Phidata

Phidata was a Python framework for building multi-modal agents with built-in memory, knowledge stores, and tools. The team open-sourced it under the Mozilla Public License and built a sizable community around it. In early 2025 they relaunched the project as Agno, and Phidata is now effectively a legacy codebase. If you're starting something new, you should be looking at Agno, not Phidata.

Phidata had a clean three-year run. It launched in early 2023, built a community of Python developers who wanted agent infrastructure without the ceremony of LangChain, and grew to tens of thousands of GitHub stars. Then in early 2025, the team announced a full rebranding and rebuild under the name Agno. The Phidata documentation now redirects to Agno. The codebase is in maintenance mode. If you search for Phidata today, you'll find plenty of tutorials that predate the transition and a framework that its own creators have moved on from.

This review covers what Phidata was, what it got right, and what the transition to Agno actually means for people who used it or are considering what to build on now.

Quick verdict

Phidata is a legacy framework. If you're building something new, don't use it. The same team that built Phidata is now working on Agno, which carries the same ideas forward with active development and a growing community. If you have existing Phidata code, the migration to Agno is not automatic but it's not prohibitive either, and it's worth doing before you build more on a foundation that won't receive bug fixes.

What Phidata was

Phidata started as a practical answer to a specific frustration. Around 2022 and 2023, building an AI agent in Python usually meant either using LangChain's sprawling abstraction tree or writing everything from scratch. LangChain was powerful but dense. Its abstraction layers stacked on top of each other, and debugging a chain that went wrong required understanding which layer had introduced the problem. A lot of developers found that the framework cost more in comprehension overhead than it saved in boilerplate.

Phidata's approach was different. You created an Agent object, gave it a model, gave it tools, gave it a knowledge base if you needed one, and ran it. The API surface was small. A basic agent with web search and a memory store fit in under twenty lines of Python. That was the pitch, and it worked. Developers who wanted to prototype fast without learning a framework's internals found Phidata approachable in a way that LangChain wasn't.

The multi-modal angle was built in from early on. Phidata treated text, images, audio, and video as first-class inputs rather than things you bolted on through a plugin. That mattered as the model ecosystem matured and people started working with models like GPT-4o and Gemini that handled different modalities natively. You didn't have to restructure your agent to start working with image inputs. You passed the image.

Memory and knowledge worked similarly. Phidata included built-in integrations for storing agent memory in databases and retrieving relevant context through vector search. This wasn't revolutionary, every serious agent framework eventually added RAG and memory support, but Phidata had it early and it was integrated cleanly rather than being a third-party plugin.

The features that made Phidata worth using

Multi-modal agents (text, images, audio)

Phidata designed for multi-modal from the start. An agent in Phidata could receive a text message, an image, or an audio clip and respond appropriately, without a separate configuration for each modality. The framework routed the input type to the model correctly as long as the underlying model supported it.

In practice this mattered most for teams building tools where the input format wasn't fixed. Customer support agents that received both text tickets and screenshots. Document processing pipelines that handled PDFs, images of handwritten notes, and typed text in the same flow. The single Agent abstraction that handled all of them was genuinely simpler than maintaining separate pipelines per input type.

Built-in memory and knowledge stores

Agent memory in Phidata came in two forms. Session memory kept context within a running conversation. Long-term memory persisted across sessions, letting an agent remember that a particular user preferred metric units or that a company's tech stack was Python and PostgreSQL.

Knowledge stores worked through a retrieval layer. You connected a vector database, indexed documents into it, and the agent retrieved relevant chunks before responding. Phidata included built-in connectors for PgVector, Pinecone, and a handful of other databases, so you weren't managing the indexing pipeline manually.

The significance of having these features in the framework rather than in plugins is that they worked with the same API as everything else. You didn't have to figure out how to connect a third-party memory library to a framework that wasn't designed for it. The surface was consistent.

Tool ecosystem and integrations

Phidata shipped a large library of pre-built tools: web search, code execution, file I/O, weather APIs, financial data, database queries, and more. The framework's design made adding tools a matter of passing a list into the Agent constructor. Tools were Python functions with a type signature and a docstring, which the framework converted into the tool definitions the model needed.

This was the right design. Because tools were just Python functions, you could write a custom tool in the same time it took to write any other function. The pre-built library covered the common cases, your own code covered the specific ones, and the interface was the same either way.

The breadth of that library, over a hundred integrations by the time Phidata transitioned to Agno, was one of the concrete advantages Phidata held over frameworks that treated tool integration as the developer's problem.

Agent UI for testing and demoing

Phidata included a local web interface for testing agents without building a frontend. You started the Agent UI from the command line, pointed it at your agent, and got a chat interface in the browser where you could send messages, inspect tool calls, and see the agent's reasoning chain.

