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.
@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.
@ansub Congrats on launching Agno! Building a lightweight and open-source library for multimodal AI agents is a fantastic contribution to the community.
Agno looks super promising—love the clean approach to collaborative knowledge management. Having a single, organized space to keep the team aligned and reduce information chaos is exactly what teams (and brains!) need. Definitely bookmarking it to test with my workflow 🙏
🧩 Kudos to Agno – pushing the boundary in multi-agent infrastructure and seamless integrations. Their approach aligns with Compozy’s mission to enable flexible, open, and collaborative agent ecosystems.
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.
Agno looks super promising—love the clean approach to collaborative knowledge management. Having a single, organized space to keep the team aligned and reduce information chaos is exactly what teams (and brains!) need. Definitely bookmarking it to test with my workflow 🙏