parallel-agents 0.4.1

Parallel multi-agent pipeline for code analysis and transformation.

parallel-agents

A parallel multi-agent pipeline for code analysis and transformation, powered by Claude.

Fan out code analysis to 8 specialist AI agents running concurrently, then merge results into a unified report with patches, risk assessments, and PR summaries.

Architecture

User Task / GitHub Issue / Repo
  |
Planner Agent (analyzes repo, creates task plan)
  |
Task Splitter (dependency resolution, batch grouping)
  |
Parallel Workers (asyncio fan-out):
  security, test, perf, devops, arch, docs, code, review
  |
Evidence Store (JSON / SQLite)
  |
Judge Agent (merge, resolve conflicts, produce patch)
  |
Final Output (patch + PR summary + risk report)

Installation

From PyPI

pip install parallel-agents
# or with pipx for isolated CLI
pipx install parallel-agents

From npm (wrapper)

npx parallel-agents run --repo ./my-project "Fix security issues"

Standalone binary

Download from GitHub Releases - no Python required.

First Run Checklist

1. Install package with pip install parallel-agents.
2. Install Claude Code CLI and complete authentication.
3. If using GitHub issue URLs, install GitHub CLI and run gh auth login.
4. Run parallel-agents workers to verify worker config.
5. Run parallel-agents run --repo ./my-project "Review for security and quality issues".

Quick Start

# Analyze a local repo
parallel-agents run --repo ./my-project "Fix security issues and improve code quality"

# From a GitHub issue
parallel-agents run "https://github.com/org/repo/issues/42" --repo ./local-clone

# Select specific workers
parallel-agents run --workers security,code,review "Refactor the auth module"

# Use SQLite evidence store
parallel-agents run --store sqlite --repo ./project "Add input validation"

# Output as JSON or patch
parallel-agents run --output json --repo ./project "Fix bugs"
parallel-agents run --output patch --repo ./project "Fix bugs" > fix.patch

CLI Commands

Command	Description
parallel-agents run <task>	Run the pipeline
parallel-agents workers	List available workers
parallel-agents show <run-id>	View results of a previous run
parallel-agents history	List all previous runs
parallel-agents init	Generate default configuration

Run Options

Flag	Description
--repo, -r	Path to repository
--workers, -w	Comma-separated workers to enable
--disable-workers, -d	Comma-separated workers to disable
--output, -o	Output format: rich, json, patch
--model, -m	Override model for all agents
--permission-mode	Override permission mode: default, acceptEdits, plan, bypassPermissions
--store, -s	Evidence store: file or sqlite
--apply-patch	Apply generated patch to repo via git apply (explicit opt-in)
--streaming/--no-streaming	Toggle live progress display

Programmatic Usage

import asyncio
from parallel_agents import Pipeline, PipelineConfig
from parallel_agents.config import WorkerConfig

config = PipelineConfig(
    workers={
        "security": WorkerConfig(enabled=True),
        "code": WorkerConfig(enabled=True, model="opus"),
        "review": WorkerConfig(enabled=True),
    },
    max_parallel_workers=3,
)

async def main():
    pipeline = Pipeline(config)
    result = await pipeline.run(
        "Review for security issues",
        repo_path="./my-project",
    )
    print(result.summary)
    if result.patch:
        print(result.patch)

asyncio.run(main())

Workers

Worker	Focus Area
security	OWASP Top 10, dependency vulns, secret scanning
test	Coverage gaps, edge cases, test generation
perf	Complexity analysis, N+1 queries, bottlenecks
devops	CI/CD, Docker, deployment configuration
arch	SOLID principles, design patterns, coupling
docs	README, docstrings, API documentation
code	Implementation, refactoring (uses Opus)
review	Code style, best practices, anti-patterns

Configuration

Set via environment variables (prefix PA_), .env file, or programmatically:

export PA_PLANNER_MODEL=opus
export PA_JUDGE_MODEL=opus
export PA_PERMISSION_MODE=default
export PA_PARSE_RETRY_ATTEMPTS=1
export PA_MAX_PARALLEL_WORKERS=4
export PA_STORE_BACKEND=sqlite

PA_PERMISSION_MODE supports default, acceptEdits, plan, and bypassPermissions.

Current Strengths

• Parallel specialist analysis with a single merged result.
• CLI, MCP server, PyPI package, npm wrapper, and release workflows.
• Evidence storage for run history (file or sqlite).

Current Limitations

• Generated patches should be reviewed before applying.
• GitHub issue mode requires gh installation and authentication.
• Output quality still depends on model behavior and repository context quality.

Project Docs

• CHANGELOG.md
• COMPATIBILITY.md
• CONTRIBUTING.md
• SECURITY.md
• SUPPORT.md

Exit Codes

• 0: success
• 1: runtime failure
• 2: authentication/API key failure
• 3: parse failure in planner/judge/worker structured output
• 4: worker execution failure (status=error)
• 5: no patch available when patch output/apply is requested
• 6: patch apply/check failure

Requirements

• Python 3.11+
• Claude Code CLI installed and authenticated
• GitHub CLI installed and authenticated for GitHub issue URL mode
• An Anthropic API key (via Claude Code authentication)

License

MIT