Middleware that catches when LLM conversations slowly drift off-topic

Description and use

Here's a brief summary of the open-source project:

This middleware detects when Large Language Model (LLM) conversations slowly drift off-topic, enabling developers to add AI capabilities without starting from scratch. The tool can be used for prototyping AI features, building Retrieval-Augmented Generation (RAG) or agent workflows, and evaluating model tooling. While it has potential, its adoption requires manual inspection and verification of its quality signals, license, maintenance, documentation, issues, and release cadence.

**Value:**
The project provides a valuable solution for developers who want to leverage AI capabilities without building a complete model stack from scratch. It can help streamline the development process by catching subtle off-topic drifts in LLM conversations, making it easier to build and evaluate AI-powered applications.

**Practical Adoption Path:**
To adopt this middleware, developers should follow these steps:

1. Review the project's documentation and issues to understand its functionality and limitations.
2. Verify the license and ensure it aligns with their project's requirements.
3. Inspect the project's maintenance and release cadence to ensure it receives regular updates.
4. Integrate the middleware into their project, taking note of any potential dependencies or compatibility issues.
5. Manually inspect the integration signals to ensure the middleware is working

Use in a workflow

Middleware that catches when LLM conversations slowly drift off-topic helps add AI capability without starting from a blank model stack.

• prototype AI features
• build RAG or agent workflows
• evaluate model tooling

Limitations and review notes

Medium: useful for prototypes or internal workflows, with dependency and maintenance checks before production.