Reviewed by Zoran P — Editor, AI Agents List · Last verified: June 10, 2026 · How we test
Choose LangGraph if you are building a production system where execution order, persistent state, and human-in-the-loop checkpoints must be explicit and auditable — and you accept a harder learning curve for that control.
Choose CrewAI if you want a working multi-agent prototype fast — you define agents by role and task, CrewAI orchestrates them, and hosted deployment with monitoring starts free (50 executions/month) and costs $25/month at the Professional tier.
Last verified: June 10, 2026
Key facts
LangGraph and CrewAI are both free, open-source frameworks for orchestrating multi-agent systems; the differences that matter are abstraction level, state control, language support, and what hosted deployment costs.
| LangGraph Graph-Based Framework for Stateful Multi-Agent Workflows | CrewAI Multi-Agent Orchestration Platform | |
|---|---|---|
| Pricing tiers | Framework: $0 (MIT) · LangSmith observability: Developer free (5,000 traces/mo) · Plus $39/seat/mo · Enterprise custom | OSS: $0 · Hosted: Basic free (50 executions/mo) · Professional $25/mo · Enterprise custom |
| Free plan | Yes — the entire framework is free and self-hosted | Yes — the full open-source framework is free; only hosted deployment is paid |
| Key capabilities | Explicit graph orchestration (nodes and edges), persistent typed state, human-in-the-loop interrupts, real-time streaming, conditional branching | Role-based agent teams, high-level task workflows, automatic orchestration, built-in monitoring and observability on hosted plans |
| Model support | OpenAI, Anthropic, Google Gemini, Azure OpenAI — usable with any LLM provider directly | OpenAI, Anthropic, and other major LLMs with minimal boilerplate |
| Language | Python + JS/TS — LangGraph.js runs on Node.js, Deno, Cloudflare Workers, Vercel Edge Functions, and browsers | Python only — 3.10–3.13, installs via pip install crewai |
| Deployment | Self-hosted, or hosted deployment with usage-based compute pricing on top of LangSmith plans | Self-hosted OSS, CrewAI AMP (managed SaaS), or CrewAI Factory (containerized — private cloud or on-premises) |
| Multi-agent model | Single, parallel, and hierarchical agent architectures within one explicit graph | Crews of role-specialised agents with automatic task handoffs |
| Notable limits | Steep learning curve; more boilerplate for simple tasks; debugging complex graphs without LangSmith is difficult | Less granular execution control; hosted free tier capped at 50 executions/mo, 1 live crew, 1 seat; API changes possible between versions |
LangGraph and CrewAI take documented but opposite approaches to the same job: CrewAI asks you to describe agents by role and task and orchestrates them for you, while LangGraph asks you to define the execution graph yourself — every node, edge, and state transition is explicit in your code.
On a multi-agent research pipeline, CrewAI's documented path is to define a researcher agent and a synthesis agent with roles and tasks, then let the crew run the handoffs automatically — faster to stand up, with execution flow handled by the framework. LangGraph's documented path is to define each step as a graph node with conditional edges between them, which costs more wiring but adds things CrewAI's model does not express cleanly: typed state that persists across sessions, interrupt points where execution pauses for human approval, and token-level streaming at every step. We have not yet published side-by-side results from running both frameworks on an identical task.
LangGraph and CrewAI cost the same to start: both are free, open-source frameworks, so the only mandatory spend with either is the LLM API usage your agents generate. The cost differences appear at production. LangGraph's framework has no paid tiers, and LangSmith — LangChain's separate observability platform — starts free with a Developer tier (5,000 traces/month, 14-day retention, 1 seat) before the $39/seat/month Plus plan; hosted deployment adds usage-based compute costs on top. CrewAI's hosted platform publishes its entry pricing: a free Basic plan capped at 50 executions/month, 1 live crew, and 1 seat, then Professional at $25/month, with only Enterprise requiring a sales conversation. If you self-host everything yourself, both LangGraph and CrewAI stay at $0 beyond your LLM bills.
Yes — CrewAI is easier to learn than LangGraph: CrewAI is rated medium setup difficulty in our data while LangGraph is rated hard, and tasks that take about 10 lines of code in CrewAI can take about 40 in LangGraph. The gap comes from abstraction level. CrewAI lets you define agents by role and task and handles orchestration automatically, so a working multi-agent crew is achievable in an afternoon. LangGraph requires you to understand its graph execution model first — every node, edge, and state transition is defined explicitly — which is real up-front cost. That cost buys control CrewAI's model does not express cleanly: deterministic execution order, typed persistent state, and human-in-the-loop checkpoints. A common path in practice is to prototype in CrewAI and move to LangGraph when production requirements exceed what CrewAI's abstractions can express.
Neither LangGraph nor CrewAI is a safe default — each has documented limitations worth weighing before you commit.
Most teams choosing between LangGraph and CrewAI can decide on three things: how much execution control they need, how fast they need a working prototype, and whether they want a managed hosting option.
Quick answers to the questions developers ask most when choosing between LangGraph and CrewAI.
Neither — LangGraph and CrewAI are both free, open-source frameworks, so you pay only for LLM API usage. Optional paid layers: LangSmith (LangGraph's observability companion) has a free Developer tier with 5,000 traces/month and a $39/seat/month Plus plan, while CrewAI's hosted platform has a free 50-executions/month tier and a $25/month Professional plan.
Typically you pick one — LangGraph and CrewAI solve the same multi-agent orchestration problem, and each lists the other as its main alternative. The common pattern is sequential rather than combined: teams prototype in CrewAI for speed, then move to LangGraph when they need explicit control over execution order, state, or human-in-the-loop steps.
LangGraph is the stronger fit for production pipelines that need deterministic, auditable execution — its explicit graph model records every step, decision, and state transition. CrewAI is production-capable by a different route: its hosted platform (CrewAI AMP, from a free tier to $25/month Professional) and CrewAI Factory for private cloud add monitoring, error handling, and observability on top of the role-based framework.
Only CrewAI does — it requires Python 3.10–3.13 and installs via pip install crewai. LangGraph ships in both Python and JavaScript/TypeScript: LangGraph.js shares the same core API and runs on Node.js, Deno, Cloudflare Workers, Vercel Edge Functions, and browsers, making LangGraph the only option of the two for non-Python teams.
LangGraph and CrewAI each have a full profile with pricing, limitations, and alternatives — start there if you are still deciding.
LangGraph — full profile
Graph-Based Framework for Stateful Multi-Agent Workflows
CrewAI — full profile
Multi-Agent Orchestration Platform
Browse all agent frameworks tools → /categories/agent-frameworks