A self-evolving multi-agent AI architecture that combines:
- LLM swarm orchestration (Ollama + OpenAI)
- adversarial code generation
- execution-based fitness evaluation
- evolutionary opcode compiler system
- self-repairing memory (software immune system)
- IR-based intermediate execution layer
- ncurses live cognitive visualization
This system behaves like a self-improving computational organism:
Input → IR Compiler → Agent Swarm → LLM Swarm → Execution Engine
↓ ↓ ↓
Opcode Evolution Debate System Adversarial Tests
↓ ↓ ↓
Repair Memory ← Fitness Scoring ← Failure Clustering
- Execution > Prediction (code must run to be valid)
- Adversarial pressure improves robustness
- Memory stores failure patterns (immune system)
- Multiple LLM roles simulate cognitive diversity
- Evolutionary selection drives opcode survival
Below is a breakdown of all system modules.
Initial system entrypoint.
- builds controller
- initializes swarm
- restores persistent memory
- launches ncurses dashboard
Central cognitive runtime.
- runs full system cycles
- coordinates agents, IR, swarm, and memory
- executes evolution loop per step
- returns live UI state
NCurses interface runner.
- renders system state
- displays agent reasoning
- shows evolution progress live
Unified abstraction over all LLM providers.
- OpenAI integration
- Ollama local models
- unified
.call()interface
Intelligence scheduler.
- selects best model per task
- learns performance over time
- routes reasoning vs generation tasks
Individual reasoning units.
- generate solutions
- participate in debate
- execute IR logic
Dynamic role allocation system.
- assigns agent responsibilities
- adapts based on performance
- supports adversarial roles
Multi-agent reasoning conflict system.
- agents compete for best solution
- scoring determines winner
- improves reasoning diversity
Executes intermediate representation instructions.
Converts agent reasoning → IR programs.
Optimizes IR execution paths.
Base opcode registry and execution logic.
LLM swarm-based opcode generation system.
Genetic evolution of opcode implementations.
- mutation
- selection
- survival pressure
Runs generated code safely.
- sandbox execution
- timeout protection
- runtime scoring
Generates inputs designed to break code.
- edge cases
- hostile inputs
- robustness testing
LLM-based automatic code repair.
Stores known bug → fix mappings.
- long-term failure memory
- reuse of fixes
Groups similar failure types.
- bug classification system
- enables predictive fixes
Stores:
- agent states
- opcode evolution history
- system sessions
Tracks patterns that evolve into reusable “functions”.
NCurses UI renderer.
- agent activity
- execution traces
- evolution progress
- system health
1. Input received
2. IR compiled
3. Agents debate
4. Swarm generates code
5. Adversarial tests attack code
6. Execution verifies correctness
7. Failure triggers repair system
8. Repair memory stores fix
9. Evolution selects best variants
10. System repeats
Over time this system develops:
- stable opcode languages
- self-correcting behavior
- reduced failure rate
- reusable fix patterns
- model specialization per task
This system executes generated code.
Recommended safeguards:
- sandbox environment
- no production filesystem access
- strict timeout enforcement
- multi-language opcode compilation (Rust / JS / WASM)
- distributed swarm execution
- reinforcement learning reward tuning
- semantic embedding memory layer
- full cognitive trace debugger
For additional modules:
Purpose: Short description of role in system
Inputs:
- what it receives
Outputs:
- what it returns
Connected Systems:
- dependencies
Evolution Role:
- generator / validator / optimizer / memory / UI
Self-evolving multi-agent computational intelligence system with adversarial training, execution grounding, and memory-driven repair.