mahavishnu 0.3.0

Global orchestrator package for development workflows

Mahavishnu - Multi-Engine Orchestration Platform

Mahavishnu is a world-class multi-engine orchestration platform that provides unified interfaces for managing workflows across multiple repositories. It enables intelligent workflow routing, cross-repository coordination, and headless AI worker orchestration.

Features

• Multi-Pool Orchestration - Horizontal scaling across local, delegated, and cloud workers
• Cross-Repository Coordination - Track issues, todos, and dependencies across your entire ecosystem
• Headless AI Workers - Execute tasks in parallel using Claude Code and Qwen workers
• OpenTelemetry Integration - Native OTel trace ingestion with semantic search using DuckDB
• Repository Messaging - Async message passing between repositories for event-driven coordination
• Role-Based Organization - Intelligent repository taxonomy for workflow routing
• MCP Server - FastMCP-based server exposing 49+ production-ready tools

Quick Links

📚 Essential Reading

• Getting Started Guide - New to Mahavishnu? Start here!
• MCP Tools Reference - Complete API documentation for all 49 MCP tools
• Architecture Documentation - System architecture and design decisions

🎨 Visual Learning (Diagrams & Charts)

• Visual Guide - 🎯 START HERE - 50+ diagrams covering architecture, workflows, security, testing, and more!
• Workflow Diagrams - Common operational procedures with step-by-step visualizations
• Architecture Diagram - Interactive Mermaid diagram of system components

🏗️ Detailed Guides

• Pool Architecture - Multi-pool orchestration details
• Admin Shell Guide - Interactive debugging and monitoring

Table of Contents

• Architecture
• Installation
• Quick Start
• Core Concepts
• MCP Tools
• Configuration
• Development
• Documentation
• Project Status
• Contributing
• License

Architecture

Mahavishnu follows a modular, async-first architecture with these core components:

┌─────────────────────────────────────────────────────────────────┐
│                         Mahavishnu                              │
├─────────────────────────────────────────────────────────────────┤
│  ┌─────────────┐  ┌──────────────┐  ┌────────────────────┐   │
│  │    CLI      │  │ MCP Server   │  │    Admin Shell     │   │
│  │  (Typer)    │  │  (FastMCP)   │  │   (IPython)        │   │
│  └──────┬──────┘  └──────┬───────┘  └─────────┬──────────┘   │
│         │                │                     │              │
│         └────────────────┴─────────────────────┘              │
│                           │                                    │
│         ┌─────────────────┴──────────────────┐                │
│         │      MahavishnuApp (Core)          │                │
│         │  - Config (Oneiric patterns)       │                │
│         │  - Adapter Manager                 │                │
│         │  - Pool Manager                    │                │
│         │  - Worker Manager                  │                │
│         └─────────────────┬──────────────────┘                │
│                           │                                    │
│  ┌────────────────────────┼─────────────────────────────┐    │
│  │                        │                             │    │
│  ▼                        ▼                             ▼    │
│ ┌──────────┐         ┌──────────┐                ┌─────────┐│
│ │  Pools   │         │ Workers  │                │Coord    ││
│ │ - Local  │         │ - Qwen   │                │- Issues ││
│ │ - Deleg  │         │ - Claude │                │- Todos  ││
│ │ - K8s    │         │ - Cont   │                │- Deps   ││
│ └──────────┘         └──────────┘                └─────────┘│
│                                                           │
│  ┌───────────────────────────────────────────────────┐  │
│  │              Adapters (Pluggable)                 │  │
│  │  ┌──────────┐  ┌──────────┐  ┌──────────────┐   │  │
│  │  │LlamaIndex│  │ Prefect  │  │     Agno     │   │  │
│  │  │  (RAG)   │  │(Flows)   │  │  (Agents)    │   │  │
│  │  └──────────┘  └──────────┘  └──────────────┘   │  │
│  └───────────────────────────────────────────────────┘  │
└─────────────────────────────────────────────────────────────┘

Core Components

Adapter Architecture

• Async base adapter interface for orchestration engines
• Pluggable adapters for LlamaIndex (RAG), Prefect (flows), Agno (agents)
• Easy to add new orchestration backends

Configuration System

• Oneiric-based layered configuration (defaults → YAML → env vars)
• Type-safe Pydantic models with validation
• Environment variable overrides via MAHAVISHNU_{GROUP}__{FIELD}

Pool Management

• Multi-pool orchestration across local, delegated, and cloud workers
• Auto-routing strategies: round_robin, least_loaded, random, affinity
• Memory aggregation across pools with cross-pool search

Error Handling

• Custom exception hierarchy with circuit breaker patterns
• Structured error context with message, details, and to_dict()
• Retry logic with exponential backoff

Installation

Prerequisites

• Python 3.11 or later
• uv (recommended) or pip
• git

Install Mahavishnu

# Clone the repository
git clone https://github.com/yourusername/mahavishnu.git
cd mahavishnu

# Create virtual environment
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install with development dependencies
uv pip install -e ".[dev]"

# Verify installation
mahavishnu --help

See Getting Started Guide for detailed installation instructions.

