dbnt 0.5.0

Universal feedback protocol and learning system for AI agents. Turn corrections into persistent, weighted rules.

DBNT — Do Better Next Time

Universal feedback protocol and learning system for AI agents. Turn corrections into persistent, weighted rules that survive across sessions.

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The Problem

Your AI agents make the same mistakes every session. You correct them, they improve — and then the context window resets and they're back to square one. Traditional memory systems record what went wrong, which creates agents that know a hundred ways to fail but can't reliably replicate success. DBNT encodes both sides of the feedback loop with information-theoretic weighting: success signals carry 1.5x weight because a working path is rarer and more valuable than a broken one.

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What DBNT Does

Five subsystems, one goal — agents that get better over time:

• Protocol Engine — Escalating correction commands (DB → DBN → DBNM → DBYC) with point scoring and structured action routing
• Signal Detection — Classifies natural language feedback without requiring special syntax. "That's not quite right" is as valid as dbn
• Rule Encoding — Stores learnings as human-readable markdown with weighted frontmatter. Success files and failure files, separately tracked
• Learning System — Pattern detection groups similar corrections. Three occurrences of the same pattern auto-promotes it to a permanent rule
• FSRS Decay Engine — Rules that get applied grow stronger. Rules that sit unused fade toward archival. Based on the FSRS-6 spaced-repetition algorithm

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Why Success Signals Outweigh Failure

Traditional approaches minimize loss. DBNT maximizes learning.

The intuition: there are infinite ways to fail a task, but only a handful of ways to do it well. A failure signal tells you one path to avoid out of infinite bad paths. A success signal tells you one path that works out of very few good paths — that's a higher information density per signal.

This is the Ralph Wiggum Problem: knowing 100 things not to do doesn't tell you what to do. Doctors study healthy patients. Athletes watch film of good plays. DBNT weights the game film accordingly.

Failure: 1.0x weight — avoid this path Success: 1.5x weight — replicate this path

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Quick Start

Installation

Claude Code Plugin (recommended for Claude Code users):

/install github:idirectships/dbnt

Python Package (for library usage, other adapters, or CLI):

pip install dbnt

60-Second Example

# Your agent made a mistake. Signal it.
dbnt process "dbn"
# → Command: DBN | Action: encode_success | Response: "Yes Chef!"

# Check what signal natural language carries
dbnt detect "that's not quite right"
# → NEGATIVE | moderate | weight=0.8

dbnt detect "perfect, ship it"
# → POSITIVE | strong | weight=1.5

# Encode what worked
dbnt success "Use bun not npm" -c code -x "Project standard"

# Encode what failed
dbnt failure "Pushed directly to main" -c protocol -x "Always use feature branches"

# Run the decay sweep — archives stale rules, boosts active ones
dbnt sweep

# Full system view
dbnt status

Python API

from dbnt import Protocol, detect_signal, encode_success

# Process a feedback command
protocol = Protocol()
response = protocol.process("dbnm")
# → Command: DBNM | Action: encode_success | "Yes Chef! Fixed, encoded, moving on."

# Classify a natural language signal
signal = detect_signal("that's not quite right")
# → SignalResult(polarity=NEGATIVE, strength=moderate, weight=0.8)

signal = detect_signal("perfect, ship it")
# → SignalResult(polarity=POSITIVE, strength=strong, weight=1.5)

# Record a success
encode_success(
    category="code",
    pattern="Used dataclass for config objects",
    context="Clean, typed, no dict key errors"
)

from dbnt import LearningStore, PatternDetector, DecayEngine

store = LearningStore()
store.add("Always use timezone-aware datetimes", domain="code", importance=3)
store.add("Use timezone-aware datetime objects", domain="code", importance=2)
store.add("Always use UTC for datetime storage", domain="code", importance=4)

# Three similar learnings → pattern detected
detector = PatternDetector()
patterns = detector.detect(store.get_unpromoted())
# → [PatternGroup(count=3, confidence="low", should_promote=True)]

# Rules decay when unused, strengthen when applied
engine = DecayEngine(store)
engine.boost("rule_timezone_abc")       # Applied → stability increases
status = engine.check("rule_old_123")   # → {"status": "archive", "retrievability": 0.2}

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The Learning Path

DBNT is designed for developers who've moved past basic AI chat. Here's how the capability layers stack:

Level 1: Single Agent Feedback Loop

Wire DBNT into your AI tool. Every mistake your agent makes, every correction you give, gets encoded as a rule. The next session, that rule is injected back into context. The agent stops repeating itself.

