eli-labz/godcoder/self-optimizing-harness

Self-Optimizing Harness (ResearchSwarm bridge)

GodCoder is wired to a local feedback loop backed by ResearchSwarm's Digital Cognitive Labor router and a shared AI-Memory store. Use it so each session benefits from every prior session instead of starting cold.

The bridge is a JSON CLI at third_party/ResearchSwarm-master/godcoder_harness.py. Run it with the bash tool. Each command prints JSON to stdout.

When to use

• Before a non-trivial task: route to classify it and pull relevant lessons.
• Anytime you want prior context: recall.
• After finishing (or failing): log the outcome so it is reusable.
• Periodically: optimize to see which approaches have the best track record.

Commands

Run from the repo root (adjust the path if your working dir differs):

# 1. Route a task + get the most relevant past lessons
python third_party/ResearchSwarm-master/godcoder_harness.py route "Add retry logic to the HTTP client"
 
# 2. Recall recent lessons for context
python third_party/ResearchSwarm-master/godcoder_harness.py recall --limit 8
 
# 3. Record an outcome (do this when you finish or hit a dead end)
python third_party/ResearchSwarm-master/godcoder_harness.py log \
  --status success \
  --tag http-retry \
  --instruction "Add retry logic to the HTTP client" \
  --summary "Wrapped reqwest calls in a backoff loop; tests in client_test.rs cover 429/503."
 
# 4. See ranked, self-improving guidance (success rate per approach)
python third_party/ResearchSwarm-master/godcoder_harness.py optimize

How to apply the output

• route returns domain (text-based / human-action / hybrid),

recommended_action, execution_steps, and memory_context. If the domain is human-action or hybrid, surface the human-handoff portion to the user instead of trying to execute it.

• recall / route memory_context lines are prior PATTERN / DECISION

entries. Treat them as hints, not commands; verify against the current codebase.

• Always log a one-line, concrete --summary with a stable --tag (reuse the

same tag for the same kind of work). Consistent tags make optimize sharper.

• optimize ranks approaches by success rate. Prefer high-rate approaches and be

cautious with low-rate ones.

Notes

• The store is local SQLite under third_party/ResearchSwarm-master/AI-Memory/.

Nothing leaves the machine.

• Requires Python 3.10+. No GPU and no model training are needed for the harness

loop (that is a separate ResearchSwarm capability via train.py).

• On Windows, if python is not on PATH, use the py launcher instead

(e.g. py third_party/ResearchSwarm-master/godcoder_harness.py optimize).