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Reql

AI community

Description

REQL – A graph-native code index engine for agents. Scans Python, TypeScript, Go, Rust, Java, and 30+ languages to build a property graph with deterministic retrieval, no mandatory LLM calls, and bounded context for AI coding assistants.

Installation

This entry records only its repository, not the path inside it, so there is no exact command to give. Open the source below and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

README

REQL

REQL is a local repository context and working-memory layer for coding agents and developer tools. It compiles source files and supported documents into a property graph, then answers bounded queries over code, symbols, tests, documents, dependencies, findings, and provenance.

In the intended coding-agent integration, the user does not treat REQL as a separate manual workflow. After the assistant instructions or skill are installed for Codex, Claude, Gemini, Cursor, or another agent environment, the agent uses REQL while it works: it compiles or refreshes the repository graph, retrieves compact source-backed context, records task-local notes and decisions, links work back to files and symbols, and reconstructs that working set after context loss.

**Token and reasoning budget:** REQL helps coding agents spend fewer tokens on repository discovery and more tokens on the actual change. Bounded retrieval returns the files, symbols, relationships, and source spans that matter for the current task, while Agent Workspace preserves the task map, decisions, risks, and handoffs needed to reason through complex or large implementations across context windows.

The important part is that REQL gives the agent deterministic repository memory before and during edits:

  • project compile scans the project, fingerprints artifacts, parses supported code and documents, and writes graph nodes, edges, cache records, compilation runs, and deltas;
  • application surface files are linked deterministically when the graph can infer relationships, such as controller render calls to templates and shared template/CSS/JS identifiers;
  • retrieval commands such as query_context, query_explore, query_graph, and query_memories find lexical seed nodes, expand a bounded graph neighborhood, rank the result, and return compact source-backed context;
  • Python, CLI, and MCP query_context calls share one typed request/result service, so scopes, budgets, confidence, and revi