Experimentando con Evolución Sintética Dirigida
Cuando la teoría se encuentra con la realidad y el código comienza a evolucionar Inspired by thinking about extensions to mostlylucid.mockllmapi and material for the (never to be released but I like to think about it 😜) sci-fi novel "Michael" about emergent AI
**Nota:**Nota:
También es muy experimental, un poco loco, y definitivamente "codificado por vida". Te han advertido.
De la teoría a la práctica:
En realidad lo construí.
Después de seis partes de la teoría sobre inteligencia emergente, sistemas multi-agente, consenso global, y cognición a escala planetaria, tuve una comprensión:
Estaba dilatando.**Es fácil especular sobre los gremios sintéticos y la inteligencia en evolución.**Es más difícil construirlo.
Así que dejé de hablar y empecé a codificar.
Lo que surgió es algo que estoy llamandoEvolución sintética dirigida (DSE)—un flujo de trabajo autoensamblante y autooptimizador que utiliza un sistema dinámico multinivel, multi-agente, alimentado por LLM.
¡O algo así! (Mira, estoy inventando esto a medida que voy.)El paso del ascensor: ¿Qué pasa si en lugar de generar código una vez y con la esperanza de que funcione, creamos un sistema donde el códigoevoluciona continuamente
a través de la planificación, ejecución, evaluación y mutación?
¿Y si pudiéramos enseñar a un sistema a aprender de sus errores, reutilizar patrones exitosos y ser más inteligentes con el tiempo?Alerta de spoiler:. En realidad funciona.*Y es raro.*Y fascinante.
Vamos a zambullirnos.
You: "Write me a function that does X"
LLM: "Here's some code! [generates 50 lines of Python]"
You: *runs it*
Code: *explodes spectacularly*
You: "Fix it"
LLM: "Oh, sorry! Here's a new version!"
You: *runs it*
Code: *different explosion*
Esto es un experimento que no es tan estable y que no es nada rápido.
Pero hace lo que dice en el TIN,
De verdad que sí.ahora
hacer todas las operaciones simplemente no bien todavía.
El problema del que no estamos hablando
He aquí cómo funciona hoy en día la mayor parte de la generación de código basada en LLM:
Las cuestiones son fundamentales:
Generación de un solo disparo
- Sin iteración, sin refinamiento, sin segundas oportunidades
Sin memoria
Sin comentarios de calidad
[1. PLAN] → [2. GENERATE] → [3. EXECUTE] → [4. EVALUATE] → [5. EVOLVE]
↑ ↓
└────────────────────── [6. LEARN] ←─────────────────────────┘
graph TD
A[User Request] --> B[Overseer LLM<br/>llama3]
B -->|Strategic Plan| C[Generator LLM<br/>codellama]
C -->|Generated Code| D[Executor<br/>Sandboxed Python]
D -->|stdout/stderr/metrics| E[Triage LLM<br/>tinyllama]
E -->|Pass?| F{Quick Check}
F -->|Pass| G[Evaluator LLM<br/>llama3]
F -->|Fail| H[Escalation<br/>qwen2.5-coder]
G -->|Fitness Score| I[RAG Memory]
H -->|Improved Code| D
I -->|Store & Learn| J[Future Reuse]
style B stroke:#e1f5ff,stroke-width:3px
style C stroke:#ffe1f5,stroke-width:3px
style D stroke:#f5ffe1,stroke-width:3px
style E stroke:#fff5e1,stroke-width:3px
style G stroke:#e1ffe1,stroke-width:3px
style I stroke:#f0e1ff,stroke-width:3px
- Los mismos errores se repiten sin cesar en problemas similares
**Necesitábamos algo fundamentalmente diferente.**No sólo mejores avisos.
class OverseerLLM:
"""Plans execution strategies and creates specifications."""
def create_plan(self, task_description: str) -> ExecutionPlan:
"""
Create detailed execution plan from task description.
Returns:
ExecutionPlan with strategy, steps, and expected metrics
"""
# Ask overseer to break down the problem
prompt = f"""Create a detailed execution plan for: {task_description}
Include:
1. High-level strategy
2. Step-by-step implementation plan
3. Expected quality score (0.0-1.0)
4. Expected execution time (ms)
5. Algorithm/data structure choices
6. Edge cases to handle
"""
response = self.client.generate(
model="llama3",
prompt=prompt,
model_key="overseer"
)
return ExecutionPlan(
plan_id=f"plan_{uuid.uuid4().hex[:8]}",
task_description=task_description,
strategy=response,
steps=self._parse_steps(response),
expected_quality=0.8,
expected_speed_ms=1000
)
**No sólo modelos más grandes.**Un sistema que realmente aprende, recuerda y mejora.
def generate_code(self, specification: str) -> str:
"""Generate code from specification (no creative interpretation)."""
prompt = f"""Implement this specification EXACTLY:
{specification}
Requirements:
- Follow the spec precisely
- No additional features
- Include error handling
- JSON input/output interface
- Return only Python code
"""
code = self.client.generate(
model="codellama",
prompt=prompt,
model_key="generator",
temperature=0.3 # Low temperature for consistency
)
return self._clean_code(code)
**Eso es lo que el DSE intenta ser.**Introducir Evolución Sintética Dirigida
def triage(self, metrics: Dict[str, Any], targets: Dict[str, Any]) -> Dict[str, Any]:
"""Quick triage evaluation using tiny model."""
prompt = f"""Quick evaluation:
Metrics:
- Latency: {metrics['latency_ms']}ms (target: {targets['latency_ms']}ms)
- Memory: {metrics['memory_mb']}MB (target: {targets['memory_mb']}MB)
- Exit code: {metrics['exit_code']} (target: 0)
Does this PASS or FAIL? One word answer."""
response = self.client.generate(
model="tinyllama",
prompt=prompt,
model_key="triage"
)
verdict = "pass" if "pass" in response.lower() else "fail"
return {
"verdict": verdict,
"reason": response.strip(),
"metrics": metrics
}
**La Evolución Sintética Dirigida toma prestados conceptos de algoritmos evolutivos pero los aplica a la generación de código.**Aquí está el flujo de trabajo principal:
def evaluate(self, stdout: str, stderr: str, metrics: Dict) -> Dict[str, Any]:
"""Comprehensive evaluation with multi-dimensional scoring."""
prompt = f"""Evaluate this code execution:
OUTPUT:
{stdout[:500]}
ERRORS:
{stderr[:500] if stderr else "None"}
METRICS:
- Latency: {metrics['latency_ms']}ms
- Memory: {metrics['memory_mb']}MB
- Exit code: {metrics['exit_code']}
Provide scores (0.0-1.0):
1. Correctness: Does output match expected?
2. Quality: Code robustness, patterns, style
3. Speed: Performance vs targets
Format: JSON with correctness, quality, speed, overall_score
"""
response = self.client.evaluate(
code_summary=stdout,
metrics=metrics
)
return {
"correctness": 0.95,
"quality": 0.88,
"speed": 0.92,
"overall_score": 0.92,
"details": response
}
Pero aquí es donde se pone interesante: no usamos un solo LLM para todo.
agentes especializados**, cada uno con un papel específico:**.
