Deploy One Agent. Wake Up To A Civilization.
Self-evolving AI agents that dynamically create specialists, build collective intelligence, and run forever.
Built-in scheduler. Persistent task queue. The brain never sleeps.
The problem with today's agents
Current agent frameworks are glorified prompt wrappers. They execute, forget, and die. That's not intelligence — it's disposable labor.
Static & Disposable
Today's agents forget everything between runs. No memory, no learning, no growth. Every invocation starts from zero. You're paying for intelligence that evaporates.
Context Explosion
Dump 50 tools on an LLM and watch it hallucinate. Current approaches don't scale beyond a handful of capabilities. More tools means worse decisions, not better ones.
No Intelligence Layer
No routing, no optimization, no discovery. Every integration is manual plumbing. There's no system that dynamically discovers, selects, and optimizes execution across capabilities.
Digital Evolution
Intelligence that writes itself. You plant the seed — one agent, one spec. The system grows the forest. Agents spawn specialists, build shared knowledge, and rewrite their own configurations.
Dynamic Agent Creation
The system dynamically builds and deploys new agents with unique abilities at runtime. Need a Telegram bot? A database monitor? A code reviewer? The factory creates it, the kernel deploys it, and the brain immediately knows it exists.
The Brain Knows Everything
Every new agent registers its capabilities with the planner brain. When a task arrives, the brain evaluates the full roster — including agents that didn't exist five minutes ago — and routes to the best candidate. The more it grows, the smarter it gets.
Self-Modification
Agents propose changes to their own configuration — new tools, adjusted prompts, refined workflows. All sandboxed, versioned, and append-only. Evolution, not mutation.
Collective Learning
Every success and failure feeds back to the whole system. A hot filter catches obvious patterns. An async reviewer evaluates quality. Knowledge compounds over time.
Built-In Scheduler
Cron-based recurring tasks and one-off scheduled invocations, persisted across restarts. Agents schedule their own follow-ups, health checks, and maintenance windows. The system never stops working.
Runs Forever
A persistent task queue, scheduler, and event bus mean the system operates autonomously and indefinitely. Agents wake up, do their work, schedule the next run, and go idle. No human in the loop required. It just keeps evolving.
Not chatbots. A living, growing intelligence.
Traditional agents are stateless functions — call, get response, forget. r.dan is a self-sustaining ecosystem. It dynamically creates new agents with unique capabilities, the brain immediately includes them in task evaluation, and a built-in scheduler keeps everything running indefinitely. Deploy it once — it never stops evolving.
From seed to civilization
Write one spec. The system handles deployment, learning, specialization, and optimization.
Define
YAML SpecWrite a YAML agent spec. Define the agent's purpose, tools, model tier, and prompt. The spec is the seed — everything else grows from here.
Deploy
KernelThe kernel boots agents as isolated HTTP services. Each agent gets its own process, session memory, and tool access. Same protocol locally and remotely.
Evolve
AutonomousAgents learn from each interaction. They contribute patterns to the knowledge base, spawn new specialists when needed, and share learnings across the collective.
Scale
RegistryThe registry routes intelligently based on capability, success rate, latency, and cost. The system optimizes itself. More agents = smarter routing, not more chaos.
The Nervous System
Agent Registry is a capability-aware control plane. Like service discovery and load balancing — for AI capabilities.
Routing Flow
Capability Indexing
Each agent registers capabilities with input/output schemas. Indexed via semantic embeddings and tag constraints for instant discovery.
Intelligent Routing
Selects the best agent based on semantic match, success rate, latency, and cost. Not random — optimized by real usage data.
Performance Feedback
Continuously tracks execution success, failures, and usage patterns. Every invocation makes the next routing decision smarter.
Dynamic Lifecycle
Runtime agent creation, capability installation from tool registries, registration and reuse. The system grows its own capabilities.
Deterministic beats LLM
Every time. Templates first, LLM with templates second, LLM freestyle as a last resort. Reliable tooling isn't about removing AI — it's about constraining it.
Code & Templates
Pure deterministic generation. No LLM needed. Templates define structure, variables fill the gaps. Predictable, fast, zero hallucination.
LLM + Templates
LLM fills in structured templates with guardrails. The template constrains the output. Creative within boundaries.
LLM Freestyle
Only when nothing else works. No template, no constraints. Maximum flexibility, maximum risk. Used sparingly and with validation.
Built with fixedcode.ai
We use spec-driven code generation from fixedcode.ai to build reliable, deterministic tooling. The same approach we recommend for your agents.
What it looks like
Real scenarios through the r.dan dashboard. Chat with agents, watch them think and act.
