What is LangGraph — Build Stateful AI Agent Workflows?

Framework for building stateful, multi-step AI agent workflows as graphs. LangGraph enables cycles, branching, human-in-the-loop, and persistent state for complex agent systems.

Is LangGraph — Build Stateful AI Agent Workflows free to use?

Yes. LangGraph — Build Stateful AI Agent Workflows is freely available on TokRepo. Check the Source & Thanks section on the asset page for the specific open-source license.

How do I install LangGraph — Build Stateful AI Agent Workflows?

Visit the asset page on TokRepo and click "Copy for agent" to get the installation instructions. Most assets can be installed with a single command.

LangGraph — Build Stateful AI Agent Workflows

pip install langgraph

from langgraph.graph import StateGraph, START, END
from typing import TypedDict

class State(TypedDict):
    messages: list
    next_step: str

def research(state: State) -> State:
    # Agent researches the topic
    return {"messages": state["messages"] + ["Research complete"], "next_step": "write"}

def write(state: State) -> State:
    # Agent writes based on research
    return {"messages": state["messages"] + ["Draft written"], "next_step": "review"}

def review(state: State) -> State:
    # Agent reviews the draft
    return {"messages": state["messages"] + ["Review done"], "next_step": "end"}

# Build graph
graph = StateGraph(State)
graph.add_node("research", research)
graph.add_node("write", write)
graph.add_node("review", review)
graph.add_edge(START, "research")
graph.add_edge("research", "write")
graph.add_edge("write", "review")
graph.add_edge("review", END)

app = graph.compile()
result = app.invoke({"messages": ["Write about AI agents"], "next_step": "research"})

What is LangGraph?

LangGraph is a framework by LangChain for building stateful, multi-step AI agent workflows as directed graphs. Unlike simple chains, LangGraph supports cycles (agent loops), conditional branching, human-in-the-loop approval, and persistent state across steps. It is the go-to framework for complex agent orchestration that goes beyond linear pipelines.

Answer-Ready: LangGraph builds stateful AI agent workflows as graphs. Supports cycles, branching, human-in-the-loop, and persistent state. By LangChain team. Used for multi-step agents, tool-calling loops, and approval workflows. LangGraph Cloud for managed deployment. 8k+ GitHub stars.

Best for: Teams building complex multi-step agent workflows. Works with: OpenAI, Claude, any LLM via LangChain. Setup time: Under 5 minutes.

Core Concepts

1. State

class AgentState(TypedDict):
    messages: list      # Conversation history
    context: str        # Retrieved context
    plan: list          # Agent's plan
    iteration: int      # Loop counter

2. Nodes (Steps)

Each node is a function that transforms state:

def analyze(state: AgentState) -> AgentState:
    # LLM call, tool use, or pure logic
    return {"messages": state["messages"] + [analysis]}

3. Edges (Transitions)

# Unconditional
graph.add_edge("research", "write")

# Conditional branching
def should_continue(state):
    if state["iteration"] > 3:
        return "end"
    return "retry"

graph.add_conditional_edges("check", should_continue, {"retry": "research", "end": END})

4. Cycles (Agent Loops)

# Agent keeps iterating until satisfied
graph.add_edge("act", "observe")
graph.add_conditional_edges("observe", check_done, {"continue": "act", "done": END})

5. Human-in-the-Loop

from langgraph.checkpoint.memory import MemorySaver

checkpointer = MemorySaver()
app = graph.compile(checkpointer=checkpointer, interrupt_before=["deploy"])

# Runs until "deploy" node, then pauses for human approval
result = app.invoke(state, config={"configurable": {"thread_id": "1"}})
# Human reviews...
app.invoke(None, config={"configurable": {"thread_id": "1"}})  # Resume

Common Patterns

Pattern	Graph Shape
ReAct Agent	think → act → observe → (loop)
Research + Write	research → outline → write → review
Approval Workflow	draft → review → [approve/reject] → publish
Multi-Agent	coordinator → [agent_a, agent_b] → merge
Retry with Feedback	execute → validate → [pass/fail → retry]

LangGraph vs Alternatives

Feature	LangGraph	CrewAI	AutoGen
Graph-based	Yes	No (sequential/parallel)	No
Cycles	Yes	Limited	Yes
Human-in-loop	Built-in	No	Yes
State persistence	Built-in	No	Limited
Managed cloud	LangGraph Cloud	No	No

FAQ

Q: Do I need LangChain to use LangGraph? A: No, LangGraph is a standalone library. It integrates well with LangChain but doesn't require it.

Q: Can I use Claude with LangGraph? A: Yes, use ChatAnthropic from langchain-anthropic as the LLM in your nodes.

Q: What is LangGraph Cloud? A: Managed hosting for LangGraph applications with API endpoints, monitoring, and auto-scaling.

LangGraph — Build Stateful AI Agent Workflows

Use it first, then decide how deep to go

What is LangGraph?

Core Concepts

1. State

2. Nodes (Steps)

3. Edges (Transitions)

4. Cycles (Agent Loops)

5. Human-in-the-Loop

Common Patterns

LangGraph vs Alternatives

FAQ

Source & Thanks

Discussion

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