LangGraph vs CrewAI vs AutoGen

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

Live tool data as of 2026-07-06
Reviewed by our team on
Saved

At a glance

DimensionLangGraphCrewAI
Pricingfreemium · from Developer $0/seat per monthfreemium · from Basic $0/mo
Best forDevOps and backend engineers building production agents, Teams needing fine-grained control over agent workflowsEnterprise teams needing to discover and prioritize automation opportunities across tickets, chats, and apps, Organizations deploying multi-agent workflows with compliance and governance requirements
Standout featuresHuman-in-the-loop checks for agent moderation · Built-in memory for cross-session context · Token-by-token streaming for real-time UXNo-code visual editor with AI copilot · Code-first API for custom orchestration · Role-based agents for complex workflows
Viability score95/10095/100
APIYesYes

LangGraph is the stronger pick for devops and backend engineers building production agents; CrewAI fits better for enterprise teams needing to discover and prioritize automation opportunities across tickets, chats, and apps.

Built from live tool data, last verified 2026-07-06.

LangGraph
LangGraph

Open-source framework for building reliable, stateful AI agents with fine-grained control.

Visit Website
CrewAI
CrewAI

Enterprise multi-agent orchestration with built-in discovery and governance.

Visit Website
AutoGen
AutoGen

Build multi-agent AI workflows with Microsoft's open-source framework.

Visit Website
Pricing
Freemium
Freemium
Free
Plans
$0/seat per month
$39/seat per month
Custom
$0/mo
Custom
$0/mo (MIT)
Popularity
3.0k views
4.2k views
5.3k views
Skill Level
Advanced
Advanced
Intermediate
API Available
Platforms
APIDesktop
API
APIDesktopWeb
Categories
💻 Code & Development🤖 Automation & Agents
🤖 Automation & Agents
🤖 Automation & Agents
Features
Human-in-the-loop checks for agent moderation
Built-in memory for cross-session context
Token-by-token streaming for real-time UX
Support for single, multi-agent, and hierarchical workflows
Low-level primitives for custom agent architectures
Graph-based state management and control flow
Integration with LangSmith for observability and deployment
Fault tolerance: retries, timeouts, error handlers
Rubrics for agent self-evaluation and correction
Model-agnostic support for any LLM provider
Sandboxes for safe code execution
Prompt caching for reduced latency and cost
Deep Agents: batteries-included agent with VFS and subagent spawning
LangSmith Engine for autonomous evaluation and fix generation
Wiki Memory for long-term agent memory
No-code visual editor with AI copilot
Code-first API for custom orchestration
Role-based agents for complex workflows
Discovery engine ranks automation opportunities from tickets/chats
Real-time tracing of LLM calls, tool calls, memory reads
Full cost accounting per execution
Human-in-the-loop approval gates
RBAC and immutable audit trails
Runtime PII redaction hooks
Automated and human-guided training
Multi-LLM testing for model swapping
Native evaluation with Arize, Galileo, DataDog, Patronus
Cognitive memory for agentic systems
Integration with NVIDIA NemoClaw for self-evolving agents
Export as MCP server or UI component
Multi-agent conversation orchestration
Flexible agent role definition
Customizable conversation patterns
Integration with various LLMs
Extensible tool use
Support for human-in-the-loop
Open-source with community contributions
AutoGen Studio visual prototyping UI
Code execution sandboxing (requires Docker)
Modular agent composition
Integrations
LangSmith
OpenAI
Anthropic
Google
Ollama
Azure
AWS Bedrock
HuggingFace
Fireworks
Baseten
Mistral
Meta
Box AI
Claude MCP
OpenRouter
Arize
Galileo
DataDog
Patronus
NVIDIA NemoClaw
GitHub
Slack
Microsoft Teams
MS Entra
Okta
Azure OpenAI
Hugging Face
LLaMA
Claude

Who should pick which

  • Solo developer building a custom agent
    Pick: LangGraph

    LangGraph is free, open-source, and gives full control to implement any agent architecture without licensing overhead.

  • Enterprise team needing governance and audit trails
    Pick: CrewAI

    CrewAI provides built-in RBAC, immutable audit logs, and PII redaction necessary for compliance.

  • Team wanting to discover automation opportunities from tickets/chats
    Pick: CrewAI

    CrewAI's discovery engine ranks tasks from existing data, which LangGraph lacks.

  • Backend engineer building hierarchical multi-agent workflow
    Pick: LangGraph

    LangGraph's low-level primitives and dynamic subagents (June 2026) enable complex, custom graphs.

  • Organization moving from no-code prototyping to production
    Pick: CrewAI

    CrewAI offers a no-code editor initially, then code-first API for production, easing the transition.

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

CrewAI vs DeepAgents comparison

Choose DeepAgents if you are a developer seeking a free, open-source, model-agnostic agent harness with sub-agents and filesystem access, and you're comfortable with a code-first setup. Choose CrewAI

CrewAI vs OpenAI Agents SDK comparison

If you're a Python developer prototyping multi-agent workflows with OpenAI and want a free, lightweight SDK with sandbox agents and realtime voice, choose OpenAI Agents SDK. For enterprise teams that

CrewAI vs Google Agent Development Kit comparison

If you're an enterprise team already invested in Google Cloud and need deterministic graph-based workflows with multi-language SDKs, Google ADK 2.0's free open-source framework is a powerful choice. F

Langfuse vs LangGraph comparison

Choose Langfuse if your priority is observability, debugging, and prompt management for production LLM apps, with a need for multi-modal evals and alerts. Choose LangGraph if you're building complex,

DeepAgents vs LangGraph comparison

Choose DeepAgents if you want a full-featured agent out of the box—with sub-agents, filesystem access, and human approval—without wiring everything from scratch. Choose LangGraph if you need low-level

Haystack vs LangGraph comparison

If you need production RAG with hybrid retrieval and multimodal support, pick Haystack. If you must build complex, stateful multi-agent loops with human oversight and low-level control, pick LangGraph

Explore each tool further

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

Browse these categories

Best AI Code & Development toolsBest AI Automation & Agents tools
Still deciding? Get the weekly AI tools brief

One email a week — new tools, honest comparisons, no spam.