Metadata-Version: 2.4
Name: anaxigraph
Version: 0.8.5
Summary: Shared architecture intelligence for humans and AI coding agents
Author: AnaxiGraph contributors
License-Expression: Apache-2.0
Project-URL: Homepage, https://github.com/hcekne/anaxigraph
Project-URL: Repository, https://github.com/hcekne/anaxigraph
Keywords: architecture,static-analysis,knowledge-graph,mcp,repository-analysis
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Software Development :: Quality Assurance
Requires-Python: >=3.11
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: fastapi<1,>=0.115
Requires-Dist: filelock<4,>=3.16
Requires-Dist: mcp<2,>=1.28
Requires-Dist: PyYAML<7,>=6
Requires-Dist: tree-sitter==0.26.0
Requires-Dist: tree-sitter-javascript==0.25.0
Requires-Dist: tree-sitter-typescript==0.23.2
Requires-Dist: uvicorn<1,>=0.30
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Dynamic: license-file

<p align="center">
  <img src="https://raw.githubusercontent.com/hcekne/anaxigraph/519b4385eb55023bdd4f23623e4631453da0c658/src/anaxigraph/dashboard/favicon.svg" width="112" alt="AnaxiGraph logo" />
</p>

<h1 align="center">AnaxiGraph</h1>

<p align="center">
  <strong>Understand the system. Guide the agent. Keep the architecture coherent.</strong><br />
  Shared architecture intelligence for people and AI coding agents.
</p>

<p align="center">
  <a href="https://github.com/hcekne/anaxigraph/actions/workflows/ci.yml"><img alt="CI status" src="https://github.com/hcekne/anaxigraph/actions/workflows/ci.yml/badge.svg" /></a>
  <a href="LICENSE"><img alt="Apache 2.0 license" src="https://img.shields.io/badge/license-Apache--2.0-167a96" /></a>
  <img alt="Python 3.11+" src="https://img.shields.io/badge/python-3.11%2B-315f9f" />
  <img alt="MCP Streamable HTTP" src="https://img.shields.io/badge/MCP-Streamable_HTTP-7652a4" />
</p>

<p align="center">
  <a href="docs/onboarding.md">Get started</a> ·
  <a href="docs/releases/0.8.5.md">What's new in 0.8.5</a> ·
  <a href="docs/agent-plugin.md">Agent plugin</a> ·
  <a href="docs/docker.md">Docker</a> ·
  <a href="docs/advanced-operations.md">Advanced</a> ·
  <a href="docs/brand.md">Brand</a> ·
  <a href="CONTRIBUTING.md">Contribute</a>
</p>

AI makes it easy to add code faster than a team can understand the architecture absorbing it.
Hidden coupling, duplicated responsibilities, inconsistent abstractions, and one-off agent changes
quietly become spaghetti code.

AnaxiGraph is the shared architecture intelligence layer for humans and AI agents. It turns a
repository and its Git history into a living model of what the software does, how its parts work
together, why they exist, and how the design is changing. A person can explore that model in the
dashboard; a coding agent can use the same evidence through AnaxiMCP before and after it edits code.

| Promise | For a person | For a coding agent |
|---|---|---|
| 🕸️ **Understand the system** | Move from product responsibilities and architecture areas to subsystems, files, named code parts, dependencies, and history. | Reuse a current repository-wide memory instead of rediscovering the architecture in every session. |
| 🧭 **Guide the agent** | See where a change belongs, what already exists, which patterns may fit, and what could be affected. | Receive a bounded working set, extension points, constraints, relevant tests, evidence, and counter-evidence. |
| 🧹 **Keep the architecture coherent** | Catch growing modules, repeated responsibilities, boundary erosion, cycles, and possible dead code before they harden into the design. | Rescan and compare the same goal after a change instead of treating passing tests as proof of good architecture. |

AnaxiGraph does not hand out a magic architecture score and does not edit the analyzed repository.
It keeps facts read from code, AI-created interpretations, and recommendations distinct so that
beginners can read a plain-language conclusion and experts can inspect the evidence behind it.

