---

Every AI agent session starts from scratch. Claude Desktop doesn't know what Cursor just learned. DeepSeek Harness (dsh) doesn't know what you told Claude Code yesterday. Whatever agent you're running — international or domestic, IDE plugin or CLI harness — it forgets your preferences every time you start a new conversation.

You've been manually repeating context — project conventions, personal preferences, past decisions — across agents that should already know. This isn't a limitation of the models. It's a missing infrastructure layer.

MemVault is that layer. A lightweight, self-hosted memory router that sits between your agents and their context. It speaks plain MCP — no MemVault-specific SDK, no per-agent API integration. Any MCP-compatible agent, from any vendor, automatically shares the same persistent memory the moment it connects.

Who it's for:

• Anyone running an MCP-capable agent — Claude Code, Claude Desktop, Cursor, Cline, Continue, DeepSeek Harness (dsh), or any other MCP client, domestic or international — who wants preferences, project context, and past decisions to persist across sessions without repeating yourself
• Multi-agent power users running several of the above side by side, on different models, from different vendors — all sharing the same memory without configuration
• Platform teams deploying AI-assisted workflows where consistency matters across a mixed agent fleet: code review conventions, architecture decisions, project-specific preferences
• Anyone tired of telling their AI the same thing twice — MemVault works the way your brain should: you say it once, it's there when you need it, no matter which agent is asking

Quick Start  ·  How It Works  ·  What MemVault Gives You  ·  Why MemVault  ·  MCP Server  ·  CLI Reference  ·  Integrations  ·  Architecture  ·  Project Status  ·  Testing  ·  Documentation  ·  Contributing  ·  License

---

Quick Start

# Install (Linux / macOS): official script, auto-verifies SHA-256
curl -fsSL https://raw.githubusercontent.com/dreamor/memvault/master/scripts/install.sh | bash
export PATH="$HOME/.memvault/bin:$PATH"

# Windows (PowerShell):
#   powershell -ExecutionPolicy Bypass -File scripts\install.ps1

# Homebrew (Apple Silicon):
#   brew install dreamor/tap/memvault

# Or from crates.io:
#   cargo install memvault-cli memvault-mcp

# Save a MUST-level preference (injected as instruction, agent must follow)
memvault save --content "User prefers Python" --priority MUST --type preference \
  --instruction "Use Python for code, not Java" --tags "coding,python"

# Search across all memory (hybrid: keyword + semantic when embedding is enabled)
memvault search --query "Python"

# See what context gets injected when a specific agent connects
memvault session-start --agent-id claude-desktop --context "Help me write code"

# Extract structured memories from free text
memvault extract --text "I prefer dark mode. Our project uses Rust." --save

# Auto-generate agent instruction files from memory
memvault sync

# All in one: dedup, decay, archive stale memories
memvault dedup && memvault decay

Verify your install in 5 seconds:

memvault-cli --version
# memvault 0.3.0

# sanity check: list saved memories (verifies DB is healthy)
memvault-cli list

Local Ollama Demo (Zero Cost, Stays on Your Machine)

MemVault is plug-and-play with a local Ollama: unconfigured LLM extraction (full-text understanding / failure reflection / relation extraction) auto-detects a local Ollama; embeddings use the local model with the ollama or auto provider.

# 1. Install and start Ollama
brew install ollama && brew services start ollama    # or the official installer

# 2. Pull models
ollama pull nomic-embed-text        # embeddings, 768-dim (default for ollama/auto)
ollama pull qwen2.5:3b-instruct     # chat: LLM extraction/reflection (default qwen2.5:7b, use 3b on small machines)

# 3. (Optional) Pin the providers explicitly — persist them in ~/.memvault/.env
#    (shell exports also work — env vars take precedence over the file — but the file survives reboots)
mkdir -p ~/.memvault
cat >> ~/.memvault/.env <<'EOF'
MEMVAULT_EMBEDDING_PROVIDER=ollama
MEMVAULT_LLM_EXTRACTION_PROVIDER=ollama
MEMVAULT_LLM_EXTRACTION_MODEL=qwen2.5:3b-instruct
EOF

# 4. Verify
memvault status     # Embedding provider: configured and reachable
memvault save --content "Build server IP is 10.20.30.40"   # output (embedded int8)
memvault outcome --task "Deploy trading service" --status failure --cause "Disk space insufficient" --task-type deploy
#   → Lesson (Llm): ... means failure reflection ran through the local LLM (not the rule-based fallback)

