threadkeeper 0.4.1

Multi-agent shared brain across Claude Code/Desktop, Codex, Gemini, Copilot, VS Code. Cross-session memory, self-improving skill loops, inter-agent signaling — one local MCP server.

thread-keeper

Multi-agent shared brain across Claude Code/Desktop, Codex, Gemini, Copilot, and VS Code. Cross-session memory, self-improving skill loops, and inter-agent signaling — one local MCP server turns parallel agent instances into a coordinated multi-agent system instead of N isolated chats.

Every connected client (Claude Code, Claude Desktop, Codex CLI + desktop, Gemini, Copilot, every MCP-aware VS Code extension) shares one SQLite store, one set of threads, one user model, and one learning loop that improves the skill library autonomously over time.

The brief format is dense — structural tags, opaque IDs, ~6 KB per session-start injection. Optimized for agent consumption, not human reading.

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Why

Every agent CLI starts cold. Context dies at session boundaries. Skills you taught Claude don't transfer to Codex. Threads you closed in yesterday's Gemini chat are invisible to today's Copilot. Parallel agent instances running the same task don't know about each other and duplicate work or step on each other's writes.

thread-keeper is the substrate underneath. Three things that together make it more than a memory store:

• Collective memory — threads, notes, verbatim quotes, dialectic claims about you. Survives session, restart, CLI swap. One agent records, every other agent (any CLI) reads. The brief injected at session start gives a new agent everything the previous one knew.
• Multi-agent coordination — spawn primitive launches child agents in parallel, each gets a self_cid + sees the same memory. broadcast / whisper / inbox / wait / ask / respond let concurrent sessions signal each other across CLIs. Parent / children / sibling agents become a coordinated swarm, not isolated chats.
• Self-improving skill library — four autonomous background loops (auto-review on thread close, shadow-review daemon, extract harvester, weekly Curator) materialize class-level skills as the agents work. Hermes Agent v0.12 pattern adapted to multi-CLI: SKILL.md is the primary write target and gets mirrored to every detected CLI's skills directory simultaneously (~/.claude/skills/, ~/.codex/skills/, ~/.threadkeeper/skills/), with lessons.md as a fallback for CLIs without a native skills loader.

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Quickstart

The shortest path — PyPI + pipx (recommended):

pipx install 'threadkeeper[semantic]' && thread-keeper-setup

thread-keeper-setup detects every CLI you have installed (Claude Code / Claude Desktop / Codex CLI + desktop / Gemini / Copilot / VS Code), registers the MCP server in each one's config, copies hooks to ~/.threadkeeper/hooks/, and writes a managed instructions block into each CLI's per-user instructions file (CLAUDE.md / AGENTS.md / GEMINI.md / copilot-instructions.md — Claude Desktop and VS Code have no global instructions file, so that step is skipped for them).

Restart your CLI of choice. The SessionStart hook injects a brief on first message; no manual brief() call required.

Alternative installs

If you don't have pipx and don't want to install it:

# uv (Rust-fast Python tool runner) — no clone, single binary on PATH
uv tool install 'threadkeeper[semantic]' && thread-keeper-setup

# Plain pip into a venv
python3 -m venv ~/.threadkeeper-venv
~/.threadkeeper-venv/bin/pip install 'threadkeeper[semantic]'
~/.threadkeeper-venv/bin/thread-keeper-setup

For development (editable install from a git checkout) or to track the bleeding edge:

# One-liner installer — clones to ~/thread-keeper, makes a venv,
# editable-installs, wires every detected CLI. Idempotent — re-run to
# update (it git-pulls + reinstalls).
curl -fsSL https://raw.githubusercontent.com/po4erk91/thread-keeper/main/install.sh | bash -s -- --semantic

# Or fully manual
git clone https://github.com/po4erk91/thread-keeper ~/thread-keeper
cd ~/thread-keeper && python3 -m venv .venv
.venv/bin/pip install -e '.[semantic]'
.venv/bin/thread-keeper-setup

To preview without writing anything:

thread-keeper-setup --dry-run

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Multi-CLI integration

CLI	MCP config	Instructions file	Hooks	Transcripts ingested
Claude Code	~/.claude.json mcpServers	~/.claude/CLAUDE.md	~/.claude/settings.json hooks	~/.claude/projects/**/*.jsonl
Claude Desktop	~/Library/Application Support/Claude/claude_desktop_config.json mcpServers (macOS); %APPDATA%\Claude\… (Win); ~/.config/Claude/… (Linux)	none (GUI-only)	not supported by the app	none — chats live in Electron IndexedDB
Codex (CLI + desktop)	~/.codex/config.toml [mcp_servers] (shared between CLI and Codex.app)	~/.codex/AGENTS.md	not supported	~/.codex/sessions/**/rollout-*.jsonl
Gemini	~/.gemini/settings.json mcpServers	~/.gemini/GEMINI.md	~/.gemini/settings.json hooks	~/.gemini/tmp/<user>/chats/session-*.jsonl
Copilot	~/.copilot/mcp-config.json mcpServers	~/.copilot/copilot-instructions.md	~/.copilot/hooks.json	~/.copilot/session-store.db (sqlite)
VS Code	~/Library/Application Support/Code/User/mcp.json servers (macOS); %APPDATA%\Code\User\mcp.json (Win); ~/.config/Code/User/mcp.json (Linux)	none (per-workspace only)	not supported	none — extensions own their history

