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docs: update README for v1.9.1 — 21 tools, autonomic features, memory_graph

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Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Sam Valladares 2026-02-21 02:04:58 -06:00
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README.md
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> Your AI forgets everything between sessions. Vestige fixes that. Built on 130 years of memory research — FSRS-6 spaced repetition, prediction error gating, synaptic tagging — all running in a single Rust binary, 100% local.
### What's New in v1.8.0
### What's New in v1.9.1
- **One-call session init** — new `session_context` tool replaces 5 calls (~15K → ~500 tokens)
- **Token budgeting** — `token_budget` parameter on `search` and `session_context` for cost control
- **Reader/writer split** — concurrent SQLite reads via WAL mode, `Arc<Storage>` everywhere
- **int8 vectors** — 2x memory savings with <1% recall loss
- **FTS5 porter stemmer** — 15-30% better keyword search via stemming
- **Self-regulating memory** — Retention Target System auto-GCs decaying memories, Auto-Promote boosts frequently accessed memories, Waking SWR Tags give promoted memories preferential dream replay
- **`memory_health`** — retention dashboard with distribution buckets, trend tracking, and recommendations
- **`memory_graph`** — knowledge graph visualization with Fruchterman-Reingold force-directed layout
- **Dream persistence** — dream-discovered connections now persist to database, enabling graph traversal across your knowledge network
- **21 MCP tools** — up from 19
See [CHANGELOG](CHANGELOG.md) for full version history.
@ -33,7 +33,7 @@ claude mcp add vestige vestige-mcp -s user
# 3. Test
# "Remember that I prefer TypeScript over JavaScript"
# New session → "What are my coding preferences?"
# New session -> "What are my coding preferences?"
# It remembers.
```
@ -98,6 +98,7 @@ RAG is a dumb bucket. Vestige is an active organ.
| **Decay** | Nothing ever expires | **FSRS-6** — memories fade like yours do, keeping context lean |
| **Duplicates** | Manual dedup or none | **Self-healing** — automatically merges "likes dark mode" + "prefers dark themes" |
| **Importance** | All memories are equal | **Synaptic tagging** — retroactively strengthens memories that turn out to matter |
| **Health** | No visibility | **Retention dashboard** — track avg retention, distribution, trends, and recommendations |
| **Privacy** | Usually cloud-dependent | **100% local** — your data never leaves your machine |
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@ -118,17 +119,19 @@ This isn't a key-value store with an embedding model bolted on. Vestige implemen
**Memory States** — Active, Dormant, Silent, Unavailable. Memories transition between states based on usage patterns, exactly like human cognitive architecture.
**Memory Dreaming** *(v1.5.0)* — Like sleep consolidation. Replays recent memories to discover hidden connections, strengthen important patterns, and synthesize insights. Based on the [Active Dreaming Memory](https://engrxiv.org/preprint/download/5919/9826/8234) framework.
**Memory Dreaming** *(v1.5.0)* — Like sleep consolidation. Replays recent memories to discover hidden connections, strengthen important patterns, and synthesize insights. Connections persist to a graph database for traversal. Based on the [Active Dreaming Memory](https://engrxiv.org/preprint/download/5919/9826/8234) framework.
**ACT-R Activation** *(v1.5.0)* — Retrieval strength depends on BOTH recency AND frequency of access, computed from full access history. A memory accessed 50 times over 3 weeks is stronger than one accessed once yesterday. Based on [Anderson, 1993](http://act-r.psy.cmu.edu/).
**Automatic Consolidation** *(v1.5.0)* — FSRS-6 decay runs automatically every 6 hours + inline every 100 tool calls. Episodic memories auto-merge into semantic summaries. Cross-memory reinforcement strengthens neighbors on access. No manual maintenance needed.
**Waking SWR Tagging** *(v1.9.0)* — Memories promoted during waking use get sharp-wave ripple tags for preferential replay during dream consolidation. 70/30 tagged-to-random ratio ensures important memories get replayed first. Based on [Buzsaki, 2015](https://doi.org/10.1038/nn.3963).
[Full science documentation →](docs/SCIENCE.md)
**Autonomic Regulation** *(v1.9.0)* — Self-regulating memory health. Auto-promotes memories accessed 3+ times in 24h (frequency-dependent potentiation). Auto-GCs low-retention memories when average retention falls below target. Consolidation triggers on 6h staleness or 2h active use.
[Full science documentation ->](docs/SCIENCE.md)
---
## Tools — 19 MCP Tools
## Tools — 21 MCP Tools
### Context Packets (v1.8.0)
| Tool | What It Does |
@ -147,10 +150,16 @@ This isn't a key-value store with an embedding model bolted on. Vestige implemen
### Cognitive Engine
| Tool | What It Does |
|------|-------------|
| `dream` | Memory consolidation via replay — discovers hidden connections, synthesizes insights |
| `dream` | Memory consolidation via replay — discovers hidden connections, synthesizes insights, persists connections to graph database |
| `explore_connections` | Graph traversal — reasoning chains, associations via spreading activation, bridges between memories |
| `predict` | Proactive retrieval — predicts what memories you'll need next based on context and activity patterns |
### Autonomic (v1.9.0)
| Tool | What It Does |
|------|-------------|
| `memory_health` | Retention dashboard — avg retention, distribution buckets (0-20%, 20-40%, etc.), trend (improving/declining/stable), recommendations |
| `memory_graph` | Knowledge graph visualization — subgraph export with Fruchterman-Reingold force-directed layout, up to 200 nodes with edge weights |
### Scoring & Dedup
| Tool | What It Does |
|------|-------------|
@ -189,7 +198,7 @@ At the start of every session:
| "Remind me..." | Creates a future trigger |
| "This is important" | Saves + strengthens |
[Full CLAUDE.md templates →](docs/CLAUDE-SETUP.md)
[Full CLAUDE.md templates ->](docs/CLAUDE-SETUP.md)
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@ -208,7 +217,7 @@ vestige restore <file> # Restore from backup
## Technical Details
- **Language:** Rust (52,000+ lines, 1,100+ tests)
- **Language:** Rust (55,000+ lines, 1,100+ tests)
- **Binary size:** ~20MB
- **Embeddings:** Nomic Embed Text v1.5 (768-dim, local ONNX inference via fastembed)
- **Vector search:** USearch HNSW (20x faster than FAISS)
@ -217,6 +226,7 @@ vestige restore <file> # Restore from backup
- **Dependencies:** Zero runtime dependencies beyond the binary
- **First run:** Downloads embedding model (~130MB), then fully offline
- **Platforms:** macOS (ARM/Intel), Linux (x86_64), Windows
- **Cognitive modules:** 28 stateful modules (15 neuroscience, 11 advanced, 2 search)
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@ -264,7 +274,7 @@ Cache locations:
- **Windows**: `%LOCALAPPDATA%\vestige\cache\fastembed`
</details>
[More troubleshooting →](docs/FAQ.md#troubleshooting)
[More troubleshooting ->](docs/FAQ.md#troubleshooting)
---