Your AI stops forgetting. Set up once. Stays out of the way.
No cloud. No account. No telemetry.
You correct your agent. "Got it," it says. Next session, same mistake.
aelfrice runs in the background and stops the amnesia. Write a rule once and every relevant prompt thereafter ships with that rule already attached — before the model sees your message. No rules-file chain to maintain, no cross-references for the agent to skip; the matched beliefs are in the prompt, not in a file the agent is supposed to consult.
For developers using AI coding agents — first-class via the UserPromptSubmit hook for hosts that expose it; any MCP host via the included stdio server. Local-only by design — embeddings, vector RAG, and cloud sync are explicitly out of scope. See Philosophy for the trade-off.
uv tool install aelfrice # requires uv — https://docs.astral.sh/uv/
aelf setup # wire the UserPromptSubmit hook into your agent
aelf onboard . # deterministic project scan (regex classifier). For LLM-quality with no API key, run /aelf:onboard in your agent.
aelf lock "never push directly to main; use scripts/publish.sh"That's it. The next prompt that mentions "push" already has the rule. From here on out aelfrice is invisible — no command to remember to run, no file to keep updated.
You type a message in your agent. aelfrice's hook fires before the model sees it and prepends matched beliefs as an <aelfrice-memory> block:
<aelfrice-memory>
The following are retrieved beliefs from the local memory store. ...
<belief id="a1f3c2d0" lock="user">never push directly to main; use scripts/publish.sh</belief>
<belief id="91e02d3c" lock="user">commits must be SSH-signed with ~/.ssh/id_ed25519</belief>
<belief id="77c01b2a">the publish script runs the release checks before tagging</belief>
</aelfrice-memory>
push the release
The model reads the whole thing as one message. Your rules arrive every relevant time, not when the agent decides to check a file.
- Stops the AI forgetting your rules. Lock a rule once with
aelf lock "..."— it comes back attached to every prompt after that, in every new session. You don't have to remind the AI; aelfrice does. - The AI can't skip it. What aelfrice remembers is in the prompt before the model sees it — not stored in a file the AI is supposed to check on its own. The reminding happens for the AI, not by you.
- Nothing to maintain. Passive capture runs in the background: every turn is logged and ingested, successful
git commitmessages too. Your memory grows while you work, without you typingaelfat all. - Stays on your computer, leaves on command. One SQLite file on your machine. No cloud account. No telemetry. If you stop trusting aelfrice,
aelf uninstallremoves it cleanly in one command (--archiveencrypts the DB to a file first).
A rules file is advice the agent may read; aelfrice is context the model has already read. And by Leonard Lin's bar, "a vector store with a similarity query" is not a memory system either — a memory system has to answer who wrote this, when, via what ingress, what supersedes it, and how do I take it back. aelfrice meets the four pillars (provenance, write gates, conflict handling, reversibility) directly. The side-by-side against hand-maintained rules files and vector stores lives in COMPARISON.md.
After aelf setup you rarely type aelf again. The everyday surface:
aelf onboard . # once per project — deterministic scan (or /aelf:onboard for the no-key subagent flow)
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
aelf feed # read the belief-write event log (v3.5+)
aelf stale --older-than 90 --cold-for 30 # surface forgotten beliefs (v3.5+)
aelf review --generate # weekly keep / remove / lock checkpoint (v3.5+)
aelf --help shows the everyday surface; aelf --help --advanced lists the rest. Full reference: COMMANDS. The same operations are exposed as MCP tools and /aelf:* slash commands — same library underneath. See MCP and SLASH_COMMANDS.
Three retrieval lanes run on every prompt (a fourth, BFS graph expansion, is opt-in), the best matches get prepended to your prompt, and the model reads the lot as one message:
L0: locked beliefs -> rules you marked permanent (always returned, never trimmed)
L2.5: entity index -> deterministic NER-extracted entity lookup, exact + stem match
L1: FTS5 keyword -> SQLite full-text search, BM25 + posterior-weighted rerank
L3: graph walk -> typed-edge BFS from the L0+L2.5+L1 seed set (DERIVED_FROM, CONTRADICTS,
SUPERSEDES, RELATES_TO, ...) — opt-in: [retrieval] bfs_enabled = true
Illustrative — not a trace of any real store. L0 locked rules always return; an FTS5/BM25 query seeds L1; the opt-in L3 graph walk steps along typed edges hop by hop; the separate structural-HRR lane (retrieve_v2) bridges to matches that share no vocabulary with the query. Color is the lane; distance from center is graph-walk depth. The L2.5 entity-index lane is omitted for legibility. Rendered by render_retrieval_lanes.py.
