Cross-platform · Windows · macOS · Linux

Run a whole engineering team of AI agents. On your machine. Right now.

Mahalaxmi orchestrates dozens of AI coding agents working in parallel — each with its own isolated workspace, intelligently assigned tasks, and only the code context it needs. Ship in hours what used to take days.

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Trusted by engineering teams using Claude Code, OpenAI, AWS Bedrock, Google Gemini, and more.

You're paying for AI. You're using 5% of it.

Every AI coding assistant works the same way: one conversation. One stream of output. One task at a time.

Your codebase has 50 independent modules that could be built in parallel. Your AI is working on them sequentially. That's not a tool problem. That's an orchestration problem.

Mahalaxmi: Multi-Agent AI Orchestration for Real Codebases

What a single AI agent does	What Mahalaxmi does
One conversation, one task	8+ agents, 8+ tasks simultaneously
Full codebase context (overwhelming)	Relevant files only, per task
One AI provider	Any combination of providers
Manual retries on failure	Self-verification + auto-retry with error context
No oversight structure	Plan review, budget gate, file accept/reject

How a Mahalaxmi cycle works

Manager Phase — Build the plan

Manager AI agents analyze your codebase and requirements, then propose an execution plan. Multiple managers debate via a consensus engine that semantically deduplicates overlapping proposals.

You review — before work begins

The execution plan surfaces as a visual list. Add tasks, remove tasks, adjust scope. Every modification is audit-logged. Only when you approve do workers start.

Worker Phase — Execute in parallel

Worker agents claim tasks in dependency order. Each worker runs in a Git worktree — a fully isolated copy of the repo. Context is pre-filtered to only the relevant files.

Results — Clean PRs, one per worker

Each worker produces a branch and pull request. Post-cycle validation checks acceptance criteria. You accept or reject individual file changes. The work is done.

Built for developers who ship

PTY-Native Terminal Control

Not screen capture. Real pseudo-terminal control that works with any AI CLI tool — reliable in ways that OCR never is.

Consensus Engine

Four merge strategies (Union, Intersection, WeightedVoting, ComplexityWeighted) with semantic deduplication using Jaccard similarity and LLM arbitration.

Intelligent Context Routing

Workers receive only the files relevant to their task — scored by keyword overlap, import-graph proximity, and historical co-occurrence.

Security Pipeline

Every worker diff is scanned for secrets, CVEs, SAST issues, and license violations before the PR is created.

AI-Agnostic

Claude Code, OpenAI Foundry, AWS Bedrock, Google Gemini, Kiro, Goose, DeepSeek, Qwen Coder — mix providers in a single cycle.

Git Worktree Isolation

Every worker runs in a dedicated Git worktree. Workers cannot interfere with each other. Each output is a clean pull request.

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What engineers say

“We went from 4-hour AI sessions with one agent to 20-minute parallel cycles with 8. The plan review step alone saves us from shipping garbage.”

— Senior Engineer, fintech startup

“The AI-agnostic part is what sold us. We had Claude and Bedrock contracts already. Mahalaxmi uses both in the same cycle — the right task goes to the right model.”

— CTO, Series A SaaS company

“The security pipeline running automatically on every worker PR means we stopped playing whack-a-mole with secrets in diffs.”

— DevSecOps Lead, healthcare tech