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langchain-ai/langgraph-fundamentals

langchain-ai

langgraph-fundamentals

INVOKE THIS SKILL when writing ANY LangGraph code. Covers StateGraph, state schemas, nodes, edges, Command, Send, invoke, streaming, and error handling.

v1.0LATEST
NewUpdated Oct 12, 2026
  • 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.

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.
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

State Management

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

Nodes

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


Edges

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

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

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.

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.


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

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

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

Error Handling

Match the error type to the right handler:

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

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:

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

Files3
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Overall Score

87/100

Grade

A

Excellent

Grades are signals, not a certification. Always review a skill yourself before use.

Safety

92

Quality

85

Clarity

87

Completeness

82

Summary

LangGraph Fundamentals is an instructional skill that teaches AI agents how to design and implement stateful agent workflows using LangGraph's core concepts: StateGraph, nodes, edges, Command routing, Send API, and error handling. It provides language-specific implementation references for Python and TypeScript with practical code examples and common pitfalls.

Detected Capabilities

code generationstate machine designdocumentation readingconceptual instruction followingreference material lookup

Trigger Keywords

Phrases that agents use to match this skill to user intent.

langgraph workflowstateful agent designconditional edge routingparallel worker orchestrationstreaming graph executionlanggraph debugging

Use Cases

  • Design multi-step agent workflows with branching logic
  • Implement stateful graphs with accumulators and conditional routing
  • Build parallel worker patterns using the Send API
  • Handle errors and retries in agent orchestration
  • Stream LLM tokens and custom data in real-time
  • Debug common LangGraph mistakes (missing reducers, infinite loops, etc.)

Quality Notes

  • Comprehensive coverage of core LangGraph concepts with clear design methodology
  • Excellent use of tables for decision-making (state strategies, edge types, error handling)
  • Language-specific references are well-organized and include practical code examples
  • Strong emphasis on common mistakes with side-by-side correct/wrong code patterns
  • Good boundary documentation: when to use LangGraph vs. alternatives
  • Clear activation criteria: skill triggers on any LangGraph code writing task
  • State management explanation includes reducers and partial updates, two common failure points
  • Command and Send patterns well-explained with warnings about static edges
  • Error handling strategy matrix maps error types to appropriate handlers
Model: claude-haiku-4-5-20251001Analyzed: Oct 12, 2026

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