agora-mnemo 1.24.4

The self-correcting memory layer for AI agents: when a fact is corrected, mnemo serves the new value and resists the old one resurfacing. Zero-dependency single file + MCP server — value-ranked recall, per-type decay, consolidation, revert, and tamper-evident erasure.

agora-mnemo

Mnemosyne — the self-correcting memory layer for AI agents.

Correct a fact once and it stays corrected: mnemo serves the new value and refuses to let the old one creep back — deterministically, with no LLM on the write path. Extracted from an autonomous research OS that has run it daily over 10,000 notes.

pip install agora-mnemo → import mnemo · PyPI · Hugging Face · DOI · Homepage · MIT · v1.24.1

If mnemo's saved you some time, a ⭐ would mean a lot — it's how other people find it. Thank you!

Built by Rastislav Drahoš — extracted from Agora, an autonomous research OS that runs it daily.

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Every claim below is checked by a script you can run

python claims_audit.py          # downloads the published wheel from PyPI and audits THAT

It fetches the released artifact, prints its sha256, and runs each claim on it — never on the working tree. The write-path claim is enforced rather than asserted: sockets are disabled for the duration, so a write that reached for a model would fail the check instead of passing it quietly. Claims about other systems are listed separately and marked untestable here; verifying those means running those systems, so they are never counted as passing.

auditing : agora_mnemo-1.24.1-py3-none-any.whl
13 passed · 0 FAILED · 0 skipped · 5 not testable here

This exists because the exercise pays for itself: the first time we ran a README sentence against the published wheel, it failed. Erasure did delete the record and scrub the bytes, but plain forget() left no receipt, so the store's own verify_writes() reported the deletion as out-of-band — flagging a legitimate API call as tampering. Fixed in 1.24.0, with a regression probe, and the audit now covers it. The tightened audit then caught a second one: forget_subject() was writing two receipts per record, one of them with the wrong reason (fixed in 1.24.3).

On certification. There is no certification body for an agent-memory library, and anyone claiming otherwise is selling a logo. SOC 2 and ISO 27001 certify organisations running services; no scheme certifies that a Python file deletes what it says it deletes. So instead:

• governance_audit.py attacks the strongest claim here — tell it to forget everything about a subject and it can prove it — across three scenarios and three repeats each: erasure through derived_from lineage, absence from the records, from recall under several phrasings, and from the bytes of every file the store wrote including sidecars; exactly one receipt carrying the caller's stated basis; tamper detection; survival across a reload; unrelated records intact; and an identical end state on every run.
• The audit must be able to fail. GOV_FALSIFY=1 skips the erasure, and CI requires the run to report CLAIM BROKEN. A green falsification control would mean the checks measure nothing, so it is treated as a build failure.
• It runs where we cannot touch it — every push and daily, on Linux, Windows and macOS, against both this source and the wheel published on PyPI. The badge above is the result. If it is red, believe the badge and not this paragraph.

What that does not certify: this store, not your vector index, prompt logs or backups; the receipt proves the act of deletion, never the content; and an operator holding the receipt key can forge receipts, so anchor the chain head externally if your adversary is the operator. Those limits are in the docstrings too, and they are the reason the word "certified" does not appear anywhere else on this page.

Why mnemo — the one thing no other agent memory does

Every mainstream agent-memory library puts an LLM on the write path: it calls a model to extract, summarize, or build a graph every time you store something. mem0 runs LLM fact-extraction on add() by default; Zep/Graphiti runs LLM entity/edge extraction on every add_episode(). That one choice is why their stored state is non-deterministic, costs a model call per write, and can silently drop a fact.

mnemo has no LLM on the write path. Storing a fact is a deterministic, zero-cost operation — and that is what makes three things possible the mainstream libraries don't offer:

What that costs, measured on someone else's benchmark. On the MemOps long-context scenarios (24 scenarios, ~50 sessions each), ingesting one scenario through mem0's default pipeline took 600–730 s of LLM extraction; mnemo's write path made zero model calls. Read the rest before quoting that: on the same run, answer accuracy was statistically indistinguishable — mnemo 0.593, a naive keep-all store 0.592, mem0 0.544, with every bootstrap CI crossing zero. So the honest claim is same answers, no write-time model cost, not better answers. About 2% of mem0's extraction calls failed to parse and those memories are missing from its store, which handicaps it slightly. MemOps is published by MemTensor, who also make a competing system. Harness, pre-registration and the full result: agora/agora_output/lab/memops.

