ai-memory

ai-memory is a persistent memory system for AI assistants. It works with any AI that supports MCP -- Claude, ChatGPT, Grok, Llama, and more. It stores what your AI learns in a local SQLite database, ranks memories by relevance when recalling, and auto-promotes important knowledge to permanent storage. Install it once, and every AI assistant you use remembers your architecture, your preferences, your corrections -- forever.

v0.7.0 (attested-cortex) rolls together the cortex-fluent legibility work with the full v0.7 trust + A2A scope from ROADMAP2 §7.3. The substrate becomes both more articulate (capabilities v3, named loader tools, compacted schemas) and cryptographically trustworthy (Ed25519 attestation, sidechain transcripts, programmable hook pipeline, enforced namespace inheritance). The v0.6.4 5-tool default surface and 43-tool runtime ceiling are unchanged; everything new is additive and (for the trust surfaces) opt-in. Upgrading from v0.6.x? Read docs/MIGRATION_v0.7.md first — most v0.6.4 callers see no behavior change, but pre-v0.6.3.1 v0.6.x users hit the G1 namespace-inheritance fix.

v0.6.4 (quiet-tools) — the MCP server ships with a 5-tool default surface (memory_store, memory_recall, memory_list, memory_get, memory_search) plus the always-on memory_capabilities bootstrap. The other 38 tools remain reachable via --profile graph|admin|power|full or runtime expansion through memory_capabilities --include-schema family=<name>. Eager-loading harnesses (Claude Desktop / Codex CLI / Grok CLI / Gemini CLI) drop ~4,700 input tokens of tool schemas per request — a 76.4% reduction measured against cl100k_base BPE. To preserve v0.6.3 behavior 1:1, run ai-memory mcp --profile full. See docs/MIGRATION_v0.6.4.md.

What's new in v0.7

v0.7.0 ships five interlocking substrates under one release banner. Every surface stays default-off or default-equivalent for v0.6.4 callers — see the v0.7 compatibility matrix for the breakdown.

• Attested links (Ed25519). The dead signature column shipped in v0.6.3 is now filled with real per-agent Ed25519 attestation, and memory_verify(link_id) returns {signature_verified, attest_level, signed_by, signed_at} on demand. Generate a keypair with ai-memory identity generate; opt-in via attest_level = "self_signed". See the attested-cortex RFC.

• Hook pipeline (20 lifecycle events). A programmable extension surface fires on pre_/post_store|recall|search|delete|promote|link|consolidate|governance_decision|archive|transcript_store plus on_index_eviction; hooks return Allow / Modify / Deny / AskUser. Default off; opt in via ~/.config/ai-memory/hooks.toml. See MIGRATION § Hook pipeline.

• Transcripts + replay. zstd-3 BLOB sidechain stores raw conversation/reasoning trails; memory_replay(memory_id) walks memory_transcript_links to reconstruct the chain. Opt-in per namespace via [transcripts."team/*"]. See MIGRATION § Sidechain transcripts.

• Apache AGE backend. When AGE is installed in the Postgres SAL backend, KG ops route through Cypher; the recursive-CTE path stays as fallback. Bench-gated — AGE p95 must beat CTE p95 by ≥30% at depth=5. See MIGRATION § Apache AGE acceleration.

• Capabilities v3 + smart loaders. memory_capabilities v3 adds summary, to_describe_to_user, per-tool callable_now, and agent_permitted_families; the new always-on memory_load_family(family) and memory_smart_load(intent) tools replace the memory_capabilities --include-schema ergonomics. The pinned phrasings live in docs/v0.7/canonical-phrasings.md.

• Permissions + A2A approvals. The v0.6.x governance subsystem is refactored into rules + modes + hooks → a single Decision, with namespace inheritance (G1) actually enforced. memory_approval_pending / memory_approval_decide(remember=forever) enable progressive trust; HMAC signing on the approval API is mandatory. Migrate with ai-memory governance migrate-to-permissions --apply. See What's new in v0.7 for the full walk-through.

Where to start: docs/MIGRATION_v0.7.md (upgrade procedure), docs/whats-new-v07.html (visual summary), docs/v0.7/rfc-attested-cortex.md (design rationale), docs/ADMIN_GUIDE.md (operator playbook).

