LangGraph vs CrewAI vs AutoGen

Side-by-side comparison of features, pricing, and ratings

Analysis reviewed Live tool data as of 2026-10-22
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At a glance

DimensionLangGraphCrewAI
Pricingfreemium · from Developer $0/seat/mofreemium · from Basic $0/mo
Best forBackend and platform engineers shipping stateful agents to production, Teams that need human approval gates inside an agent loopEnterprise platform teams governing many agents across departments on one runtime, Regulated organizations needing RBAC, audit trails, SSO, and runtime PII redaction
Standout featuresGraph-based state management for explicit agent control flow · Human-in-the-loop checks to steer and approve agent actions · Built-in memory persisting conversation history and context across sessionsNo-code visual editor (Studio) with AI copilot, exportable to Python · Crew Studio automated multi-agent builder guided by 700k agent workflow patterns · Deterministic agent workflow construction for repeatable runs
Viability score81/10087/100
APIYesYes

LangGraph is the stronger pick for backend and platform engineers shipping stateful agents to production; CrewAI fits better for enterprise platform teams governing many agents across departments on one runtime.

Built from live tool data, last verified 2026-10-20.

LangGraph
LangGraph

LangGraph is an MIT-licensed, low-level agent orchestration framework for building stateful, controllable AI agents as graphs of states and transitions.

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CrewAI
CrewAI

CrewAI is an enterprise agent build and runtime platform: a visual multi-agent builder plus a governed control plane for production workflows.

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AutoGen
AutoGen

AutoGen is Microsoft's open-source framework for building conversational and event-driven multi-agent AI apps in Python and .NET.

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Pricing
Freemium
Freemium
Free
Plans
$0/seat/mo
$39/seat/mo
Custom
$0/mo
Custom
$0
Popularity
3.1k views
4.2k views
5.3k views
Skill Level
Advanced
Intermediate
Intermediate
API Available
Platforms
API
WebAPICLI
—
Categories
🕸️ Agent Frameworks & Orchestration📡 LLM Observability & Evals
🕸️ Agent Frameworks & Orchestration🛡️ AI Governance & Guardrails
🕸️ Agent Frameworks & Orchestration
Features
Graph-based state management for explicit agent control flow
Human-in-the-loop checks to steer and approve agent actions
Built-in memory persisting conversation history and context across sessions
First-class token-by-token streaming of reasoning and actions
Single-agent, multi-agent, and hierarchical architectures in one framework
Low-level primitives for fully customizable agent design
Model-agnostic: build and ship with any model provider
MCP support in the open-source stack, including elicitation and the stateless protocol
Expose your agent as an MCP server
1-click deploy to a horizontally scalable service
Scale-to-zero when idle for dedicated deployments
30+ API endpoints including state and memory (Assistants API)
Cron scheduling for agent runs
Sandboxed ephemeral code execution for agent-generated code
LLM Gateway cost limits, rate limiting, model fallbacks, and PII redaction
No-code visual editor (Studio) with AI copilot, exportable to Python
Crew Studio automated multi-agent builder guided by 700k agent workflow patterns
Deterministic agent workflow construction for repeatable runs
Code-first Python API for custom multi-agent orchestration
Role-based agents that separate and simplify orchestration
Canvas and prompt-style builder for long-horizon operational agents
Discovery ranks automation opportunities from tickets, chats, apps, and workflows
Interactive AI suggestions that refine recommended automations
Automations packaged as shareable presentation decks
One-click context to accelerate building automations
Export agents as an MCP server
Export agents as an embeddable UI component
Real-time tracing of every LLM call, tool call, and memory read with cost accounting
RBAC, immutable audit trails, Enterprise IAM, and SSO via MS Entra or Okta
Human-in-the-loop approval gates and live intervention during execution
AutoGen Studio web UI for prototyping agents without writing code (pip install, port 8080)
AgentChat Python framework for conversational single- and multi-agent apps, Python 3.10+
Core event-driven framework for scalable multi-agent systems
Deterministic and dynamic agentic workflows for business processes
Distributed multi-language agents via GrpcWorkerAgentRuntime over gRPC
AssistantAgent with model clients such as OpenAIChatCompletionClient
Customizable conversation patterns including group chat and sequential turns
McpWorkbench for connecting agents to Model Context Protocol (MCP) servers
OpenAIAssistantAgent for using the OpenAI Assistant API
DockerCommandLineCodeExecutor for running model-generated code in a Docker container
Community extensions you can discover, use, or publish
Human-in-the-loop approval and intervention inside agent workflows
Parallel .NET API surface alongside Python
Documented migration path for projects moving off AutoGen 0.2
MIT-licensed source you can self-host and modify
Integrations
OpenAI
Anthropic
Google Gemini
OpenRouter
Fireworks
Baseten
Ollama
Azure
AWS Bedrock
HuggingFace
GitHub
Slack
Microsoft Teams
MS Entra
Okta
Arize
Galileo
DataDog
Patronus
OpenTelemetry
Model Context Protocol (MCP) servers
Docker
gRPC
OpenAI Assistant API

