parallel-agents 0.2.0

Parallel multi-agent pipeline for code analysis and transformation, powered by Claude

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.

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
--store, -s	Evidence store: file or sqlite
--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_MAX_PARALLEL_WORKERS=4
export PA_STORE_BACKEND=sqlite

Requirements

• Python 3.11+
• Claude Code CLI installed and authenticated
• An Anthropic API key (via Claude Code authentication)

License

MIT