GSD Core

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What is rgsd

rgsd is a fork of gsd-core that replaces the linear subagent model with an RLM (Recursive Language Model) inference loop — agents that can decompose problems by recursively calling themselves, rather than spawning one-shot leaf agents.

Where gsd-core runs planning and execution in flat, isolated subagents, rgsd treats each agent as a first-class RLM node: it can examine the context it receives, decide what sub-problems to delegate, and invoke child agents as function calls — all within a structured phase loop.

The core insight from Recursive Language Models: a model operating inside a code environment can call itself as a function, deferring context decomposition decisions to the model itself rather than hardcoding them in the orchestrator.

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How it works

Each milestone repeats the same five-step loop, one phase at a time:

1. Discuss — capture implementation decisions before anything is planned
2. Plan — research, decompose, and verify the plan fits a fresh context window
3. Execute — run plans in recursive waves; each executor can spawn child agents and call back into itself when context exceeds its budget
4. Verify — walk through what was built; diagnose and fix before declaring done
5. Ship — create the PR, archive the phase, repeat for the next one

The difference from gsd-core is in step 3: instead of a flat executor receiving a fixed context slice, rgsd executors operate in an RLM loop — they receive a structural skeleton of large files via the context-slice engine, query the graphify knowledge graph for relevant symbols, and recursively narrow context before generating code.

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Key features

RLM inference loop

Agents call themselves recursively to decompose arbitrarily large contexts. No fixed context-window ceiling on what a single phase can reason about.

Context-slice engine

rgsd-tools context-slice <file> pre-filters large source files into a structural skeleton (all function/class signatures) plus pattern-ranked excerpt windows, capped by a configurable token budget. Files below 3 000 tokens pass through unchanged. Dropped regions are reported explicitly — never silently truncated.

rgsd-tools config-set context-slice.enabled true
rgsd-tools context-slice src/graphify.cts --budget-tokens 2000

Symbol-aware graphify queries

The graphify knowledge graph resolves queries through a two-tier seed matcher: exact substring matching first, fuzzy symbol-tokenized fallback second. Natural-language queries like "find authentication middleware" reliably expand to the right graph nodes even when no exact token matches exist.

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Quickstart

npx rgsd@latest

The installer prompts for your runtime (Claude Code, Gemini CLI, Codex, Cursor, Windsurf, and more) and whether to install globally or locally.

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Configuration

rgsd-tools config-set context-slice.enabled true   # enable context-slice
rgsd-tools config-set graphify.enabled true         # enable knowledge graph

Full configuration reference: docs/CONFIGURATION.md

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CLI reference

rgsd-tools context-slice <file> [--pattern <regex>] [--budget-tokens N] [--context-lines N]
rgsd-tools graphify build
rgsd-tools graphify query <term>
rgsd-tools graphify status

Full CLI reference: docs/CLI-TOOLS.md

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Relationship to gsd-core

rgsd is a fork of gsd-core. It inherits the full phase-loop workflow, all commands, and the installer. The additions are:

Feature	gsd-core	rgsd
Phase loop (discuss/plan/execute/verify/ship)	✓	✓
Subagent isolation	flat	recursive (RLM)
Context-slice engine	✓	✓ (extended)
Graphify knowledge graph	✓	✓ + fuzzy symbol matching
RLM inference loop in executor	—	✓

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License

MIT — see LICENSE.