ai-memory v0.6.4 collapses the default MCP tool surface from 43 to 5 — saving ~4,700 input tokens per request on every eager-loading harness — without removing a single tool. The other 38 are still there, still callable, still functional. They just don't pre-pay their schema cost on every turn.
Same release, three framings — pick yours.
Every time your AI assistant (Claude, ChatGPT, Cursor, Codex, Grok, Gemini) reaches for ai-memory, it used to spend ~6,200 input tokens just describing the available memory tools before reading your message. v0.6.4 cuts that to ~1,500. Your AI still does everything it did before — it just doesn't pre-pay for tools it doesn't need every turn. First-message responses on Codex / Grok / Gemini should feel snappier; your subscription cost drops automatically.
brew upgrade ai-memory && ai-memory doctor --tokens
That second command shows you exactly how much you're saving.
--profile {core,graph,admin,power,full,custom} flag (CLI + AI_MEMORY_PROFILE env + [mcp].profile config). Resolution order: CLI > env > config > core.memory_capabilities extended with family=<name> + include_schema=true for runtime tool registration.-32601 with an actionable diagnostic naming the family and suggesting both --profile and --include-schema recovery paths.@alphaone/ai-memory on npm + ai-memory-mcp on PyPI. requireProfile helper raises ProfileNotLoaded with structured .hint.ai-memory install <name>.[mcp.allowlist] per-agent capability allowlist. audit_log table (schema v20) records every capability-expansion event.Boris Cherny's published 90-day Claude Code instrumentation: 73% of tokens go to nine waste patterns. v0.6.4 closes Pattern 6 ("just-in-case tool definitions") in one release — 76.4% input-token reduction, ~$107/user/year on heavy single-user pricing, ~$107K/year per 1,000 daily-active agent seats. Empirically validated by a 4-tier discovery matrix vs. live xAI Grok 4.3 (6/6 PASS, GATE GREEN). Apache-2.0, single Rust binary, runs on the dev's laptop — no SaaS, no PII exfiltration, no vendor lock-in. The architecture decision you make today doesn't have to be revisited.
Every number on this page is anchored to a publicly verifiable source. Click through to see the methodology.
tools/list
Honesty correction shipped with this release: the v0.6.4 RFC drafts originally claimed "~25,800 tokens / 87% reduction." Those numbers were measured against MiniLM (a sentence-embedder vocabulary that systematically over-counts JSON by ~4× vs. cl100k_base, the BPE Claude/GPT actually use for input accounting). Real measurement: 6,198 → 1,465 / 76.4%. We corrected the public claim before the release. Methodology →
Most "AI memory" products are chat memory. ai-memory is the agent substrate. v0.6.4 ships the first tier of NHI (Non-Human Identity) guardrails: per-agent capability allowlists and a capability-expansion audit log. Phase 2 (Ed25519 attestation) lands in v0.7.
The new [mcp.allowlist] config table maps agent_id patterns to allowed family sets. Pattern resolution: exact > longest-prefix > * wildcard. Default disabled for backward compat — flip it on for production.
[mcp.allowlist] "ai:claude-code@*" = ["full"] "ai:codex-cli@*" = ["core", "graph"] "ai:grok-cli-*" = ["core"] "*" = ["core"] # wildcard default
memory_capabilities --include-schema call gets loggedSchema migration v19 → v20 adds the audit_log table with columns for agent_id, event_type, requested_family, granted, attestation_tier, timestamp. Three indexes (agent_id, timestamp, event_type) for SOC/SIEM-friendly queries. Idempotent migration; preserves every existing row.
SELECT agent_id, requested_family, granted
FROM audit_log
WHERE timestamp > datetime('now', '-24 hours')
AND granted = 0;
ai-memory's architecture scales from one developer's laptop to a multi-region hive of agents without switching products. Every primitive listed below is in the v0.6.4 binary today; you turn flags on as you grow.
ai-memory mcp| Capability | Vendor memory (Claude/ChatGPT) |
SaaS memory (mem0 / Letta) |
Vector DBs (Chroma / pgvector) |
ai-memory v0.6.4 |
|---|---|---|---|---|
| Local-first, no cloud roundtrip | ❌ | ❌ | ✅ | ✅ |
| Universal across AI vendors | ❌ vendor-locked | ⚠️ paid plans | ⚠️ glue code | ✅ MCP-native |
| Zero-token cost until recall | ❌ | ⚠️ varies | n/a | ✅ |
| Self-curating (auto-tag, dedup, contradictions) | ❌ | ⚠️ partial | ❌ | ✅ |
| Multi-agent federation built in | ❌ | ❌ | ❌ | ✅ |
| Quorum W-of-N writes + CRDT merge | ❌ | ❌ | ❌ | ✅ |
| Per-agent NHI capability allowlist | ❌ | ❌ | ❌ | ✅ (v0.6.4 phase 1) |
| Capability-expansion audit log | ❌ | ❌ | ❌ | ✅ (schema v20) |
| Webhook event bus (HMAC-signed) | ❌ | ❌ | ❌ | ✅ |
| Apache-2.0, no vendor risk | ❌ | ❌ | ✅ | ✅ |
| Single binary install | n/a | n/a | ❌ | ✅ |
| Public per-release evidence pages | ❌ | ❌ | n/a | ✅ |
Most product pages cite numbers without sources. Every claim above is anchored to a public, verifiable artifact:
Public 4-tier acceptance matrix — T1 Awareness / T2 Reactive / T3 Proactive / T4 Mesh. Run against a real OpenClaw harness driving live xAI Grok 4.3 against the v0.6.4 release binary. Per-cell evidence: full LLM transcripts, MCP wire logs, verdict JSON.
Per-release CERT verdicts with green/red status, scenario-by-scenario evidence, schema-migration validation against real production-shaped DBs. v0.6.4 campaign: CERT GREEN across S25-S32 (new) + S1-S22 (functional replay under --profile full).
Documents how the 6,198 → 1,465 measurement was taken (which tokenizer, which BPE vocabulary, which harnesses), and includes the explicit honesty correction on the original RFC's 25,800 / 87% claim — the methodology gap explained, not papered over.
The evidence page tracks v0.6.x baselines (1,809 / 93.08% on v0.6.3, 1,886 / 93.84% on v0.6.3.1) alongside v0.6.4 metrics. CI-enforced ≥92% coverage gate per-module. Performance budgets fail PRs whose measured p95 exceeds the published target by >10%.
brew install alphaonedev/tap/ai-memory ai-memory install claude-code --apply
cargo install ai-memory --version 0.6.4
docker pull ghcr.io/alphaonedev/ai-memory:0.6.4
sudo dnf copr enable alpha-one-ai/ai-memory sudo dnf install ai-memory
npm install @alphaone/ai-memory
pip install ai-memory-mcp # import name remains ai_memory