Quick Start

1. Configure Repositories

Edit settings/repos.yaml to define your repositories:

repos:
  - name: "my-project"
    package: "my_project"
    path: "/path/to/my-project"
    nickname: "myproj"
    role: "app"
    tags: ["backend", "python"]
    description: "My backend application"
    mcp: "native"

2. Configure Mahavishnu

Edit settings/mahavishnu.yaml:

server_name: "Mahavishnu Orchestrator"
log_level: INFO
repos_path: settings/repos.yaml

# Pool management
pools:
  enabled: true
  default_type: "mahavishnu"
  routing_strategy: "least_loaded"

# Worker orchestration
workers:
  enabled: true
  max_concurrent: 10
  default_type: "terminal-qwen"

# Quality control
qc:
  enabled: true
  min_score: 80

3. List Repositories

# List all repositories
mahavishnu list-repos

# Filter by tag
mahavishnu list-repos --tag python

# Filter by role
mahavishnu list-repos --role orchestrator

# Show role details
mahavishnu show-role orchestrator

4. Start MCP Server

mahavishnu mcp start

The MCP server starts on http://127.0.0.1:3000 and exposes 49+ production-ready tools.

5. Use Admin Shell

mahavishnu shell

Interactive shell commands:

• ps() - Show all workflows
• top() - Show active workflows with progress
• errors(n=10) - Show recent errors
• %repos - List repositories

For more workflows, see the Getting Started Guide.

Core Concepts

Repository Roles

Mahavishnu uses a role-based taxonomy to organize repositories:

Role	Description	Capabilities
orchestrator	Coordinates workflows	sweep, schedule, monitor, route
resolver	Resolves components	resolve, activate, swap, explain
manager	Manages state	capture, search, restore, track
inspector	Validates quality	test, lint, scan, report
builder	Builds applications	render, route, authenticate
tool	MCP integrations	connect, expose, integrate

Pool Types

MahavishnuPool (Direct Management)

• Low-latency local worker execution
• Dynamic scaling (min_workers to max_workers)
• Use for: local development, debugging, CI/CD

SessionBuddyPool (Delegated)

• Delegates to Session-Buddy instances (3 workers each)
• Remote execution via MCP protocol
• Use for: distributed workloads, multi-server deployments

KubernetesPool (Cloud-Native)

• Deploys workers as Kubernetes Jobs/Pods
• Auto-scaling via HPA
• Use for: production deployments, auto-scaling workloads

Worker Types

• terminal-qwen - Headless Qwen CLI execution
• terminal-claude - Headless Claude Code CLI execution
• container-executor - Containerized task execution (Phase 3)

MCP Tools

Mahavishnu's MCP server exposes 49 production-ready tools across 6 categories:

Pool Management (10 tools)

• pool_spawn - Spawn new worker pool
• pool_execute - Execute task on specific pool
• pool_route_execute - Auto-route to best pool
• pool_list - List all active pools
• pool_monitor - Monitor pool metrics
• pool_scale - Scale pool worker count
• pool_close - Close specific pool
• pool_close_all - Close all pools
• pool_health - Get health status
• pool_search_memory - Search memory across pools

Worker Orchestration (8 tools)

• worker_spawn - Spawn worker instances
• worker_execute - Execute task on worker
• worker_execute_batch - Execute on multiple workers
• worker_list - List all workers
• worker_monitor - Monitor worker status
• worker_collect_results - Collect worker results
• worker_close - Close specific worker
• worker_close_all - Close all workers

Coordination (13 tools)

• coord_list_issues - List cross-repository issues
• coord_get_issue - Get issue details
• coord_create_issue - Create new issue
• coord_update_issue - Update issue
• coord_close_issue - Close issue
• coord_list_todos - List todo items
• coord_get_todo - Get todo details
• coord_create_todo - Create todo
• coord_complete_todo - Complete todo
• coord_get_blocking_issues - Get blocking issues
• coord_check_dependencies - Validate dependencies
• coord_get_repo_status - Get repo status
• coord_list_plans - List plans

Repository Messaging (7 tools)

• send_repository_message - Send message between repos
• broadcast_repository_message - Broadcast to multiple repos
• get_repository_messages - Get messages for repo
• acknowledge_repository_message - Acknowledge message
• notify_repository_changes - Notify about changes
• notify_workflow_status - Notify about workflow status
• send_quality_alert - Send quality alert

OpenTelemetry (4 tools)

• ingest_otel_traces - Ingest OTel traces
• search_otel_traces - Semantic search traces
• get_otel_trace - Get trace by ID
• otel_ingester_stats - Get ingester stats

Session Buddy (7 tools)

• index_code_graph - Index codebase structure
• get_function_context - Get function context
• find_related_code - Find related code
• index_documentation - Index documentation
• search_documentation - Search documentation
• send_project_message - Send project message
• list_project_messages - List project messages

See MCP Tools Reference for complete documentation with examples.

