attune-ai

Attune AI

Spec-driven development for Claude Code — turn requirements into reliable software.

🌐 Docs & guides: attune-ai.dev

Attune AI gives Claude Code persistent memory. Your agent stops starting from zero: a stash → recall → promote loop carries decisions, bugs, and hard-won lessons from one session into the next, and a retrievable lessons corpus surfaces the right lesson at the exact moment a prompt needs it — local-first, working from a plain pip install attune-ai. The economics are measured, not promised: recall loads a few hundred exactly-relevant tokens instead of your whole corpus — 67× fewer tokens on our own 800+ lesson store, retrieved at P@3 96% on a frozen trap-moment benchmark (details in the memory suite below).

Around that memory core, the same package also ships a spec-driven, multi-agent toolkit: 20 workflows and 53 MCP tools dispatching 2–6 domain-specific subagents behind Socratic quality gates, RAG grounding with a citation-per-claim contract (mean per-claim faithfulness CI-gated at ≥ 0.97; 0.98 currently measured, N=20 runs on the 40-query golden set), and generation fact-checking — one install, one MCP server.

We run our own knowledge base on it: the docs, help templates, and 800+ engineering lessons at attune-ai.dev are authored, grounded, and maintained entirely by Attune's own stack.

Managing and creating help-content, docs, or knowledge-bases? That's attune-gui — a dedicated Living Docs dashboard wrapping attune-rag, attune-help, and attune-author in a single UI. attune-ai is the developer workflow hub; attune-gui is the docs hub.

New in 10.0.0 — one memory architecture, no legacy layer

Major version, one breaking change: the legacy memory-graph API (attune.memory.MemoryGraph and its node/edge types) is removed. Curated memory has been plain .md files served from Redis since 9.6.0 — the graph was the layer nothing living called, confirmed by a usage audit before deletion (receipts in docs/specs/archive/memorygraph-value-gate/). If you never imported MemoryGraph — telemetry says that's everyone — nothing changes: same install, same memory loop, same measured economics below. Accessing a removed name raises an error naming the successor, and there is no data migration because the graph store was already retired.

The memory suite — out of the box, measured

Your agent stops starting from zero — with a plain pip install attune-ai. The memory suite matured across recent releases (curated promotion in 9.5, files-canonical unification in 9.6, Redis / Agent Memory Server client as a core dependency in 9.7). The loop: