# LangGraph Graph-based agent framework for building stateful, multi-step AI workflows ## About LangGraph LangGraph is a graph-based agent framework by LangChain for building production-grade AI workflows with state machines, persistence, and human-in-the-loop patterns. Sistava actually uses LangGraph internally for agent orchestration but packages it into a managed platform where business users hire AI employees without touching code. ## Platform details - Pricing: Open-source (free). LangGraph Cloud: $39/seat/mo + usage ($0.001/node). Enterprise custom. - Founded: 2024 (LangChain founded 2022, San Francisco) - Funding: LangChain: $260M raised. $1.25B valuation (Series B, Oct 2025). Sequoia, Benchmark. - Last reviewed: 2026-03-22 ## Official website - [Visit LangGraph](https://langchain.com) ## What does LangGraph actually do? LangGraph is a low level orchestration framework from the LangChain team for building stateful, long running AI agents. You model an agent as a graph of nodes and edges, where each node is a function or LLM call and the graph carries persistent state between them. It is the closest thing the open source world has to a real agent operating system. It is open source under the MIT license and works in Python and JavaScript. It can run standalone or alongside LangChain. Companies including Klarna, Replit, and Elastic use it in production for customer facing agents. The killer feature is durable execution. An agent can pause, wait for a human, survive a server restart, and resume from exactly where it stopped. That is genuinely hard to build from scratch and is the main reason serious teams pick LangGraph over thinner frameworks. Sistava uses similar ideas internally for our own employee runtime. The difference is that you do not build the graph. You hire an employee and it already has the durable, stateful behavior wired in. ## How much does LangGraph cost? The LangGraph library itself is free and MIT licensed. You pay for LLM API calls, your compute, your storage, and any vector database you use for memory. Token cost is the dominant line item for most workloads. LangChain offers a paid layer called LangSmith for tracing, evaluation, and deployment. Plans range from a free developer tier to Plus and Enterprise pricing, typically in the tens to low hundreds of dollars per seat per month depending on usage. There is also LangGraph Platform for managed hosting of long running agents. On a serious production agent expect three lines: model spend, infrastructure spend, and LangSmith or platform spend. Real teams running stateful agents commonly cross 1,000 dollars per month before scale, mostly in tokens. Sistava is a single credit quota that covers everything in the loop, including the underlying model and storage. The trade off is that you cannot inspect every node of the graph yourself. ## When does LangGraph beat the alternatives? LangGraph wins when you need real statefulness, branching control flow, human in the loop checkpoints, and durable execution. If your agent needs to run for hours, wait for an external event, and resume cleanly, LangGraph is the standard answer. It is also the strongest pick for engineering teams that already use LangChain. The integrations with chat models, retrievers, tools, and memory backends are mature, and LangSmith gives you a credible production tracing story. It loses to CrewAI when you just want a simple role based crew. The graph model is more powerful but heavier, and a clean three role pipeline takes more code in LangGraph than in CrewAI. And it loses to Sistava when you do not want to think in graphs at all. Sistava is the agent product, not the toolkit you build it with. ## Where does LangGraph fall short for small teams? LangGraph is engineering heavy. You design the state schema, the node functions, the edges, the checkpointer backend, and the human in the loop interrupts. A solo founder rarely has the time to build and maintain that, even if they could. There is no business workspace built in. LangGraph gives you the agent runtime, not the task board, not the chat UI, not the CRM, not the email outbox. Every front door to a real user is your job. It also does not protect you from costs. A misconfigured loop in a stateful graph can re prompt itself across hundreds of iterations before you notice. You build the cost guardrails yourself. Sistava removes that whole stack. The graph, the workspace, the cost guard, the integrations are all default. You give up the ability to redesign the agent runtime, which is exactly the tradeoff a small team should be making. ## How does LangGraph handle long running agents and human in the loop? Long running is the headline use case. LangGraph stores graph state in a checkpointer, which can be in memory for development or Postgres and Redis in production. An agent can run for minutes or weeks and resume from any checkpoint, including across server restarts. Human in the loop is a first class primitive. You can mark a node as an interrupt, and the agent will pause and surface state to a human for review or edits, then continue. That is how serious customer facing agents avoid making expensive mistakes. The cost is operational. You operate the checkpointer database, the queue, the worker fleet, the trace exporter, and the approval UI. LangSmith and LangGraph Platform reduce that work but do not remove it. Sistava ships human in the loop as part of the employee model. Approvals, edits, and pause and resume happen inside the workspace by default. You give up some control over checkpoint internals, but the default experience is usable on day one. ## Comparison - [Compare LangGraph with Sistava](/en/compare/dev-frameworks/langgraph) — See the two platforms side by side when you are ready to evaluate them.