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A bi-temporal, provenance-carrying memory primitive for AI agents. One SQLite file. No LLM or network calls in the storage layer. Exposed to any agent via MCP.

Most agent memory is a bag of strings. memharness stores facts, and combines three semantics that incumbents tend to split apart:

Bi-temporal: every fact records when it became true in the world (valid_from/valid_to) separately from when the agent learned it (tx_at). So you can ask: "what did you believe on March 1st?"

Supersession, never deletion: corrections close the old fact and link it to its successor. "What did you think before I corrected you?" has an answer.

Provenance per fact: every memory cites who said it, where, and when. "Why do you believe that?" has an answer. So does "forget everything from that session."

The storage layer is deterministic: no LLM, no network, no background daemon. It's plain SQLite, so you can open the file with any client.

Run it yourself: cd examples && npm install && npm run demo

When to use this (and when not to)

memharness is not a magic accuracy upgrade, and it is honest about that. If your agent's memory is small and static and comfortably fits the context window, a CLAUDE.md file (or just stuffing the history into the prompt) is simpler, and on short histories full context will match or beat any external memory system.

Reach for memharness when:

History outgrows the window: months of facts, many subjects, more than you want to (or can) paste into every prompt.

You need an audit trail: "what did the agent believe when it made this decision?" (as_of), "what changed since Monday?" (diff), "why does it believe this?" (why). These are queries a bag of strings cannot answer.

You need provenance-scoped deletion: "forget everything from that session/file/source" in one call (GDPR-shaped, not a string search).

Beliefs change over time: corrections should supersede, not silently overwrite, so old reasoning stays explainable.

How it compares

Honest, and pointed at the thing memharness actually does differently: it is a deterministic, auditable storage layer rather than an extraction service.

Storage LLM calls to write as_of / diff / why Embeddable / self-host

memharness one SQLite file none yes: bi-temporal + provenance yes, it's a library

mem0 hosted / OSS service yes (extraction pipeline) partial / no partial

Zep / Graphiti hosted graph yes (LLM ingestion) bi-temporal, but LLM-built partial

Letta / MemGPT agent framework + DB yes (agent-managed) no yes

Anthropic memory tool client-side files model edits files no (model picks) yes

plain CLAUDE.md / files text files none no yes

Where the others win, plainly: mem0 and Zep do automatic fact extraction from raw conversation, which memharness deliberately does not (the write path stays model-free; a client or skill decides what is worth remembering). Plain CLAUDE.md needs no install at all. memharness earns its place when you need the temporal and provenance queries the others don't offer.

Packages

Package What it is

@memharness/core TypeScript library: schema, migrations, write path, recall ranking. No model, no network.

@memharness/mcp MCP server (stdio) exposing the seven tools to any MCP client.

@memharness/embed Optional. A local embedding model for hybrid (semantic) recall. Not installed by default.

Quick start (MCP)

The default install is small (SQLite plus the MCP SDK); the embedding model is opt-in, see Hybrid recall.

Claude Code:

claude mcp add memharness -- npx -y @memharness/mcp

Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json) and Cursor (~/.cursor/mcp.json) use the same JSON shape:

{ "mcpServers": { "memharness": { "command": "npx", "args": ["-y", "@memharness/mcp"] } } }

Codex (~/.codex/config.toml) uses TOML, not JSON:

[mcp_servers.memharness] command = "npx" args = ["-y", "@memharness/mcp"]

The database lives at ~/.memharness/memory.db (override with MEMHARNESS_DB; XDG_DATA_HOME is honored on Linux). Nothing else is written unless you turn on the optional debug log.

First run

Add the server with one of the commands above, then restart your client so it picks up the new MCP server.

In a conversation, hand the agent a durable fact, e.g. "remember that I deploy this project with Fly.io." It calls remember.

Later (or in a fresh session) ask "what do you know about how I deploy?" It calls recall and answers from memory. Correct it and it calls revise; the old belief becomes history, queryable with as_of / why / diff.

No API key, no signup, no network. The first remember creates the SQLite file and that's the whole setup. To watch the tools work end to end without an agent, run the demo: cd examples && npm install && npm run demo.

Optional: make recall automatic

By default the agent decides when to call recall. To push relevant memory in at the start of every session instead (more reliable than hoping the model remembers to look), add a Claude Code SessionStart hook that runs the bundled memharness-context tool, whose stdout is injected into context:

{ "hooks": { "SessionStart": [ { "hooks": [ { "type": "command", "command": "npx -y -p @memharness/mcp memharness-context --subject user" } ] } ] } }

It prints a compact dump of the most relevant current beliefs (and exits quietly if there's nothing yet), so the agent starts each session already knowing the durable facts. Pass --subject more than once to inject several entities.

