CarlosVallejoRuiz/slurp — Token-budget-aware graph navigation for AI coding agents.

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.
3. Integrate the project into their

Problem

CarlosVallejoRuiz/slurp helps add AI capability without starting from a blank model stack.

Outcome

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

Workflow

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

Evidence

Token-budget-aware graph navigation for AI coding agents. Serve exactly the noodles your LLM needs. 🍜

Source