La arquitectura multi-agente
User: "Write a fibonacci function"
LLM: [Generates code + tests + documentation + explanation all at once]
[Might invent requirements you didn't ask for]
[Might miss requirements you did ask for]
Responsabilidades del agente:
User: "Write a fibonacci function"
↓
Overseer: Creates detailed specification
{
"problem": "Generate first N fibonacci numbers",
"algorithm": "Iterative DP approach",
"inputs": {"n": "integer"},
"outputs": {"result": "list[int]"},
"constraints": {
"timeout_ms": 5000,
"max_n": 100
},
"test_cases": [
{"input": {"n": 5}, "expected": [0,1,1,2,3]},
{"input": {"n": 10}, "expected": [0,1,1,2,3,5,8,13,21,34]}
]
}
↓
Generator: Implements ONLY the specification
[No creative interpretation]
[No added features]
[Just clean, focused code]
Supervisor (llama3)
Generador (codellama)
nomic-embed-text- Anotación multidimensional integralsequenceDiagram
participant U as User
participant S as System
participant R as RAG Memory
participant Q as Qdrant DB
participant E as Embedding Model
U->>S: Request: "validate email"
S->>R: Search similar artifacts
R->>E: Generate embedding
E-->>R: 768-dim vector
R->>Q: Semantic search
Q-->>R: Top 5 similar artifacts
R-->>S: Found: email_validator (0.92 similarity)
alt High Similarity (>0.9)
S->>S: Reuse as-is
else Medium Similarity (0.7-0.9)
S->>S: Use as template
else Low Similarity (<0.7)
S->>S: Generate from scratch
end
S->>U: Return solution
S->>R: Store with metadata
R->>E: Generate embedding
E-->>R: Vector
R->>Q: Index artifact
Q-->>R: Stored
Aquí está la innovación clave que hace que el DSE funcione:
class QdrantRAGMemory:
"""RAG memory using Qdrant vector database for semantic search."""
def __init__(
self,
qdrant_url: str = "http://localhost:6333",
collection_name: str = "code_evolver_artifacts",
embedding_model: str = "nomic-embed-text",
vector_size: int = 768 # nomic-embed-text dimension
):
self.qdrant = QdrantClient(url=qdrant_url)
self.embedding_model = embedding_model
self.vector_size = vector_size
# Create collection if needed
self._init_collection()
def store_artifact(
self,
artifact_id: str,
artifact_type: ArtifactType,
name: str,
content: str,
tags: List[str],
metadata: Dict[str, Any],
auto_embed: bool = True
):
"""Store artifact with semantic embedding."""
# Generate embedding
if auto_embed:
embedding = self._generate_embedding(content)
else:
embedding = None
# Create artifact
artifact = Artifact(
artifact_id=artifact_id,
artifact_type=artifact_type,
name=name,
content=content,
tags=tags,
metadata=metadata
)
# Store in Qdrant with metadata as payload
if embedding:
self.qdrant.upsert(
collection_name=self.collection_name,
points=[
PointStruct(
id=hash(artifact_id) & 0x7FFFFFFF, # Positive int
vector=embedding,
payload={
"artifact_id": artifact_id,
"name": name,
"type": artifact_type.value,
"tags": tags,
"quality_score": metadata.get("quality_score", 0.0),
"latency_ms": metadata.get("latency_ms", 0),
"usage_count": metadata.get("usage_count", 0),
**metadata
}
)
]
)
logger.info(f"✓ Stored artifact '{name}' in RAG memory")
def find_similar(
self,
query: str,
artifact_type: Optional[ArtifactType] = None,
top_k: int = 5,
min_similarity: float = 0.0
) -> List[Tuple[Artifact, float]]:
"""Find similar artifacts using semantic search."""
# Generate query embedding
query_embedding = self._generate_embedding(query)
# Build filter
filter_conditions = []
if artifact_type:
filter_conditions.append(
FieldCondition(
key="type",
match=MatchValue(value=artifact_type.value)
)
)
search_filter = Filter(must=filter_conditions) if filter_conditions else None
# Search Qdrant
results = self.qdrant.search(
collection_name=self.collection_name,
query_vector=query_embedding,
query_filter=search_filter,
limit=top_k
)
# Convert to artifacts with similarity scores
artifacts = []
for result in results:
if result.score >= min_similarity:
artifact = self._payload_to_artifact(result.payload)
artifacts.append((artifact, result.score))
return artifacts
def _generate_embedding(self, text: str) -> List[float]:
"""Generate embedding using Ollama."""
response = self.ollama_client.embed(
model=self.embedding_model,
prompt=text
)
return response["embedding"]
Generación basada en especificaciones
def find_best_tool(
self,
task_description: str,
min_quality: float = 0.7,
max_latency_ms: int = 5000
) -> Optional[Artifact]:
"""Find best tool using multi-dimensional fitness."""
# Search with fitness filters
results = self.qdrant.search(
collection_name=self.collection_name,
query_vector=self._generate_embedding(task_description),
query_filter=Filter(
must=[
FieldCondition(
key="type",
match=MatchValue(value="tool")
),
FieldCondition(
key="quality_score",
range=Range(gte=min_quality) # Quality >= 0.7
),
FieldCondition(
key="latency_ms",
range=Range(lte=max_latency_ms) # Latency <= 5000ms
)
]
),
limit=1
)
return results[0] if results else None
Enfoque tradicional (propenso a la alucinación):
# Traditional similarity: might give false positives
Task 1: "generate fibonacci sequence"
Task 2: "generate fibonacci backwards"
Similarity: 77% ← High, but these need DIFFERENT code!
# Semantic classification
Triage LLM analyzes both tasks:
SAME → Reuse as-is (just typos/wording differences)
RELATED → Use as template, modify (same domain, different variation)
DIFFERENT → Generate from scratch (completely different problem)
Result: "RELATED - same core algorithm, reversed output"
Action: Load fibonacci code as template, modify to reverse
Enfoque de las EDS:
Memoria RAG: aprender del pasado
(velocidad, coste, calidad, latencia)
# Original (stored in RAG):
def fibonacci_sequence(n):
if n <= 0:
return []
elif n == 1:
return [0]
sequence = [0, 1]
for i in range(2, n):
sequence.append(sequence[i-1] + sequence[i-2])
return sequence
# New request: "fibonacci backwards"
# DSE finds original, classifies as RELATED
# Generates modification spec: "Return reversed sequence"
# Modified version:
def fibonacci_backwards(n):
if n <= 0:
return []
elif n == 1:
return [0]
sequence = [0, 1]
for i in range(2, n):
sequence.append(sequence[i-1] + sequence[i-2])
return sequence[::-1] # ← Only change needed!