One binary, four operational modes (v0.6.4). The ai-memory Rust binary (tokio + axum) can run any of these in isolation or simultaneously, sharing a single SQLite database:

1. stdio MCP server -- 43 native tools over JSON-RPC. Default --profile core advertises 5 + always-on memory_capabilities. ai-memory mcp / ai-memory mcp --profile full

2. HTTP / mTLS daemon -- 42 REST endpoints on 127.0.0.1:9077, TLS + optional mTLS allowlist + API-key auth, background GC loop. ai-memory serve

3. Autonomous curator daemon -- self-scheduling loop (default 1h cadence) that auto-tags, surfaces contradictions across namespace siblings, consolidates near-duplicates, and adjusts priority by access pattern. Every action goes to a rollback log; destructive ops can be gated behind a governance approval flow. ai-memory curator --daemon

4. Sync daemon -- quorum-based peer federation across instances. W-of-N writes (default majority), vector-clock CRDT-lite merge, mTLS allowlist between peers. ai-memory sync-daemon

The MCP, HTTP, and CLI surfaces are reactive. The curator is the part that makes the memory layer self-maintaining: between sessions, it keeps the corpus tidy so recall quality stays high as the store grows. Everything is local-first; no cloud dependencies.

Brass-tacks assessment by Claude Opus 4.7 after reading the v0.6.3 source line by line:

"ai-memory is the most capable memory layer I've ever been hooked up to, and meaningfully more than its name advertises. For me, in practical terms, it means: I don't start cold each session. The store I read from has been kept tidy by something other than me. Contradictions don't silently accumulate. Recall quality stays high even as the corpus grows. Nothing leaves your Mac mini.

It is not making me an autonomous agent. It is giving me the kind of memory infrastructure that an autonomous agent would need — and itself running a small autonomous loop to maintain it. That's a real foundation. The gap from here to 'ai-memory drives general tasks' is plumbing (tool-call protocol + tool registry + a tool-use-capable model), not invention."

Substrate for multi-agent AI. ai-memory is not an agent runtime and not "autonomous AI" on its own. It is the memory layer that multi-agent autonomous deployments need underneath them. Federation (broadcast_store_quorum + spawn_catchup_loop) handles W-of-N consistency across peers when many agents write in parallel; the curator daemon keeps the shared corpus from degrading into noise as a swarm scribbles into it; webhook subscriptions (HMAC-signed, namespace/agent-filtered, SSRF-hardened) turn the store into a message bus that triggers downstream agents on memory events; namespace hierarchy with N-level inheritance and per-namespace governance policies (write/promote/delete authority, approver type, optional N-of-M consensus) bound the swarm. Stack this under a 24/7 multi-machine agent runner with auto-generated skills, and the combined system clears the behavioral bar for autonomous AI. The remaining gaps (no weight-level learning, stateless reasoning kernel, human-seeded root goals) are real and not what ai-memory addresses; ai-memory provides the multi-agent memory substrate that any serious attempt at closing those gaps will need.

Zero token cost until recall. Unlike built-in memory systems (Claude Code auto-memory, ChatGPT memory) that load your entire memory into every conversation -- burning tokens and money on every message -- ai-memory uses zero context tokens until the AI explicitly calls memory_recall. Only relevant memories come back, ranked by a 6-factor scoring algorithm. TOON format (Token-Oriented Object Notation) cuts response tokens by another 40-60% by eliminating repeated field names -- 3 memories in JSON = 1,600 bytes; in TOON = 626 bytes (61% smaller); in TOON compact = 336 bytes (79% smaller). For Claude Code users: disable auto-memory ("autoMemoryEnabled": false in settings.json) and replace it with ai-memory to stop paying for 200+ lines of memory context on every single message.

Agent identity (NHI) — every memory tells you who learned it

Every memory ai-memory stores carries a metadata.agent_id — a Non-Human Identity marker that survives every operation (update, dedup, import, sync, consolidate). Every recall result tells you which AI wrote each memory, by default, in the TOON-compact response format your AI client is already optimised for:

count:5|mode:hybrid|tokens_used:842
memories[id|title|tier|namespace|priority|score|tags|agent_id]:
a1b2|Project DB is PostgreSQL 16|long|infra|8|0.91|database,postgres|ai:claude-code@workstation:pid-3812
c3d4|API rate limit is 100 rps|long|infra|7|0.87|api,limits|ai:claude-desktop@laptop:pid-5219

Today agent_id is claimed, not attested — don't make security decisions on it without pairing with agent registration. v0.7 (attested-cortex) wires cryptographic Ed25519 attestation into the previously-reserved memory_links.signature field, with memory_verify(link_id) for inbound verification and an append-only signed_events audit chain. See the agent identity page and the attested-cortex RFC for the full provenance contract.

