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.

Zur Installation springen

Repository: existential-birds/beagle
Letzte Quellaktivität: 31. Mai 2026 um 22:49
Erkannte Sprache von SKILL.md: Englisch
Sterne: 79
Forks: 8

Installationsoptionen

Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.

Quelldateien prüfen

Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.

Zurück zur Suche Zurück zur Installation

# 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

# 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]

# 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

# 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())

# 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)

# 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")]

# 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}

# 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]

# 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")

# BAD - orphan node
builder.add_node("main", main_fn)
builder.add_node("orphan", orphan_fn)  # Never reached!
builder.add_edge(START, "main")
builder.add_edge("main", END)

# Check with visualization
print(graph.get_graph().draw_mermaid())

# BAD - missing path in conditional
def router(state) -> Literal["a", "b", "c"]:
    ...

builder.add_conditional_edges("node", router, {"a": "a", "b": "b"})
# "c" path missing!

# GOOD - include all possible returns
builder.add_conditional_edges("node", router, {"a": "a", "b": "b", "c": "c"})
# Or omit path_map to use return values as node names

# BAD - Command return without destinations (breaks visualization)
def dynamic(state) -> Command[Literal["next", "__end__"]]:
    return Command(goto="next")

builder.add_node("dynamic", dynamic)  # Graph viz won't show edges

# GOOD - declare destinations
builder.add_node("dynamic", dynamic, destinations=["next", END])

# BAD - async node called with sync invoke
async def my_node(state):
    result = await async_operation()
    return {"result": result}

graph.invoke(input)  # May not await properly!

# GOOD - use ainvoke for async graphs
await graph.ainvoke(input)
# Or provide both sync and async versions

# BAD - blocking call in async node
async def my_node(state):
    result = requests.get(url)  # Blocks event loop!
    return {"result": result}

# GOOD - use async HTTP client
async def my_node(state):
    async with httpx.AsyncClient() as client:
        result = await client.get(url)
    return {"result": result}

# BAD - AI message with tool_calls but no tool execution
messages = [
    HumanMessage(content="search for X"),
    AIMessage(content="", tool_calls=[{"id": "1", "name": "search", ...}])
    # Missing ToolMessage! Next LLM call will fail
]

# GOOD - always pair tool_calls with ToolMessage
messages = [
    HumanMessage(content="search for X"),
    AIMessage(content="", tool_calls=[{"id": "1", "name": "search", ...}]),
    ToolMessage(content="results", tool_call_id="1")
]

# BAD - model may call multiple tools including interrupt
model = ChatOpenAI().bind_tools([interrupt_tool, other_tool])
# If both called in parallel, interrupt behavior is undefined

# GOOD - disable parallel tool calls before interrupt
model = ChatOpenAI().bind_tools(
    [interrupt_tool, other_tool],
    parallel_tool_calls=False
)

# BAD - in-memory checkpointer loses state on restart
graph = builder.compile(checkpointer=InMemorySaver())  # Testing only!

# GOOD - use persistent storage in production
from langgraph.checkpoint.postgres import PostgresSaver
checkpointer = PostgresSaver.from_conn_string(conn_string)
graph = builder.compile(checkpointer=checkpointer)

# BAD - subgraph with explicit False prevents persistence
subgraph = sub_builder.compile(checkpointer=False)

# GOOD - use None to inherit parent's checkpointer
subgraph = sub_builder.compile(checkpointer=None)  # Inherits from parent
# Or True for independent checkpointing
subgraph = sub_builder.compile(checkpointer=True)

# BAD - returning large data in every node
def node(state):
    large_data = fetch_large_data()
    return {"large_field": large_data}  # Checkpointed every step!

# GOOD - use references or store
from langgraph.store.memory import InMemoryStore

def node(state, *, store: BaseStore):
    store.put(namespace, key, large_data)
    return {"data_ref": f"{namespace}/{key}"}

# BAD - no protection against infinite loops
def router(state):
    return "agent"  # Always loops!

# GOOD - check remaining steps or use RemainingSteps
from langgraph.managed import RemainingSteps

class State(TypedDict):
    messages: Annotated[list, add_messages]
    remaining_steps: RemainingSteps

def check_limit(state):
    if state["remaining_steps"] < 2:
        return END
    return "continue"