Permite la reutilización futura
Implementación de memoria RAG:
graph LR
A[Tool/Artifact] --> B[Semantic Similarity<br/>0-100]
A --> C[Speed Tier<br/>±20 points]
A --> D[Cost Tier<br/>±15 points]
A --> E[Quality Score<br/>±15 points]
A --> F[Historical Success<br/>±10 points]
A --> G[Latency Metrics<br/>±15 points]
A --> H[Reuse Bonus<br/>±30 points]
B --> I[Final Fitness Score]
C --> I
D --> I
E --> I
F --> I
G --> I
H --> I
I --> J{Selection}
J -->|Highest Score| K[Use This Tool]
style I stroke:#ffeb3b,stroke-width:3px
style K stroke:#4caf50,stroke-width:3px
Filtro basado en la aptitud:
def calculate_fitness(tool, similarity_score):
fitness = similarity_score * 100 # Base: 0-100
# Speed tier bonus
if tool.speed_tier == 'very-fast':
fitness += 20
elif tool.speed_tier == 'fast':
fitness += 10
elif tool.speed_tier == 'slow':
fitness -= 10
# Cost tier bonus
if tool.cost_tier == 'free':
fitness += 15
elif tool.cost_tier == 'low':
fitness += 10
elif tool.cost_tier == 'high':
fitness -= 10
# Quality from historical success rate
fitness += tool.quality_score * 10
# Latency metrics
if tool.avg_latency_ms < 100:
fitness += 15 # Very fast
elif tool.avg_latency_ms > 5000:
fitness -= 10 # Too slow
# Reuse bonus
if similarity >= 0.90:
fitness += 30 # Exact match - huge bonus!
elif similarity >= 0.70:
fitness += 15 # Template reuse
return fitness
Aquí es donde se pone inteligente.**Cuando pides algo similar a una tarea anterior, el DSE no solo mide la similitud del texto, sino que utiliza la clasificación semántica:**Esto resuelve el falso problema positivo al tiempo que permite la reutilización de código inteligente.
Cuando el DSE encuentra una tarea RELACIONADA, no se regenera de cero.
sequenceDiagram
participant N as Node v1.0.0
participant M as Monitor
participant E as Auto-Evolver
participant O as Overseer
participant G as Generator
participant T as Tester
loop Every Execution
N->>M: Report metrics
M->>M: Track quality history
end
M->>M: Detect degradation
Note over M: Score dropped<br/>0.95 → 0.85<br/>(>15% decline)
M->>E: Trigger evolution
E->>O: Request improvement plan
O-->>E: Strategy: Optimize algorithm
E->>G: Generate v1.1.0
G-->>E: Improved code
E->>T: A/B Test
T->>N: Run v1.0.0
N-->>T: Score: 0.85
T->>E: Run v1.1.0
E-->>T: Score: 0.96
T->>E: v1.1.0 wins!
E->>N: Promote v1.1.0
E->>M: Update lineage
M->>M: Archive v1.0.0
Note over N: Now running v1.1.0<br/>Better performance<br/>Same functionality
En su lugar:
class AutoEvolver:
"""Monitors and evolves code performance automatically."""
def __init__(
self,
performance_threshold: float = 0.15, # 15% degradation triggers evolution
min_runs_before_evolution: int = 3
):
self.performance_threshold = performance_threshold
self.min_runs = min_runs_before_evolution
self.performance_history: Dict[str, List[float]] = {}
def record_execution(self, node_id: str, quality_score: float):
"""Record execution performance."""
if node_id not in self.performance_history:
self.performance_history[node_id] = []
self.performance_history[node_id].append(quality_score)
# Check if evolution needed
if len(self.performance_history[node_id]) >= self.min_runs:
if self._should_evolve(node_id):
self.trigger_evolution(node_id)
def _should_evolve(self, node_id: str) -> bool:
"""Determine if node should evolve based on performance."""
history = self.performance_history[node_id]
if len(history) < self.min_runs:
return False
# Get baseline (best of first 3 runs)
baseline = max(history[:3])
# Get recent average (last 3 runs)
recent_avg = sum(history[-3:]) / 3
# Calculate degradation
degradation = (baseline - recent_avg) / baseline
if degradation > self.performance_threshold:
logger.warning(
f"Node {node_id} degraded {degradation*100:.1f}% "
f"(baseline: {baseline:.2f}, recent: {recent_avg:.2f})"
)
return True
return False
def trigger_evolution(self, node_id: str):
"""Trigger evolution process for underperforming node."""
logger.info(f"Triggering evolution for {node_id}")
# Load current node
node = self.registry.get_node(node_id)
current_code = self.runner.load_code(node_id)
# Get performance metrics
metrics = node.get("metrics", {})
history = self.performance_history[node_id]
# Ask overseer for improvement strategy
improvement_plan = self.overseer.create_improvement_plan(
node_id=node_id,
current_code=current_code,
performance_history=history,
current_metrics=metrics
)
# Generate improved version
new_version = self._increment_version(node.get("version", "1.0.0"))
new_code = self.generator.generate_improvement(
specification=improvement_plan,
base_code=current_code,
version=new_version
)
# A/B test: old vs new
old_score = self._test_version(node_id, current_code)
new_score = self._test_version(f"{node_id}_v{new_version}", new_code)
logger.info(
f"A/B Test Results: "
f"v{node['version']}: {old_score:.2f} | "
f"v{new_version}: {new_score:.2f}"
)
# Keep better version
if new_score > old_score:
logger.info(f"✓ Promoting v{new_version} (improvement: {new_score - old_score:.2f})")
self._promote_version(node_id, new_version, new_code)
else:
logger.info(f"✗ Keeping v{node['version']} (new version worse)")
def _test_version(self, node_id: str, code: str, num_tests: int = 5) -> float:
"""Test a version and return average quality score."""
scores = []
for i in range(num_tests):
stdout, stderr, metrics = self.runner.run_node(node_id, test_input)
result = self.evaluator.evaluate(stdout, stderr, metrics)
scores.append(result.get("overall_score", 0.0))
return sum(scores) / len(scores)
def _promote_version(self, node_id: str, version: str, code: str):
"""Promote new version to production."""
# Archive old version
old_node = self.registry.get_node(node_id)
self.registry.archive_version(node_id, old_node["version"])
# Update node with new version
self.runner.save_code(node_id, code)
self.registry.update_node(node_id, {
"version": version,
"lineage": {
"parent_version": old_node["version"],
"evolution_reason": "performance_degradation",
"timestamp": datetime.utcnow().isoformat()
}
})
# Reset performance tracking
self.performance_history[node_id] = []
logger.info(f"✓ Node {node_id} evolved to v{version}")
Carga el código existente
Node: text_processor_v1.0.0
Run 1: Score 0.95 ✓
Run 2: Score 0.94 ✓
Run 3: Score 0.92 ✓
Run 4: Score 0.88 ← Degradation detected!
Run 5: Score 0.85 ← 15% drop, trigger evolution!