With no configuration at all (neither env vars nor ~/.memvault/.env), LLM extraction auto-detects a local Ollama and enables itself (default model qwen2.5:7b; pull it in advance with ollama pull qwen2.5:7b, or point MEMVAULT_LLM_EXTRACTION_MODEL at an installed model). Embeddings still default to the in-process native embedder; set MEMVAULT_EMBEDDING_PROVIDER=auto in ~/.memvault/.env to prefer Ollama and fall back to native when it isn't running.

---

How It Works

MemVault is a pipeline, not a single script. Every stage below is a shipping module:

Agent connects (MCP stdio/SSE)
        │
        ▼
┌───────────────────┐
│  Agent Router      │  ← match agent type/tag → filter relevant memory
│  (Agent Registry)  │
└────────┬──────────┘
         │
         ▼
┌───────────────────┐
│  Memory Retrieval  │  ← keyword (BM25) + vector (embedding) + RRF fusion
│  (3 search modes)  │     synonym expansion · scoring · soft filtering
└────────┬──────────┘
         │
         ▼
┌───────────────────┐
│  Auto-Injection    │  ← MUST-level → instruction prompt
│                    │     REFERENCE → context resource
│                    │     NORMAL    → search result
└────────┬──────────┘
         │
         ▼
  Agent receives context ──→ makes better decisions

• Storage: SQLite with bundled FTS5 (full-text search); embeddings stored int8-quantized (~1/4 the size of f32 at near-identical ranking quality, legacy f32 rows still readable)
• Retrieval: BM25 keyword search over FTS5 with CJK bigram tokenization (Chinese two-character words match correctly) and tiered match fallback (strict → relaxed unigram → synonym OR; relaxations are reported, never silent), local-first embedding (in-process native by default — switch to local Ollama or any OpenAI-compatible model), RRF fusion with per-result recall provenance (kw#2/vec#5), synonym expansion, relevance scoring, soft intent filtering
• Pipeline: Automatic entity extraction, delta-write on save (near-duplicates skipped, similar memories absorb only the residual), semantic deduplication, time-based decay, archive of stale memories
• Sync: Zero-invasion file generation — memvault sync produces CLAUDE.md, AGENTS.md, etc. directly from database contents

