tomevault-io Self-Evolving Agent
Trit: 0 (ERGODIC - coordinator) Color: Green (#26D826)
Overview
Implements self-evolving agent patterns from recent research:
- Darwin Gödel Machine (DGM) for self-improving code
- Open-ended evolution of agent capabilities
- Lifelong learning with long-term memory
- Feedback loops for continual adaptation
Key Papers
- Darwin Gödel Machine - Zhang et al. 2025
- Self-Evolving Agents Survey - Gao et al. 2025
- Long Term Memory for AI Self-Evolution - Jiang et al. 2024
- Open-Endedness is Essential for ASI - Hughes et al. 2024
- Static Sandboxes Are Inadequate - Chen et al. 2025
Core Concepts
Darwin Gödel Machine Architecture
┌─────────────────────────────────────────────────────┐
│ DARWIN GÖDEL MACHINE │
├─────────────────────────────────────────────────────┤
│ ┌─────────────┐ ┌─────────────┐ │
│ │ Archive │───▶│ Sampler │ │
│ │ (agents) │ │ (select) │ │
│ └─────────────┘ └──────┬──────┘ │
│ ▲ │ │
│ │ ┌──────▼──────┐ │
│ │ │ Mutator │ │
│ │ │ (LLM-based)│ │
│ │ └──────┬──────┘ │
│ │ │ │
│ ┌──────┴──────┐ ┌──────▼──────┐ │
│ │ Validator │◀────│ Evaluator │ │
│ │ (benchmark)│ │ (fitness) │ │
│ └─────────────┘ └─────────────┘ │
└─────────────────────────────────────────────────────┘
Evolution Loop
\text{For each generation } t:
1. \text{Sample agent } A_t \text{ from archive}
2. \text{Mutate: } A'_t = \text{LLM}(A_t, \text{context})
3. \text{Evaluate: } f(A'_t) = \text{benchmark}(A'_t)
4. \text{If } f(A'_t) > \theta: \text{add to archive}
5. \text{Prune archive to maintain diversity}
Three Dimensions of Self-Evolution
- What to Evolve: Model, memory, tools, architecture
- When to Evolve: Intra-test-time, inter-test-time, offline
- How to Evolve: Scalar rewards, textual feedback, multi-agent
API
Python Implementation
from dataclasses import dataclass
from typing import List, Dict, Any, Callable
import random
@dataclass
class Agent:
"""Self-evolving agent representation."""
code: str
fitness: float
generation: int
parent_id: str = None
metadata: Dict[str, Any] = None
def __hash__(self):
return hash(self.code)
class DarwinGodelMachine:
"""Darwin Gödel Machine for self-improving agents."""
def __init__(
self,
initial_agent: Agent,
mutator: Callable[[Agent], Agent],
evaluator: Callable[[Agent], float],
archive_size: int = 100,
diversity_threshold: float = 0.3
):
self.archive = [initial_agent]
self.mutator = mutator
self.evaluator = evaluator
self.archive_size = archive_size
self.diversity_threshold = diversity_threshold
self.generation = 0
def sample_agent(self) -> Agent:
"""Sample agent from archive (fitness-proportionate)."""
fitnesses = [max(a.fitness, 0.01) for a in self.archive]
total = sum(fitnesses)
probs = [f / total for f in fitnesses]
return random.choices(self.archive, weights=probs, k=1)[0]
def mutate(self, agent: Agent) -> Agent:
"""Mutate agent using LLM-based code modification."""
new_code = self.mutator(agent)
return Agent(
code=new_code,
fitness=0.0,
generation=self.generation,
parent_id=hash(agent.code)
)
def evaluate(self, agent: Agent) -> float:
"""Evaluate agent on benchmarks."""
try:
fitness = self.evaluator(agent)
agent.fitness = fitness
return fitness
except Exception as e:
agent.fitness = 0.0
agent.metadata = {"error": str(e)}
return 0.0
def is_novel(self, agent: Agent) -> bool:
"""Check if agent is sufficiently novel for archive."""
for existing in self.archive:
similarity = self.code_similarity(agent.code, existing.code)
if similarity > (1 - self.diversity_threshold):
return False
return True
def evolve_step(self) -> Agent:
"""Run one evolution step."""
