Dify vs AutoGPT
Two of the top agent platforms, side by side: score, setup, license, activity and what each review found.
Dify
Visual LLM app platform with workflows, RAG pipeline, agents and APIs
AutoGPT
Block-based builder for agents that run on demand, schedule or trigger
| What we compare | Dify | AutoGPT |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 93, widely used | 99, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 93, healthy | 79, fair |
| Easy to run | 33, some setup | 17, hard |
| Agent-ready | 45, minimal | 85, ready |
| Facts from GitHub and the README | ||
| Stars | 158.1k | 187.5k |
| License | custom license (read the license) | custom license (read the license) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Sep 2026 | Oct 2026 |
| Language | Not stated | Not stated |
| Docker | Yes | No |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Not stated | Mentioned |
Dify
Dify is an LLM app platform started with Docker Compose (dashboard on port 80) that needs 2 CPU cores and 4 GiB RAM. One canvas covers visual workflows, a prompt IDE, a RAG pipeline that ingests PDFs and PPTs, sandboxed agents using Marketplace tools, MCP servers or your own APIs, plus LLMOps tracing via Opik, Langfuse or Arize Phoenix. Hundreds of models work, including OpenAI-compatible endpoints.
Who it is for: Product teams building LLM apps and RAG workflows without heavy code
Strengths
- Workflow, RAG, agents, prompt IDE and model management in one canvas
- Hundreds of models: GPT, Mistral, Llama3 and any OpenAI-compatible API
- Observability through Opik, Langfuse and Arize Phoenix
- Every feature is exposed through an API (backend-as-a-service)
Weaknesses
- Dify Open Source License adds conditions on top of Apache 2.0
- SSO, RBAC and support SLAs are reserved for Dify Enterprise
- Minimum 2 CPU cores and 4 GiB RAM for the Compose stack
- Dashboard binds to port 80 by default
- RAM ≥ 4 GB
- no GPU
- Docker + Compose
- Models: OpenAI GPT, Mistral, Llama 3, OpenAI-compatible APIs, dozens of inference providers
- port 80
AutoGPT
AutoGPT Platform lets you describe a job in plain English (AutoPilot) or wire blocks on a visual canvas, then run the agent on demand, on a schedule or from a trigger, with a dashboard of runs and costs and a marketplace of shared agents. It connects to 45+ platforms such as Gmail, Slack, GitHub and Notion. Self-hosting is free with your own Docker host and model API keys; the hosted platform is paid.
Who it is for: Non-developers and teams wanting scheduled agents over SaaS tools
Strengths
- Plain-English AutoPilot and a drag-and-connect block builder for the same agent
- Agents run on demand, on schedules or from triggers with a run and cost dashboard
- 45+ integrations including Gmail, Google Sheets, GitHub, Slack, Notion, Jira, Salesforce
- Classic standalone agent still shipped under MIT in classic/
Weaknesses
- Platform code is Polyform Shield: no offering it as a competing hosted service
- README has no self-host commands; the single-container installer is still unreleased
- Windows self-hosting is manual-guide only
- Hosted platform charges per agent run; README is largely marketing
- no GPU
- Needs Docker