JarvisCore: Multi-Agent Orchestration
Build scalable agent swarms without the infrastructure headache. Orchestrate complex workflows from single-node prototypes to distributed mesh networks.
Install and configure
One command to install. Choose your LLM provider or bring your own agents.
pip install jarviscore-framework
python -m jarviscore.cli.scaffoldJarvisCore creates your mesh
AutoAgent generates code from prompts, or wrap your existing agents with CustomAgent.
from jarviscore import Mesh
from jarviscore.profiles import AutoAgent
mesh = Mesh(mode='autonomous')
mesh.add(YourAgent)
await mesh.start()Execute workflows
Run multi-step workflows with dependencies. JarvisCore handles orchestration, P2P mesh, and distributed execution.
results = await mesh.workflow('task-id', [
{'agent': 'analyzer', 'task': 'Process data'},
{'agent': 'reporter', 'task': 'Generate report', 'dependencies': ['step1']}
])LLM Code Generation
AutoAgent writes and executes Python code from natural language prompts using Claude, Azure, or Gemini.
P2P Mesh Networking
Agents discover each other across machines using SWIM protocol. No central coordinator needed.
Workflow Orchestration
Define multi-step pipelines with dependencies and context passing between agents.
Bring Your Own Agents
CustomAgent profile wraps existing code from LangChain, CrewAI, or any Python framework.
Nexus Protocol
Standardizing Identity and Connection Orchestration for Autonomous Agents
The Nexus Protocol addresses the identity crisis in autonomous agents. It enables secure, scalable agent identities through decoupled authentication, zero-trust security, and universal provider integration.
Zero-Trust Security
Decoupled authentication with least-privilege access control
Universal Integration
Works with any identity provider - OAuth, SAML, custom systems
Connection Orchestration
Intelligent credential management and service routing