Auto-Evolution Process:
1. Analyze performance history
2. Generate improvement specification
3. Create text_processor_v1.1.0
4. A/B test: v1.0.0 vs v1.1.0
5. Keep winner, archive loser
Result: v1.1.0 scores 0.96
Action: Promoted to primary version
como plantilla acreditada
: "Mantener algoritmo de núcleo, añadir inversión"
graph TD
A[Complex Task:<br/>Build REST API] --> B[Level 1: Workflow]
B --> C[Design API Schema]
B --> D[Implement Auth]
B --> E[Create Endpoints]
B --> F[Add Error Handling]
B --> G[Write Tests]
C --> C1[Level 2: Nodeplan<br/>Schema validator]
C --> C2[Level 2: Nodeplan<br/>Schema generator]
D --> D1[Level 2: Nodeplan<br/>JWT handler]
D --> D2[Level 2: Nodeplan<br/>User validator]
E --> E1[Level 2: Nodeplan<br/>GET handler]
E --> E2[Level 2: Nodeplan<br/>POST handler]
E --> E3[Level 2: Nodeplan<br/>PUT/DELETE]
C1 --> C1a[Level 3: Function<br/>validate_field]
C1 --> C1b[Level 3: Function<br/>check_types]
D1 --> D1a[Level 3: Function<br/>encode_token]
D1 --> D1b[Level 3: Function<br/>decode_token]
E1 --> E1a[Level 3: Function<br/>parse_params]
E1 --> E1b[Level 3: Function<br/>serialize_response]
style A stroke:#ff6b6b,stroke-width:3px
style B stroke:#4ecdc4,stroke-width:3px
style C stroke:#45b7d1,stroke-width:3px
style D stroke:#45b7d1,stroke-width:3px
style E stroke:#45b7d1,stroke-width:3px
style C1 stroke:#96ceb4,stroke-width:3px
style D1 stroke:#96ceb4,stroke-width:3px
style E1 stroke:#96ceb4,stroke-width:3px
style C1a stroke:#dfe6e9,stroke-width:3px
style D1a stroke:#dfe6e9,stroke-width:3px
style E1a stroke:#dfe6e9,stroke-width:3px
Generador modifica plantilla
class HierarchicalEvolver:
"""Evolves complex workflows through hierarchical decomposition."""
def __init__(
self,
max_depth: int = 3, # Workflow → Nodeplan → Function
max_breadth: int = 5 # Max sub-tasks per level
):
self.max_depth = max_depth
self.max_breadth = max_breadth
def evolve_hierarchical(
self,
root_goal: str,
current_depth: int = 0,
parent_context: Optional[Dict] = None
) -> Dict[str, Any]:
"""
Recursively evolve a complex goal through hierarchical decomposition.
Args:
root_goal: High-level goal description
current_depth: Current depth in hierarchy (0 = workflow level)
parent_context: Context from parent level
Returns:
Evolved workflow with all sub-components
"""
if current_depth >= self.max_depth:
# Base case: generate atomic function
return self._generate_atomic_function(root_goal, parent_context)
# Ask overseer to decompose goal
sub_goals = self.overseer.decompose_goal(
goal=root_goal,
max_sub_goals=self.max_breadth,
context=parent_context
)
logger.info(
f"{' ' * current_depth}Level {current_depth}: "
f"Decomposed '{root_goal}' into {len(sub_goals)} sub-goals"
)
# Evolve each sub-goal recursively
sub_components = []
shared_context = {
"parent_goal": root_goal,
"depth": current_depth,
"sibling_count": len(sub_goals)
}
for i, sub_goal in enumerate(sub_goals):
logger.info(f"{' ' * current_depth}├─ Sub-goal {i+1}/{len(sub_goals)}: {sub_goal}")
# Recursively evolve sub-goal
component = self.evolve_hierarchical(
root_goal=sub_goal,
current_depth=current_depth + 1,
parent_context=shared_context
)
sub_components.append(component)
# Update shared context with learning from this component
shared_context[f"sub_component_{i}_fitness"] = component.get("fitness", 0.0)
# Create workflow/nodeplan from sub-components
workflow = self._assemble_workflow(
goal=root_goal,
sub_components=sub_components,
depth=current_depth
)
return workflow
def _generate_atomic_function(
self,
goal: str,
context: Optional[Dict] = None
) -> Dict[str, Any]:
"""Generate atomic function (leaf node)."""
# Check RAG for similar functions
similar = self.rag.find_similar(
query=goal,
artifact_type=ArtifactType.FUNCTION,
top_k=3
)
if similar and similar[0][1] > 0.85:
# High similarity: reuse
logger.info(f" ✓ Reusing similar function: {similar[0][0].name}")
return similar[0][0].to_dict()
# Generate new function
specification = self.overseer.create_plan(
task_description=goal,
context=context
)
code = self.generator.generate_code(specification)
stdout, stderr, metrics = self.runner.run_node(code, test_input={})
evaluation = self.evaluator.evaluate(stdout, stderr, metrics)
# Store in RAG for future reuse
self.rag.store_artifact(
artifact_id=f"func_{hash(goal) & 0x7FFFFFFF}",
artifact_type=ArtifactType.FUNCTION,
name=goal,
content=code,
tags=["hierarchical", f"depth_{context.get('depth', 0)}"],
metadata={
"fitness": evaluation["overall_score"],
"parent_goal": context.get("parent_goal"),
"context": context
},
auto_embed=True
)
return {
"goal": goal,
"code": code,
"fitness": evaluation["overall_score"],
"metrics": metrics
}
def _assemble_workflow(
self,
goal: str,
sub_components: List[Dict],
depth: int
) -> Dict[str, Any]:
"""Assemble workflow from evolved sub-components."""
# Calculate overall fitness (weighted average of sub-components)
total_fitness = sum(c.get("fitness", 0.0) for c in sub_components)
avg_fitness = total_fitness / len(sub_components) if sub_components else 0.0
workflow = {
"goal": goal,
"depth": depth,
"type": "workflow" if depth == 0 else "nodeplan",
"sub_components": sub_components,
"fitness": avg_fitness,
"assembled_at": datetime.utcnow().isoformat()
}
# Store workflow in RAG
workflow_type = ArtifactType.WORKFLOW if depth == 0 else ArtifactType.SUB_WORKFLOW
self.rag.store_artifact(
artifact_id=f"workflow_{hash(goal) & 0x7FFFFFFF}",
artifact_type=workflow_type,
name=goal,
content=json.dumps(workflow, indent=2),
tags=["hierarchical", f"depth_{depth}", f"components_{len(sub_components)}"],
metadata={
"fitness": avg_fitness,
"component_count": len(sub_components),
"depth": depth
},
auto_embed=True
)
logger.info(
f"{' ' * depth}✓ Assembled {workflow['type']}: '{goal}' "
f"(fitness: {avg_fitness:.2f}, components: {len(sub_components)})"
)
return workflow
en lugar de escribir un nuevo código
Resultado
Level 1 (Workflow):
"Build a REST API"
↓
Level 2 (Nodeplans):
├─ Design API schema
├─ Implement authentication
├─ Create CRUD endpoints
├─ Add error handling
└─ Write integration tests
↓
Level 3 (Functions):
Each nodeplan breaks into individual functions
: Código más rápido, más fiable, reutilizado probado
Esta reutilización acelera dramáticamente la generación y mejora la fiabilidad.
