Side-by-side comparison
CrewAI vs LangChain
vs
Side-by-side comparison based on our agenticness evaluation framework
At a glance
Quick Facts
| Feature | CrewAI | LangChain |
|---|---|---|
| Category | Multi-Agent Orchestration, Agent Frameworks & Orchestration | Agent Frameworks & Orchestration |
| Deployment | Hybrid (cloud + self-hosted) | Self-hosted |
| Autonomy Level | Semi-autonomous | Copilot (human-in-loop) |
| Model Support | Single model | Multi-model |
| Open Source | Yes | Yes |
| MCP Support | -- | Yes |
| Team Support | Enterprise | Small team |
| Pricing Model | Freemium | Free / open source |
| Interface | gui, web, api | api, cli |
36-point evaluation
Agenticness
11/36
Guided Assistant
CrewAI
9/36
Guided Assistant
LangChain
Dimension Breakdown (0-4 each)
Action Capability
CrewAI
2
LangChain
2
Autonomy
CrewAI
1
LangChain
1
Planning
CrewAI
2
LangChain
1
Adaptation
CrewAI
0
LangChain
1
State & Memory
CrewAI
0
LangChain
0
Reliability
CrewAI
1
LangChain
0
Interoperability
CrewAI
1
LangChain
1
Safety
CrewAI
2
LangChain
1
Scores from our agenticness evaluation framework. Higher is more autonomous.
Features & Use Cases
CrewAI
Features
- Visual editor for building agentic workflows
- AI copilot for workflow creation
- Integrated tools and triggers
- Workflow execution limits by plan
- Cloud SaaS deployment
- Self-hosted deployment via Kubernetes and VPC for Enterprise
- SSO for Enterprise
- Secret manager integration for Enterprise
Use Cases
- Teams building production AI agent workflows with a visual interface
- Organizations that want to deploy agents in a managed cloud environment
- Enterprises that need self-hosted agent infrastructure on private cloud or on-prem systems
- Developers who want to prototype an agent workflow and later scale it for production
LangChain
Features
- Python framework for building agents and LLM applications
- Interoperable interfaces for models, embeddings, vector stores, and retrievers
- Third-party integrations for data sources, tools, and model providers
- Modular component-based architecture for composing workflows
- Works with LangGraph for more controllable agent orchestration
- Integrates with LangSmith for debugging, evaluation, and deployment support
- Open-source MIT-licensed codebase
Use Cases
- Building custom AI agents that call tools and external systems
- Prototyping LLM applications before hardening them for production
- Connecting language models to retrieval and data-augmentation workflows
- Swapping model providers while keeping application logic stable
- Developing and debugging agent workflows alongside LangGraph and LangSmith
Pricing
CrewAI
- **Free (Basic):** Free tier with a visual editor, AI copilot, integrated tools and triggers, and 50 workflow executions per month.
- **Professional ($25/month):** Includes everything in Basic, plus 1 additional seat, 100 workflow executions per month, and support via the community forum.
- **Enterprise:** Custom pricing. Includes SaaS or self-hosted deployment via Kubernetes and VPC, SOC2, SSO, secret manager integration, PII detection and masking, dedicated support, uptime SLAs, Slack or Teams support channels, and forward-deployed engineers.
LangChain
- **Free / open source** — full functionality available at no cost.
Analysis
Our Verdict
Pick CrewAI when you want a team-friendly, production workflow system—visual workflow building with an AI copilot, hybrid cloud/self-hosted execution (Kubernetes + VPC), and enterprise necessities like SSO, secret management, and PII detection/masking with managed support. Pick LangChain when you’re an engineering team building custom agent workflows in Python and want full control over composition using a modular framework, extending into LangGraph for controllable orchestration and LangSmith for debugging/evaluation/deployment, with everything running self-hosted and modeled around interchangeable components and integrations.
Choose CrewAI if...
- +Choose CrewAI if you want a more production-oriented agent *workflow platform* with a **visual editor** for building agentic workflows and **hosted or hybrid deployment** options (cloud SaaS by default, with **self-hosted on Kubernetes + VPC** for enterprise).
- +Choose CrewAI if your team needs **enterprise operations features out of the box**—SSO, a secret manager integration, and **PII detection/masking**, plus enterprise-grade support/uptime SLAs—rather than assembling those capabilities yourself.
- +Choose CrewAI if you want to move from prototype to managed execution with **workflow execution limits tied to plans** and an ecosystem that’s explicitly built for **building, testing, deploying, and managing** agentic workflows rather than writing everything from scratch in code.
- +Choose CrewAI if non-engineers or cross-functional team members benefit from **workflow creation via an AI copilot + visual tooling**, accelerating iteration on multi-step agent workflows.
Choose LangChain if...
- +Choose LangChain if you’re building custom agentic behavior in **Python** and want an **open-source framework** to wire together model calls, tools, retrieval components, and external integrations in a modular way.
- +Choose LangChain if you expect to need **more controllable orchestration** via **LangGraph** and want a workflow/dev lifecycle with **LangSmith** for debugging, evaluation, and deployment support (i.e., you’re building your own orchestration and observability stack).
- +Choose LangChain if you want flexibility to **swap model providers** and keep your application/tooling logic stable, because it’s designed around interoperable interfaces (models/embeddings/vector stores/retrievers) and third-party integrations.
- +Choose LangChain if you prefer **self-hosted** development without adopting a hosted agent execution platform—starting from installing the library and composing workflows directly in your codebase.