Memtether

MemTether

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The only AI agent memory with a tamper-evident audit chain.

*If your agent can't prove what it knows, it doesn't know.*

Remember every fact with its source · Recall it at three depths · Audit who changed what, when, and why — with corrections that never delete and a hash chain anyone can verify.

pip install memtether

$ memtether court fact-20261007-abc

+============================================================+ | MEMTETHER MEMORY COURT - CASE FILE | +============================================================+ | 1. CONTENT Clash proxy port is 7897 | | 2. TEMPORAL valid: 10-05 -> present | known: 10-05 -> now | | 3. PROVENANCE #1 remember by=codex -> #2 correct by=user | | 4. CONFLICTS case #3: vs fact-...old verdict=superseded | | 5. INTEGRITY chain: PASS (3 anchors, 42 entries hashed) | +============================================================+

$ python examples/audit_chain_demo.py

Before tamper: verify_chain: PASS Simulating attacker editing DB directly... After tamper: verify_chain: FAIL {"reason": "chain hash mismatch"}

Hash chain detected the tampering. That is the point.

MemTether is a local-first memory hub for multi-client AI agents. While most memory systems compete on retrieval accuracy, MemTether competes on governance: who wrote each fact, when it took effect, what it superseded, which conflicts a human adjudicated, and a tamper-evident hash chain anyone can verify — no code, no dependencies. Retrieval is provided (SGM deterministic SQL routing + hybrid embedding), but it is not the headline: agent memory that you can audit, correct, and trust is.

Think of it as an append-only memory ledger: every fact is a frame with its source and checksum attached; correcting a fact appends a new frame instead of overwriting the old one; and as_of rewinds the whole ledger to any past date. The DB is the source of truth — export-md gives you a human-readable, diffable, Git-friendly view of it anytime.

Coming from mem0, Zep, or Letta? examples/mem0_migration.py shows a complete round-trip migration from a mem0 JSON export — no mem0 SDK required.

$ memtether court fact-20261007-abc

+============================================================+ | MEMTETHER MEMORY COURT - CASE FILE | | 1. CONTENT Clash proxy port is 7897 | | 2. TEMPORAL valid: 10-05 → present | known: 10-05 → now | | 3. PROVENANCE #1 remember by=codex → #2 correct by=user | | 4. CONFLICTS case #3: vs fact-...old verdict=superseded | | 5. INTEGRITY chain: PASS (3 anchors, 42 entries hashed) | +============================================================+

Demo

Memory Court — audit any memory

Why MemTether Instead of mem0?

TL;DR

• Your AI clients forget each other? Every agent keeps its own memory. MemTether gives them one shared, governed brain via file-level pointers — no cloud, no sync.
• Your AI writes things it should not? Every memory carries a source identity and a hash-chained audit trail. Tamper with the database and the chain detects it — run python examples/audit_chain_demo.py to see it turn red.
• Your AI changed its mind? Corrections never delete. Bi-temporal supersession keeps who-changed-what-when-why forever, queryable at any past point in time.

Run it yourself:

$ python examples/audit_chain_demo.py

写入 25 条记忆（自动生成 2 个审计锚点）... 篡改前 verify_chain: PASS {'anchors': 2, 'entries_covered': 20} 模拟攻击者直接改库... 篡改后 verify_chain: FAIL {'reason': 'chain hash mismatch'} ✅ 哈希链成功检测到篡改！

Note: ntries_covered: 20 with 25 writes is expected — the hash chain anchors every 10 audit entries, so the last 5 writes sit in the unanchored tail until the next batch. Tampering with an *anchored* entry is what turns verification red.

## Migrating from mem0

Already using mem0? examples/mem0_migration.py shows a complete round-trip: mem0 JSON export → MemTether Exchange v1 → live retrieval → back to mem0 format. No mem0 SDK required, bi-temporal fields honestly marked temporal_source="backfilled".

Performance (microbenchmark, n=200, local SQLite)

Reproduce: python benchmarks/bench_latency.py --n 200

Release channels

main only holds merges that passed the full test suite. Alpha builds are tagged automatically on every successful build (v0.1.0aN), so tag numbers are not contiguous and some tags point at commits that were later squashed into a merge. Install from PyPI for a validated build:

pip install memtether -i https://pypi.org/simple/

Documentation

• REPRODUCING.md - Paper numbers to commands: every headline number in the paper maps to an artifact or command here

Under the hood: supersession chains (corrections never delete), bi-temporal timestamps (when facts were true vs when the system learned them), human-adjudicated conflict resolution, and a hash-chained audit log designed with EU AI Act Article 12 record-keeping requirements in mind (not certified compliance) record-keeping requirements.

One physical SQLite database, shared by every AI client via file-level pointers. No cloud. No API fees. No sync daemon. Greppable, git-able, yours — and auditable.

*Compared to mem0 (66.6K★), Zep/Graphiti (31.5K★), and agentmemory (29.2K★):

those systems either overwrite or delete old values. MemTether's supersession

chain preserves the full history with source attribution and Q-Value ranking.*

EAF retrieval: 75.7% strict on LongMemEval 500Q (paired delta +19.4pp, McNemar p=7.8e-7).

How MemTether compares

*'Each cell reflects the feature's presence in the project's public documentation as of 2026-10-07. MemTether's differentiator is not any single feature — it's the combination of all governance dimensions in one local-first deployment.'*

Artifact bundle with per-question records.

mcp-name: io.github.lanbass869-cell/memtether

Project page · Install · Docs · Reproduce · 中文文档 · Español

Install · Usage · Why MemTether · Benchmarks · Architecture · Security · FAQ

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• Reproduce MemTether - Every claim has a command
• Founding 10 Challenge - 10 spots, 14 days, honest outcomes
• Docs (zh) - Reproduce / Challenge in Chinese

Quick Start

30 seconds, zero keys

pip install memtether

generate a 100-fact demo DB and query it immediately

memtether demo memtether search "投影" --list

$ memtether search "投影" --list

1. [incident/deltahub] 巡检发现投影超限：3904 / 3900 字符——超限是静默整体截断 uid=demo-incident-0100 1.0000
2. [fact/epsilon_ai] 投影是给注入侧看的精简版，真源始终是数据库文件。 uid=demo-fact-0061 0.0536

In your agent

from memtether import remember, search

remember("User prefers dark mode", source="my-agent") # hash-chained on write results = search("dark mode")

# Or as an MCP server for Claude Code / Cursor

memtether mcp

Optional: pre-download the embedding model (semantic search works

without it too — keyword path is always on)

memtether download-models

# Node.js / TypeScript — thin REST client, zero dependencies

npm install @memtether/client

Agent Skills

MemTether ships skills that teach your coding agent *when* to use memory — install with npx skills add:

npx skills add MemTether/MemTether

Framework Int