Give the agent your outcome, examples, boundaries and design preferences, then inspect and refine
the result together. The [directed development loop](docs/working-with-agents.md) shows how to
find ownership, check dependencies and tests, and compare each edit against an explicit baseline.

## 🚀 Start in four steps

You need Git, Python 3.11+, and [`uv`](https://docs.astral.sh/uv/).
**Recommended for agent integration: run AnaxiGraph on the host with uv**, under the same account
as your authenticated Codex or Claude Code CLI. Dashboard workers can then use those logins and
available models directly. AnaxiGraph needs **no model key** in `agent` mode; the chosen workers
use your own agent account's model allowance. Model availability and limits depend on that account.

### 1. Start the host service in your repository

```bash
cd /path/to/your/repository
uvx anaxigraph up . --open --semantic agent --connect codex
```

Use `--connect claude` for Claude Code. Omit `--semantic agent --connect codex` when you only want
the code-only map. Install and authenticate the selected agent CLI before running AI steps.

`uvx` runs AnaxiGraph in an isolated tool environment without adding it to your repository's
dependencies. For regular use, install the command once with `uv tool install anaxigraph` and use
`anaxigraph up . --open --semantic agent --connect codex`. Update with
`uv tool upgrade anaxigraph`, then restart the service. To update the one-command setup, restart
with `uvx --refresh anaxigraph up . --open --semantic agent --connect codex`.

This command creates or loads repository policy, stores AnaxiIndex outside the target, completes
the current scan, starts the loopback dashboard and AnaxiMCP. Historical mapping is opt-in with
`--history-snapshots auto` or an explicit count. Stop it with Ctrl-C; restart with the same command.

### 2. Build understanding through Workflows

Visit <http://127.0.0.1:8765>. Current architecture is ready before background history finishes.

The dashboard opens on **Workflows**. The diagrams are the control surface for building and
reviewing the map: select a step, configure its models and workers, run it, and open its results.

| Workflow | What you get |
|---|---|
| **Mapping** | File descriptions, dependencies, a reviewed responsibility map and the Living Architecture Charter. Start here. |
| **Pattern analysis** | Candidate patterns assessed against the saved map, independent critiques and evidence-backed evaluations. Run after Mapping. |
| **Fresh eyes** | Independent design proposals from the capability brief, reconciliation and a comparison with the current implementation and available Pattern evaluations. Run after Mapping. |
| **Architecture planning** | Compare options, let the agent select a target, visualize current versus proposed boundaries and follow migration milestones. Your direction is optional. |

Use **Configure → Try 2 jobs** for a small sample, **Run this step** for one stage, or **Run full mapping**, **Run full pattern analysis**, **Run full fresh eyes** or
**Run full architecture planning** for the selected sequence. Choose provider, model, effort and worker count per step; settings show
what will run next separately from the recorded models that produced saved results. Saving settings
alone makes no model calls. Optional analysis stays **Not run** until requested.

Progress shows saved coverage, reused results, active workers, elapsed time and reported token
usage. Mapping step 5 reviews independent batches in parallel within each ordered OpenAI/Claude
pass, saves each batch immediately, then shows **Final reconciliation**. Resume retains successful
batches and retries failed work. The worker queue refills available slots as jobs finish.

The header identifies the checkout, branch, indexed commit and current commit. Structural scans,
AI mapping, Patterns and Fresh Eyes have separate freshness indicators. Current views load while
history builds; file lists show loaded/total counts and fetch full descriptions when opened.
See the [workflow controls](docs/onboarding.md#explore-and-run-repository-workflows) for details,
or install the [persistent uv service](docs/advanced-operations.md#persistent-host-dashboard-with-uv)
to keep the host dashboard running between sessions.

### 3. Restart Codex in the repository

The explicit `--connect codex` option configures `http://127.0.0.1:8765/mcp` on the machine where
Codex runs. Restart it after first-time setup:

```bash
cd /path/to/your/repository
codex
```

### 4. Direct the agent with the shared map

Once Mapping is ready, give the agent an outcome, examples, design preferences and boundaries:

> Use AnaxiGraph to show me where this behavior lives, what depends on it, and which tests matter.
> Extend the existing owner, preserve its public contract, and compare the result with the
> pre-edit snapshot. Here is the behavior I want: …

**Guide** can copy a prompt with your goal, exact checkout and comparison baseline. The agent uses
the same map as the dashboard to inspect ownership, reuse existing code and check the change.
Guidance and impact default to an 8 KB response budget; request `details=true` or CLI `--details`
when more evidence is needed. Documentation and benchmark data remain identifiable as supporting
artifacts rather than implementation starting points.

