> **langgraph-fundamentals** — skill 15 of 22 in [langchain-ai/langchain-skills](https://skillsdocs.com/langchain-ai/langchain-skills).
>
> Book (all skills, one file): https://skillsdocs.com/langchain-ai/langchain-skills.md
> Machine manifest: https://skillsdocs.com/langchain-ai/langchain-skills/.well-known/agent-skills/index.json
> Install the book: `npx skills add langchain-ai/langchain-skills`
> Upstream: https://github.com/langchain-ai/langchain-skills/blob/main/config/skills/langgraph-fundamentals/SKILL.md @ `main`
> Raw bytes, no header: https://raw.githubusercontent.com/langchain-ai/langchain-skills/main/config/skills/langgraph-fundamentals/SKILL.md
> Base for relative paths: https://raw.githubusercontent.com/langchain-ai/langchain-skills/main/config/skills/langgraph-fundamentals/
> Licence: MIT — https://spdx.org/licenses/MIT.html
>
> Bundled files (2), referenced from this skill's directory:
>   - `references/python.md` — https://raw.githubusercontent.com/langchain-ai/langchain-skills/main/config/skills/langgraph-fundamentals/references/python.md
>   - `references/typescript.md` — https://raw.githubusercontent.com/langchain-ai/langchain-skills/main/config/skills/langgraph-fundamentals/references/typescript.md
>
> Content © its authors, served unmodified. Takedown: https://github.com/DreambaseAI/skillsdocs/issues/new?labels=takedown&title=Takedown+request

<!-- Verbatim upstream SKILL.md follows, YAML frontmatter included. -->

---
name: langgraph-fundamentals
description: "INVOKE THIS SKILL when writing ANY LangGraph code. Covers StateGraph, state schemas, nodes, edges, Command, Send, invoke, streaming, and error handling."
---

<overview>
LangGraph models agent workflows as **directed graphs**:

- **StateGraph**: Main class for building stateful graphs
- **Nodes**: Functions that perform work and update state
- **Edges**: Define execution order (static or conditional)
- **START/END**: Special nodes marking entry and exit points
- **State with Reducers**: Control how state updates are merged

Graphs must be `compile()`d before execution.
</overview>

<design-methodology>

### Designing a LangGraph application

Follow these 5 steps when building a new graph:

1. **Map out discrete steps** — sketch a flowchart of your workflow. Each step becomes a node.
2. **Identify what each step does** — categorize nodes: LLM step, data step, action step, or user input step. For each, determine static context (prompt), dynamic context (from state), retry strategy, and desired outcome.
3. **Design your state** — state is shared memory for all nodes. Store raw data, format prompts on-demand inside nodes.
4. **Build your nodes** — implement each step as a function that takes state and returns partial updates.
5. **Wire it together** — connect nodes with edges, add conditional routing, compile with a checkpointer if needed.

</design-methodology>

<when-to-use-langgraph>

| Use LangGraph When | Use Alternatives When |
|-------------------|----------------------|
| Need fine-grained control over agent orchestration | Quick prototyping → LangChain agents |
| Building complex workflows with branching/loops | Simple stateless workflows → LangChain direct |
| Require human-in-the-loop, persistence | Batteries-included features → Deep Agents |

</when-to-use-langgraph>

---

## State Management

<state-update-strategies>

| Need | Solution | Example |
|------|----------|---------|
| Overwrite value | No reducer (default) | Simple fields like counters |
| Append to list | Reducer (operator.add / concat) | Message history, logs |
| Custom logic | Custom reducer function | Complex merging |

</state-update-strategies>

---

## Nodes

<node-function-signatures>

Node functions return partial state updates. Signatures for configuration and runtime access differ by language; use the applicable implementation reference.

</node-function-signatures>

---

## Edges

<edge-type-selection>

| Need | Edge Type | When to Use |
|------|-----------|-------------|
| Always go to same node | `add_edge()` | Fixed, deterministic flow |
| Route based on state | `add_conditional_edges()` | Dynamic branching |
| Update state AND route | `Command` | Combine logic in single node |
| Fan-out to multiple nodes | `Send` | Parallel processing with dynamic inputs |

</edge-type-selection>

---

## Command

Command combines state updates and routing in a single return value. Fields:
- **`update`**: State updates to apply (like returning a dict from a node)
- **`goto`**: Node name(s) to navigate to next
- **`resume`**: Value to resume after `interrupt()` — see human-in-the-loop skill

<command-return-type-annotations>

**Python**: Use `Command[Literal["node_a", "node_b"]]` as the return type annotation to declare valid goto destinations.

**TypeScript**: Pass `{ ends: ["node_a", "node_b"] }` as the third argument to `addNode` to declare valid goto destinations.

</command-return-type-annotations>

<warning-command-static-edges>

**Warning**: `Command` only adds **dynamic** edges — static edges defined with `add_edge` / `addEdge` still execute. If `node_a` returns `Command(goto="node_c")` and you also have `graph.add_edge("node_a", "node_b")`, **both** `node_b` and `node_c` will run.

</warning-command-static-edges>

---

## Send API

Fan-out with `Send`: return `[Send("worker", {...})]` from a conditional edge to spawn parallel workers. Requires a reducer on the results field.

---

## Running Graphs: Invoke and Stream

<invoke-basics>

Call `graph.invoke(input, config)` to run a graph to completion and return the final state.

</invoke-basics>

<stream-mode-selection>

| Mode | What it Streams | Use Case |
|------|----------------|----------|
| `values` | Full state after each step | Monitor complete state |
| `updates` | State deltas | Track incremental updates |
| `messages` | LLM tokens + metadata | Chat UIs |
| `custom` | User-defined data | Progress indicators |

</stream-mode-selection>

---

## Error Handling

Match the error type to the right handler:

<error-handling-table>

| Error Type | Who Fixes | Strategy | Example |
|---|---|---|---|
| Transient (network, rate limits) | System | `RetryPolicy(max_attempts=3)` | `add_node(..., retry_policy=...)` |
| LLM-recoverable (tool failures) | LLM | `ToolNode(tools, handle_tool_errors=True)` | Error returned as ToolMessage |
| User-fixable (missing info) | Human | `interrupt({"message": ...})` | Collect missing data (see HITL skill) |
| Unexpected | Developer | Let bubble up | `raise` |

</error-handling-table>

---

## Core boundaries

- Return partial state updates from nodes instead of mutating state directly.
- Route loops through a named node; `START` is entry-only.
- Define reducers for accumulated list fields; otherwise, the last write wins.
- Account for static edges when using `Command` with `goto`, because both routes execute.

## Implementation references

If writing, modifying, or debugging LangGraph code, determine the project's language from its existing files, then read the applicable reference before implementing:

- For Python, read [references/python.md](references/python.md).
- For TypeScript, read [references/typescript.md](references/typescript.md).

Read both only when the task covers both languages. For conceptual questions that require no code, do not load either reference.