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https://github.com/taniwhaai/arai

AI coding rules that actually work. Enforce instruction files via hooks — CLAUDE.md, .cursorrules, copilot-instructions, and more.
https://github.com/taniwhaai/arai

ai-guardrails claude-code cli code-quality copilot cursor-rules developer-tools llm rust windsurf

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AI coding rules that actually work. Enforce instruction files via hooks — CLAUDE.md, .cursorrules, copilot-instructions, and more.

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README 

          
# Arai



**Instruction files that actually work.** One command. Runs locally. Zero cost.



Arai makes your AI coding assistant instruction files structurally enforceable — not just suggestions that get forgotten as context grows.



![Arai blocking a forbidden command at the PreToolUse hook](demos/block.gif)



## Quick Start



```bash

curl -sSf https://arai.taniwha.ai/install | sh



cd your-project

arai init

```



That's it. Arai discovers your instruction files, extracts the rules, classifies their intent, scans your codebase for context, and sets up native hooks so guardrails fire at the right moment.



## What It Does



When your AI coding assistant (Claude Code or Grok TUI) is about to do something your rules cover, Arai injects the relevant guardrail — right when it matters. Rules derived from prohibitive predicates (`never`, `forbids`, `must_not`) actually **block the tool call** instead of just advising.



```

You: "Create a new database migration"



  PreToolUse: Write migrations/versions/001_add_users.py

  → Arai: deny

    reason: "Alembic never: hand-write migration files"

            [from your rules:12, layer-1 imperative]



Assistant: "I should use alembic revision --autogenerate instead..."

```



Rules only fire when relevant. No noise on `ls`. No repeating principles already in your instruction files.



Every firing is written to a local audit log, and every PostToolUse is correlated with the matching PreToolUse to produce a **compliance verdict** — so you can measure whether the model actually honours the rules you wrote.



## How It Works



1. **Discovers** instruction files in your project and home directory

2. **Extracts** rules by pattern-matching imperative language ("never", "always", "don't", "must")

3. **Classifies** each rule's intent — what action it governs, which tools it applies to, when it should fire

4. **Scans** your codebase with tree-sitter to understand which tools own which directories

5. **Tracks** session state — knows if you've already run tests before pushing

6. **Fires** only relevant rules at the right moment via native hooks (where supported)



## Supported Instruction Files



| File | Tool | Enforcement |

|------|------|-------------|

| `CLAUDE.md` | Claude Code | Hooks (block + advise) |

| `AGENTS.md` / `Agents.md` | Grok TUI (native) | Hooks (block + advise) |

| `~/.claude/CLAUDE.md` | Claude Code (global) | Hooks (block + advise) |

| `~/.grok/` AGENTS.* files | Grok TUI (global) | Hooks (block + advise) |

| `.cursorrules` / `.cursor/rules` | Cursor | MCP (advise) |

| `.windsurfrules` | Windsurf | MCP (advise) |

| `.github/copilot-instructions.md` | GitHub Copilot | Ingest only |



Rules from every file are parsed, classified, and stored the same way — but

enforcement strength depends on what surface the assistant exposes.



- **Claude Code** and **Grok TUI** both support real PreToolUse hooks, so Arai

  can issue `deny` decisions and actually block tool calls.

- Cursor and Windsurf are MCP clients today — they get strong advisory

  enforcement via the MCP server.

- GitHub Copilot currently has no live enforcement surface; the file is

  still ingested for `arai stats`, `arai diff`, and the audit log.



Arai hooks several more events alongside the standard tool-call events

(when the assistant supports them) so the rule set stays accurate to the live

working tree:



- **`FileChanged` + `InstructionsLoaded`** — when an instruction file

  (CLAUDE.md, rules-dir, memory file, ...) is edited on disk or loaded

  into context, Arai spawns an `arai scan` in the background. The next

  tool-call hook sees the updated guardrails — no manual rescan.

