agno-agi/agno
Build, run, manage agentic software at scale.
Builds and runs multi-agent AI systems as scalable production APIs
User requests enter through HTTP endpoints, get validated and routed to the appropriate agent or team. The agent loads its session context from the database, processes the message using its LLM and tools, potentially queries knowledge bases for additional context, and returns a response. Throughout this process, the learning system captures insights that get stored back into knowledge bases for future use.
Under the hood, the system uses 4 feedback loops, 4 data pools, 6 control points to manage its runtime behavior.
A 10-component ml inference. 3554 files analyzed. Data flows through 7 distinct pipeline stages.
How Data Flows Through the System
User requests enter through HTTP endpoints, get validated and routed to the appropriate agent or team. The agent loads its session context from the database, processes the message using its LLM and tools, potentially queries knowledge bases for additional context, and returns a response. Throughout this process, the learning system captures insights that get stored back into knowledge bases for future use.
- HTTP request parsing — FastAPI receives incoming HTTP/WebSocket requests, validates them against Pydantic schemas (AgentRunRequest), and extracts user_id, session_id, message content, and configuration
- Session context loading — SessionRouter retrieves or creates user session from database, loads conversation history (num_history_runs messages), and prepares session state for agent execution [AgentRunRequest → SessionState]
- Agent message processing — Agent.run() method processes the user message by constructing context from session history, knowledge base queries, and system instructions, then calls the configured LLM model to generate a response [AgentRunRequest]
- Tool invocation execution — If the LLM response includes tool calls, agents execute them through SQLTools for database queries, MCPTools for web searches, or CodingTools for file operations, collecting results to include in the final response
- Knowledge base querying — Knowledge.search() performs semantic similarity search against vector embeddings to find relevant documents, chunks text using configured chunking strategy, and returns context for LLM reasoning
- Learning extraction — LearningMachine analyzes the completed interaction to extract insights, patterns, and useful information, then stores these learnings in the dynamic knowledge base for future agent improvements
- Response serialization — AgentRunResponse is constructed with the final content, execution metrics, session data, and any media attachments, then serialized to JSON for HTTP response or streamed incrementally for real-time interaction
Data Models
The data structures that flow between stages — the contracts that hold the system together.
libs/agno/agno/agent.pyclass with model: LLM, db: BaseDb, tools: list[Tool], knowledge: Knowledge, instructions: str, add_history_to_context: bool, num_history_runs: int
Created during server startup, persists agent configuration and capabilities, executed for each user request with session context
libs/agno/agno/os/routers/agents.pyPydantic model with message: str, user_id: str, session_id: str, stream: bool, additional_messages: list, images: list
Parsed from incoming HTTP requests, validated against schema, passed to agent for processing
libs/agno/agno/os/routers/agents.pyPydantic model with content: str, metrics: dict, session_id: str, messages: list, media: list
Generated by agent after processing request, serialized to JSON for HTTP response or streamed incrementally
libs/agno/agno/knowledge/knowledge.pyclass with vector_db: VectorDb, reader: Reader, embedder: Embedder, id: str, description: str, num_documents: int
Created during agent setup, populated with documents via readers, queried during agent reasoning for context retrieval
libs/agno/agno/os/routers/session.pydict with user_id: str, session_id: str, agent_data: dict, messages: list, created_at: datetime, updated_at: datetime
Created on first user interaction, updated with each message exchange, persisted in database for conversation continuity
Hidden Assumptions
Things this code relies on but never validates. These are the things that cause silent failures when the system changes.
