aelfrice 1.6.0

Persistent memory for AI agents. Set up once. Stays out of your way. Local SQLite, auditable, no GPU, no network.

aelfrice

Persistent memory for AI agents. Set up once. Stays out of your way.

Local SQLite. Auditable. No GPU, no network.

You correct your agent. "Got it," it says. Next session, same mistake.

aelfrice runs in the background and stops the forgetting. You write a rule once and it gets attached to every prompt thereafter — no cross-references for the agent to skip, no markdown files to maintain, nothing to remember to do.

pip install aelfrice
aelf onboard .
aelf lock "never push directly to main; use scripts/publish.sh"
aelf setup       # wire the hook into Claude Code

That's it. Restart Claude Code and your next prompt that mentions "push" already has the rule attached. From here on out aelfrice is invisible — no command to remember to run, no file to keep updated.

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What it does

When you submit a prompt in Claude Code, aelfrice's UserPromptSubmit hook fires before the model sees your message. It runs a two-layer search:

L0: locked beliefs   -> rules you marked permanent (always returned)
L1: FTS5 keyword     -> SQLite full-text search, BM25-ranked

The matching beliefs come back as an <aelfrice-memory> block prepended to your prompt. The agent reads it as part of the prompt — it doesn't have to remember to check a file.

<aelfrice-memory>
[locked] never push directly to main; use scripts/publish.sh
[locked] commits must be SSH-signed with ~/.ssh/id_rrs
         the publish script runs gitleaks before tagging
</aelfrice-memory>

push the release

Default budget is 2,000 tokens per prompt. Locked beliefs always go first; the rest is BM25-ranked and truncated to fit.

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What it remembers

You run	It stores
aelf lock "never commit .env files"	Permanent rule. Returned on every retrieval.
aelf onboard .	Walks the project — git log, README headings, code structure — and ingests structural facts.
aelf feedback <id> used	Bayesian feedback. Strengthens the belief's posterior.
aelf feedback <id> harmful	Weakens it. After enough independent harmfuls, locks auto-demote.

Each belief carries a (α, β) Beta-Bernoulli posterior. α / (α+β) is the confidence. Locks short-circuit decay; everything else fades over time so stale beliefs eventually drop out of retrieval.

aelf stats
# beliefs:    142   locked: 8   threads: 67
# feedback:   31    avg_confidence: 0.71

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Why files don't solve this

The standard workaround for "agent keeps forgetting" is more files: STATE.md, DECISIONS.md, a CLAUDE.md with cross-references to runbooks. Every cross-reference is a bet that the agent will read the file, find the right section, and follow what it says.

The failure modes are predictable. The agent reads the rule and runs git push anyway. Cross-references break silently after compaction. State files rot the moment you forget to update them. Each new failure mode begets another file.

aelfrice replaces the chain with a mechanism. The hook injects matched beliefs as part of your prompt, before the agent sees it. Nothing voluntary. Nothing the agent can skip.

Manual approach	What breaks	aelfrice
Rules in CLAUDE.md	Agent reads them, doesn't follow them	Injected per-prompt, not per-session
Cross-references	Agent skips or reads the wrong section	Matched beliefs injected directly
Hand-maintained state files	One missed update breaks the chain	State is the SQLite DB; no manual sync

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Determinism

Same store + same query gives the same beliefs. The retrieval path is stdlib + SQLite — no embeddings, no learned re-rankers, no LLM — so every result traces back to a specific belief and the user action that wrote it.

Tradeoff: no fuzzy semantic recall. See PHILOSOPHY.md.

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Your data stays yours

• 100% local. SQLite at <repo>/.git/aelfrice/memory.db. No network calls in the retrieval path.
• No telemetry. No accounts, no signup, no phone-home.
• No GPU, no vector DB. Stdlib + SQLite. The optional [mcp] extra adds fastmcp. That's it.
• Per-project isolation. Beliefs from project A cannot leak into project B (they live in different .git/ directories).
• Removable. aelf uninstall --archive backup.aenc encrypts the DB to a file, then deletes it. Or --purge for a full wipe.

docs/PRIVACY.md for verifiable specifics.

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Day-to-day surface

After aelf setup you should rarely type aelf again. The day-to-day commands are six:

aelf onboard .                      # once per project — scan and ingest
aelf lock "never push to main"      # add a permanent rule
aelf locked                          # see what rules are active
aelf search "push to main"           # check what the agent will see
aelf status                          # quick health summary
aelf setup / aelf doctor            # initial install + verification

Everything else (deeper diagnostics, archive/uninstall, migration tools, hook entry-points called by Claude Code itself) is callable but not something you reach for in normal use. aelf --help shows the everyday surface; aelf --help --advanced lists the rest. Full reference: COMMANDS.

The same operations are also available as MCP tools and /aelf:* slash commands — same library underneath. See MCP and SLASH_COMMANDS.

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Roadmap

Version	Status	Theme
v1.0.x	shipped	core memory, CLI, MCP, hook wiring, install routing
v1.1.0	shipped	per-project DBs (.git/aelfrice/), aelf migrate, edges→threads rename, aelf health rewrite
v1.2.0	shipped	auto-capture pipeline (transcript-ingest, commit-ingest, SessionStart), agent_inferred → user_validated promotion, triple extractor, --batch JSONL ingest, CLI consolidation, INEDIBLE per-file opt-out
v1.2.x	planned	search-tool PreToolUse hook — memory-first context on Grep/Glob
v1.3	shipped	retrieval wave — entity index (L2.5), BFS multi-hop (L3), LLM-Haiku onboard classifier (opt-in), partial Bayesian-weighted ranking
v1.4	shipped	context rebuilder — PreCompact retrieval-curated continuation (augment mode); manual + threshold trigger; continuation-fidelity scorer (exact-match)
v1.5	shipped	retrieval plumbing — composition plumbing + per-lane telemetry (#232), BM25F anchor text (#148), search-tool Bash matcher (#155), v3 federation version-vector schema (#204), v1.4 dynamic-trigger re-park (#188)
v1.5.x	planned	default-on LLM-Haiku onboard via host-driven classification (#238)
v1.6	planned	graph signal wave — signed Laplacian + eigenbasis (#149, offline already merged), heat kernel authority (#150), posterior-weighted ranking full (#151), Plate FFT HRR primitives (#216)
v1.7	planned	structural retrieval lane + composition default-on flip — HRR bind/probe (#152), uri_baki post-rank adjuster retest (#153), benchmark-gate default-on flip (#154)
v2.0	planned	feature parity with the original research line + benchmark reproducibility. v2.0's component issues land incrementally across v1.5–v1.7; final v2.0 tag is the reproducibility cut.

Per-version detail: docs/ROADMAP.md. Open issues: docs/LIMITATIONS.md.

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Documentation

• Getting started: Install · Quickstart
• Reference: Commands · MCP · Slash commands · Config
• Background: Architecture · Philosophy · Privacy · Limitations
• Development: Releasing · Changelog · Contributing · Security

Citation

@software{aelfrice2026,
  author = {robotrocketscience},
  title  = {aelfrice: deterministic Bayesian memory for AI coding agents},
  year   = {2026},
  url    = {https://github.com/robotrocketscience/aelfrice},
  license = {MIT}
}

MIT