pixeltable/pixeltable

The backend agents build with - Multimodal database, orchestration, and serving in one file

What it solves

Pixeltable simplifies the creation of multimodal AI backends by consolidating the database, orchestration, and serving layers into a single application file. It eliminates the need to manage separate object storage, vector databases, and endpoint code, allowing developers to build and deploy AI agents and media pipelines more efficiently.

How it works

Pixeltable uses a table-based approach where images, video, audio, and documents are stored as columns. It introduces "computed columns" (transforms) that automatically run when data is inserted or updated. The entire application—including the data schema, the processing logic (UDFs), and the HTTP routes for serving—is defined in one Python file. This file can be deployed locally or to Pixeltable Cloud using a CLI tool (pxt).

Who it’s for

It is designed for developers building multimodal AI applications, such as chat agents or video search tools, and specifically caters to those using AI coding agents (like Cursor or Claude) by providing a single-file architecture that agents can easily manage and modify.

Highlights

  • Unified Backend: Combines a multimodal database, orchestrator, and API serving in one file.
  • Automatic Processing: Computed columns trigger processing pipelines automatically upon data insertion.
  • Multimodal Support: Native handling of images, video, audio, and documents.
  • Agent-Friendly: Provides specialized plugins and MCP servers for AI coding agents to build and deploy apps.

Related

  • Project
    pykale/pykale

    A PyTorch-based library for multimodal and transfer learning that provides a standardized, pipeline-based API to create sustainable and reusable AI workflows.

  • Project
    TechyNilesh/DeepImageSearch

    A Python library for building AI-powered image search systems supporting text, image, and hybrid search using multimodal embeddings and vector indexing.

  • Project
    unum-cloud/UStore

    A modular, multi-modal transactional database for AI and semantic search that integrates document, graph, and vector storage into a single ACID-compliant system.

  • Project
    lancedb/lancedb

    A multimodal lakehouse and vector database built on the Lance columnar format for fast, scalable vector search across petabytes of multimodal data.

  • Project
    CVHub520/X-AnyLabeling

    X‑AnyLabeling is a cross‑platform desktop application for AI‑assisted annotation of images, video, text, and 3D point clouds. It bundles dozens of state‑of‑the‑art vision models (YOLO, SAM, Grounding‑DINO, etc.) and supports local or remote inference, offering tools for detection, segmentation, OCR, pose, VQA, and more, with export to common dataset formats.