Documentation Index
Fetch the complete documentation index at: https://docs.agno.com/llms.txt
Use this file to discover all available pages before exploring further.
You have a deployed agent platform with two reference agents, evals, and JWT auth. The sections below cover the moves that turn it into a real product: teams and workflows, scheduled tasks, and connecting to Slack.
Going beyond agents
Rule of thumb: agents for open questions, teams for routing, workflows for processes. Most of your platform will be agents. A few will be teams or workflows.
| Pattern | Use it when | Reference |
|---|
| Agent | A single LLM with tools and instructions can handle the request. | Agents overview |
| Team | The right specialist isn’t known up front. A leader routes or coordinates. | Teams overview |
| Workflow | The process needs to run the same way every time. Determinism matters. | Workflows overview |
| Mode | Behavior |
|---|
| Coordinate | A leader plans the work, calls the right specialists, synthesizes. |
| Route | A router picks one specialist to handle the request. |
| Broadcast | Every specialist runs in parallel; you aggregate. |
Scheduled tasks
The scheduler is on by default in app/main.py:
agent_os = AgentOS(
name="AgentOS",
scheduler=True,
...
)
| Use case | Example |
|---|
| Maintenance | Purge sessions older than 90 days. Vacuum Postgres tables. |
| Proactive runs | Every weekday morning, summarize overnight news and post to Slack. |
| Catch regressions | Run python -m evals weekly against production agents. |
Connect to interfaces
Your agents should be available where your users are. Slack threads. Discord channels. Telegram for the field team. Or a custom UI inside your product.
The pattern is the same for each. Expose the agent via an interface in app/main.py:
from agno.os.interfaces.slack import Slack
interfaces: list = []
if SLACK_BOT_TOKEN and SLACK_SIGNING_SECRET:
interfaces.append(
Slack(
agent=code_search,
streaming=True,
token=SLACK_BOT_TOKEN,
signing_secret=SLACK_SIGNING_SECRET,
resolve_user_identity=True,
)
)
agent_os = AgentOS(
...
interfaces=interfaces,
)
resolve_user_identity=True ties the Slack user to the same user_id they have in the AgentOS UI, so sessions follow the person across surfaces.
Agno ships 100+ toolkit integrations. See the full toolkit reference.
from agno.tools.slack import SlackTools
from agno.tools.googlesheets import GoogleSheetsTools
my_agent = Agent(
...
tools=[
SlackTools(),
GoogleSheetsTools(),
],
)
.env for local and to .env.production for Railway, then ./scripts/railway/env-sync.sh.
Swap model providers
Switch from OpenAI to Anthropic:
- Set
ANTHROPIC_API_KEY in .env and .env.production.
- Add
anthropic to pyproject.toml.
- Update the agent’s
model=.
- Run
./scripts/generate_requirements.sh && docker compose up -d --build.
from agno.models.anthropic import Claude
my_agent = Agent(
...
model=Claude(id="claude-sonnet-4-5"),
)
google-genai), Mistral (mistralai), and the other supported providers.
Keep the repo coherent
As you ship more agents, docs drift, env vars rot, and new agents miss imports. The template ships a fourth Claude Code prompt for the recurring sweep:
Run docs/review-and-improve.md
example.env entries, agents on disk not registered in app/main.py) and surfaces the rest as a punch list. Run it before public releases and periodically during active development.
Going deeper
| To learn | See |
|---|
| The full AgentOS feature set | Demo OS |
| How knowledge and Agentic RAG work | Knowledge |
| How MCP integrates with Agno | MCP |
| Comparable templates | Scout, Dash, Coda |
| Building a fully custom AgentOS app | Build a product |
