_{Scripted local session-room capture · real AIT routing, PTYs, context refs, and replay}

Why ait exists

Single-agent AI coding tools — Claude Code, Cursor, Aider — are fast but lock you to one model's view of the codebase. If that model misses something, nothing catches it before the code hits your tree.

ait runs multiple agents as a team. One investigates, another implements, a third reviews — and the review gate can block the apply if it finds a critical issue. Cross-agent context is handed off via AIT_CONTEXT_FILE, not re-pasted. Everything runs locally; your prompts, diffs, and provenance never leave your machine.

It wraps the agent CLIs you already use. MIT, Python 3.14+, zero runtime dependencies, no SaaS, no telemetry.

→ Read the manifesto: Multi-agent AI coding belongs on your laptop

Install and try

pipx install ait-vcs            # or: npm install -g ait-vcs
cd your-repo
ait init                        # one-time per repo
direnv allow                    # only if prompted
claude ...                      # or codex / aider / gemini / cursor — ait wraps them
ait status                      # see what ran
ait apply latest                # apply when you are ready

The package is ait-vcs on PyPI and npm. The installed command is ait.

_{Static HTML from ait graph --html: attempts, evidence, memory, hot files, and query filters in one local report.}

Key Capabilities

Problems ait Solves

ait is not another agent. It is the local attempt workflow around the agents you already trust.

Core Concept

AIT does not replace Claude Code, Codex, or any other coding agent. It gives them an attempt-first workflow. Every agent run becomes an attempt with its own isolated worktree, provenance, queryable metadata, and explicit apply/recover flow.

That keeps AI-generated changes in a reviewable proposal state. You can compare attempts from different agents, inspect what each changed, keep failed runs for recovery, and decide which result should land in your checkout.

Memory in AIT is attempt-derived, evidence-backed repo memory, not a hidden chat-window transcript. It is not an external vector database product, not a CLAUDE.md generator, and not a bag of unreviewed prompt snippets. AIT recalls policy-allowed context from prior attempts, commits, notes, accepted facts, and review findings, then federates that AIT-owned memory with live external sources such as CLAUDE.md, AGENTS.md, .claude/memory.md, .codex/memory.md, and .cursor/rules at recall/run/review time. Those files remain their own source of truth; AIT does not auto-import them.

Agent-to-agent communication

AIT gives agents a shared, local handoff channel that is tied to Git state:

1. A wrapped agent run creates an attempt with prompt, output, changed files, commits, status, and memory candidates.
2. Useful facts, decisions, failures, and review findings stay queryable under .ait/ or in live repo memory files.
3. The next wrapped run receives AIT_CONTEXT_FILE, a compact handoff assembled from policy-allowed prior attempts, accepted facts, notes, commits, and live agent memory files.

That makes communication asynchronous and inspectable. Claude can investigate, Codex can implement, Aider can patch, Cursor can follow repo rules, and a reviewer agent can challenge the result without depending on one model's private chat history.

What It Feels Like

Initialize once:

ait init
direnv allow   # only if prompted

Confirm the current shell is actually routed through AIT:

ait status claude-code
ait status codex
ait status --all

Bypass detection: wrapped means the agent command resolves to the repo-local AIT wrapper. Bypass detection: bypass_risk means this shell would call the real agent binary directly, so AIT would not capture prompt, attempt, or failure evidence. Re-run shell activation, direnv allow, or ait repair, then check status again.

Then keep using your agent:

claude ...
codex ...
aider ...
gemini ...
cursor ...

After a successful wrapped run, inspect the result:

ait status
ait recover latest --debug   # optional low-level details

Apply only when you are ready:

ait apply latest

Until apply, your root checkout stays unchanged. If apply is unsafe because your local edits overlap with the result, AIT holds the result for recovery instead of stashing or overwriting your work.

Inspect cleanup candidates before reclaiming old internal workspaces:

ait cleanup
ait cleanup --apply

ait cleanup defaults to a dry run. Apply mode removes safe terminal workspaces such as applied or discarded attempts while retaining active, pending, and reviewable attempts by default.

Core Features

Quick Examples

Set explicit intent and commit text:

AIT_INTENT="Update README" \
AIT_COMMIT_MESSAGE="update README with Claude" \
claude -p --permission-mode bypassPermissions \
  "Shorten the README and improve the quickstart"

Wrap a command directly:

ait run --adapter claude-code --intent "Refactor query parser" -- claude
ait run --adapter codex --intent "Implement parser edge cases" -- codex
ait run --adapter aider --intent "Fix auth expiry" -- aider src/auth.py
ait run --adapter shell --intent "Regenerate fixtures" -- \
  python scripts/regenerate_fixtures.py

Use repo-local memory:

ait memory
ait memory sources
ait memory search "auth adapter"
ait memory recall "billing retry"
ait memory backfill --dry-run
ait memory backfill --import

ait memory sources and default ait memory recall are zero-touch reads: they do not create .ait/, do not mutate source files, and read current repo-local agent memory live. ait memory backfill --dry-run is also zero-write preview. Use backfill --import only as an explicit mutation when you want AIT to add advisory memory under .ait/; it is not required for live recall. Global or out-of-repo memory requires an explicit --global --path ....

Run adversarial review before apply:

ait review attempt latest-reviewable \
  --mode adversarial \
  --review-adapter claude-code \
  --review-budget standard

ait review finding list --severity high --format text
ait review report --attempt latest --format json
ait apply latest --mode current

Repair local setup if wrappers drift:

ait repair
ait repair codex

Integrations

ait ships first-class adapters for the agents most teams already run. Each adapter wraps the upstream CLI, isolates its work in a Git worktree, and records the attempt locally in .ait/.

ait init detects every supported agent CLI on $PATH and wires it up automatically — wrappers under .ait/bin/, hooks merged into the relevant .claude/, .codex/, .gemini/ config. The per-adapter sections below assume you already ran ait init. To re-run setup explicitly (e.g. after upgrading an agent), use ait adapter setup <name>.