• Corrections that stick. Write a new value for a key and it supersedes the old one; echo_guard blocks a later restatement of the retired value from resurfacing. No config, no model call. Honest scope: the guard engages on keyed or extractor-derived assertions (the shipped extractors derive the key from raw text); a free-text write that nothing keys is stored as an independent record and ranks on its own.
• Revert on command. m.revert(key) rolls a corrected fact back to its predecessor. Of the leading systems we checked — mem0, Zep/Graphiti, Letta, Cognee, Memobase, MemoryScope, LangMem, txtai — none exposes a revert-to-predecessor command (mem0's history() is a read-only log; Graphiti invalidates but never un-invalidates; Letta has no undo).
• Deletes the value, not just the pointer. forget_subject removes the value from mnemo's records (subject
  • its derived_from lineage) and leaves a content-free, tamper-evident signed receipt — so what remains is a proof-of-deletion, not the data. Since 1.24.0 every deletion path leaves that receipt, including plain forget(ids=…, where=…); before that only forget_subject and forget_pii did, so a record removed with forget() was erased correctly but unaccounted-for, and verify_writes() reported it as an out-of-band deletion — the store flagging its own legitimate API call as tampering. Pass request_id= / basis= to forget() to bind the reason into the receipt's committed hash. Most agent-memory libraries instead retain the deleted value by design: mem0 keeps it in its SQLite history table (a full reset() purges it); Graphiti stamps the old edge invalid_at and keeps it. For secure erasure at rest (against raw-disk/backup forensics — which a plaintext store of ANY library, mnemo included, does not give you) use an encrypted store + shred() (NIST SP 800-88 crypto-erasure: destroy the key and every at-rest copy dies).

	LLM on write	corrections stick	revert to predecessor	deleted value retained?
mnemo	no — deterministic	✅ supersession + echo_guard	✅ revert(key)	✅ no — value scrubbed, content-free receipt (+ shred() for at-rest)
mem0	yes (by default)	LLM decides ADD/UPDATE	✗ history is read-only	✗ kept in the history table by design
Zep / Graphiti	yes	temporal invalidation	✗ no un-invalidate	✗ invalidated edge retained
Letta / MemGPT	yes	LLM rewrites the block	✗ no undo	✗

(Every competitor cell was checked against that project's current source/docs — see the integrity benchmark, which also names each system that shares an individual property. Cryptographic deletion receipts do exist in purpose-built provenance systems like Engram and Heartwood; the claim here is scoped to mainstream agent-memory libraries.)

The mechanism underneath — no LLM on the write path — is the part a competitor can't copy without abandoning its extraction design. That is the moat.

And it doesn't cost you recall

Integrity would be hollow if mnemo retrieved worse. It doesn't. On the standard LOCOMO benchmark (full set, n=1536), with the built-in tuned recipe (a semantic embedder + hybrid recall + a soft speaker prefilter), mnemo's retrieval-recall@25 is 0.78 (a supporting turn is retrieved) / 0.65 (all supporting turns) — top-tier, and measured the honest way: LLM-free and reproducible, with no LLM judge to inflate it. Run it: python mnemo/probes/retrieval_recall_locomo.py.

(We deliberately don't headline an LLM-judged end-to-end QA score. Those are judge-dependent and not comparable across harnesses — mem0 reports 66.9% and Zep 71.2% under their own judges — so a cross-system "we win" claim would need running them through this harness, which we haven't done. What we publish is our own reproducible number.)

Every number in this README traces to a runnable probe in mnemo/probes/. Nothing is asserted that you can't reproduce.

