3. Use the AntigravityAgent class directly. A purpose-built BaseExternalAgent that exposes the full API: native sessions, streaming, and UI through AgentOS.
Image search using text classification
Image search has generally been a HARD problem but not every use-case needs google photos like infrastructure.
Some just need a quick and dirty image search and now that's possible in about 100 lines of code.
Link below
Excited to release v2.6.8 with native support for Google Gemini’s Antigravity agent 🚀
There are multiple ways to make full use of Antigravity and its capabilities with Agno. More in the thread 🧵
A git-backed LLM wiki, built with @AgnoAgi
> Ingest web pages via @p0
> Compile into a markdown wiki (stored in a git repo)
> Every write auto-commits and pushes
> Query it using the same agent
Git gives you the history, storage, all the good stuff.
Code below, video next.
An epidemiologist with no software engineering background built a production multi-agent healthcare analytics platform with Agno.
His early prototypes used raw API calls. Each step took 30 seconds. Fragile, slow, impossible to extend.
He evaluated LangChain, OpenAI Agents SDK, and others. Most added complexity or required vendor lock-in.
Agno clicked in a day. He validated his core hypothesis and now ships new features from idea to production in a single week.
The results:
→ Multi-step queries: under 60 seconds (was minutes)
→ Users get answers 10x faster than manual analysis
→ Scaled without adding engineering headcount
→ Architecture ready for PHI/PII use cases
"It had the main things I was looking for: simple implementation, a community to help onboard, and pragmatism, not the fluffy language"