🧰 Frameworks
CrewAI vs n8n: Which Should You Choose?
Both are developer framework agents, but they are built for different people. Here is the honest split.
CrewAI
by CrewAI Inc.
An open-source Python framework for orchestrating role-playing agents that collaborate on a task.
n8n
by n8n GmbH
A source-available workflow automation platform with AI agent nodes you can self-host and own outright.
Side by side
CrewAI vs n8n at a glance
| 🚢 CrewAI | 🔗 n8n | |
|---|---|---|
| Editor score | 4.0★★★★★ | 4.5★★★★★ |
| Vendor | CrewAI Inc. | n8n GmbH |
| Autonomy | Fully autonomous | Semi-autonomous |
| Deployment | Self-hosted, Cloud, API | Self-hosted, Cloud, On-prem, API |
| Starting price | Free tier | Free tier |
| Pricing model | Free open-source framework; optional paid hosted platform | Free self-hosted (fair-code); cloud priced per workflow execution |
| Free tier | Yes — the framework is fully free and open source | Yes — unlimited self-hosted community edition |
| Integrations | 6 native | 11 native |
| Best for | Python engineers building agent products | Technical ops and automation engineers |
Decision
Which one should you pick?
Choose CrewAI if…
- Python engineers building agent products. The core audience. Full control, no per-run platform fee, and an abstraction that keeps multi-agent code readable.
- Teams running agents at high volume. Paying only model costs is dramatically cheaper than any credit-metered platform once volume is real.
- Data and research teams. Good fit for automated research pipelines, though you own the reliability and monitoring work.
Skip it if: Python engineering required is a dealbreaker for you.
Choose n8n if…
- Technical ops and automation engineers. The ideal user. Comfortable with APIs, JSON and a bit of JavaScript, and rewarded with almost unlimited flexibility.
- Teams with data residency requirements. Self-hosting means customer data never leaves your infrastructure except for model calls — and you can point those at a local model too.
- High-volume automation on a budget. Self-hosted execution is effectively free at the margin, which beats every per-task competitor at scale.
Skip it if: Steeper learning curve than no-code competitors is a dealbreaker for you.
Trade-offs
Strengths and weaknesses
CrewAI
- Free, open source and model agnostic
- Clearest abstraction of any multi-agent framework
- Cheapest option at high volume by a wide margin
- Python engineering required
- Autonomous crews are hard to make reliable
- Token spend can spiral without explicit limits
n8n
- Genuinely free at unlimited volume if you self-host
- Enormous integration library
- Mix deterministic steps with agent decision points
- Steeper learning curve than no-code competitors
- Self-hosting is real operational work
- Cloud pricing is less compelling than the free tier suggests
FAQ
CrewAI vs n8n FAQ
Is CrewAI better than n8n?
On our scoring n8n edges it at 4.5/5 against 4.0/5, but the gap is smaller than the difference in who they suit. CrewAI is the better choice for python engineers building agent products; n8n is stronger for technical ops and automation engineers.
Which is cheaper, CrewAI or n8n?
CrewAI: Free tier — free open-source framework; optional paid hosted platform. n8n: Free tier — free self-hosted (fair-code); cloud priced per workflow execution. Compare the pricing models rather than the headline numbers; consumption-based plans can overtake a higher flat fee quickly once usage is real.
Can I use CrewAI and n8n together?
Yes, and plenty of teams do. They share 6 integrations, so both can sit on the same data without duplicated plumbing. The usual pattern is to run each on the work it is strongest at rather than forcing one to cover everything.
What are the main differences between CrewAI and n8n?
Three things matter most. Autonomy: CrewAI is fully autonomous while n8n is semi-autonomous. Deployment: CrewAI runs self-hosted/cloud/api, n8n runs self-hosted/cloud/on-prem/api. Pricing model: free open-source framework; optional paid hosted platform versus free self-hosted (fair-code); cloud priced per workflow execution.