from langgraph import Graph, State
class MyState(State):
query: str
plan: str | None
artifacts: list
issues: list
# Nodes
def search_node(state):
# call web search tool, write sources
return {"artifacts": state.artifacts + ,.
---
## Key Takeaways
- Model your workflow as a graph with explicit state to reduce drift.
- Use validators and checkpoints to make failures cheap and recoverable.
- Start small, prove routing logic, then layer in concurrency and subgraphs.
- Consider CrewAI/AutoGen if you prefer role/dialog metaphors over state machines.
### FAQ
Q1:What is LangGraph and how does it differ from LangChain Agents?
LangGraph is an agentic state machine that models AI workflows as nodes and edges with explicit shared state. Compared to LangChain Agents’ prompt-first ReAct style, LangGraph emphasizes deterministic routing, loops, and recoverable execution.
Q2:Is LangGraph good for multi-agent systems?
Yes. You can represent roles as nodes or subgraphs and coordinate them with policies and shared state, making multi-agent collaboration more predictable than dialog-only approaches.
Q3:When should I use LangGraph instead of CrewAI or AutoGen?
Choose LangGraph when you need strict flow control, loops, validation gates, and checkpoints. CrewAI or AutoGen may be better when you want role-based or conversational collaboration with less emphasis on explicit state.
Q4:Does LangGraph support self-reflection loops?
Yes. Builders commonly implement reflection and critique cycles that iteratively improve outputs, a pattern frequently discussed by the community.
Q5:How does LangGraph handle reliability and recovery?
LangGraph supports checkpoints and explicit state, enabling retries, resumability, and safer failure handling—features highlighted in in-depth reviews and practitioner guides.