---

What MemVault Gives You

• Auto-Injected Context: Session start automatically pulls relevant memory by agent identity — MUST-level rules land as instructions, not just chat history
• Hybrid Retrieval: BM25 + vector + RRF fusion with synonym expansion, relevance scoring, and per-result provenance (which path recalled each memory, at what rank) — available via CLI, MCP tool, and REST API
• Explainable Injection: every candidate dropped on the way into an agent's context is recorded with a reason (budget, caps, intent/type penalties) — "why didn't the agent get this memory?" always has an answer
• MUST Enforcement: MUST-priority memories are never filtered or truncated. Always in context, always obeyed — trust comes from provenance (human-authored/reviewed), with an opt-in fallback for memories independently corroborated by multiple identity-verified agents (MEMVAULT_CORROBORATION_GATE), so a single spoofed/compromised agent can't unilaterally inject a binding MUST
• Multi-Agent Awareness: Agent Registry with type/tag-based soft filtering (score demotion, not hard exclusion)
• MCP Proxy: Transparent proxy that injects memory into ANY upstream MCP server's responses — zero client changes
• Compliance Tracking: inject_session_id traces what was injected and measures follow-through rate
• Cross-Platform: CLI + MCP Server (stdio & SSE) + Web Dashboard (browser) + Obsidian Plugin
• Zero-Invasion Sync: Generate AGENTS.md / CLAUDE.md from memory — no per-agent config files to edit
• Contextual Extraction, Local-First: Rule-based keyword extraction by default; optionally understands a full user+assistant exchange via an LLM, auto-detecting a local Ollama for free before ever touching a remote API
• History & Rollback: Every update/delete is snapshotted into memory_history — memvault checkpoints + memvault restore roll one memory back without touching the rest
• Self-Diagnostics: memvault status reports exactly which features are degraded when no embedding provider is configured, plus a schema fingerprint (migration version + checksum) for cross-database comparison
• Episodic Memory: record_outcome records task results; failures are distilled into lessons and auto-injected next time (REFERENCE → MUST only with human approval), so the same trap isn't hit twice
• Procedural Skill Activation: skills whose trigger matches intent are injected as structured [SKILL] blocks with success-rate stats (shown after ≥3 runs); failures flag the skill for revision (version++ on human edit), repeated successes auto-draft new skills into the review inbox
• Semantic Knowledge Links: lightweight relation triples, repeated facts consolidated into a linked semantic fact with provenance, and superseded facts archived (never re-injected, still listable & restorable)
• Team Shared Pool & SOP Import: memories marked shared are injected into every session (capped at 20); Markdown SOPs can be batch-imported as verifiable skills
• Delta-Write on Save: every save is checked against its namespace first — near-duplicates are skipped, similar memories absorb only the residual (what's genuinely new) and get their strength refreshed, so the library converges instead of accumulating near-copies; --force / force_insert bypasses
• Task-Level Evaluation: memvault bench samples your own failure history (episodes that distilled a lesson) and measures lesson retrieval/injection rates — and with --judge, an LLM scores "plan without vs. with memory" against the known failure cause, so you see task-success lift, not just retrieval recall
• Two-Phase Injection (never blocks): MUST rules resolve deterministically with zero embedding calls and are served immediately; the semantic pipeline prefetches in the background and lands within a short window (250ms) — if it doesn't, the deterministic baseline is served and the request moves on (two-phase design)
• Conversation-N-Gram Retrieval: retrieval keys are conditioned on the recent turn window, weighted by recency so the current focus dominates — not a single flat query
• Single Canonical Injection Channel: per-agent inject_channel (mcp / proxy / sync in agents.yaml) restricts automatic injection to one delivery path, so the same memory is never sent to the same agent twice
• Data You Own: Single SQLite file. Full export/import. No cloud dependency. Your data, your machine.

---

Why MemVault

Feature	Plain CLAUDE.md	Vector DB + RAG	MemVault
Context injection	Manual edits	Query-time only	Auto on session start
Multi-agent sharing	Copy-paste	Separate indexes	Single shared store
MUST enforcement	None	None	Instruction-layer injection
Search modes	File grep	Embedding only	BM25 + Vector + Hybrid
Synonym expansion	No	No	Built-in
Deduplication	No	No	Semantic dedup pipeline
Write-time delta merge	No	No	Near-dupes skip, similar absorb the residual at save
Task-level evaluation	None	Recall metrics only	bench: with-vs-without-memory task success delta
Decay / archival	No	No	Time-based + auto archive
Memory extraction	Manual	N/A	Rule-based by default; optional local-first LLM extraction
MCP native	No	No	stdio + SSE + Proxy
Agent differentiation	Global file	Query filter	Type/tag registry
Compliance tracking	None	None	inject_session_id + rate
Self-hosted	Yes	Varies	Single binary, no cloud

MemVault complements your existing agent setup rather than replacing it. Keep your LLM, your IDE, and your workflow exactly as they are. MemVault adds the memory layer underneath.

---

MCP Server

Tier-1 agents (Claude Code, OpenCode, dsh, Gemini CLI, Codex) have one-command plugin installs — see Installing into your agents first. Everything below is the universal fallback for any other MCP client.

stdio (any standard MCP client)

MemVault speaks plain MCP stdio — the same mcpServers JSON works verbatim in Claude Desktop, Cursor, Cline, Continue, and any other client that reads this format:

{
  "mcpServers": {
    "memvault": {
      "command": "/path/to/memvault-mcp",
      "args": ["--db", "~/.memvault/data.db"],
      "env": { "OPENAI_API_KEY": "sk-..." }
    }
  }
}

A couple of clients use their own one-liner instead of hand-editing JSON:

# Claude Code
claude mcp add memvault /path/to/memvault-mcp -- --db ~/.memvault/data.db

DeepSeek Harness (dsh) — a domestic (China) agent harness — gets deeper treatment than a generic stdio config: a native Cordis plugin (dsh-plugin/) that auto-injects memory into the system prompt and auto-extracts at turn end, with no per-turn cooperation required from the agent. See docs/INSTALL.md §2.5 for both the zero-code MCP route and the deep-integration plugin.

