Agno

Sweet spot: a Python team starting a fresh agent project and wanting something cleaner and faster than LangChain. Agno's API is tight, the docs are good, and the performance benchmarks (while self-published) hold up reasonably well in practice. Failure modes. The community-size gap is real — when you hit an edge case, you will find fewer Stack Overflow threads and blog posts than the LangChain equivalent. Migrating existing LangChain code is not a trivial lift; treat Agno as a choice for new projects, not a drop-in replacement. Multi-modal support is strong but still depends on whichever model you choose actually supporting the modality. What to pilot. Build a small production-realistic agent (5-ish tools, knowledge base, streaming UI) in Agno and a matched version in LangChain. Measure cold-start latency, tokens per run, and lines of code. If Agno wins on at least two of the three for your workload, it is the right bet going forward; if the metrics are close, stick with whatever your team already knows.

Agno (formerly Phidata) is an open-source Python framework for building agents that combines a clean API, serious focus on performance, and first-class support for multi-model and multi-modal workflows. Agents are defined with a few lines of Python — give them a model, tools, memory, and a knowledge base — and Agno handles the tool-use loop, streaming, and state. Where Agno differentiates: published benchmarks showing multi-thousand-x faster agent instantiation than LangChain equivalents, a unified reasoning primitive (think before answer), multi-modal inputs (image, audio, video) as a first-class concept, and native support for agent teams with leader-follower patterns. The framework ships adapters for OpenAI, Anthropic, Gemini, Groq, Cohere, Ollama, xAI, and essentially every major API provider. Beyond the library, Agno offers a free agent UI (FastAPI + Next.js template) and a paid hosted platform for deployment, monitoring, and team workflows. It has a growing footprint in production agent stacks, particularly for teams that tried LangChain, hit its performance ceiling, and wanted something lighter.