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.
I love Agno. It's so simple to use even I with my low dev knowledge can build powerful agents.
I built an RSA generator and working on my own Google Ads agent using Agno. Great job team.
@ansub Agno’s focus on speed, multimodal flexibility, and open-source ethos makes it a tantalizing foundation for developers pushing agentic systems beyond today’s latency-heavy norms. The claims around memory efficiency and multi-agent orchestration could redefine how teams scale AI workflows. But how does Agno handle resource contention in complex, real-world deployments—say, when 100+ agents with competing priorities access shared tools or memory? Does the framework prioritize tasks dynamically (e.g., via cost-based optimization), or does it rely on developers to manually define agent hierarchies?
Check out my review after building AI agents using Agno and CrewAI. IMO, Agno FTW!
Hey there! Tried it out and loved the one time link feature. Super useful when I don’t want the message hanging around.
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 🙏
10,000x faster instantiation and 50x less memory usage compared to LangGraph is a huge deal! Agno has the potential to become a standard framework for building AI Agents! It's also open source and model agnostic which is great. Are there any plans on improving its reasoning capabilities or add more features?
@ansub Congrats on launching Agno! Building a lightweight and open-source library for multimodal AI agents is a fantastic contribution to the community.
