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Mine your AI conversations — every tool, every month — for the ideas you forgot you had. Local-first.

Project description

Alluvia

ci MIT python local-first

Alluvia

Pan your AI history for gold.

Every conversation you've ever had with an AI tool is sediment. Most of it is sand — but scattered through it are the nuggets: ideas you never chased, solutions you solved once and forgot, threads you meant to finish. alluvia is the pan.

You think through problems in Claude Code. You debug in Cursor. You explore in ChatGPT. Each tool remembers nothing about the others, and neither do you. The idea you need today is sitting in a session from last spring, in a different app, under a title you'll never search for. alluvia finds it.

Local-first memory for AI-assisted work — across tools, with provenance and human judgment. Not another "AI memory": your raw sessions never leave the machine, every surfaced idea cites its source, and you rate what's gold.

alluvia ingests all of it into one local store, distills it into atomic ideas, clusters those into themes, and then does the part nothing else does: it finds the bridges — the places where your past self already met the problem your present self is holding.

A true story from alluvia's own validation gate: a security review in one tool flagged a server-side validation gap. alluvia connections linked it to debugging sessions in a different tool from 14 months earlier — same root cause, long forgotten. Then alluvia propose turned that bridge into a concrete fix plan, cited back to both sources. The human kept it. Every claim in this README traces to a logged validation gate — see docs/validation.

Sixty seconds

git clone <this-repo> && cd alluvia
uv run alluvia init        # detects your sources, sets up your LLM provider
uv run alluvia refresh     # distill → embed → cluster → map (local embeddings)
uv run alluvia themes

(PyPI package coming; installs the alluvia command.)

The four lenses

$ alluvia themes            # D — your thinking, clustered
• Docker Issues  [84 sessions/2 sources]  (2025-03→2026-06)
• Refresh Token Storage  [9 sessions/2 sources]
    Insecure localStorage tokens vulnerable to XSS; approaches discussed...

$ alluvia connections       # A — bridges across tools and months
🔗 "no cross-check between ids enables forgery"   [tool-A · 2026-06]
   ↔ "service isn't storing the id on upload"      [tool-B · 2025-04]
   why: same missing validation, found twice, 14 months apart.

$ alluvia unfinished        # B — threads you keep circling, never closing
🧵 Test Infra Reorganization   open · 4 sessions over 388 days

$ alluvia propose           # C — new next-steps, grounded in YOUR notes
[prop:50bda956] Add server-side consistency check  (feasibility 4/5)
    ...cites: note:104966a3, note:93de85cc
$ alluvia rate prop:50bda956 --keep

Plus a weekly digest (alluvia digest run --if-due) that brings ≤5 interrupt-worthy items to you — and stays silent when nothing clears the bar.

See it: the dashboard

alluvia serve --open        # http://localhost:8177

Five views over your map — corpus overview, theme bubbles by status, the cross-tool bridge graph, a weekly activity timeline with your longest-unfinished threads, and your full judgments history. One self-contained page, zero external requests, served only on 127.0.0.1.

Inside your assistant (MCP)

claude mcp add alluvia -- uv run --directory <repo> alluvia mcp

Eight tools let Claude Code / Cursor / any MCP client query your idea-map mid-conversation: "you circled this in April — here's where you landed."

What leaves your machine

Data Where it goes
Raw conversations Nowhere. Local SQLite, forever yours
Embeddings Nowhere. Computed locally (fastembed/ONNX)
Distill / label / propose calls Your configured LLM provider, under your API key, secret-scrubbed first
Telemetry There is none.

Provider is your choice — Groq (free tier works; alluvia chains per-model daily budgets automatically), OpenAI, or Anthropic — with per-role model overrides (SIFT_LLM_MODEL_PROPOSE=... for a stronger generator, cheap models for bulk extraction).

How it works

sources ─► ingest ─► RAW (never mutated) ─► distill ─► notes ─► embed
                                                                  │
              lenses ◄── themes/links/status ◄── cluster/link/track
                │
   CLI · MCP · weekly digest        ratings ─► the eval corpus (yours)

Three data classes with different guarantees: raw (source of truth, never touched), derived (rebuildable from raw — improve the pipeline, re-run, nothing lost), judgments (your ratings and digests — durable, never regenerated).

Honest limits

  • Windsurf/Antigravity transcripts live in schema-less protobuf stores; alluvia detects and skips them cleanly. ChatGPT ingestion uses the official data export (ZIP), not live capture.
  • Generated proposals are guardrailed (must cite your notes, novelty-gated, feasibility-labeled) but they're LLM output — you rate, alluvia learns.
  • All accepted trade-offs live in docs/DEBT.md, each with the condition that triggers fixing it.

MIT · built local-first on purpose: the research this project started from found that for developers, trust in this category is owned data or nothing.

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