Other MCP-compatible agents — international or domestic, IDE plugin or CLI harness — should work the same way: any client implementing standard MCP stdio/SSE can connect without MemVault-side changes. The ones above are the ones we've actually verified; if you get MemVault working with another one, a PR to this list is welcome.

SSE (multi-client, network-accessible)

memvault-mcp --transport sse --port 3777
# Clients connect at http://127.0.0.1:3777/mcp

SSE features: multi-client simultaneous connections, auto-triggered embedding backfill on initialization, HTTP remote access.

Note: --transport sse only mounts the MCP-over-HTTP endpoint (/mcp) — it does not expose the REST API (/api/*). The Web Dashboard is served by the REST backend (memvault-mcp --transport http --serve-web <dist>), and the Obsidian plugin also uses the REST API and requires --transport http instead. See docs/INSTALL.md §2.6.

18 MCP Tools

Tool	Description
save_memory	Save with auto-embedding; delta-write by default (near-dupes skip, similar merge) — force_insert to bypass
record_outcome	Record a task outcome (episodic memory); failures reflect into lessons
import_skills	Import skills from a Markdown SOP (headings → skills, list items → steps)
search_memory	Keyword / semantic / hybrid
session_start	Agent-aware context injection; honors the agent's inject_channel (skips with an explanation when another channel is canonical)
review_memory	Approve / reject / edit
delete_memory	Remove a memory
extract_memories	Structured extraction from text
run_dedup	Deduplication scan
run_decay	Decay + auto-archive
confirm_read	Mark read (updates access_count)
list_inbox	List memories pending human review
run_promote	Promote pipeline (L1→L2→L3), archive sources to L0
report_compliance	Report follow/violate status for an injected session
get_compliance_report	Compliance rates per session or aggregate
add_evidence	Record evidence relations: supports / contradicts / sourced_from
get_memory_evidence	Get the raw-evidence chain a memory was distilled from (its L0 trace rows) plus its evidence profile — read-only grounding, so an agent can quote the original session text and name its sources
get_effectiveness_report	Automatic effectiveness judgments for injected memories (useful/neutral/harmful/insufficient-context rates, judged from record_outcome) — independent of the manual report_compliance flow

2 MCP Resources

URI	Content
memory://user-profile	MUST-level rules, auto-loaded on connect
memory://project-context	REFERENCE-level project context

Configuration (.env file & environment variables)

Copy .env.example to ~/.memvault/.env and uncomment what you need — it is also the single source of truth documenting every key.

Precedence (high → low): CLI flags > process environment > ~/.memvault/.env > built-in defaults. Every binary loads the env file first thing at startup; --env-file <path> or MEMVAULT_ENV_FILE points elsewhere, and a missing file is silently skipped. memvault status prints where each setting came from (env / file / default).

Two groups are intentionally not in the table below: the host installation contract (MEMVAULT_AGENT_ID, MEMVAULT_HOOK_EXTRACT, … — per-agent values authored in each host's plugin/mcpServers config), and the proxy's upstreams topology (structured data, lives in ~/.memvault/proxy.yaml).

