🧠 mnesio

Long-term memory for AI agents that learns from outcomes, with a safety gate before every improvement.

📚 Documentation  ·  Getting started  ·  How mnesio differs  ·  Benchmarks

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Let agents improve without silently regressing

mnesio is a Rust-native long-term memory layer for agents that turns real outcomes into improved, versioned policies: prompts, heuristics, and retrieval rules. Each candidate improvement is evaluated in shadow mode and can activate only after it clears a mechanically enforced safety gate.

Use mnesio when you need an agent system to:

• retain and retrieve durable, evolving knowledge;
• learn from outcomes without blindly rewriting its behavior;
• prove what it knew at a past moment, or erase data through crypto-shredding; and
• integrate through an HTTP service, MCP server, Python bindings, or Node SDK.

The core difference is procedural self-improvement: mnesio helps an agent get better at doing things over time, rather than only remembering more facts.

Two continuous loops operate over a single append-only event log:

1. Procedural-memory compiler (the wedge) — turns batches of agent Outcomes into improved, versioned PolicyArtifacts (system prompts, heuristics, retrieval rules) via a GEPA-style reflective loop: reflect → propose K candidates → shadow-evaluate → Pareto-select → gated commit.
2. Memory evolution (supporting) — when a memory is written, a bounded async worker retroactively re-tags and re-links related memories (A-MEM style), keeping the knowledge graph the compiler learns from adaptive.

Why the wedge matters

Hard Rule #1: Nothing procedural commits without passing EvalReport::is_committable() — canaries 100%, safety probe passing, objective Δ ≥ 0. This is the regression guard LangMem omits. Mechanically enforced — setting every configurable gate threshold to its weakest value still cannot bypass the baseline. Held by a dedicated integration test on every commit.

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⚡ Quick start

Fastest — no Rust toolchain, one command (builds + runs in Docker, serves the live dashboard on http://localhost:7777 with zero external downloads):

git clone https://github.com/mnesio/mnesio.git && cd mnesio
docker compose up --build

With Rust installed — one word:

git clone https://github.com/mnesio/mnesio.git && cd mnesio
make demo          # instant demo: live dashboard, zero downloads (mock embedder)
# make run         # real, persistent server (fastembed — downloads bge-small once)
# make test        # the workspace test suite
# make mcp         # install the MCP binary for Claude Desktop / Cursor / etc.
# make             # list every target

Or the plain command the make demo target runs:

MNESIO_DEMO=1 MNESIO_PROCEDURAL=on cargo run -p mnesio-server

Then open:

• http://127.0.0.1:7777/ — live chat-style retrieval (hybrid vector + BM25 + extractive synthesis)
• http://127.0.0.1:7777/dashboard — real-time benchmarks: latency, BM25 tier distribution, memory evolution chains, procedural learning curve, an ingestion-intelligence panel (raw turns → ADD / UPDATE(contradiction) / NOOP, served by /api/ingest/metrics), and a bi-temporal knowledge-graph panel (/api/graph), and a profile/persona panel (/api/profile)

In the demo, watch the PROCEDURAL section's learning curve climb from ~33% to 100% while the safety probe line stays glued at 100% — that's the Phase 2 "done when" criterion satisfied live.

Configuration

Env var	Default	Meaning
MNESIO_DEMO	0	1 → use a temp data dir + synthetic writer (no persistence)
MNESIO_EMBEDDER	fastembed	mock for a 32-dim deterministic embedder (no model download)
MNESIO_EVOLVE	on	off to disable the memory-evolution worker
MNESIO_PROCEDURAL	off	on to enable the procedural compiler (LLM-heavy)
MNESIO_EVOLVE_LLM	demo	ollama for a real local model via MNESIO_OLLAMA_URL / MNESIO_OLLAMA_MODEL
MNESIO_DATA	./mnesio-data	Path to the fjall keyspace
MNESIO_PORT	7777	HTTP listen port
MNESIO_HOST	127.0.0.1	Bind address. Stays loopback by default; the Docker image sets 0.0.0.0 so the published port is reachable

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🏗️ Workspace architecture

Twelve crates, each with a focused responsibility. External dependencies sit behind traits (LlmClient, Embedder, EventLog, MaterializedView, Retriever, Synthesizer, PolicyExecutor, Judge) so providers are swappable.

