INSOMNIQ
initializing system...
Give INSOMNIQ a problem. It assembles a team of autonomous AI agents — Claude Sonnet, Haiku, Gemini, or local Ollama — designs the pipeline, executes in parallel, and self-improves with every run. Not a wrapper. An AI firm.
6 autonomous AI agents ready · Type a problem, watch the team form
How it works
Describe the problem. INSOMNIQ recruits the right autonomous AI agents (LLM-powered), designs a custom pipeline, runs it in parallel, scores the output, and self-iterates — until quality is met.
01
Problem In
YAML or natural language. Set budget, acceptance criteria, constraints.
02
AI Strategy
Coordinator reads KB for proven patterns, designs the team, assigns LLM models per node.
03
Parallel Execute
Agents run concurrently. Auditor monitors. Predictor flags risks in advance.
04
Evaluate
Evaluator scores output 0–100%. If can_improve → Coordinator replans and retries.
05
Learn
Librarian writes insights to KB. Run 50 is fundamentally smarter than run 1.
The AI Agent Team
Each agent is an LLM-powered autonomous worker — Claude Sonnet, Claude Haiku, Gemini Flash, or local Ollama. Agent Resources (UCB1) learns which model excels at each task type and improves with every run.
Plans the entire pipeline. Reads KB for proven strategies, designs agent teams, assigns LLM models per node, handles replanning when something fails. The brain of every run.
Watches every node run. Manages the inbox — users and Investor send signals here. Delivers consolidated summaries to Coordinator at each replan. Never misses an anomaly.
Breaks safe-play loops. When quality stagnates, calls a board meeting with concrete goals. Reads quality signals and forces the system to take calculated risks or reboot entirely.
Scores output quality 0–100%. Spawns parallel sub-evaluators for complex output. Decides if can_improve — triggering the next iteration cycle. Your quality bar, automated.
Consolidates the Knowledge Base after every run. Merges duplicates, drops noise, surfaces actionable insights for the next Coordinator. LanceDB + ModernBERT, 3ms semantic search.
Before each node runs, queries KB for past failures on similar tasks. Flags risks in advance — so the pipeline doesn't repeat history. Future failure prevention, automated.
Meta moment
Right now — attempt 19. Pipeline: web_spark → html_architect → qa_polish. Budget: $22.52 of $80.00 spent. Not a demo. Actual autonomous execution, live.
19
ATTEMPT
$23
SPENT OF $80
3
PIPELINE NODES
Live session
A real INSOMNIQ pipeline session. Parallel LLM execution — Claude Sonnet for strategy & code, Claude Haiku for speed & memory, Ollama for local runs. Autonomous AI agents, unfiltered.
Why INSOMNIQ
Devin costs $9/hour and resets after each session. Cursor doesn't remember last week. INSOMNIQ gets fundamentally better with every run — because it has a persistent, searchable memory of what worked.
672
Tests Passing
17.5K
Lines of Code
3ms
KB Search
9
Core Pillars
Community & Roadmap
INSOMNIQ isn't just another AI wrapper. The Investor Center, shared Knowledge Base, and public patterns make the system smarter for everyone — while your local data stays completely private.
Investor Center
Cross-project knowledge aggregation. Your Investor learns from every project in your portfolio. Public Investor Center lets the community share collective AI team patterns and strategies.
Public Knowledge Base
Opt-in shared KB. Strategies, agent patterns, domain knowledge — contributed across the community. Your local KB stays private. Public KB is read-only unless you choose to contribute.
Server Mode
Run INSOMNIQ as a service. Submit problems via API, get autonomous AI agent results asynchronously. Deploy on your own infrastructure or use the hosted version.
AGPL-3.0 · Python 3.11+ · Claude · Gemini · Ollama · uv sync and go