Holographic Memory

WARM

WritersLogic Adaptive Retention & Memory.

Hybrid search, graph indexing, and vector-symbolic memory.

Local memory for agents and apps: full-text and vector search, a memory API and a vector-symbolic fact store in one embeddable Rust engine that makes no network calls.

[CI] [MSRV 1.89] [OpenSSF Scorecard] [OpenSSF Best Practices] [License: Apache-2.0] [WARM MCP MCP server – quality and maintenance score on Glama]

[Bemula forgetting score, DeepSeek backbone] [LongMemEval dev, gpt-4o reader and judge] [VIBE llama-128-ip max recall@100, one thread]

Website · Docs · Get started · Standards and provenance · Results · Capabilities · Limits · Contribute

What it is

WARM is a Rust library that stores text, vectors and facts in a directory on your machine and finds them again. It ships with Node.js bindings, C, WASM and UniFFI bindings, an HTTP server with a Python client, and an MCP server. There are no service calls, no telemetry and no model downloads unless you turn on a feature that asks for one.

Who it's for

Headline results

Every row links to the committed file it comes from. These are single machines and stated protocols, not a substitute for testing your own data.

Where it's behind

Method, intervals and every loss: docs/benchmarks and the benchmarks pages.

Generated from benchmarks/results/competitive/ by warm-bench table; method and caveats in docs/benchmarks/COMPARISON.md. W = WARM ahead (95% interval excludes parity), T = tie, L = a comparator is ahead.

Get started

Clone once: git clone https://github.com/writerslogic/warm && cd warm. Run the offline agent-memory demo with cargo run --release --example demo-offline-agent.

cargo add warm-rust

use writerslogic_warm::{DocumentInput, WarmCore, SearchOptions};
let dir = tempfile::tempdir()?;let warm = WarmCore::new(4096, Some(dir.path().display().to_string()), None)?;warm.memorize_document(DocumentInput {    id: "backups".into(),    text: "Restore deleted documents from verified backups.".into(),    source_uri: Some("operations.md".into()),    version: Some("1".into()),    metadata: Some(serde_json::json!({ "project": "alpha" })),    chunk_words: None, overlap_words: None, store_text: None, embeddings: None, title_weight: None,})?;let hits = warm.search_documents("restore backups", &SearchOptions::default())?;println!("{} {:?}", hits[0].document_id, hits[0].source_uri);

npm install writerslogic-warm

const { WarmMemorySystem } = require('writerslogic-warm')const { DocumentMemory } = require('writerslogic-warm/semantic')
async function main() {  const dir = require('node:fs').mkdtempSync(require('node:path').join(require('node:os').tmpdir(), 'warm-readme-'))  const memory = new DocumentMemory(new WarmMemorySystem(4096, dir))  await memory.memorize({    id: 'backups', text: 'Restore deleted documents from verified backups.',    sourceUri: 'operations.md', version: '1', metadata: { project: 'alpha' },  })  const [hit] = await memory.search('restore backups', { filter: { project: 'alpha' } })  console.log(hit.documentId, hit.sourceUri, hit.text)  await memory.flush()}main().catch(error => { console.error(error); process.exit(1) })

cargo build --release --features server --bin warm-serverWARM_API_KEY=change-me target/release/warm-server --input-dim 384 &uv pip install warm-sdk

from writerslogic_warm_sdk import WarmClient
client = WarmClient(url="http://127.0.0.1:8080", api_key="change-me", tenant_id="demo")client.add_documents([{"id": "a", "vector": [0.1] * 384, "text": "hello", "metadata": {}}])print(client.query([0.1] * 384, top_k=1)["matches"][0]["id"])

npx -y writerslogic-warm-mcp                                # stdio; downloads a verified binary on first runnpx -y writerslogic-warm-mcp --transport http               # http://127.0.0.1:8765/mcp

cargo build --release --features mcp,security --bin warm-mcp   # add local-models for semantic recall./target/release/warm-mcp                                       # stdio./target/release/warm-mcp --transport http                      # http://127.0.0.1:8765/mcp

The five-minute quickstart walks through ingest, search, a SQLite FTS5 comparison and encryption in Rust, Python and Node, and CI runs that code on every pull request. For a whole agent memory with no embeddings and no network, run cargo run --example demo-offline-agent.

What ships today

Every capability above except document search and the vector index is behind a Cargo feature or runtime option that is off by default; Cargo features lists them all.

Architecture

flowchart TB  subgraph bindings["Bindings and servers"]    rust["Rust crate"]    node["Node.js (napi)"]    ffi["C, WASM, UniFFI"]    python["Python: warm-sdk"]    server["warm-server (HTTP)"]    mcp["warm-mcp (stdio, HTTP)"]  end  subgraph engine["Engine API"]    core["WarmCore: documents, vectors, facts, mutation gate"]    prov["Security and provenance: signed audit log, COSE/SCITT"]  end  subgraph indexes["Indexes and memory"]    text["Text index: blocked postings, BM25, Boolean and phrase"]    vector["Vector index (qgraph): vertex file, updatable delta, query-aware projection"]    memory["Memory API: remember, recall, forget, conversation memory"]    vsa["Vector-symbolic layer: bind, bundle, Hopfield cleanup"]  end  subgraph storage["Storage"]    journal["Journal and checkpoints"]    segments["Append-only segments (CRC32, LZ4)"]    mapped["Memory-mapped files"]    crypto["AES-256-GCM encryption (optional)"]  end  python --> server  rust --> core  node --> core  ffi --> core  server --> core  mcp --> core  core --> text  core --> vector  core --> memory  core --> vsa  core --> prov  memory --> text  text --> journal  vector --> journal  memory --> journal  journal --> segments  segments --> mapped  segments --> crypto

Module map: docs/ARCHITECTURE.md.

Limits

• Not anonymous. Hypervector sketches and term counts are not encryption; anyone with a store can encode candidate text and compare. storeText: false keeps term counts, IDs and embeddings, so it is not a privacy control.
• Document search is conventional. It does not use the hypervectors; the holographic parts are the algebra and the meaning memory.
• One writer per store. A second writing process fails at open with a lock error.
• Capacity numbers are points, not guarantees, and are stated only over the tested range (capacity).
• Benchmark results are single machines. Conditions are in each results file; the single-core ann-benchmarks run and the VIBE high-recall points are losses (see Where it's behind).
• Masking is obfuscation. Client-side vector masking preserves every pairwise similarity.
• A store is a directory and WarmCore::new(.., None, ..) uses the current directory.

Full statement: docs/PRIVACY.md. Report vulnerabilities privately via SECURITY.md.

Contributing

CONTRIBUTING.md: setup, the checks CI runs, DCO sign-off (git commit -s, no CLA). Start with a good first issue or Discussions. See the Code of Conduct. Benchmark changes regenerate the results file they touch; the README cites files, not remembered figures.

License

Apache-2.0. See LICENSE. Use, modify and embed it in open-source or proprietary software, including network services, with no copyleft obligation. Releases up to and including crate 0.6.1 were published under AGPL-3.0-or-later and stay available under those terms.