Agno vs Stream (2026)
Updated April 29, 2026
Overview
Agno (formerly Phidata) is an open-source Python framework for building production-grade AI agents and multi-agent systems. 39K+ GitHub stars, 23+ LLM providers supported, 100+ pre-built tool integrations, MCP-compatible. Includes AgentOS — a stateless FastAPI runtime — and a control plane UI for monitoring.
Stream builds Vision Agents, an open-source Python framework for real-time voice and video AI agents, on top of its chat, video, and feeds APIs.
Key features
Core capabilities each platform advertises.
- 23+ LLM providers (OpenAI, Anthropic, Gemini, etc.) and 100+ tool integrations
- AgentOS — stateless FastAPI runtime for production deployment
- Control plane UI for monitoring, sessions, and team management
- Built-in memory + knowledge (RAG) stored in your database
- ReliabilityEval for evaluating agent quality at scale
- Open-source Python framework for voice and video agents
- Real-time WebRTC streaming of audio and video to models
- Pluggable vision pipelines (YOLO, Roboflow, Moondream)
- Turn detection, interruptions, and speaker diarization
- Tool calling and MCP support
- Phone calls through Twilio or Telnyx
- Works with any WebRTC network, not only Stream's edge
Strengths and tradeoffs
What each tool does well, and the limitations to keep in mind.
Pros
- Production-first design with stateless scaling and AgentOS runtime
- 39K+ GitHub stars and 424 active contributors
- 100+ pre-built tools including MCP support
- Control plane UI is rare in OSS agent frameworks
- Memory in your DB — no vendor lock-in
Cons
- Newer than LangChain ecosystem — fewer third-party integrations
- Python-only — no JS/TS SDK
- Smaller community than LangChain at production scale
- Less battle-tested at Fortune 500 scale than LangGraph
Pros
- Open source and works with any video edge network
- Wide integration list across LLMs, speech, and vision models
- Stream's edge includes 333k free participant-minutes per month for developers
Cons
- Built for real-time voice and video, not general text-based agents
- Python only
- Still pre-1.0 (v0.5), so APIs may change
What people are saying
Agno has emerged as one of the fastest-growing AI agent frameworks in 2026 — 39,100+ stars and a 424-contributor community.
Read full reviewAgno or Stream — which should you choose?
Choose Agno if you wantChoose if you want
- Production multi-agent systems with stateless scaling
- Customer-facing AI assistants with session management
- Internal-tool agents with monitoring dashboards
- Multi-modal agents combining vision, audio, and text
- Slack-native agents for ops and engineering workflows
Choose Stream if you wantChoose if you want
- Real-time voice agents and phone agents
- Video understanding, such as coaching or sports commentary
- Avatars and video restyling
- Agents that join live video calls
Compare Agno and Stream on your own traffic
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Try Respan freeFrequently Asked Questions
An agent framework provides the building blocks for creating AI agents that can autonomously plan, use tools, and complete multi-step tasks. Instead of building tool use, memory, and orchestration from scratch, you get pre-built components that handle the common patterns.
For simple single-tool agents, raw API calls work fine. Frameworks become valuable when you need multi-step planning, tool orchestration, error recovery, memory, or multi-agent coordination. They save significant development time for complex agent architectures.
LangChain and LlamaIndex are the most mature with the largest ecosystems. CrewAI is best for multi-agent workflows. Vercel AI SDK is ideal for TypeScript/Next.js applications. Evaluate based on your language preference, use case complexity, and integration needs.