You can also ask the agent to start or resume Mapping instead of using the dashboard:

> Use AnaxiGraph to build or resume the AI-created code map for this repository. Choose an
> explicit economical worker model, start or reuse the background run, and return its run ID.
> Let the worker manage the queue. Check compact progress when I ask; do not supervise it in an
> LLM loop or edit source while mapping.

For CLI examples below, install the command with `uv tool install anaxigraph`, or prefix each
command with `uvx`. The durable command survives the invoking Codex session:

```bash
anaxigraph understand . --executor codex --model '<worker-model>' --parallel-jobs 4 --background --json
anaxigraph semantic-status . --compact --json
```

Replace `<worker-model>` with your chosen Codex model. For Claude Haiku workers, use
`--executor claude --model haiku`. The repository's `max_parallel_jobs` setting limits total
concurrency across executors. Start small and increase workers within your account's limits.

`--background` keeps the worker alive after the invoking agent session exits. Return the run
handle and read compact status on request; ordinary software manages scheduling, leases and retries.
Choose a model explicitly to control cost. Provider limits and the repository's shared worker cap
still apply. Direct MCP work is a fallback when an authenticated host worker is unavailable.

The worker verifies the exact checkout before using a matching service's index. Linked worktrees
remain distinct; sharing a Git remote is insufficient. The completed map includes a reviewed
responsibility hierarchy and Living Architecture Charter, with evidence, provenance and unknowns.
Unchanged inputs reuse saved understanding. See [advanced operation](docs/advanced-operations.md)
for executor choices, index authority, Charter corrections and cost details.

The complete [onboarding guide](docs/onboarding.md) explains the normal coding loop and setup
diagnostics.

The installed AnaxiGraph skill carries the normal coding loop. The dashboard's `/api/glossary`
response exposes the same finding-state and measurement meanings for clients that render the
human interface; they are not duplicated as another agent tool.

## 🔁 Use one coding loop

**Architecture planning** can choose a direction for you. Leave its direction blank to use suggested
defaults, or add your goals and constraints. The agent compares preserving the current design with
incremental evolution and other justified options, selects a target, and records its reasoning.
You can choose another option later. The workflow shows current and proposed diagrams, Mermaid
exports, and ordered milestones with owners, contracts, verification and rollback. A selected plan
survives code changes; verification reports identify the scan they checked.

Read its summary with `anaxigraph plan .`; add `--details` for full options, diagrams and milestones.
Request it with `anaxigraph plan . --start`, or
`ANAXIGRAPH_GUIDE(intent="plan", start=true)`. Planning uses the existing background worker queue.
Ordinary coding guidance includes the saved target and next milestone.

Keep one persistent AnaxiGraph service running throughout the coding session. The service supervises
its structural watcher internally, so ordinary saves update cheap structural facts without a second
container or another model-backed repository pass. Build the complete AI map once when needed.

Give the connected agent one concrete goal. `ANAXIGRAPH_GUIDE` is the front door: use
`intent="understand"` for the system map, `build` for placement, `improve` for a bounded structural
change, `redesign` for the capability-first fresh-eyes review, and `reassess` after editing.

> Use AnaxiGraph to guide “add saved prompt exports.” Find the smallest relevant file set, tell me
> where the code belongs, inspect what depends on shared files, identify the focused checks, and
> refresh the shared map after implementation.