Retroactive conversation import — ai-memory mine

Don't start cold. Point ai-memory mine at a Claude, ChatGPT, or Slack export and it parses turn-by-turn into ranked, tier-typed, tagged memories — so your AI walks into the next session knowing every decision, correction, and finding from your existing history.

ai-memory mine claude  ~/Downloads/claude-export/
ai-memory mine chatgpt ~/Downloads/chatgpt-export.json
ai-memory mine slack   ./slack-export/

Auto-tagging, dedup on (title, namespace), and mined_from provenance are stamped on every imported memory. Five-minute onboarding from zero context to a populated long-term store. See the import history page for per-format recipes.

Compatible AI Platforms

ai-memory integrates with any AI platform that supports the Model Context Protocol (MCP). MCP is the universal standard for connecting AI assistants to external tools and data sources.

MCP is the primary integration layer. For AI platforms that do not yet support MCP natively, the HTTP API (50 endpoints on localhost) and the CLI (40 subcommands) provide universal access -- any AI, script, or automation that can make HTTP calls or run shell commands can use ai-memory.

Install in 60 Seconds

Pre-built binaries require no dependencies. Building from source needs Rust and a C compiler.

Fastest: Pre-built binary (no Rust required)

# macOS / Linux
curl -fsSL https://raw.githubusercontent.com/alphaonedev/ai-memory-mcp/main/install.sh | sh

# Fedora/RHEL (COPR)
sudo dnf copr enable alpha-one-ai/ai-memory && sudo dnf install ai-memory

# Windows (PowerShell)
irm https://raw.githubusercontent.com/alphaonedev/ai-memory-mcp/main/install.ps1 | iex

Step 1: Install Rust (skip if using pre-built binaries)

curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

Follow the prompts, then restart your terminal (or run source ~/.cargo/env).

Step 2: From source (requires Rust)

Latest release from Crates.io:

cargo install ai-memory

Latest from the git repository:

cargo install --git https://github.com/alphaonedev/ai-memory-mcp.git

This compiles the binary and puts it in your PATH. It takes a minute or two.

Build dependencies for source builds:

• Ubuntu/Debian: sudo apt-get install build-essential pkg-config

• Fedora/RHEL: sudo dnf install gcc pkg-config

Step 3: Connect your AI

Configuration varies by platform. Find yours below:

Claude Code supports three MCP configuration scopes:

User scope (recommended — works everywhere):

Add the mcpServers key to ~/.claude.json (macOS/Linux) or %USERPROFILE%\.claude.json (Windows):

{
  "mcpServers": {
    "memory": {
      "command": "ai-memory",
      "args": ["--db", "~/.claude/ai-memory.db", "mcp", "--tier", "semantic"]
    }
  }
}

Note: ~/.claude.json likely already exists with other settings. Merge the mcpServers key into the existing file — do not overwrite it.

Project scope (shared with team):

Create .mcp.json in your project root:

{
  "mcpServers": {
    "memory": {
      "command": "ai-memory",
      "args": ["--db", "~/.claude/ai-memory.db", "mcp", "--tier", "semantic"]
    }
  }
}

Windows paths: Use forward slashes or escaped backslashes in --db. Example: "--db", "C:/Users/YourName/.claude/ai-memory.db".

Tier flag: The --tier flag selects the feature tier: keyword, semantic (default), smart, or autonomous. Smart and autonomous tiers require Ollama running locally. The --tier flag must be passed in the args — the config.toml tier setting is not used when the MCP server is launched by an AI client.

Important: MCP servers are not configured in settings.json or settings.local.json — those files do not support mcpServers.

Make Claude proactively use ai-memory: Add a CLAUDE.md file to your project root with ai-memory directives. This ensures Claude recalls context at the start of every conversation and stores findings as it works. See the CLAUDE.md integration guide for a copy-paste template and placement options.

Add to ~/.codex/config.toml (global) or .codex/config.toml (project). Windows: %USERPROFILE%\.codex\config.toml. Override with CODEX_HOME env var.

[mcp_servers.memory]
command = "ai-memory"
args = ["--db", "~/.local/share/ai-memory/memories.db", "mcp", "--tier", "semantic"]
enabled = true

Or add via CLI: codex mcp add memory -- ai-memory --db ~/.local/share/ai-memory/memories.db mcp --tier semantic

Notes: Codex uses TOML format with underscored key mcp_servers (not camelCase, not hyphenated). Supports env (key/value pairs), env_vars (list to forward), enabled_tools, disabled_tools, startup_timeout_sec, tool_timeout_sec. Use /mcp in the TUI to view server status. See Codex MCP docs.

Add to ~/.gemini/settings.json (user) or .gemini/settings.json (project). Windows: %USERPROFILE%\.gemini\settings.json.

