MCPHub LabRegistryagno-agi/agno
agno-agi

agno agi/agno

Built by agno-agi • 39,055 stars

What is agno agi/agno?

Build, run, manage agentic software at scale.

How to use agno agi/agno?

1. Install a compatible MCP client (like Claude Desktop). 2. Open your configuration settings. 3. Add agno agi/agno using the following command: npx @modelcontextprotocol/agno-agi-agno 4. Restart the client and verify the new tools are active.
šŸ›”ļø Scoped (Restricted)
npx @modelcontextprotocol/agno-agi-agno --scope restricted
šŸ”“ Unrestricted Access
npx @modelcontextprotocol/agno-agi-agno

Key Features

Native MCP Protocol Support
Real-time Tool Activation & Execution
Verified High-performance Implementation
Secure Resource & Context Handling

Optimized Use Cases

Extending AI models with custom local capabilities
Automating system workflows via natural language
Connecting external data sources to LLM context windows

agno agi/agno FAQ

Q

Is agno agi/agno safe?

Yes, agno agi/agno follows the standardized Model Context Protocol security patterns and only executes tools with explicit user-granted permissions.

Q

Is agno agi/agno up to date?

agno agi/agno is currently active in the registry with 39,055 stars on GitHub, indicating its reliability and community support.

Q

Are there any limits for agno agi/agno?

Usage limits depend on the specific implementation of the MCP server and your system resources. Refer to the official documentation below for technical details.

Official Documentation

View on GitHub
<div align="center" id="top"> <a href="https://agno.com"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://agno-public.s3.us-east-1.amazonaws.com/assets/logo-dark.svg"> <source media="(prefers-color-scheme: light)" srcset="https://agno-public.s3.us-east-1.amazonaws.com/assets/logo-light.svg"> <img src="https://agno-public.s3.us-east-1.amazonaws.com/assets/logo-light.svg" alt="Agno"> </picture> </a> </div> <p align="center"> Build, run, and manage agentic software at scale. </p> <div align="center"> <a href="https://docs.agno.com">Docs</a> <span>&nbsp;•&nbsp;</span> <a href="https://github.com/agno-agi/agno/tree/main/cookbook">Cookbook</a> <span>&nbsp;•&nbsp;</span> <a href="https://docs.agno.com/first-agent">Quickstart</a> <span>&nbsp;•&nbsp;</span> <a href="https://www.agno.com/discord">Discord</a> </div>

What is Agno

Agno is the runtime for agentic software. Build agents, teams, and workflows. Run them as scalable services. Monitor and manage them in production.

LayerWhat it does
FrameworkBuild agents, teams, and workflows with memory, knowledge, guardrails, and 100+ integrations.
RuntimeServe your system in production with a stateless, session-scoped FastAPI backend.
Control PlaneTest, monitor, and manage your system using the AgentOS UI.

Quick Start

Build a stateful, tool-using agent and serve it as a production API in ~20 lines.

from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.anthropic import Claude
from agno.os import AgentOS
from agno.tools.mcp import MCPTools

agno_assist = Agent(
    name="Agno Assist",
    model=Claude(id="claude-sonnet-4-6"),
    db=SqliteDb(db_file="agno.db"),
    tools=[MCPTools(url="https://docs.agno.com/mcp")],
    add_history_to_context=True,
    num_history_runs=3,
    markdown=True,
)

agent_os = AgentOS(agents=[agno_assist], tracing=True)
app = agent_os.get_app()

Run it:

export ANTHROPIC_API_KEY="***"

uvx --python 3.12 \
  --with "agno[os]" \
  --with anthropic \
  --with mcp \
  fastapi dev agno_assist.py

In ~20 lines, you get:

  • A stateful agent with streaming responses
  • Per-user, per-session isolation
  • A production API at http://localhost:8000
  • Native tracing

Connect to the AgentOS UI to monitor, manage, and test your agents.

  1. Open os.agno.com and sign in.
  2. Click "Add new OS" in the top navigation.
  3. Select "Local" to connect to a local AgentOS.
  4. Enter your endpoint URL (default: http://localhost:8000).
  5. Name it "Local AgentOS".
  6. Click "Connect".

https://github.com/user-attachments/assets/75258047-2471-4920-8874-30d68c492683

Open Chat, select your agent, and ask:

What is Agno?

The agent retrieves context from the Agno MCP server and responds with grounded answers.

https://github.com/user-attachments/assets/24c28d28-1d17-492c-815d-810e992ea8d2

You can use this exact same architecture for running multi-agent systems in production.

Why Agno?

Agentic software introduces three fundamental shifts.

A new interaction model

Traditional software receives a request and returns a response. Agents stream reasoning, tool calls, and results in real time. They can pause mid-execution, wait for approval, and resume later.

Agno treats streaming and long-running execution as first-class behavior.

A new governance model

Traditional systems execute predefined decision logic written in advance. Agents choose actions dynamically. Some actions are low risk. Some require user approval. Some require administrative authority.

Agno lets you define who decides what as part of the agent definition, with:

  • Approval workflows
  • Human-in-the-loop
  • Audit logs
  • Enforcement at runtime

A new trust model

Traditional systems are designed to be predictable. Every execution path is defined in advance. Agents introduce probabilistic reasoning into the execution path.

Agno builds trust into the engine itself:

  • Guardrails run as part of execution
  • Evaluations integrate into the agent loop
  • Traces and audit logs are first-class

Built for Production

Agno runs in your infrastructure, not ours.

  • Stateless, horizontally scalable runtime.
  • 50+ APIs and background execution.
  • Per-user and per-session isolation.
  • Runtime approval enforcement.
  • Native tracing and full auditability.
  • Sessions, memory, knowledge, and traces stored in your database.

You own the system. You own the data. You define the rules.

What You Can Build

Agno powers real agentic systems built from the same primitives above.

  • Pal → A personal agent that learns your preferences.
  • Dash → A self-learning data agent grounded in six layers of context.
  • Scout → A self-learning context agent that manages enterprise context knowledge.
  • Gcode → A post-IDE coding agent that improves over time.
  • Investment Team → A multi-agent investment committee that debates and allocates capital.

Single agents. Coordinated teams. Structured workflows. All built on one architecture.

Get Started

  1. Read the docs
  2. Build your first agent
  3. Explore the cookbook

IDE Integration

Add Agno docs as a source in your coding tools:

Cursor: Settings → Indexing & Docs → Add https://docs.agno.com/llms-full.txt

Also works with VSCode, Windsurf, and similar tools.

Contributing

See the contributing guide.

Telemetry

Agno logs which model providers are used to prioritize updates. Disable with AGNO_TELEMETRY=false.

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Global Ranking

8.5
Trust ScoreMCPHub Index

Based on codebase health & activity.

Manual Config

{ "mcpServers": { "agno-agi-agno": { "command": "npx", "args": ["agno-agi-agno"] } } }