amnitex 0.2.1

atex (amni-tex): a lossless byte-page memory layer for MCP-capable AI coding assistants. Local, no embeddings, no cloud.

atex (amni-tex)

A lossless byte-page memory layer for MCP-capable AI coding assistants. Local. No embeddings. No cloud. MIT-licensed. Zero runtime dependencies beyond stdlib.

Install name on PyPI is amnitex; the command-line tool itself is atex.

pipx install amnitex
cd /path/to/your/project
atex init
atex demo            # auto-detects MCP clients (Claude Desktop, Claude Code, Cursor, Cline, Continue, Zed) and wires the config with [y/N] consent

That's it. Restart your AI client; atex_search, atex_recall, atex_remember, atex_list_keys, atex_stats are now available as tools.

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What it does

Every AI coding assistant forgets your project the moment a new session starts. atex persists project knowledge to a local lossless byte-page key-value store and exposes it over the Model Context Protocol (MCP) so any compliant client can call it mid-conversation.

Numbers

Scale-validated retrieval (v0.2.0 spatial tex-grid)

The default retriever is keyword-scan (O(N), simple). v0.2 adds a spatial tex-grid backend (O(num_query_tokens), texture-shaped inverted index, CPU-only, no extra deps).

Corpus-scale (20 queries per cell, auto-sized tex-grid):

Tokens	Entries	KB-scan recall@1	tex-grid recall@1	KB-scan avg query	tex-grid avg query	Speedup
2	1	100%	100%	6.4 ms	0.015 ms	425×
500	10	100%	100%	6.7 ms	0.004 ms	1675×
50K	1,000	100%	95%	0.85 ms	0.004 ms	212×
1M	20,000	100%	100%	24.3 ms	0.004 ms	6075×

Tex-grid maintains 95-100% recall@1 across four orders of magnitude of corpus size, with sub-microsecond query latency. KB-scan stays at 100% because it's exhaustive but at 1M tokens its average query is 24 ms (linear in corpus size); tex-grid stays at 4 µs because it's keyed by query length, not KB size.

Long-session (no context degradation, 5 round-counts × 2 backends):

Rounds	Recall (probe round 1, mid, last)	KB-scan p99	tex-grid p99
2	3/3 ✓	sub-ms	sub-ms
5	3/3 ✓	sub-ms	sub-ms
50	3/3 ✓	2.95 ms	0.04 ms
500	3/3 ✓	3.75 ms	0.014 ms
2000	3/3 ✓	4.93 ms	0.023 ms

Round 2000 retrieves the round-1 fact in 23 microseconds with the tex-grid. Conversation length does not degrade recall — the data lives in the KB, not the model context window.

Replay: atex demo --scenario corpus-scale --out scale.json and atex demo --scenario long-session --out session.json.

20-query smoke (v0.1)

	atex (KB-scan)	naive substring scan
recall@1 (20-query bench)	95%	90%
recall@3	100%	95%
recall@5	100%	95%
avg query latency	0.5 ms	0.05 ms
p99 query latency	8.5 ms	1.0 ms

Replay: atex bench. Full numbers in bench_results.md, source in atex/bench/.

Validated end-to-end with a 0.5B open-source model

$ ollama pull qwen2.5:0.5b-instruct
$ atex demo --model qwen2.5:0.5b-instruct
[atex-validate] model=qwen2.5:0.5b-instruct steps=4 pass=4 fail=0 wall=5.54s
  ✓ pre-clear: probe key cleared: True
  ✓ remember-then-recall: wrote 103 bytes; round-trip exact-match=True
  ✓ rag-search-finds-fact: top_keys=['manual::atex-validation-probe', ...]
  ✓ rag-answer-quotes-fact: answer='The validation cookie value is azure-marmot-7421.'

The 0.5B model retrieved the validation cookie out of the seeded atex knowledge base via RAG and quoted it back exactly. Works with any model your ollama server has pulled.

How it differs from other memory layers

	atex	Vector DB (Pinecone / Chroma)	mem0 / basic-memory	"Just paste it"
Local	yes	no (or self-host)	yes / hybrid	yes
Cost	free	$$/mo	free / hybrid	free
Lossless exact recall	yes	no (embedding-lossy)	varies	yes
Multi-AI-client (MCP)	yes	typically one client	yes	every session
Persists across vendors	yes	per integration	yes	no
Embedding model required	no	yes	usually yes	no
Setup time	~10 seconds	hours	minutes	none (but never ends)

Tools exposed over MCP

Tool	Use when
atex_search(query, k)	The assistant should look something up before asking you to re-explain it
atex_recall(key)	Exact path lookup, e.g. project::src/auth.ts
atex_remember(key, text)	You explicitly tell it "remember that X"
atex_list_keys(prefix, max)	Discover what is known about an area
atex_stats()	Inspect KB size / fill

Server-side validation: atex_remember keys must match ^[a-zA-Z0-9_\-./:]{1,256}$, must not contain .., text payload capped at 1 MiB.

Storage format

Lossless byte-page key-value store. Pages are mem-mappable; the index is JSON. No proprietary formats, no embedding indices, no ML dependencies.

• .atex/pages/page_<idx>.kb.page — raw byte stream, default 1 MiB per page (configurable via ATEX_PAGE_W / ATEX_PAGE_H)
• .atex/index.json — entry index {key: {page_idx, offset, length, meta}}
• .atex/manual/ — user-taught facts (kept in git for team sharing)
• .atex/config.json — project config (kept in git)

Reproducibility

git clone https://github.com/Amnibro/amnitex
cd atex
pip install -e ".[dev]"
pytest                                                    # 31 tests pass in ~6 s
python -m atex.cli bench --out bench.json --md bench.md   # bench numbers above
ollama pull qwen2.5:0.5b-instruct
atex demo --model qwen2.5:0.5b-instruct                   # live RAG validation

Paper

paper/atex.tex — arXiv preprint draft, cs.IR + cs.CL. The paper expands every number above and includes the full case study of using atex to track context while building atex (see DOGFOOD.md).

License

MIT — see LICENSE.

Citation

@misc{atex2026,
  title  = {atex: A Lossless Byte-Page Memory Layer for MCP-Capable AI Coding Assistants},
  author = {Anthony Reffelt},
  year   = {2026},
  note   = {arXiv preprint forthcoming}
}

Contributing

Issues and PRs welcome at github.com/Amnibro/amnitex. See DOGFOOD.md for the case study of using atex while building atex (19/19 search hits, 18/18 recall hits across the build).