Description
JarvisCore is an open-source Python framework tailored for constructing autonomous multi-agent AI systems. It is developed by Prescott Data and is designed to facilitate the creation of agents that can operate unattended in production environments. The framework emphasizes peer-to-peer agent orchestration, ensuring that there is no single point of failure. This is achieved through a SWIM gossip mesh that allows agents to discover and communicate with each other seamlessly.
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One of the standout features of JarvisCore is its robust memory system. It incorporates a four-tier memory structure that includes a working scratchpad, an episodic ledger, LLM-compressed long-term summaries, and optional cross-session semantic memory via Athena MemOS. This memory system ensures that agents can retain context across sessions, making them more efficient and effective over time.
JarvisCore also prioritizes security with its Nexus credential layer, which allows agents to interact with third-party APIs without exposing raw credentials. This zero-trust approach ensures that OAuth2, API keys, and basic authentication are managed securely, keeping them out of agent reasoning.
The framework supports a wide range of integrations, offering prebuilt actions for platforms like Slack, GitHub, Zoom, SAP, NetSuite, MS Graph, and Salesforce. This extensive integration capability allows developers to connect JarvisCore agents to various business systems, enhancing their functionality.
For observability, JarvisCore provides full-stack tracing, enabling developers to monitor every agent turn, tool call, and LLM request. This is complemented by Redis PubSub for live streams and Prometheus for operational dashboards, ensuring comprehensive monitoring and compliance.
JarvisCore is ideal for developers looking to deploy AI agents in production environments. It offers two agent profiles: AutoAgent, which runs a full cognitive loop, and CustomAgent, which allows for deterministic control. This flexibility makes it suitable for a wide range of applications, from research and analysis to API workers and deterministic pipelines.
- CategoryAI Agent Builders
- Pricing ModelOpen source
- DeploymentCloud
- PlatformsPython
JarvisCore's Core Features
Open-source Python framework
Peer-to-peer agent orchestration
Four-tier agent memory system
Zero-trust credential management
Extensive service integrations
Full-stack observability
Two agent profiles: AutoAgent and CustomAgent
Self-organizing agent mesh
How to use JarvisCore?
Install: Set up JarvisCore in your Python environment
Configure: Add an LLM provider and necessary credentials
Run: Execute your first autonomous agent
Optimize: Integrate additional services and enhance agent capabilities
JarvisCore's Use Cases
- Autonomous Research
- API Integration
- Production Monitoring
- Data Analysis
- Workflow Automation
