Pixelagent

You define an agent by declaring its name, system prompt, and tools, and the framework handles the rest. The type-safe Python model catches mismatched arguments at build time rather than deep inside a request. For a developer, that means less boilerplate and fewer silent failures when an agent evolves.

Pixelagent ships agent classes for OpenAI, Anthropic, AWS Bedrock, and Gemini, and the same shape of code works across all four. Switching providers is mostly a matter of changing the import. This keeps a project from being locked to a single model vendor while you compare cost and quality.

Agent memory and tool-call history are written to Pixeltable tables as the agent runs. You can query those tables afterward to replay a conversation or check which tools fired. Persistent memory also means an agent can pick up an earlier thread instead of starting cold each session.

Any Python function can become a tool. You decorate a function, hand it to the agent, and the model decides when to call it during a chat. Because tools live as ordinary code, you can reuse existing libraries, such as pulling live stock data, without wrapping them in a new schema.

The framework handles text, images, audio, and video as first-class data, since the storage layer underneath is built for it. An agent can reason over a mix of media in one workflow instead of pushing everything through a text-only pipe. That matters for retrieval and analysis tasks where the source isn't plain text.

Messages, tool calls, and performance metrics are logged automatically, giving you a trace of what the agent did and how long it took. On top of that, the project ships examples for memory, reflection, reasoning, and multimodal retrieval that you can drop into an agent. The result is a framework you extend instead of a black box you accept.