The agent follows one sequence:

1. **Guide** — `ANAXIGRAPH_GUIDE(intent="build"|"improve")` returns one evidence-backed
   recommendation, likely files, placement, counter-reasons, bounded impact, tests, and risks.
   Retain its `snapshot_id` as the pre-edit baseline.
2. **Impact** — `ANAXIGRAPH_IMPACT` shows direct dependants of the shared files before they change.
3. **Change** — edit source and run focused tests through the normal coding workflow. AnaxiGraph
   observes the repository; it does not edit it.
4. **Refresh and reassess** — request `ANAXIGRAPH_SCAN(refresh_semantics=true)`, finish any prepared
   AI work, then call `ANAXIGRAPH_GUIDE(intent="reassess")` with `from_snapshot_id` set to that
   baseline. Keep the same baseline across implementation phases until you deliberately choose
   another; the dashboard's **Changes → Compare from** provides the same control. The scan compares code fingerprints,
   reuses unchanged file meanings, and prepares only structurally changed files plus context whose
   interfaces or relationships changed. The shared before/after response explains observed changes,
   architectural consequences, possible improvements or regressions, reasons to leave the design
   alone, and the smallest safe verification step. It creates no approval or change-management
   state. The same result is visible under **Changes** or from `anaxigraph reassess .`.
   Use History when you need the wider introduction, recurrence, churn, or co-change context.

With the default `semantic.refresh: on_scan`, an explicit scan prepares those changed and affected
scopes automatically. `anaxigraph update . --prepare-semantics` is the explicit equivalent for a
repository that keeps manual refresh. Run `anaxigraph understand . --executor codex --background`
to execute the prepared work. Static reassessment is available immediately; ask again after
`semantically_ready` when the decision needs refreshed responsibility, duplication, pattern, or
possible-unused-code evidence.

The reviewed responsibility map has its own incremental validation boundary. It is carried forward
when the included file inventory and map policy still match and the fraction of unchanged intrinsic
module roles meets `semantic.taxonomy.stability_bias` (default `0.8`). New or removed files, changed
map constraints, or broader responsibility drift trigger a fresh proposal and its configured agent
reviews. A carried taxonomy does not suppress affected context, group, repository, or pattern work.
Planning responses expose this decision and the currently queued cascade under `work_plan`.

Each guidance and impact reply includes server time, payload size, and model-token use. Semantic
status groups AI jobs by action with current-snapshot and lifetime time/token totals, while scan
results and detached execution records show wall-clock duration. Successful and failed attempts
contribute token totals when the executor reports them. A process killed before it emits usage is
still labeled unreported; zero is never presented as proof that the model call was free.

A changed metric or finding is not automatically an improvement. Expected behavior, focused tests,
and architecture evidence must agree. The [onboarding guide](docs/onboarding.md#use-one-coding-loop)
explains the same loop; lower-level and operator workflows stay in the
[advanced guide](docs/advanced-operations.md).

## 👀 Challenge the design with fresh eyes

For a major refactor, AnaxiGraph can deliberately step outside the current package layout instead
of asking an agent already immersed in the repository to redesign what it just read. Start the
fixed review explicitly:

```bash
anaxigraph fresh-eyes . --start --proposals 2
anaxigraph understand . --executor codex --background
anaxigraph fresh-eyes .
```

The clean-sheet agents receive only the behavior-only Capability Brief and external constraints—no
current paths, frameworks, findings, history, or architecture map. A blind adjudicator preserves
meaningful disagreement, then a repository-aware pass compares that reference design with what is
actually built. A final mission filter keeps only small, justified recommendations and records
reasons not to proceed. One proposal is the lower-cost mode, two is the recommended default, and
three is optional.

Two providers can answer one review. Pin an executor family to each proposal slot, then start one
host worker per executor; each executor owns its own background run slot for the repository:

```bash
anaxigraph fresh-eyes . --start --proposals 2 --proposal-executors codex,claude
anaxigraph understand . --executor codex --background
anaxigraph understand . --executor claude --background
```

A pinned slot is only handed to the executor family it names, so a worker that finds only the other
executor's slot reports `waiting_for_executor` with the exact command that starts it and keeps
polling instead of calling the queue complete. Both workers claim at the same time only when
`semantic.max_parallel_jobs` is 2 or more. The review reports `cross_provider: true` only when two
different executor families actually produced the proposals; two sessions of one provider stay
false with the recorded caveat. A pin applies to a new review or a `--restart` generation, and
`anaxigraph fresh-eyes . --unpin` releases every assignment when the second executor is never
started.

