LangGraph

LangGraph is the "adult in the room" for agent frameworks—trading the magic of prompt-chains for the rigid reliability of state machines. While tools like CrewAI offer a friendly role-playing interface where you just tell an agent to "be a researcher," LangGraph forces you to define exactly how data flows between steps using nodes and edges. It is effectively a library for building cyclic computational graphs, making it the only serious choice for engineers who need to guarantee an agent won't hallucinate itself into an infinite loop.

The cost trade-off is stark. The open-source library is free (MIT license), but if you use the managed LangGraph Cloud, the pricing is aggressive. A workload processing 5,000 documents/day with a 5-step agent workflow equals roughly 750,000 node executions/month. On LangGraph Cloud, after the 100k free tier, you pay $0.001 per node plus uptime fees, easily pushing bills past $800/month before you even pay for LLM tokens. Self-hosting the library on a $50/month VPS is the obvious move for high-volume production apps, provided you have the DevOps chops to manage the persistence layer yourself.

Technically, LangGraph excels at "human-in-the-loop" patterns. Its checkpointer system saves the state of an agent after every step, allowing you to pause execution, wait for a human to approve a draft, and then resume days later from the exact same state. This is nearly impossible to do reliably in conversational frameworks like AutoGen. However, the developer experience is verbose. A simple linear chain that takes 3 lines in LangChain might take 20 lines of graph definition here. You aren't just writing prompts; you are architecting a system.

Skip LangGraph if you are building a quick prototype or a simple chatbot; the setup time isn't worth it. Use it if you are building a complex, long-running agentic workflow (like a coding assistant or a support bot) where state persistence and deterministic control flow are non-negotiable requirements.

Pricing

The open-source library is 100% free with no hidden limits. The "gotcha" lies in LangGraph Cloud (managed hosting). It offers a free tier of 100k node executions/month, but scales to $0.001 per node execution thereafter. Crucially, Cloud also charges "uptime fees" for keeping agents active ($0.0036/min for production), which can silently double your bill. For a standard heavy workload, self-hosting via Docker is practically mandatory to avoid paying 10x the infrastructure cost of a simple VM.

Technical Verdict

Production-grade but verbose. It creates a CompiledGraph that behaves like a standard LangChain Runnable, making integration seamless if you already use the ecosystem. Native support for cyclic graphs (loops) and persistence (Postgres/SQLite checkpointers) is best-in-class. Documentation is dense but accurate. Expect to write significant boilerplate code defining TypedDict states and conditional edges before your agent does anything.

Quick Start

# pip install langgraph
from langgraph.graph import StateGraph, START, END
from typing import TypedDict
 
class State(TypedDict): count: int
def step(s): return {"count": s["count"] + 1}
 
workflow = StateGraph(State)
workflow.add_node("step", step)
workflow.add_edge(START, "step")
workflow.add_edge("step", END)
print(workflow.compile().invoke({"count": 0}))

Watch Out

• LangGraph Cloud charges for 'uptime' minutes, not just executions, which can lead to surprisingly high bills.
• You must define a strict TypedDict schema for your state; dynamic data passing is painful.
• Async execution requires careful handling of the checkpointer to avoid race conditions.
• Simple linear chains feel over-engineered; only use it if you actually need loops or persistence.