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pixelagent

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Multi-Agent Platforms & Frameworks
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Basic Information

Pixelagent is an agent engineering blueprint and lightweight framework for engineers to build custom, stateful agentic applications. It unifies an LLM, storage, and orchestration into a declarative, type-safe Python interface built on the Pixeltable data infrastructure. The repository provides blueprints that connect to model providers such as Anthropic, OpenAI, and AWS Bedrock as well as multiprovider setups. It emphasizes native multimodal support for text, images, audio, and video, and promotes build-your-own functionality for memory, tool-calling, reflection, reasoning, and team workflows. The project includes examples, tutorials, and recommendations for packaging agent blueprints as distributable PyPI packages. The README documents quick start code snippets showing how to instantiate agents, perform chat interactions, call tools, and persist conversational state in Pixeltable tables.

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App URL
https://github.com/pixeltable/pixelagent
Github Repository
https://github.com/pixeltable/pixelagent/blob/main/README.md

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App Details

Features
The project includes data orchestration and storage built on Pixeltable infrastructure and native multimodal support for text, image, audio, and video. It offers a declarative, type-safe Python framework that is model-agnostic and extensible to multiple providers. Observability is built in with automatic logging of messages, tool calls, and performance metrics for traceability. Agentic extensions are available for memory, tool-calling, reflection, reasoning, planning, and team workflows. Tools can be added as user-defined functions and invoked via tool_call. Memory and tool logs are persisted in tables and accessible through the Pixeltable API. The repo contains provider-specific blueprints, multiprovider examples, tutorials, and sample patterns like ReAct, reflection loops, and planning loops.
Use Cases
Pixelagent accelerates development of custom agent applications by providing a unified pattern for combining models, storage, and orchestration so engineers can focus on behavior rather than infrastructure. Built-in persistence and table-based memory let agents retain conversational context and tool histories without manual database wiring. Plug-and-play tool UDFs, example ReAct and planning patterns, and provider blueprints reduce integration effort for OpenAI, Anthropic, Bedrock, and multiprovider setups. Observability and automatic logging make debugging, auditing, and performance analysis easier. Tutorials, examples, and the ability to package blueprints to PyPI help teams distribute and deploy repeatable agent designs. The framework supports extensible multimodal retrieval and agentic RAG patterns for building more capable assistants.

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