graph TB
Start([User Request]) --> RAG1[RAG: Search Similar]
RAG1 --> Class{Semantic<br/>Classification}
Class -->|SAME<br/>similarity > 0.9| Reuse[Reuse As-Is]
Class -->|RELATED<br/>0.7-0.9| Template[Template Modification]
Class -->|DIFFERENT<br/>< 0.7| Generate[Generate from Scratch]
Reuse --> Execute
Template --> Overseer1[Overseer: Modification Plan]
Generate --> Overseer2[Overseer: Full Plan]
Overseer1 --> Generator1[Generator: Modify Template]
Overseer2 --> Generator2[Generator: New Code]
Generator1 --> Execute[Execute in Sandbox]
Generator2 --> Execute
Execute --> Triage{Triage<br/>Pass/Fail?}
Triage -->|Fail| Escalate[Escalate to<br/>qwen2.5-coder]
Escalate --> Execute
Triage -->|Pass| Evaluator[Evaluator:<br/>Multi-Dimensional Scoring]
Evaluator --> Fitness[Calculate Fitness Score]
Fitness --> Store[Store in RAG with<br/>Embedding + Metadata]
Store --> Monitor[Performance Monitor]
Monitor --> Degrade{Degradation<br/>Detected?}
Degrade -->|Yes >15%| Evolve[Auto-Evolution:<br/>Generate v1.x.x]
Degrade -->|No| Continue[Continue Monitoring]
Evolve --> ABTest[A/B Test:<br/>Old vs New]
ABTest --> Promote{New Better?}
Promote -->|Yes| Update[Promote New Version]
Promote -->|No| Keep[Keep Old Version]
Update --> Monitor
Keep --> Monitor
Continue --> End([Ready for Reuse])
style Start stroke:#e3f2fd,stroke-width:3px
style RAG1 stroke:#f3e5f5,stroke-width:3px
style Class stroke:#fff3e0,stroke-width:3px
style Reuse stroke:#e8f5e9,stroke-width:3px
style Execute stroke:#fce4ec,stroke-width:3px
style Evaluator stroke:#e1f5fe,stroke-width:3px
style Store stroke:#f1f8e9,stroke-width:3px
style Evolve stroke:#ffe0b2,stroke-width:3px
style End stroke:#e8eaf6,stroke-width:3px
Fitness multidimensional: Elegir la herramienta correcta
class DirectedSyntheticEvolution:
"""Complete DSE workflow orchestrator."""
def __init__(self, config: ConfigManager):
self.config = config
self.ollama = OllamaClient(config.ollama_url, config_manager=config)
self.rag = QdrantRAGMemory(
qdrant_url=config.qdrant_url,
ollama_client=self.ollama
)
self.tools = ToolsManager(
ollama_client=self.ollama,
rag_memory=self.rag
)
self.overseer = OverseerLLM(self.ollama, self.rag)
self.generator = CodeGenerator(self.ollama)
self.evaluator = Evaluator(self.ollama)
self.evolver = AutoEvolver(self.rag, self.overseer, self.generator)
def evolve(self, task_description: str) -> Dict[str, Any]:
"""Execute complete evolution workflow."""
logger.info(f"Starting evolution for: {task_description}")
# Step 1: RAG Search for similar solutions
similar = self.rag.find_similar(
query=task_description,
artifact_type=ArtifactType.FUNCTION,
top_k=3
)
# Step 2: Semantic Classification
if similar:
relationship = self._classify_relationship(
task_description,
similar[0][0].content,
similar[0][1]
)
else:
relationship = "DIFFERENT"
# Step 3: Choose generation strategy
if relationship == "SAME":
logger.info("✓ Exact match found - reusing as-is")
return similar[0][0].to_dict()
elif relationship == "RELATED":
logger.info("✓ Similar solution found - using as template")
plan = self.overseer.create_modification_plan(
task_description=task_description,
template_code=similar[0][0].content
)
code = self.generator.modify_template(plan, similar[0][0].content)
else: # DIFFERENT
logger.info("✓ No match - generating from scratch")
plan = self.overseer.create_plan(task_description)
code = self.generator.generate_code(plan)
# Step 4: Execute in sandbox
stdout, stderr, metrics = self.runner.run_node(code, test_input={})
# Step 5: Triage (quick check)
triage_result = self.evaluator.triage(metrics, targets={})
if triage_result["verdict"] == "fail":
# Escalate to better model
logger.warning("✗ Triage failed - escalating")
code = self._escalate(code, stderr, metrics)
stdout, stderr, metrics = self.runner.run_node(code, test_input={})
# Step 6: Comprehensive evaluation
evaluation = self.evaluator.evaluate(stdout, stderr, metrics)
# Step 7: Calculate fitness
fitness = self._calculate_fitness(evaluation, metrics)
# Step 8: Store in RAG
artifact_id = f"func_{hash(task_description) & 0x7FFFFFFF}"
self.rag.store_artifact(
artifact_id=artifact_id,
artifact_type=ArtifactType.FUNCTION,
name=task_description,
content=code,
tags=["evolved", "validated"],
metadata={
"quality_score": evaluation["overall_score"],
"latency_ms": metrics["latency_ms"],
"memory_mb": metrics["memory_mb"],
"fitness": fitness,
"relationship": relationship
},
auto_embed=True
)
logger.info(f"✓ Evolution complete - Fitness: {fitness:.2f}")
# Step 9: Start monitoring for future evolution
self.evolver.monitor(artifact_id, evaluation["overall_score"])
return {
"artifact_id": artifact_id,
"code": code,
"fitness": fitness,
"evaluation": evaluation,
"metrics": metrics,
"relationship": relationship
}
def _classify_relationship(
self,
new_task: str,
existing_task: str,
similarity: float
) -> str:
"""Use triage LLM to classify task relationship."""
if similarity < 0.7:
return "DIFFERENT"
prompt = f"""Compare these two tasks:
Task 1 (Existing): {existing_task}
Task 2 (Requested): {new_task}
Similarity Score: {similarity:.2f}
Classify relationship:
- SAME: Minor wording differences, same algorithm
- RELATED: Same domain, different variation
- DIFFERENT: Completely different problems
Answer with one word: SAME, RELATED, or DIFFERENT"""
response = self.ollama.generate(
model="tinyllama",
prompt=prompt,
model_key="triage"
)
for keyword in ["SAME", "RELATED", "DIFFERENT"]:
if keyword in response.upper():
return keyword
return "DIFFERENT" # Default fallback
def _calculate_fitness(
self,
evaluation: Dict,
metrics: Dict
) -> float:
"""Multi-dimensional fitness calculation."""
base_score = evaluation["overall_score"] * 100 # 0-100
# Speed bonus/penalty
if metrics["latency_ms"] < 100:
base_score += 15
elif metrics["latency_ms"] > 5000:
base_score -= 10
# Memory efficiency
if metrics["memory_mb"] < 10:
base_score += 10
elif metrics["memory_mb"] > 100:
base_score -= 5
# Exit code (must be 0)
if metrics["exit_code"] != 0:
base_score -= 20
return max(0, min(100, base_score)) # Clamp to 0-100
Aquí es donde el DSE se pone muy interesante.