L0 always ships. L1, L2.5, and (when enabled) L3 are budget-trimmed against the merged candidate set in score-descending order; locked beliefs win every overflow. Default budget: 1,500 tokens per hook-injected prompt (the aelf search / library retrieve() default is 2,400). A separate structural-HRR lane (Plate-FFT bind/probe) routes queries that parse as structural markers in the retrieve_v2 API; ordinary prompts never touch it.
Lock count is the operator's baseline-context budget knob. If you lock 200 things, every session opens with all 200, by design. Everything non-locked is BM25-ranked and budget-trimmed. The first prompt of a new session carries one extra block — a <session-start> sub-block listing all locks plus load-bearing unlocked beliefs (corroboration ≥ 2, or posterior mean ≥ ⅔ with α+β ≥ 4); subsequent prompts in the same session skip it.
Bench evidence on the labelled query-strategy corpus measured +0.2851 absolute NDCG@k (+94.8%) versus the v1.4 raw-BM25 baseline (v3.0 30-row corpus, 2026-05-12) at +0.96 ms p99 over legacy-bm25 (re-measured 2026-05-26; gate budget +5 ms delta; see tests/bench_gate/test_query_strategy.py). Full lane wiring, composition, and federation peer DBs: ARCHITECTURE § Retrieval.
Every belief carries a (α, β) Beta-Bernoulli posterior: α / (α+β) is the confidence; α + β is how much evidence backs that confidence. New beliefs sit at low evidence (high variance, retrievable but discounted); locked beliefs short-circuit decay and pin as ground truth.
| You run | It stores |
|---|---|
aelf lock "never commit .env files" |
Permanent rule. Returned on every retrieval. |
aelf onboard . |
Walks the project — git log, prose headings, code structure — and ingests structural facts as agent_inferred beliefs via the deterministic regex classifier. |
/aelf:onboard |
Same scan, higher-quality classification driven by in-session subagents — no API key, no billing. The preferred path in an agent; bare aelf onboard is the deterministic fallback. |
aelf feedback <id> used |
α += 1. Strengthens the belief's posterior. |
aelf feedback <id> harmful |
β += 1. Weakens it. Locks resist passive feedback by design — change with aelf unlock / aelf delete. |
aelf promote <id> |
Flips origin from agent_inferred to user_validated. With --to-scope <SCOPE>, also moves federation visibility (project / global / shared:<name>). |
/aelf:wonder <topic> |
Researches the topic and writes the findings as speculative phantoms; /aelf:reason <topic> can then walk them. |
| (passive — no command) | Default-on auto-capture: every prompt/response turn is logged and ingested at compaction; successful git commit events are ingested too. Opt out per-hook via aelf setup --no-transcript-ingest, --no-commit-ingest, --no-session-start, --no-stop-hook, --no-sessionstart-recap, --no-search-tool, --no-search-tool-bash, --no-pre-issue-guard, --no-claude-memory-mirror, --no-agent-context — see INSTALL § default-on hooks. |
Each belief has an origin column tying it to the action that wrote it — one of user_stated, user_corrected, user_validated, user_transcript, agent_inferred, agent_remembered, document_recent, speculative, unknown. The store is a single SQLite file; open it in any browser, nothing is hidden.
Two slash commands let the agent reach back into the belief graph mid-turn, beyond the auto-injected retrieval block. They pair: /aelf:wonder grows the graph by researching; /aelf:reason walks the enriched graph for structured verdicts.
/aelf:wonder <topic> — the research surface. Given a topic, aelfrice runs gap analysis on what the store already knows, generates 2–6 orthogonal research axes (always-on domain_research + internal_gap_analysis; conditional contradiction_resolution / uncertainty_deep_dive / coverage_extension), has the host agent fan out one research task per axis to research and write up findings, then persists the merged research as new speculative beliefs via wonder_ingest. Those phantoms sit in the graph at low evidence — discoverable by retrieval and by the next /aelf:reason <topic> — until you promote them with aelf promote (or lock the underlying statement, which auto-promotes a matching phantom). Agent-count shorthand like quick 2-agent / deep 4-agent is recognised in the query string — the integer sets the agent count (quick / deep are optional qualifiers).
