reasoningbank-intelligence

ReasoningBank Intelligence

Trajectory-based learning pipeline for moflo-enabled agents. Records what an agent did, judges the outcome, distills successful runs into reusable patterns, and retrieves relevant prior experience on the next task.

Prerequisites

• moflo (the neural subsystem ships inline as part of the moflo package)
• Moflo's memory DB at .swarm/memory.db (created on first run)

Quick Start

import { createInitializedReasoningBank } from 'moflo/dist/src/cli/neural/reasoning-bank.js';

const rb = await createInitializedReasoningBank({
  namespace: 'reasoning-bank',
  vectorDimension: 768,
  retrievalK: 3,
  mmrLambda: 0.7,              // 0=pure relevance, 1=pure diversity
  distillationThreshold: 0.6,  // min verdict score to keep
  dedupThreshold: 0.95,
});

The Pipeline

Four stages. You typically call each once per task:

1. Record trajectory  →  storeTrajectory({ id, input, actions, outcome, reward, ... })
2. Judge              →  const verdict = await rb.judge(trajectory)
3. Distill            →  const memory  = await rb.distill(trajectory)   // if verdict good enough
4. Retrieve (next task) →  const hits  = await rb.retrieveByContent(query, k)

1. Record

const trajectory = {
  id: taskId,
  input: userRequest,
  actions: ['read_file', 'edit_file', 'run_tests'],
  outcome: 'success' as const,
  reward: 1.0,                // 0..1
  metadata: { toolCalls: 3, durationMs: 1800 },
  timestamp: new Date(),
};

rb.storeTrajectory(trajectory);

2. Judge

The built-in judge scores on outcome + reward + action-step quality. No external LLM call.

const verdict = await rb.judge(trajectory);
// { score: 0-1, outcome: 'success' | ..., reasoning: string }

Swap in your own judge by extending ReasoningBank if you want LLM-in-the-loop scoring — this was designed as a rule-based baseline so the hot path stays cheap.

3. Distill

Compresses a trajectory into a reusable DistilledMemory (signature, approach, outcome-tagged). Skips if the verdict is below distillationThreshold.