Variable	Purpose	Default
MEMVAULT_EMBEDDING_PROVIDER	Provider: native (in-process, default), auto (Ollama-first, native fallback), ollama/local, openai, or openai-compatible (any OpenAI-compatible endpoint)	native
MEMVAULT_EMBEDDING_API_KEY (legacy fallback: OPENAI_API_KEY)	API key for remote providers (not needed for local Ollama); the default native provider needs no key	(not needed — native local model)
MEMVAULT_EMBEDDING_API_BASE	Any OpenAI-compatible base URL (OpenAI / Azure / vLLM / gateway...). For ollama/local the embedder uses Ollama's native endpoint http://localhost:11434/api	https://api.openai.com/v1 / http://localhost:11434/api (Ollama)
MEMVAULT_EMBEDDING_MODEL	Embedding model: bge-small-zh (zh, ~95MB) / multilingual/e5-base for native; nomic-embed-text (768-dim) for Ollama; or any model name for API providers	bge-small-zh (native) / nomic-embed-text (Ollama) / text-embedding-3-small (API)
MEMVAULT_EMBEDDING_DIM	Vector dimensions	768 (local/Ollama) / 1536 (API)
MEMVAULT_LLM_EXTRACTION_PROVIDER	Optional: enables LLM-based contextual memory extraction (understands a full user+assistant exchange, not just keyword lines). Unset/auto → local-first: auto-detects a running local Ollama and uses it for free, no config needed; falls back to rule-based if none is running. openai/openai-compatible/custom → explicit remote provider (never auto-enabled just because an API key exists elsewhere — remote calls cost money and carry hallucination risk). off/disabled/none → force pure rule-based, even if local Ollama is running	(unset — local-first, rule-based if no local Ollama)
MEMVAULT_LLM_EXTRACTION_API_KEY (falls back to OPENAI_API_KEY) / MEMVAULT_LLM_EXTRACTION_API_BASE / MEMVAULT_LLM_EXTRACTION_MODEL	Chat-completions endpoint config for LLM extraction	local: http://localhost:11434/v1 / qwen2.5:7b (no key) — remote: https://api.openai.com/v1 / gpt-4o-mini
MEMVAULT_RELATIONS	Opt-in LLM relation extraction: true makes extract_memories (mode=llm) also persist supports/contradicts/sourced_from triples	(unset / false)
MEMVAULT_DELTA_WRITE	Delta-write on save: dedup within the same namespace first — near-duplicates skipped, similar memories absorb the residual. false turns it off; per-save bypass via --force / force_insert	true
MEMVAULT_CONTEXT_NGRAM_WINDOW	How many recent observed turns build the recency-weighted retrieval key used by proxy auto-injection	5
MEMVAULT_HOOK_EXTRACT_MIN_FRICTION	Minimum friction score (tool retries, rejected tool calls, mid-session corrections) a Stop-hook-triggered extract must reach before it saves anything. 0 disables the gate — extracts on every Stop, as before this existed	1
MEMVAULT_IDENTITY_VERIFICATION	Record whether a save_memory call's agent_id actually had a registered agents.yaml API key checked (Memory.identity_verified), vs. running unauthenticated. false stops recording it; recording alone never changes trust decisions	true
MEMVAULT_CORROBORATION_GATE	Opt-in MUST trust path: a MUST memory independently corroborated by enough distinct identity-verified agents (see MEMVAULT_CORROBORATION_MIN_AGENTS) is treated as trusted even without human review. true turns it on — off by default, so is_trusted output is unchanged unless you opt in	false
MEMVAULT_CORROBORATION_MIN_AGENTS	Minimum distinct identity-verified agents required for the corroboration gate above	2
MEMVAULT_DB_POOL_SIZE	SQLite connection pool size	5
MEMVAULT_CORS_ORIGIN	Comma-separated allowed CORS origins for REST (unset = localhost only)	(localhost only)
MEMVAULT_DB	SQLite database path	~/.memvault/data.db
RUST_LOG	Log verbosity	info
MEMVAULT_HOME	Base directory: where .env lives, plus the model cache (~/.memvault/models) and agents.yaml. Environment-only — it can't be set inside the .env file itself (a file can't define its own location)	~/.memvault
HF_ENDPOINT	HuggingFace endpoint override for native model downloads (e.g. https://hf-mirror.com on CN networks)	(HuggingFace default)
MEMVAULT_EXTRACT_ASSISTANT	Proxy-path contextual extraction of agent-produced text: on by default but saved downgraded (review:required, lowered confidence — nothing agent-produced is trusted before human review). off/disabled/false/0 disables extracting from agent responses entirely	(on — downgraded)

---

CLI Reference

save · outcome · search · list · review · delete · session-start · resource · extract · dedup · decay · doctor · promote · backup · export · import · import-skills · import-agent · ingest · confirm-read · sync · checkpoints · restore · supersede · status · bench · eval-history