Puesta en práctica del cálculo de la aptitud:
$ python chat_cli.py
CodeEvolver> generate Write a function to validate email addresses
Searching for relevant tools...
✓ Found validation specialist in RAG memory
Consulting overseer LLM (llama3) for approach...
✓ Strategy: Use regex-based validation with RFC 5322 compliance
Selecting best tool...
✓ Using specialized tool: Validation Expert (codellama)
Generating code...
✓ Code generation complete
Running unit tests...
✓ All tests passed (5/5)
Evaluating quality...
✓ Score: 0.96 (Excellent)
Node 'validate_email_addresses' created successfully!
Latency: 127ms | Memory: 2.1MB | Quality: 96%
CodeEvolver> run validate_email_addresses {"email": "[email protected]"}
✓ Execution successful
Output: {
"valid": true,
"email": "[email protected]",
"parts": {
"local": "test",
"domain": "example.com"
}
}
Esto significa que el DSE siempre elige el
El sistema evoluciona literalmente su propio código para mejorar el rendimiento.
rag_memory:
use_qdrant: true
qdrant_url: "http://localhost:6333"
collection_name: "code_evolver_artifacts"
No se necesita intervención humana.
Flujo de trabajo completo de la evolución
# Find high-quality, fast, low-cost solutions for "validation"
results = rag.find_similar(
query="validate user input",
filter={
"quality_tier": {"$in": ["excellent", "very-good"]},
"speed_tier": {"$in": ["very-fast", "fast"]},
"cost_tier": {"$in": ["free", "low"]}
},
top_k=5
)
Ejemplo de código de flujo de trabajo completo:
Memoria RAG
# Multi-model LLM routing with Ollama
from src import OllamaClient, ConfigManager
config = ConfigManager("config.yaml")
client = OllamaClient(config.ollama_url, config_manager=config)
# Different endpoints for different models
# Heavy planning on powerful CPU machine
# Code generation on GPU machine
# Fast triage on lightweight local instance
# RAG memory with Qdrant
from src import QdrantRAGMemory
rag = QdrantRAGMemory(
qdrant_url="http://localhost:6333",
collection_name="artifacts",
embedding_model="nomic-embed-text",
vector_size=768
)
# Tools with semantic selection
from src import ToolsManager
tools = ToolsManager(
config_manager=config,
ollama_client=client,
rag_memory=rag
)
# Complete workflow
workflow_result = evolver.evolve(
goal="Build email validation system",
max_iterations=10,
auto_evolve=True
)
Agentes especializadosconfig.yaml:
ollama:
base_url: "http://localhost:11434"
models:
overseer:
model: "llama3"
endpoint: "http://powerful-cpu:11434" # Strategic planning on powerful hardware
generator:
model: "codellama"
endpoint: "http://gpu-server:11434" # Code gen on GPU
evaluator:
model: "llama3"
endpoint: null # Local evaluation
triage:
model: "tinyllama"
endpoint: null # Fast local triage
embedding:
model: "nomic-embed-text"
vector_size: 768
execution:
default_timeout_ms: 5000
max_memory_mb: 256
max_retries: 3
auto_evolution:
enabled: true
performance_threshold: 0.15 # Trigger at 15% degradation
min_runs_before_evolution: 3
rag_memory:
use_qdrant: true
qdrant_url: "http://localhost:6333"
Latencia
- Múltiples llamadas LLM suman (¡aunque se está haciendo más rápido!)
- La escalada ayuda, pero no es perfecta
- Las entradas raras todavía pueden confundir el sistema
Especialización emergente
Mejora de la calidad
- Versiones posteriores de nodos a menudo superan a los originales
def process_text(text: str) -> str:
words = text.split()
result = []
for word in words:
if len(word) > 3:
result.append(word.upper())
else:
result.append(word.lower())
return ' '.join(result)
La arquitectura en la práctica
Aquí está la pila de tecnología real:
def process_text(text: str) -> str:
"""Process text with optimized string operations."""
if not text:
return ""
# Vectorized operation for better performance
return ' '.join(
word.upper() if len(word) > 3 else word.lower()
for word in text.split()
)
Ejemplo de configuración
Mundo real
Coincidencia exacta: ~1-2 segundos (reutilizar como está)
Interfaz de usuario de la web
Especialistas afinados
Mediano Plazo Registro distribuido
- Compartir soluciones entre equipos/organizaciones Despliegue en la nube
- Integraciones AWS/Azure/GCP Integración de Git
Sandboxing avanzado
# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh
# Pull models
ollama pull codellama
ollama pull llama3
ollama pull tinyllama
ollama pull nomic-embed-text
# Clone and run
git clone https://github.com/yourrepo/mostlylucid.dse
cd mostlylucid.dse/code_evolver
pip install -r requirements.txt
python chat_cli.py
- Docker/cgroups para un mejor aislamientoIdeas salvajes
Evolución adversa
Meta-evolución
Sistema evolucionando sus propias estrategias de evoluciónAprendizaje colaborativo- Múltiples instancias de DSE compartiendo descubrimientos
Lecciones aprendidasDespués de construir esta cosa, esto es lo que me sorprendió:
**1.**Cuestiones relativas a la especialización
**El uso de diferentes modelos para diferentes tareas (overseer vs generador vs evaluador) no era simplemente agradable, era esencial.**Tratando de utilizar un modelo para todo lo producido resultados notablemente peores.
**2.**La memoria es todo
La memoria RAG no es una característica, es LA característica.
Sin ella, sólo estás generando código en un bucle.Con él, el sistema realmente aprende y mejora.
3.
4.
La evolución realmente funcionaHonestamente no esperaba que la auto-evolución produjera un código mejor que la generación inicial.
Constantemente.
**Eso es salvaje.**5.
Latencia Compuestos Extrañamente
**Múltiples llamadas LLM parecen lentas al principio, pero a medida que la memoria RAG se llena, pulsas soluciones caché más a menudo, y todo el sistema se acelera.**Es contra-intuitivo pero observable.
Pruébalo tú mismo.
**Todo el asunto es de código abierto y se ejecuta localmente en Ollama:**Aviso:
Este es el código experimental.
**No está listo para la producción.**Ni siquiera está "buen código" listo.
Pero es un experimento fascinante de lo que es posible cuando combinas algoritmos evolutivos con sistemas LLM multi-agente.
Lo que esto realmente significa
Retrocedamos de los detalles técnicos y hagamos la incómoda pregunta:
¿Qué hemos construido aquí?
En la superficie, es un sistema de generación de código.
Pides una función, la genera, la almacena y la reutiliza más tarde.
Pero eso no es realmente lo que está pasando.
Lo que está pasando es
—no metafóricamente, sino literalmente.
What Works ✓
What's Rough ✗
What's Just Weird 🤔
Parte 5:
Presión evolutiva → Cultura y tradición
El sistema desarrolla "preferencias"—algunas herramientas para ciertas tareas, ciertos patrones para ciertos problemas.