The connected Codex or Claude executor supplies the model context and tokens; AnaxiGraph supplies
bounded evidence, validates each result, and resumes the saved stages after interruption. The
Codex adapter delivers oversized requests through temporary evidence pages in the same isolated,
read-only workspace, preserving the complete payload without exceeding the CLI's inline-input
limit. The pages add no review stages and are removed on completion or a handled failure. The
dashboard exposes the same review under **Improve → Fresh eyes**. A connected agent can read or
start it through `ANAXIGRAPH_GUIDE(intent="redesign", start=true, proposal_count=2)`. The older
`fresh_eyes=true` form remains accepted. Starting a
review never edits source or automatically accepts a recommendation.

Changing executor, model, or reasoning effort does not silently invalidate a completed review.
When you deliberately want a new architectural judgment, run `anaxigraph fresh-eyes . --restart`
(a connected agent requests the same rerun with
`ANAXIGRAPH_GUIDE(intent="redesign", start=true, restart=true)`) and then run
`anaxigraph understand` with your explicit runtime model and reasoning-effort choices.
The new generation reruns every Fresh-Eyes stage while preserving the earlier documents for audit;
it does not force unchanged module dossiers to be regenerated.

Every review lists the generations it has recorded, with the executor models, stage timings, output
volume, token counts, and attempts of each. Read an earlier one in full with
`anaxigraph fresh-eyes . --generation 2`, `GET /api/fresh-eyes?generation=2`,
`ANAXIGRAPH_GUIDE(intent="redesign", generation=2)`, or the generation selector in
**Improve → Fresh eyes**. A recorded generation is history: it reports `superseded` and cannot be
started, retried, or restarted.

## Carry the architecture with Git

Workflow runs save reusable results in a tracked root `anaxigraph/` folder when writable. It carries
concise current file understanding, the reviewed architecture, selected Pattern/Fresh Eyes results,
model provenance, workflow choices and architecture plans. Git preserves previous records. Each
server rebuilds dependency graphs, symbols and other code-derived indexes locally; historical SQL
caches, runtime data and worker queues are never exported by the current record format.

The default budget is **100 MB of active analysis state**: at most **25 MB and 32 files** in the
tracked record and **64 MB** for its local index. Configured budgets cannot exceed **200 MB**.
Exports apply today's exclusions and check size/file limits before writing. Unchanged scans and
exports reuse their existing state. Current plus two comparison checkpoints are retained locally;
detailed historical mapping is opt-in. Markdown and Mermaid keep the chosen plan readable in Git.

After pulling a repository, start AnaxiGraph normally. It validates and loads the shared record into
its local query cache, reuses compatible results and checks local source changes. An artifact-only
commit leaves the source map current. Each worktree keeps its own identity and local edits.

For Flowthink or another scheduler, run on a checkout with authenticated agent CLIs:

```bash
uvx anaxigraph refresh . --workflows mapping,patterns,fresh-eyes,architecture --executor codex --model '<worker-model>' --json
```

The command saves completed and partial results, returns a nonzero exit status for unfinished work
or publication failure, and leaves Git commits/pushes to your scheduler. Configure Mapping nightly
and the other workflows at their own cadence; unchanged model work is reused. Use
`anaxigraph record save .` for an explicit save, or **Save shared architecture files** in Planning.
Local mapping, record export and Git commit/push are separate results. For an existing bloated
index, run `anaxigraph record compact . --db /path/to/anaxi-index.db --json` on its server. This
backs up original saved understanding, preserves selected analyses, prunes disposable history, reclaims SQLite
disk space and replaces the record with the compact format. Recovery backups are reported separately
and remain until the operator retires them. See [repository records and scheduled runs](docs/advanced-operations.md#repository-records-and-scheduled-runs)
for migration, limits and scheduling.

## 🐳 Durable Docker sidecar

If you prefer an isolated, persistent container beside the repository:

```bash
cd /path/to/your/repository
uvx anaxigraph init . --start --semantic agent --connect codex
```

The generated Compose service mounts source read-only, drops Linux capabilities, enables
no-new-privileges, persists AnaxiIndex in a named volume, and publishes only to loopback by
default. Use `--connect claude` for Claude Code. Preview the full repository and client change with
`--dry-run --json`.

