scone-core 0.2.0

Your AI forgets everything between conversations, and the memory services that fix it want your memories in their cloud. Scone fixes both: a complete memory engine, episodic and semantic, temporal and searchable, that runs entirely on your machine and embeds in anything.


🧠 Temporal memory	Facts extracted from what you store, with validity intervals. A contradiction closes the old fact with a recorded reason, so history stays queryable. Stale facts decay; recalled facts strengthen.
🕰️ Time travel	`search --as-of 2026-03-15` answers "what did I believe in March?". Validity is a WHERE clause, not a version-chain walk.
🔍 Hybrid search	BM25, vectors, facts, and recency fused in one query, with provenance on every result. Local ONNX embeddings by default; it works on a plane.
👤 Profiles	Identity facts + recent activity in one call, on the CLI, MCP, and HTTP surfaces.
📉 Context economy	Every recall reports bytes returned vs stored ("97.7% saved"). Token optimization is a product surface, not a benchmark footnote.
📦 Portable & embeddable	`scone export` writes JSONL with full fact history. The C ABI (`include/scone.h`) embeds the engine in any language. Zero TypeScript.

Use Scone

🧑‍💻 I use AI tools

Give Claude Code (or any MCP client) persistent memory across sessions.

→ Agent memory

🔧 I'm building

Embed the engine as a Rust crate or through the C ABI; or call the HTTP API.

→ Build with Scone

🖥️ I run my own infra

One binary, one config file, Bearer keys each bound to a space.

→ Self-host

Quickstart

cargo install scone-cli

scone add --note "changed the oil on the truck"
scone search "vehicle maintenance"      # semantic hit, fully offline
scone watch ~/notes --once              # ingest a directory
scone add paper.pdf --tag research      # PDFs become searchable text
scone distill                           # extract temporal facts (any LLM)
scone facts list --all                  # history, with closure reasons
scone search "tools" --as-of 2026-03-15T00:00:00Z
scone profile                           # identity + recent activity
scone export > memory.jsonl             # your memory is portable

The semantic lane uses whatever LLM you configure (Ollama, OpenAI-compatible, or Anthropic) and pauses loudly when none is set. Episodic search never needs one. ~/.scone/config.toml:

[llm]
provider = "ollama"
model = "llama3.1:8b"

Give your AI memory

claude mcp add scone -- scone --space myproject mcp

Tool	What it does
`memory_store`	Save an observation; duplicates are recognized, facts distill immediately when an LLM is configured.
`memory_recall`	Hybrid recall with your profile prepended; `as_of` for time travel.
`memory_facts_about`	What's currently known about an entity (aliases resolved).
`memory_forget`	Close a fact with your reason. Recorded, never deleted.

Each --space is an isolated brain: one per project, per client, per team.

Build with Scone

cargo add scone-core

let mut engine = Engine::open(dir, Box::new(OnnxEmbedder::new(cache)?))?;
let space = auth::resolve(&mut engine, "notes", true)?;
engine.ingest(&space, IngestInput::Note { text: "…".into() })?;
let pack = engine.recall(&space, "what do I know about X", &RecallOpts::default())?;

API	Purpose
`Engine::ingest`	Store content: chunked, embedded, indexed, queued for distillation
`Engine::recall`	Hybrid retrieval: facts, cited chunks, context economy
`Engine::distill`	Drain the queue through your LLM into temporal facts
`Engine::profile`	Identity facts + recent activity
`Engine::export_jsonl` / `import_jsonl`	Full-fidelity portability
`scone-ffi`	The same engine via C ABI, from any language

Self-host

# ~/.scone/config.toml
[server]
listen = "127.0.0.1:7437"
[[server.keys]]
key = "sk-alice"
space = "alice"

scone serve

POST /v1/episodes · GET /v1/recall · GET /v1/facts · POST /v1/facts/{id}/close · GET /v1/profile · GET /v1/status. Every key is bound to exactly one space, and the server refuses to start keyless.

How it works

SQLite is the single source of truth; tantivy (BM25) and usearch (HNSW) are derived, rebuildable indexes (scone doctor --rebuild). Ingestion is two lanes: episodic (synchronous, offline-complete) and semantic (async LLM distillation that never blocks a write). Four invariants are property-tested: chunks reassemble exactly; no two active facts share subject+predicate; contradiction closes intervals, never deletes; every fact carries provenance.

Measured on Apple Silicon (criterion): recall ~300 µs over 5k chunks, 3.6 ms end-to-end including local query embedding, ingest 2.9 ms/note.

License

MIT. Built by studying what came before and keeping the receipts.