Th Memory MCP Server

th-memory-mcp

Status: v2.2.6 — a temporal, conflict-aware, hybrid-retrieval memory engine. 16 MCP tools, 25 passing test suites. Non-destructive schema migration from v1 (all v1 data preserved). New in v2.2: lifecycle states, temporal validity, conflict/dedup resolution with USER/SESSION/PROJECT/GLOBAL scope, hybrid FTS+vector retrieval (RRF), memory graph, get_context assembly, periodic consolidation, and link_memory / merge_memory / update_memory / import_memory / extract_memories. New in v2.2.3: scope-enforced retrieval, graph scope isolation, export/import round-trip, hardened import path (realpath), strict import validation, N+1 query elimination, cold/ablation benchmark, and MEMORY_RETRIEVAL_MODE switch.

Requirements

• Node.js >= 20 — the server uses Node-only APIs (the better-sqlite3 native build and import.meta.url resolution) and the MCP SDK requires a modern runtime. CI tests on Node 20.x and 22.x.
• npm — to install dependencies and run the build/test scripts (npm install, npm run build, npm test).
• OpenCode — the host that loads this MCP server and the auto-capture plugin. Any build supporting MCP over stdio + plugins works; the plugin runs on OpenCode's bundled Bun runtime.
• OS: Windows / macOS / Linux — the server is cross-platform (Node). The auto-capture plugin runs wherever OpenCode's Bun runtime runs. Windows note: MEMORY_DB_PATH is easiest to set with setx; on macOS/Linux use export in your shell profile.

No external services, accounts, or API keys are required — everything lives in a single local SQLite file.

Quick Start

Fastest path: after cloning, run npm run quickstart — it builds, wires opencode.json, deploys the plugin, and sets MEMORY_DB_PATH for you in one command. The steps below show exactly what it does (use them if you prefer manual control).

Install via npm (alternative): install the server globally with npm install -g th-memory-mcp (or run it on demand with npx th-memory-mcp), then point the mcp command in opencode.json to th-memory-mcp instead of the built dist/index.js. The auto-capture plugin still comes from this repo (copy src/plugin/learning-capture.ts as described in step 4 below).

# 1. Clone and build
git clone https://github.com/worakorn-prince/th-memory-mcp.git
cd th-memory-mcp
npm install
npm run build

# 2. Share one DB between the server and the plugin
#    Windows (PowerShell):
setx MEMORY_DB_PATH "$PWD/data/memory.db"
#    macOS / Linux (add to your shell profile, e.g. ~/.zshrc):
# export MEMORY_DB_PATH="$PWD/data/memory.db"

1. Merge this into your ~/.config/opencode/opencode.json (replace <REPO> with the absolute clone path):

{
  "instructions": ["<REPO>/AGENTS.memory.example.md"],
  "mcp": {
    "memory": {
      "type": "local",
      "command": ["node", "<REPO>/dist/index.js"],
      "enabled": true,
      "environment": { "MEMORY_DB_PATH": "<REPO>/data/memory.db" }
    }
  }
}

1. (Optional) Auto-capture: copy src/plugin/learning-capture.ts → ~/.config/opencode/plugins/
2. Restart OpenCode
3. Try it: "Remember that I prefer pnpm" → new session → "What package manager do I prefer?"

Architecture

OpenCode ──┬─ Plugin learning-capture (Bun)  ── auto-captures prompts/tool/error into DB
            │                                   └─ injects profile back into context on compaction
             └─ MCP th-memory-mcp (Node.js stdio)  ── 16 tools read/write the same SQLite DB
                                                      ▲
                               Global instructions (memory-protocol.md) teach the AI to use the tools

See ARCHITECTURE_v2.md for the full architecture spec.

Why th-memory-mcp?

LLMs don't remember you between sessions — every new chat starts blank. th-memory-mcp gives your AI a private, local long-term memory:

• Context-based learning, not fine-tuning — it captures your preferences, corrections, and habits, then recalls them into context next time. Same mechanism as the memory features of leading AI products, without sending any data off your machine.
• 100% local & private — a single SQLite file, no cloud, no external API. Secrets are filtered before anything is stored.
• Low overhead — each tool call is capped (latency < 10 ms, bounded output size) and the AI only queries memory when it's actually useful, so it never bloats your context.
• Resilient — every tool degrades gracefully; if the DB is unavailable the AI keeps working instead of crashing.
• Open & extensible — MIT licensed, 16 documented tools, a rule-based distill, and an auto-capture plugin you can adapt.

Works with other harnesses

th-memory-mcp is a standard MCP server, so the 9 tools run anywhere MCP-over-stdio is supported. Full auto-capture (background prompt/tool/error capture + profile injection) needs a hook runtime — OpenCode has it built in; Claude Code gets it via our hooks bridge; Codex and Cursor use the tools manually (no hook runtime yet).

• Claude Code: see CLAUDE_CODE_HOOKS.md — drop-in hooks replicate the OpenCode plugin (capture + profile injection on UserPromptSubmit/PreCompact, rule-based distill on SessionEnd).
• Qwen Code: see QWEN_SETUP.md — MCP works fully; hooks use the Gemini-CLI schema so auto-capture needs a small adapter.
• Codex: see CODEX_SETUP.md
• Cursor: see CURSOR_SETUP.md

All harnesses share one SQLite file via MEMORY_DB_PATH, so memory captured anywhere is readable everywhere.

