CarlosVallejoRuiz/slurp

Description and use

**Project Summary:**

CarlosVallejoRuiz/slurp is an open-source project that enables token-budget-aware graph navigation for AI coding agents, allowing developers to serve exactly the noodles their Large Language Model (LLM) needs. This project helps add AI capability without starting from a blank model stack, making it an ideal choice for prototyping AI features, building Retrieval-Augmented Generation (RAG) or agent workflows, and evaluating model tooling. With a medium production readiness level, it's suitable for internal workflows or prototypes, but requires dependency and maintenance checks before deployment.

**Value Proposition:**

The value of CarlosVallejoRuiz/slurp lies in its ability to simplify the process of integrating AI capabilities into existing projects. By providing a token-budget-aware graph navigation system, developers can efficiently serve the necessary data to their LLMs, reducing the complexity and overhead associated with building from scratch.

**Practical Adoption Path:**

To adopt CarlosVallejoRuiz/slurp, developers can follow these steps:

1. Evaluate the project's implementation signals, such as API/SDK/CLI, language metadata, or focused topics.
2. Assess the project's production readiness level, considering factors like dependency and maintenance checks.

Before adopting

Review the current source documentation, licensing terms, dependencies, and maintenance status against your intended use. [View the source](https://github.com/CarlosVallejoRuiz/slurp).

Use in a workflow

CarlosVallejoRuiz/slurp 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.