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toolfront

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Basic Information

ToolFront is a developer library that provides simple, fast, and controlled data retrieval for AI applications. It wraps common data sources so language models can query databases, REST APIs with OpenAPI specs, and local documents using a consistent ask interface. The project aims to translate natural language queries into structured results such as strings, typed lists, or Pydantic models. It supports many database backends via optional extras, works with a broad set of AI model providers, and can be installed from PyPI. The README includes examples for Text2SQL against Postgres, API retrieval using an OpenAPI spec, document information extraction into Pydantic schemas, and a sample MCP server configuration for Snowflake. Documentation and community channels are available for integration guidance.

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App URL
https://github.com/kruskal-labs/toolfront
Github Repository
https://github.com/kruskal-labs/toolfront/blob/main/README.md

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App Details

Features
ToolFront exposes a small set of high-level adapters: a Database class to let models answer questions against SQL databases, an API class that queries any OpenAPI-described service, and a Document class for extracting structured data from files into Pydantic models. It provides Text2SQL examples, typed return values, and model-agnostic integration supporting OpenAI, Anthropic, Google, xAI and 14+ providers. The package is distributed on PyPI with optional extras for specific databases and shows a sample MCP server config for Snowflake. The repo includes examples, badges for CI and packaging, and links to documentation and community support channels. The design emphasizes control, precision, and speed when retrieving data for AI workflows.
Use Cases
ToolFront reduces the engineering work needed to connect LLMs to real data by offering reusable primitives for common retrieval tasks. Developers can ask natural language questions and receive structured answers instead of wiring ad hoc parsers or manual SQL generation. The library supports typed outputs via Pydantic, OpenAPI-based API calls, and database-specific extras to simplify integration with production data stores including Snowflake. It is installable via pip and documented for multiple providers and backends, which speeds prototyping and lowers maintenance for applications that combine LLMs with business data. Community channels and GitHub issues provide support for adoption and troubleshooting.

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