SuperAPI AI Directory

Phidata

Phidata (now named Agno) is a self-driving agent platform that runs in the user's own cloud environment. It provides three major components: the Agno SDK, the AgentOS runtime, and the Agno control plane, and supports building agents, teams, and multi-agent workflows with code, no-code, or natural language. The platform comes with more than 100 built-in toolkits, natively supports more than 30 model providers, and is compatible with frameworks such as LangGraph, DSPy, and Claude SDK. Users can build agents for customer-facing customer service, sales, research, finance, weekly reports, and more, and deploy agents to their own products, ChatGPT, Claude, Slack, Discord, and other channels via REST, MCP, or APIs. AgentOS provides durable execution, distributed state, request isolation, resumable streaming, JWT-based RBAC, audit logs, and observability; the control plane can manage all runs, traces, sessions, evaluations, and usage metrics in real time, and supports roles and permissions, approvals, and guardrails. The platform emphasizes that data stays in your own database, can run in any container-capable cloud, on-premises, or hybrid environment, is priced at a flat rate, does not store data or run compute, and therefore has no storage fees, retention fees, or per-trace billing. Official information shows it has 41K GitHub stars, Fortune 5 customers, and is SOC 2 compliant.

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About this tool

Phidata (now called Agno) is a self-driving agent platform that runs in the user's own cloud environment, used to build, deploy, and improve customer-facing and internal agents. It is officially positioned as "from prompt to platform in just three steps," and it emphasizes that the platform is built for coding agents, with the entire lifecycle from setup to deployment manageable by coding agents.

Purpose

Agno is aimed at teams that need to build agent platforms, customer service agents, sales development agents, research and analysis agents, and internal automation workflows. Official examples include: answering customer tickets using product documentation, order history, and past solutions; making a product agentic so that users can ask questions in natural language and have the agent perform actions; finding qualified leads and drafting personalized outreach; support agents where billing and escalation specialists collaborate in the same queue; distributing issues to multiple analysis agents and merging them into a single briefing; reading code diffs, drafting release notes, and submitting a release checklist for approval; performing data ingestion, extraction, validation, posting, and setting approval gates before shipment; summarizing product usage and sending a report every Friday morning; and enriching newly registered users with company information and routing them to the account owner.

Main capabilities

The platform consists of three parts. The Agno SDK is used to build agents, providing three modes: agents, teams, and workflows, including more than 100 toolkits and a large number of capabilities that can be added. The AgentOS runtime is used to run agents, turning them into APIs callable by products and MCP servers that ChatGPT and Claude can connect to, and providing durable execution, distributed state, request isolation, resumable streaming, JWT-based RBAC, audit logs, and deep observability with built-in monitoring and tracing. The Agno control plane is used to manage agents, enabling real-time management of the operation of every agent, team, and workflow, viewing traces, sessions, evaluations, and usage metrics in one place, and providing audit logs, admin approvals, user roles, and service account access.

The platform supports using coding agents to build, evaluate, and improve the platform, using code, no-code, or natural language; any agent or workflow can be implemented with a single prompt. Agents can serve the user's own products, AI applications (ChatGPT, Claude), or chat applications (Slack, Discord), and be distributed through REST, MCP, or interfaces.

How to use

It is officially described in three steps: Step one, set up the agent platform with a prompt. The platform is built for coding agents, and the coding agent manages the full lifecycle from setup to deployment; officiallyThe second step is to build agents with coding agents, using code, no-code, or natural language to build, evaluate, and improve the platform. The third step is to serve agents anywhere, connecting them to your own products, ChatGPT, Claude, Slack, Discord, using REST, MCP, or interfaces.

Applicable Scenarios

It is suitable for scenarios such as customer support, embedded agents within products, sales lead development, multi-agent collaborative support, research briefings, release notes automation, financial process automation, periodic reporting, user data enrichment and routing, etc. The platform is also suitable for teams that need self-hosting, keeping data in their own databases, and running in any container environment.

Limitations and Unprovided Information

Official materials do not provide specific price figures, feature differences between versions, free quotas, specific installation commands, SDK code examples, the specific list of supported models, toolkit lists, performance metrics, or regional availability. The charging method is only described as fixed pricing, does not store data, and does not run computations, so there are no storage fees, retention fees, or per-trace billing. Compliance information only mentions SOC 2 compliance, 41K GitHub stars, and Fortune 5 customers. Other details have not yet been provided.