MCP server MIT licensed Python JSON-RPC 2.0 over stdio v2.1.0rc2

Auditable agent memory — the memory layer you can prove.

mem20 is an MIT-licensed MCP server that gives AI agents persistent memory with a hard partition between grounded and simulated content, contamination auditing, evidence-based promotion, and an append-only, origin-tagged ledger. 270 tools across 27 domains. Self-hosted. Zero lock-in.

Persistent memory for AI agents — with the audit trail that regulated teams need. Grounded vs simulated, enforced in the architecture.

github.com/bythewayz66-glitch/mem20
Hit rate · direct
1.000
Published, own harness
Hit rate · multi-hop
0.800
Published, own harness
Hit rate · adversarial
0.000
Known weakness — see caveat
Leak rate · clean stores
0.000
No simulated bleed detected
The wedge

Not a retrieval-quality race. A trust and provenance race.

Every memory layer on the market competes on retrieval quality. None of them lead with auditability — the ability to show why a memory is in the store and where it came from. In a regulated context that is the whole ballgame: you don't need "probably relevant," you need "here is the source, and here is the evidence it was promoted."

01

Prove it, don't just retrieve it.

A hard grounded/simulated partition plus an append-only origin-tagged ledger tells you where every memory came from.

02

Catch contamination before it ships.

Auditing detects simulated content bleeding into grounded memory — leak rate 0.000 on clean stores.

03

Own your memory layer.

MIT licensed, Python, self-hosted. No lock-in, no black box, no vendor holding your audit trail.

How the partition works

Two memory classes, one gate, and a ledger that keeps the receipts.

Grounded and simulated memory are not the same store with a boolean flag. They are separate classes, so "the agent made this up" is structurally visible instead of silently plausible.

Grounded memory

What the agent actually knows

Content that traces to a real source. This is the only class a downstream decision should be allowed to rest on — and every entry carries its origin.

Simulated memory

What the agent generated

Hypotheses, drafts, inferences and speculative content the model produced. Useful, kept, and quarantined by construction — never silently indistinguishable from fact.

simulatedpromotion gategrounded
→

Evidence-based promotion — with an append-only audit trail

Nothing graduates from simulated to grounded by assertion. Content is promoted only with evidence attached to the promotion, and that promotion is recorded. The claim is auditable — an append-only trail you can inspect — not a claim of cryptographic tamper-proofing.

☰

Append-only, origin-tagged ledger

Every memory carries its source. History is written forward and tagged with origin, so the record can't be quietly rewritten after the fact — and an audit can reconstruct what the agent knew, and when.

Under the hood

01 Hard grounded/simulated partition Not a boolean flag on one store — two distinct memory classes.
02 Contamination auditing Detect when simulated content has entered grounded memory, before it becomes a decision.
03 Evidence-based promotion Nothing graduates from simulated to grounded without evidence attached.
04 Append-only origin-tagged ledger Every memory carries its source; history cannot be quietly rewritten.
05 MCP-native and broad JSON-RPC 2.0 over stdio, 270 tools across 27 domains — drops into LangGraph, CrewAI, Pydantic AI and the wider MCP ecosystem.
06 MIT, self-hosted, Python Audit the code you depend on. Run it in your own VPC.
Published benchmarks

The numbers, including the one that isn't flattering.

Benchmarks are self-published on the mem20 v2.1.0rc2 harness. We show the full table — including the adversarial row — because a memory layer you're meant to audit shouldn't get to hide the row it loses.

Query class Hit rate What it means
Direct recall 1.000 Explicit, well-formed lookups resolve reliably.
Multi-hop 0.800 Chains that require composing several memories.
Adversarial known weakness 0.000 Hard adversarial queries defeat the retrieval path today. On the roadmap — not hidden.
Leak rate (clean stores) 0.000 No simulated content detected bleeding into grounded memory.
Read this before you read the table

These benchmarks are self-published on our own harness. mem20 does not yet have a published LoCoMo or LongMemEval result comparable to Mem0 or Zep, so we make no parity claim and cite no one else's figure as ours.

