Attune AI
Persistent memory and receipt-verified workflows for Claude Code.
🌐 Docs & guides: attune-ai.dev
Your agent stops starting from zero, its word stops being the evidence, and it asks you with structure instead of prose.
Memory: a stash → recall → promote loop carries decisions, bugs,
and hard-won lessons from one session into the next, and surfaces the
right lesson at the exact moment a prompt needs it. Local-first, from
a plain pip install attune-ai. 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 benchmark (details).
Receipts: state the outcome you want and how to verify it, and get back a receipt — not a promise:
attune fix "imports resolve after the rename" \
--scope src/attune/cli_minimal.py \
--probe "pytest tests/unit/test_cli_minimal.py" \
--run
The probes are re-run independently of the workflow that claims it finished. Exit 0 means the probes passed — not that the agent felt good about it.
Interactive forms: the agent asks with structure, not prose — a decision card with its recommendation and tradeoffs, a pushback card when it disagrees, a progress report, a ranking or triage — one tap each, validated on the way back. One form renders to whatever surface your client draws: a native dialog, a rich widget, or a plain menu (the vocabulary).
Around that core: 21 workflows and 61 MCP tools dispatching 2–6 domain-specific subagents behind Socratic quality gates, RAG grounding with a citation-per-claim contract, and generation fact-checking — one install, one MCP server. We run our own knowledge base on it: the docs and 800+ engineering lessons at attune-ai.dev are authored, grounded, and maintained by Attune's own stack.
Contents: Install · Costs · Memory · Receipts · Multi-LLM · Workflows & tools · Forms · Accuracy · Install options · Privacy
Get Started in 60 Seconds
Plugin (works standalone)
claude plugin marketplace add Smart-AI-Memory/attune-ai
claude plugin install attune-ai@attune-ai
Then say "what can attune do?" in Claude Code.
Add the Python package (unlocks CLI + MCP)
pip install attune-ai
attune # shows your next steps
Then check your setup with attune validate and run your first
workflow: attune workflow run code-review --path src/.
Setup fight you? Tell me where — I'm actively fixing this.
What each layer adds
| Capability | Plugin only | Plugin + pip |
|---|---|---|
| 28 auto-triggering skills | Yes | Yes |
| Security hooks | Yes | Yes |
| Prompt-based analysis | Yes | Yes |
| 61 MCP tools | -- | Yes |
attune CLI + multi-agent workflows |
-- | Yes |
Ops dashboard (attune ops) — run history, cost tiles, telemetry |
-- | Yes |
What this costs
| How you run it | What it costs |
|---|---|
| Plugin in Claude Code (skills, hooks, forms) | Your Claude subscription. No API key, no extra charge. |
attune CLI + MCP tools |
Direct Anthropic API calls — needs ANTHROPIC_API_KEY with API credits. |
The one thing people get wrong: a Claude Pro/Max subscription does not include API credits — they are separate products. If you only use the plugin, this never comes up. Free on either path (they never call a model): elicitation forms, security hooks, path validation, memory storage and recall, and every local transform.
New in 13.0.0 — correctness you can trust under failure
The code paths that only matter when something goes wrong got a
library-wide review. Memory writes that reported success without
landing now actually persist or surface the failure; telemetry and
security-gate state a single malformed record could corrupt are
locked down; external input that could crash a parse now degrades
instead. Shared stores (AgentStateStore, ComplianceDatabase) write
atomically under a lock. And agents gain interactive forms — richer
than yes/no: a decision card with per-option tradeoffs, a pushback card
for disagreement, progress reports, plus ranking, triage, and more, each
rendered to whatever surface your client supports — a native dialog, an
HTML widget, or multiple-choice (via attune-forms 0.7.0). The major
bump is the removal of
the dormant in-package hook-execution engine — dead code with no live
caller (the hooks Claude Code runs are unchanged); the
memory-durability and schema changes alter observable behavior, so
check the CHANGELOG's migration notes if you depend on them.
The memory suite — measured
Stash on stop. Recall at the door. Promote what endures.
- Stash — a
Stophook extracts decisions, bugs, and references from the session and writes them to the memory store (local file by default, Redis Agent Memory Server when reachable). - Recall — a
SessionStarthook surfaces the most recent findings for your project;/recall <topic>searches on demand. - Promote — a reviewed stash→curated path lands git-tracked
.mdfiles in your corpus. Files are the store; Redis serves them. - Lessons at the trap moment — hooks retrieve the exact lesson a prompt or tool call needs, budget-capped no matter how large the corpus grows.
Memory is local-first — nothing leaves your machine, and without Redis everything degrades to the file backend with clear guidance. The economics are measured, not promised (2026-07-05 snapshot; ratios improve as the corpus grows):
| Memory-suite recall | Instead of loading | You load | Win |
|---|---|---|---|
| Trap-moment lessons | 202,042 tok (583 lessons) | ≤3,000 tok | 67× fewer tokens |
| SessionStart digest | 16 corpus files (4.6 ms) | one Redis call (0.6 ms) | ~7× faster |
Numbers from benchmarks/memory_savings.py on our dogfood store.
