Skip to content
Development
Skill

/langgraph-code-review

Reviews LangGraph code for bugs, anti-patterns, and improvements. Use when reviewing code that uses StateGraph, nodes, edges, checkpointing, or other LangGraph features. Catches common mistakes in state management, graph structure, and async patterns.

From plugin
beagle
85139 skills2 commands

Claude Code

$ npx -y skills add existential-birds/beagle --skill langgraph-code-review --agent claude-code

SKILL.md

langgraph-code-review.SKILL.md
name: langgraph-code-review
description: Reviews LangGraph code for bugs, anti-patterns, and improvements. Use when reviewing code that uses StateGraph, nodes, edges, checkpointing, or other LangGraph features. Catches common mistakes in state management, graph structure, and async patterns.

LangGraph Code Review

When reviewing LangGraph code, check for these categories of issues.

Anti-confabulation (gate 0 — runs before every other gate)

Before issuing **any** finding — flag a bug, anti-pattern, or improvement — you MUST echo the exact artifact you are judging, quoted from a source you read in **this** turn:

  • The code finding: its `file:line` plus the cited code, read freshly now.
  • The graph/state code under review: the `StateGraph`, node, edge, or state-schema snippet your finding depends on, quoted from the file you just read.

> The artifact is the only source of truth. **Never** infer what you are reviewing from the branch name, the working directory, surrounding files, or recollection. If your mental model differs from the freshly read source, **the source wins.** A finding issued without a same-turn echo of its target is invalid — emit the echo first, or do not emit the finding.

This gate exists because an LLM under contextual priming will confidently flag code that is not in the file. It runs **before** the gates below.

Review gates (sequenced)

Complete in order. Each step has an objective pass condition before moving on.

1. **Locate graph code** — Search the review scope for `StateGraph`, `compile(`, `invoke`, `ainvoke`, `add_node`, `add_edge`, `add_conditional_edges`. **Pass:** a short list of file paths (or explicit “none in scope” after searching).

2. **Map state schema** — For each graph state type (`TypedDict`, `BaseModel`, etc.), list fields that hold lists, dicts, or messages and whether `Annotated` + reducers (`add_messages`, `operator.add`, …) are present. **Pass:** every such field is either covered by a reducer pattern below or explicitly flagged as intentional overwrite.

3. **Trace persistence** — If interrupts, `thread_id`, or checkpoint APIs appear, follow them to `compile(..., checkpointer=...)` and invocation `config`. **Pass:** behavior matches the interrupt/checkpointer/thread_id guidance below—or you document a concrete mismatch with file:line.

4. **Report with evidence** — For each finding you will deliver, record **file path and line number(s)** (or a minimal quoted snippet). **Pass:** no critical or high-severity issue is stated without that citation.

5. **Run the checklist** — Use the checklist at the end of this skill; each item is **satisfied**, **not applicable** (with reason), or **open** with evidence. **Pass:** no item left silently unchecked.

Critical Issues

1. State Mutation Instead of Return

# BAD - mutates state directly
def my_node(state: State) -> None:
    state["messages"].append(new_message)  # Mutation!

# GOOD - returns partial update
def my_node(state: State) -> dict:
    return {"messages": [new_message]}  # Let reducer handle it

2. Missing Reducer for List Fields

# BAD - no reducer, each node overwrites
class State(TypedDict):
    messages: list  # Will be overwritten, not appended!

# GOOD - reducer appends
class State(TypedDict):
    messages: Annotated[list, operator.add]
    # Or use add_messages for chat:
    messages: Annotated[list, add_messages]

3. Wrong Return Type from Conditional Edge

# BAD - returns invalid node name
def router(state) -> str:
    return "nonexistent_node"  # Runtime error!

# GOOD - use Literal type hint for safety
def router(state) -> Literal["agent", "tools", "__end__"]:
    if condition:
        return "agent"
    return END  # Use constant, not string

4. Missing Checkpointer for Interrupts

# BAD - interrupt without checkpointer
def my_node(state):
    answer = interrupt("question")  # Will fail!
    return {"answer": answer}

graph = builder.compile()  # No checkpointer!

# GOOD - checkpointer required for interrupts
graph = builder.compile(checkpointer=InMemorySaver())

5. Forgetting Thread ID with Checkpointer

# BAD - no thread_id
graph.invoke({"messages": [...]})  # Error with checkpointer!

# GOOD - always provide thread_id
config = {"configurable": {"thread_id": "user-123"}}
graph.invoke({"messages": [...]}, config)

State Schema Issues

6. Using add_messages Without Message Types

# BAD - add_messages expects message-like objects
class State(TypedDict):
    messages: Annotated[list, add_messages]

def node(state):
    return {"messages": ["plain string"]}  # May fail!

# GOOD - use proper message types or tuples
def node(state):
    return {"messages": [("assistant", "response")]}
    # Or: [AIMessage(content="response")]

7. Returning Full State Instead of Partial

# BAD - returns entire state (may reset other fields)
def my_node(state: State) -> State:
    return {
        "counter": state["counter"] + 1,
        "messages": state["messages"],  # Unnecessary!
        "other": state["other"]          # Unnecessary!
    }

# GOOD - return only changed fields
def my_node(state: State) -> dict:
    return {"counter": state["counter"] + 1}

8. Pydantic State Without Annotations

# BAD - Pydantic model without reducer loses append behavior
class State(BaseModel):
    messages: list  # No reducer!

# GOOD - use Annotated even with Pydantic
class State(BaseModel):
    messages: Annotated[list, add_messages]

Graph Structure Issues

9. Missing Entry Point

# BAD - no edge from START
builder.add_node("process", process_fn)
builder.add_edge("process", END)
graph = builder.compile()  # Error: no entrypoint!

# GOOD - connect START
builder.add_edge(START, "process")

10. Unreachable Nodes

# BAD - orphan node
builder.add_node("main", main_fn)
builder.add_node("orphan", orphan_fn)  # Neve
Read more
@existential-birds
Ships withbeagle

Image: NASA, Public Domain. Source Beagle is an Agent Skills marketplace: framework-aware code review, documentation, testing, architectural analysis, and git workflows for any compatible coding agent.

Stars on GitHub

Momentum

stars in total
85
Star history of existential-birds/beagle on GitHub, a chart by star-history.com

Other skills on beagle.

More skills from other plugins.

brainstorming
Auto-invoked

brainstorming

You MUST use this before any creative work - creating features, building components, adding functionality, modifying behavior, or…

@obra@obraInstall Skill
ask-matt

ask-matt

Ask which skill or flow fits your situation. A router over the skills in this repo.

skills10d309k
@mattpocock@mattpocockInstall Skill
accessibility
Auto-invoked

accessibility

Design, implement, and audit accessible UI to WCAG 2.2 Level AA across Web, iOS, and Android — semantic ARIA roles and labels,…

ecc12d293k
@affaan-m@affaan-mInstall Skill
compiler-commit

compiler-commit

Use when you want to verify compiler changes and commit with the correct convention. Runs tests, lint, and format, then commits…

react1d252k
@facebook@facebookInstall Skill

Questions about langgraph-code-review.

Which coding agents can use langgraph-code-review?
langgraph-code-review installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.