Every capability on this page runs entirely within your infrastructure. Your prompts, documents, and model outputs never leave your network unless you explicitly configure an external integration. This makes Jarvis suitable for sensitive workloads where cloud AI services are not an option.
Local AI inference
Run 10+ models on your own GPU hardware with no usage limits or API costs.
Multi-agent delegation
Break complex tasks into subtasks and delegate them to specialized agents.
526+ MCP tools
Give agents access to ServiceNow, custom tools, and your own integrations.
57+ automations
Trigger n8n workflows from agents, schedules, webhooks, or system events.
RAG memory
Agents recall past conversations and search document corpora via vector retrieval.
Fleet management
Load, unload, and monitor models across all nodes from a single interface.
Local AI inference
Jarvis serves models entirely on your hardware through Ollama and LiteLLM. You get full control over which models run, on which nodes, and under what load conditions — with no per-token costs, no rate limits, and no data leaving your network. Your model fleet includes options across a range of sizes and specializations: general-purpose chat models, coding assistants, and fast lightweight models for high-throughput tasks. You can add, remove, or swap models at any time.Model fleet
View every model available in your Jarvis instance.
Multi-agent task delegation
When you send a complex request to Jarvis, the agent layer breaks it into subtasks and routes each one to the agent role best suited to handle it. Roles include:- CEO — strategy, prioritization, and goal decomposition
- CTO — technical architecture and system decisions
- Engineer — implementation, debugging, and execution
- Writer — structured output, documentation, and communication
Paperclip
Primary orchestrator that coordinates multi-agent workflows.
Hermes
Handles external communication and output formatting.
526+ MCP tool integrations
Agents in Jarvis can call tools via the Model Context Protocol (MCP). Your instance includes over 526 tools out of the box, with the largest integration being ServiceNow — giving agents the ability to query, create, and update records directly. Beyond ServiceNow, you can expose any API, script, or local function as an MCP tool. Agents discover available tools at runtime and choose which ones to call based on the task.MCP tools
Browse the full tool catalog and learn how to add your own.
57+ n8n automation workflows
Jarvis ships with over 57 pre-built n8n workflows covering infrastructure management, deployment pipelines, monitoring alerts, and data synchronization. You can run these on a schedule, trigger them from agents, or fire them via webhook. Agents and workflows are connected in both directions: agents can invoke workflows as tools, and workflows can call back into the agent layer through the Jarvis API. This lets you combine deterministic automation with dynamic agent reasoning.n8n integration
Explore workflows and learn how to connect them to agents.
RAG memory across conversations
Agents remember. Jarvis uses three memory systems together to give agents durable context:- Qdrant stores semantic embeddings so agents can retrieve relevant past conversations and documents by meaning, not just keyword.
- Neo4j stores relationships between entities — useful when tasks involve understanding how systems, people, or decisions are connected.
- Mem0 coordinates what gets stored and retrieved, so agents automatically have the right context without you managing it manually.
Memory setup
Configure your memory backends and retention policies.
Fleet-level model management
You manage your entire model fleet — across all five nodes — through a single interface. You can see which models are loaded, which nodes they are running on, and how much VRAM each is consuming. Loading or unloading a model on a specific node takes one action.Nodes
View node status and hardware resources across the brain mesh.
Monitoring
Set up dashboards and alerts for your Jarvis instance.