language:
- en
license: mit
tags:
- agent
- multi-agent
- autonomous
- self-improving
- recursive
- learn-from-scratch
- huggingface
pipeline_tag: text-generation
Agron — Self-Improving Multi-Agent AI
Agron is an autonomous, self-improving AI agent system that learns from scratch. It starts with empty memory and bootstrap rules, then recursively improves through:
- Observation of task outcomes
- Memory consolidation and knowledge graph growth
- Tool/strategy evolution and validation
- Safe self-modification with tests
- Optional synchronization to Hugging Face
Agent Team
| Agent | Role | Specialty |
|---|---|---|
| Coordinator | Orchestrator | Decomposes work, delegates, synthesizes |
| Learner | Knowledge | Ingests, distills, updates memory from scratch |
| Builder | Improvement | Generates new tools/strategies recursively |
| Guardian | Safety | Validates changes, prevents regressions |
Learn From Scratch
Agron begins with:
- Empty episodic memory
- No imported knowledge
- Seed evaluation rules only
- Bootstrap loop enabled
First cycles prioritize:
- Environment probing
- Capability discovery
- Minimal safe tool creation
- Curriculum expansion
- Test-backed improvement
Recursive Self-Improvement
- XP / generation tracking
- Gap analysis against current capabilities
- Candidate generation with validation
- Test-gated commits
- Misalignment/secret checks before push
- Rollback on failed validation
Hugging Face Sync
Agron can push validated improvements to:
https://huggingface.co/teddyoas/Agron
Trained LLM
Agron's initial LLM has now been trained from scratch on WikiText-2 using a 4-layer, 256-dim, 4-head LLaMA-style architecture (about 8.4M parameters). Training artifacts and weights are committed in this repository, including model.safetensors, tokenizer files, and the checkpoint.
Future recursive self-improvement can fine-tune or grow this base model.
Cybersecurity Focus
Agron and its spawned agents are constrained to cybersecurity exploitation and analysis only:
- Exploitation (authorized targets, CVEs, payloads)
- Vulnerability analysis and research
- Reverse engineering and malware analysis
- Network / host / application reconnaissance
- Defensive hardening and detection engineering
Agents can be spawned in parallel for tasks using the built-in multi-agent orchestration.