Agno

What is Agno?

Agno is a Python framework designed for building, running, and managing AI Agent platforms; its core code is available under the Apache 2.0 license. It offers an abstraction for individual agents, as well as support for multi-agent teams, deterministic workflows, knowledge, memory, tools, evaluation, and production deployment.

Currently, Agno divides its platform into three layers: the SDK is responsible for construction, AgentOS Runtime handles execution, and the Control Plane is in charge of management and debugging. Teams can deploy the runtime, sessions, memory, knowledge, and traces within their own infrastructure and databases.

Core functions

• Use Python to quickly create agents with tools and commands.
• Organize multiple professional Agents to work together and form a Team
• Use Workflow to orchestrate deterministic, multi-step, and stateful processes
• Connect to multiple providers of closed-source and open-source models
• Process multi-modal input and output such as text, images, audio, and video.
• Add a knowledge base, RAG, memory, and session storage to the Agent.
• Use structured output to return data with clear types.
• Automatically generate production APIs and streaming endpoints via AgentOS.
• Check sessions, metrics, knowledge, and traces in the Control Plane
• Configure Guardrails, manual approval, RBAC, and request isolation.
• Compatible with Claude Agent SDK, LangGraph, and DSPy Agent

Three-layer architecture of the platform

Agno Agent

An Agent is a stateful control loop based on a stateless model; the model carries out reasoning in response to instructions and invokes tools until the task is completed or the stopping conditions are met. Developers can add elements such as knowledge, storage, memory, human intervention, and Guardrails as needed.

• Define roles, objectives, instructions, and expected outputs.
• Select the model along with its temperature, context, and invocation parameters.
• Register a tool and control the actions that the Agent can perform.
• Save session history and operation status
• Retrieve specialized knowledge and incorporate relevant content into the context.
• Remember the selected user information and historical summaries.
• Return text, streaming events, or structured objects
• Check tool calls, errors, and complete trace.

Multi-Agent Team

A Team allows multiple Agents to work together within the same system, with the Team Leader coordinating the members to complete tasks. This team structure facilitates the separation of functions such as retrieval, analysis, writing, verification, and execution, thereby reducing the burden on any single Agent to handle all responsibilities.

• Assign dedicated models, knowledge, and tools to each member.
• The leading Agent selects or coordinates suitable members.
• Share designed contexts and task results.
• Complete different sub-tasks in parallel or sequentially
• Summarize, compare, and review the outputs of the members.
• Record assignments, tool usage, and final answers
• Add manual approval for the capabilities of high-risk members.

Agentic Workflows

Workflow organizes Agents, Teams, functions, and business logic through clear steps, making it suitable for tasks that require tracking of status, the ability to retry actions, and a predictable execution order. It complements fully autonomous Agents by limiting dynamic reasoning to those areas where such judgment is truly necessary.

• Define steps, conditions, branches, and inputs/outputs
• Combines regular functions, Agents, and Teams
• Saves workflow status and supports restoration of long-running tasks
• Set retries and alternative paths for failed steps.
• Set approvals for external writes and sensitive actions.
• Use structured data to connect to downstream steps
• Create separate traces and metrics for each stage.

Model support

Agno is not tied to any single model provider; it can be connected to various commercial APIs, open-weight models, and local inference services. The actual capabilities of the models, their costs, data policies, and the regions in which they are available are determined by the respective providers.

• Select a model among speed, cost, and quality based on the task.
• Configure different model providers for various agents.
• Use reasoning models or Reasoning Tools to handle complex tasks.
• Reduce the transmission of sensitive data by using local models.
• Use structured output or JSON format to constrain the results.
• Create the same evaluation dataset for model switching.
• Check for provider failures, quotas, and version changes.

Multimodal capabilities

Agno Agent can receive text, images, audio, and video, and generate outputs in various forms based on the capabilities of the model. Multimodal support depends on the specific model and tools used; it is not possible to assume that all combinations will be available just by looking at the framework’s interfaces.

• Understanding images, charts, scans, and screen captures
• Handle audio transcription, voice input, and audio output.
• Analyze video or related media content
• Retrieve visual information along with textual knowledge.
• Generate images or other supported media
• Retain the original files, model results, and manual verification records.

