tencentdb-agent-memory (Claude Code plugin)

Four-layer long-term memory (L0 Conversation → L1 Atom → L2 Scene → L3 Persona) for Claude Code, inspired by Tencent/TencentDB-Agent-Memory.

Fully local — no external Gateway, no paid API, no Python. All extraction and consolidation is done by the Claude agent itself.

Installation

# Add marketplace
claude plugin marketplace add https://github.com/baodq97/tencentdb-agent-memory

# Install plugin
claude plugin install tencentdb-agent-memory

Quick start

# Inside Claude Code:
/memory-init
# → installs deps, links tmem CLI, creates store
# → hints: "ask me to seed memories"
# then say "seed memories" → agent extracts L1 atoms
# then say "consolidate memories" → agent builds scenes + persona
# done — hybrid recall is now active automatically

What happens automatically

Hook	Action
`UserPromptSubmit`	Hybrid recall (FTS5 + vector + RRF) + L2 scene-navigation index → inject `<memory-context>`
`Stop`	Auto-capture turn + background consolidation after N turns
`SessionEnd`	Mark session as pending for later seeding

Hooks never block — failures degrade to no injection.

How recall works

Each turn, the UserPromptSubmit hook builds a <memory-context> block from three layers:

L3 persona — a short summary of who you are / your standing preferences.
L1 atoms — hybrid search (FTS5 keyword + EmbeddingGemma vector, merged via RRF) over the most relevant memories, within a token budget.
L2 scene-navigation — a heat-ranked index of scene blocks (name + heat + summary), project scenes first then global, with its own token budget. Full scene content is not inlined; load it on demand with tmem scene <name> (progressive disclosure — cheap always-on index, full read only when needed).

Tune the scene-navigation budget with tmem config scene-max-tokens N (0 disables it).

Components

Type	Name	Purpose
Command	`/memory-init`	Install deps, link tmem CLI, init store
Skill	`memory-seed`	Agent extracts L1 atoms from conversation history
Skill	`memory-consolidate`	Agent builds L2 scenes + L3 persona
Skill	`tmem-cli`	CLI reference for memory inspection/management
Agent	`memory-consolidator`	Background worker dispatched by asyncRewake

tmem CLI

Installed automatically by /memory-init. Available in terminal and used by skills.

tmem status                     Memory stats
tmem search <query>             FTS5 keyword search (global + current project)
tmem search <query> --all       Cross-project: search every project store, labelled by store
tmem projects                   List all memory stores (slug, records, scenes)
tmem migrate-fragments [--apply]  Collapse legacy cwd-keyed fragment stores into their project root
tmem recall <query>             Hybrid recall (FTS5 + vector + RRF) + L2 scene-navigation
tmem persona                    Show persona
tmem scenes list                List scene blocks
tmem scene <name>               Print one full scene block (project-first, then global)
tmem scenes dedup [--dry-run]   Remove duplicate scenes
tmem changelog [--last N]       Recent memory changes
tmem sync [--full]              Embed missing vectors (delta); --full rebuilds
tmem atoms [global|project|all] Dump L1 atoms as JSON
tmem sessions                   List pending sessions
tmem init                       Initialize memory store
tmem mark-done                  Mark consolidation complete
tmem config consolidate-every N Set consolidation threshold (default 20)
tmem config scene-max-tokens N  Set L2 scene-navigation token budget (default 200, 0 disables)
tmem daemon start               Warm + serve the embed daemon (foreground, like `ollama serve`)
tmem daemon status              Health-ping the daemon (ready/warming/failed/down + pid)
tmem daemon stop                Stop the daemon + clear its pidfile

Contributor intelligence (`/contrib`)

Profile how a top GitHub engineer works — and learn from them.

Prerequisite: an authenticated gh CLI (gh auth login). All data lives in
<global>/contributors/ — the self-memory feature is never touched.

Quickest way — just drop a link

Paste a GitHub link (or a handle) and say what you want — the contrib-profile
skill takes it A→Z for you:

"Analyze how this engineer works: https://github.com/sindresorhus/ky"
"Profile https://github.com/torvalds and show me the playbook"

It resolves the target (picks the right repo if you only give a user), runs the
whole pipeline, and hands back the persona + learnable playbook. Prefer to drive
it yourself? Ask "how do I use /contrib" and it guides you through the steps
below instead.

Usage — first run (manual)

Declare a subject (a GitHub user in one repo):
```
/contrib add <user> <owner/repo>
```
Ingest their public activity — gh fetches their PRs, commits (all
branches), review threads and issues, then the agent classifies it into
evidence-linked atoms across the 11 dimensions. Incremental by default
(--full to refetch):
```
/contrib ingest <user>@<repo>
```
Build the persona — consolidate the atoms into one profile:
```
/contrib build <user>@<repo>
```

Learn from it:

/contrib persona  <user>@<repo>    # the full dossier (11 dimensions + evidence)
/contrib playbook <user>@<repo>    # emulable heuristics you can copy
/contrib compare  <user>@<repo>    # you (your existing self-persona) vs this role model

Going further

Capability model — add a 2nd engineer and see what the top engineers
share (needs ≥2 built personas; they don't have to include you):
```
/contrib add <user2> <org2/repo2> ; /contrib ingest <user2>@<repo2> ; /contrib build <user2>@<repo2>
/contrib capabilities
```
Two-engineer table — /contrib compare <a> <b> (per-dimension, side by side).
Trajectory — /contrib trajectory <id> (per-year cadence + commit-style arc).
Team — /contrib team add <teamId> <id...> then /contrib team capabilities <teamId>.
Recall — /contrib search "<query>" (keyword; vector too if the embed daemon
is warm — run /contrib sync once to index).

The 11 dimensions

Activity is classified into 11 dimensions across 3 clusters — Technical Craft
(idea/plan/solve/craft), Collaboration & Influence (comms/mentor/conflict),
and Outcomes & Ownership (scope/ownership/execution). Every atom and persona
claim is evidence-linked to a PR or commit. v0.3.0 measures cadence/style, not
PR diff size (the GitHub search API omits it).

Architecture

~/.memory-tencentdb/
├── global/           index.db (FTS5) + vectors.db (sqlite-vec) + persona.md + scenes/
├── projects/{hash}/  index.db + vectors.db + scenes/
└── models/           embeddinggemma-300m (~80MB, downloaded on first init)

Tech stack

FTS5 — keyword search via node:sqlite (built-in)
sqlite-vec — vector cosine search (npm)
EmbeddingGemma-300m — local embedding via node-llama-cpp (npm, ~80MB model)
Resident embed daemon — keeps the model warm over local IPC (named pipe / unix socket); degrades to FTS-only on failure. Manage explicitly with tmem daemon start|status|stop
RRF (k=60) — merges FTS5 + vector results

Changelog

See CHANGELOG.md for per-version history.

License

Plugin: MIT. Upstream inspiration: MIT (c) TencentDB Agent Memory Team.