Skill 15 · Langgraph Fundamentals
Subchapter 15.1
references/python.mdMarkdown9 KBView on GitHub
Use this reference only for LangGraph projects written in Python. The shared concepts, decisions, and invariants remain in ../SKILL.md.
Define state schema with reducers for accumulating lists and summing integers.
from typing_extensions import TypedDict, Annotated
import operator
class State(TypedDict):
name: str # Default: overwrites on update
messages: Annotated[list, operator.add] # Appends to list
total: Annotated[int, operator.add] # Sums integersWithout a reducer, returning a list overwrites previous values.
# WRONG: List will be OVERWRITTEN
class State(TypedDict):
messages: list # No reducer!
# Node 1 returns: {"messages": ["A"]}
# Node 2 returns: {"messages": ["B"]}
# Final: {"messages": ["B"]} # "A" is LOST!
# CORRECT: Use Annotated with operator.add
from typing import Annotated
import operator
class State(TypedDict):
messages: Annotated[list, operator.add]
# Final: {"messages": ["A", "B"]}Nodes must return partial updates, not mutate and return full state.
# WRONG: Returning entire state object
def my_node(state: State) -> State:
state["field"] = "updated"
return state # Don't mutate and return!
# CORRECT: Return dict with only the updates
def my_node(state: State) -> dict:
return {"field": "updated"}| Signature | When to Use |
|---|---|
def node(state: State) | Simple nodes that only need state |
def node(state: State, config: RunnableConfig) | Need thread_id, tags, or configurable values |
def node(state: State, runtime: Runtime[Context]) | Need runtime context, store, or stream_writer |
from langchain_core.runnables import RunnableConfig
from langgraph.runtime import Runtime
def plain_node(state: State):
return {"results": "done"}
def node_with_config(state: State, config: RunnableConfig):
thread_id = config["configurable"]["thread_id"]
return {"results": f"Thread: {thread_id}"}
def node_with_runtime(state: State, runtime: Runtime[Context]):
user_id = runtime.context.user_id
return {"results": f"User: {user_id}"}Simple two-node graph with linear edges.
from langgraph.graph import StateGraph, START, END
from typing_extensions import TypedDict
class State(TypedDict):
input: str
output: str
def process_input(state: State) -> dict:
return {"output": f"Processed: {state['input']}"}
def finalize(state: State) -> dict:
return {"output": state["output"].upper()}
graph = (
StateGraph(State)
.add_node("process", process_input)
.add_node("finalize", finalize)
.add_edge(START, "process")
.add_edge("process", "finalize")
.add_edge("finalize", END)
.compile()
)
result = graph.invoke({"input": "hello"})
print(result["output"]) # "PROCESSED: HELLO"Route to different nodes based on state with conditional edges.
from typing import Literal
from langgraph.graph import StateGraph, START, END
class State(TypedDict):
query: str
route: str
result: str
def classify(state: State) -> dict:
if "weather" in state["query"].lower():
return {"route": "weather"}
return {"route": "general"}
def route_query(state: State) -> Literal["weather", "general"]:
return state["route"]
graph = (
StateGraph(State)
.add_node("classify", classify)
.add_node("weather", lambda s: {"result": "Sunny, 72F"})
.add_node("general", lambda s: {"result": "General response"})
.add_edge(START, "classify")
.add_conditional_edges("classify", route_query, ["weather", "general"])
.add_edge("weather", END)
.add_edge("general", END)
.compile()
)Command lets you update state AND choose next node in one return.
from langgraph.types import Command
from typing import Literal
class State(TypedDict):
count: int
result: str
def node_a(state: State) -> Command[Literal["node_b", "node_c"]]:
"""Update state AND decide next node in one return."""