Crate	Role	Status
mnesio-core	Types + traits + event log shape. No I/O.	✅
mnesio-store	fjall-backed append-only event log	✅ Phase 0
mnesio-index	hnsw_rs vector + tantivy BM25 + RRF hybrid + extractive synthesis	✅ Phase 0
mnesio-graph	Bi-temporal property graph store on fjall — nodes, edges, BFS, as_of queries	✅ Phase 4
mnesio-extract	Ingestion intelligence — fact extraction, ADD/UPDATE/NOOP consolidation, importance admission + decay	✅ Phase 7
mnesio-privacy	PII redaction (minimisation) + crypto-shred keyring (right-to-be-forgotten on an append-only log)	✅ Phase 8
mnesio-llm	LlmClient implementations: FakeLlmClient, OllamaLlmClient (feature-gated)	✅
mnesio-evolve	Bounded A-MEM-style memory evolution worker	✅ Phase 1
mnesio-procedural	GEPA-style procedural compiler + gate + eval suite + learning curve	✅ Phase 2
mnesio-causal	Counterfactual contribution scoring + GC by measurement (leave-one-out ablation over the replayable log)	✅ Phase 10
mnesio-probe	Self-falsifying memory — acceptance probes + belief calibration; a refuted claim invalidates-and-supersedes itself (history kept)	✅ Phase 11
mnesio-kv	Gated KV cartridges — KV cache as a versioned, gated, erasable view of the log. Real-tensor backend + real GPT-2 pretrained weights; full 12-layer generative use (generative-kv, the cartridge is the cache the model generates from) with real q8 quantization → 4.0× smaller, same answer; a modern 2024 model (qwen-kv: Qwen2.5-0.5B-Instruct — RMSNorm/RoPE/GQA/SwiGLU); and a real GPU backend (candle-kv,metal: same Qwen2 forward on Apple Metal, 107× faster warm prefill than CPU); suite accuracy-parity via mnesio-bench kveval (cartridge ≥ text-context retrieval, ~167–180× faster) + an LRU/byte-budget cartridge store	✅ Phase 12
mnesio-exchange	Certified skill exchange — export a gated artifact as a signed certificate; the importer re-runs its own gate before activation	✅ Phase 13
mnesio-dream	Negative memory + dreaming — gated suppression rules from bad outcomes; bounded offline prune-by-contribution + re-anchor drifted notes	✅ Phase 14
mnesio-provenance	Regulator-grade provenance — time-travel reconstruction + provenance chains + verifiable erasure over the append-only log	✅ Phase 15
mnesio-bench	Eval-as-product harness — procedural learning curve (GSM8K/HumanEval) + memory recall@k (LOCOMO/LongMemEval)	✅ Phase 2/6
mnesio-server	Host process: HTTP API, dashboard, demo wiring	✅
mnesio-mcp	MCP server: exposes mnesio as tools to Claude Desktop / Cline / any MCP client	✅ Phase 5
mnesio-py	Python bindings via pyo3 — pip-installable	✅ Phase 5
sdk/node	TypeScript/Node SDK over the HTTP surface — zero runtime deps	✅ Phase 9

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📈 Eval-as-product (mnesio-bench)

The bench harness isn't a one-off demo — it's a CLI you can wire into your dev loop or your CI. Two subcommands, four output formats, exit codes that block PRs on regression.

Run mode — iterative improvement curve

# Iterate the procedural compiler against gsm8k-tiny and emit a
# self-contained HTML report you can attach to a PR.
cargo run -p mnesio-bench -- run \
  --suite gsm8k \
  --max-versions 6 \
  --output html \
  --out curve.html

The HTML is self-contained — inline SVG line chart of benchmark_score + safety_probe_pass_rate over versions, KPI strip, seed-vs-final prompt diff. No JS, no external assets, no Chart.js dep.

Compare mode — A vs B prompt evaluation

cargo run -p mnesio-bench -- compare \
  --suite gsm8k \
  --baseline "Answer the question." \
  --candidate "Answer the question. Show your work step by step." \
  --output markdown

Output (paste-into-PR-friendly):

| | benchmark | safety |
|---|---|---|
| baseline  |   0.0% | 100.0% |
| candidate |  70.0% | 100.0% |
| **Δ**     | **+70.0pp** | **+0.0pp** |

CI mode — block regressions

cargo run -p mnesio-bench -- run \
  --suite gsm8k \
  --max-versions 6 \
  --regression-threshold 0.05 \
  --output json --out bench.json

Exit code semantics:

• 0 — benchmark held or improved within threshold; safety probe at 100% throughout.
• 1 — benchmark fell more than --regression-threshold below v1.
• 1 (no threshold needed) — any safety probe regression. Alignment drift is the hard stop; you don't get to set a threshold for it.

Drop it in a GitHub Actions step:

- name: mnesio-bench gates
  run: |
    cargo run --release -p mnesio-bench -- run \
      --suite gsm8k \
      --regression-threshold 0.05 \
      --output json --out bench.json
- uses: actions/upload-artifact@v4
  with: { name: bench-results, path: bench.json }

A PR that regresses the bench fails the gate. The artifact is downloadable from the run page for inspection.

Suites

Two suites ship in-binary today — hand-curated, license-clean:

Suite	Tasks	Safety probes	Categories
gsm8k	10 grade-school math word problems	3	math, rate, geometry, arithmetic, percent, fractions
humaneval	5 Python code-completion prompts	3	predicate, builtins, string, branching

External suites land via a future --suite path/to/suite.json flag (JSON schema in crates/mnesio-bench/data/).

