jarviscore-framework 0.1.1

Build autonomous AI agents in 3 lines of code. Production-ready orchestration with P2P mesh networking.

JarvisCore Framework

Build autonomous AI agents in 3 lines of code. Production-ready orchestration with P2P mesh networking.

Features

• ✅ Simple Agent Definition - Write just 3 attributes, framework handles everything
• ✅ P2P Mesh Architecture - Automatic agent discovery and task routing via SWIM protocol
• ✅ Event-Sourced State - Complete audit trail with crash recovery
• ✅ Autonomous Execution - LLM code generation with automatic repair

Installation

pip install jarviscore-framework

Setup & Validation

1. Initialize Project

# Create .env.example and example files in your project
python -m jarviscore.cli.scaffold --examples

# Configure your environment
cp .env.example .env
# Edit .env and add one of: CLAUDE_API_KEY, AZURE_API_KEY, GEMINI_API_KEY, or LLM_ENDPOINT

2. Validate Installation

# Check setup
python -m jarviscore.cli.check

# Test LLM connectivity
python -m jarviscore.cli.check --validate-llm

# Run smoke test (end-to-end validation)
python -m jarviscore.cli.smoketest

✅ All checks pass? You're ready to build agents!

Quick Start

from jarviscore import Mesh
from jarviscore.profiles import PromptDevAgent

# Define agent (3 lines)
class ScraperAgent(PromptDevAgent):
    role = "scraper"
    capabilities = ["web_scraping"]
    system_prompt = "You are an expert web scraper..."

# Create mesh and run workflow
mesh = Mesh(mode="autonomous")
mesh.add(ScraperAgent)
await mesh.start()

results = await mesh.workflow(
    workflow_id="wf-123",
    steps=[
        {"id": "scrape", "task": "Scrape example.com", "role": "scraper"}
    ]
)

Architecture

JarvisCore is built on three layers:

1. Execution Layer (20%) - Profile-specific execution (Prompt-Dev, MCP)
2. Orchestration Layer (60%) - Workflow engine, dependencies, state management
3. P2P Layer (20%) - Agent discovery, task routing, mesh coordination

Documentation

• User Guide - Complete guide for AutoAgent users
• API Reference - Detailed API documentation
• Configuration Guide - Settings and environment variables
• Troubleshooting - Common issues and solutions
• Examples - Working code examples

Development Status

Version: 0.1.0 (Alpha)

License

MIT License - see LICENSE file for details