescript>
<typescript>
Deep Agents project — provider-agnostic starting point.
```json
{
  "dependencies": {
    "deepagents": "latest",
    "@langchain/core": "^1.0.0",
    "langchain": "^1.0.0",
    "langsmith": "^0.3.0"
  }
}

</typescript> </ex-deepagents-typescript>

---

Step 4 — Set Your Environment Variables

<environment-variables> ```bash # LangSmith — always recommended for observability LANGSMITH_API_KEY=<your-key> LANGSMITH_PROJECT=<project-name> # optional, defaults to "default"

Model provider — set the one(s) you use

OPENAI_API_KEY=<your-key> ANTHROPIC_API_KEY=<your-key> GOOGLE_API_KEY=<your-key> MISTRAL_API_KEY=<your-key> GROQ_API_KEY=<your-key> COHERE_API_KEY=<your-key> FIREWORKS_API_KEY=<your-key> TOGETHER_API_KEY=<your-key> HUGGINGFACEHUB_API_TOKEN=<your-key>

Common tool/retrieval services

TAVILY_API_KEY=<your-key> PINECONE_API_KEY=<your-key>

</environment-variables>

---

## Step 5 — Load the Right Skill Next

Based on the framework and archetype you chose above, invoke these skills **now** before writing any code:

<next-skills>

### If you chose LangChain

| Your archetype | Load next |
|----------------|-----------|
| Any LangChain agent (QA bot, SQL, search, RAG, tool-calling) | **`langchain-fundamentals`** — always |
| Adding external tools/packages (Tavily, Pinecone, etc.) | **`langchain-dependencies`** — package patterns and version guidance |
| Need streaming or async responses | **`langchain-fundamentals`** then `langgraph-fundamentals` |

### If you chose LangGraph

| Your archetype | Load next |
|----------------|-----------|
| Any LangGraph graph | **`langgraph-fundamentals`** — always |
| Approval pipeline, HITL, or pause/resume | **`langgraph-fundamentals`** + `langgraph-human-in-the-loop` |
| State that must survive restarts or cross-thread memory | **`langgraph-persistence`** |
| Streaming output token by token | **`langgraph-fundamentals`** |

### If you chose Deep Agents

**Always load `deep-agents-core` first — it is the mandatory starting point for any Deep Agents project.**

| Your archetype | Load next (after `deep-agents-core`) |
|----------------|--------------------------------------|
| Research Assistant — delegates to specialist subagents | **`deep-agents-orchestration`** — subagent config, TodoList, HITL |
| Personal Assistant — remembers users across sessions | **`deep-agents-memory`** — MemoryMiddleware, Store backends |
| Coding Assistant — reads/writes files, plans refactors | `deep-agents-core` is sufficient; add `deep-agents-orchestration` if using HITL |
| Orchestrator — routes work across multiple named subagents | **`deep-agents-orchestration`** — SubAgentMiddleware patterns |
| Long-running task agent — survives restarts | **`deep-agents-memory`** + `deep-agents-orchestration` |
| On-demand skills agent | `deep-agents-core` covers SkillsMiddleware setup |
</next-skills>