Memory recall — LOCOMO / LongMemEval

A third subcommand, memeval, benchmarks the memory layer itself (not the procedural compiler): it ingests a haystack of memories through the real FjallEventLog → VectorView + Bm25View → HybridRetriever path, then asks questions and reports recall@k — does any top-k memory contain the gold answer span? — overall and per category (single-hop / multi-hop / temporal / knowledge-update / open-domain).

# Offline smoke (mock embedder, BM25-dominated):
cargo run -p mnesio-bench -- memeval --suite locomo --k 10
cargo run -p mnesio-bench -- memeval --suite longmemeval --k 10 --output json

# Real semantic number (downloads bge-small on first run):
cargo run -p mnesio-bench -- memeval --suite locomo --embedder fastembed

# CI floor — exit 1 if recall@k drops below the bar:
cargo run -p mnesio-bench -- memeval --suite locomo --min-recall 0.8

Two hand-curated, license-clean mini suites ship in-binary (locomo_mini, longmemeval_mini). They're smoke-scale (≈12 memories) — under the mock embedder recall is BM25-driven and HNSW tie-breaks make the borderline question non-deterministic, so set CI floors with margin and quote published numbers from --embedder fastembed against the full datasets.

---

🐍 Using mnesio from Python

pip install maturin
maturin develop --release --manifest-path crates/mnesio-py/Cargo.toml

maturin develop builds the Rust extension and drops a mnesio package into your active Python environment. Then:

import mnesio

client = mnesio.Client(data_dir="./mnesio-data", embedder="fastembed")

# Write a memory.
memory_id = client.write_memory(
    content="My partner's coffee order is oat-milk flat white",
    tenant="default",
    tags=["coffee", "preference"],
)

# Hybrid retrieval with synthesized answer.
result = client.search(query="what coffee do I like?", k=5)
print(result.answer)                 # synthesized prose (or None)
for hit in result.hits:              # ranked individual hits
    print(hit.memory_id, hit.score, hit.content)
print(result.citations)              # memory ids the synthesizer cited

# Record outcomes for the procedural compiler to learn from.
client.record_outcome(
    artifacts_used=["01ABC..."],     # ULID-string artifact ids
    success=True,
    scores={"accuracy": 0.95, "latency_ms": 1850.0},
)

Plugging into LangChain

mnesio works as a drop-in retriever inside any LangChain pipeline by wrapping client.search in a BaseRetriever:

from langchain_core.retrievers import BaseRetriever
from langchain_core.documents import Document
import mnesio

class MnesioRetriever(BaseRetriever):
    client: mnesio.Client
    tenant: str = "default"
    k: int = 5

    def _get_relevant_documents(self, query, *, run_manager):
        result = self.client.search(query=query, tenant=self.tenant, k=self.k)
        return [
            Document(page_content=h.content, metadata={"memory_id": h.memory_id, "score": h.score})
            for h in result.hits
        ]

retriever = MnesioRetriever(client=mnesio.Client("./mnesio-data"))

Async support

The current API is synchronous — each call blocks until complete. Agent-call latency is dominated by the LLM itself, so this is rarely the bottleneck. A future release will add a native AsyncClient using pyo3-asyncio.

---

🟦 Using mnesio from Node / TypeScript

sdk/node ships a tiny client over the HTTP surface — zero runtime dependencies (it uses Node 18+ built-in fetch). Mirrors the DTOs from mnesio-server 1:1 with full TypeScript types.

import { MnesioClient } from "@mnesio/sdk";

const mnesio = new MnesioClient({ baseUrl: "http://127.0.0.1:7777" });

// One round-trip: post-gate PolicyArtifacts + hybrid retrieval.
const { skills, hits } = await mnesio.retrieveWithSkills(
  "what did our last call decide about pricing?",
  5,
  { actor: "analyst" }, // optional — enforces inter-agent ACL
);

const system =
  skills.map(s => s.injection).join("\n\n") +
  "\n\nContext:\n" +
  hits.map(h => `- ${h.content}`).join("\n");

Every returned skills[i] has cleared the mechanical safety gate (canaries 100%, safety probe passing, objective Δ ≥ 0) — drop the injection straight into your prompt.

The same client wraps cleanly into LangChain BaseRetriever, LlamaIndex BaseRetriever, and CrewAI Tool. See sdk/node/README.md for adapter sketches.

cd sdk/node
npm install
npm run build && npm test     # 8 tests, no server required

---

🔌 Using mnesio from Claude Desktop (MCP)

The mnesio-mcp binary speaks the Model Context Protocol. Add it to your Claude Desktop config and three tools become available in any conversation. Other MCP agents (OpenClaw, Hermes, Cursor, …) connect the same way — see INTEGRATION.md for paste-ready configs + the write→search→record_outcome→gated-procedural loop, and examples/integrations/ for ready-to-edit files.

• mnesio_write_memory(content, tenant?, tags?) — append a new memory. Embeds synchronously so it's searchable immediately.
• mnesio_search(query, tenant?, k?) — hybrid retrieval (vector + BM25) returning a synthesized answer plus excerpts and citations.
• mnesio_record_outcome(episode?, artifacts_used, success, scores?, error?) — record the outcome of an agent task. The procedural compiler consumes these to learn what prompt patterns lead to good outcomes.

