NeuroWeave Timeline (NWT)

Process memory for AI agents and humans. NWT remembers how a project became what it is — not just what it is now.

Most tools remember results. NWT remembers evolution.

Traditional memory:  User → Context → Summary → Memory
Timeline memory:     User → Action  → Timeline Event → Evolution Graph

Every meaningful action in your project — a decision, a refactor, a file creation, a bug fix — becomes a node in a durable timeline. The links between nodes form an Evolution Graph that explains why the project looks the way it does today.

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What you can ask

Question	One-liner
Why does this file exist?	nwt explain activation.py
Why was this architecture chosen?	nwt search "architecture decision"
What happened three months ago?	nwt history
What decisions led to the current design?	nwt story
Show me the evolution graph	nwt graph
What did that commit actually do?	nwt git-hook-status
What changed between two events?	nwt diff 1 5
Merge small consecutive events	nwt compact

AI agents reach the same answers over MCP — see MCP integration.

Auto-grow: timeline from git commit

The single biggest reason NWT timelines are empty is friction. The v0.2 fix is a post-commit hook that logs an event on every commit — no human or agent typing required.

nwt init
nwt install-git-hook
git commit -m "Refactor retrieval layer" -m "Reason: lookup latency was high"
nwt: logged [12] Refactor retrieval layer

Add --strict to refuse events without a Reason: line, and --ai-command "claude -p" to let an external model fill the reason when the human didn't. See docs/git-hook.md.

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30-second quick start

pip install -e .
cd your-project
nwt init
nwt log "Add activation engine" \
      --files activation.py \
      --reason "retrieval was slow"
nwt history
nwt graph

That's it. Storage is plain JSON under .nwt/. No database, no embeddings, no vendor lock-in, no daemon.

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A tour of the output

nwt history — what happened, in order

  [1] 2026-06-15  Project scaffolded  [setup, milestone]
      reason: Kickoff the MVP
      files:  pyproject.toml, README.md
  [2] 2026-06-15  Add memory engine  [core, milestone]
      reason: Need a place to put things
      files:  memory.py
  [3] 2026-06-15  Add activation spreading  [memory, optimization]
      reason: Retrieval was sequential and slow
      files:  activation.py, retriever.py
  [4] 2026-06-15  Add decay mechanism  [memory]
      reason: Stale nodes should fade
      files:  activation.py
  [5] 2026-06-15  Vectorize activation  [refactor, performance]
      reason: Loop was the hot path in profiling
      files:  activation.py

nwt graph — the evolution as a tree

○    1  Project scaffolded
│
│    2  Add memory engine
      ├─ sibling  → [   4] Add decay mechanism
      └─ extends  → [   3] Add activation spreading
│
│    3  Add activation spreading
│
│    5  Vectorize activation

nwt story — 100 events compressed to one page

# memory-engine-demo — evolution summary

span: 2026-06-15 → 2026-06-15  (5 events)

milestones:
  - 1  Project scaffolded          — Kickoff the MVP
  - 2  Add memory engine           — Need a place to put things
  - 3  Add activation spreading    — Retrieval was sequential and slow
  - 4  Add decay mechanism         — Stale nodes should fade
  - 5  Vectorize activation        — Loop was the hot path in profiling

spine file: activation.py

decisions (events with stated reasons):
  - [1] Project scaffolded: Kickoff the MVP
  - [2] Add memory engine: Need a place to put things
  ...

nwt explain activation.py — why a file exists

# activation.py
created in:  event 3
modified in: 4, 5

reason:
  Retrieval was sequential and slow

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New in v0.2

Events chain automatically

nwt log appends after the latest event by default, so a plain sequence of logs forms one linear history. Start an explicit branch with --parent none, or continue after a specific event with --parent ID.

Event importance

Events now have an importance field: low, normal (default), high, milestone.

nwt log "Fix login bug" --summary "..." --importance high
nwt log "Project launched" --summary "..." --importance milestone

The story command reports events grouped by importance.

nwt diff — compare two events

nwt diff 1 5
# Output:
# Diff: [1] → [5]
# Events: 5 between these points
# Added: new_feature.py
# Removed: old_feature.py
# In both (likely modified): main.py

nwt compact — merge small events

When you have many small consecutive events with the same tags, compact them:

nwt compact
# Compacted: 50 → 35 events (merged 15)
# backup: .nwt/snapshots/pre-compact-20260920T120000

Options:

• --time-window 3600 — group events within 1 hour (default)
• --min-group 3 — minimum group size to merge (default)
• --dry-run — preview the merge without touching disk

Compact backs up timeline/, relations/ and the counter to .nwt/snapshots/ before rewriting, and remaps parents, typed relations, indices, and the id counter onto the renumbered events.

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How it works

NWT lives in your project as a single .nwt/ directory:

your-project/
└── .nwt/
    ├── metadata.json       # project name, schema version
    ├── .counter.json       # next event id
    ├── timeline/           # one JSON file per event
    │   ├── 000001.json
    │   ├── 000002.json
    │   └── ...
    ├── relations/          # typed edges out of each source event
    ├── snapshots/          # pre-compact backups land here
    └── indices/            # derived, rebuildable
        ├── files.json
        └── tags.json

Everything is JSON, atomically written. The whole workspace is grep-friendly and git diff-friendly. See docs/architecture.md for the rationale.

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MCP integration

For agent developers — NWT ships an MCP server exposing the same answers as tools:

Tool	Returns
create_event	A persisted event with id and timestamp
search_history	Matching events across task/summary/reason/files/tags
get_project_story	Compressed project story (milestones, decisions, spine file)
explain_file	Created/modified-in + earliest reason for a file

Wire it up in your MCP client:

{
  "mcpServers": {
    "nwt": {
      "command": "nwt-mcp",
      "env": { "NWT_ROOT": "/absolute/path/to/your/project" }
    }
  }
}

The server picks the workspace from $NWT_ROOT if set, else its own cwd. See docs/mcp.md for the recommended agent loop:

1. Session start: call get_project_story to load context.
2. For unfamiliar files: call explain_file rather than reading cold.
3. As work is done: call create_event with a reason explaining why.
4. When uncertain: call search_history with a hypothesis from the current code.

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安装

# from a clone (editable)
git clone https://github.com/Thatgfsj/neuroweave-timeline
cd neuroweave-timeline
pip install -e .

# from PyPI (coming soon)
pip install NWtimeline

需要 Python 3.10+。CLI 依赖于 click；MCP 服务器依赖于 mcp。两者都是自动安装的。

安装开发依赖（pytest）并运行测试套件：

pip install -e ".[dev]"
pytest -q

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Roadmap

v0.2 (this release) is the "auto-grow" cut: git hook integration with strict mode and AI-filled reasons, automatic event chaining, the importance field, diff, and compact. The timeline fills itself now.

• v0.3 — snapshots (nwt snapshot / nwt restore) and deeper git linkage (commit SHAs on every event, nwt rewind).
• v0.4 — multi-agent collaboration history (concurrency-safe writes, per-author identity).
• v0.5 — NWC integration, only if NWT earns it on its own.

See docs/roadmap.md and docs/standalone.md for the full story.

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Contributing

Issues and PRs are welcome. The whole project is ~1,500 lines of Python plus docs — easy to read end-to-end. Start with docs/architecture.md for the layout and CONTRIBUTING.md for the workflow.

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Security

NWT stores only what you give it, on disk, in your project's .nwt/. It does not phone home, does not read environment variables other than NWT_ROOT, and writes nowhere else. The .gitignore refuses to track tokens, keys, or .env files. See SECURITY.md for the full policy.

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License

MIT — see LICENSE.