Every CLI that produces parseable transcripts feeds the same dialog_messages table with a source tag, so dialog_search() finds matches regardless of where the conversation happened. Claude Desktop and the VS Code adapter are the exceptions — MCP registration only; their chats don't reach the table for now (Electron IndexedDB on the Claude Desktop side; per-extension stores on the VS Code side).

VS Code's user-level mcp.json is the central host that every MCP-aware VS Code extension consumes — GitHub Copilot Chat, the Anthropic Claude IDE plugin, the OpenAI Codex IDE plugin, Continue, Cline, … — so a single registration there reaches all of them at once.

Adding a new CLI = one file under threadkeeper/adapters/ implementing the CLIAdapter contract. See CONTRIBUTING.md.

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Core systems

Spawn — primary parallelism primitive

spawn(prompt, slim=True, role=..., visible=False, ...) launches a child Claude session via a claude -p subprocess. By default slim=True: the child loads only the thread-keeper MCP, no embeddings, no third-party servers. ~500 MB RSS versus ~1.3 GB for a full child. Heuristic for the parent: N≥2 modular independent units of ≥5 min each = spawn signal.

A daemon measures combined child RSS every 10 s; admission control refuses a new spawn that would exceed THREADKEEPER_SPAWN_BUDGET_MB (3 GB default). Slim children that need semantic search delegate to the parent via search_via_parent — no per-child copy of sentence-transformers.

Learning loop (hermes-style)

Four loops materialize knowledge into Anthropic-style Skill files (SKILL.md under each detected CLI's skills directory — Claude's ~/.claude/skills/, Codex's ~/.codex/skills/, plus the canonical ~/.threadkeeper/skills/ mirror) with a CLI-agnostic ~/.threadkeeper/lessons.md fallback for CLIs that don't auto-trigger on the Skill format (Gemini / Copilot / bare MCP clients):

• Auto-review on close_thread — when a closed thread is rich (≥5 notes, ≥2 insight/move), close_thread spawns a slim child with SKILL_REVIEW_PROMPT + the thread's notes. The prompt is rubric-form (Q1–Q5 yes/no) with explicit positive examples for incident-vs-rule classification. The fork also receives a "recently active skills" block so it prefers PATCHing existing umbrellas over creating new ones (Hermes Agent v0.12's active-update bias). Child appends a lesson via lesson_append, optionally mirrors to ~/.claude/skills/<name>/SKILL.md, then closes with mark_skill_materialized. Opt in with THREADKEEPER_AUTO_REVIEW=1.
• Shadow-review daemon — every THREADKEEPER_SHADOW_REVIEW_INTERVAL_S seconds (default off; 15 min recommended), scans the diff of dialog_messages since the last cursor across all CLIs. The window filters internal review-child sessions (no self-pollution) and strips adapter [tool_result] / [tool_call] noise — Hermes v0.12's "clean context" rule. If ≥500 chars of meaningful signal remain, spawns a slim observer child that decides on class-level learning. Idempotent through events.kind='shadow_review_pass'.
• Extract daemon — every THREADKEEPER_EXTRACT_INTERVAL_S seconds (default off; 10 min recommended), scans recent dialog_messages with heuristic matchers (locale-aware "I want / next time / always" patterns, headers + insight markers, bullet regularities, paraphrase clusters via cosine ≥ 0.80) and enqueues candidates in extract_candidates.status='pending' for the agent to review via review_candidates() / accept_candidate(). The same self-pollution filter as shadow_review excludes internal review-child sessions. Where shadow extracts CLASS-LEVEL durable rules, extract harvests PER-INCIDENT decision-shaped utterances — sidesteps the empirical problem that agents focused on their primary task don't call note() / verbatim_user() on their own.
• Autonomous Curator — every THREADKEEPER_CURATOR_INTERVAL_S seconds (default off; 7 days recommended), spawns a slim child that reviews the EXISTING lessons.md + skill_usage inventory and writes ~/.threadkeeper/curator/REPORT-<isodate>.md with KEEP / PATCH / CONSOLIDATE / PRUNE recommendations. Pinned and foreground-authored entries are marked [PROTECTED] in the inventory so the curator never proposes destructive changes against them. Phase 1 is advisory-only — user reviews the REPORT and applies changes manually. Inspired by Hermes Agent v0.12's hermes curator cron agent.