- **`CwdChanged`** — when Claude `cd`s into a different directory

  (monorepo navigation), Arai re-scans rooted at the new directory so

  the next tool call matches against the right project's rules.

- **`PostToolBatch`** — when Claude does a batch of parallel tool calls,

  Arai correlates each call individually against any PreToolUse firings

  in the same session, so per-rule compliance verdicts (Obeyed /

  Ignored / Unclear) stay accurate on parallel workloads.



## Smart Matching



Arai doesn't just do keyword matching. It understands your rules:



- **Intent classification** — "never hand-write migration files" only fires on Write, not Edit (editing existing migrations is fine)

- **Code graph** — writing to `migrations/versions/` triggers alembic rules even if the file doesn't mention alembic, because sibling files import it

- **Content sniffing** — detects `from alembic import op` in file content being written

- **Session awareness** — "never push without running tests" suppresses after tests have been run

- **Timing routing** — domain rules fire on tool calls, principles stay silent (already in CLAUDE.md)

- **Broad imperative coverage** — recognises `never/always/don't/must`, `should/shouldn't`, `cannot/refuse`, `make sure/be sure`, `consider/recommend`, bare `No X` prohibitions, conditional shapes (`When X, do Y` / `Before X: do Y` / `If X → do Y`), and the section-aware `Use X` style-guide pattern. Severity mapping mirrors grammatical weight: `should` is `Inform` (soft), `should not` is `Block` (the writer chose to call out a specific prohibition).



## Compliance & audit



Beyond firing rules, Arai produces a tamper-evident local record of *every*

guardrail decision and correlates it with what the model actually did. This

is what tech leads and compliance reviewers want to see — the trail behind

the enforcement.



- **Local JSONL audit log** — one line per firing at

  `~/.taniwha/arai/audit//.jsonl`. Append-only, day-bucketed,

  queryable with `arai audit` (filters: `--since`, `--tool`, `--event`,

  `--outcome`, `--rule`). Owner-only on disk (0700 dir / 0600 file on Unix;

  `icacls`-pinned on Windows).

- **Hash-chained — actually tamper-evident** — every line carries `prev_hash`

  and `hash` (SHA-256 over canonical bytes); the chain is anchored per-day in

  a `.head.YYYYMMDD` sidecar. `arai audit --verify` walks the chain across

  every day-bucket and exits non-zero on any tamper / reorder / deletion —

  drop it in a cron or pre-archive job to gate evidence integrity.

- **Retention controls** — `arai audit --purge --older=90` drops day-buckets

  older than 90 days; `arai audit --purge --project=` wipes a specific

  project (offboarding / decommission). Today's bucket is always preserved

  and whole files are deleted (never individual lines), so the hash chain on

  retained days stays valid. Pair with `--dry-run` (and `--json`) for a

  pre-purge review, or wire into a scheduled job for time-based retention

  policy.

- **Derivation trace per firing** — each rule entry records source file,

  line number, and parser layer (`from CLAUDE.md:42, layer-1 imperative`).

  Auditors can answer "why did this rule fire?" without code spelunking.

- **Compliance verdicts** — every PostToolUse is correlated against recent

  PreToolUse firings to produce **Obeyed / Ignored / Unclear** per rule.

  `arai stats --by-rule` rolls these up into per-rule ratios with a ⚠ flag

  on rules the model is routing around.

- **Graduated enforcement** — severity tiers (Block / Warn / Inform) derive

  from rule predicate; `arai severity` pins individual rules so you can

  ship a rule set in advise mode and escalate one at a time.

  `ARAI_DENY_MODE=off` is the project-wide rollback path.

- **Regression-tested policy** — `arai test` replays scenarios through the

  live `match_hook` pipeline; `arai record` captures real firings as

  fixtures. Rule changes become CI assertions, not vibes.

