Side-by-side comparison
CrewAI vs LangChain
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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
If you’re a team looking to build and operate agentic workflows with a **visual workflow builder**, managed or enterprise **self-hosted** deployment options (Kubernetes/VPC), and operational features like **SSO, secret management, and PII masking**, go with **CrewAI**. If you’re primarily a developer assembling bespoke agent behaviors in Python—connecting LLMs to tools, retrieval, and external systems with a modular architecture, and you want to pair with **LangGraph** for orchestration and **LangSmith** for debugging/evaluation/deployment—choose **LangChain**.
Choose CrewAI if...
- +Choose CrewAI if you want a production-oriented, team-focused agent workflow platform with a **visual editor** plus an **AI copilot for creating workflows**, and you’d rather manage agent behavior as runnable “workflows” than hand-code orchestration.
- +Choose CrewAI if you need **managed cloud deployment** for agents today, but also a clear **enterprise path to self-hosting** on **Kubernetes with VPC**—including enterprise-grade items like **SSO**, **secret manager integration**, and **PII detection/masking**.
- +Choose CrewAI if your priority is moving from prototype to operational reliability with **workflow execution limits by plan**, plus **dedicated support and uptime SLAs** for Enterprise (and “forward-deployed engineers” per the description).
Choose LangChain if...
- +Choose LangChain if you want an **open-source Python framework** to directly build custom agent/LLM applications by composing **models, tools, retrieval, and external integrations** in code.
- +Choose LangChain if you specifically benefit from a modular engineering workflow—e.g., leveraging its interoperability for **models/embeddings/vector stores/retrievers**—and you plan to orchestrate more controllably using **LangGraph**.
- +Choose LangChain if you want a developer stack for **debugging/evaluation/deployment support** via **LangSmith**, and you prefer fully self-hosted control from the start (with the core framework available at no cost).