Run Claude Code safely

claude -p --permission-mode bypassPermissions "Refactor the auth module"

ait captures the prompt, edited files, status, and resulting commits as one attempt. Apply it with ait apply latest once you are happy with the result, or use ait run --apply auto ... when you want safe results applied immediately.

Run Codex CLI safely on a real repository

ait run --adapter codex --intent "Implement parser edge cases" -- codex

Each Codex session edits in isolation. Failed attempts are kept for recovery; only applied attempts touch your root checkout.

Run Aider in isolation

ait run --adapter aider --intent "Fix auth expiry" -- aider src/auth.py

Aider's commits are captured as attempt results, with full provenance linking the prompt, edited files, and commits.

Run Gemini CLI with attempt history

ait run --adapter gemini --intent "Add config validation" -- gemini

Gemini sessions are recorded as attempts the same way as Claude Code and Codex. ait memory recall later surfaces what each agent tried.

Run Cursor agents with reviewable provenance

ait run --adapter cursor --intent "Migrate to new SDK" -- cursor

Cursor edits are confined until you apply them. The attempt log keeps the changed files, exit status, and commits available for review and recovery.

Wrap any other shell agent

ait run --adapter shell --intent "Regenerate fixtures" -- \
  python scripts/regenerate_fixtures.py

Use the generic shell adapter to give attempt provenance to any custom agent or script.

Compared to alternatives

ait is the layer around the agents, not a replacement.

How It Works

your prompt
    |
    v
agent CLI wrapped by ait
    |
    v
internal isolated workspace
    |
    v
attempt metadata + commits + memory
    |
    v
review, apply, recover, or inspect

The wrapped process receives:

AIT_INTENT_ID
AIT_ATTEMPT_ID
AIT_WORKSPACE_REF
AIT_CONTEXT_FILE   # when context is enabled

AIT_CONTEXT_FILE contains a compact repo-local handoff selected from previous attempts, commits, curated notes, accepted facts, review findings, and live external memory files such as CLAUDE.md, AGENTS.md, .claude/memory.md, .codex/memory.md, and .cursor/rules. AIT records a versioned ait.context_manifest next to the context file. The manifest separates trusted baseline, advisory, and excluded memory; candidate, stale, superseded, and policy-blocked memory cannot become trusted baseline, and policy-blocked body text is not copied into the context or manifest.

Install

Recommended:

pipx install ait-vcs
ait --version

Virtual environment:

python3.14 -m venv .venv
.venv/bin/pip install ait-vcs
.venv/bin/ait --help

npm wrapper:

npm install -g ait-vcs
ait --version

Tagged GitHub release:

pipx install "git+https://github.com/m24927605/ait.git@v0.55.67"

Upgrade:

ait upgrade
ait --version

Preview an upgrade:

ait upgrade --dry-run

Useful Commands

ait status
ait status claude-code
ait status codex
ait status --all
ait doctor
ait doctor --fix

ait adapter list
ait adapter doctor claude-code
ait adapter setup claude-code

ait attempt list
ait attempt show <attempt-id>
ait resume latest
ait intent show <intent-id>
ait context <intent-id>

ait memory
ait memory sources
ait memory search "auth adapter"
ait memory recall "billing retry"
ait memory backfill --dry-run
ait memory backfill --import
ait memory lint
ait memory lint --fix

ait graph
ait graph --html
ait console --read-only
ait console action apply --attempt latest --dry-run --format json

ait policy validate --format json
ait metadata export --dry-run --output ait-metadata.bundle.json --format json
ait metadata import --input ait-metadata.bundle.json --dry-run --format json

Shell auto-activation:

ait shell show --shell zsh
ait shell install --shell zsh
ait shell uninstall --shell zsh

Requirements

• Python 3.14+
• Git
• SQLite from the Python standard library
• Node.js 18+ only when installing through npm

Status

ait is currently 0.55.67 and alpha quality. It is intended for local dogfooding, power users, and infra-minded engineers who are comfortable with Git workflows.

Metadata is local to one repository under .ait/. It is not synchronized across machines.

The visual model is becoming usable: ait graph --html remains a static local report, and ait console --read-only writes or serves a loopback-only daily console over the same attempt graph, evidence, memory, hot files, and review results. Browser mutation UI is not enabled. A CLI action dry-run layer now exists for apply/recover/discard preflight and append-only journaling, but real apply/recover/discard still go through the existing CLI/domain paths.

Team-readiness hardening is still local-first: .ait/policy.json validation is fail-closed and is now consumed by apply, review, console action preflight, and context trust filtering. Metadata export/import currently supports dry-run planning only. There is still no cross-machine sync, SaaS dashboard, telemetry, automatic push, or automatic merge.

Product direction:

Development

Set up the repository:

python3.14 -m venv .venv
.venv/bin/pip install -e .
.venv/bin/pip install pytest

Verify:

.venv/bin/pytest -q
.venv/bin/ait --version
.venv/bin/ait --help

Before a release:

git status --short
.venv/bin/pytest -q

The release version in pyproject.toml, the Git tag, and this README should match.

Documentation

• Documentation site — full docs in English and 繁體中文
• Why ait — the ten problems ait solves
• Getting started
• AI search facts (Q&A)
• Compare: naked git-worktree vs ait
• Compare: agent managers, memory layers, review bots
• Command reference
• Adversarial code review
• SEO strategy (internal)

For internal design notes (specs, memory architecture, refactor plans), see docs/, including the product weakness response plan.