- **No data egress** — no network on the hook hot path. Anonymous opt-out

  telemetry is architecturally separate from the audit log; they share no

  code path. The audit data physically cannot leak via the telemetry

  channel. The telemetry queue is hard-capped at 2 MiB on disk.

- **Supply-chain hardened** — every install path verifies the binary

  against published `checksums.txt` (SHA-256). `arai:extends` upstream

  policy fetches refuse loopback / RFC1918 / link-local / cloud metadata

  and disable redirects; **cached upstream policies carry a SHA-256

  sidecar** so a tampered cache file is detected before its rules reach

  the parser.

- **MCP authentication** — the agent-facing MCP server supports an optional

  shared-secret via `ARAI_MCP_AUTH_TOKEN`. When set, `initialize` must

  present a matching token (constant-time compare) before any tool call

  succeeds.



Designed to align with the **SOC 2 Trust Service Criteria** (CC6.1 logical

access, CC6.6 supply-chain, CC7.2 monitoring, CC7.3 detection, CC8.1 change

management, CC9.2 vendor management). Arai is not itself a certified

product — it gives you the controls and the evidence trail; the

certification is yours to pursue. A complete TSC mapping and enterprise /

procurement-team feature inventory is in

[`docs/arai-compliance-features.pdf`](docs/arai-compliance-features.pdf).

The Word source (`.docx`) is committed alongside it for editing.



## Enrichment



Three tiers of rule understanding, each more accurate:



```bash

arai scan                  # Tier 1: Built-in verb taxonomy (free, instant)

arai scan --enrich         # Tier 2: Sentence transformer model (local, ~80MB download)

arai scan --enrich-llm     # Tier 3a: LLM classification via CLI

arai scan --enrich-api     # Tier 3b: LLM classification via API (no CLI needed)

```



Configure your LLM:

```bash

# Via CLI tool (shell-out)

ARAI_LLM_CMD="claude -p" arai scan --enrich-llm

ARAI_LLM_CMD="ollama run llama3" arai scan --enrich-llm



# Via API (OpenAI-compatible endpoints)

ARAI_API_KEY=sk-... arai scan --enrich-api                    # OpenAI (default)

ARAI_API_URL=http://localhost:11434/v1 arai scan --enrich-api  # Ollama (auto-detected)

ARAI_API_URL=https://api.groq.com/openai/v1 ARAI_API_KEY=gsk-... ARAI_API_MODEL=llama-3.3-70b-versatile arai scan --enrich-api



# Or in ~/.taniwha/arai/config.toml

[enrich]

llm_command = "llm -m gpt-4o-mini"       # for --enrich-llm

api_url = "https://api.openai.com/v1"     # for --enrich-api

api_key_env = "OPENAI_API_KEY"

model = "gpt-4o-mini"

```



## Commands



```bash

arai init                  # Discover, extract, classify, scan, set up hooks

arai status                # Show what's being enforced

arai guardrails            # List all active rules

arai why "git push --force" # Explain which rules would fire (dry-run, no audit write)

arai scan                  # Re-scan instruction files

arai scan --code           # Also scan source code (tree-sitter AST)

arai scan --enrich-llm     # Enhance rules via LLM CLI

arai scan --enrich-api     # Enhance rules via API (OpenAI-compatible)

arai add "Never X"         # Add a rule manually

arai audit                 # Inspect the local log of rule firings

arai audit --outcome=ignored # Compliance verdicts where the model ignored a rule

arai audit --rule alembic  # Filter audit by rule subject/predicate/object substring

arai audit --verify        # Verify the SHA-256 hash chain across every day-bucket

arai stats                 # Aggregate audit log — top rules, compliance, token economics

arai stats --by-rule       # Just the per-rule compliance + token economics

arai severity alembic block # Pin a rule's severity (incremental deny rollout)

arai severity --reset alembic # Drop the override; severity reverts to predicate-derived

arai diff CLAUDE.md        # Preview rule-set delta before saving an edit

arai test scenarios.json   # Replay synthetic hook scenarios against rules

arai record --since=1h     # Capture recent firings as a scenario skeleton

arai lint CLAUDE.md        # Parse a file and preview extracted rules

arai trust                 # Manage URLs trusted for shared-policy extends

arai mcp                   # Run the MCP server (stdio) for agent-authored guards

arai upgrade --full        # Switch to full binary (with ONNX enrichment)

