🧠 Axon

A tiny brain that never stops learning.

Axon is a lightweight, continuously self-learning knowledge engine written in pure Rust. It crawls the web, extracts entities and relations, builds a knowledge graph, and answers your questions — all with zero ML frameworks, minimal compute, and a single SQLite file as its brain.

Knowledge isn't static. A brain that stops learning is already dead.

Axon treats knowledge like memory: facts have confidence that grows when reinforced and decays when neglected. Feed it URLs, let it crawl, and watch it build understanding over time. Old, unreinforced facts fade away — just like real memory.

# Build
cargo build --release

# Feed it a URL
axon feed https://en.wikipedia.org/wiki/Rust_(programming_language)

# Ask questions
axon ask "Rust programming"

# See what it knows about something
axon about "Rust"

# Show related entities
axon related "Mozilla"

# See recently learned facts
axon recent

# Show top knowledge areas
axon topics

# View brain statistics
axon stats

# Watch a URL for changes
axon watch https://news.ycombinator.com

# Crawl known sources
axon crawl --max-pages 20

# Forget old, low-confidence facts
axon forget --threshold 0.1 --min-age-days 30

# Fuzzy search (typo-tolerant)
axon fuzzy "Einstien"            # finds "Einstein"
axon fuzzy "Motzart" --distance 2

# Ingest local markdown files
axon ingest notes.md
axon ingest ./docs/ --recursive

# Import URLs from sitemaps
axon sitemap https://example.com --max 200

# Detect contradictions in the knowledge graph
axon contradictions

# Run as daemon (continuously learn)
axon daemon --interval 30m

┌─────────────┐     ┌──────────────┐     ┌─────────────────┐
│  Web Crawler │────▶│ Text Process │────▶│ Knowledge Graph  │
│  (reqwest)   │     │   (NLP)      │     │   (SQLite)       │
└─────────────┘     └──────────────┘     └─────────────────┘
                                                   │
                                          ┌────────┴────────┐
                                          │  Query Engine    │
                                          │  (ask/about/etc) │
                                          └─────────────────┘

• Entities: Named things with types, confidence scores, and access counts
• Relations: Subject → predicate → object triples with provenance
• Facts: Key-value pairs attached to entities
• Frontier: Crawl queue with priority and change detection

• Sentence splitting & tokenization
• TF-IDF keyword extraction
• Entity extraction: capitalized phrases, years, URLs
• Relation extraction: subject-verb-object patterns
• Deduplication via Levenshtein distance

• Reinforcement: Seeing something again increases confidence
• Temporal decay: Unused knowledge fades over time
• Forgetting: Prune low-confidence old facts to keep the brain lean
• Change detection: Content hashing to learn only from new/changed pages

• Respects robots.txt
• Rate limiting with politeness delays
• Crawl frontier with priority queue
• Content hash-based change detection
• Sitemap parsing — auto-discover URLs from sitemap.xml and sitemap indexes
• Markdown ingestion — feed local .md files and directories

• Levenshtein distance with Damerau transposition
• Auto-calibrated edit distance based on query length
• Substring and word-level matching
• Bigram similarity for phrase matching
• Typo-tolerant entity lookup across CLI, API, and queries

• Detects conflicting facts for the same entity/key
• Severity levels: HARD (equal confidence), SOFT (one dominant), MINOR (numeric rounding)
• Boolean contradiction detection (true/false, alive/dead, etc.)
• Numeric closeness analysis (within 5% = minor)

For daemon mode, create an axon.toml:

[crawl]
max_pages = 20

axon daemon --interval 1h --config axon.toml

Pure Rust, minimal footprint:

• reqwest — HTTP client (rustls-tls, no OpenSSL)
• scraper — HTML parsing
• rusqlite — SQLite (bundled, zero system deps)
• clap — CLI parsing
• chrono — Time handling
• tokio — Async runtime
• texting_robots — robots.txt parsing

axon serve --port 8080

PROMETHEUS is axon's discovery engine. It finds patterns in the knowledge graph, detects gaps, generates hypotheses, and validates them — automated scientific reasoning over your data.

1. Pattern Discovery — Frequent subgraph mining, co-occurrence analysis, temporal sequence detection, statistical anomaly finding
2. Gap Detection — Structural holes (missing links between connected entities), type-based gaps (entities missing expected relations), analogy detection
3. Hypothesis Engine — Generates testable hypotheses from discovered gaps, scores confidence, checks for contradictions, builds full reasoning chains
4. Validation — Cross-references hypotheses against existing knowledge, updates confidence based on evidence
5. Meta-learning — Tracks which discovery patterns lead to confirmed hypotheses, adjusts pattern weights over time

# Run full discovery pipeline
axon discover
axon discover --format json
axon discover --format markdown

# List hypotheses
axon hypotheses
axon hypotheses --status proposed

# Explain a hypothesis (full reasoning chain)
axon explain 42

• Hypothesis: subject-predicate-object triple with confidence, evidence for/against, reasoning chain, status (proposed → testing → confirmed/rejected)
• Pattern: recurring structural motif with type, frequency, involved entities
• Discovery: confirmed hypothesis with evidence sources
• Pattern weights: meta-learning — tracks confirmation/rejection rates per pattern type

All stored in SQLite alongside the existing brain tables.

MIT