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AI · October 23, 2024

Phidata – Build AI Agents with memory, knowledge, tools and reasoning

Build, run, and manage agent platforms. Contribute to agno-agi/agno development by creating an account on GitHub.

Phidata – Build AI Agents with memory, knowledge, tools and reasoning launched on October 23, 2024 with 27 votes, #772 of 5,185 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 1 year on.

Visit github.com ↗Alternativestop 15% of October 2024

In plain words

Phidata is a framework for building AI agents that can retain memory, access knowledge bases, use external tools, and perform reasoning tasks. It's designed for developers who want to create intelligent agents with capabilities beyond basic language models. The framework emphasizes giving agents persistent memory and the ability to interact with external systems and information sources, making it useful for building more autonomous and capable AI applications.

written from the facts on this page · September 2026

From the sources

Agno is a framework and runtime for agent platforms. Build agents, run them as a service, manage your platform using a web UI. Agno allows you to own your agent stack. Maintain control of your data, memory, and security posture (JWT-based RBAC), and turn your agent platform into a learning loop with simulations and usage data. Help me set up my agent platform. Clone https://github.com/agno-agi/agentos-railway into a folder called agent-platform, cd in, read the README, and follow the get started guide. Your coding agent will set up your agent platform and run it locally using Docker, giving you a REST API for serving your agents, a Postgres database for storing your data and traces, an MCP…from github.com

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  • Phidata – Building Blocks for Data Engineering2023 · docs.phidata.com · ▲16

    Hi HN, I’ve been a data engineer for over a decade and have slowly been working on a way for 1 person to run a data platform (mostly to automate my job) When I was starting, I found the wide range of tools required to build data products overwhelming. Infrastructure setup, app deployment, pipeline development, creating metrics, dashboards and finally, integrating ML into product. No wonder companies have 100s of people working on this. So last year I quit the best job in the world to figure this out. I’d like to introduce Phidata: Building Blocks for Data Engineering It works like this: 1.…

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