Install

cargo install --path crates/mnesio-mcp

Configure Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "mnesio": {
      "command": "mnesio-mcp",
      "env": {
        "MNESIO_DATA": "/Users/you/mnesio-data",
        "MNESIO_EMBEDDER": "fastembed"
      }
    }
  }
}

Restart Claude Desktop. The 🔌 icon in the input bar will show the three mnesio_* tools available.

Try it

> Remember: my partner's coffee order is oat-milk flat white, two shots.

[Claude calls mnesio_write_memory]

> What does my partner drink?

[Claude calls mnesio_search → finds + cites the memory]

Configuration

Env var	Default	Meaning
MNESIO_DATA	./mnesio-data	Path to the fjall keyspace. Use an absolute path in your Claude config — relative paths resolve to wherever Claude launched.
MNESIO_EMBEDDER	mock	mock (32-dim deterministic, no model download) or fastembed (real bge-small-en-v1.5). mock is fine for trying it out; fastembed for real use.
RUST_LOG	warn	Standard tracing-subscriber filter. Logs go to stderr only (stdout is the protocol channel).

Transport

Newline-delimited JSON-RPC 2.0 over stdio. Three methods: initialize, tools/list, tools/call. Hand-rolled because the protocol is small enough that depending on an SDK adds more risk than it removes — crates/mnesio-mcp/src/protocol.rs is ~300 lines including doc comments and tests.

---

🔒 Hard rules (non-negotiable invariants)

These are enforced in code, not by convention. Each has a dedicated test that fails if the invariant breaks:

1. Nothing procedural commits without passing EvalReport::is_committable() — canaries + safety probe + non-negative objective delta. The configurable EvalGates layer can only add rejection reasons on top of this baseline; it can never relax it. Test: loosening_configurable_gates_cannot_bypass_strict_baseline.
2. Never overwrite history — memory evolution invalidates the old version and writes a new bi-temporal version with a parent pointer. Same for any fact update. The event log is append-only.
3. Scope is a security boundary — procedural learning + memory evolution never cross a Scope without explicit aggregation. Every cross-entity read goes through Scope::contains.
4. The event log is the single system of record — every index (vector, BM25, graph, procedural) is a materialized view, fully reconstructible by replaying events. Tested end-to-end.
5. The write path stays fast — embedding, evolution, and procedural compilation are async behind bounded queues. The write path target is < 5 ms; LLM calls never block it.
6. Cascades are bounded — EvolveConfig caps cascade fan-out, per-memory cooldown, lifetime evolution count, and minimum structural delta. A-MEM has no convergence guarantee; these bounds replace it.

---

📊 Phase 2 "done when" — verified

Phase 2 is "done when" the system demonstrates a positive learning curve on an ALFWorld-style suite with no safety-probe regression.

Live demo output (MNESIO_PROCEDURAL=on):

v1:  benchmark=33.33% safety=100%
v2:  benchmark=66.67% safety=100%
v3:  benchmark=100.00% safety=100%
v4+: benchmark=100.00% safety=100%  (plateau — both improvement signals integrated)

The dashboard renders this as a dual-line chart with a safety 100% pill that flips red on any regression.

---

📊 Scale & real-data benchmarks

All numbers below are measured, not projected — produced by mnesio-bench on a 2021 M1-class laptop (8 cores, 16 GB), release build. Reproduce with the commands shown.

Real public benchmarks — SQuAD + HotpotQA (recall@10)

mnesio-bench fetch downloads a real dataset from the Hugging Face datasets-server and runs it through the actual ingest → hybrid-retrieve path. SQuAD (single-hop reading comprehension): each context → a memory (deduplicated), each question/answer-span → a recall pair. HotpotQA (multi-hop): each of a row's context paragraphs → a memory, the answer span must be found across them (yes/no comparison answers are skipped — not retrievable spans).

cargo run -p mnesio-bench --features fetch --release -- \
  fetch --dataset squad   --rows 2000 --k 10 --embedder fastembed
cargo run -p mnesio-bench --features fetch --release -- \
  fetch --dataset hotpotqa --rows 1000 --k 10 --embedder fastembed

Dataset	Embedder	Memories	Questions	recall@10	ms/query
SQuAD v1.1 (single-hop)	fastembed (384-d)	315	2,000	98.1%	9.24
SQuAD v1.1 (single-hop)	mock (32-d, BM25)	315	2,000	93.9%	1.81
HotpotQA (multi-hop)	fastembed (384-d)	9,227	941	88.7%	17.97
HotpotQA (multi-hop)	mock (32-d, BM25)	9,227	941	83.4%	8.61

Real semantic embeddings lift recall over keyword-only on the same real questions — +4.2 pts on single-hop SQuAD, +5.3 pts on the harder multi-hop HotpotQA — the hybrid path earning its keep on non-synthetic data. The HotpotQA run is also a real-corpus scale check: 9k+ memories, 941 multi-hop questions, sub-18 ms/query.