What real users say: LangGraph vs CrewAI

Not marketing copy and not our opinion — a structured sweep of public discussion (reviews, forums, communities and video comments), showing what people praise and what they complain about for each tool.

LangGraph

117 mentions across 6 sources · 53% positive — mixed (averaged across 6 sources)

Hacker News, YouTube, Bluesky, Stack Overflow, GitHub, Lemmy

What users praise

  • • Fine-grained control over agent workflows and state transitions.
  • • Excellent for building complex multi-agent and hierarchical systems.
  • • Human-in-the-loop checks provide reliable agent moderation.
  • • Graph-based orchestration makes deterministic workflows intuitive.

What frustrates them

  • • Steep learning curve and API confusion for new users.
  • • Gets complicated fast for simple or single-agent tasks.
  • • Long tool calls silently re-execute on cloud, wasting cost.
  • • Security vulnerabilities can expose files and secrets.

Researched Jul 25, 2026

CrewAI

122 mentions across 7 sources · 57% positive — mixed (averaged across 7 sources)

Hacker News, YouTube, Product Hunt, Bluesky, Stack Overflow, GitHub, Lemmy

What users praise

  • • Role-based agents keep responsibilities clear in multi-agent systems.
  • • Enterprise governance layer with RBAC and audit trails for compliance.
  • • Discovery engine ranks automation opportunities from tickets and chats.
  • • Real-time tracing with full cost accounting per execution is useful.

What frustrates them

  • • Non-deterministic agent paths make production reliability a concern.
  • • No built-in idempotency guard for tool retries risks double actions.
  • • Dependency conflicts when installing optional tool packages are common.
  • • Default OpenAI embedding leads to SSL errors with other LLMs.

Researched Jul 25, 2026

Frequently Asked Questions

Which is better, LangGraph or CrewAI?

The best choice between LangGraph and CrewAI depends on your specific use case — we compare them independently on features, current pricing, integrations, and real-world signals (with an on-demand sentiment scan available for each). See the side-by-side breakdown above to match them to your needs.

What are the main differences between LangGraph and CrewAI?

The key differences include pricing model, feature set, platform support, and skill level requirements. Review the full comparison on RightAIChoice for a detailed breakdown.

Is there a free version of LangGraph or CrewAI?

Check the pricing section in the comparison for the latest pricing details on both tools, including free tiers, trial options, and paid plans.

More LangGraph or CrewAI comparisons

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DeepAgents vs LangGraph comparison
Haystack vs LangGraph comparison

Explore each tool further

LangGraph
View LangGraph reviewLangGraph alternatives
CrewAI
View CrewAI reviewCrewAI alternatives
AutoGen
View AutoGen reviewAutoGen alternatives

Browse these categories

Best AI Agent Frameworks & Orchestration toolsBest AI LLM Observability & Evals toolsBest AI AI Governance & Guardrails tools
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Last reviewed: April 21, 2026