```



## Deny mode — actually block bad actions



Starting in v0.2.3, Arai no longer just *advises*: rules derived from

prohibitive predicates (`never`, `forbids`, `must_not`) emit

`permissionDecision: "deny"` (or equivalent) so the assistant refuses the tool call. Advisory

rules (`always`, `requires`, `prefers`) keep the previous behaviour.



Severity is inferred from the predicate at extract time:



| Predicate | Severity | Hook behaviour |

|-----------|----------|----------------|

| `never`, `forbids`, `must_not` | `block`  | `permissionDecision: "deny"` + reason |

| `always`, `requires`, `enforces` | `warn` | `permissionDecision: "allow"` + context |

| `prefers`, `learned_from` | `inform` | `permissionDecision: "allow"` + context |



Rolling Arai out incrementally? Flip deny mode off at the env level:



```bash

ARAI_DENY_MODE=off   # advisory-only — rules still fire in additionalContext

```



Useful pattern: ship Arai in advise mode for a week, watch `arai audit

--outcome=ignored`, tune the rules the model keeps flouting, then enable

deny mode when the rule set is trustworthy.



## Compliance tracking



After every PostToolUse, Arai correlates the call against recent

PreToolUse firings in the same session and emits a `Compliance` event to

the audit log per rule:



- **obeyed** — forbidden phrase absent from the executed command (for

  prohibitive rules), or the required evidence present (for affirmative

  rules).

- **ignored** — forbidden phrase still in the executed command.

  The model ran the thing anyway (either deny was off or the assistant

  chose to proceed).

- **unclear** — not enough signal to decide (short object text, or

  affirmative rule without evidence in this call).



```bash

arai audit --event=Compliance     # all verdicts

arai audit --outcome=ignored      # shortcut for the painful ones

arai audit --outcome=obeyed       # show the rules doing their job

```



This closes the feedback loop the audit log was missing: not just *which*

rules fired, but *which ones the model actually honoured*.



## arai why — explain before you commit



`arai why ` replays a hypothetical tool call through the live

matching pipeline and prints the rules that would fire, with severity,

derivation (source + line + parser layer), and match percentage. No audit

write; read-only against the rule set.



```bash

arai why "git push --force origin main"

arai why --tool Write /src/migrations/001_init.py

arai why --tool Bash --event PostToolUse "rm -rf /data"

arai why "git push --force" --json   # machine-readable

```



Use it to: debug "why did that rule fire?", preview new rules before

committing them, or include the output in a PR description when you

change a CLAUDE.md.



## Rule expiry — self-pruning rules



Annotate rules with `(expires YYYY-MM-DD)` or `(until YYYY-MM-DD)` at the

end of the line. The annotation is stripped from the rule body at parse

time and stored separately; `load_guardrails` filters out expired rows so

the rule stops firing on its own, without you having to remember to

clean it up.



```markdown

- Never touch the old auth module (expires 2026-09-01)

- Always rebase against release-1.8 until 2026-12-31

- Prefer the new payment SDK over the legacy one (until 2027-06-30)

```



Perfect for `learned_from` incidents that have a shelf life, migration

windows, and "temporarily forbid X until we finish the refactor" rules.