Quickstart (2 minutes)

pip install agora-mnemo          # zero required dependencies

from mnemo import Mnemo

m = Mnemo("memory.json")                      # persists to JSON; drop the path for pure in-memory
m.remember("The API rate limit is 1000 req/min", key="api::rate_limit")
m.recall("what is the rate limit")            # -> ["The API rate limit is 1000 req/min"]

# Correction is first-class: writing the same key supersedes the old value — no config, no LLM call.
m.remember("The API rate limit is 5000 req/min", key="api::rate_limit")
m.recall("rate limit")                        # -> ["...5000 req/min"]  (only the current value)
m.revert("api::rate_limit")                   # roll back to the predecessor, on command
m.history("api::rate_limit")                  # full audit trail, oldest to newest

New in 1.11.0: ready-made write-path extractors (regex_extractor, deterministic; make_llm_extractor, opt-in) that can derive a key from text without an explicit one, and a first-class LangChain integration (from mnemo.integrations.langchain import MnemoRetriever — a retriever that never hands a superseded fact back to your chain). pip install "agora-mnemo[langchain]".

Honest scope of regex_extractor (measured 2026-07-20, corrected from an earlier overclaim). It keys clean declarative statements — "My ZIP code is 94107", "Alice's email is …", "The API rate limit is 500 rps". It does not reliably key natural conversational prose: measured on an external dialogue corpus (the MemOps dataset, arXiv 2607.12893) it derived a key for 5.2% of sentences (1,037 of 19,851 across six transcripts), and — the part that matters — it does not hold a stable key across a real correction chain, because "my official title … was Junior Data Analyst" and "so my current title is Data Analyst" yield different keys that never meet. On raw chat transcripts, supersession therefore mostly does not fire and mnemo behaves as a verbatim store. If you control the write, pass key= explicitly — that is the path where corrections-stick, revert and the erasure guarantees actually hold. (This README previously said the extractors exist "so supersession engages over free text"; that was too strong. See CHANGELOG 1.23.1, which also fixes a real data-loss bug found in the same measurement.)

Give your agent this memory in 60 seconds (MCP)

Using Claude Code? One command registers mnemo as your agent's memory (uv fetches it, nothing else to install):

claude mcp add mnemo -e MNEMO_PATH=~/.mnemo_memory.json -- uvx --from "agora-mnemo[mcp]" mnemo-mcp

Claude Desktop / Cursor / any MCP client — add to your MCP config (claude_desktop_config.json, .cursor/mcp.json, …):

{
  "mcpServers": {
    "mnemo": {
      "command": "uvx",
      "args": ["--from", "agora-mnemo[mcp]", "mnemo-mcp"],
      "env": { "MNEMO_PATH": "~/.mnemo_memory.json" }
    }
  }
}

Your agent now has remember / recall / history — and corrections that stick: when a fact is superseded, recall serves the current value, a restated stale value can't resurrect it (echo_guard), and revert / route undo a correction on an unmarked "go back". recall returns compact records by default (drops internal fields; get(id) / neighbors(id) for detail on demand). Eighteen tools total; details below.

Jump to: Correction (measured) · Governance & erasure · Org-wide erasure receipt · Install · MCP server · Shell CLI · Framework integrations · The four operations · Five rules · Provenance & receipts · Threat model

Use

The full API reference — every method, argument and return shape, with runnable examples — lives in docs/API.md. The four operations you actually need are further down this page; everything else is there when you need it.

Framework integrations

Adapters for LangGraph, CrewAI, LangChain, LlamaIndex, AutoGen and the rest, with copy-paste snippets: docs/INTEGRATIONS.md.

Use it as an MCP server (any Claude / Cursor / agent client)

mnemo ships an MCP stdio server so any MCP-compatible agent can use it as long-term memory — remember (with a per-type decay prior), value-ranked recall, consolidate, consolidate_clusters, contradictions, value_by_cohort, forget (verified erasure). Correction is first-class over MCP too: revert / route undo a correction on an unmarked "go back", and the read-path review layer observe / reopened / resolve_reopened (1.9.2–1.9.5) reopens a settled record for steward review on a corroborated contradiction (a lone restatement stays an echo, never an auto-change). The MCP remember exposes key (deterministic supersession) plus object / reaffirm, and the server runs with echo_guard ON by default (0.6.11) so a corrected fact stays corrected even if the old value is re-stated later — the failure mode a plain keyed/add-based store shows on RAMR's ECHO-RESISTANCE (keyed-without-guard 0.00, a real add-based system 0.57, guard 1.00). Set MNEMO_ECHO_GUARD=0 to disable. Install and run the server straight from PyPI (the [mcp] extra pulls the MCP SDK; the core library stays dependency-free):

pip install "agora-mnemo[mcp]"     # the library + the MCP server SDK
mnemo-mcp                          # speaks MCP over stdio