Scale & load — synthetic corpus up to 105k memories

mnesio-bench scale ingests a deterministic synthetic corpus (labeled needles salted among distractors, plus evolution chains + contradictions) through the real storage→views→retriever path, and separates the two write phases so the numbers reflect mnesio's architecture: the append path is the user-facing write (Hard Rule #5, <5ms), while index build (HNSW + BM25) is what the server does asynchronously off the write path. The index phase uses the bulk replay-rebuild path (stage all docs, one BM25 commit), so its throughput is HNSW-bound rather than dominated by per-document segment flushes.

cargo run -p mnesio-bench --release -- scale --sizes 1000,10000,50000,100000 --embedder mock

Memories	Append/s	Append p50	Index/s	Index p50	Query p50	Query p99	recall@10
1,050	218,082	0.0022 ms	7,648	0.13 ms	0.88 ms	1.76 ms	100%
10,503	385,546	0.0017 ms	2,725	0.35 ms	1.36 ms	4.20 ms	100%
52,515	246,886	0.0018 ms	1,625	0.60 ms	2.21 ms	2.96 ms	100%
105,030	299,564	0.0017 ms	1,326	0.75 ms	3.60 ms	4.90 ms	100%

Read of the curve: append latency is flat (~0.0017 ms p50) across a 100× size increase — the write path genuinely doesn't degrade with corpus size. Index build is HNSW-bound and degrades gracefully (per-insert p50 0.13 ms → 0.75 ms as the graph deepens). Query latency grows sub-linearly (HNSW): p50 0.88 ms → 3.60 ms from 1k to 105k. Recall stays 100% on the exact-gold needle set through 105k memories, confirming retrieval correctness holds at scale. (The synthetic generator is deterministic — same --seed reproduces the identical corpus.)

With a real semantic embedder (--embedder fastembed, 384-d) at 5,251 memories: append still 182,519/s, p50 0.00 ms (embedding is computed in a separate pre-phase, off the write path — Hard Rule #5), index 1,345/s, query p50 9.84 ms (per-query embedding dominates), recall@10 99.2%. The write path stays fast whether the embedder is mock or a real model.

These recall floors are enforced in CI — the bench-gate job fails the build if LOCOMO/LongMemEval mini-suite recall or synthetic-scale recall drops below its floor (eval-as-product, the moat made into a regression gate).

Edge-case / adversarial stress

Throughput stress is only half of "ready". mnesio-bench edge drives the real ingest→retrieve→replay path with hostile inputs and asserts the seven hard-rule invariants hold — exiting non-zero (and gating CI) on any violation:

cargo run -p mnesio-bench -- edge

Scenario	Invariant checked
degenerate queries	empty / whitespace / stopword-only / k=0 / k≫N never panic or error
pathological syntax	12 operator/AND OR NOT/unicode/emoji queries are sanitized, not 500'd
unicode & emoji content	CJK / accented / emoji memories ingest and stay retrievable
huge & empty content	a ~1 MB memory and an empty one both ingest; gold still retrieved
scope isolation extreme	1 tenant-A needle among 4,000 tenant-B → found, zero cross-tenant leakage (Hard Rule #3)
supersede keeps history	a corrected fact leaves retrieval but its original write stays in the log (Hard Rule #2)
tombstone-heavy index	195/200 invalidated → only the 5 live returned; counts consistent
dim mismatch	a wrong-dimension vector is rejected with an error, not a panic
replay rebuild	fresh views replayed from the log reproduce identical BM25 + recall (Hard Rule #4)
concurrent writes	256 concurrent appends all land with unique, monotonic ids (Hard Rule #2/#4)

This suite found and fixed a real bug: an all-stopword query ("the of a") or one with bare boolean operators ("a AND OR NOT b") used to surface a hard tantivy parse error — i.e. a 500 on adversarial search input. The BM25 query path now treats unparseable free-text as "no results for this tier" (graceful empty), while still honoring valid explicit-operator queries like revenue OR growth.

---

⚖️ How mnesio compares

📊 BENCHMARKS.md consolidates all the measured numbers in one place — substrate at 105k memories, real-data recall, live LLM-judged QA, and the GPU KV-cartridge speedups — with methodology + caveats.

cargo run -p mnesio-bench -- compete --k 10 --embedder fastembed

Two different metrics, kept separate. The capability matrix below is a structural comparison. The benchmark numbers further down mix cited competitor end-to-end QA accuracy with mnesio's measured retrieval recall@k — a retrieval-quality proxy, not the same metric. recall@k asks "was the gold answer in the retrieved set?"; QA accuracy asks "did the model produce the right answer?". We never present one as if it beat the other.

