jarviscore-framework 0.3.0

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

JarvisCore Framework

Build autonomous AI agents with P2P mesh networking.

Features

• ✅ AutoAgent - LLM generates and executes code from natural language
• ✅ CustomAgent - Bring your own logic (LangChain, CrewAI, etc.)
• ✅ ListenerAgent - API-first agents with background P2P (just implement handlers)
• ✅ P2P Mesh - Agent discovery and communication via SWIM protocol
• ✅ Workflow Orchestration - Dependencies, context passing, multi-step pipelines
• ✅ FastAPI Integration - 3-line setup with JarvisLifespan
• ✅ Cloud Deployment - Self-registering agents for Docker/K8s

Installation

pip install jarviscore-framework

Setup

# Initialize project
python -m jarviscore.cli.scaffold --examples
cp .env.example .env
# Add your LLM API key to .env

# Validate
python -m jarviscore.cli.check --validate-llm
python -m jarviscore.cli.smoketest

Quick Start

AutoAgent (LLM-Powered)

from jarviscore import Mesh
from jarviscore.profiles import AutoAgent

class CalculatorAgent(AutoAgent):
    role = "calculator"
    capabilities = ["math"]
    system_prompt = "You are a math expert. Store result in 'result'."

mesh = Mesh(mode="autonomous")
mesh.add(CalculatorAgent)
await mesh.start()

results = await mesh.workflow("calc", [
    {"agent": "calculator", "task": "Calculate factorial of 10"}
])
print(results[0]["output"])  # 3628800

CustomAgent (Your Code)

from jarviscore import Mesh
from jarviscore.profiles import CustomAgent

class ProcessorAgent(CustomAgent):
    role = "processor"
    capabilities = ["processing"]

    async def execute_task(self, task):
        data = task.get("params", {}).get("data", [])
        return {"status": "success", "output": [x * 2 for x in data]}

mesh = Mesh(mode="distributed", config={'bind_port': 7950})
mesh.add(ProcessorAgent)
await mesh.start()

results = await mesh.workflow("demo", [
    {"agent": "processor", "task": "Process", "params": {"data": [1, 2, 3]}}
])
print(results[0]["output"])  # [2, 4, 6]

ListenerAgent + FastAPI (API-First)

from fastapi import FastAPI
from jarviscore.profiles import ListenerAgent
from jarviscore.integrations.fastapi import JarvisLifespan

class ProcessorAgent(ListenerAgent):
    role = "processor"
    capabilities = ["processing"]

    async def on_peer_request(self, msg):
        # Handle requests from other agents
        return {"result": msg.data.get("task", "").upper()}

# That's it - 3 lines to integrate with FastAPI
app = FastAPI(lifespan=JarvisLifespan(ProcessorAgent(), mode="p2p"))

Execution Modes

Mode	Profile	Use Case
autonomous	AutoAgent	Single machine, LLM code generation
p2p	CustomAgent, ListenerAgent	Agent-to-agent communication, swarms
distributed	CustomAgent, ListenerAgent	Multi-node workflows + P2P

What's New in 0.3.0

Developer Experience Improvements:

• ListenerAgent - No more writing run() loops. Just implement on_peer_request() and on_peer_notify() handlers.
• JarvisLifespan - FastAPI integration reduced from ~100 lines to 3 lines.
• Cognitive Discovery - peers.get_cognitive_context() generates LLM-ready peer descriptions. No more hardcoded agent names in prompts.

Cloud Deployment:

• Self-Registration - agent.join_mesh() lets agents join existing meshes without central orchestrator.
• Remote Visibility - Agents on different nodes are automatically discovered and callable.

Documentation

• User Guide - Complete documentation
• Getting Started - 5-minute quickstart
• AutoAgent Guide - LLM-powered agents
• CustomAgent Guide - Bring your own code
• API Reference - Detailed API docs
• Configuration - Settings reference

Version

0.3.0

License

MIT License