atlas-recall

Deterministic markdown memory for Claude Code: index a directory of notes, retrieve the relevant ones, inject them into a prompt via a hook. No vector database required.

Retrieval and search are two interfaces on one engine, not two separate tools bolted together. The same corpus, the same config, the same index: recall query answers "what's relevant" automatically for an agent (a Claude Code UserPromptSubmit hook), and recall find / recall map / recall trace answer the same question interactively for a human, plus a wikilink graph over your notes -- as of this writing (2026), mem0, Letta, and Zep don't ship that combination; check their current docs, this is the kind of claim that goes stale.

The problem

Claude Code forgets everything between sessions unless you paste context in by hand. The usual fix is a vector database: embed every note, embed the query, take the top-K by cosine distance. In practice that ceiling doesn't discriminate well on a personal notes corpus (tens to low hundreds of files), it needs an embedding model running somewhere, and it's one more service that has to be up before the first command works.

The measured result

A dense-only retriever at a swept 0.45 cosine cutoff, measured against a 15-query labeled should-hit set over a 108-document corpus, admitted an average of 16.1 of 108 documents per query at 100% hit-recall (14.9% of the corpus) - the cutoff wasn't discriminating anything, it was a junk floor. Swapping the embedding model and re-sweeping the cutoff (0.48, still dense-only) brought that down to 2.9 of 119 documents at the same 100% hit-recall (2.4% of the corpus), roughly 5.5x fewer false admits.

The retriever this package ships (retrieval.py) goes further: BM25 lexical search fused with the dense path via Reciprocal Rank Fusion (RRF), gated by a conservative admission floor, scored by recency decay and a light priority tiebreak. RRF fuses by RANK, not by raw score, which is what lets a keyword-shaped query win on BM25 and a paraphrase win on dense without needing a BM25 score and a cosine distance to live on the same scale. The 5.5x number above is the dense half of that measurement, reproduced honestly here because it's the reason RRF replaced a plain distance cutoff rather than the whole story - re-run recall query against your own corpus and judge the fused result on your own notes; a number measured on someone else's corpus is a reason to expect a good default, not a guarantee.

The design decision that makes this a product, not a demo

The dense path is optional. Most retrieval-over-markdown projects need a Python venv with chromadb, plus a running Ollama with an embedding model pulled, before the first query works. Most people bounce off that step and the repo dies unused.

• Default: BM25 lexical search, plus recency decay, plus a priority weight. Pure Python, zero external services, works immediately after install.
• recall index --dense opts into Chroma + Ollama for semantic retrieval on top of BM25.
• Everything that touches the dense path degrades instead of crashing when it isn't configured: retrieval fuses BM25-only, curation dedup falls back to BM25 lexical overlap, and recall index --dense without chromadb installed prints exactly what to run rather than a stack trace.

Install

Not registered on PyPI yet -- install straight from source:

pipx install git+https://github.com/Abydin/atlas-recall

(or pip install git+https://github.com/Abydin/atlas-recall inside a venv). A plain PyPI install (pipx install atlas-recall) is coming once the name is registered there; until then that command won't find this package. The dense path is a separate extra, not a hard dependency:

pip install "atlas-recall[dense] @ git+https://github.com/Abydin/atlas-recall"

The three commands, with real output

$ recall init ~/notes
Wrote config to ~/.config/atlas-recall/config.json
notes_dir = ~/notes

Paste this block into your Claude Code settings.json ...
{
  "hooks": {
    "UserPromptSubmit": [
      { "hooks": [ { "type": "command", "command": "recall hook" } ] }
    ]
  }
}

$ recall index
index: discovered=3 changed=3 unchanged=0 removed=0 edges_verified=1 edges_divergent=0

$ recall query "what temperature should the water be for a light roast pour over"
== LIKELY-RELEVANT NOTES (read before acting) ==
water-temperature -- ~/notes/water-temperature.md -- hybrid retrieval (BM25+dense, RRF)
  BODY: 96C for light roasts, 92C for dark roasts. Off the boil by about 30 seconds.
coffee-brewing -- ~/notes/coffee-brewing.md -- pour-over ratio and grind size notes

That ran with no Chroma collection built and no Ollama process running - recall init never mentions either. recall hook (the piece that actually wires into Claude Code) reads a UserPromptSubmit payload on stdin and emits the same block as hookSpecificOutput.additionalContext; paste the block recall init printed into your settings.json and every prompt gets the relevant notes injected automatically.