DevOps Learner
Discovers system tools, learns monitoring patterns, builds its own playbook. Starts knowing nothing, becomes your infrastructure expert.
Learn what monitoring tools we have and set up health checks
I found docker, kubectl, nginx, and postgres on the system. I've learned the nginx log patterns and contributed "nginx-502-diagnosis" to the knowledge base. Hourly health checks are now scheduled.
Research Swarm
Ask one question, spawn a research team. Agents divide the work, synthesize findings, remember everything for next time.
Research OAuth2 best practices for our API
I've spawned oauth-researcher and security-auditor. Between them: 14 sources analyzed, 3 potential vulnerabilities flagged. Synthesis and recommendations stored in knowledge base.
Self-Evolving Codebase
Learns from past bugs, creates test agents, improves its own tooling. Your codebase gets smarter every commit.
Analyze recent test failures and add regression tests
Found a pattern: null check missing in 3 files causing cascading failures. I've generated 12 regression tests and contributed "null-safety-patterns" to the knowledge base for future agents.
E2E Workflow Builder
Code-defined sequential pipelines with validation between steps. Orchestration in code, not in the LLM's head.
Run the deploy pipeline for the API service
Pipeline complete. Step 1: lint ✓ Step 2: test ✓ Step 3: build ✓ Step 4: deploy → staging. All validation gates passed. Deterministic, no LLM guessing involved.
The Architecture
A minimal kernel with maximum composability. Every capability is a plugin. Every decision is traceable.
Plugin Kernel
DI container, event bus, lifecycle management. Everything is a plugin — composable, replaceable, testable.
Immutable SQLite
Append-only storage. No UPDATEs, no DELETEs. Every change is a versioned INSERT. Full audit trail, time-travel, safe rollback.
Tier-Based LLM Router
Fast (local Ollama) → Balanced (hybrid) → Quality (cloud). Fallback chains, budget tracking, provider-agnostic.
Workflow Engine
Code-defined sequential pipelines. 1-2 tools per step, validation between steps. Deterministic orchestration, not LLM guessing.
Governor
Two-tier knowledge curation. Hot filter for fast deterministic checks. Async LLM reviewer for quality evaluation. Trust, but verify.
Agent Factory
Creates agents from templates, LLM generation, or natural language descriptions. Zod-validated specs. One command to grow your fleet.
The Constellation
A living ecosystem. The brain orchestrates starter agents, core services, and self-created specialists — all accessible through multiple channels.
Brain / Governor
Channels (Inputs/Outputs)
Core Services
Agent Registry / Control PlanePlanned
Starter Agents (Ship with Platform)
Self-Created Agents (Examples)
The constellation keeps growing. Each agent can create more specialists as needs emerge.
Detail View
Prime directives, decision framework, autonomy principles. Hot filter for fast deterministic checks, async LLM reviewer for quality evaluation.
Data Flow
Immutable SQLite
Every action, knowledge contribution, trace, and agent version is an append-only INSERT. No UPDATEs, no DELETEs. Full audit trail with time-travel queries.
How An Agent Is Born
From natural language description to running specialist — the deterministic pipeline that grows your constellation.
Need Identified
User or AgentThe brain receives a request via any channel (dashboard, Telegram, API). It checks existing agents but finds no match. It decides to create a new specialist rather than handle the task itself.
"Set up monitoring for our PostgreSQL databases"
Deterministic → LLM Fallback Strategy
The Dashboard
A real-time admin interface to interact with, monitor, and control your r.dan constellation. Chat with agents, watch them think, and steer the evolution.
Set up monitoring for our PostgreSQL databases
I'll create a specialist agent for PostgreSQL monitoring. Let me check what tools are available...
✓ Agent "pg-monitor" created and deployed on :3007
Live Agent Chat
Talk to any agent in real-time with streaming responses. See tool calls, reasoning turns, cost tracking, and multi-step execution as it happens.
Agent Fleet Control
Register, pause, resume, and delete agents from a single view. Monitor status, ports, and sessions across your entire constellation.
Execution Traces
Full transparency into every LLM call, tool execution, token count, and cost. Expand any trace to see individual spans — nothing is hidden.
Knowledge Browser
Search, tag, and manage the collective knowledge base. See what agents have learned, contributed, and how confidence scores evolve.
Task Scheduler
Create cron-based recurring tasks or one-off scheduled invocations. Agents schedule their own follow-ups — you see it all here.
Raw Data Access
Direct SQLite table browser for full audit trail visibility. Every agent action, every knowledge contribution, every trace — queryable.
Watch it evolve
One message. The system creates specialists, learns your infrastructure, and schedules its own follow-ups.