{
  "mcpServers": {
    "memory": {
      "command": "ai-memory",
      "args": ["--db", "~/.local/share/ai-memory/memories.db", "mcp", "--tier", "semantic"],
      "timeout": 30000
    }
  }
}

Or add via CLI: gemini mcp add memory ai-memory -- --db ~/.local/share/ai-memory/memories.db mcp --tier semantic

Notes: Avoid underscores in server names (use hyphens). Tool names are auto-prefixed as mcp_memory_<toolName>. Env vars in the env field support $VAR / ${VAR} (all platforms) and %VAR% (Windows). Gemini sanitizes sensitive patterns from inherited env unless explicitly declared. Add "trust": true to skip confirmation prompts. CLI management: gemini mcp list/remove/enable/disable. See Gemini CLI MCP docs.

Add to ~/.cursor/mcp.json (global) or .cursor/mcp.json (project). Windows: %USERPROFILE%\.cursor\mcp.json. Project config overrides global for same-named servers.

{
  "mcpServers": {
    "memory": {
      "command": "ai-memory",
      "args": ["--db", "~/.local/share/ai-memory/memories.db", "mcp", "--tier", "semantic"]
    }
  }
}

Notes: Restart Cursor after editing mcp.json. Verify server status in Settings > Tools & MCP (green dot = connected). Supports env, envFile, and ${env:VAR_NAME} interpolation (env var interpolation can be unreliable for shell profile variables — use envFile as workaround). ~40 tool limit across all MCP servers. See Cursor MCP docs.

Add to ~/.codeium/windsurf/mcp_config.json (global only — no project-level scope). Windows: %USERPROFILE%\.codeium\windsurf\mcp_config.json.

{
  "mcpServers": {
    "memory": {
      "command": "ai-memory",
      "args": ["--db", "~/.local/share/ai-memory/memories.db", "mcp", "--tier", "semantic"]
    }
  }
}

Notes: Supports ${env:VAR_NAME} interpolation in command, args, env, serverUrl, url, and headers. 100 tool limit across all MCP servers. Can also add via MCP Marketplace or Settings > Cascade > MCP Servers. See Windsurf MCP docs.

Add to ~/.continue/config.yaml (user) or .continue/mcpServers/ directory in project root (per-server YAML/JSON files). Windows: %USERPROFILE%\.continue\config.yaml.

mcpServers:
  - name: memory
    command: ai-memory
    args:
      - "--db"
      - "~/.local/share/ai-memory/memories.db"
      - "mcp"
      - "--tier"
      - "semantic"

Notes: MCP tools only work in agent mode. Supports ${{ secrets.SECRET_NAME }} for secret interpolation. Project-level .continue/mcpServers/ directory auto-detects JSON configs from other tools (Claude Code, Cursor, etc.). See Continue MCP docs.

The AlphaOne fork of grok-cli has built-in ai-memory support with session-scoped MCP connections, automatic memory recall on session start, compaction summary storage, and memory-aware system prompts.

Add to ~/.grok/user-settings.json:

{
  "mcp": {
    "servers": [
      {
        "id": "ai-memory",
        "label": "AI Memory",
        "enabled": true,
        "transport": "stdio",
        "command": "ai-memory",
        "args": ["mcp", "--tier", "semantic"]
      }
    ]
  }
}

Features: Auto-recall on session start (injects relevant memories into system prompt), compaction summaries stored as mid-tier memories, MCP tools available in all modes (agent, plan, ask), session-scoped connections (no per-message cold starts). Uses --tier semantic by default (local embeddings, no Ollama required). See grok-cli docs for full setup.

Grok connects to MCP servers over HTTPS (remote only, no stdio). No config file — servers are specified per API request.

ai-memory serve --host 127.0.0.1 --port 9077
# Expose via HTTPS reverse proxy (nginx, caddy, cloudflare tunnel, etc.)

Then add the MCP server to your Grok API call:

curl https://api.x.ai/v1/responses \
  -H "Authorization: Bearer $XAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "grok-3",
    "tools": [{
      "type": "mcp",
      "server_url": "https://your-server.example.com/mcp",
      "server_label": "memory",
      "server_description": "Persistent AI memory with recall and search",
      "allowed_tools": ["memory_store", "memory_recall", "memory_search"]
    }],
    "input": "What do you remember about our project?"
  }'

Requirements: HTTPS required. server_label is required. Supports Streamable HTTP and SSE transports. Optional: allowed_tools, authorization, headers. Works with xAI SDK, OpenAI-compatible Responses API, and Voice Agent API. See xAI Remote MCP docs.