Highlights

• Structured memory — preferences with confidence scoring plus dedicated lesson records (situation → mistake → correction) for capturing corrections, not just flat facts.
• Lifecycle & temporal — every memory has a lifecycle state (active/stale/superseded/archived), confidence/importance/salience scoring, per-type decay, and validity intervals so the AI can reason about point-in-time truth and supersession chains.
• Conflict-aware — duplicate detection, contradiction detection, and update/supersession resolution preserve both sides of ambiguous evidence instead of silently overwriting.
• Hybrid retrieval — get_context blends FTS5 keyword search with a dependency-free lexical fuzzy matching (hashed n-gram similarity, 512-dim FNV-1a) (RRF fusion + scoring), then assembles a token-budgeted context with optional memory-graph expansion.
• Consolidation — periodic clustering of similar memories into derived memories with full provenance (derived_from links).
• First-class Thai / i18n — Thai-aware tokenization in distill; the AI accepts Thai and English interchangeably.
• Private by default — a single local SQLite file, no cloud, no API keys, with secret lines (api_key=, password:, token) filtered before storage.
• Cross-harness — runs on OpenCode, Claude Code, Codex, and Cursor sharing one DB; auto-capture + profile injection via OpenCode plugin or Claude hooks.
• Lightweight & resilient — Node + better-sqlite3, no extra native extensions; every tool degrades gracefully so the AI keeps working if the DB is unavailable.

Scripts

Tools (16)

Install with OpenCode

1. Merge the mcp section from opencode.example.json into your opencode.json (global or project-level)
   • Important: set MEMORY_DB_PATH to the SAME database file for both the server and the plugin (the example uses <ABSOLUTE_PATH>/th-memory-mcp/data/memory.db), otherwise the auto-capture plugin writes to a different DB than the one the AI reads
   • How to set it (pick one):
     • define it in the mcp environment (see example) — covers the MCP server only
     • or set it as a system/user-level environment variable (e.g. setx MEMORY_DB_PATH "D:/path/to/memory.db" on Windows) — covers both server and plugin, since the plugin runs in the same process as OpenCode
2. Attach the global memory rules — add to opencode.json:

   "instructions": ["C:/Users/<user>/.config/opencode/memory-protocol.md"]
   

   (example rule content is in AGENTS.memory.example.md — can be attached at project level instead)
3. (Optional) Deploy the auto-capture plugin: copy src/plugin/learning-capture.ts → ~/.config/opencode/plugins/learning-capture.ts
4. Restart OpenCode (config loads at startup only)
5. Test: "Remember that I prefer pnpm" → open a new session and ask back

Daily usage

The AI accepts both Thai and English interchangeably — you can switch languages at any time without warning.

Long-term care: run npm run distill occasionally to summarize stats and prune interactions older than 30 days.

data/ structure

data/
├── memory.db          # SQLite (WAL mode) — main DB (+ .db-wal, .db-shm)
└── exports/           # JSON files from export_memory (writeable only in this dir)

• DB path can be overridden via the MEMORY_DB_PATH env var
• everything in data/ is git-ignored

Benchmark — internal self-reported (not third-party)

⚠️ Internal self-reported benchmark — not third-party benchmark

• internal small-N: 180 records/30 topics (B.retrieval: 30 topics × 5 relevant + 30 distractors = 180; full run also uses small-N storage/temporal/context subsets)
• single-machine self-run: single developer machine, single OS/Node/better-sqlite3 build — not cross-machine, not independently verified
• not third-party benchmark: self-reported, not independently verified; do not compare as if from an external evaluator
• Dataset and harness are in repro/ (commitable) and benchmark/ (full framework, see TH_MEMORY_MCP_BENCHMARK_SPEC.md and benchmark/README.md).

Two modes

Reproduce:

npm run build
# Normal — quick
npm run benchmark
npm run benchmark -- --k 10
npm run benchmark -- --out repro/results

# Heavy — full framework, more data
npm run benchmark:heavy
# or custom:
node benchmark/run.mjs --suite all --topics 100 --distractors 100 --scale 2000 --out benchmark/results

Viewer — compare last 3 versions (table + charts)

npm run benchmark:viewer
# or: npx serve . -l 3000
# open http://localhost:3000/benchmark/viewer/  or  http://localhost:3000/result/viewer.html

The viewer loads benchmark/results/history.jsonl, groups by version, takes the latest run of the 3 most recent versions (e.g. 2.2.2 / 2.2.3 / 2.2.4) and shows a highlighted table (1 row per version) + bar charts for Recall@5 / MRR / NDCG@5 and Latency p95. Results are also saved per version in result/v*_benchmark_result.md and benchmark/results/versions/<ver>/.

Last internal run (v2.2.4, warm, same dataset — not third-party): Recall@5=0.92, Precision@5=0.92, MRR=1.00, NDCG@5=0.94 over 30 topics/180 records. See result/v2.2.4_benchmark_result.md and repro/README.md for details and caveats (internal small-N, single-machine self-run).

Known Limitations

• No encryption at rest (plaintext-at-rest) — data/memory.db (WAL mode, better-sqlite3) is a plain, unencrypted SQLite file. 100% local & private means no cloud or network exfiltration — it does not mean encrypted at rest. Anyone with filesystem access (shared machine, backup, malware, stolen device) can read preferences/lessons/interactions in plaintext. For sensitive data, use OS-level full-disk encryption (BitLocker / FileVault / LUKS) or an opt-in SQLCipher build (requires native rebuild and key management). No SQLCipher/in-code encryption is applied by default and src/db/index.ts documents this explicitly.

License

MIT © 2026 worakorn-prince

This project is licensed under the MIT License — see the LICENSE file for the full text.