Tool ecosystem

Agno offers a large number of pre-built tools, and it also allows developers to turn Python functions, third-party APIs, or MCP Servers into Agent capabilities. These tools should have clear schemas, minimal permissions, and verifiable results.

• Call search, financial, database, and business APIs
• Execute authorized Python functions and internal services
• Connect standardized external tools via MCP
• Limit tool names, parameters, networks, and permission scopes.
• Run input/output Guardrails before and after the call
• Manual confirmation is required for writing, deleting, paying, and sending.
• Record tool usage time, errors, and actual business impact

Knowledge base and Agentic RAG

Agno can be connected to various vector databases, embedding models, and content sources, providing agents with the capability to perform searches at runtime. Agentic RAG enables agents to decide when to search, which knowledge to use, and whether to proceed with additional queries.

• Read knowledge from documents, web pages, databases, and custom sources
• Split the content, embed it, and write it to the vector database.
• Supports over 20 connection methods for vector databases.
• Search for relevant segments based on the query and return them.
• Allow the agent to continue searching when there is insufficient information.
• The source metadata is retained to facilitate verification.
• Implement governance for knowledge updates, deletions, and access rights.

Memory, sessions, and storage

Agno distinguishes between the history of the current session, long-term memory, knowledge, and application status, and offers various database-driven solutions. Production systems need to specify what information can be stored, for how long, and how users can view and delete that information.

Reasoning ability

Agno treats reasoning as a framework capability, supporting native reasoning models, Reasoning Tools, and custom reasoning methods. Reasoning helps in breaking down complex tasks, but it also increases latency, token costs, and unpredictability.

• Explicitly plan intermediate steps in complex problems
• Use professional tools to verify calculations against facts.
• Have Team members analyze from different perspectives respectively.
• Reflect on or re-examine the conclusions.
• Limit the maximum number of steps and total running time
• Do not expose the results of private system reasoning to end users.

Structured output

Agents can return structured results using type models or JSON schemas, which facilitates their use in APIs, workflows, and databases. The correctness of the structured syntax does not guarantee the accuracy of the business-related values; field validation and rule checks are still necessary.

• Use Python types to define the expected fields and hierarchy.
• Have the model generate objects according to a fixed schema.
• Validate enums, dates, amounts, and identifiers.
• Maintain the status for fields that are missing or have low confidence levels.
• Reject directly triggering high-risk actions with unverified data.
• Recording the original response and parsing errors is used for debugging.

AgentOS Runtime

AgentOS Runtime is a FastAPI service that runs Agents, Teams, and Workflows; it automatically provides over 50 production endpoints and supports SSE streaming of events. It operates within the user’s own containers and cloud environments, and its API can also deliver functionality directly to the end products.

• Register one or more Agents, Teams, and Workflows
• Start operations and retrieve history through a unified API.
• Output tokens, tool calls, and status events via SSE.
• Connect to the enterprise database to save sessions and traces.
• Add application middleware, authentication, and business routing.
• Deploy using containers to any supported environment.
• Connect the same Runtime to the Control Plane

Control Plane

The Control Plane is a visual interface for managing and debugging AgentOS; it allows users to view sessions, metrics, knowledge, memory, traces, and evaluations. The browser connects directly to the user’s Runtime, and Agno does not act as a proxy for conversations or runtime data.

• View real-time and historical Agent operations
• Check model output, tool calls, and latency.
• Managing knowledge content and long-term memory
• Interaction testing with Agents, Teams, and Workflows
• Run system evaluations and compare configuration changes
• Assign permissions to personnel responsible for development, operation, and governance.
• Connect to a local or production AgentOS environment

Data ownership and privacy

AgentOS stores sessions, memories, knowledge, and traces in the company’s own database, while the Runtime operates within the company’s cloud environment. According to the official documentation, the Control Plane browser connects directly to the Runtime; Agno merely stores the endpoints involved in operations without forwarding any business data.

• Select a database that meets the regional and industry requirements.
• Deploy AgentOS Runtime in the enterprise network
• Avoid sending traces to unnecessary third-party platforms.
• Set encryption, backup, and data retention policies as needed.
• Restrict the Runtime endpoints that the Control Plane can access
• It is still necessary to evaluate separately the data flow of the selected model provider.
• Turn off unnecessary telemetry and check the actual network outbound connections.