self.generation += 1
# Sample and mutate
parent = self.sample_agent()
child = self.mutate(parent)
# Evaluate
fitness = self.evaluate(child)
# Add to archive if good and novel
if fitness > 0 and self.is_novel(child):
self.archive.append(child)
# Prune if too large
if len(self.archive) > self.archive_size:
self.archive.sort(key=lambda a: a.fitness, reverse=True)
self.archive = self.archive[:self.archive_size]
return child
def evolve(self, generations: int) -> Agent:
"""Run evolution for multiple generations."""
for _ in range(generations):
self.evolve_step()
# Return best agent
return max(self.archive, key=lambda a: a.fitness)
@staticmethod
def code_similarity(code1: str, code2: str) -> float:
"""Compute code similarity (simple Jaccard)."""
tokens1 = set(code1.split())
tokens2 = set(code2.split())
intersection = len(tokens1 & tokens2)
union = len(tokens1 | tokens2)
return intersection / union if union > 0 else 0.0
class LLMMutator:
"""LLM-based code mutator for agent evolution."""
def __init__(self, model, mutation_prompts: List[str]):
self.model = model
self.prompts = mutation_prompts
def __call__(self, agent: Agent) -> str:
"""Generate mutated code using LLM."""
prompt = random.choice(self.prompts)
system = """You are an AI agent code mutator.
Your task is to improve the given agent code while maintaining correctness.
Return ONLY the improved code, no explanations."""
user = f"""{prompt}
Current agent code:
```python
{agent.code}
Current fitness: {agent.fitness} Generation: {agent.generation}
Generate improved code:"""
response = self.model.generate(system=system, user=user)
return self.extract_code(response)
@staticmethod
def extract_code(response: str) -> str:
"""Extract code from LLM response."""
if "```python" in response:
start = response.index("```python") + 9
end = response.index("```", start)
return response[start:end].strip()
return response.strip()
class LongTermMemory: """Long-term memory for self-evolution (OMNE framework)."""
def __init__(self, embedding_model, max_entries: int = 10000):
self.entries = []
self.embeddings = []
self.embedding_model = embedding_model
self.max_entries = max_entries
def store(self, experience: Dict[str, Any]):
"""Store experience in long-term memory."""
text = self.experience_to_text(experience)
embedding = self.embedding_model.encode(text)
self.entries.append(experience)
self.embeddings.append(embedding)
# Prune old entries if needed
if len(self.entries) > self.max_entries:
self.entries = self.entries[-self.max_entries:]
self.embeddings = self.embeddings[-self.max_entries:]
def retrieve(self, query: str, k: int = 5) -> List[Dict]:
"""Retrieve relevant experiences."""
query_emb = self.embedding_model.encode(query)
# Compute similarities
similarities = [
self.cosine_similarity(query_emb, emb)
for emb in self.embeddings
]
# Get top-k
indices = sorted(range(len(similarities)),
key=lambda i: similarities[i], reverse=True)[:k]
return [self.entries[i] for i in indices]
def consolidate(self):
"""Consolidate memories (compress similar experiences)."""
# Group similar experiences
# Extract patterns
# Update with generalized knowledge
pass
### Multi-Agent Self-Evolution
```python
class MultiAgentEvolution:
"""Co-evolution of multiple agent populations."""
def __init__(self, populations: Dict[str, DarwinGodelMachine]):
self.populations = populations
self.interaction_history = []
def co_evolve_step(self):
"""Evolve all populations with interaction."""
for name, dgm in self.populations.items():
# Evolve independently
child = dgm.evolve_step()
# Cross-pollinate: share best agents
for other_name, other_dgm in self.populations.items():
if other_name != name:
best_other = max(other_dgm.archive, key=lambda a: a.fitness)
# Learn from other population
self.share_knowledge(dgm, best_other)
def share_knowledge(self, recipient: DarwinGodelMachine, donor: Agent):
"""Transfer knowledge between populations."""