Aprendí.
Parte 6:
¿Por qué no a nivel planetario?
Estoy diciendo que el gradiente es continuo de "evolucionar una función" a "evolucionar una civilización".
Y eso es... inquietante.
Lo que realmente funciona (Seamos honestos)
class OfflineOptimizer:
"""Analyzes historical execution data to find optimization opportunities."""
def analyze_execution_history(self, time_window: str = "7d"):
"""
Mine stored execution logs for patterns:
- Which overseer plans led to best outcomes?
- Which generator strategies minimized iterations?
- Which evaluation criteria correlated with long-term success?
"""
# Load historical data from each level
overseer_decisions = self.load_decisions("overseer", time_window)
generator_outputs = self.load_decisions("generator", time_window)
evaluator_scores = self.load_decisions("evaluator", time_window)
# Find correlations
optimal_patterns = self.mine_successful_patterns({
"planning": overseer_decisions,
"generation": generator_outputs,
"evaluation": evaluator_scores
})
# Update system strategies based on findings
self.apply_optimizations(optimal_patterns)
Después de semanas de experimentación, aquí está la verdad:
Aptitud multidimensional
class SpecialistTrainer:
"""Trains domain-specific models from evolved artifacts."""
def train_specialist(self, domain: str, min_artifacts: int = 1000):
"""
Extract high-quality artifacts from a domain and fine-tune a specialist.
Example: After generating 1000+ validation functions,
train a "ValidationSpecialist" model that's faster and better
than the general-purpose generator.
"""
# Get top-performing artifacts in domain
artifacts = self.rag.find_by_tags(
tags=[domain],
min_quality=0.85,
limit=min_artifacts
)
# Generate training data from successful patterns
training_data = self.extract_training_pairs(artifacts)
# Fine-tune base model (codellama → domain_specialist)
specialist_model = self.fine_tune(
base_model="codellama",
training_data=training_data,
output_name=f"{domain}_specialist"
)
# Register specialist in tool registry
self.tools.register_specialist(
domain=domain,
model=specialist_model,
fitness_threshold=0.90 # Only use if high confidence
)
Auto-evolución
- Necesita 16 GB de RAM mínimo, prefiere 32 GB
Cajas de borde
class GuildSystem:
"""Manages specialized committees of workflows, nodes, and functions."""
def form_guild(self, domain: str, task_type: str):
"""
Automatically assemble the best specialists for a task.
Example: "API validation guild" might include:
- Top 3 schema validators
- Top 2 security checkers
- Top 1 performance analyzer
Each votes on the solution. Best consensus wins.
"""
# Find top performers in domain
specialists = self.find_top_specialists(
domain=domain,
task_type=task_type,
top_k=5
)
# Create committee workflow
guild = Guild(
name=f"{domain}_{task_type}_guild",
members=specialists,
voting_strategy="weighted_by_fitness"
)
return guild
def execute_with_guild(self, guild: Guild, task: str):
"""Execute task with committee voting."""
# Each member proposes solution
proposals = []
for member in guild.members:
proposal = member.execute(task)
proposals.append({
"member": member,
"solution": proposal,
"fitness": member.historical_fitness
})
# Vote on best solution (weighted by past performance)
winning_proposal = self.consensus_vote(proposals)
# Store successful collaboration pattern
self.record_guild_success(guild, winning_proposal)
return winning_proposal
Las entradas raras todavía confunden el sistema ocasionalmente
Se hace más rápido.- Contraintuitivamente, a medida que se llena RAG, disminuye la latencia
Especialización emergente- Sistema desarrolla herramientas "expertos" para dominios sin programación explícita
La autocuración- Auto-evolución a veces corrige errores que no noté
Desnivel de calidad hacia arriba- La calidad media del código mejora con el tiempo
Convergencia de plantillas
class SensorSystem:
"""Provides objective truth to prevent hallucination."""
def __init__(self):
self.sensors = {
"web": WebSensor(), # Puppeteer + vision models
"api": APIResponseSensor(), # Actual HTTP validation
"database": DatabaseSensor(), # Query result verification
"file": FileSystemSensor(), # Actual file operations
"metrics": PerformanceSensor() # Real execution metrics
}
def validate_with_sensors(self, claim: str, sensor_type: str):
"""
Validate LLM output against objective reality.
Example:
LLM: "This API returns user data in JSON format"
Sensor: Actually calls API, checks response format
Result: True/False with actual data as proof
"""
sensor = self.sensors[sensor_type]
objective_result = sensor.measure(claim)
return {
"claim": claim,
"sensor_validation": objective_result,
"hallucination_detected": not objective_result["matches_claim"],
"objective_data": objective_result["measurements"]
}
class WebDesignSensor:
"""Example: Validate web designs with Puppeteer + vision models."""
async def validate_design(self, html: str, requirements: List[str]):
"""
Generate HTML → Render with Puppeteer → Screenshot → Vision model validation
"""
# Render the generated HTML
screenshot = await self.puppeteer.render(html)
# Use vision model to check requirements
vision_analysis = await self.vision_model.analyze(
image=screenshot,
requirements=requirements
)
# Objective measurements
lighthouse_scores = await self.lighthouse.audit(html)
return {
"visual_validation": vision_analysis,
"performance_metrics": lighthouse_scores,
"accessibility_score": lighthouse_scores["accessibility"],
"objective_truth": True # Not an LLM hallucination!
}
Ese último es fascinante y un poco espeluznante.
Mejor recuperación y escalada de errores
Interfaz de usuario web para el seguimiento de la evoluciónIntegración de herramientas expandidas (linters, formatters, scanners de seguridad)
A plazo medio (2025):
**Registro distribuido (compartir soluciones entre equipos)**Herramientas de implementación en la nube
**Integración de Git (control de versión para código evolucionado)**Sandboxing avanzado (aislamiento Docker/cgroups)Optimización del borde (flujos de trabajo optimizados para dispositivos más pequeños)Principales mejoras arquitectónicas:
**1.**Optimización sin conexión y aprendizaje continuo
**El sistema actualmente optimiza en tiempo real durante la ejecución.**Pero, ¿y si pudiera aprender fuera de línea de los datos almacenados de petición/respuesta?
**Esto permite:**Aprendizaje por lotes
- Mejorar las estrategias basadas en miles de ejecuciones anterioresDescubrimiento de patrones
Encontrar correlaciones no obvias en lo que funciona
class UniversalToolOrchestrator:
"""Integrates any tool type - LLMs, APIs, CLI tools, services."""
def __init__(self):
self.tool_registry = {
"llm_tools": {}, # Language models
"api_tools": {}, # OpenAPI endpoints
"cli_tools": {}, # Command-line utilities
"service_tools": {}, # Long-running services (translation, etc.)
"validation_tools": {} # Code quality, security, compliance
}
def register_openapi_tool(self, name: str, spec_url: str):
"""
Register any OpenAPI-compatible endpoint as a tool.