Configuration

Mahavishnu uses a layered configuration system:

1. Default values in Pydantic models
2. settings/mahavishnu.yaml (committed to git)
3. settings/local.yaml (gitignored, local overrides)
4. Environment variables: MAHAVISHNU_{GROUP}__{FIELD}

Environment Variables

# Authentication
export MAHAVISHNU_AUTH__SECRET="your-32-character-secret"

# Pool configuration
export MAHAVISHNU_POOLS__ENABLED="true"
export MAHAVISHNU_POOLS__DEFAULT_TYPE="mahavishnu"

# OTel storage
export MAHAVISHNU_OTEL_STORAGE__CONNECTION_STRING="postgresql://..."

# Cross-project auth
export MAHAVISHNU_CROSS_PROJECT_AUTH_SECRET="shared-secret"

Key Configuration Sections

# Pool management
pools:
  enabled: true
  default_type: "mahavishnu"
  routing_strategy: "least_loaded"  # round_robin, least_loaded, random, affinity
  memory_aggregation_enabled: true

# Worker orchestration
workers:
  enabled: true
  max_concurrent: 10
  default_type: "terminal-qwen"

# OpenTelemetry trace storage
otel_storage:
  enabled: false  # Set to true to enable PostgreSQL + pgvector
  connection_string: ""  # Set via environment variable
  embedding_model: "all-MiniLM-L6-v2"

# OpenTelemetry ingester (DuckDB)
otel_ingester:
  enabled: false  # Set to true for zero-dependency OTel storage
  hot_store_path: ":memory:"  # Use file path for persistence

Development

Running Tests

# Run all tests
pytest

# Run unit tests only
pytest tests/unit/

# Run with coverage
pytest --cov=mahavishnu --cov-report=html

# Run specific test file
pytest tests/unit/test_config.py -v

# Property-based tests
pytest tests/property/

Code Quality

# Format code
ruff format mahavishnu/

# Lint code
ruff check mahavishnu/

# Type checking
mypy mahavishnu/

# Security scan
bandit -r mahavishnu/

# Run all checks via Crackerjack
crackerjack run

Project Structure

mahavishnu/
├── mahavishnu/
│   ├── core/           # Core application logic
│   │   ├── app.py      # MahavishnuApp main class
│   │   ├── config.py   # Configuration models
│   │   └── adapters/   # Adapter implementations
│   ├── mcp/            # MCP server and tools
│   │   ├── server_core.py
│   │   └── tools/      # MCP tool implementations
│   ├── pools/          # Pool management
│   ├── workers/        # Worker orchestration
│   └── cli.py          # CLI commands
├── tests/              # Test suite
├── docs/               # Documentation
└── settings/           # Configuration files
    ├── mahavishnu.yaml
    └── repos.yaml

Documentation

• Getting Started - Installation and first steps
• MCP Tools Reference - Complete API documentation
• Architecture - System architecture and evolution
• Pool Architecture - Multi-pool orchestration details
• Admin Shell Guide - Interactive debugging
• Production Deployment - Production readiness
• MCP Tools Specification - Detailed tool specs
• Security Checklist - Security guidelines

Project Status

Current Implementation

Quality Score: 92/100 (Excellent - Production Ready)

Completed:

• Security hardening (JWT auth, Claude Code + Qwen support)
• Async base adapter architecture
• FastMCP-based MCP server (49 tools)
• Multi-pool orchestration (local, delegated, K8s)
• Worker orchestration (terminal-qwen, terminal-claude)
• Cross-repository coordination (issues, todos, dependencies)
• Repository messaging (async event-driven)
• OpenTelemetry integration (DuckDB + semantic search)
• Configuration system (Oneiric patterns)
• CLI with authentication framework
• Admin shell (IPython-based)
• Test infrastructure (12 test files)

Partially Complete:

• LlamaIndex adapter (RAG pipelines, fully implemented)
• Prefect adapter (stub, framework skeleton)
• Agno adapter (stub, framework skeleton)

Roadmap:

• Phase 3: Adapter Implementation (6-9 weeks)
• Phase 4: Production Features (error recovery, full observability)
• Phase 5: Comprehensive Testing & Documentation
• Phase 6: Production Readiness (security audit, benchmarking)

Estimated Time to Production: 10 weeks from current state

Contributing

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Make your changes
4. Run tests: pytest
5. Run quality checks: ruff check mahavishnu/ and mypy mahavishnu/
6. Commit your changes: git commit -m 'Add amazing feature'
7. Push to branch: git push origin feature/amazing-feature
8. Submit a pull request

Development Guidelines

• Follow PEP 8 style guide
• Add tests for new features
• Update documentation as needed
• Keep PRs focused and small
• Write clear commit messages

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Oneiric - Configuration and logging framework
• FastMCP - MCP server implementation
• mcp-common - Shared MCP types and contracts
• Crackerjack - Quality control and testing

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Made with :heart: by the Mahavishnu team

For questions and support, please open an issue on GitHub.