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
12/36
Guided Assistant
CrewAI
10/36
Guided Assistant
LangChain
Dimension Breakdown (0-4 each)
Action Capability
CrewAI
2
LangChain
2
Autonomy
CrewAI
1
LangChain
1
Planning
CrewAI
1
LangChain
1
Adaptation
CrewAI
0
LangChain
0
State & Memory
CrewAI
1
LangChain
0
Reliability
CrewAI
2
LangChain
0
Interoperability
CrewAI
1
LangChain
2
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
Choose CrewAI AMP when the priority is a visual, lifecycle-oriented platform for getting collaborative agent workflows deployed and managed, particularly when enterprise hosting, governance, and support requirements matter. Choose LangChain when your engineering team wants code-level control over a custom Python LLM application—especially its models, retrieval stack, external integrations, and orchestration—and can build the surrounding operational experience from its ecosystem components such as LangGraph and LangSmith.
Choose CrewAI if...
- +Choose CrewAI AMP if your team wants to design agentic workflows in a visual editor, with an AI copilot, pre-integrated tools, and triggers rather than assembling the workflow entirely in application code.
- +Choose CrewAI AMP if you need an opinionated path from a limited free prototype (50 executions/month) to operated production workflows with execution-based plans and team seats.
- +Choose CrewAI AMP if enterprise deployment requires a managed SaaS option or self-hosting through Kubernetes and a VPC, plus enterprise controls such as SSO, secret-manager integration, PII detection/masking, SLAs, and dedicated support.
- +Choose CrewAI AMP if non-developer stakeholders need to participate directly in creating or operating collaborative agent workflows through a hosted platform.
Choose LangChain if...
- +Choose LangChain if developers need a Python-native, MIT-licensed framework to build LLM applications directly in a codebase using modular components.
- +Choose LangChain if your application must interchange models, embeddings, vector stores, retrievers, data sources, and tool/model-provider integrations while preserving your own application architecture.
- +Choose LangChain if retrieval-augmented or data-augmentation workflows are central, and you want to compose the retrieval and model layers programmatically.
- +Choose LangChain if you want more controlled orchestration through LangGraph and a separate developer-oriented path for debugging, evaluation, and deployment support through LangSmith.