langgraph-functional-api

LangGraph Functional API Skill

Expert assistance for the LangGraph Functional API: imperative, Python-native agent workflows using @entrypoint and @task decorators. Gets you LangGraph persistence, streaming, and human-in-the-loop with minimal boilerplate — especially useful for wrapping existing agents or frameworks.

When to choose Functional API over Graph API:

• You're wrapping an existing agent (CrewAI, AutoGen, Strands, etc.)
• Your workflow is naturally imperative (sequential Python logic with branches)
• You need parallel task execution without defining a graph topology
• You want to add persistence/streaming to an agent you didn't write in LangGraph

Full corpus: ../langgraph/references/llms-txt.md (5.4 MB) and ../../langchain-deepagents/references/llms-full.md (10 MB).

When to Use This Skill

Activate when:

• Writing @entrypoint workflows — defining the top-level entry to a LangGraph application
• Defining @task functions — wrapping individual operations with checkpointing and retries
• Running tasks in parallel — invoking multiple tasks concurrently and awaiting results
• Wrapping third-party agents — integrating CrewAI, AutoGen, Strands, or any framework
• Configuring durable execution — choosing exit, async, or sync durability modes
• Using tasks inside Graph API nodes — mixing both APIs in one graph
• Handling RunControl / GraphDrained — controlling graph execution lifecycle programmatically
• Streaming from functional workflows — getting token-level or state-level streaming output

Quick Reference

Minimal @entrypoint + @task

from langgraph.checkpoint.memory import InMemorySaver
from langgraph.func import entrypoint, task

@task
def fetch_data(query: str) -> str:
    return call_api(query)  # Any IO operation

@task
def process_data(raw: str) -> str:
    return transform(raw)

@entrypoint(checkpointer=InMemorySaver())
def my_workflow(inputs: dict) -> dict:
    raw = fetch_data(inputs["query"]).result()
    processed = process_data(raw).result()
    return {"output": processed}

# Invoke with thread_id for persistence
result = my_workflow.invoke(
    {"query": "hello"},
    config={"configurable": {"thread_id": "1"}},
)

Parallel task execution

from langgraph.func import entrypoint, task

@task
def search_web(query: str) -> str:
    return web_search(query)

@task
def search_docs(query: str) -> str:
    return doc_search(query)

@entrypoint(checkpointer=InMemorySaver())
def research_workflow(inputs: dict) -> dict:
    # Launch both tasks concurrently
    web_future = search_web(inputs["query"])
    docs_future = search_docs(inputs["query"])

    # Collect results (blocks until both done)
    web_result = web_future.result()
    docs_result = docs_future.result()

    return {"web": web_result, "docs": docs_result}

Task retry policy

from langgraph.func import entrypoint, task
from langgraph.types import RetryPolicy

@task(retry=RetryPolicy(max_attempts=3, backoff_factor=2.0))
def flaky_api_call(payload: dict) -> dict:
    return requests.post(API_URL, json=payload).json()

@entrypoint(checkpointer=InMemorySaver())
def workflow(inputs: dict) -> dict:
    result = flaky_api_call(inputs).result()
    return {"result": result}

Durable execution modes

from langgraph.runtime import RunControl

# Choose durability level — trade-off: performance vs crash recovery
control = RunControl(
    durability="sync"    # "exit" | "async" | "sync"
    # exit: fastest, saves only at exit (no mid-run crash recovery)
    # async: saves asynchronously (good balance)
    # sync: saves before every step (slowest, most durable)
)

Human-in-the-loop interrupt in a task

from langgraph.types import interrupt, Command
from langgraph.func import entrypoint, task

@task
def get_approval(action: str) -> str:
    return interrupt({"prompt": f"Approve: {action}?"})

@entrypoint(checkpointer=InMemorySaver())
def workflow(inputs: dict) -> dict:
    decision = get_approval(inputs["action"]).result()
    if decision == "yes":
        return {"status": "approved"}
    return {"status": "rejected"}

# Resume after interrupt
workflow.invoke(
    Command(resume="yes"),
    config={"configurable": {"thread_id": "1"}},
)

Wrap a third-party agent (Strands, CrewAI, AutoGen)

from langgraph.func import entrypoint
from langgraph.checkpoint.memory import InMemorySaver

# Example: wrap a Strands agent
from strands import Agent as StrandsAgent
from strands_tools import http_request

strands_agent = StrandsAgent(tools=[http_request])

@entrypoint(checkpointer=InMemorySaver())
def strands_workflow(inputs: dict) -> dict:
    # Your existing agent runs inside @entrypoint
    # Gets LangSmith persistence, streaming, HITL for free
    result = strands_agent(inputs["prompt"])
    return {"output": str(result)}

# Now deployable to LangSmith with full observability

Tasks inside Graph API nodes (mixing APIs)

from langgraph.graph import StateGraph, START
from langgraph.func import task
from langgraph.types import RetryPolicy

@task(retry=RetryPolicy(max_attempts=2))
def llm_call(prompt: str) -> str:
    return llm.invoke(prompt)

# Use @task inside a StateGraph node
def my_node(state: State) -> State:
    # Convert node operations to tasks for granular checkpointing
    result = llm_call(state["input"]).result()
    return {"output": result}

graph = StateGraph(State)
graph.add_node("my_node", my_node)

Streaming from an @entrypoint

# Functional API supports the same stream modes as Graph API
config = {"configurable": {"thread_id": "1"}}

for chunk in my_workflow.stream(inputs, config, stream_mode="updates"):
    print(chunk)

for chunk in my_workflow.stream(inputs, config, stream_mode="messages"):
    print(chunk)  # LLM tokens

Functional API Concepts

Concept	Decorator/Type	Notes
Workflow entry	@entrypoint(checkpointer=...)	Top-level callable; required for persistence
Unit of work	@task	Individually checkpointed; retryable
Invoke	workflow.invoke(inputs, config)	Blocking call
Stream	workflow.stream(inputs, config, stream_mode=...)	Streaming call
Parallel	Launch multiple tasks, call .result() to collect	Like asyncio.gather
Retry	@task(retry=RetryPolicy(...))	Per-task retry logic
Durability	RunControl(durability="sync|async|exit")	Checkpoint frequency
Interrupt	interrupt(payload) inside task	Pause for human input
Resume	workflow.invoke(Command(resume=value), config)	Continue after interrupt
Third-party	Any callable inside @entrypoint	Wrap CrewAI, AutoGen, etc.

Functional API vs Graph API

Need	Use
Explicit graph topology / visualization	Graph API
Imperative Python logic	Functional API
Wrapping an existing agent	Functional API
Named, inspectable stages	Graph API
Parallel tasks with dynamic count	Functional API
Complex conditional branching	Graph API
Minimal boilerplate	Functional API
Fan-out with Send	Graph API

Reference Files

File	Location
LangGraph Python docs index	../langgraph/references/llms.md
1473-page corpus summaries	../langgraph/references/llms-txt.md
Full content + all code	../../langchain-deepagents/references/llms-full.md

Key doc pages: docs.langchain.com/oss/python/langgraph/functional-api, /use-functional-api, /choosing-apis, /durable-execution