Dialectic user model

A model of you, accumulated as you use the agent. dialectic_claim, dialectic_evidence (support / contradict / clarifying), dialectic_synthesis, dialectic_supersede. Honcho-inspired smoothed ratio (s-c)/(s+c+3) → low / medium / high / disputed confidence. Grouped by domain (style, values, workflow, ...) in brief().

i18n bundle

All multilingual regex and prompt fragments live in threadkeeper/i18n.py — the rest of the codebase stays English-only. Currently ships ten locales: English, Mandarin Chinese, Hindi, Spanish, Portuguese, French, German, Arabic, Russian, Japanese (~82 % of the world's speakers).

Adding a new language is a two-file PR — see CONTRIBUTING.md.

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Configuration

The most-used env knobs (full list in threadkeeper/config.py):

Knob	Default	Purpose
THREADKEEPER_DB	~/.threadkeeper/db.sqlite	SQLite file
THREADKEEPER_AUTO_REVIEW	"" (off)	auto-review on close_thread
THREADKEEPER_SHADOW_REVIEW_INTERVAL_S	0 (off)	shadow daemon tick (s)
THREADKEEPER_SHADOW_REVIEW_WINDOW_S	900	sliding window for shadow scan (s)
THREADKEEPER_EXTRACT_INTERVAL_S	0 (off)	extract daemon tick (s); 600 = 10 min recommended
THREADKEEPER_EXTRACT_WINDOW_MIN	30	sliding dialog window per extract pass (min)
THREADKEEPER_CURATOR_INTERVAL_S	0 (off)	curator daemon tick (s); 604800 = 7d recommended
THREADKEEPER_CURATOR_MIN_LESSONS	3	min lessons before curator engages
THREADKEEPER_CURATOR_DESTRUCTIVE	"" (advisory)	when "1": curator child applies its own PATCH/PRUNE/CONSOLIDATE directly instead of writing advisory REPORT only
THREADKEEPER_SPAWN_BUDGET_MB	3072	combined child RSS cap (MB); 0 disables
THREADKEEPER_INGEST_INTERVAL_S	30	transcript ingest tick (s)
THREADKEEPER_NO_EMBEDDINGS	""	force-disable sentence-transformers
THREADKEEPER_SKILL_NUDGE_INTERVAL	10	events between skill_hint nudges

Persist them via ~/.claude/settings.json's env block (Claude Code) or the equivalent env section in each CLI's config. Hot-config reload is tracked.

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Storage

~/.threadkeeper/db.sqlite (overridable via THREADKEEPER_DB). WAL mode for multi-writer concurrency. Optional notes_vec / dialog_vec HNSW indexes through sqlite-vec for sub-linear semantic search; fallback to Python-side cosine when the extension is missing.

One file. Backup = cp. Wipe memory = rm.

Hooks and small runtime artifacts: ~/.threadkeeper/hooks/.

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Verifying ingest across CLIs

python scripts/tk_verify_ingest.py

Walks every installed CLI adapter, parses recent transcripts in an isolated tempdir DB, reports per-source message counts and any silent parse failures. Read-only with respect to live state.

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Tests

pip install -e '.[semantic,dev]'
python -m pytest

412 tests passing on Python 3.11 / 3.12 / 3.13 (1 skipped). CI runs the suite on every push and PR.

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Project layout

threadkeeper/
├── server.py             # MCP entry: python -m threadkeeper.server
├── _setup.py             # `thread-keeper-setup` installer
├── config.py             # env-driven defaults
├── db.py                 # SQLite schema + sqlite-vec loader
├── identity.py           # session, self-cid, daemon launchers
├── ingest.py             # adapter-driven transcript ingest
├── brief.py              # render_brief / render_context
├── shadow_review.py      # autonomous learning observer
├── i18n.py               # 10 locales of regex + prompt bundles
├── adapters/             # one file per supported CLI
│   ├── claude_code.py
│   ├── claude_desktop.py
│   ├── codex.py
│   ├── gemini.py
│   ├── copilot.py
│   └── vscode.py
└── tools/                # @mcp.tool entries — 83 of them
    ├── threads.py
    ├── peers.py
    ├── spawn.py
    ├── skills.py
    └── ...

Detailed map in docs/ARCHITECTURE.md. Open work in docs/ROADMAP.md and the Issues tab.

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Contributing

PRs welcome — see CONTRIBUTING.md for the project map, test workflow, and recipes for adding a new CLI adapter or a new locale. Look for the good-first-issue label.

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License

MIT — see LICENSE.