Register it with any MCP client — Claude Code (.mcp.json), Claude Desktop (claude_desktop_config.json), Cursor, Windsurf, Codex, Gemini. Zero-setup with uvx (installs on first run):

{
  "mcpServers": {
    "mnemo": {
      "command": "uvx",
      "args": ["--from", "agora-mnemo[mcp]", "mnemo-mcp"],
      "env": { "MNEMO_PATH": "./mnemo_memory.json" }
    }
  }
}

Or, after pip install "agora-mnemo[mcp]", with the console script directly:

{
  "mcpServers": {
    "mnemo": {
      "command": "mnemo-mcp",
      "env": { "MNEMO_PATH": "./mnemo_memory.json" }
    }
  }
}

For semantic recall, point it at any OpenAI-compatible embeddings endpoint via MNEMO_EMBED_URL / MNEMO_EMBED_MODEL / MNEMO_EMBED_KEY; with none set it uses the lexical fallback. The agent then calls recall(query) before reasoning and remember(fact) as it learns — its memory is value-ranked and append-only, not a recency buffer. If MNEMO_EMBED_MODEL contains nomic (nomic-embed-text is asymmetric — see its model card; like E5's passage:/query:), mnemo auto-applies its required task prefixes — search_document: for stored text, search_query: for the query (opt out with MNEMO_NOMIC_PREFIX=0). Omitting them was simply using the model wrong; with prefixes on, our own reinforcement-controlled re-measure lands recall_any@1 at 0.397 on one LoCoMo config (n=1536, deterministic retrieval-recall — an upper bound, not end-to-end QA; a self-comparison, not a cross-system claim; the earlier 0.19→0.29 delta was contaminated by a since-fixed recall-reinforcement confound — see the 1.15.0 CHANGELOG correction). In the library, pass a separate Mnemo(embed=…, embed_query=…) for any asymmetric embedder. If you use persist_vectors=True, also pass Mnemo(embed_id="…") (a recipe fingerprint): when it changes, mnemo re-embeds the persisted vectors once so a new-space query can't silently mis-match old vectors.

Compact recall + progressive disclosure (1.14.0). Over MCP, recall returns a compact projection — {id, text, score, value, tags} — dropping internal bookkeeping fields the model doesn't reason over, and k is hard-capped (MNEMO_MAX_K, default 50), so a recall drops cheaply into the prompt. Full text is kept by default; snippet truncation is opt-in (snippet_chars>0) — off by default on purpose, since truncating a hit could cut off a corrected value past the boundary and defeat the echo-guard. Pull detail on demand: get(id) returns one full record, neighbors(id, k) a bounded local expansion (excludes self). recall(full=True) returns complete records. token_report(query, k) is a deterministic, no-LLM (~chars/4) payload-size estimate comparing the compact projection to the full records for the same k hits — an apples-to-apples sizing aid, not a whole-store comparison and not a measured token saving. None of this is novel — it's standard MCP/RAG context-economy practice (progressive disclosure / small-to-big retrieval); mnemo never emitted embedding vectors.