The 0.000 adversarial hit rate is a genuine limitation, not a marketing footnote: an adversary who knows how to phrase around the retrieval path can defeat it today. If your threat model includes adversarial query injection, weigh that honestly.

Where mem20 sits

A different question, not a better scoreboard.

The mature players have more benchmark mileage and, in several cases, larger communities. mem20's bet is orthogonal: none of them leads with provenance. Figures below are the vendors' own published claims and pricing, verified October 2026; we present them as claimed, not as independently confirmed.

mem20 Mem0 Zep / Graphiti Letta (MemGPT)
Approach Memory + cognition substrate with a grounded/simulated partition Extract-and-retrieve memory layer Temporal knowledge graph Stateful agent runtime, self-editing memory
License MIT Apache 2.0 Managed / enterprise Apache 2.0
Provenance & audit surface Grounded/simulated partition + contamination auditing + append-only origin-tagged ledger Not positioned as an audit layer Temporal graph edges; auditability not a lead claim Self-editing memory; no provenance partition
Vendor-published benchmark Hit rate 1.000 / 0.800 / 0.000 · leak 0.000 own harness LoCoMo 92.5 · LongMemEval 94.4 DMR 94.8% LoCoMo 74.0%
Independent benchmark Not published no comparable LoCoMo/LongMemEval 49.0 on LongMemEval reported in independent testing 45-point gap vs vendor claim Not in our verified set Not in our verified set
Pricing Free & open source — self-host Free → $19 → $79 → $249 (graph) → Enterprise Flex $125/mo (50k credits) · Flex Plus $375/mo · Enterprise Free → Pro $20/mo → API $20/mo + $0.10/active agent/mo
Community signal v2.1.0rc2 — early ~59.9k stars · $24M Series A Managed platform ~23.6k stars · $10M seed
How to read the comparison fairly

Mem0 and Letta are Apache-2.0 and self-hostable too, and both have substantially more production mileage than mem20. Zep's temporal graph is a genuinely different design that solves problems our ledger does not attempt. The row that matters here is provenance: this is the one axis where mem20 leads, and it leads by positioning, not by a headline benchmark number.

Straight talk

What we don't claim.

Three limits we'd rather state ourselves than have someone else discover in production.

No LoCoMo / LongMemEval result

mem20 has no published benchmark comparable to Mem0 or Zep. We do not imply parity, and we never present their numbers as ours.

Adversarial hit rate is 0.000

Hard adversarial queries defeat the retrieval path today. It's on the roadmap, and it stays visible wherever the benchmark table appears.

The promotion gate has known gaps

A self-attestation hole in the promotion gate was flagged in a prior audit. Promotion is auditable, with an append-only trail — not unfakeable, and not cryptographic.

Install

Run it on your own infrastructure.

If your memory layer is the thing you have to audit, you can't audit someone else's black box. mem20 is MIT-licensed and self-hosted by design — and not yet on PyPI, so install is from source.

shell
# not on PyPI — install from source
git clone https://github.com/bythewayz66-glitch/mem20
cd mem20
python3 -m venv .venv
.venv/bin/pip install -r requirements.txt

# run the MCP server over stdio (JSON-RPC 2.0)
MEM20_STORE_PATH=~/.mem20/store .venv/bin/python mcp/mcp_server.py
  • ✓MIT licensed — read every line before it runs in your VPC.
  • ✓MCP-native: JSON-RPC 2.0 over stdio, 270 tools across 27 domains.
  • ✓Drops into LangGraph, CrewAI, Pydantic AI and the wider MCP ecosystem.
  • ✓Zero lock-in — your memory, your ledger, your infrastructure.
MIT Python MCP server self-hosted v2.1.0rc2

Stop guessing what your agent knows.

Auditable agent memory — two memory classes, a promotion gate with a trail, and a ledger that keeps the receipts.