Agno vs AutoGPT
Side-by-side comparison of two agent options that often come up together when people are choosing between self-hosted frameworks, managed assistants, and extensible AI tooling.
Open source40k stars
Agno
High-performance multi-agent framework — build, run and manage teams of AI agents at scale
Open source184k stars
AutoGPT
The pioneer of autonomous AI agents — task decomposition, web browsing, file management, and code execution
Category
Agno
AutoGPT
Tagline
High-performance multi-agent framework — build, run and manage teams of AI agents at scale
The pioneer of autonomous AI agents — task decomposition, web browsing, file management, and code execution
Deployment
Self-hosted / Agno Cloud
Self-hosted
Pricing
Open source and free to self-host. Agno Cloud available for managed deployments.
Free and open source. Requires your own API key for the LLM backend (OpenAI, Anthropic, or local via Ollama).
Channels
Web, api, CLI
Web, api
Open source
Yes
Yes
Privacy
Self-hosted deployments keep data on your infrastructure.
Data sent to your chosen LLM provider. Use local models via Ollama for air-gapped privacy.
Agno pros
- Extremely fast — benchmarks show 3x LangGraph speed.
- Native multi-agent team support built-in.
- Strong memory architecture with multiple storage backends.
AutoGPT pros
- The original autonomous agent — most recognized name in the space.
- Plugin ecosystem for extending capabilities.
- Supports multiple LLM backends including local Ollama models.
Agno cons
- Developer-focused — no visual builder.
- Ecosystem smaller than LangChain/LangGraph.
- Requires Python knowledge.
AutoGPT cons
- Complex multi-service setup (Postgres, Redis, web UI).
- Generates many LLM API calls per task — costs can escalate quickly.
- Newer frameworks have surpassed it in reliability and ease of use.
Agno gotchas
- Formerly called Phidata — old docs may use old name.
- Start with single agent before multi-agent orchestration.
AutoGPT gotchas
- Loops and hallucinations are common on complex multi-step tasks.
- Token usage per task is high — set a budget cap before long runs.
- Documentation can lag behind the codebase.
Not sure which one fits you?
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