Receipts, not promises
If you know acceptance-test-driven development, this is that rebuilt for agent workflows: acceptance probes are declared up front, and the agent's own word is never the evidence.
- Fix Receipts (
attune fix) — outcome-first fixing. Preview a contract (done conditions, constraints, probes) with nothing executing; add--runfor an attributed diff whose probes are re-run independently. Exit 0 only when the probes pass. - Spec Ladders (
/spec) — goal-driven specs you approve rung by rung: requirements, design, and a gated task ladder, every ruling recorded in a decision file that outlives the session. - Guided intakes —
/fixand/speccompose their contracts through a form: goal pre-filled, scope picker from paths you've touched, probe suggestions from matching tests. - Receipts all the way down — a failed or absent security auditor fails the Security gate; spec-closure claims draw a rotating skeptic seat; risk-class diffs authored by the lead model are reviewed by a different model before promotion.
Multi-LLM collaboration
As of 10.6.0, attune treats Claude Code, OpenAI Codex, and Google Antigravity as seats at the same table — with the discipline that a claim without a receipt doesn't ship:
/roundtable— the three models deliberate a question on a Redis-backed board; you chair what gets promoted./cross-review— an advisory second opinion on a real diff from a different model than the one that wrote it.- Cross-provider handoff + shared session memory — portable resume briefs and a provider-neutral stash/recall surface with a PII/secrets gate that redacts at rest and fails closed.
- A projected collaboration contract — one master file projects
to
AGENTS.mdand per-provider mirrors.
Codex installs the same plugin from its marketplace
(codex plugin install attune-ai@attune-ai); Antigravity connects
over MCP. The 10.6.1 release exists because a cross-provider receipt
probe caught a protocol bug the primary client silently tolerated.
Workflows and MCP tools
Skills trigger from natural language — "review my code", "scan for
vulns", "generate tests", "plan this feature" — and every workflow
dispatches 2–6 subagents (Opus for deep reasoning, Sonnet for
analysis, Haiku for fast scanning), synthesized by an orchestrator.
Ready-made Claude Code subagents (security-reviewer, spec-author,
refactor-planner, …) appear in your /agents list on install.
All 21 workflows
| Workflow | Agents | What It Does |
|---|---|---|
| code-review | security, quality, perf, architect | 4-perspective code review |
| security-audit | vuln-scanner, secret-detector, auth-reviewer, remediation | Finds vulnerabilities and generates fix plans |
| deep-review | security, quality, test-gap | Multi-pass deep analysis |
| perf-audit | complexity, bottleneck, optimization | Identifies bottlenecks and O(n²) patterns |
| bug-predict | pattern-scanner, risk-correlator, prevention | Predicts likely failure points |
| health-check | dynamic team (2–6) | Project health across tests, deps, lint, CI, docs, security |
| test-gen | identifier, designer, writer | Writes pytest code for untested functions |
| test-audit | coverage, gap-analyzer, planner | Audits coverage and prioritizes gaps |
| doc-gen | outline, content, polish | Generates documentation from source |
| doc-audit | staleness, accuracy, gap-finder | Finds stale docs and drift |
| dependency-check | inventory, update-advisor | Audits outdated packages and advisories |
| refactor-plan | debt-scanner, impact, plan-generator | Plans large-scale refactors |
| simplify-code | complexity, simplification, safety | Proposes simplifications with safety review |
| release-prep | health, security, changelog, assessor | Go/no-go readiness check |
| release-gate | parallel agent team (4 stages) | Release readiness assessment / go-no-go gate |
| release-notes | agent-prep | Drafts release notes + LLM readiness advice |
| doc-orchestrator | inventory, outline, content, polish | Full-project documentation |
| secure-release | security, health, dep-auditor, gater | Release pipeline with risk scoring |
| research-synthesis | summarizer, pattern-analyst, writer | Multi-source research synthesis |
| discovery-sweep | pattern-scanner, verifier | Repo-wide bug-pattern sweep with verification |
| rag-code-gen | retriever, generator | Citation-forced code generation grounded in the local corpus |
| orchestrated-health-check | dynamic team | health-check with explicit meta-orchestration |
| fix | agent-fix | Minimal in-place fix within a contract's scope, verified by a receipt |
All 61 MCP tools — 50 core in 7 categories, plus 11 memory tools registered by the bundled Redis plugin
Workflow (22): security_audit code_review bug_predict
discovery_sweep performance_audit refactor_plan simplify_code
deep_review test_generation test_audit test_gen_parallel
doc_gen doc_audit doc_orchestrator release_notes
health_check dependency_check secure_release
research_synthesis analyze_batch analyze_image
rag_knowledge_query
Help (5): help_lookup help_init help_status help_update
help_maintain
Memory (4): memory_store memory_retrieve memory_search
memory_forget
Personal Memory (4): personal_memory_capture
personal_memory_recall personal_memory_topics
personal_memory_forget
Utility (8): auth_status auth_recommend telemetry_stats
context_get context_set attune_get_level attune_set_level
list_capabilities
Elicitation (5): elicitation_ask elicitation_render_form
elicitation_collect_response elicitation_render_widget
chart_render_widget
Handoff (2): handoff_create handoff_resume
Redis memory (11): session_memory_*, redis_memory_*,
redis_health_check
Interactive forms — the agent asks with structure
Agent↔you exchanges are interactive forms, not prose Q&A. The agent presents a decision with its recommendation, rationale, and per-option tradeoffs; disagrees through a pushback card (your approach vs. its alternative, side by side); reports progress as done / in-flight / blocked; and has you rank, triage, confirm, deliberate, or review its assumptions — one tap each. Every question is validated on the way back, so a malformed answer is re-asked, not silently accepted.
