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CHAOS
Context Hydrated Agentic Orchestration System
Equip your AI with the context, tools, workflows, git awareness, and live visibility it needs in order to effectively operate inside your development environment.
SYSTEM MODEL
AI agents can reason, plan, and generate, but today they forget context, repeat work, drift into hallucination, and need constant babysitting. CHAOS fixes that by becoming the runtime layer underneath the LLM through MCP.
RESULT
Five systems
Move through the runtime path from memory to integrations. Each system has one job, and each makes the next step more reliable.
01 / Context & Memory
CHAOS keeps project knowledge, living docs, previous decisions, and active task state close to every run so agents start from current ground instead of reconstructed memory.
System proof
02 / Session & Orchestration
The orchestration layer turns a prompt into bounded work: intake, planning, execution, verification, handoff, and the next action all stay visible inside the same operating loop.
System proof
03 / Automation & Git
Skills, hooks, file watchers, branch movement, and integration checks connect agent intent to repository changes that can be reviewed, verified, and shipped.
System proof
04 / Governance & Auditability
Permission scopes, tool records, prompts, outputs, decisions, and file changes become part of the run record so agent work can be inspected instead of guessed after the fact.
System proof
05 / Local-First & Integration Layer
CHAOS runs near the code, exposes MCP-facing capability surfaces, and connects editors, clients, providers, and local runtime services through one controlled engine.
System proof
System 01 / 05
01 / Context & Memory
CHAOS keeps project knowledge, living docs, previous decisions, and active task state close to every run so agents start from current ground instead of reconstructed memory.
Know your agents have the right context.
System proof
02 / Session & Orchestration
The orchestration layer turns a prompt into bounded work: intake, planning, execution, verification, handoff, and the next action all stay visible inside the same operating loop.
Coordinate every task from one place.
System proof
03 / Automation & Git
Skills, hooks, file watchers, branch movement, and integration checks connect agent intent to repository changes that can be reviewed, verified, and shipped.
Move from prompt to pull request.
System proof
04 / Governance & Auditability
Permission scopes, tool records, prompts, outputs, decisions, and file changes become part of the run record so agent work can be inspected instead of guessed after the fact.
Keep every run controlled and explainable.
System proof
05 / Local-First & Integration Layer
CHAOS runs near the code, exposes MCP-facing capability surfaces, and connects editors, clients, providers, and local runtime services through one controlled engine.
Use any client through one local engine.
System proof
Operator flow
CHAOS does not replace your AI provider or editor. It becomes the local system between your project, your provider, and the tools agents use while work is running.
01
Install, configure, launch, and control the local engine.
02
Runs CHAOS services, MCP servers, daemon processes, and local state.
03
Connects compatible AI clients to the local CHAOS capability surface.
04
Your chosen model supplies reasoning and generation through BYOK.
05
Shows logs, workflow activity, health, and dash-log visibility.
06
Keeps tool calls, decisions, and repo changes inspectable.
Outcomes
CHAOS preserves the context, documentation, workflow state, and engineering guardrails around agent work so each session builds on the last.
Project knowledge, prior decisions, task state, and run history carry forward so new sessions do not start from zero.
CHAOS helps generate and maintain technical documentation as workflows move, keeping implementation details, decisions, and changes easier to recover later.
The context layer reinforces repo structure, instructions, workflows, and software engineering principles instead of relying on one-off prompt memory.
With better context, visible state, and reviewable execution history, agents waste less time guessing and more time moving work through a controlled path.
Trust & Control
CHAOS gives AI agents explicit context, bounded tools, repo-aware workflows, and visible execution state so work moves through a system you can inspect.
Agents start from ranked project context, living docs, task state, and relevant memory instead of reconstructing the repo from scratch.
CHAOS connects compatible clients to explicit local capabilities rather than vague, hidden access.
Tasks, handoffs, dependencies, and results stay part of a visible execution path.
Repo-aware sessions, git watchers, and integration paths keep parallel work trackable.
Logs, process state, tool activity, workflow movement, and health indicators stay visible while work runs.
Prompts, tool calls, decisions, file changes, and outputs remain inspectable after the run.
Tell us how you work, what kinds of agent workflows you want to run, and how CHAOS could fit into your development process.
Note: Beta applications are reviewed manually. Paid access is now handled through the billing flow on the pricing page.