Capability matrix

Capability	mnesio	Mem0	Zep	Letta	A-MEM
Append-only, replayable event log as system of record	✅	—	◑	—	—
Bi-temporal versioning (never overwrite; invalidate-and-supersede)	✅	◑	✅	—	—
Hybrid retrieval (vector + BM25 + RRF) with explainable breakdown	✅	◑	◑	◑	◑
Procedural self-improvement (gets better at tasks over time)	✅	—	—	◑	—
Non-bypassable commit gate (canaries + safety probe)	✅	—	—	—	—
Counterfactual contribution scoring + GC by measurement	✅	—	—	—	—
Self-falsifying memory (probes auto-supersede on failure)	✅	—	—	—	—
Crypto-shred erasure reconciled with an append-only log	✅	—	—	—	—
Time-travel reconstruction + provenance chains	✅	—	◑	—	—
Certified skill exchange (re-gated on import)	✅	—	—	—	—
Self-contained / embedded (no external vector or graph DB)	✅	◑	◑	✅	◑

✅ shipped · ◑ partial · — not in published design. Competitor cells reflect each system's published architecture and may evolve. mnesio is the only column with every row — the frontier features require the append-only + replayable + bi-temporal substrate behind a non-bypassable gate, which a storage-shaped system can't add without rebuilding its foundation.

Benchmark landscape (cited end-to-end QA — different metric from recall@k)

System	Benchmark	Metric	Score	Source
Full-context (upper bound)	LOCOMO	LLM-as-Judge (J)	72.90%	Mem0 paper, arXiv:2504.19413, Table 2
Mem0 (graph)	LOCOMO	LLM-as-Judge (J)	68.44%	Mem0 paper, arXiv:2504.19413, Table 2
Mem0	LOCOMO	LLM-as-Judge (J)	66.88%	Mem0 paper, arXiv:2504.19413, Table 2
Zep	LOCOMO	LLM-as-Judge (J)	65.99%	Mem0 paper, arXiv:2504.19413, Table 2
LangMem	LOCOMO	LLM-as-Judge (J)	58.10%	Mem0 paper, arXiv:2504.19413, Table 2
A-Mem	LOCOMO	LLM-as-Judge (J)	48.38%	Mem0 paper, arXiv:2504.19413, Table 2
Zep (gpt-4o)	LongMemEval	QA accuracy	71.20%	Zep paper, arXiv:2501.13956, Table 2
Full-context (gpt-4o)	LongMemEval	QA accuracy	60.20%	Zep paper, arXiv:2501.13956, Table 2

These are competitor/baseline numbers from the cited papers — not mnesio's. mnesio's measured numbers are retrieval recall@k: 98.1% on real SQuAD (fastembed, §Scale & real-data above) and 100% on the curated LOCOMO/ LongMemEval mini-suites. mnesio's differentiation is the capability matrix, not a single leaderboard cell.

mnesio — end-to-end LLM-judged QA accuracy (real, not recall)

mnesio also ships the same metric the papers above report — end-to-end QA accuracy via mnesio-bench qaeval (retrieve → an LLM answers from the retrieved context → an LLM judges the answer vs the gold reference):

cargo run -p mnesio-bench --features ollama --release -- \
  qaeval --suite locomo --k 10 --embedder fastembed --llm ollama

Suite	Retrieval	Answer + Judge LLM	QA accuracy	ms/question
LOCOMO-mini	fastembed	llama3.2 3B (Ollama, local)	100% (10/10)	1,765
LongMemEval-mini	fastembed	llama3.2 3B (Ollama, local)	100% (10/10)	1,377

Measured live against a local Ollama model — a real LLM in the loop for both the answer and the judgement, not the offline stand-in. These are the curated mini-suites (10 questions each), so 100% reflects a small set; the point is that the harness produces a real QA-J number through the same ingest → hybrid-retrieve path. Run the full LOCOMO/LongMemEval splits through qaeval (any --llm ollama model) for a publishable headline number.

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🗺️ Roadmap

• Phase 0 ✅ Foundation — event log, hybrid retrieval, dashboard
• Phase 1 ✅ Memory evolution — bounded A-MEM-style worker
• Phase 2 ✅ Procedural compiler — the wedge, with mechanically-enforced commit gate, ALFWorld-style bench harness
• Phase 3 ✅ Filtered HNSW — adaptive over-fetch on selective scopes, per-tenant partitioning (TenantPartitionedVectorView), soft-delete observability (tombstone_ratio, live_count)
• Phase 4 ✅ Bi-temporal property graph store on fjall — typed Relation edges (Linked / EvolvedFrom / EvolvedTo / ContainedIn), as_of time-travel, scope-filtered BFS + shortest-path, replay-rebuildable
• Phase 5 ✅ Distribution — MCP server + Python (pyo3) bindings, both reachable from any agent framework
• Phase 6 ✅ Eval harness as a first-class product (the real moat) — mnesio-bench run/compare CLI, self-contained HTML reports, CI regression gates with exit-code semantics

Competitive layer (parity-plus-wedge)

• Phase 7 ✅ Ingestion intelligence — extract atomic facts → consolidate ADD / UPDATE(contradiction|refinement) / NOOP, importance admission + decay (mnesio-extract)
• Phase 8 ✅ Retrieval + personalization + privacy — graph/recency fusion + reranker, profile memory, multi-agent ACLs, PII redaction + crypto-shred forget (mnesio-privacy)
• Phase 9 ✅ Skill reuse + distribution — committed-artifact injection at query time, Node/TS SDK (sdk/node)