See [Docker operation](docs/docker.md) for manual Compose review, updates, watchers, and the
experimental multi-repository registry.

The image does not bundle Codex or Claude CLIs or their credentials. Dashboard model execution
needs those CLIs inside the service's environment; an installation on the host is not available
inside the container. Host execution with uv is the recommended route for direct dashboard worker
integration. Docker can still serve the shared map to connected agents and host workers.

## 🔌 Install the guided agent workflow

The shared plugin teaches Codex and Claude Code how to select the right indexed repository, build
or resume the AI-created code map, find a small set of likely files and affected callers, hand off
a planned finding, and verify a completed change.

Codex:

```bash
codex plugin marketplace add hcekne/anaxigraph && \
  codex plugin add anaxigraph@anaxigraph
```

Invoke `$anaxigraph`. Claude Code:

```bash
claude plugin marketplace add hcekne/anaxigraph && \
  claude plugin install anaxigraph@anaxigraph --scope user
```

Invoke `/anaxigraph:anaxigraph`. The plugin includes the default loopback MCP connection, so
plugin users may omit `--connect` from the start command. See the
[agent plugin guide](docs/agent-plugin.md) for the safety contract and custom endpoint behavior.

## How it works

```text
source + Git ── facts, hashes, relationships, history ──→ versioned AnaxiIndex
                                                               │
                                  ┌────────────────────────────┴──────────────────────┐
                                  ▼                                                   ▼
                         human dashboard                                      AnaxiMCP for agents
                    understand and investigate                         plan, inspect impact, verify
                                  │                                                   │
                                  └────────────────────┬──────────────────────────────┘
                                                       ▼
                                             better shared decisions

changed or stale modules ── bounded evidence ──→ connected agent using its own tokens
                                                       │
                                                       └──→ checked descriptions in AnaxiIndex
```

Structural refresh and semantic execution are separate operations. A dashboard **Refresh scan**
runs asynchronously with observable progress and safe cancellation; semantic prepare/resume uses
the already-current snapshot and never hides a structural rescan inside the command.

Three named surfaces share one index:

- **AnaxiGraph** is the scanner, dashboard, and overall project.
- **AnaxiIndex** is the SQLite record of repositories, files, named code parts, direct code links,
  findings, history, and AI-created descriptions.
- **AnaxiMCP** gives coding agents size-limited repository evidence and controlled ways to update
  the external index.

AnaxiGraph does not execute target code or edit application source. A generated sidecar mounts
the target read-only. The local index serves fast queries; the tracked `anaxigraph/` record carries
reusable analysis and plans between checkouts. Explicit workflows save that record when writable.
Read-only services can load it, and report when they cannot publish updated files.

### Facts are not opinions

AnaxiGraph deliberately separates:

1. **facts read directly from repository data**—hashes, code structure, named code parts, direct
   links, Git changes, branch counts, imported test coverage, and which analyzer produced them;
2. **AI explanations**—purpose, responsibilities, role in the repository, related behavior, and
   pattern opportunities, each with the model, instructions, evidence, and evidence-strength
   rating that produced it; and
3. **recommendations**—reviewable proposals with evidence, counter-evidence, cost, safety, and
   lifecycle state.

Relationship edges say whether they are resolved, ambiguous, unresolved, or external. Dynamic
runtime wiring can still be invisible, so a missing edge is never presented as proof of dead code.

### One index, several views

The map selector distinguishes four sources instead of blending them: **Current view** uses
optional declared intent first, then the AI-reviewed **Responsibility map**, then the deterministic
**Path map** fallback; **Declared map** shows repository policy alone. Stable group identities are
kept separate from their display labels, and history uses today's current-view frame by default so
the same regions visibly fill and connect over time.