All MCP tools in mcp_tools list have a connect() method that returns an awaitable and establishes connections successfully on first call
If this fails: If any MCP tool lacks connect() method or connection fails, the entire application startup fails with AttributeError or connection timeout, taking down all agents
libs/agno/agno/os/app.py:mcp_lifespan
Static knowledge (table schemas, validated queries) remains valid throughout agent lifecycle and database schema changes don't invalidate stored knowledge
If this fails: When database schema evolves, agents continue using outdated table definitions leading to SQL errors, failed queries, and incorrect data analysis without any cache invalidation
cookbook/01_demo/agents/dash/agent.py:dash_knowledge
The workspace directory has write permissions, sufficient disk space, and won't be modified by external processes during agent operation
If this fails: If workspace becomes read-only or fills up, CodingTools silently fail to write files or partially write corrupted files, causing agents to work with incomplete code
cookbook/01_demo/agents/gcode/agent.py:WORKSPACE
MCP tools are connected in the order they appear in the mcp_tools list, with no circular dependencies or connection ordering requirements
If this fails: If tool B depends on tool A being connected first, but A appears later in the list, tool B connection fails and agents lose access to external capabilities
libs/agno/agno/os/app.py:mcp_lifespan
Dynamic learnings storage can handle unlimited growth as the agent discovers new patterns, type errors, and business rules over time
If this fails: As learnings accumulate without bounds, vector database storage costs grow linearly, search performance degrades, and old irrelevant learnings pollute context retrieval
cookbook/01_demo/agents/dash/agent.py:LearningMode.ACTIVE
Business rules and semantic model definitions in BUSINESS_CONTEXT and SEMANTIC_MODEL_STR match the actual database structure and business logic
If this fails: When business rules change but context isn't updated, agent provides analysis based on outdated assumptions, leading to incorrect insights and business decisions
cookbook/01_demo/agents/dash/agent.py:BUSINESS_CONTEXT
PostgreSQL database connection parameters (host, port, credentials, database name) are available through environment variables or default configuration
If this fails: If database environment isn't properly configured, agent_db initialization fails and agent cannot access any database functionality, but error might be deferred until first database operation
cookbook/01_demo/agents/dash/agent.py:get_postgres_db
CodingTools(base_dir=WORKSPACE) properly sandboxes all file operations to prevent access outside the workspace directory
If this fails: If path traversal protection fails, agents could read/write sensitive files outside workspace, creating security vulnerabilities or corrupting system files
cookbook/01_demo/agents/gcode/agent.py:CodingTools
create_knowledge function creates unique knowledge instances per agent and doesn't share state between 'dash_knowledge' and 'gcode_knowledge' instances
If this fails: If knowledge instances share underlying storage, learnings from one agent pollute another agent's context, causing cross-contamination of domain-specific knowledge
cookbook/01_demo/agents/dash/agent.py:create_knowledge
Individual coding sessions and file operations within workspace directory stay within reasonable size limits for the filesystem
If this fails: Large code generation tasks could create files exceeding filesystem limits, causing partial writes, corruption, or filesystem errors that break subsequent operations
cookbook/01_demo/agents/gcode/agent.py:WORKSPACE
System Behavior
How the system operates at runtime — where data accumulates, what loops, what waits, and what controls what.
Data Pools
Stores agent conversation history, session state, and execution metadata with per-user isolation
Maintains document embeddings and metadata for semantic search during agent reasoning
Maintains persistent connections to external MCP servers with connection lifecycle management
Temporarily holds active user sessions for fast access during request processing
Feedback Loops
- Learning accumulation loop (self-correction, reinforcing) — Trigger: Agent completes interaction successfully. Action: LearningMachine extracts insights and stores them in knowledge base. Exit: Learning stored and available for future queries.
- Session continuity loop (recursive, reinforcing) — Trigger: User sends follow-up message in same session. Action: Agent loads previous conversation context and builds upon it. Exit: User explicitly starts new session.
- Tool retry loop (retry, balancing) — Trigger: Tool execution fails with recoverable error. Action: Agent retries tool call with modified parameters or error handling. Exit: Tool succeeds or max retries exceeded.
- Knowledge refinement loop (training-loop, balancing) — Trigger: Agent uses knowledge that proves incorrect or incomplete. Action: Learning system updates knowledge base with corrections. Exit: Knowledge accuracy improves.
