Get the verified binary
The installer selects the supported prebuilt for your platform, verifies its published SHA-256, and installs without root access.
Prebuilts: universal macOS (Apple silicon + Intel) and Linux x86_64.
Give every MCP- or HTTP-capable agent one durable store on your machine. Mnema is free to download, local by default, and built on a formally specified, machine-checked engram engine.
curl -fsSL https://mnema.zimac.ai/install.sh | sh
Confidence describes retrieval strength,
not factual truth.
Install the headless binary, connect an MCP client, and the client gains a shared memory it can consult before acting and update after learning.
The installer selects the supported prebuilt for your platform, verifies its published SHA-256, and installs without root access.
Prebuilts: universal macOS (Apple silicon + Intel) and Linux x86_64.
In local mode, mnema mcp starts the loopback memory server if it is not already running, then bridges tools over stdio.
Claude Code is one example. Any client that can launch an MCP stdio server can use the same command and arguments.
Each compatible client reaches the same local memory through four explicit tools.
consult returns ranked memories and a retrieval-confidence band. Low confidence should be treated as a hint, not a fact.
MCP is a stdio bridge. HTTP and the Pensieve use the loopback server. The CLI calls the same API. They are distinct interfaces over one engram store—not three names for the same transport.
~/.mnemabinary-framed engram logSet MNEMA_URL and MNEMA_TOKEN to bridge MCP or CLI calls to an authenticated remote store. A non-loopback server bind is refused unless authentication is configured or the explicit unsafe override is set.
The Mac app renders a deterministic field of memories and an evidence rail. It is not a black-box claim about what recall did: the layout and the confidence inputs have explicit meanings.
Public consult runs a deterministic retrieval pipeline. Default recall makes no model call. Optional external neural embeddings can expand the semantic leg when you configure them.
The embedded server fuses lexical retrieval with deterministic feature-hash vectors and HNSW-backed candidate search.
A field-weighted BM25 pass seeds pseudo-relevance feedback, adding salient terms before the candidate pool is ranked again.
Frequency and recency contribute a cognitive activation signal, so repeatedly useful memories remain easier to retrieve.
Reinstated temporal context, tightly gated neighbors, entity affinity, and named-person source cues help recover scattered evidence.
A deduplication-floored MMR selection reduces repeated near-duplicates while preserving the strongest ranked evidence.
Evidence strength, winner margin, and corroboration produce grounded, tentative, or exploratory retrieval bands.
Connections are explicit. Two tools, two jobs: consult ranks with the cognitive-fusion pipeline above, and related traverses the graph links you or your agents authored — so a hop in the graph always means something a person or agent actually recorded.
curl -sS -H 'content-type: application/json' 127.0.0.1:7077/v1/consult -d '{"query":"retry loop"}'
Mnema keeps committed baselines for retrieval quality and performance. The results below compare Mnema cognitive recall with its own semantic-RAG baseline—not with competitor marketing claims.
Overall recall@5: semantic-RAG baseline 0.451 → cognitive recall 0.548.
| Recall@5 | Semantic RAG | Mnema cognitive | Lift |
|---|---|---|---|
| Overall | 0.451 | 0.548 | +9.7pp |
| Multi-hop | 0.185 | 0.291 | +10.6pp |
Gold supporting turns are scored deterministically — no LLM judge — so the number is a reproducible measurement of the retrieval layer itself.
HNSW approximate top-10 p50 was 0.284ms versus 3.128ms for brute-force cosine, with recall@10 of 1.0 on that run.
Component latency is hardware-specific. Full search_cogni p50 was 19.4ms at the same 10k scale in the committed baseline.
The default standalone store is a local binary-framed log under ~/.mnema. Its file format, network boundary, and failure behavior are explicit.
The log appends and fsyncs CRC32-framed records. A per-segment SHA-256 chain makes segment history tamper-evident.
On reopen, incomplete active-tail bytes are truncated after the last complete frame. Corrupt completed data is detected rather than silently accepted.
HTTP defaults to 127.0.0.1:7077. A non-loopback bind requires configured auth tokens or an explicit unsafe override.
Know the boundary. Default recall stays local; an external embeddings endpoint or a remote MNEMA_URL touches the network only because you configured it. At-rest protection comes from your disk encryption — FileVault on a Mac covers ~/.mnema — and the store is a plain directory you can back up or move while Mnema is stopped.
Mnema maintains a property catalog and names the discharge mechanism for each claim. Every release runs the full layered suite on the exact commit it ships from: property tests and exhaustive conformance checking, bounded symbolic proofs, and a model-checked replication protocol.
Property tests, lints, and the exhaustive CRDT conformance checker pass on the release commit before any binary is built.
Bounded symbolic proofs discharge the pure invariants, and a three-replica protocol model is checked for consistency, convergence, and liveness on the same commit.
The overflow-safe frame-boundary component is proved, transpiled, and compiled into mnema-log.
Every claim points at a specific check. Exhaustive enumeration, bounded symbolic proof, finite model checking, and proof-carrying compile-time arithmetic each discharge the properties they are best suited to, and the property catalog records exactly which mechanism stands behind which guarantee.
The real CRDT implementation is compared with an independent reference fold. The checker enumerates every delivery order through seven operations per scenario (7! = 5,040), then runs a deterministic 3,000-schedule sample for larger cases. It checks causal delivery, convergence, liveness, and idempotence at the implementation layer.
const EXHAUSTIVE_MAX: usize = 7; const SAMPLE_ORDERS: usize = 3000; for_each_schedule(ops.len(), |perm| { replica.ingest(one_op_at_a_time(perm)); assert_eq!(replica.materialize(), canonical); });
Kani harnesses cover selected pure invariants over their stated symbolic bounds, including merge laws, add-wins resolution, clock monotonicity, and the recall cost ladder. TLC checks a finite three-replica model for consistency, convergence, causal behavior, GC safety, trust, and liveness. Every bound is stated in the property catalog, so each result is reproducible exactly as claimed.
\\* Finite protocol model; invariants named in the spec.
INVARIANTS == {
Consistency,
Convergent,
Causal,
Unforgeable,
GCSafe
}A frame length is read from disk and must be treated as untrusted. The Zi component proves the remaining-bytes form equivalent to the naive bound while keeping the host calculation overflow-safe. The surrounding Rust still owns byte reads, CRC, JSON, chain checks, and truncation.
fn frame_fits (off: Int) (header: Int) (payload_len: Int) (buf_len: Int) -> Bool req off + header <= buf_len ens result == (payload_len <= buf_len - off - header) ens result == (off + header + payload_len <= buf_len) { ret payload_len <= buf_len - off - header }
The installer and the DMG are different distributions. The headless binary provides CLI, server, and MCP. The Mac app adds the Pensieve interface.
Best for agent clients, scripts, servers, and CI. The installer verifies the published checksum before placing mnema in ~/.local/bin by default.
curl -fsSL https://mnema.zimac.ai/install.sh | sh
The universal DMG includes the native Pensieve window and the same local memory server.
Standalone Mnema keeps a separate ~/.mnema store. Zimac embeds the same formally specified, machine-checked engine inside a native chief-of-staff app with specialists, integrations, dashboards, staffing, and team workflows.
Install the headless server for agents and scripts, or download the Mac app for the Pensieve. Local mode is free to use and needs no account.
curl -fsSL https://mnema.zimac.ai/install.sh | sh
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