• Install DBNT, run dbnt install --adapter claude-code (or --adapter generic)
• Agent makes a mistake → you say "not quite" → signal detected → rule encoded
• Next session: rule is loaded, mistake doesn't recur

This alone eliminates 80% of repeat errors.

Level 2: Persistent Rules with Lifecycle Management

Rules accumulate. Without management, you end up with hundreds of stale files that slow context loading and contradict each other. FSRS-6 handles this automatically.

• Frequently-applied rules gain stability — they're harder to decay
• Unused rules fade — dbnt sweep archives them
• dbnt dissonance surfaces conflicting rules before they cause issues

The rule store stays lean. Only actively relevant rules survive.

Level 3: Skill Improvement Through Pattern Promotion

When you correct the same class of mistake three or more times, DBNT detects the pattern and auto-promotes it to a permanent, high-confidence rule. Individual learnings become structural improvements.

• Similar corrections cluster automatically
• Promotion threshold: 3+ occurrences with pattern confidence
• Skills versioned: code-review v1 → v2 → v3
• Rollback available if a new version performs worse

Your agent's behavior across a domain improves without manual rule-writing.

Level 4: Multi-Agent Coordination (The Horizon)

This is where DBNT becomes a swarm memory layer. The protocol and storage architecture already support it — shared rule stores, cross-agent learning propagation, probabilistic peer review between agents.

Agent A learns something. Agent B gets that learning without making the mistake itself. Agents critique each other's outputs. The swarm's collective rule base evolves.

Level 4 is where this framework is heading. We run a production system across multiple nodes that uses DBNT as its learning substrate — agents coordinating autonomously, rules propagating across the network, skills compounding over time. The implementation details of that system aren't open source, but the foundation you'd build it on is exactly this.

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Bring Your Own Everything

Bring Your Own Model

DBNT doesn't call any LLM APIs. It processes feedback signals and manages rule storage. Your model choice is completely orthogonal. Run it with Claude, GPT-4, Ollama, LM Studio, llama.cpp — anything that generates text and can receive context injection.

If you want transcript-based signal extraction (parsing conversation history for implicit feedback), that processing happens on your stack with your model.

Bring Your Own Tools

Adapters connect DBNT to whatever AI tooling you use. The Claude Code adapter hooks UserPromptSubmit and Stop events. The generic adapter uses filesystem watching and markdown files — it works with anything. Adding your own adapter is around 50 lines implementing a simple interface.

dbnt install --adapter claude-code    # Installs to ~/.claude/hooks/ and ~/.claude/rules/
dbnt install --adapter generic         # Installs to ~/.dbnt/rules/

Bring Your Own Keys

DBNT has no API keys, no cloud dependencies, no telemetry. Everything runs locally. The rule store is a directory of markdown files. The learning store is a SQLite file. The score history is JSON. You own all of it.

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Protocol Commands

The escalation ladder — each level signals increasing severity and triggers different encoding behavior:

Command	Meaning	Points	Agent Response
db	Do Better — recoverable mistake	−1	Fix it + encode the success pattern
dbn	Do Better Now — same class of mistake	−1	Fix it faster + encode
dbnm	Do Better Now Move — fix it and keep going	−1	Fix + encode + don't stop to discuss
dbyc	Critical — you had to take over	−2	Encode BOTH the failure AND what worked
good / fixed / ship it	Confirmed working	+3	Acknowledge (1.5x weighted)
tweak / almost	Close, iterate	+0.5 → −1	Degrades on repetition

The required response to any correction command is "Yes Chef!" — then fix, encode, continue. The kitchen protocol framing is intentional: corrections are instructions, not critiques.

dbyc is the most important signal. When a human has to step in and finish the work themselves, there are two learnings to capture: what the agent did wrong, and what the human did right. Both get encoded.

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Signal Detection

DBNT classifies feedback from natural language, so you don't need to remember commands in the moment. Common signal mappings:

Natural Language	Signal	Weight
"perfect", "ship it", "exactly right"	POSITIVE_STRONG	1.5x
"good", "that works", "correct"	POSITIVE_MODERATE	1.2x
"ok", "sure", "fine"	NEUTRAL	1.0x
"not quite", "close but", "almost"	NEGATIVE_MODERATE	0.8x
"wrong", "that's broken", "no"	NEGATIVE_STRONG	1.0x (encode failure)
"i had to fix this myself"	CRITICAL	2.0x (encode both)

Silence is treated as neutral approval. The system doesn't require active positive feedback to function — only corrections.

