Encrypted memory and cache for AI agents, in one file.

import mnemo_engine
from sentence_transformers import SentenceTransformer

MODEL = "sentence-transformers/all-MiniLM-L6-v2"
model = SentenceTransformer(MODEL)
def embed(texts):
    return model.encode(texts, normalize_embeddings=True).tolist()

with mnemo_engine.open("agent.mnemo", "a long passphrase", dimensions=384) as db:
    db.set_embedder(embed, name=MODEL, dimensions=384)   # checked against the file

    # Durable memory, plain text in and out
    db.remember_text("the user prefers dark mode", "semantic", importance=0.8)
    for hit in db.recall_text("which theme does the user like?", top_k=5):
        print(round(hit["score"], 3), hit["content"])

    # Cache an expensive call
    prompt = "Summarize the release notes"
    hit = db.cache_get("llm", prompt)
    if hit is None:
        answer = call_llm(prompt)                # your LLM call
        db.cache_put("llm", prompt, answer, ttl_secs=3600)
    else:
        answer = hit["value"].decode()

use mnemo_engine::{CachePutOpts, EmbedCmd, EmbedderIdentity, MemoryType, Mnemo, MnemoConfig, RecallRequest};

fn main() -> mnemo_engine::Result<()> {
    let cfg = MnemoConfig { dimensions: 384, ..Default::default() };
    let mut db = Mnemo::create("agent.mnemo", "a long passphrase", cfg)?;

    // Any program that turns text into vectors (see examples/embedder_server.py)
    let id = EmbedderIdentity {
        name: "sentence-transformers/all-MiniLM-L6-v2".into(), dimensions: 384, normalize: true,
    };
    db.set_embedder(EmbedCmd::new("python3 embedder_server.py", id)?)?;

    db.remember_text("the user prefers dark mode", MemoryType::Semantic)?;
    db.cache_put("llm", "prompt-42", br#"{"answer":"..."}"#,
        CachePutOpts { content_type: "json".into(), ttl_secs: Some(3600), cost_hint_ms: None })?;
    db.flush()?; // one atomic, crash-safe commit

    let req = RecallRequest::from_text(db.embedder().unwrap(), "which theme does the user like?")?.top_k(5);
    for hit in db.recall(&req)? {
        println!("{:.3}  {}", hit.score, hit.memory.content);
    }
    Ok(())
}

$ export MNEMO_PASSPHRASE='a long passphrase'        # never put it on the command line

$ mnemo init agent.mnemo --dimensions 384         # new file, self-describing from the start
$ mnemo about agent.mnemo                          # what is this file and how do I use it?
$ EMB=(--embed-cmd "python3 embedder_server.py" --embed-name sentence-transformers/all-MiniLM-L6-v2)
$ mnemo remember agent.mnemo --text "the user prefers dark mode" --importance 0.8 "${EMB[@]}"
$ mnemo recall   agent.mnemo --text "which theme does the user like?" --top-k 5 "${EMB[@]}"

$ mnemo cache put agent.mnemo --ns llm --key prompt-42 --value '{"answer":"..."}' --content-type json --ttl 3600
$ mnemo cache get agent.mnemo --ns llm --key prompt-42

$ mnemo snapshots agent.mnemo                      # every commit is restorable
$ mnemo restore agent.mnemo --to-txn 12

$ mnemo serve --mcp agent.mnemo "${EMB[@]}"       # expose it to MCP clients, text in