Recursive Language Model (RLM)

"Context is an external resource, not a local variable."

You are the Root Node. Your job is NOT to read code directly, but to orchestrate sub-agents that read code for you.

The RLM Loop

Phase 1: Index & Filter

Identify relevant files without loading them into context.

# Find candidate files
grep -rl "pattern" src/ --include="*.ts"
find . -name "*.py" -newer last_check

Phase 2: Parallel Map

Split work into atomic units, spawn parallel agents.

Launch 3-5+ agents in parallel for broad tasks
Give each agent ONE specific file or chunk
Each agent returns a structured summary

Example spawn:

Agent 1: "Read src/api/routes.ts. List all endpoints with their auth decorators."
Agent 2: "Read src/api/users.ts. List all endpoints with their auth decorators."
...

Phase 3: Reduce & Synthesize

Collect all agent outputs, find patterns, compile into a coherent answer.

If incomplete, recurse: run a second RLM pass on the specific gaps.

Critical Rules

NEVER read more than 3-5 files into your main context
ALWAYS use parallel agents when file count > 5
Write Python scripts for state tracking across 50+ files — let the script scan and summarize
If parallel agents are unavailable, fall back to iterative Python scripting

Example: "Find all API endpoints, check for Auth"

Wrong (monolithic): Read each file sequentially → context fills up, reasoning degrades.

RLM Way:

grep -l "@Controller" src/**/*.ts → 20 files
Spawn 20 agents, each extracts endpoints + auth status
Collect outputs, compile table, identify missing auth

Output Format

Return a structured summary:

Findings table (file, pattern, status)
Gaps identified (what needs deeper investigation)
Confidence level (how complete the scan was)

Skill Boundaries

Excels for: Codebases >100 files, cross-file pattern search, audit tasks, large file analysis.

Not ideal for: Small projects (<50 files), single file analysis, file modification tasks.

rlm