Agno AI is revolutionizing autonomous AI agents by blending simplicity, speed, and versatility into a uniquely powerful framework. Its lightweight, open-source architecture empowers developers to build multimodal agents capable of dynamic reasoning, collaboration, and problem-solving—far surpassing traditional chatbots or rigid scripted systems.
What makes Agno truly exceptional is its performance: it reportedly instantiates agents 10,000x faster than LangGraph while using 50x less memory, making it perfect for scalable, high-demand applications. The framework’s model-agnostic design ensures maximum flexibility, seamlessly integrating OpenAI, Claude, and custom LLMs without vendor lock-in (OpenRouter being an excellent choice for model access).
With features like Agentic RAG, multi-agent teams, and built-in debugging tools, Agno takes AI development to the next level. Developers can effortlessly create specialized agents for finance, research, customer support, and more—then orchestrate them into efficient workflows with minimal boilerplate code. Native support for text, audio, images, and video further unlocks groundbreaking multimodal applications.
Agno AI is not just another framework—it’s a paradigm shift in how businesses deploy AI. Whether for enterprise automation, real-time analytics, or intelligent assistants, Agno is setting a new standard.
For anyone serious about the future of AI agents, Agno isn’t just worth exploring—it’s the smart bet.
Flowtica Scribe
Hi everyone!
Sharing Agno, a new open-source library focused on building high-performance, multimodal AI agents. If you're building agentic systems, this looks seriously impressive, especially regarding speed and memory efficiency.
Agno acts as a lightweight framework, providing a unified API for various LLMs and adding capabilities like memory, knowledge stores, tool use, and reasoning.
Key aspects that stand out:
🚀 Lightning Fast & Lightweight: They report huge performance gains over frameworks like LangGraph (claiming 10,000x faster instantiation and 50x less memory on their benchmarks).
🔌 Model Agnostic: No lock-in! Use models from OpenAI, Anthropic, Cohere, or open-source ones via Ollama, Together, Anyscale, etc.
👁️ Multimodal: Native support for agents working with text, image, audio, and video.
🤝 Multi-Agent Teams: Built to orchestrate teams of specialized agents.
🧠 Memory, Knowledge, Tools: Built-in support for memory, vector DBs (for RAG), and adding custom tools.
📊 Monitoring: Integrates with agno.com for real-time agent monitoring.
🔓 Open Source (Apache 2.0): Freely available for use and contribution.
For developers building high-performance, multimodal AI agents, Agno offers a powerful and efficient open-source foundation.
Hunting credit to @sentry_co 🙌
@sentry_co @zaczuo Hey Zac, Nice work. Congrats. btw, what do you provide for Audio and Video AI agents as LLM options. Not every models excels at these.
Agno
@sentry_co @zaczuo @imraju Hey!
Agno is model-agnostic, so you can choose the best models for your use case.
We’ve actually documented a few multimodal use cases here:
docs.agno.com/examples/concepts/multimodal
Let us know what you're building—would love to help brainstorm the right setup!
@ansub Congrats on launching Agno! Building a lightweight and open-source library for multimodal AI agents is a fantastic contribution to the community.
Strawberry
This looks cool! Coming to typescript any time soon? :)
Model-agnostic agents simplify my workflow! Thanks! 👍
So good, best of luck guys!
Wing Assistant
I built a multi-agent system with Agno, really simple to use. Great job!
chatWise