/aelf:reason <query> — the structured-walk surface. Walks the belief graph from BM25-seeded starting points and emits a typed reasoning trace: hops with edge-type breadcrumbs, a VERDICT (SUFFICIENT / PARTIAL / UNCERTAIN / INSUFFICIENT / CONTRADICTORY), IMPASSES (typed gaps, ties, or constraint failures), and SUGGESTED UPDATES — (belief_id, direction, note) rows that map straight to aelf feedback so the conclusion closes the loop on the beliefs that fed it. Each impasse is dispatched by the host agent to a role-tagged worker (Verifier / Gap-filler / Fork-resolver). Peer hops in foreign federation scopes are annotated [scope:<name>].
The pair-rhythm is the point: /aelf:wonder adds fresh thinking to the graph, then /aelf:reason draws conclusions across it. Both surfaces are deterministic in the aelfrice layer (verdict classification, impasse derivation, axis generation, suggested-update mapping). The only LLM calls happen when the host agent dispatches one worker per impasse or research axis — and those calls run under the host's own credentials, not aelfrice's. Specs: COMMANDS § wonder, COMMANDS § reason.
Running in the background. No action required after aelf setup.
- Passive capture. Ten default-on hooks:
UserPromptSubmitretrieval, four-event transcript-ingest,PostToolUse:Bashcommit-ingest,SessionStartlocked-belief injection (with a belief-write recap line, v3.5+),Stoplock-prompt,PreToolUse:Grep|Globmemory-first search,PreToolUse:Bashmemory-first search, thePreToolUse:Bashissue-dup guard, thePostToolUse:Write|Edit|MultiEditclaude-memory mirror (v3.7+ — installed on by default but inert until opted in viaAELFRICE_MIRROR_CLAUDE_MEMORY=1), and thePreToolUse:Agentworker-context injection (dispatched subagents inherit locked + task-relevant beliefs; opt out via--no-agent-context). Session activity flows into the belief graph without you typingaelfat all; opt out per-hook — see INSTALL § default-on hooks. - Determinism. SQLite + a deterministic numeric stack (numpy / scipy / snowballstemmer — no GPU, no network). No embeddings, no learned re-rankers, no LLM in the retrieval path. Every result traces to the action that wrote it.
- Local-only. SQLite at
<git-common-dir>/aelfrice/memory.db. By default, the only outbound call aelfrice itself makes is the update notifier — a TTL-gated, read-only GET tohttps://pypi.org/pypi/aelfrice/json(disable withAELF_NO_UPDATE_CHECK=1). No telemetry; no accounts. The memory/retrieval path never touches the network. (LLM dispatches in/aelf:wonder//aelf:reasonflows do reach the network — under the host agent's credentials, not aelfrice's; the retrieval path stays local.) Per-project isolation by construction. Read-only cross-project federation viaknowledge_deps.json— peer DBs are opened read-only, foreign-id mutations are rejected at the API surface. See PRIVACY.md. - Removable.
aelf uninstall --archive backup.aencencrypts the DB to a file, then deletes it. Or--purgefor a full wipe.
If you already live in Obsidian, aelf export-obsidian <vault-path> emits the belief graph as one Markdown note per belief under <vault>/aelfrice/. Typed edges land in YAML front-matter for Dataview; the same edges appear in the note body as wikilinks so the graph view has something to draw. The export is one-way (DB → vault): SQLite stays the source of truth, and the <vault>/aelfrice/ subdirectory is wiped and rewritten on each run.
Scopes: --scope all (everything, capped by --max-notes), --scope recent (newest first), --scope query "<text>" (BM25 seeds + N-hop neighbourhood). Default cap is 500 notes; the hard ceiling is 5000 unless --force is passed.
Two structural limits, shipped with the feature: Obsidian's built-in graph view chokes around a few thousand nodes (bound the export with
--scope query/--max-notes, or useaelf graphfor query-anchored visualization at any store size), and the graph view is untyped — edge types are preserved in YAML front-matter and queryable via Dataview, but the graph view will not show them.
Latest stable: v3.8.0 (2026-06-30). Per-entry detail in CHANGELOG § 3.8.0. Per-version history: docs/concepts/ROADMAP.md. Known limits: docs/user/LIMITATIONS.md.
- Getting started: Install · Quickstart
- Reference: Commands · MCP · Slash commands · Config
- Background: Architecture · Philosophy · Comparison · Privacy · Limitations
- Development: Releasing · Changelog · Contributing · Security
@software{aelfrice2026,
author = {robotrocketscience},
title = {aelfrice: deterministic Bayesian memory for AI coding agents},
year = {2026},
url = {https://github.com/robotrocketscience/aelfrice},
license = {MIT}
}