Security and Governance

AgentOS offers JWT RBAC, hierarchical Scope, request isolation, Guardrails, Human-in-the-Loop, and approval processes. The capabilities of this framework do not automatically create a compliance system; companies still need to properly configure identities, permissions, data, and operational boundaries.

• Use JWT to authenticate the identities of users, applications, and services.
• Set the scope at the tenant, user, Agent, and resource levels.
• Prevent state interference between different requests, sessions, and users
• Configure Guardrails at the input, output, and tool invocation points.
• Introduce a manual approval process for sensitive operations.
• Recording permission verification, Agent decision-making, and manual modification
• Create test sets for injection attacks, privilege escalation, and data leakage.

Multi-framework support

AgentOS enables the execution of native Agno Agents, as well as Agents developed using the Claude Agent SDK, LangGraph, and DSPy, all within the same Runtime, API, and UI. Adapters are responsible for unified routing, stream-based output, and session persistence.

External framework Agents can take advantage of unified endpoints, SSE, session storage, and tool visibility, but Agno-specific delegation, knowledge, dependencies, Hooks, Skills, and Learning are not added automatically.

Channels and agreements

• Agents are available via Slack, Telegram, WhatsApp, and Discord.
• Use the front-end interaction protocols supported by AG-UI for connection.
• Collaborate with other compatible Agent systems through A2A
• Use the MCP connection tool and document service.
• Embed the AgentOS API in web or mobile products
• Configure identity mapping and session isolation for each channel.
• Verify message length, attachments, and retry rules for the channel.

Scheduling and background tasks

Agno supports scheduled tasks and background jobs based on Cron, allowing periodic Agent processes to run without the need for a separate scheduling infrastructure. In a production environment, issues related to idempotency, concurrency, retries, and fault recovery still need to be addressed.

• Generate research, reports, and data summaries on a scheduled basis
• Periodically update the knowledge base and indexes
• Process long documents and batch tasks in the background
• Limit the concurrent execution of tasks under the same plan.
• Use the business primary key to avoid repeated executions.
• Monitor timeouts, failures, and queued tasks

Applicable scenarios

• In-product chat assistant and context Copilot
• Research, analysis, writing, and review of multi-Agent systems
• Document extraction, classification, and structured processing
• Labeling of text, image, audio, and video data
• Enterprise Knowledge Assistant and Agentic RAG
• Business automation with manual approval
• Scheduled reporting, monitoring, and background agent tasks
• Unified management of Agno, LangGraph, DSPy, and Claude Agent
• Agent platform that requires private data, Trace, and its own database.

Which users are it suitable for

• Development teams that use Python to create AI products
• Researchers in need of open-source multi-Agent frameworks
• Companies that wish to have Agent operation data and Trace information
• The platform engineering team responsible for upgrading from the prototype to a production API
• Organizations that maintain multiple Agent frameworks simultaneously
• Teams that require unified monitoring and governance through the Control Plane
• Enterprises that are able to deploy containers, databases, and security barriers on their own.

Agno price

Agno offers three tiers: Free, Pro, and Enterprise. The SDK and the local AgentOS can be used for free under an open-source license; the Pro tier is intended for teams that need to connect to production AgentOS, while the Enterprise tier provides additional support, customization options, and the ability to host the Control Plane independently.

Pro additional fees

What Pro refers to as “Unlimited” applies primarily to Control Plane monitoring, retention, knowledge storage, memory functions, and chat; it does not mean that third-party models, vector databases, or cloud infrastructure are available at no cost. The total cost includes expenses related to model tokens, databases, networking, and maintenance.

Quick Start Tutorial

1. Prepare the Python environment and install the Agno SDK.
2. Create a basic Agent with a clearly defined model and instructions.
3. Add a read-only tool and test normal and abnormal inputs.
4. Connect to the database to save Sessions and Traces.
5. Add Knowledge, Memory, and structured output as needed.
6. Use AgentOS to register agents and generate FastAPI services.
7. Connect to the local Control Plane to check operations and tool calls.
8. Deploy to production only after completing evaluation, security, and load testing.

Tutorial on Building Multi-Agent Systems

1. Break down business objectives into specialized tasks with clear responsibilities.
2. Select the appropriate model, knowledge, and tools for each task.
3. Create a member Agent and restrict its permissions and output scope.
4. Use Team to configure leadership, delegation, and aggregation methods.
5. For the determination step, use a Workflow rather than relying entirely on autonomous reasoning.
6. Establish structured handover and failure recovery pathways.
7. Evaluate collaborative stability and total cost through multiple runs.
8. Use Trace to determine which member or tool is causing the error.

