Agno — AI Agent Framework: Live Stats & TrendScore

Live GitHub stats, community sentiment, and trend data for Agno. TrendingBots tracks star velocity, fork activity, and what developers are saying — updated from real data sources.

GitHub data synced: May 5, 2026 • Sentiment updated: Apr 19, 2026

GitHub Statistics

Community Sentiment

Community Buzz: Agno framework for orchestrating multi-agent workflows, as mentioned on Reddit. Another user on GitHub said, 'I built a 100% local AI agentic workflow that automatically tests chatbots using GPT-OSS 20B via llama.cpp + Agno workflow framework.'

Pros & Cons

What People Love

Agno framework for orchestrating multi-agent workflows (Reddit), Built a 100% local AI agentic workflow using Agno (GitHub)

Common Complaints

Security concerns, NULL Pgvector embeddings

Biggest Positive: Agno framework

Biggest Negative: Security concerns

Why Agno Stands Out

Agno introduces a new interaction model, where agents stream reasoning, tool calls, and results in real-time, and a new governance model, where agents choose actions dynamically. This approach enables more flexible and scalable agentic software. By building trust into the engine itself, Agno provides a unique solution for building and managing AI agents. With its stateless, horizontally scalable runtime and native tracing, Agno is well-suited for production environments.

Built With

Build a personal agent that learns your preferences — Agno's architecture enables stateful agents with streaming responses, Build a self-learning data agent grounded in six layers of context — Agno's integrations with 100+ tools facilitate complex workflows, Build a self-learning context agent that manages enterprise context knowledge — Agno's guardrails and approval workflows ensure secure execution, Build a post-IDE coding agent that improves over time — Agno's support for long-running execution and human-in-the-loop approval enables dynamic coding, Build a multi-agent investment committee that debates and allocates capital — Agno's multi-agent orchestration capabilities facilitate complex decision-making

Getting Started

  1. Run `pip install agno` to install the Agno library
  2. Create a new agent using `from agno.agent import Agent` and configure its properties, such as `name`, `model`, and `db`
  3. Define the agent's tools and integrations, such as `MCPTools` and `SqliteDb`
  4. Run the agent using `uvx --python 3.12 --with agno[os] --with anthropic --with mcp fastapi dev agno_assist.py`
  5. Try interacting with the agent through the AgentOS UI to verify it works

About

Run agents as production software.

Official site: https://docs.agno.com

Category & Tags

Category: development

Tags: agents, ai, ai-agents, developer-tools, python

Market Context

Competing with other AI workflow frameworks