Jarvus — The
Self-Learning Agent
Powered by Spiking Neural Networks. Learns from every task. Grows smarter over time.
// Live SNN firing pattern — temporal event processing
One agent, every surface.
Jarvus runs wherever you work — terminal, API, browser, or fully autonomous in the background.
CLI
Run Jarvus in any terminal, shell script, or editor. Full task orchestration from the command line.
Install → pip install jarvusAPI
Embed Jarvus into your own systems. REST interface with streaming support and webhook callbacks.
Read docs →Autonomous
Long-running background agents. Set a goal, walk away. Jarvus handles the rest, learns as it goes.
Learn more →Doesn't just run tasks.
Learns from them.
Jarvus is built on Spiking Neural Networks — a biologically-inspired architecture that processes information as temporal event streams, not static snapshots. The same architecture the brain uses.
Every task completed updates Jarvus's internal model. No retraining pipelines. No dataset curation. Just experience.
Temporal reasoning
SNNs process event sequences over time. Jarvus understands when things happen, not just what — enabling causal inference across task steps.
On-device self-learning
Jarvus updates its own synaptic weights from experience using spike-timing-dependent plasticity. Each task makes it sharper.
Efficient inference
SNN-based compute fires only when needed. Inference uses a fraction of the power of equivalent transformer-based agents.
SNN Architecture Diagram
Interactive visualization
Watch it think.
A running Jarvus session shows you its reasoning process, self-corrections, and what it learned — in full. No black box.
Jarvus emits structured events for every decision: why it spawned a subagent, why it rejected a plan, what it stored in episodic memory.
Analyzing repository structure and dependency graph
subagent-1 · active · 2.1s elapsed
RunningConflicting schema in step 3 — rewrote execution plan without user input
self-correction · autonomous
Self-correctedMemory written: user prefers structured JSON with confidence scores
episodic memory · persistent across sessions
LearnedSpawned 3 parallel subagents to accelerate ingestion
orchestrator · parallel execution
ParallelGenerate final report with anomaly flags and delta from baseline
queued · awaiting upstream completion
PendingSpans the full development cycle.
From planning to execution to learning — Jarvus handles every phase autonomously, with full transparency into its reasoning at each step.
01
Plan
Jarvus asks clarifying questions, builds a task dependency graph, then executes. Complex tasks get decomposed into parallel subagent workstreams automatically.
02
Execute
Subagents run in parallel. Jarvus monitors intermediate outputs and self-corrects when it detects anomalies — no human prompting required mid-task.
03
Learn
Post-task, Jarvus writes episodic memories and updates synaptic weights based on what worked. Future runs start smarter.
04
Report
Structured output with confidence scores, anomaly flags, learned insights, and a diff of its internal model state before and after the task.
Episodic Memory Graph
Interactive visualization
An agent that remembers — and evolves.
Jarvus maintains persistent episodic memory across sessions. Past tasks, user preferences, domain knowledge, and learned heuristics all persist and compound.
This isn't retrieval-augmented generation. Jarvus doesn't look things up — it internalizes them into its network weights via spike-timing-dependent plasticity.
Equipped to do real work.
Jarvus comes with a full toolkit out of the box. Extend it with custom plugins or connect your existing stack via configuration.
Terminal
Run shell commands, builds, and scripts directly. Sandboxed by default.
Web search
Search the web, fetch documentation, and gather context autonomously.
File I/O
Read, write, and manipulate files across your filesystem securely.
External APIs
Connect to any REST or GraphQL API via configuration.
Custom plugins
Extend Jarvus with custom tools using our open plugin format.
Your stack
Connect to databases, monitoring systems, IoT endpoints, and more.
Research highlights.
Latest findings from the Jarvus research team on SNN architectures, self-learning systems, and agent benchmarks.
Why SNNs for agents — the architectural argument
Exploring the advantages of spiking neural networks for autonomous agent systems compared to traditional transformer architectures.
Dec 2024LearningSelf-modification without catastrophic forgetting
How Jarvus learns new patterns without losing previously acquired knowledge through synaptic weight management.
Nov 2024BenchmarksBenchmarks vs. transformer-based agents
Comprehensive performance analysis comparing SNN-based agents to transformer alternatives on speed, power, and task accuracy.
Oct 2024Latest releases.
Added episodic memory persistence and improved SNN training stability.
Fixed subagent orchestration race conditions and added parallel execution limits.
Initial public release with core SNN architecture and CLI interface.
Try Jarvus now.
Get started with the self-learning agent powered by Spiking Neural Networks.