The four operations

op	what it does
remember(text, tags, value, mtype, key)	append-only raw capture, absolute UTC time, never edited; mtype ∈ {episodic, semantic, procedural} sets the decay prior (events fade fast, durable facts slow, rules barely). Optional key = a deterministic (subject, relation) supersession key: a new value retires every active record with the same key — no similarity threshold, no LLM — so recall never serves the stale value (bi-temporal: a back-filled earlier value can't overwrite the current one)
recall(query, k, where=…)	value-ranked retrieval: relevance × value, decayed by the memory's per-type half-life (access resets the clock), so important durable memories beat both merely-similar and stale ones. Optional where = a metadata pre-filter (the cheap filter-before-you-rank lever): field → scalar / list / operator ($gte $lte $gt $lt $in $nin $ne $contains), matched top-level then meta, ALL fields AND-ed — e.g. a hard time-range where={"valid_from":{"$gte":t0,"$lte":t1}} or a closed-set entity where={"speaker":{"$in":[…]}}. Measured to beat retriever choice on LoCoMo (probes/locomo_metadata_prefilter.py); it's a HARD filter, so on lossy/predicted extraction keep it loose (a wrong filter hard-deletes the answer). Reinforcement is relevance-weighted (a bullseye hit reinforces value more than one that squeaked into top-k, so a weak-but-frequent false positive can't go immortal); a repeatedly-recalled episodic memory graduates to semantic only when corroborated — by an earned outcome, or by ≥2 distinct canonical sources (entity-resolved before counting, so sybil variants of one origin — Wikipedia / wikipedia.org / a full URL — collapse to one and can't mint durability); and a memory whose source was later contradicted is provenance-demoted + flagged stale_derived
consolidate(keep)	the dream pass: flag universal-matcher hubs, link near-duplicates, apply the state-toggle guard (a polarity clash supersedes, doesn't merge), supersede the low-value surplus — only adds a derived layer
consolidate_clusters(threshold)	cluster-triggered consolidation: consolidate a semantic cluster only once it's grown past threshold — sparse topics keep their raw episodes, dense ones don't grow unbounded
contradictions()	flag mutually-incompatible related memories (similarity-gated) for human review
forget(ids, where)	the one op that truly deletes (the rest is append-only): hard-removes the matched records and scrubs their ids from every survivor's links + toggle pointers + the vec/token caches, so a forgotten memory can't resurface via recall, a consolidation link, or the dream pass. For erasure / right-to-be-forgotten, poison removal, or a hard correction — measured 15/15 on a verified-forgetting severe-test

Five rules it won't break (each one cost us to learn)

1. Raw capture is immutable. Consolidation adds links and markers; it never overwrites the source. This is what stops the slow accuracy drift of LLM-rewritten memory.
2. Absolute timestamps at write time. Relative/derived times rot the moment they're consolidated.
3. Value-ranked, type-aware decay. Retention is value × a per-type half-life, not recency or access-frequency alone. A uniform access-reset clock keeps merely-popular memories while a load-bearing-but-cold fact — queried once a month, prevents a destructive action — starves; we measured exactly that failure. The fix is that the half-life is set by kind, not by read count: episodic events fade in days, semantic facts in months, procedural rules barely at all. A cold-but-critical fact survives by being typed semantic/procedural (long half-life × its high value), not by frequent reads; access only resets the clock within a type's window.
4. Value is reported at the cohort level (tag / time-block), never per-memory.
5. Contradictions are flagged, never auto-resolved. Silent rewrites destroy trust in the whole memory.

Provenance — why these rules, with receipts

Why these rules — the measured receipts behind mnemo's design. Click to expand.

mnemo's design isn't taste; it's what Agora's lab measured:

