Build, run, manage agentic software at scale.
npx skills add https://github.com/agno-agi/agno --skill system-infoInstallieren Sie diesen Skill über die CLI und beginnen Sie mit der Verwendung des SKILL.md-Workflows in Ihrem Arbeitsbereich.
Build, run, and manage agentic software at scale.
Agno is the runtime for agentic software. Build agents, teams, and workflows. Run them as scalable services. Monitor and manage them in production.
| Layer | What it does |
|---|---|
| Framework | Build agents, teams, and workflows with memory, knowledge, guardrails, and 100+ integrations. |
| Runtime | Serve your system in production with a stateless, session-scoped FastAPI backend. |
| Control Plane | Test, monitor, and manage your system using the AgentOS UI. |
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:
Connect to the AgentOS UI to monitor, manage, and test your agents.
http://localhost:8000).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.
Agentic software introduces three fundamental shifts.
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.
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:
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:
Agno runs in your infrastructure, not ours.
You own the system. You own the data. You define the rules.
Agno powers real agentic systems built from the same primitives above.
Single agents. Coordinated teams. Structured workflows. All built on one architecture.
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.
See the contributing guide.
Agno logs which model providers are used to prioritize updates. Disable with AGNO_TELEMETRY=false.