jarviscore-framework 0.3.1

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 with P2P message 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
• Cognitive Discovery - LLM-ready peer descriptions for autonomous delegation
• 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 + FastAPI (Recommended)

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

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

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

# 3 lines to integrate with FastAPI
app = FastAPI(lifespan=JarvisLifespan(ProcessorAgent(), mode="p2p"))

CustomAgent (Workflow Mode)

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]

Profiles

Profile	You Write	JarvisCore Handles
AutoAgent	System prompt	LLM code generation, sandboxed execution
CustomAgent	on_peer_request() and/or execute_task()	Mesh, discovery, routing, lifecycle

Execution Modes

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

Framework Integration

JarvisCore is async-first. Best experience with async frameworks.

Framework	Integration
FastAPI	JarvisLifespan (3 lines)
aiohttp, Quart, Tornado	Manual lifecycle (see docs)
Flask, Django	Background thread pattern (see docs)

Documentation

Documentation is included with the package:

python -c "import jarviscore; print(jarviscore.__path__[0] + '/docs')"

Available guides:

• GETTING_STARTED.md - 5-minute quickstart
• CUSTOMAGENT_GUIDE.md - CustomAgent patterns and framework integration
• AUTOAGENT_GUIDE.md - LLM-powered agents
• USER_GUIDE.md - Complete documentation
• API_REFERENCE.md - Detailed API docs
• CONFIGURATION.md - Settings reference

Version

0.4.0

License

MIT License