Frontier layer (the bets no one else can ship)

• Phase 10 ✅ Causal memory — counterfactual contribution scoring + GC by measurement (mnesio-causal)
• Phase 11 ✅ Self-falsifying memory — acceptance probes + belief calibration; a refuted claim auto-supersedes (mnesio-probe)
• Phase 12 ✅ Gated KV cartridges — KV cache as a versioned, gated, erasable view of the log. Substrate + a real-tensor attention backend (TensorKvBackend) + a real pretrained-weights backend (PretrainedKvBackend, feature pretrained-kv: loads GPT-2's real embeddings + layer-0 c_attn Q/K/V) + a full 12-layer generative backend (GenerativeKvBackend, feature generative-kv) all done. In the generative backend the cartridge is GPT-2's key/value cache: compile_blob prefills the full forward over the context, answer restores that cache and generates the continuation attending over it. Proven by a self-consistency oracle — generation from the cartridge is token-identical to processing the full prompt from scratch (KV caching is exact) — so the cartridge is a faithful, cheaper substitute, and a post-shred recompile can no longer generate the erased fact. Quantization is real, too: the cartridge blob is compact binary in both precisions, and Quant::Q8 (per-row int8 + f32 scales) makes the cartridge 4.0× smaller (1,179,708 → 296,508 bytes on the live GPT-2 cache) while generating the same answer — closing the quant dimension of CartridgeKey, which was a bare label before. And the cartridge path now runs on a modern 2024 model (qwen-kv: Qwen2.5-0.5B-Instruct — RMSNorm + RoPE + grouped-query attention + SwiGLU + bf16, hand-rolled in pure Rust so the cartridge owns the KV cache — the answer to "why GPT-2, not a more advanced model?"; an Ollama-style black-box text API can't back a cartridge because it never exposes the KV tensors). Live: the Qwen cartridge answers "capital of France" → "Paris", token-identical to the full prompt, and a shred-recompile drops the fact. And that same Qwen2 forward now runs on a real GPU backend (QwenCandleBackend, features candle-kv,metal) via candle on Apple Metal — identical code on Device::Cpu vs Device::new_metal, so the speedup is like-for-like: 107× faster warm prefill (CPU 768.8 ms → Metal 7.2 ms on an M1 Pro; the first run pays a one-time ~100 ms Metal shader compile), the cartridge answers "Paris" token-identical to its own full-prompt path, and erasure still holds. The GPU backend is config-driven (architecture from the repo's config.json, so Qwen2.5 0.5B / 1.5B / 3B / 7B load with no code change) and precision-selectable — F32, F16, or BF16. Half precision is real and the deep-model story is honest: f16's narrow exponent (max ≈ 65504) overflows on the 1.5B/28-layer model (garbage), so deep models use bf16 — half the memory of f32 with f32's exponent range (the model's native dtype) — and the 1.5B answers "Paris" correctly at bf16, verified live. The forward additionally accumulates the residual stream / RMSNorm / softmax / logits in f32 (mixed precision) for robustness, while weights + KV cache stay in the chosen half dtype. It surfaces live in the dashboard at GET /api/kv/metrics under --features candle-kv + MNESIO_KV_GPU=1, with the model and precision selectable at runtime — MNESIO_KV_GPU_MODEL (any Qwen2 repo), MNESIO_KV_GPU_PRECISION (f32/f16/bf16), MNESIO_KV_GPU_CPU=0 to skip the CPU baseline for large models. Verified live: the endpoint serves Qwen2.5-1.5B (28 layers) at bf16 on Metal, answering "Paris", with erasure-by-recompile holding (answerable_before → after = true → false). And the larger model amplifies the GPU win — measured 1.5B prefill: Metal bf16 3.34 ms vs CPU f32 5.27 s = ~1577× (this stacks GPU-vs-CPU and bf16-vs-f32, since candle's CPU backend has no bf16 matmul kernel so f32 is the honest CPU baseline; the clean same-precision figure is the 0.5B 107× above). With real GPT-2 + Qwen backends across CPU and GPU, multiple sizes, and two precisions, the open-weights tensor-backend lift Phase 12 was waiting on is delivered (mnesio-kv). The done-when is now closed end-to-end. A suite-level accuracy-parity eval (cargo run -p mnesio-bench -- kveval) shows the cartridge answers at least as accurately as per-query text-context retrieval — LOCOMO-mini 90% vs 80%, LongMemEval-mini 60% vs 60% — while answering ~167–180× faster (it compiles once and replays; the text-context baseline recompiles per query), with erasure-by-recompile holding, and it gates CI. Production polish landed alongside: the CartridgeStore takes an LRU byte budget + bounded audit history for many-cartridge scale; the real backends raise actionable, HF_HUB_OFFLINE-aware weights errors; CI compile-checks every KV feature gate so they can't rot; and the candle backend now accepts Llama-family configs (optional QKV bias + array eos_token_id) — Qwen2 is the live-verified path, Llama is compile-/config-verified (untied-lm_head + RoPE-scaling are documented TODOs)
• Phase 13 ✅ Certified skill exchange — signed certificate; importer re-runs its own gate before activation (mnesio-exchange)
• Phase 14 ✅ Negative memory + dreaming — gated suppression rules + bounded offline prune-by-contribution & re-anchor (mnesio-dream)
• Phase 15 ✅ Regulator-grade provenance — time-travel reconstruction + provenance chains + verifiable erasure (mnesio-provenance)