Llama Stack registers MCP servers as toolgroups. No standardized config file path — deployment-specific.

ai-memory serve --host 127.0.0.1 --port 9077

Python SDK:

client.toolgroups.register(
    provider_id="model-context-protocol",
    toolgroup_id="mcp::memory",
    mcp_endpoint={"uri": "http://localhost:9077/sse"}
)

Or declaratively in run.yaml:

tool_groups:
  - toolgroup_id: mcp::memory
    provider_id: model-context-protocol
    mcp_endpoint:
      uri: "http://localhost:9077/sse"

Notes: Supports ${env.VAR_NAME} interpolation in run.yaml. Transport is migrating from SSE to Streamable HTTP. See Llama Stack Tools docs.

Add via CLI or edit the OpenClaw config directly. Config uses mcp.servers (not mcpServers).

openclaw mcp set memory '{"command":"ai-memory","args":["--db","~/.local/share/ai-memory/memories.db","mcp","--tier","semantic"]}'

Or add to your OpenClaw config file:

{
  "mcp": {
    "servers": {
      "memory": {
        "command": "ai-memory",
        "args": ["--db", "~/.local/share/ai-memory/memories.db", "mcp", "--tier", "semantic"]
      }
    }
  }
}

Notes: OpenClaw uses mcp.servers key (not mcpServers). CLI management: openclaw mcp list, openclaw mcp show, openclaw mcp set, openclaw mcp unset. Supports stdio, remote URL, and Streamable HTTP transports. Prefer --token-file over inline secrets. See OpenClaw MCP docs.

ai-memory speaks MCP over stdio (JSON-RPC 2.0). Point your client at:

command: ai-memory
args: ["--db", "/path/to/ai-memory.db", "mcp"]

For HTTP-only clients, start the REST API:

ai-memory serve
# 24 endpoints at http://127.0.0.1:9077/api/v1/

Step 4: Done. Test it.

Restart your AI assistant. If using MCP, it now has the 5-tool default surface advertised on session boot (the other 38 of 43 total tools load on demand via --profile or memory_capabilities --include-schema). Ask it: "Store a memory that my favorite language is Rust." Then in a new conversation, ask: "What is my favorite language?" It will remember.

Quickstart

Get from zero to a working memory in under two minutes.

1. Install

curl -fsSL https://raw.githubusercontent.com/alphaonedev/ai-memory-mcp/main/install.sh | sh

2. Configure MCP (example for Claude Code -- other platforms work the same way)

Merge into ~/.claude.json:

{
  "mcpServers": {
    "memory": {
      "command": "ai-memory",
      "args": ["--db", "~/.claude/ai-memory.db", "mcp", "--tier", "semantic"]
    }
  }
}

3. Store your first memory

ai-memory store -T "Project uses PostgreSQL 15" -c "Main DB is PG 15 with pgvector." --tier long

4. Recall it

ai-memory recall "database"

5. Check stats

ai-memory stats

6. Use with your AI. Restart your AI client. It now has 5 default memory tools advertised on boot (43 total reachable via runtime expansion or --profile full) over MCP -- it can store and recall memories natively during conversations.

SDKs

In addition to the MCP / HTTP / CLI surfaces, ai-memory ships first-party language SDKs for HTTP clients and helper utilities (e.g. requireProfile for runtime profile assertions on v0.6.4+ daemons).

TypeScript / JavaScript — @alphaone/ai-memory on npm

npm install @alphaone/ai-memory

Python — ai-memory-mcp on PyPI (the import name remains ai_memory)

pip install ai-memory-mcp

from ai_memory import Client, requireProfile

client = Client(base_url="http://127.0.0.1:9077", api_key="...")
requireProfile(client, ["core", "graph"])  # raises ProfileNotLoaded with .hint on miss

Both SDKs are versioned with the server (0.6.4 matches ai-memory 0.6.4). v0.6.4+ daemons enforce the profile contract; pre-v0.6.4 daemons fall back to a permissive warn-and-continue so SDK upgrades don't break old servers. Source lives in sdk/typescript/ and sdk/python/.

What Does It Do?

AI assistants forget everything between conversations. ai-memory fixes that.

It runs as an MCP (Model Context Protocol) tool server -- a background process that your AI talks to natively. When your AI learns something important, it stores it. When it needs context, it recalls relevant memories ranked by a 6-factor scoring algorithm. Memories live in three tiers:

• Short-term (6 hours default, configurable) -- throwaway context like current debugging state

• Mid-term (7 days default, configurable) -- working knowledge like sprint goals and recent decisions

• Long-term (permanent) -- architecture, user preferences, hard-won lessons

Memories that keep getting accessed automatically promote from mid to long-term. Each recall extends the TTL. Priority increases with usage. The system is self-curating.