• Semantic recall beats keyword recall, and the gap widens with scale — as the store grows to a corpus of several thousand notes, lexical recall@5 decays from 0.94 (small store) to 0.25, while semantic holds at ~0.65 — ≈2.6× at full scale (Agora Lab b4c260); on paraphrase queries semantic recall@5 is 0.86 vs 0.20 lexical (3501f1). The embedder is the real lever at scale; the lexical overlap match is the zero-dependency floor that still runs anywhere on a small store. (Honest footnote: pruning universal-matcher hub notes lifts lexical recall ~20% only when a store is link-spammed, and does not move semantic recall — it's a lexical/hybrid optimisation, not a headline.)
• Value-ranked consolidation — under a keep-budget, ranking what to keep by value beats FIFO/random, and the advantage scales super-linearly as the budget shrinks (≈1.8× at half budget → ≈4× at one-eighth), surviving heavy estimation noise.
• Retention must blend value with recency, not decay on access alone — we simulated a half-life-with-access-reset policy (a popularity signal) against a value-aware blend under a shrinking budget, with value made deliberately anti-correlated with access-frequency for a load-bearing-but-cold subset. At a 30% keep-budget the access-decay policy retained only 2.8% of the high-value/low-frequency memories and 20% of total value, vs 100% and 64% for the blend — about 3× more value kept (the gap persists, ≈2.2× retained value, even at a 7% budget). Pure access-frequency decay starves the rarely-queried-but-critical memories; forgetting must consume an explicit value channel separate from access recency. (Agora Lab 19d802.)
• Supersession needs a deterministic key, not embedding similarity — replicating an external result (MemStrata / Yadav, arXiv 2606.26511) on our own local nomic stack: a cosine-similarity classifier separating a contradicted fact from a rephrased duplicate scores AUROC ~0.61 (near chance) — a contradiction is often more embedding-similar to the original than a true rephrase is. A similarity-based store therefore serves the stale value ~42% of the time; the deterministic (subject, relation, object) supersession key (remember(..., key=...)) drives that to 0% (Agora Lab exp_supersession_replication, severe-test 8/8). This is why supersession is a key, not a threshold.
• No single recall mechanism survives all operating points — only the layered store does — head-to-head on a synthetic evolving + contaminated stream (stable / superseded / poisoned facts, local nomic): a naive cosine top-1 store scores 42% (fine on stable, but blind to supersession — 0/8 on updated facts — and fooled by repeated lies); a recency store 67% (fixes supersession but serves the freshest lie — 0/8 on poison); mnemo — deterministic supersession key + corroboration gate + value-ranking — is 100%, robust across all three. Each single mechanism wins one regime and loses another (the memory operating-point trap), which is why the durable layer needs all three together (probe mnemo/probes/operating_point_memory.py).
• Cohort-level value — per-memory outcome attribution is statistically underpowered at n-of-1 (the best proxy reached only ~0.36 power at realistic sample sizes); the cohort is where the signal lives. Hence rule 4.
• Contradiction detection runs in production over the 10,000-note vault; the lesson that it must flag, not auto-edit (rule 5) is why silent rewrites are forbidden.

(Methods + numbers live in the Agora track record: https://dancenitra.github.io/agora/.)

Threat model & layered defense (adversarial memory integrity)

The full adversarial threat model + layered defenses. Click to expand.

An untrusted-ingestion memory store cannot decide whether a written claim is true. mnemo doesn't try to; it makes the attacker pay, and the honest map of what each layer buys — worked to bedrock across a public practitioner thread with adversarial review — is below. Every claim here has a runnable receipt in mnemo/probes/; this is textbook mechanism with a receipt, not a new theory.

A defense the attacker can also write is a suggestion, not a defense. Content-declared provenance is theater: Source: X and corroborated by N are strings a writer controls, so default (distinct source strings) corroboration falls to a sybil that mints two labels (~0.9 attack-success across 10 models — memory_defense_layer_probe). Only channels the writer does not control hold — distinct verified keys (strict_corroboration, Ed25519 attest) whose issuance is itself costly/rate-limited (a free-to-mint key is just another string a sybil spends), an earned Beta(good,bad) outcome credit a session can't self-grant, and system write-history. That is Biba integrity (1977) / Cheng-Friedman (2005): no symmetric reputation is sybilproof; the escape is an exogenous, un-writable anchor.

The layers, and the exact residual each leaves:

• Provenance — did the call happen? Bind standing to a runtime signature over the real (tool, result), not the session's log (execution_receipt_gate). Closes fabricated logs iff the executor is a trust domain separate from the session; in-process tools → the key is session-readable → theater.
• Veracity — is a real call's output true? No write-time oracle (a novel fact has no priors; attestation = authorship, not truth). Standing is earned at use time from an observed outcome (veracity_outcome_grounding) — only helps where harm is observable.
• The irreversible tail. A provenance-scaled spend_irreversible budget makes a low-provenance memory bind an honest action's irreversible spend against itself (soft_influence_taint); a reversibility checkpoint bounds a one-shot defector. Bounds, doesn't close (Sybil relocates it per identity).
• The sub-threshold coordinated campaign. A temporal change-detector is evaded for free at bias h→0, but B≈N·h forces a growing correlated low-provenance population a cross-sectional detector catches (two_detector_trap); and randomized retrieval raises a targeted attacker's coverage from O(1) to ~Θ(fN) with no anchor (moving_target_retrieval).