One declarative form, written once, renders to the richest surface your
client supports — a native dialog, a rich HTML widget, or a plain
multiple-choice menu on a text-only surface — so the same question
works everywhere and degrades gracefully. The full construct vocabulary
ships via attune-forms 0.7.0 (new in 13.0.0). Chart specs render
through the same sealed SVG kernel (chart_render_widget, nine chart
types).
Accuracy & Faithfulness
RAG generation enforces citation-per-claim: 0.98 mean per-claim faithfulness, CI-gated at ≥ 0.97 (40-query golden set, N=20 runs). The contract was chosen by A/B measurement — the per-query hallucination bucket rate dropped from 46.7% to 6.7% with it (methodology). Retrieved passages are sentinel-wrapped against prompt injection. The help resolver passes 48/48 benchmark queries at P@1 (golden set).
Installation Options
pip install attune-ai works out of the box — the CLI, all
workflows, the MCP server, RAG (attune-rag and attune-verify are
core dependencies), cross-session memory, and the Agent SDK. Memory
features activate when a Redis Stack server is reachable and degrade
with guidance when not. Add extras only for the surfaces you use:
| You want | Install |
|---|---|
| Everything most users need, incl. Redis memory | pip install attune-ai |
| Claude API mode + optional LangChain/LangGraph interop adapters | pip install 'attune-ai[developer]' |
The ops dashboard (attune ops) |
pip install 'attune-ai[ops]' |
Extras combine — pip install 'attune-ai[developer,ops]'. Keep the
quotes: zsh and bash treat square brackets as glob characters.
Contributing? Clone and install the dev toolchain instead:
git clone https://github.com/Smart-AI-Memory/attune-ai.git
cd attune-ai && pip install -e '.[dev]'
API mode
The CLI and MCP tools call the Anthropic API directly (the plugin never needs this):
export ANTHROPIC_API_KEY="sk-ant-..." # requires API credits
export REDIS_URL="redis://localhost:6379" # optional
Model routing assigns Opus/Sonnet/Haiku by task complexity
(ATTUNE_AGENT_MODEL_* to override); depth budgets run $0.50 /
$2.00 / $5.00 (ATTUNE_MAX_BUDGET_USD to override); --cheap
forces pattern-matching workflows onto Haiku. Live spend tiles on
the dashboard (attune ops).
Platform support
| Platform | Support |
|---|---|
| macOS / Linux / WSL2 | Full |
| Windows native + Git Bash | Supported (Bash tool, POSIX-ish syntax) |
| Windows native + PowerShell tool | Limited — security validation fails closed |
Redis has no native Windows build — use Docker
(docker run -d -p 6379:6379 redis:7-alpine). Without reachable
Redis, memory degrades gracefully to the file backend and
attune.memory.session_stash.backend_status() reports
fallback: true.
Ecosystem
| Package | Role | Install |
|---|---|---|
attune-ai |
Developer workflow hub (this package) | pip install attune-ai |
attune-rag |
RAG pipeline (core dep) | bundled |
attune-verify |
Generation fact-checker (core dep) | bundled |
attune.authoring |
Help authoring + staleness detection (absorbed the former attune-author package in 11.0.0) |
bundled |
attune-help |
Progressive-depth template runtime | pip install attune-help |
Security, Privacy & Telemetry
Path traversal protection on all file ops, a PreToolUse guard that blocks eval/exec, MCP rate limiting, prompt sanitization, and automated scanning (CodeQL, bandit, detect-secrets) — details in SECURITY.md.
Usage data is local-first. An opt-in, anonymous usage ping
(OFF by default) carries only package, version, workflow name, OS,
Python version, a resettable anonymous id, and a timestamp — never
paths, code, prompts, or filenames; the payload is frozen in source
and guarded by a regression test. attune telemetry status|enable|disable;
DO_NOT_TRACK=1 always wins.
Links
Apache License 2.0 — Free and open source.
If you find Attune useful, give it a star — it helps others discover the project.
Acknowledgments
- Anthropic — For Claude AI, the Model Context Protocol, and the Agent SDK patterns behind the multi-agent orchestration layer
- Boris Cherny — Creator of Claude Code, whose workflow posts validated Attune's plan-first, multi-agent approach
- Affaan Mustafa — For battle-tested Claude Code configurations that inspired the hook system
Built by Patrick Roebuck using Claude Code.
1 maintainer
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