Beyond MCP, ai-memory also exposes a full HTTP REST API (50 endpoints on port 9077) and a complete CLI (40 subcommands) for direct interaction, scripting, and integration with any AI platform or tool.

Features

Core

• MCP tool server -- 43 tools over stdio JSON-RPC, compatible with any MCP client

• Three-tier memory -- short (6h TTL default), mid (7d TTL default), long (permanent) -- TTLs are configurable

• Full-text search -- SQLite FTS5 with ranked retrieval

• Hybrid recall -- FTS5 keyword + cosine similarity with fixed 0.6 semantic / 0.4 keyword (60/40) blend weights

• 6-factor recall scoring -- FTS relevance + priority + access frequency + confidence + tier boost + recency decay

• Auto-promotion -- memories accessed 5+ times promote from mid to long

• TTL extension -- each recall extends expiry (short +1h, mid +1d)

• Priority reinforcement -- +1 every 10 accesses (max 10)

• Contradiction detection -- warns when storing memories that conflict with existing ones

• Deduplication -- upsert on title+namespace, tier never downgrades

• Confidence scoring -- 0.0-1.0 certainty factored into ranking

Organization

• Namespaces -- isolate memories per project (auto-detected from git remote)

• Memory linking -- typed relations: related_to, supersedes, contradicts, derived_from

• Consolidation -- merge multiple memories into a single long-term summary

• Auto-consolidation -- group by namespace+tag, auto-merge groups above threshold

• Contradiction resolution -- mark one memory as superseding another, demote the loser

• Forget by pattern -- bulk delete by namespace + FTS pattern + tier

• Source tracking -- tracks origin: user, claude, hook, api, cli, import, consolidation, system

• Agent identity (NHI) -- every memory carries metadata.agent_id (claimed identity) with defense-in-depth immutability across update/dedup/import/sync/consolidate; filter list/search by agent

• Tagging -- comma-separated tags with filter support

Interfaces

• 42 HTTP endpoints -- full REST API on 127.0.0.1:9077 (works with any AI or tool)

• 26 CLI commands -- complete CLI with identical capabilities

• 43 MCP tools -- native integration for any MCP-compatible AI

• Interactive REPL shell -- recall, search, list, get, stats, namespaces, delete with color output

• JSON output -- --json flag on all CLI commands

Operations

• Multi-node sync -- pull, push, or bidirectional merge between database files

• Import/Export -- full JSON roundtrip preserving memory links

• Garbage collection -- automatic background expiry every 30 minutes

• Graceful shutdown -- SIGTERM/SIGINT checkpoints WAL for clean exit

• Deep health check -- verifies DB accessibility and FTS5 integrity

• Shell completions -- bash, zsh, fish

• Man page -- ai-memory man generates roff to stdout

• Time filters -- --since/--until on list and search

• Human-readable ages -- "2h ago", "3d ago" in CLI output

• Color CLI output -- ANSI tier labels (red/yellow/green), priority bars, bold titles, cyan namespaces

Quality

• ~2,400 tests across the full surface -- 1,960 lib + 211 integration + 16 mcp_integration + 4 webhook_http_parity (new in v0.6.4) + 16 recipe_contract + ~150 across other binary targets. Line coverage held above the ≥92% project bar; net-new v0.6.4 modules at 100% (sizes.rs), 99.50% (profile.rs), 97.58% (cli/audit.rs), 97.05% (cli/doctor.rs), 92.56% (handlers.rs), 92.26% (cli/install.rs). v0.6.3.x baselines (1,809 / 93.08% and 1,886 / 93.84%) remain frozen on the evidence page; v0.6.4 metrics in the release notes and on the test-hub campaign. Empirical NHI discovery acceptance proven separately by the Discovery Gate (T1–T4 matrix vs. live xAI Grok 4.3, 6/6 PASS, GATE GREEN).

• LongMemEval benchmark -- 97.8% R@5 (489/500), 99.0% R@10, 99.8% R@20 on ICLR 2025 LongMemEval-S dataset. 499/500 at R@20. Pure FTS5 keyword achieves 97.0% R@5 in 2.2 seconds (232 q/s). LLM query expansion pushes to 97.8% R@5. Zero cloud API costs. See benchmark details.