memvault <command> --help   # detailed usage per command

Key Commands

Command	What It Does
save	Save a memory with priority, type, optional instruction. Delta-write by default: near-duplicates are skipped, similar memories absorb the residual; --force to bypass
outcome	Record a task result (success/failure/partial); failures are distilled into lessons that auto-inject into similar future tasks
search	Hybrid retrieval with relevance scoring; flags: --query, --top-k, --namespace
session-start	Simulate what context an agent receives on connect; a multi-line --context is treated as a turn sequence and weighted by recency
extract	Parse free text, extract structured memories
import-skills	Import skills from a Markdown SOP (# / ## headings → skills, list items → steps); enters the review inbox unless --approve
import-agent	Cold-start import from another agent's native memory files: Claude Code/Desktop (CLAUDE.md/auto-memory), Codex CLI (AGENTS.md), Hermes Agent (USER.md/MEMORY.md/skills), Qoder (.qoder/rules), OpenClaw (experimental); --scan to detect-only, --path to override, --paste/stdin as a generic fallback for any other agent, enters the review inbox unless --approve
ingest	Incrementally ingest agent session transcripts (Claude Code / Codex / Hermes) into memory: turns with extractable signal are kept as L0 evidence rows, and their extracted candidates carry source_trace_ids back to that evidence; a per-session watermark means each turn is processed once. --dry-run to preview, --approve to skip the review inbox, --agent/--home/--max-sessions to scope
sync	Generate agent instruction files (AGENTS.md / CLAUDE.md / MEMORY-INDEX.md, …) from memory (with --watch)
dedup	Scan and merge semantically duplicate memories (vector-assisted when an embedding provider is configured)
checkpoints	List memory history snapshots (per-memory or global); flags: --memory-id, --limit
restore	Revert a memory to the state captured by a checkpoint (--history-id)
supersede	Archive an old fact and point it at its replacement (nothing is deleted; search skips superseded, list keeps them)
status	Show embedding provider readiness (distinguishes not-configured / explicitly-disabled / configured-but-unavailable) and which features degrade without it
doctor	Read-only memory hygiene lint: dangling/stale/duplicate/contradicted + machine-readable --json
bench	Task-level memory benchmark: samples your own outcome history, measures lesson retrieval/injection rates; --judge adds an LLM-scored "plan without vs. with memory" success delta; each run persists itself for eval-history
eval-history	Trend-over-time view of past bench/doctor runs — every run persists itself automatically, this just lists what accumulated
decay	Archive stale memories based on access recency
backup	Create a consistent point-in-time SQLite backup
export / import	Backup and restore — JSON to a file or a directory (writes export.json inside); Markdown to/from a directory or a single .md file; import is idempotent (ids already present are skipped, never overwritten)
confirm-read	Mark memories as read (updates access_count)

---

Integrations

Installing into your agents

MemVault ships native adapters for most agents — one shared store, per-host identity via MEMVAULT_AGENT_ID, four tiers (Tier 1/2/3 details below; per-client registration snippets in integrations/mcp-clients/).

Tier 1 — one-command native plugins (memory injected by hooks; extraction opt-in where the host exposes lifecycle hooks):

Agent	Install	Recall	Extract
Claude Code	/plugin marketplace add dreamor/memvault, then /plugin install memvault@memvault (two separate prompts) — bundles the MCP server, 4 skills, 3 slash commands	✅ SessionStart hook	✅ Stop hook, enable with MEMVAULT_HOOK_EXTRACT=1
OpenCode	merge integrations/opencode/opencode.json into your project	✅ system transform	✅ on session.idle
DeepSeek Harness (dsh)	built-in Cordis plugin dsh-plugin/ — see docs/INSTALL.md §2.5	✅ system prompt	✅ turn-end
Gemini CLI / Antigravity	gemini extensions install https://github.com/dreamor/memvault	⚠️ rule context + tools	❌
Codex CLI	integrations/codex/: config.toml MCP + memvault sync + custom prompts	⚠️ rules + tools	❌

⚠️ = the host has no injection hooks; recall is rule-driven (the agent calls session_start once) with the bundled canonical rule text.

Tier 2 — paste an MCP snippet. Strict-JSON registrations with per-host identities in integrations/mcp-clients/ (target paths in its README): Cursor · Windsurf · Cline/Roo · Continue · Zed · JetBrains AI/Junie · VS Code (Copilot Chat) · Claude Desktop.

Tier 3 — native manifests, verify-on-install. Qoder (.qoder/rules/ + .qoder-plugin/ + a UserPromptSubmit hook template), Grok Build (grok plugin install dreamor/memvault --trust), the Hermes Python plugin (integrations/hermes/, pre_llm_call recall + extraction helper) and the pi extension (pi-extension/, pi install git:github.com/dreamor/memvault) ship in-repo; OpenClaw and Swival consume the generated root skills/ (also exported to .openclaw/skills/); Devin stays a manual recipe in integrations/README.md.