The bedrock. Checked from ~8 directions (detection, reputation, elicitation-without-ground-truth à la Bayesian Truth Serum, robust aggregation, moving-target), every layer we examined reduces to one of two exogenous anchors — a bounded corruption count (Byzantine; sybils break it) or a shared attacker-independent prior (peer-prediction; a coalition coordinating its reports breaks it). (A synthesis over those cases, not a proof.) You cannot separate a large coordinated coalition from genuine consensus from internal signals alone (Cheng-Friedman + Lamport-Shostak-Pease 1982; and no internal truth-oracle, by analogy to Tarski's undefinability). What that leaves is not "give up" but a shape: localize the one exogenous check at the rare high-consequence irreversible step (a human, a separately-provenanced feed — a channel the poison can't reach), and don't let evidence-free consensus drive an irreversible action (weight it ~0; on an observable target, require an independent evidentiary provenance, which is super-linear to forge, not N reputations). The residual is the integrity of that one minimal anchor — a standard, bounded problem, not the intractable verify-all-memory one.

Prior art credited throughout: Biba 1977 · Douceur 2002 · Cheng-Friedman 2005 · Friedman-Resnick 2001 · Lamport-Shostak-Pease 1982 · Lorden 1971 / Moustakides 1986 (CUSUM delay floor) · Tarski (undefinability of truth, used by analogy) · Doyle 1979 (truth-maintenance) · Garcia-Molina & Salem 1987 (Sagas) · Prelec 2004 (Bayesian Truth Serum) · Blanchard 2017 / Yin 2018 (Byzantine-robust aggregation) · PoisonedRAG (Zou 2024) · MINJA (Dong, arXiv:2503.03704) · AgentPoison (Chen, arXiv:2407.12784) · the shilling / Sybil-detection line (Mobasher-Burke 2007, Mehta-Nejdl 2009, SybilRank/Cao 2012, Viswanath 2010).

The second_brain thinking layer

An optional layer on top of the store — dialectic, contradiction surfacing, question generation: docs/SECOND_BRAIN.md.

Status

v0.2 — the core, honest and runnable, now with two MCP servers (mnemo_mcp for memory, second_brain_mcp for the thinking layer over your notes) and a deterministic supersession key (remember(..., key=...)) that closes the embedding supersession blind spot. Roadmap: pluggable vector stores, a hosted tier. Open-core; the core stays free.

MIT-licensed · part of Agora.

Self-maintaining (maintain.py)

The #1 second-brain frustration is maintenance, not capture. maintain.py runs the chore people stop doing — over a folder of Markdown notes it finds dead [[wikilinks]], orphan notes, stale notes, near-duplicate clusters, and a vault health score (self_legibility = % of notes in the link graph's giant component — knowledge debt is a percolation collapse, so it warns before the cliff). Crucially it turns findings into actions: for each orphan it suggests which existing note to link it to (re-connecting it to the graph), and flags archive candidates (old + isolated). It resolves links by filename or frontmatter alias, and dates notes by frontmatter (not git-reset mtime) — both learned from dogfooding it on a real ~7,700-note vault (it rescued ~300 falsely-flagged orphans). Advisory + safe: it returns a plan and an action list; it never edits, moves, or deletes a note. And it can apply the fix when you ask: apply_suggestions appends a marked ## Related (auto-suggested) block of [[links]] to each orphan — additive only, idempotent (re-running replaces its own block), dry-run by default. python maintain.py runs a verified round-trip on a synthetic vault (diagnose → suggest → apply); maintenance_report and apply_links in second_brain_mcp.py expose it to any MCP agent.

mcp-name: io.github.DanceNitra/mnemo