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Rule Storage

Rules live in ~/.dbnt/rules/:

~/.dbnt/
├── rules/
│   ├── successes/     # What worked — 1.5x weighted
│   └── failures/      # What failed — 1.0x weighted
├── learnings.db       # SQLite — pattern detection, decay tracking
└── score.json         # Running score history

A rule file looks like this:

---
id: rule_timezone_2024_abc1
category: code
weight: 1.5
stability: 4.2
retrievability: 0.87
created: 2024-11-03
last_applied: 2024-11-14
---

# Always Use Timezone-Aware Datetimes

Always use timezone-aware datetime objects. Store in UTC, display in local time.

## Context
Three separate corrections on datetime handling across different projects.
Auto-promoted from pattern after 3+ occurrences.

Human-readable. Diffable. Version-controllable if you want.

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FSRS-6 Decay

Rules use the FSRS retrievability formula:

R(t, S) = (1 + t / (9 × S))^(-1)

Where t = days since last application, S = stability score. Apply a rule → stability increases, slower decay. Ignore a rule → retrievability drops toward the archival threshold.

This prevents the rule store from bloating with stale context that hurts more than it helps.

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CLI Reference

# Protocol
dbnt process "dbnm"              # Detect and route a command
dbnt score                        # View scoring history

# Signals
dbnt detect "that's perfect"      # Classify a signal

# Rules
dbnt success "Use bun not npm" -c code -x "Project standard"
dbnt failure "Pushed to main" -c protocol -x "Always use feature branches"

# Learning
dbnt learn "Always validate at boundaries" -d code -i 3
dbnt patterns                     # Show recurring patterns
dbnt promote                      # Auto-promote qualifying patterns to rules
dbnt sweep                        # Run FSRS decay check — archives stale rules

# Status
dbnt status                       # Full system overview
dbnt dissonance                   # Surface conflicting success/failure signals

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Adapters and Integrations

Adapter	Status	Description
Claude Code	Stable	Hooks UserPromptSubmit + Stop, injects rules into context
Generic	Stable	File-based, filesystem events — works with any tool
LangChain	Planned	Callback handler on chain completion
CrewAI	Planned	Task completion hook
AutoGen	Planned	Agent feedback loop integration
Cursor	Planned	.cursorrules injection
MCP Server	Planned	Model Context Protocol adapter

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Architecture

Human feedback
    │
    ├─ "dbnm" ────────► Protocol Engine ──► Score tracking + encode action
    ├─ "perfect" ─────► Signal Detector ──► POSITIVE (1.5x weight)
    └─ "not quite" ───► Signal Detector ──► NEGATIVE (1.0x weight)
                                │
                                ▼
                     Rule files (markdown)
                                │
                                ▼
                    Learning Store (SQLite)
                                │
                                ▼
                     Pattern Detector
                     (3+ similar → promote)
                                │
                                ▼
                    FSRS-6 Decay Engine
                    ├─ Applied? ──► Boost stability
                    └─ Unused?  ──► Fade → archive

No middleware. No cloud calls. The signal goes in, the rule comes out, the agent gets better.

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Comparison

Feature	DBNT	Traditional Logging	Vector Memory
Persists across sessions	Yes	No	Partial
Success/failure weighting	1.5x / 1.0x	Equal	N/A
Human-readable rules	Markdown	Logs	Vectors
Decay / lifecycle	FSRS-6	Manual	None
LLM-agnostic	Yes	Yes	Usually not
Local-first	Yes	Varies	Usually cloud
Zero cloud dependencies	Yes	Varies	Heavy
Pattern auto-promotion	Yes	No	No
Multi-agent ready	Yes (shared store)	No	Partial

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Contributing

Issues, PRs, and discussion welcome on GitHub.

What we accept without prior discussion:

• New adapter implementations
• Signal detection improvements and edge cases
• Test coverage additions
• Documentation fixes

What needs a discussion issue first:

• Changes to the core protocol command set
• Modifications to the FSRS decay parameters
• New storage backends

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License

MIT

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What's Next

DBNT is a foundation layer, not a finished product. A single agent with persistent memory is useful. An agent whose skills compound over weeks of corrections is more useful. A network of agents sharing a rule store and improving collectively is something else entirely.

We run a production system built on this foundation — multiple nodes coordinating autonomously, rules propagating across agents, skills versioning and rolling back based on performance signals. That system isn't open source. But the protocol it runs on is exactly what you're installing.

Start at Level 1. Wire it into your current setup. Watch the correction rate drop over a few weeks. Then decide how far you want to take it.

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Built by Dru Garman. MIT licensed.