Delays
- LLM response generation (async-processing, ~1-10 seconds) — User waits for agent response, can be streamed to reduce perceived latency
- Vector embedding computation (async-processing, ~100-500ms) — Knowledge search latency affects agent response time
- MCP connection establishment (async-processing, ~1-3 seconds) — First tool use in session has connection overhead
- Database query execution (async-processing, ~10-1000ms) — Session loading and history retrieval affect response time
Control Points
- Model selection (architecture-switch) — Controls: Which LLM backend and model size agents use for reasoning. Default: OpenAI GPT-4
- History context window (hyperparameter) — Controls: Number of previous messages included in agent context (num_history_runs). Default: 3
- Streaming response mode (feature-flag) — Controls: Whether responses are streamed incrementally or returned in full. Default: configurable per request
- Tracing enabled (feature-flag) — Controls: Whether request tracing and monitoring is active. Default: true
- Learning mode (runtime-toggle) — Controls: Whether agents actively learn from interactions (LearningMode.ACTIVE/PASSIVE). Default: ACTIVE
- Knowledge chunk size (hyperparameter) — Controls: Document chunking strategy for vector embeddings. Default: 1000 tokens
Technology Stack
HTTP/WebSocket server framework providing async request handling and automatic API documentation
Data validation and serialization for request/response models and configuration schemas
Database ORM for agent memory and session persistence across PostgreSQL and SQLite
Large language model providers for agent reasoning and response generation
Semantic search over knowledge bases using embeddings for context retrieval
Standardized protocol for connecting agents to external tools and services
Terminal formatting and logging for development and debugging output
Async HTTP client for external API calls and MCP server communication
Key Components
- AgentOS (orchestrator) — Creates and configures the FastAPI application that serves agents as HTTP/WebSocket APIs with session management and tracing
libs/agno/agno/os/app.py - Agent (executor) — Core autonomous system that processes user messages using LLM, tools, memory, and knowledge to generate responses
libs/agno/agno/agent.py - LearningMachine (processor) — Dynamic knowledge acquisition system that extracts insights from agent interactions and stores them for future use
libs/agno/agno/learn.py - SQLTools (adapter) — Database interface that allows agents to execute SQL queries, inspect schemas, and manipulate database state
libs/agno/agno/tools/sql.py - MCPTools (adapter) — Model Context Protocol client that connects agents to external MCP servers for web search, documentation, and other services
libs/agno/agno/tools/mcp.py - SessionRouter (gateway) — Manages user sessions and conversation history, ensuring message continuity and isolation between users
libs/agno/agno/os/routers/session.py - DatabaseManager (store) — Abstracts database operations for agent memory, providing consistent interface across PostgreSQL, SQLite, and other backends
libs/agno/agno/db/base.py - KnowledgeSystem (store) — Vector database and document management system that enables semantic search over agent knowledge bases
libs/agno/agno/knowledge/knowledge.py - TeamCoordinator (orchestrator) — Coordinates multiple agents working together, managing communication patterns and result aggregation
libs/agno/agno/team.py - WorkflowEngine (orchestrator) — Executes multi-step workflows with conditional branching, loops, and agent handoffs
libs/agno/agno/workflow.py
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?
Builds and runs multi-agent AI systems as scalable production APIs agno-agi/agno is a 10-component ml inference written in Python. Data flows through 7 distinct pipeline stages. The codebase contains 3554 files.
How is agno architected?
agno is organized into 5 architecture layers: Framework Core, Runtime Server, Storage Layer, Tools Ecosystem, and 1 more. Data flows through 7 distinct pipeline stages. This layered structure keeps concerns separated and modules independent.
How does data flow through agno?
Data moves through 7 stages: HTTP request parsing → Session context loading → Agent message processing → Tool invocation execution → Knowledge base querying → .... User requests enter through HTTP endpoints, get validated and routed to the appropriate agent or team. The agent loads its session context from the database, processes the message using its LLM and tools, potentially queries knowledge bases for additional context, and returns a response. Throughout this process, the learning system captures insights that get stored back into knowledge bases for future use. This pipeline design reflects a complex multi-stage processing system.
What technologies does agno use?
The core stack includes FastAPI (HTTP/WebSocket server framework providing async request handling and automatic API documentation), Pydantic (Data validation and serialization for request/response models and configuration schemas), SQLAlchemy (Database ORM for agent memory and session persistence across PostgreSQL and SQLite), OpenAI/Anthropic APIs (Large language model providers for agent reasoning and response generation), Vector Databases (Semantic search over knowledge bases using embeddings for context retrieval), MCP (Model Context Protocol) (Standardized protocol for connecting agents to external tools and services), and 2 more. A focused set of dependencies that keeps the build manageable.
What system dynamics does agno have?
agno exhibits 4 data pools (Agent Database, Vector Knowledge Store), 4 feedback loops, 6 control points, 4 delays. The feedback loops handle self-correction and recursive. These runtime behaviors shape how the system responds to load, failures, and configuration changes.
What design patterns does agno use?
6 design patterns detected: Dual Knowledge System, Tool Factory Pattern, Sandboxed Execution, Session Isolation, Structured Input/Output, and 1 more.
Analyzed on April 20, 2026 by CodeSea. Written by Karolina Sarna.