new_count = state["count"] + 1
if new_count > 5:
return Command(update={"count": new_count}, goto="node_c")
return Command(update={"count": new_count}, goto="node_b")
graph = (
StateGraph(State)
.add_node("node_a", node_a)
.add_node("node_b", lambda s: {"result": "B"})
.add_node("node_c", lambda s: {"result": "C"})
.add_edge(START, "node_a")
.add_edge("node_b", END)
.add_edge("node_c", END)
.compile()
)Fan out tasks to parallel workers using the Send API and aggregate results.
from langgraph.types import Send
from typing import Annotated
import operator
class OrchestratorState(TypedDict):
tasks: list[str]
results: Annotated[list, operator.add]
summary: str
def orchestrator(state: OrchestratorState):
"""Fan out tasks to workers."""
return [Send("worker", {"task": task}) for task in state["tasks"]]
def worker(state: dict) -> dict:
return {"results": [f"Completed: {state['task']}"]}
def synthesize(state: OrchestratorState) -> dict:
return {"summary": f"Processed {len(state['results'])} tasks"}
graph = (
StateGraph(OrchestratorState)
.add_node("worker", worker)
.add_node("synthesize", synthesize)
.add_conditional_edges(START, orchestrator, ["worker"])
.add_edge("worker", "synthesize")
.add_edge("synthesize", END)
.compile()
)
result = graph.invoke({"tasks": ["Task A", "Task B", "Task C"]})Use a reducer to accumulate parallel worker results (otherwise last worker overwrites).
# WRONG: No reducer - last worker overwrites
class State(TypedDict):
results: list
# CORRECT
class State(TypedDict):
results: Annotated[list, operator.add] # Accumulatesresult = graph.invoke({"input": "hello"})
# With config (for persistence, tags, etc.)
result = graph.invoke({"input": "hello"}, {"configurable": {"thread_id": "1"}})Stream LLM tokens in real-time for chat UI display.
for chunk in graph.stream(
{"messages": [HumanMessage("Hello")]},
stream_mode="messages"
):
token, metadata = chunk
if hasattr(token, "content"):
print(token.content, end="", flush=True)Emit custom progress updates from within nodes using the stream writer.
from langgraph.config import get_stream_writer
def my_node(state):
writer = get_stream_writer()
writer("Processing step 1...")
# Do work
writer("Complete!")
return {"result": "done"}
for chunk in graph.stream({"data": "test"}, stream_mode="custom"):
print(chunk)Use RetryPolicy for transient errors (network issues, rate limits).
from langgraph.types import RetryPolicy
workflow.add_node(
"search_documentation",
search_documentation,
retry_policy=RetryPolicy(max_attempts=3, initial_interval=1.0)
)Use ToolNode from langgraph.prebuilt to handle tool execution and errors. When handle_tool_errors=True, errors are returned as ToolMessages so the LLM can recover.
from langgraph.prebuilt import ToolNode
tool_node = ToolNode(tools, handle_tool_errors=True)
workflow.add_node("tools", tool_node)Must compile() to get executable graph.
# WRONG
builder.invoke({"input": "test"}) # AttributeError!
# CORRECT
graph = builder.compile()
graph.invoke({"input": "test"})Provide conditional path to END to avoid infinite loops.
# WRONG: Loops forever
builder.add_edge("node_a", "node_b")
builder.add_edge("node_b", "node_a")
# CORRECT
def should_continue(state):
return END if state["count"] > 10 else "node_b"
builder.add_conditional_edges("node_a", should_continue)# Router must return names of nodes that exist in the graph
builder.add_node("my_node", func) # Add node BEFORE referencing in edges
builder.add_conditional_edges("node_a", router, ["my_node"])
# Command return type needs Literal for routing destinations (Python)
def node_a(state) -> Command[Literal["node_b", "node_c"]]:
return Command(goto="node_b")
# START is entry-only - cannot route back to it
builder.add_edge("node_a", START) # WRONG!
builder.add_edge("node_a", "entry") # Use a named entry node instead
# Reducer expects matching types
return {"items": ["item"]} # List for list reducer, not a string