The seven tools

Tool What it does The thesis it tests

remember store an atomic fact with confidence + provenance facts > blobs

recall ranked current beliefs; as_of returns beliefs at a past instant bi-temporal

revise supersede a belief, keep history supersession > deletion

diff what changed since a date (learned/revised/retracted) the audit demo

why provenance + full revision chain for a fact trust / audit

forget tombstone by id or by source (provenance-based deletion) GDPR-shaped

stats counts, subjects, schema version —

Library use

import { Memharness } from "@memharness/core";

const mem = Memharness.open(); // ~/.memharness/memory.db

// Learn something now, then learn it was actually true earlier. const { id } = mem.remember({ subject: "user", fact: "lives in Osaka", sourceRef: "session-2026-06-09", }); mem.revise({ oldFactId: id, newFact: "lives in Tokyo", validFrom: "2026-05-01" });

mem.recall({ query: "lives" }).facts[0].fact; // "lives in Tokyo" (current belief) mem.diff({ since: "2026-06-01" }); // { learned, revised, retracted } mem.why(id); // { fact, ancestors, descendants }

recall returns a RecallResult ({ facts: ScoredFact[]; asOf; truncated; usedFallback }), not a bare string. asOf time-travels: mem.recall({ query: "lives", asOf: "2026-04-15" }) returns what was believed as held on that date. That honors transaction time, so a fact learned today is not visible to a query about the past.

Recall ranking is reciprocal-rank fusion over FTS5 BM25 (plus a vector rank when hybrid recall is enabled), times confidence, times recency decay (90-day half-life, configurable), scored in SQL. An optional maxTokens budget caps output for context windows. A substring fallback catches partial words and typos, in both FTS-only and hybrid modes.

Optional: hybrid recall

By default, recall is FTS5 keyword search plus recency/confidence ranking: no model, fully offline. Hybrid recall adds a semantic leg via a local embedding model (BGE-small, ~130MB, downloaded once from the HuggingFace hub then fully offline: no API key, no per-query network). Enable it in two steps:

Install the optional embedding package alongside the server. With npx:

npx -y -p @memharness/mcp -p @memharness/embed memharness-mcp

(or npm i -g @memharness/embed for a global install).

Set MEMHARNESS_HYBRID=1 in the server's environment.

The server then keeps stored facts embedded automatically: facts you remember become semantically searchable on the next recall, with no separate backfill step. The first hybrid recall prints download progress to stderr while the model loads. If the package isn't installed, the server says so and stays FTS-only; it never fails closed.

At the library level, recall is embedding-provider-agnostic: pass your own query vector to recall({ queryVector }) and attach document vectors with setEmbedding(...), from any model you like.

A worked example

Two sessions, weeks apart. The agent learns a preference, the user later corrects it, and a downstream question asks what the agent believed at the time:

// June 9: the agent learns a deploy target and acts on it. const { id } = mem.remember({ subject: "project:acme", fact: "deploys via Heroku", sourceRef: "session-2026-06-09", });

// June 16: turns out the team moved to Fly back on June 1. mem.revise({ oldFactId: id, newFact: "deploys via Fly.io", validFrom: "2026-06-01", sourceRef: "session-2026-06-16", });

mem.recall({ subject: "project:acme" }).facts[0].fact; // "deploys via Fly.io"

// "Why did the CI config you wrote on June 9 target Heroku?" mem.recall({ subject: "project:acme", asOf: "2026-06-09" }).facts[0].fact; // "deploys via Heroku": what the agent honestly believed that day.

mem.why(id); // the full chain: Heroku, superseded by Fly.io, with sources. mem.diff({ since: "2026-06-15" }); // surfaces the Heroku -> Fly.io revision.

No bag-of-strings memory can answer the as_of question, because it overwrote Heroku the moment it learned Fly.io.

Correctness

The property suite is the heart of the project: for randomized sequences of remember/revise/forget, recall({asOf: T}) must equal the belief set produced by a naive, SQL-free replay of the event log, probed at every event timestamp ±1ms. 10,000 cases run on every push to main.

Benchmarked at 100k facts (10% revision chains, 2% retractions) on a developer laptop (Apple Silicon): overall recall p95 ~1.3ms against a 10ms budget, across four query shapes (two-term keyword, keyword + subject, subject-only, and as_of + keyword). pnpm bench seeds the database and asserts the budget, so the number is reproducible rather than quoted.

One deliberate divergence from the original prototype: retraction stores a timestamp (retracted_at), not a flag, so as_of queries before the retraction still see history, which is what the prototype's docs promised but its SQL didn't deliver.

Development

pnpm install pnpm test # unit + behavior suites (property tests at 200 runs) pnpm test:property # 10k randomized property cases pnpm bench # seed 100k facts, assert recall p95 < 10ms

Schema migrations are forward-only, driven by PRAGMA user_version. Rows are never deleted (forget tombstones), so facts.id doubles as the insert sequence. All timestamps are canonical fixed-width UTC ISO 8601, making lexicographic comparison chronological.

Optional: local usage log

For debugging or measuring your own usage, set MEMHARNESS_DEBUG=1 and the server appends an op-name and timestamp line (never fact content) to a usage.log next to the database. It is off by default, fully local, and never networked.

License

Apache-2.0

About

Bi-temporal agent long-term memory: SQLite-ba

[truncated for AI cost control]