sweeper

Sweeper - Agent-Powered Code Maintenance

You orchestrate the sweeper CLI to run parallel AI sub-agents against a codebase with optional VM isolation and swappable providers (Claude, Codex, Ollama/local models). While lint fixing is the default, the same loop handles test repairs, dependency migrations, refactoring, and any task where you can run a command, parse issues, and dispatch agents to fix them. Tapes records every sub-agent session, enabling you to learn from past runs and optimize token spend.

Prerequisites

The sweeper binary must be in PATH. Build it if needed:

cd /path/to/sweeper && go build -o sweeper . && export PATH="$PWD:$PATH"

Setup

If no sweeper.md exists in the working directory, gather this information:

1. Target directory - Which directory to lint (default: .)
2. Lint command - What linter to run (default: golangci-lint run --out-format=line-number ./...)
3. Concurrency - How many parallel sub-agents (default: 3)
4. Max rounds - Retry rounds before stopping (default: 3)
5. VM mode - Whether to isolate sub-agents in a stereOS VM (recommended for 5+ agents or sensitive repos)
6. Constraints - Files/directories off-limits, behavioral invariants to preserve

Then create a session document:

sweeper.md - Living Session Document

# Sweeper Session

**Started:** <ISO timestamp>
**Objective:** Fix all lint issues in `<target>`
**Linter:** `<command>`
**Concurrency:** <n> sub-agents
**Max rounds:** <n>
**VM:** <yes/no>
**Constraints:** <constraints>

## Status
- Round: 0
- Issues found: (pending first run)
- Issues fixed: 0
- Token spend: (pending — check tapes after first run)

## What's Been Tried
(Updated after each round)

## Token Budget
(Updated via `sweeper observe` after each run)

Commit on a new branch: sweeper/<goal>-<date>

Running Sweeper

Use the CLI to orchestrate the full loop. The CLI handles linting, parsing, parallel sub-agent dispatch, retry escalation, telemetry, and tapes integration.

Basic runs

# Default: golangci-lint with claude (default provider)
sweeper run

# Custom linter
sweeper run -- npm run lint

# Multi-round with escalation
sweeper run --max-rounds 3

# High concurrency in VM isolation
sweeper run --vm -c 5 --max-rounds 3 -- npx eslint --quiet .

# Preview what would be fixed
sweeper run --dry-run

Alternative providers

# Use OpenAI Codex CLI instead of Claude
sweeper run --provider codex -- npm run lint

# Use a local Ollama model (no API key needed)
sweeper run --provider ollama --model qwen2.5-coder:7b

# Ollama with custom API base
sweeper run --provider ollama --model codellama --api-base http://gpu-server:11434

Available providers: claude (default, CLI), codex (CLI), ollama (API). CLI providers have built-in file tools. API providers include file content in the prompt and apply returned diffs. VM isolation (--vm) only works with CLI providers.

VM isolation (recommended for production)

# Ephemeral VM — boots before sweep, tears down after
sweeper run --vm -- npx eslint --quiet .

# Reuse existing VM
sweeper run --vm-name my-vm -- cargo clippy 2>&1

Use --vm when:

• Running inside a Claude Code session (avoids nesting conflicts)
• Working with sensitive API keys (secrets stay in VM)
• High concurrency (dedicated resources)
• CI/CD (hermetic environment)

How the CLI Orchestrates

Each sweeper run executes this loop:

1. Lint: Run linter command, parse structured output
2. Group: Issues grouped by file into parallel fix tasks
3. Strategy: Pick prompt strategy per file based on round + history:
   • Round 0 (no prior history): standard — straightforward fix
   • Round 1+ (any prior history): retry — different approach
   • Consecutive stale >= threshold: exploration — refactor surrounding code
   • Stagnant after exploration: file dropped
4. Dispatch: Parallel sub-agents fix each file (bounded by --concurrency)
5. Record: Each outcome logged to .sweeper/telemetry/ JSONL + tapes captures token usage
6. Re-lint: Verify fixes, filter retryable issues
7. Repeat or stop: Continue if issues remain and rounds left

Tapes — The Learning Center

Tapes is the backbone for self-learning. Every sub-agent session is recorded in tapes, giving you token usage, session transcripts, and outcome data.

Check tapes status

sweeper observe

This shows:

• Success rate per linter — which linters sweeper handles best
• Round effectiveness — which retry rounds contribute most fixes
• Strategy effectiveness — standard vs retry vs exploration success rates
• Token usage per linter — how much each linter costs to fix (from tapes)

Use tapes data to make decisions

Before starting a sweep, check historical performance:

sweeper observe --target /path/to/project

Use the insights to tune your run:

• If round 1 fixes 90% of issues, --max-rounds 1 saves tokens
• If exploration strategy has <10% success, lower --stale-threshold to skip stagnant files faster
• If a specific linter has low success rate, consider excluding those rules
• Compare token spend across runs to track improvement over time

Token budget tracking

After each run, update sweeper.md with token spend from tapes:

## Token Budget
- Run 1: 45,230 prompt + 12,100 completion = 57,330 total
- Run 2: 31,000 prompt + 8,200 completion = 39,200 total (31% reduction)
- Trend: improving — retry prompts getting more targeted

The goal is to fix more issues with fewer tokens over time. Tapes makes this measurable.

Resume

If sweeper.md already exists when you start:

1. Read it to understand what's been tried and token spend so far
2. Run sweeper observe to check recent success patterns
3. Read git log for recent sweeper commits
4. Choose --max-rounds and --concurrency based on tapes insights
5. Continue from where you left off

Updating sweeper.md

After each sweeper run completes, update the session document:

1. Record round results (issues found/fixed/remaining)
2. Run sweeper observe and record token spend
3. Note which strategies worked and which files are stagnant
4. Commit the updated sweeper.md