## Per-rule enrichment opt-out — `(noenrich)`



`arai scan --enrich-llm` and `--enrich-api` send the full text of every

guardrail to whatever LLM you've configured (`ARAI_LLM_CMD` /

`ARAI_API_URL`). For most rules that's fine — they're already in

`CLAUDE.md`. But if a single rule mentions an internal codename you'd

rather not ship to a third-party endpoint, append `(noenrich)`:



```markdown

- Never deploy to internal-codename-cluster (noenrich)

```



The annotation is stripped from the rule body at parse time and stored

separately; the enrichment paths filter the rule out before building the

prompt. `(noenrich)` and `(expires …)` can appear together in either

order. To opt out globally, just don't pass `--enrich-llm` /

`--enrich-api` — neither runs by default.



Before each enrichment run Arai prints a one-line notice with the

resolved destination and a locality verdict (`local` / `REMOTE` /

`unknown locality`), plus the count of rules excluded via `(noenrich)`,

so you can see at a glance whether rule text is about to leave the

host.



## Audit log



Every time a rule fires, Arai appends one line to a local JSONL log at

`~/.taniwha/arai/audit//.jsonl`. The log captures the

hook event, the tool that was called, a truncated prompt preview, the

decision (`inject`, `deny`, `review`), and every rule that matched —

with source file, line number, parser layer, severity, and confidence.



Nothing leaves your machine — this is separate from the anonymous

usage telemetry below.



```bash

arai audit                    # Today's firings, table view

arai audit --since=7d         # Last week

arai audit --tool=Bash        # Only Bash tool calls

arai audit --event=PreToolUse # Only pre-tool-use firings

arai audit --event=Compliance # Compliance verdicts (Pre/Post correlation)

arai audit --outcome=ignored  # Shortcut: Compliance events marked ignored

arai audit --rule alembic     # Filter to firings/verdicts touching this rule

arai audit --json             # JSONL stream (pipe-friendly)

arai audit --verify           # Verify the SHA-256 hash chain (exits non-zero on any tamper)

arai audit --verify --json    # Machine-readable verify report for CI / cron

```



`--rule` is a case-insensitive substring match against the rule's

subject, predicate, or object — the same shape `arai severity` uses.

Pairs naturally with `--outcome=ignored` to answer "every time the

alembic rule was ignored this week".



Useful for answering:



- *"Why did Claude suddenly change approach halfway through?"* —

  look up the firing, see which rule matched.

- *"Which rules are actually load-bearing?"* — sort firings by rule,

  prune rules that never trigger.

- *"Did the guardrail fire before that regrettable git push?"* —

  grep by session id.



## Status — health check your rule set



`arai status` shows how many rules are loaded, where they came from,

and when they were last scanned. As of v0.2.2 it also surfaces two

common rule-set health issues:



- **Duplicate rules** — the same (subject, predicate, object) ingested

  from more than one source file. Usually safe to consolidate into

  one source to reduce drift.

- **Opposing predicates** — the same subject carries both a

  prohibitive predicate (`never`, `must_not`, `avoid`) and a required

  predicate (`always`, `must`, `requires`, `ensure`). Not always a

  real conflict (the objects may differ), but worth a human look.



These are advisory only — the hook path ignores them. Fix them at the

source.



## Stats — aggregate the audit log



`arai stats` rolls up the same JSONL `arai audit` tails and answers

the questions every maintainer asks after a few weeks of use:



```bash

arai stats                # Top rules, compliance, token economics

arai stats --since=30d    # Window to the last month

arai stats --top=5        # Show only top 5 per section

arai stats --by-rule      # Compliance + token economics only

arai stats --json         # Machine-readable for dashboards

```



Output includes: total firings, most-fired rules, tools attracting the

most guardrails, day-by-day activity, **and a per-rule compliance

roll-up** — for every rule that has fired, how many Pre/Post pairs

ended up `obeyed` vs `ignored`, plus a ratio:



```

Per-rule compliance

  fires obeyed ignored unclear   ratio  rule

     12     11       1       0     92%  alembic must_not: hand-write migrations

      7      4       3       0     57%  git must_not: --no-verify  ⚠

      9      9       0       0    100%  cargo always: test before commit

```



The ⚠ flag highlights rules with low ratios and enough volume to

mean it — these are the ones to either rewrite (rule subject too

narrow / object too vague) or escalate via `arai severity` (see

below) once you trust the wording.