The human path: search and trace the same corpus

$ recall find "chain lube"
bike-maintenance | doc | bike-maintenance | chain lube schedule | ~/notes/bike-maintenance.md

$ recall node coffee-brewing
id: coffee-brewing
...
-- 1-hop edges --
  link -> water-temperature [water-temperature]

$ recall trace coffee-brewing
coffee-brewing
  coffee-brewing -[link]-> water-temperature

$ recall map coffee
# coffee

## coffee-brewing  [coffee-brewing]
pour-over ratio and grind size notes
  -link-> water-temperature

$ recall verify
verify: checked=1 divergent=0

find is full-text search (SQLite FTS5, porter-stemmed). node shows one note plus its outbound [[wikilinks]]. trace follows those links recursively. map shows the shape of a topic: search hits plus what they connect to. verify reports links that point at a note that no longer exists, so drift is visible instead of silent.

recall index builds the SQLite FTS5 + wikilink graph that find / node / trace / map / verify read (and the Chroma collection, with --dense). It does NOT need to run before recall query / recall hook work: the BM25 corpus those read is built fresh, in-process, per query -- there's no separate BM25 index step, and that's a real strength, not a gap: the automatic path works the moment notes_dir is configured, no indexing required. One directory, one config file - index and query are still two interfaces on the same engine, not two tools that happen to sit in the same repo.

Propose-only curation

As of this writing (2026), I'm not aware of another tool that ships memory curation where an agent proposes edits and a human approves every single one, with nothing auto-written -- that's a claim about what I've seen, not an exhaustive survey, so verify against current alternatives before repeating it. recall distill reads candidate notes (name, description, body, optionally which existing note to update) as JSON on stdin and returns a list of proposed ADD/UPDATE operations - an advisory near-duplicate warning on ADDs, an explicit, name-based opt-in required for UPDATE (never a fuzzy match deciding to overwrite something). It never writes a file.

$ echo '[{"description": "grind size for espresso", "body": "18g in, 36g out, 25-30s."}]' \
    | recall distill > proposed.json
$ recall distill-apply proposed.json
ADD 'grind-size-for-espresso' (type=reference)
  description: grind size for espresso
  body: 18g in, 36g out, 25-30s.
Apply this op? [y/N] y
  -> wrote ~/notes/grind-size-for-espresso.md

distill-apply is the only place in this package that writes to your notes, and it asks before every op, individually - there is no batch approve.

Configuration

recall init <dir> writes ~/.config/atlas-recall/config.json (or $ATLAS_RECALL_CONFIG). Everything the engine touches comes from that file or a CLI flag - nothing in this package points at a specific person's machine, corpus, or rules. Two fields worth knowing about:

• rules_path: an optional file (e.g. a house style guide) injected verbatim, undiluted, at the top of every hook block. Empty by default.
• keyword_rules: [[keywords], "rule line"] pairs - if a prompt contains one of the keywords, that one rule line gets echoed inline. Ships empty. See examples/keyword_rules.json for the shape; copy it, don't inherit it, the rules in there aren't yours.

What's in the box

Known limitation, carried over honestly

recall index (incremental) only re-derives outbound edges for notes it re-parses that pass. An edge whose TARGET note was deleted or renamed, but whose SOURCE note wasn't touched, doesn't get a chance to self-heal - it stays flagged divergent (recall verify will show it) until recall index --rebuild does a full pass. This isn't hidden: it's the tradeoff of incremental indexing being fast, and verify exists specifically so it's visible rather than silent.

License

MIT, see LICENSE.