agno-agi/agno
Build, run, manage agentic software at scale.
Python framework for building, serving, and managing production-ready AI agents
Data flows from user inputs through agents/teams/workflows, with telemetry reporting and session management handled by AgentOS
Under the hood, the system uses 2 feedback loops, 2 data pools, 3 control points to manage its runtime behavior.
Structural Verdict
A 9-component ml inference with 12 connections. 3485 files analyzed. Highly interconnected — components depend on each other heavily.
How Data Flows Through the System
Data flows from user inputs through agents/teams/workflows, with telemetry reporting and session management handled by AgentOS
- Request Ingestion — FastAPI receives user requests through AgentOS endpoints
- Agent Execution — Agent processes input using model, tools, and memory with optional hook processing
- Response Generation — Structured or streaming responses generated based on agent configuration
- Telemetry Recording — Execution metadata sent to telemetry API for monitoring and analytics (config: api_runtime, api_url)
- Session Persistence — Conversation state and memory persisted to database for future interactions
System Behavior
How the system actually operates at runtime — where data accumulates, what loops, what waits, and what controls what.
Data Pools
Persistent storage for conversation history and agent state
Batched execution events for analytics and monitoring
Feedback Loops
- Agent Memory Loop (recursive, reinforcing) — Trigger: New user message. Action: Agent retrieves conversation history to inform response. Exit: Session ends or memory limit reached.
- Hook Processing Loop (retry, balancing) — Trigger: Pre/post hook execution. Action: Execute hook with error handling and optional background processing. Exit: Hook completes successfully or max retries reached.
Delays & Async Processing
- Background Hook Execution (async-processing, ~variable) — Hooks marked for background execution don't block main response flow
- Telemetry API Calls (async-processing, ~60s timeout) — Non-blocking telemetry recording with graceful failure handling
Control Points
- API Runtime Environment (env-var) — Controls: Telemetry API endpoint selection (dev/stg/prd). Default: prd
- Alpha Features Flag (env-var) — Controls: Enables experimental features. Default: false
- Hook Background Execution (runtime-toggle) — Controls: Whether hooks run synchronously or in background. Default: decorator-controlled
Technology Stack
Web framework for serving agents as APIs
Data validation and schema definition
HTTP client for API communication
Default database for agent memory and sessions
Terminal formatting and logging
Key Components
- AgentOS (service) — FastAPI application factory that serves agents, teams, and workflows as stateful production APIs
libs/agno/agno/os/app.py - Agent (class) — Core agent abstraction with memory, tools, and model integration for conversational AI
libs/agno/agno/agent.py - api.Client (class) — HTTP client for telemetry reporting and external API communication
libs/agno/agno/api/api.py - hook (function) — Decorator for creating lifecycle hooks that can run synchronously or in background
libs/agno/agno/hooks/decorator.py - AgentRunCreate (type-def) — Pydantic schema for agent execution telemetry data
libs/agno/agno/api/schemas/agent.py - TelemetryRunEventType (type-def) — Enum for categorizing different types of execution events (agent, team, workflow, eval)
libs/agno/agno/api/schemas/utils.py - AgnoAPISettings (config) — Configuration settings for API endpoints and runtime environment (dev, stg, prd)
libs/agno/agno/api/settings.py - ApiRoutes (config) — Centralized API endpoint definitions for telemetry and OS communication
libs/agno/agno/api/routes.py - OSLaunch (type-def) — Schema for tracking AgentOS deployment launches for telemetry
libs/agno/agno/api/schemas/os.py
Configuration
cookbook/02_agents/02_input_output/parser_model.py (python-pydantic)
park_name(str, unknown) — default: Field(..., description="Name of the national park")best_season(str, unknown) — default: Field(signature_attractions(List[str], unknown) — default: Field(recommended_trails(List[str], unknown) — default: Field(wildlife_encounters(List[str], unknown) — default: Field(photography_spots(List[str], unknown) — default: Field(camping_options(List[str], unknown) — default: Field(safety_warnings(List[str], unknown) — default: Field(- +4 more parameters
cookbook/02_agents/09_hooks/post_hook_output.py (python-pydantic)
is_complete(bool, unknown)is_professional(bool, unknown)is_safe(bool, unknown)concerns(list[str], unknown)confidence_score(float, unknown)
cookbook/02_agents/09_hooks/pre_hook_input.py (python-pydantic)
is_relevant(bool, unknown)has_sufficient_detail(bool, unknown)is_safe(bool, unknown)concerns(list[str], unknown)recommendations(list[str], unknown)
cookbook/02_agents/09_hooks/session_state_hooks.py (python-pydantic)
topics(List[str], unknown) — default: Field(description="Topics present in the user messages")
Explore the interactive analysis
See the full architecture map, data flow, and code patterns visualization.
Analyze on CodeSeaRelated Ml Inference Repositories
Frequently Asked Questions
What is agno used for?
Python framework for building, serving, and managing production-ready AI agents agno-agi/agno is a 9-component ml inference written in Python. Highly interconnected — components depend on each other heavily. The codebase contains 3485 files.
How is agno architected?
agno is organized into 4 architecture layers: Agent Framework Core, Operating System, API & Telemetry, Cookbook Examples. Highly interconnected — components depend on each other heavily. This layered structure enables tight integration between components.
How does data flow through agno?
Data moves through 5 stages: Request Ingestion → Agent Execution → Response Generation → Telemetry Recording → Session Persistence. Data flows from user inputs through agents/teams/workflows, with telemetry reporting and session management handled by AgentOS This pipeline design reflects a complex multi-stage processing system.
What technologies does agno use?
The core stack includes FastAPI (Web framework for serving agents as APIs), Pydantic (Data validation and schema definition), httpx (HTTP client for API communication), SQLite (Default database for agent memory and sessions), Rich (Terminal formatting and logging). A focused set of dependencies that keeps the build manageable.
What system dynamics does agno have?
agno exhibits 2 data pools (Agent Memory Database, Telemetry Buffer), 2 feedback loops, 3 control points, 2 delays. The feedback loops handle recursive and retry. These runtime behaviors shape how the system responds to load, failures, and configuration changes.
What design patterns does agno use?
4 design patterns detected: Telemetry Recording, Pydantic Schemas, Async/Sync Dual API, Hook System.
Analyzed on March 31, 2026 by CodeSea. Written by Karolina Sarna.