The ratio is computed **once per Pre firing** using a first-

definitive-wins rule: the first non-`unclear` Compliance verdict

correlated against a Pre is the verdict for that Pre, regardless

of how many subsequent Posts also fall inside the 5-minute

correlation window. So a rule that fires once and is honored stays

at 1 obeyed / 1 fire, not 8 obeyed / 1 fire just because eight

unrelated commands followed.



Nothing leaves the machine — stats are a local view over your own

audit log.



## Token economics — calibrated estimates



`arai stats` also surfaces a *token economics* section with

calibrated estimates of how Arai is affecting your model's token

burn. Two streams contribute:



```

Token economics (estimates)

     12  repeat-injection suppressions  (~600 tokens, 50 ea.)

      4  denied-and-honored mistakes    (~8000 tokens, 2000 ea.)

     17  advised-and-honored events     (~8500 tokens, 500 ea.)

            total estimated tokens saved:  ~17100

            (calibrated estimates, not measurements)

```



- **Repeat-injection suppressions** — when a rule fires a second

  time in the same session, Arai emits a compact "still: subject

  predicate object" line instead of re-injecting the full source /

  layer / severity payload. The model already has that context from

  the first firing. The 50-token estimate is the rough delta

  between the full and compact forms.

- **Denied-and-honored mistakes** — a `block`-severity rule fired,

  the model would otherwise have run a destructive action, and the

  PostToolUse correlation confirms it didn't. The 2000-token

  estimate is a conservative bound on what "fix the mess" cycles

  cost (revert files, undo migrations, rollback deploys).

- **Advised-and-honored events** — a `warn` or `inform` rule fired

  and the model complied. Lower confidence saving (the model might

  have done the right thing anyway), so a smaller 500-token

  estimate.



These are **estimates, not measurements**. The constants live in

[`src/stats.rs`](src/stats.rs) and are documented there; treat the

total as an order-of-magnitude reading, not a precise number. If

you want to see the underlying counts, `arai stats --json` exposes

the `token_economics` object with all three streams broken out.



## Severity — per-rule deny-mode rollout



`arai severity` pins a rule's enforcement strength so re-running

`arai scan` won't reset it to the predicate-derived classification.

Use it for **incremental deny-mode rollout**: ship the rule set in

advise mode (`ARAI_DENY_MODE=off`), watch `arai stats --by-rule`,

and flip individual rules into `block` once the model is honouring

them in the wild — without forcing the whole rule set into a strict

mode it isn't ready for yet.



```bash

arai severity                          # List active overrides

arai severity alembic block            # Pin every rule whose subject/object

                                       # contains "alembic" to block

arai severity git warn                 # Demote git rules to advise-only

arai severity --reset alembic          # Drop the override; severity reverts

                                       # to the predicate-derived value

arai severity alembic block --json     # Machine-readable list of changes

```



Pattern matching is case-insensitive substring against the rule's

subject *or* object, so `arai severity migrate` covers both

`alembic must_not: hand-write migrations` and `migrations require:

backfill_plan`.



Overrides survive `arai scan` and `arai init` — they live in their

own column and are never touched by re-classification. Drop one with

`--reset` when you're ready to re-derive severity from the rule's

predicate.



## Diff — preview rule-set changes



`arai diff ` shows what changes a candidate edit to an

instruction file would make to the live rule set — added, removed,

moved — before you save and run `arai scan`. Read-only against

the store; pairs with `arai lint` (preview a file in isolation)

and `arai why` (preview a single tool call).



```bash

arai diff CLAUDE.md                            # Plain table view

arai diff memory/feedback_testing.md --json    # For pre-commit hooks

```



Output is grouped into three sections — `Added` (rules in the file

that aren't in the store yet), `Removed` (rules in the store whose

text isn't in the new file), `Moved` (same rule, different line

number — caught when you re-order a file without changing its rules).