Tutorial for deploying AgentOS

1. Select the database, model service, and target cloud environment.
2. Register production Agents, Teams, and Workflows in AgentOS.
3. Configure JWT, RBAC, tenant, and session isolation.
4. Use key management for database, model, and tool credentials.
5. Build the container and run a full API check in the testing environment.
6. Connect to the Control Plane and verify that no unexpected traces are sent out.
7. Configure scaling, health checks, backups, and failure recovery.
8. After going live, continuously monitor latency, errors, costs, and security incidents.

Effect evaluation

Product advantages

• The SDK and AgentOS core are open-source under the Apache 2.0 license.
• A complete abstraction layer is formed from Agents, Teams to Workflows.
• Supports multiple models, multimodal approaches, tools, knowledge, and memory.
• AgentOS can quickly generate production FastAPI and SSE endpoints.
• Sessions, memories, knowledge, and traces are stored in a dedicated database.
• Direct connection from the Control Plane browser to the Runtime reduces data transmission.
• Agno, Claude Agent SDK, LangGraph, and DSPy can all be run simultaneously.
• Includes RBAC, request isolation, Guardrail, and manual approval capabilities.
• The Pro price, as well as the costs related to seats and connections, are clearly displayed.

Usage restrictions and precautions

• Open-source frameworks require one to bear the costs related to models, databases, cloud services, and operation and maintenance.
• Pro includes only 1 production connection; additional Runtime instances require extra payment.
• The so-called unlimited use does not include costs related to third-party models and infrastructure.
• Multi-Agent systems are more difficult to debug than single-Agent systems, and they may lead to a significant increase in tokens.
• The external framework adapter does not support all of Agno’s native capabilities.
• The security features of AgentOS must be properly configured in order to function effectively.
• Knowledge, memory, and traces may contain sensitive information, so governance is required.
• Models and tools may still pose risks of errors, privilege escalation, or prompt injection.
• The framework is updated frequently; production projects should use fixed and tested versions.
• By default, telemetry records information on the usage by the model provider; it can be disabled as per configuration.

GitHub and open source

Agno’s official GitHub repository makes available the SDK, code related to AgentOS, examples, and cookbooks, under the Apache License 2.0. This license permits use, modification, and distribution, but it is required to retain the license text, copyright information, and any necessary NOTICE statements.

Self-hosting of the Control Plane, dedicated support, and customized solutions are part of the Enterprise benefits. The use of an open-source SDK does not mean that the model APIs, cloud resources, or all enterprise services are available free of charge.

Basic information

Recommendation score

4.8 / 5. Agno is suitable for teams that wish to develop their own Agent platforms using Python, to gain control over the data in use and to move from development to production environments; it offers an open-source SDK, AgentOS, as well as support for multiple frameworks. However, companies still need to handle security configurations, the costs associated with models, and the maintenance of complex systems.

Frequently Asked Questions

What is Agno mainly doing?

It helps developers create Agents, Teams, and Workflows, and it enables the operation, management, and monitoring of production Agent platforms through AgentOS.

Is Agno free?

The SDK and the local AgentOS can be used freely under an open-source license; the Pro version for the production Control Plane costs $150 per month.

Is Agno open source?

It is open source; the official core repository is currently licensed under Apache License 2.0.

What is AgentOS?

It consists of a Runtime and a Control Plane; it uses a unified API to operate the Agents and provides interfaces for monitoring, debugging, and management.

Will the data be sent to Agno?

AgentOS sessions, memories, knowledge, and traces are stored in the user database; the Control Plane connects directly to the Runtime via the official architecture, while the data flow related to model providers still needs to be evaluated separately.

Does Agno support LangGraph?

Yes, AgentOS can package and run LangGraph; it also supports the Claude Agent SDK and DSPy.

How many seats are included in Pro?

Pro includes 4 seats and 1 Live Connection; additional seats cost $30 per month.

How much does it cost to produce additional connections?

The public price is $95 per additional Live Connection per month.

Can Agno be deployed locally?

Yes, AgentOS Runtime can operate within the user’s own containers and cloud environments; a self-hosted Control Plane is part of the Enterprise features.