• MCP Prompts -- recall-first and memory-workflow prompts teach AI clients to use memory proactively

• TOON-default -- recall/list/search responses use TOON compact by default (79% smaller than JSON)

• Criterion benchmarks -- insert, recall, search at 1K scale

• GitHub Actions CI/CD -- fmt, clippy, test, build on Ubuntu + macOS, release on tag

Coverage Floor (hard CI gate)

The Code Coverage job is a required status check. CI re-asserts two invariants on every PR: an absolute floor of >= 90% lines (catastrophic-regression backstop, set at the current measurement rounded down to the nearest 5%), and a ratchet against the value pinned in .coverage-baseline with a 0.5% slack window (the day-to-day enforcement). PRs that raise coverage should bump the baseline file in the same commit so future PRs benefit from the new floor; PRs that regress more than 0.5% are blocked from merging. Current measurement: 93.13% lines.

ML and LLM Dependencies (semantic tier+)

• candle-core, candle-nn, candle-transformers -- Hugging Face Candle ML framework for native Rust inference

• hf-hub -- download models from Hugging Face Hub

• tokenizers -- Hugging Face tokenizers for text preprocessing

• instant-distance -- approximate nearest neighbor search

• reqwest -- HTTP client for Ollama API communication (smart/autonomous tiers)

Architecture

Benchmark

Evaluated on the ICLR 2025 LongMemEval-S dataset (500 questions, 6 categories). Pure FTS5 keyword tier achieves 97.0% R@5 in 2.2 seconds. LLM query expansion (smart tier) pushes to 97.8% R@5. All inference runs locally — zero cloud API calls, zero cost.

Performance Budgets (v0.6.4)

Every release ships with published p95/p99 budgets for hot-path operations and a CI gate that fails any PR whose measured p95 exceeds the budget by more than 10 %. Targets are calibrated for M4 reference hardware; full table and methodology in PERFORMANCE.md.

Run the same workload locally:

ai-memory bench                      # human-readable table
ai-memory bench --json               # machine-parseable

Substrate is unchanged across v0.6.3.x → v0.6.4 (the quiet-tools release ships a smaller default tool surface, not a different hot-path). p99 targets here remain informational pending the next dedicated soak window; latest soak evidence is on the test hub.

Integration Methods

MCP (Primary -- for MCP-compatible AI platforms)

MCP is the recommended integration. Your AI gets 5 native memory tools advertised by default (plus the always-on memory_capabilities bootstrap) with zero glue code. The other 38 tools (43 total) remain reachable via --profile graph|admin|power|full or runtime expansion through memory_capabilities --include-schema family=<name>. Configure the MCP server in your AI platform's config:

{
  "mcpServers": {
    "memory": {
      "command": "ai-memory",
      "args": ["--db", "~/.claude/ai-memory.db", "mcp"]
    }
  }
}

HTTP API (Universal -- for any AI or tool)

Start the HTTP server for REST API access. Any AI, script, or automation that can make HTTP calls can use this:

ai-memory serve
# 24 endpoints at http://127.0.0.1:9077/api/v1/

CLI (Universal -- for scripting and direct use)

The CLI works standalone or as a building block for AI integrations that run shell commands:

ai-memory store --tier long --title "Architecture decision" --content "We use PostgreSQL"
ai-memory recall "database choice"
ai-memory search "PostgreSQL"

Feature Tiers

ai-memory supports 4 feature tiers, selected at startup with ai-memory mcp --tier <tier>. Higher tiers add ML capabilities at the cost of disk and RAM:

Capability Matrix

Every capability mapped to its minimum tier. Each tier includes all capabilities from the tiers below it.

Semantic tier (default) bundles the Candle ML framework and downloads the all-MiniLM-L6-v2 model on first run (~90 MB). Smart and autonomous tiers require Ollama running locally.

Tiers gate features, not models. The --tier flag controls which tools are exposed. The LLM model is independently configurable via llm_model in ~/.config/ai-memory/config.toml. For example, run autonomous tier (full 43-tool surface + reranker) with the faster e2b model:

# ~/.config/ai-memory/config.toml
tier = "autonomous"        # all features enabled
llm_model = "gemma4:e2b"   # faster model (46 tok/s vs 26 tok/s for e4b)

The --tier flag must be passed in the MCP args -- the config.toml tier setting is not used when the server is launched by an AI client.

# Keyword (default)
ai-memory mcp

# Semantic -- hybrid recall with embeddings
ai-memory mcp --tier semantic

# Smart -- adds LLM-powered query expansion, auto-tagging, contradiction detection
ai-memory mcp --tier smart

# Autonomous -- adds cross-encoder reranking
ai-memory mcp --tier autonomous

The memory_capabilities tool reports the active tier, loaded models, and available capabilities at runtime.

