Data engineering

An MCP server that connects an AI client such as Claude Desktop or Cursor to a running ZenML instance, giving it read access to ML and LLM pipeline definitions, runs and deployments.

• MCP servers
• Data engineering

A human-in-the-loop workbench that turns a domain expert's intuitive sense of "find me content like this" into a reproducible, auditable classifier trained on a rubric-judged sample of a large text corpus.

• RAG
• Data engineering

A framework that lowers the cost of processing a dataset with large language models by deciding per record whether a cheap or an expensive model is used, guaranteeing a target accuracy.

• Inference
• Data engineering

A semantic search tool for text columns in spreadsheets, letting journalists, researchers and anyone working with survey responses or similar data search a CSV by meaning instead of exact keywords.

• RAG
• Data engineering

A Rust command-line tool that filters large synthetic and pretraining text datasets by sending each row through TypeSafe AI's Jev model for calibrated, rubric-based judgments instead of a slower general-purpose language model.

• Models
• Data engineering

An MCP server that lets AI agents search DataHub's data catalog, explore lineage and business context, and generate SQL grounded in an organization's real tables, dashboards and documentation.

• MCP servers
• Data engineering

A PDF extraction tool that checks its own output for dropped or scrambled pages and re-extracts them with a stronger backend, and can also audit any other extractor's output for the same silent failures.

• RAG
• Data engineering

An MCP server that answers questions about large CSV, Excel, Parquet and JSONL files by querying an indexed profile of the data instead of pasting the rows into a model's context window.

• MCP servers
• Data engineering

An execution engine that runs SQL extended with natural-language operators, so a query can ask a language model to evaluate a condition on every row instead of filtering only on structured columns.

• Databases
• Data engineering

A set of self-contained Jupyter notebook tutorials introducing Hadoop, Spark and MapReduce, most runnable directly on Google Colab with no separate setup.

• Data engineering
• Tutorials

A plugin-based, declarative data-access layer where an AI agent or user describes the data it needs and mloda resolves how to fetch and transform it, tracing every result back to its source.

• Data engineering

A Datasette plugin that adds an LLM-powered chat assistant for exploring a Datasette instance's databases and tables, plus background agents that write exploration reports.

• Databases
• Data engineering

A set of Jupyter notebook tutorials for PyPOTS, a Python toolbox for modeling partially observed, incomplete time series data.

• Data engineering
• Tutorials

An AI-native distributed data plane in Rust that unifies high-performance RPC, KV cache sharing, an elastic message queue and S3-compatible file acceleration behind three standardized interfaces, aimed at AI training and inference bottlenecks.

• Inference
• Data engineering

A Python tool for exploring and iterating on computer vision datasets with SQL filters, vector similarity search and a GUI dashboard, built for Ultralytics YOLO and related models.

• Databases
• Data engineering

AI agents with a chat interface built into JupyterLab, connected through MCP tools so they can execute code, manage files and interact with notebooks in response to natural-language requests.

• Coding agents
• Data engineering

A schema-based extractor that pulls structured data from documents and the web into JSON or Markdown with a single tool call, for use inside AI agent workflows.

• RAG
• Data engineering

A tool that converts Excel workbooks into structured JSON for LLM and RAG pipelines, and lets an AI agent read and patch-edit Excel files through a CLI and an MCP server.

• MCP servers
• Data engineering

A harness for agentic data science that runs an analysis multiple independent ways and checks whether the results converge, instead of trusting a single AI-generated model and conclusion.

• AI evals
• Data engineering

A Python library that generates Avro schemas from dataclasses, Pydantic models, and Faust records, and serializes or deserializes instances against those schemas.

• Libraries
• Data engineering

A self-hosted geospatial data catalog that brings files, database tables and remote service snapshots into one searchable index, with a browser-based map builder and OGC and STAC APIs for existing GIS tools.

• APIs
• Data engineering

A self-hosted platform combining AI agents with a data stack: multi-agent workflows and RAG knowledge bases on one side, and an ETL pipeline, a lakehouse, BI dashboards and no-code machine learning on the other, governed by one shared catalog and audit trail.

• Agent frameworks
• Data engineering

An open-source, extensible agent framework for chatting with data, turning natural language into SQL, charts, dashboards and reports.

• RAG
• Data engineering

An agentic AI framework built on Apache Flink, letting agent workflows run as distributed, event-driven Flink jobs.

• Agent frameworks
• Data engineering