Metadata-Version: 2.4
Name: attune-ai
Version: 14.0.0
Summary: Persistent memory and receipt-verified workflows for Claude Code — plugin, MCP server, and spec-driven dev framework.
Author-email: Patrick Roebuck <admin@smartaimemory.com>
Maintainer-email: Smart-AI-Memory <admin@smartaimemory.com>
License:                                  Apache License
                                   Version 2.0, January 2004
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Keywords: ai,claude,anthropic,llm,ai-agent,multi-agent,developer-tools,code-review,security-audit,test-generation,workflow-automation,cost-optimization,claude-code,mcp,model-context-protocol,static-analysis,code-quality,devops,ci-cd,cli
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Topic :: Software Development :: Libraries :: Application Frameworks
Classifier: Topic :: Software Development :: Quality Assurance
Classifier: Topic :: Software Development :: Testing
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Operating System :: OS Independent
Classifier: Environment :: Console
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Dynamic: license-file

# Attune AI

<!-- mcp-name: io.github.Smart-AI-Memory/attune-ai -->

**Persistent memory and receipt-verified workflows for Claude Code.**

🌐 **Docs & guides: [attune-ai.dev](https://attune-ai.dev)**

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---

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](#the-memory-suite--measured)).

**Receipts:** state the outcome you want and how to verify it, and
get back a receipt — not a promise:

```bash
attune fix "imports resolve after the rename" \
  --scope src/attune/cli_minimal.py \
  --probe "pytest tests/unit/test_cli_minimal.py" \
  --run
```

<!-- demo-gif-slot: 30-second recording of the command above producing
     an attributed diff + probe receipt goes here. -->

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](#interactive-forms--the-agent-asks-with-structure)).

Around that core: 21 workflows and <!-- cap:mcp_registered_tool_count -->61 MCP tools<!-- /cap -->
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](https://attune-ai.dev) are authored, grounded, and
maintained by Attune's own stack.

**Contents:**
[Install](#get-started-in-60-seconds) ·
[Costs](#what-this-costs) ·
[Memory](#the-memory-suite--measured) ·
[Receipts](#receipts-not-promises) ·
[Multi-LLM](#multi-llm-collaboration) ·
[Workflows & tools](#workflows-and-mcp-tools) ·
[Forms](#interactive-forms--the-agent-asks-with-structure) ·
[Accuracy](#accuracy--faithfulness) ·
[Install options](#installation-options) ·
[Privacy](#privacy--telemetry)

---

## Get Started in 60 Seconds

### Plugin (works standalone)

```bash
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)

```bash
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](https://github.com/Smart-AI-Memory/attune-ai/discussions/1325) — I'm actively fixing this.

### What each layer adds

| Capability | Plugin only | Plugin + pip |
| ---------- | ----------- | ------------ |
| <!-- cap:skill_count -->28 auto-triggering skills<!-- /cap --> | Yes | Yes |
| Security hooks | Yes | Yes |
| Prompt-based analysis | Yes | Yes |
| <!-- cap:mcp_registered_tool_count -->61 MCP tools<!-- /cap --> | -- | 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.

---

<!-- ROTATING SLOT: this "New in <version>" section is replaced each
     release with the headline feature; the displaced content moves to
     a permanent section below. Don't stack a second "New in" here. -->

## New in 14.0.0 — the release checks its own diff

`/release audit` answers a question the other release checks don't ask:
**what class of defect could *this* release have introduced?** It
resolves the range from your last release tag, proves the gates that
were green are still green *on this exact commit*, sweeps the changed
package surface with a calibrated rule pack, and hands you a capped
one-page residual — never a diff dump. Three models then sit on it for
a single round and either accept or amend a pre-filled disposition per
item. You rule; `/release publish` refuses to tag until every item
carries a ruling, and records that ruling next to the commit it was
made about.

It also knows what it didn't look at. The packet states how many files
were swept against how many changed, so an empty result can never be
mistaken for a clean one. And the class register behind it **derives**
each status from evidence — whether the gate resolves, what it still
finds, whether a deferral covers it — so a gate that gets renamed goes
loud instead of quietly reading CLOSED.

The rest you can see from the outside. Background alerting is solid:
`attune alerts watch --daemon` keeps a firm hold on its database across
the fork and writes owner-only files. The ops client token is compared
in constant time, git refs are validated before they reach the command
line, and telemetry listings cost a single Redis round trip instead of
one per record. The repo's own whole-tree scanners now survive a file
with a null byte in it rather than stopping at the first one.

The major bump is a removal: `attune.exceptions` — the nine-class tree
rooted at `EmpathyFrameworkError` — was the final unremoved surface of
the "Empathy" framework retired in 9.0.0, and nothing in the library
raised any of them once their throwers were deleted. **The migration is
to delete the handler, not repoint the import**: a `try/except` naming
one of these was already dead code, and there is deliberately no shim
for an exception that can never be caught. One trap worth naming —
`attune.config.validation.ValidationError` is a *different, live* class
(a dataclass describing a config problem, not an exception), so
repointing there gets you something you cannot catch.

---

## The memory suite — measured

**Stash on stop. Recall at the door. Promote what endures.**

- **Stash** — a `Stop` hook 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 `SessionStart` hook surfaces the most recent
  findings for your project; `/recall <topic>` searches on demand.
- **Promote** — a reviewed stash→curated path lands git-tracked
  `.md` files 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 `--run` for 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** — `/fix` and `/spec` compose 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.md` and 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.

<details>
<summary><b>All 21 workflows</b></summary>

| 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 |

</details>

<details>
<summary><b>All 61 MCP tools</b> — 50 core in 7 categories, plus 11
memory tools registered by the bundled Redis plugin</summary>

**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`

</details>

---

## 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](https://github.com/Smart-AI-Memory/attune-ai/blob/main/docs/rag/faithfulness-decision-2026-04-19.md)).
Retrieved passages are sentinel-wrapped against prompt injection.
The help resolver passes 48/48 benchmark queries at P@1
([golden set](https://github.com/Smart-AI-Memory/attune-ai/blob/main/tests/unit/help/fixtures/golden_queries.yaml)).

---

## 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:

```bash
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):

```bash
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`).

<details>
<summary><b>Platform support</b></summary>

| 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`.

</details>

---

## 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](https://github.com/Smart-AI-Memory/attune-ai/blob/main/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

- [Full Documentation](https://attune-ai.dev)
- [Plugin Setup](https://github.com/Smart-AI-Memory/attune-ai/blob/main/plugin/README.md)
- [GitHub Repository](https://github.com/Smart-AI-Memory/attune-ai)

**Apache License 2.0** — Free and open source.

If you find Attune useful,
[give it a star](https://github.com/Smart-AI-Memory/attune-ai) —
it helps others discover the project.

## Acknowledgments

- **[Anthropic](https://www.anthropic.com/)** — For Claude AI, the
  Model Context Protocol, and the Agent SDK patterns behind the
  multi-agent orchestration layer
- **[Boris Cherny](https://x.com/bcherny)** — Creator of Claude Code,
  whose workflow posts validated Attune's plan-first, multi-agent approach
- **[Affaan Mustafa](https://github.com/affaan-m/everything-claude-code)** — For battle-tested Claude Code configurations that inspired the hook system

[View Full Acknowledgements](https://github.com/Smart-AI-Memory/attune-ai/blob/main/ACKNOWLEDGEMENTS.md)

---

**Built by Patrick Roebuck using Claude Code.**

<!-- mcp-name: io.github.Smart-AI-Memory/attune-ai -->