Tier 4 — instruction-only rule copies. Canonical text + scripts/gen-rule-copies.sh (parity-checked in CI) produce AGENTS.md/CLAUDE.md blocks, .cursor/rules/, .clinerules/, .kiro/steering/, Junie guidelines; memvault sync --watch keeps them fresh from the store.

Any other MCP-speaking client (domestic or international, IDE plugin or CLI harness) connects with zero MemVault-side changes via the standard stdio config below — not individually verified; PRs adding a verified entry are welcome.

GUI surfaces are independent of agent installs: Web Dashboard (9 tabs) · Obsidian plugin (α — Vault sync + browse/capture) · MCP Proxy (transparent memory injection for any upstream server).

---

Architecture

┌────────────────────────────────────────────────┐
│  Clients (any MCP-compatible agent)             │
│  ┌────────────┐ ┌────────┐ ┌─────┐ ┌────────┐  │
│  │ Claude Code│ │ Cursor │ │ dsh │ │ Others │  │
│  └────────────┘ └────────┘ └─────┘ └────────┘  │
└──────────────────┬───────────────────────────────┘
                   │ MCP (stdio / SSE / HTTP)
┌──────────────────▼───────────────────────────────┐
│  memvault-mcp     (rmcp 3.1.1)                    │
│  ┌──────────────┐ ┌────────────────┐ ┌────────┐  │
│  │  18 tools    │ │  2 Resources   │ │ SSE    │  │
│  │   + REST API │ │  + Auto-Inject │ │ Server │  │
│  └──────┬───────┘ └──────┬─────────┘ └────────┘  │
│         └────────┬───────┘                        │
│              ┌───▼────────┐                       │
│              │ Agent       │ (Agent Registry      │
│              │ Router      │  type/tag filter)     │
│              └───┬────────┘                       │
├──────────────────┼────────────────────────────────┤
│  memvault-core    │                               │
│  ┌──────────┐  ┌─▼───────┐ ┌────────────┐ ┌───┐ │
│  │ storage  │  │retrieval│ │ pipeline   │ │sync│ │
│  │ SQLite   │  │BM25+Vec │ │extractor   │ │   │ │
│  │          │  │RRF+syn  │ │dedup/decay │ │   │ │
│  │embed     │  │onym     │ │export/     │ │   │ │
│  │backfill  │  │scoring  │ │import      │ │   │ │
│  └──────────┘  └─────────┘ └────────────┘ └───┘ │
└────────────────────────────────────────────────┘

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Project Status

MemVault is in beta. The Rust core (storage / search / injection) is CI-gated and stable; plugin adapters and GUI surfaces evolve faster. All configuration is environment-driven (see .env.example), and every change is recorded in CHANGELOG.md.

Testing

cargo test                      # ~1000 tests (full workspace)
cargo clippy --all-targets      # zero warnings
cargo fmt --all -- --check      # format check
cargo llvm-cov --workspace --all-features   # CI gate: line ≥92% / region ≥90% / function ≥85%

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Documentation

Doc	Content
docs/DESIGN.md	Product & architecture design
docs/INSTALL.md	Installation guide (all platforms)
docs/DOCKER.md	Docker deployment
docs/RUNBOOK.md	Deployment / health check / rollback runbook
docs/TROUBLESHOOTING.md	Symptom → cause → fix troubleshooting guide
docs/experiments/	Hypothesis-validation experiments (H1–H7, 2026-08-11 → 2026-08-27, all CONFIRMED) + runtime plumbing regression (2026-08-28)
docs/RELEASING.md	Release process — what CI automates (Linux/macOS binaries, Docker image, dashboard archive, Obsidian zip) vs. manual steps (Obsidian submission — no macOS signing needed)
docs/DISTRIBUTION.md	Distribution channel map — automated vs. manual channels, required credentials, MCP registries, optional channels
CHANGELOG.md	Release history
CONTRIBUTING.md	Contribution guide
SECURITY.md	Security disclosures
.env.example	Configuration template — single source of truth for every config key

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Contributing

• 🐛 Bugs: Issue Tracker
• 💡 Ideas: Feature Request
• 📖 Guide: CONTRIBUTING.md
• 🔒 Security: SECURITY.md

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License

MemVault is released under the MIT License.