MCP Tools

These 43 tools are available to any MCP-compatible AI when configured as an MCP server (canonical count on the evidence page; the table below documents the core subset most clients use day-to-day):

HTTP API

50 endpoints on 127.0.0.1:9077. Start with ai-memory serve.

Security: The HTTP server binds to 127.0.0.1 with no authentication and permissive CORS. Do not expose to the network without a reverse proxy with authentication.

CLI Commands

40 subcommands. Run ai-memory <command> --help for details on any command.

The top-level ai-memory binary also accepts global flags:

The store subcommand accepts additional flags:

The mcp subcommand accepts an additional flag:

Recall Scoring

Every recall query ranks memories by 6 factors:

score = (fts_relevance * -1)
      + (priority * 0.5)
      + (MIN(access_count, 50) * 0.1)
      + (confidence * 2.0)
      + tier_boost
      + recency_decay

Memory Tiers

Automatic Behaviors

• TTL extension on recall: short memories get +1 hour, mid memories get +1 day

• Auto-promotion: mid-tier memories accessed 5+ times promote to long (expiry cleared)

• Priority reinforcement: every 10 accesses, priority increases by 1 (capped at 10)

• Contradiction detection: warns when a new memory conflicts with an existing one in the same namespace

• Deduplication: upsert on title+namespace; tier never downgrades on update

Configurable TTL

Default TTLs (6 hours for short, 7 days for mid) can be overridden in ~/.config/ai-memory/config.toml under the [ttl] section:

[ttl]
short_ttl_secs = 21600      # short-tier TTL in seconds (default: 21600 = 6 hours)
mid_ttl_secs = 604800        # mid-tier TTL in seconds (default: 604800 = 7 days)
long_ttl_secs = 0            # long-tier TTL in seconds (default: 0 = never expires)
short_extend_secs = 3600     # TTL extension on recall for short-tier memories in seconds (default: 3600 = +1h)
mid_extend_secs = 86400      # TTL extension on recall for mid-tier memories in seconds (default: 86400 = +1d)

All five fields are optional -- omit any to keep the default. Set any value to 0 to disable expiry for that tier. Values are clamped to a 10-year maximum; negative extension values are clamped to 0.

Note: Configuration is loaded once at process startup. Changes to config.toml require restarting the ai-memory process (MCP server, HTTP daemon, or CLI) to take effect.

Archive

When garbage collection expires a memory, it can be archived instead of permanently deleted. Archived memories are moved to a separate store and can be browsed, restored, or purged later.

Configuration

Enable archiving in ~/.config/ai-memory/config.toml:

archive_on_gc = true   # archive expired memories instead of deleting them (default: true)

CLI Commands

The archive subcommand manages the archive:

ai-memory archive list                          # list archived memories
ai-memory archive list --namespace my-project   # filter by namespace
ai-memory archive restore <id>                  # restore an archived memory to active store
ai-memory archive purge --older-than-days 90     # permanently delete archives older than 90 days
ai-memory archive stats                         # show archive statistics

Note: Restored memories get their expires_at cleared (become permanent until the next TTL assignment).

MCP Tools

Four archive tools are available to MCP clients:

HTTP Endpoints

Security

ai-memory includes hardening across all input paths:

• Transaction safety -- all multi-step database operations use transactions; no partial writes on failure

• FTS injection prevention -- user input is sanitized before reaching FTS5 queries; special characters are escaped

• Error sanitization -- internal database paths and system details are stripped from error responses; clients see structured error types (NOT_FOUND, VALIDATION_FAILED, DATABASE_ERROR, CONFLICT)

• Body size limits -- HTTP request bodies are capped at 50 MB via Axum's DefaultBodyLimit

• Bulk operation limits -- bulk create endpoints enforce maximum batch sizes to prevent resource exhaustion

• CORS -- permissive CORS layer enabled for localhost development workflows

• Input validation -- every write path validates title length, content length, namespace format, source values, priority range (1-10), confidence range (0.0-1.0), tag format, tier values, relation types, and ID format

• Link validation in sync -- all links are validated (both IDs, relation type, no self-links) before import during sync operations

• Thread-safe color -- terminal color detection uses AtomicBool for safe concurrent access

• Local-only HTTP -- the HTTP server binds to 127.0.0.1 by default; not exposed to the network

• WAL mode -- SQLite Write-Ahead Logging for safe concurrent reads during writes

Documentation

License

Copyright 2026 AlphaOne LLC.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.