JSON output keeps the same shape for CI.



## Lint — preview what a file produces



`arai lint ` parses an instruction file and prints every rule it

would extract along with the intent classification, without touching

the DB. Use it to iterate on CLAUDE.md wording and see the effect

before you commit.



```bash

arai lint CLAUDE.md

arai lint memory/feedback_testing.md --json   # machine-readable

```



Output for each rule: subject / predicate / object, the classified

action (Create / Modify / Execute / General), the hook timing it routes

to (ToolCall / Stop / Start / Principle), and which tools the rule

applies to.



## Test — regression harness for rules



`arai test` replays synthetic hook payloads through the *same*

`match_hook` pipeline the live hook handler uses, so rule changes get

caught before they affect a real session.



The canonical [alembic example](scenarios/alembic-migration.json) is

checked in — run it after `arai init` on any repo with an alembic rule

in CLAUDE.md:



```bash

arai test scenarios/alembic-migration.json

```



Scenario files are JSON:



```json

{

  "scenarios": [

    {

      "name": "force-push triggers the git guardrail",

      "hook": {

        "hook_event_name": "PreToolUse",

        "tool_name": "Bash",

        "tool_input": { "command": "git push --force origin master" }

      },

      "expect": {

        "matches_subject": ["git"],

        "does_not_match_subject": ["alembic"],

        "min_matches": 1

      }

    }

  ]

}

```



```bash

arai test scenarios/guards.json

arai test scenarios/guards.json --json   # structured pass/fail for CI

```



Exit code is non-zero when any scenario fails. Matches are checked by

subject substring because full SPO triples tend to drift across

re-ingest.



## Record — seed scenarios from real firings



`arai record` turns entries in the audit log into scenario skeletons

so you don't hand-write regression tests. Flow: run your assistant, hit a

rule firing you want pinned, `arai record --since=1h > tests.json`,

tune the expectations, check in.



```bash

arai record --since=1h              # last hour

arai record --since=7d --tool=Bash  # only Bash firings from the last week

arai record --limit=50              # cap audit entries scanned

```



Deduplicates by (tool, prompt) so repeated identical firings collapse

to one scenario. Each scenario's `expect` seeds `matches_subject` with

whatever actually fired and `min_matches: 1` — tune from there.



Runtime-capturing *new rules* (as opposed to testing existing ones) is

a different loop: that goes through the MCP `arai_add_guard` tool,

documented below.



## Shared policies — `arai:extends`



Instruction files can inherit rules from a trusted upstream URL. This

is the "org-wide CLAUDE.md" pattern without a policy service — just

another markdown file hosted wherever you like.



Declare the upstream in your CLAUDE.md:



```markdown



# My project rules

- Never publish artifacts before tag push

```



Then trust the URL:



```bash

arai trust --add https://example.com/standards/rust-backend.md

arai trust                  # List trusted URLs

arai trust --remove    # Revoke

```



Ārai never fetches a URL that isn't explicitly trusted. HTTPS only,

512 KB size cap, 24-hour cache with stale-while-error fallback, and

extends are not recursive — the fetched file can't pull in further

URLs. On `arai init`, trusted upstream content is inlined ahead of the

local rules before the parser runs, so the rest of the pipeline sees

one merged file.



## MCP: agent-authored guardrails



`arai mcp` is also the integration path for assistants that don't have a

native PreToolUse hook surface. Cursor and Windsurf are both MCP clients — point

them at `arai mcp` and the agent can read the same rule set, register new guards

mid-session, and self-check recent decisions.

The strongest blocking enforcement is available in assistants with native hook

support (currently Claude Code and Grok TUI), but everything else — rule lookup,

agent-authored guards, decision history — is shared via MCP.