# Extract useful patterns from donor
# Inject into recipient's mutation prompts
pass
GF(3) Triads
This skill participates in balanced triads:
persistent-homology (-1) ⊗ self-evolving-agent (0) ⊗ jaxlife-open-ended (+1) = 0 ✓
sheaf-cohomology (-1) ⊗ self-evolving-agent (0) ⊗ forward-forward-learning (+1) = 0 ✓
temporal-coalgebra (-1) ⊗ self-evolving-agent (0) ⊗ gay-mcp (+1) = 0 ✓
Use Cases
Self-Improving Coding Agent
# Initialize with basic coding agent
initial = Agent(code=BASIC_CODER_CODE, fitness=0.2, generation=0)
# LLM mutator with improvement prompts
mutator = LLMMutator(model, prompts=[
"Add better error handling",
"Improve code efficiency",
"Add missing edge cases",
"Refactor for clarity"
])
# Benchmark evaluator
evaluator = lambda a: run_swebench(a.code)
# Create DGM
dgm = DarwinGodelMachine(initial, mutator, evaluator)
# Evolve!
best = dgm.evolve(generations=100)
print(f"Best fitness: {best.fitness}") # 0.2 -> 0.5
Continual Learning Agent
# Agent with long-term memory
class ContinualAgent:
def __init__(self):
self.dgm = DarwinGodelMachine(...)
self.ltm = LongTermMemory(embedding_model)
def interact(self, task):
# Retrieve relevant past experiences
experiences = self.ltm.retrieve(task)
# Evolve with context
agent = self.dgm.sample_agent()
agent.metadata["context"] = experiences
# Execute and store
result = self.execute(agent, task)
self.ltm.store({"task": task, "result": result})
return result
Integration with Music-Topos
;; In agents/self_evolving.clj
(defn dgm-evolve-color-agent
"Evolve color generation agents via DGM"
[initial-seed benchmark-fn]
(let [archive (atom [(make-color-agent initial-seed)])
mutate-fn (fn [agent]
(update agent :seed #(splitmix64-next %)))
evaluate-fn (fn [agent]
(benchmark-fn (generate-colors agent)))]
(loop [gen 0]
(when (< gen 100)
(let [parent (sample-archive @archive)
child (mutate-fn parent)
fitness (evaluate-fn child)]
(when (> fitness 0.5)
(swap! archive conj child))
(recur (inc gen)))))
(best-agent @archive)))
Safety Considerations
From Darwin Gödel Machine paper:
- Sandboxing: Execute evolved code in isolated environments
- Human Oversight: Review significant capability gains
- Capability Bounds: Limit what evolved agents can access
- Rollback: Maintain ability to revert to previous versions
- Alignment Verification: Test evolved agents for alignment
class SafeDGM(DarwinGodelMachine):
def evaluate(self, agent: Agent) -> float:
# Run in sandbox
with Sandbox() as sb:
result = sb.execute(agent.code, timeout=60)
# Check for safety violations
if self.safety_check(result):
return result.fitness
else:
return -1.0 # Reject unsafe agents
See Also
jaxlife-open-ended- Open-ended embodied evolutionforward-forward-learning- Local learning for agent updatesbisimulation-game- Agent coordination and skill dispersalparallel-fanout- Parallel agent dispatch
References
@article{zhang2025darwin,
title={Darwin Gödel Machine: Open-Ended Evolution of Self-Improving Agents},
author={Zhang, Jenny and others},
journal={arXiv:2505.22954},
year={2025}
}
@article{gao2025survey,
title={A Survey of Self-Evolving Agents},
author={Gao, Huan-ang and others},
journal={arXiv:2507.21046},
year={2025}
}
@article{hughes2024openended,
title={Open-Endedness is Essential for Artificial Superhuman Intelligence},
author={Hughes, Edward and others},
journal={arXiv:2406.04268},
year={2024}
}
Scientific Skill Interleaving
This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem:
Graph Theory
- networkx [○] via bicomodule
- Universal graph hub
Bibliography References
general: 734 citations in bib.duckdb
Cat# Integration
This skill maps to Cat# = Comod(P) as a bicomodule in the equipment structure:
Trit: 0 (ERGODIC)
Home: Prof
Poly Op: ⊗
Kan Role: Adj
Color: #26D826
GF(3) Naturality
The skill participates in triads satisfying:
(-1) + (0) + (+1) ≡ 0 (mod 3)
This ensures compositional coherence in the Cat# equipment structure.
Converted and distributed by TomeVault — claim your Tome and manage your conversions.