This removed a friction point that shows up early in agent development. When you're building something and you want to see how it behaves with different inputs, spinning up a full frontend is overhead that slows the iteration cycle. The Agent UI handled that part. You tested in the browser, found the failure case, fixed it in Python, and tested again. It wasn't meant for production, but as a development tool it saved real time.

Phidata Cloud for hosting

Phidata Cloud was the managed hosting layer. You deployed your agents to Phidata's infrastructure, got a persistent runtime, and could expose agent endpoints without managing your own servers. It was positioned as the paid tier above the free open-source library.

The cloud product didn't reach maturity before the Agno transition. It was available, teams used it, but it was never as fleshed out as the core framework. When the team moved to Agno, Phidata Cloud was absorbed into the Agno platform rather than continued as a separate product.

The move to Agno

In early 2025, the Phidata team announced they were rebuilding the project under a new name: Agno. The announcement framed this as a maturation rather than a replacement, the same core ideas, better architecture, with a new name to mark the shift.

The practical effect was clear and fast. The documentation at docs.phidata.com redirected to Agno's docs. Active development moved to the agno-agi/agno repository. The phidatahq/phidata repository stopped receiving new features. By May 2026, Agno is at version 2.6.5 with over 40,000 GitHub stars and an active release schedule. Phidata is not.

The Agno architecture expanded on what Phidata built. The SDK is still Python, the agent model is still memory, knowledge, and tools, but Agno added a proper runtime layer for deploying agents as production services, a control plane for managing multi-agent systems, and first-class support for running agents from other frameworks like LangGraph and DSPy within the same environment. It's a bigger system than Phidata was, aiming at production deployments rather than just prototyping.

If you have Phidata code, you'll need to update imports and some API calls to move to Agno. The conceptual model is similar enough that the migration isn't a rewrite, but it's not a package version bump either. The team published a migration guide. For any non-trivial project, budget a few days to work through it.

Phidata vs the alternatives

Phidata vs LangGraph

LangGraph is the framework to reach for when you need explicit control over how an agent moves through a workflow. You define nodes as processing steps and edges as transitions, and LangGraph executes the graph. That structure is verbose compared to Phidata's Agent class, but it's debuggable in a way that high-abstraction frameworks aren't. When a LangGraph workflow fails, you can trace it to the exact node and edge that went wrong.

Phidata was faster to prototype with. A working agent with memory and tools took fewer lines of code than an equivalent LangGraph graph. But for complex production workflows with branching conditions, human approval steps, and retry logic, LangGraph's explicitness is worth the verbosity.

Since Phidata is now Agno, the relevant comparison for new projects is Agno against LangGraph. If you want something easier to start, Agno. If you want something where the control flow is visually and structurally explicit, LangGraph.

Phidata vs CrewAI

CrewAI took a different angle than Phidata. Where Phidata modeled an individual agent with memory and tools, CrewAI modeled a team of agents with roles, each responsible for a part of a larger task. A researcher, a writer, a reviewer working in sequence.

The role-based crew model is intuitive for workflows that genuinely map to specialization. If you're building a content pipeline or a research automation where different agents do different jobs, CrewAI's abstractions fit. For a single capable agent with lots of tools, Phidata's model was simpler and more appropriate.

Neither framework is active in the original form. CrewAI continues to develop. Phidata is now Agno.

Phidata vs building for the best AI agent for coding tasks

If you're evaluating frameworks for AI agent coding tasks, Phidata's legacy matters mostly as context for why Agno exists. Agno is where the active development is. The question of whether to use Agno, LangGraph, or CrewAI for a coding agent depends more on your specific control flow requirements than on the Phidata lineage.

Should you use Phidata today?

The answer is almost certainly no, unless you have a very specific reason to stay on it.

The framework's own creators have stopped working on it. The documentation doesn't point you to Phidata; it points you to Agno. New bugs in Phidata won't be fixed. New models won't be added to its tool ecosystem. The community has moved.

If you're building something new, start with Agno. It carries everything Phidata had and keeps getting better. If you've built something on Phidata, the question is whether it's stable enough to leave alone or whether the dependency drift and lack of maintenance will create problems over time. For anything in active development, migrating to Agno is the right move.

Phidata was a good framework for its time. It showed that agent infrastructure didn't need to be as complex as LangChain made it look, that multi-modal and memory support could be first-class rather than afterthoughts, and that a clean Python API could reach developers who found the alternatives overwhelming. That lesson carried forward into Agno. The Phidata name didn't have to.

Key features

• Multi-modal agents supporting text, images, audio, and video
• Built-in memory and knowledge stores with vector database integrations
• Tool ecosystem with 100+ pre-built integrations
• Agent UI for local testing and demos
• Workflow orchestration for multi-step agent pipelines