- **Understand** combines the Living Architecture Charter, overview, file inventory, and graph.
- **Guide** answers where to build and how to refactor with the same recommendation for a person or
  coding agent.
- **Improve** combines the ranked finding record and reviewed pattern intelligence.
- **Changes** replays representative first-parent commits and supports reassessment after edits.
- **Settings** owns repositories, readiness, refresh, and progressively disclosed operations.
- **Pattern intelligence** also exposes finalized evaluations through `anaxigraph patterns` and the
  paged `/api/patterns` endpoint. Guidance includes relevant recommendations directly for coding
  agents. Each result leads with a conclusion, evidence, action, cautions, and
  verification; its nine exact ratings are grouped and explained instead of shown as a number wall.
  Candidate results likewise explain why a pair was selected or skipped, what evidence is missing,
  and why the internal selection order is not itself a pattern recommendation.

`anaxigraph search "goal or code name" .` uses the same bounded SQLite FTS ranking as dashboard
search, `ANAXIGRAPH_SEARCH`, and the first step of architecture guidance. It searches paths, filenames,
symbols, summaries, responsibilities, contracts, and normalized aliases, then reports the semantic
provenance that contributed to each result.

## 🎯 Findings are a workflow, not a wall

The default attention list shows at most 20 useful findings and excludes routine long-function
notes. The complete record remains filterable and paginated; no evidence is deleted merely to
quiet the UI.

Every finding says what AnaxiGraph saw, why it may matter, what to do, when the code may be fine as
it is, and how to check the result. The dashboard, REST API, MCP tools, guidance results, and copied
agent prompt use the same wording. Exact rule IDs, evidence values, and ordering scores remain
structured fields for automation, but each field has an adjacent ordinary-language meaning; they
are never dumped into a jargon-filled “technical details” section. **Plan agent work** selects
a finding for implementation; its handoff also says which retained code map first shows the
problem, where it disappears, and whether it later returns. Resolution and regression normally
come from a later scan. Retained maps are samples, so the named frame bounds the change rather than
claiming that every Git commit was analyzed.

## Current support boundary

Python uses its built-in AST. JavaScript, JSX, TypeScript, and TSX use pinned Tree-sitter grammars
for structural symbols, imports and re-exports, CommonJS, modern dynamic imports, source spans,
TypeScript declarations, and syntax-level type evidence. Workspace targets resolve from indexed
`package.json`, `tsconfig.json`, and `jsconfig.json` evidence with explicit provenance and honest
ambiguity; AnaxiGraph does not run the target build or pretend it has compiler/type-checker or
runtime certainty. Other recognized source and text formats have heuristic or inventory support.
The roadmap deliberately does not call extension recognition “full language support.” See the
[capability matrix](docs/language-support.md).

Linux x86-64 is release-gated. Linux ARM64, macOS, and WSL2 are best effort; Docker Desktop is the
recommended macOS path. Native Windows is not supported—use WSL2. See the
[platform matrix](docs/platform-support.md).

The REST and MCP service is a local sidecar. Keep it bound to loopback or access it through a
trusted SSH tunnel; do not expose the port to an untrusted network.

## Advanced operation

The [advanced guide](docs/advanced-operations.md) covers local Codex/Claude/custom executors,
semantic cost and privacy, SSH forwarding, custom ports/state,
optional coverage imports, durable history controls, watchers, integrity diagnostics, upgrades,
resets, lower-level CLI commands, and several repositories.

## 🛠️ Development

```bash
uv sync --extra dev
uv run pre-commit install --install-hooks
uv run python scripts/run_quality_gate.py --base origin/main
```

The quality gate includes a fresh, repeatable AnaxiGraph scan of this repository. Its full report
is retained in CI and compared with
[`quality/self-analysis-baseline.json`](quality/self-analysis-baseline.json); new or worsened
warning/error findings fail while unchanged information-level diagnostics remain visible and
non-blocking. Run it directly with
`uv run python scripts/check_self_analysis.py --output /tmp/anaxigraph-self-analysis.json`.

The product brief is [`repo_instructions.md`](repo_instructions.md), the consecutive roadmap is
[`docs/feature-development-plan.md`](docs/feature-development-plan.md), and the release contract is
[`docs/releasing.md`](docs/releasing.md). Contributions are welcome; see
[`CONTRIBUTING.md`](CONTRIBUTING.md).