`arai mcp` runs a [Model Context Protocol](https://modelcontextprotocol.io/)

server on stdio. Three tools, exposed to any MCP-capable agent:



| Tool | What it does |

|------|--------------|

| `arai_add_guard(rule, reason?)` | Register a new guardrail mid-session. Takes effect on the next PreToolUse hook — same enforcement path as rules in your CLAUDE.md. |

| `arai_list_guards(pattern?)` | List active guardrails, optionally substring-filtered, so the agent can check what constraints are live before acting. |

| `arai_recent_decisions(session_id?, limit?, since?)` | Look up recent Ārai decisions (deny / inject / review) so the agent can self-check after a refusal — closes the model-side feedback loop. |



This closes two gaps instruction files don't cover. First, when an agent

discovers a rule mid-session (*"from now on, never write to /etc"*,

*"always run the full test suite before pushing"*), it now has

somewhere to register it for deterministic enforcement rather than

hoping context retention holds. Second, after a deny, the agent can

call `arai_recent_decisions` to see what it was just refused for —

useful for avoiding "try the same thing twice" loops when a single

rule keeps getting hit.



Register it with your assistant (for example in Claude Code or Cline) by adding to your MCP settings:



```json

{

  "mcpServers": {

    "arai": {

      "command": "arai",

      "args": ["mcp"]

    }

  }

}

```



For Cline (in `cline_mcp_settings.json`, or via the MCP UI):



```json

{

  "mcpServers": {

    "arai": {

      "command": "arai",

      "args": ["mcp"],

      "disabled": false,

      "autoApprove": []

    }

  }

}

```



For Cursor and Windsurf, follow each tool's MCP server registration UI

and point it at the same `arai mcp` command — the protocol is identical.



Prerequisite: `arai` must be on your `PATH`. The install script, `cargo

install arai`, `npm install -g @taniwhaai/arai`, and the Homebrew tap all

put it there.



## Installation



```bash

# Install script (recommended)

curl -sSf https://arai.taniwha.ai/install | sh



# Full binary (with local sentence transformer)

ARAI_FULL=1 curl -sSf https://arai.taniwha.ai/install | sh



# npm

npm install -g @taniwhaai/arai



# Cargo

cargo install arai

cargo install arai --features enrich   # with ONNX model support



# Homebrew

brew install taniwhaai/tap/arai



# Docker (sandboxed install or CI-side enforcement)

docker build -t arai .

docker run --rm -i -v "$(pwd)/.taniwha/arai:/home/arai/.taniwha/arai" arai

# Or via compose with a persistent named volume:

docker compose run --rm arai

```



## Performance



| Operation | Median | p95 |

|-----------|--------|-----|

| Hook check (skip-tool — Read/Glob/Agent) | ~22 ms | ~36 ms |

| Hook check (full match pipeline) | ~32 ms | ~55 ms |

| Full init | <200 ms | — |



End-to-end wall clock per tool call (on supported assistants), measured by

`bench/hot_path.sh`. Cost is dominated by Rust binary fork+exec

(~20 ms floor on Linux/WSL); rule matching itself is sub-ms above 200

rules thanks to the LEFT-JOIN'd intent and Aho-Corasick content sniffing.

Rule count between 50 and 500 doesn't materially move the median —

matching is no longer the bottleneck.



## Telemetry



Arai collects anonymous usage data to help us understand if guardrails are actually useful. We track:



- Whether a rule fired and on which tool

- Hook response latency

- Rule counts and enrichment tier on init



We **never** collect file paths, rule text, code content, API keys, or anything that could identify you or your codebase.



**Opt out** at any time:



```bash

export ARAI_TELEMETRY=off   # or DO_NOT_TRACK=1

```



## Built By



[Taniwha.ai](https://taniwha.ai) — extracted from the [Kete](https://github.com/taniwhaai/kete) code intelligence platform.



## License



MIT / Apache-2.0