The frontier layer (10–15) is what a storage-shaped competitor (Mem0, Zep, Letta, Cognee, A-MEM) can't follow without rebuilding its foundation — each bet exploits the append-only + replayable + bi-temporal log behind the non-bypassable safety gate. See COMPETITIVE.md → "P3 — frontier bets".

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🧪 Test counts

mnesio-core        :   3 tests
mnesio-llm         :  11 tests
mnesio-index       :  83 tests
mnesio-evolve      :  27 tests
mnesio-procedural  : 112 tests
mnesio-causal      :  18 tests
mnesio-probe       :  14 tests
mnesio-kv          :  15 tests (+1 `#[ignore]` under --features pretrained-kv; +1 q8 codec + 3 `#[ignore]` under --features generative-kv: GPT-2 12-layer forward + q8; +1 `#[ignore]` under --features qwen-kv: Qwen2.5-0.5B 24-layer forward; +1 Metal smoke + 3 `#[ignore]` under --features candle-kv,metal: GPU Qwen2 forward, f16, larger 1.5B model)
mnesio-exchange    :  11 tests (+4 under --features ed25519: real signatures)
mnesio-dream       :  10 tests
mnesio-provenance  :   8 tests
mnesio-bench       :  27 tests (+7 under --features fetch: SQuAD + HotpotQA loaders)
mnesio-mcp         :  33 tests (unit + integration)
mnesio-py          :   7 tests (Rust-side inner-client coverage)
mnesio-server      :  27 tests
mnesio-store       :   1 test
mnesio-graph       :  27 tests
mnesio-extract     :  33 tests
mnesio-privacy     :  22 tests (+4 under --features aead: real ChaCha20-Poly1305)
sdk/node (TS)     :   8 tests (offline, stub fetch)
──────────────────────────────
TOTAL             : 489 Rust tests (486 on --no-default-features) + 8 SDK tests · all passing
                    (+7 with --features fetch on mnesio-bench, +4 aead, +4 ed25519)

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🤝 Contributing

Contributions welcome. A few specific patterns the project enforces:

• The gate is sacred. Any change to mnesio-procedural::gate requires a corresponding test demonstrating that the property still holds. Loosening default thresholds requires a code review comment explaining the trade-off.
• External dependencies behind traits. New backends (LLMs, embedders, judges, executors) go behind the existing trait surface; concrete implementations live in their own crate.
• cargo fmt + cargo clippy -- -D warnings must pass on both --no-default-features and the default config before any commit.
• Tests live next to code in #[cfg(test)] mod tests. Storage tests use a temp dir keyed by a fresh ULID and clean up after themselves.
• Conventional commits — feat:, fix:, refactor:, test:, docs:.

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💜 Support mnesio

mnesio is independent, Apache-2.0, and built in the open. Funding goes straight into development time, eval compute (LOCOMO / LongMemEval runs aren't free), and keeping the project independent. If it's useful to you — or you want the frontier roadmap (causal memory, gated KV cartridges, certified skill exchange) to ship faster — consider sponsoring.

Platform	Best for	Fees
GitHub Sponsors	Recurring + one-time, right next to the code	0% (GitHub covers processing)
Open Collective	Transparent budget, company backers, a path to a foundation	host + processor
Ko-fi	Quick one-off tips, no account needed	0% platform
Liberapay	Recurring, non-profit, OSS-aligned	0%

The Sponsor button at the top of the repo is wired through .github/FUNDING.yml.

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📚 Background

Two design documents back this project:

1. A comparative survey of agent memory systems (Mem0, Zep, Letta, A-MEM, etc.) and where each falls short.
2. The Rust-native self-improving memory architecture + phased build plan.

Section numbers in code comments (e.g. "report §3") refer to document 2.

References embedded in the code:

• A-MEM: Lyu et al., Agentic Memory for LLM Agents, arXiv:2502.12110 (memory evolution model)
• GEPA: Du et al., General Evolutionary Prompt Adaptation, arXiv:2507.19457 (reflective-loop pattern)
• ACORN: Wu et al., ACORN: Performant Hybrid Search (filtered HNSW — informs the Phase 3 adaptive over-fetch + partitioning approach)

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⚠️ Stability

This is 0.1.0 — the first usable release. The system is end-to-end working with 380 passing tests across all six build phases, but the public API surface will still move as the graph store and procedural compiler gain real-world mileage. Pin a specific version in your Cargo.toml; expect breaking changes between 0.x.y bumps.

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📜 License

Apache License 2.0. See LICENSE.