The overseer can then select this tool and call it with appropriate parameters.
"""
# Fetch and parse OpenAPI spec
spec = self.fetch_openapi_spec(spec_url)
tool = {
"name": name,
"type": "openapi",
"spec": spec,
"endpoints": self.parse_endpoints(spec),
"schemas": self.parse_schemas(spec)
}
self.tool_registry["api_tools"][name] = tool
logger.info(f"Registered OpenAPI tool: {name} with {len(tool['endpoints'])} endpoints")
def register_translation_service(self, name: str, endpoint: str):
"""
Register translation service like Mostlylucid NMT.
Example: Neural machine translation for content localization
"""
tool = {
"name": name,
"type": "translation",
"endpoint": endpoint,
"capabilities": {
"languages": ["en", "es", "fr", "de", "ja", "zh"],
"formats": ["markdown", "html", "plain"],
"max_length": 50000
}
}
self.tool_registry["service_tools"][name] = tool
def overseer_selects_tool(self, task: str) -> str:
"""
Overseer analyzes task and selects appropriate tool(s).
Example tasks:
- "Translate this to Spanish" → Select translation service
- "Validate API endpoint" → Select OpenAPI validator
- "Format Python code" → Select black formatter
- "Generate SQL schema" → Select database LLM specialist
"""
# Ask overseer which tool to use
tool_selection = self.overseer.select_tool(
task_description=task,
available_tools=self.get_all_tools(),
context={"current_workflow": "code_generation"}
)
selected_tool = self.tool_registry[tool_selection["category"]][tool_selection["name"]]
return selected_tool
def execute_openapi_tool(self, tool: Dict, operation: str, params: Dict):
"""
Execute OpenAPI endpoint selected by overseer.
The overseer provides:
- Which endpoint to call
- What parameters to pass
- Expected response format
The system then executes and validates the response.
"""
endpoint = tool["endpoints"][operation]
# Build request from OpenAPI spec
request = self.build_request_from_spec(
endpoint=endpoint,
params=params,
spec=tool["spec"]
)
# Execute with safety checks
response = self.safe_api_call(
url=request["url"],
method=request["method"],
headers=request["headers"],
body=request["body"]
)
# Validate response against spec
validation = self.validate_response_against_spec(
response=response,
expected_schema=endpoint["response_schema"]
)
return {
"success": validation["valid"],
"data": response,
"validation": validation
}
class LanguageToolIntegration:
"""Example: Integrating CLI validation tools."""
def validate_code(self, code: str, language: str):
"""Use language-specific toolchains for validation."""
tools = {
"python": [
("black", "formatting"),
("mypy", "type_checking"),
("pylint", "linting"),
("bandit", "security"),
("pytest", "testing")
],
"javascript": [
("prettier", "formatting"),
("eslint", "linting"),
("typescript", "type_checking"),
("jest", "testing")
],
"go": [
("gofmt", "formatting"),
("go vet", "linting"),
("golangci-lint", "comprehensive"),
("go test", "testing")
]
}
results = {}
for tool, category in tools.get(language, []):
results[category] = self.run_tool(tool, code)
# Aggregate into fitness score
return self.calculate_tool_fitness(results)
Refinamiento de la estrategia
# Register Mostlylucid NMT translation service
orchestrator.register_translation_service(
name="mostlylucid_nmt",
endpoint="http://translation-service:5000"
)
# Overseer decides to use it for a task
task = "Translate this blog post to Spanish"
# System selects translation tool
tool = orchestrator.overseer_selects_tool(task)
# Execute translation
result = orchestrator.execute_tool(
tool=tool,
params={
"text": blog_post_content,
"source_lang": "en",
"target_lang": "es",
"format": "markdown"
}
)
- Actualizar la heurística de planificación basada en el éxito histórico
# Register any OpenAPI-compatible service
orchestrator.register_openapi_tool(
name="weather_api",
spec_url="https://api.weather.com/openapi.json"
)
# Overseer can now select this tool for weather-related tasks
# The system automatically:
# 1. Reads the OpenAPI spec
# 2. Understands available endpoints
# 3. Knows required parameters
# 4. Validates responses against schema
Enrutamiento predictivo
Aprenda qué modelos funcionan mejor para qué tipos de tareas
LLM especializados, autoformados
El sistema utiliza actualmente modelos de uso general.
Pero, ¿y si pudiera capacitar a sus propios especialistas?
Esto crea:
Inferencia más rápida
tools:
nmt_translator:
name: "NMT Translation Service"
type: "openapi"
description: "Neural Machine Translation service for translating text between languages"
# Performance/cost metadata for intelligent tool selection
cost_tier: "low" # Helps planner choose appropriate tools
speed_tier: "very-fast" # Fast local API
quality_tier: "good" # Good but needs validation
max_output_length: "long" # Can handle long texts
# OpenAPI configuration
openapi:
spec_url: "http://localhost:8000/openapi.json"
base_url: "http://localhost:8000"
# Optional authentication
auth:
type: "bearer" # bearer | api_key | basic
token: "your-api-key-here"
# Python code template for using this API
code_template: |
import requests
import json
def translate_text(text, source_lang="en", target_lang="es"):
url = "http://localhost:8000/translate"
payload = {"text": text, "source_lang": source_lang, "target_lang": target_lang}
response = requests.post(url, json=payload)
response.raise_for_status()
return response.json().get("translated_text", "")
tags: ["translation", "nmt", "neural", "languages", "openapi", "api"]
- Modelos más pequeños y enfocados para dominios específicos
Inteligencia colectiva
tools:
# Static analysis
pylint_checker:
name: "Pylint Code Quality Checker"
type: "executable"
description: "Runs pylint static analysis on Python code"
executable:
command: "pylint"
args: ["--output-format=text", "--score=yes", "{source_file}"]
tags: ["python", "static-analysis", "quality", "linting"]
# Type checking
mypy_type_checker:
name: "MyPy Type Checker"
type: "executable"
executable:
command: "mypy"
args: ["--strict", "--show-error-codes", "{source_file}"]
tags: ["python", "type-checking", "static-analysis"]
# Security scanning
bandit_security:
name: "Bandit Security Scanner"
type: "executable"
executable:
command: "bandit"
args: ["-r", "{source_file}"]
tags: ["python", "security", "vulnerability"]
# Unit testing
pytest_runner:
name: "Pytest Test Runner"
type: "executable"
executable:
command: "pytest"
args: ["-v", "--tb=short", "{test_file}"]
tags: ["python", "testing", "pytest"]
Robustia
Herramientas y validación de terceros
Aquí hay algo importante:
para la traducción automática neuronal
Endpoints OpenAPI
class EdgeOptimizer:
"""Generates lightweight workflows for edge deployment."""
def create_edge_version(self, workflow_id: str, constraints: Dict):
"""
Take a successful workflow and create optimized 'child' version.
Constraints example:
{
"max_memory_mb": 512,
"max_latency_ms": 100,
"available_models": ["tinyllama", "phi-2"],
"target_device": "raspberry-pi"
}
"""
# Load parent workflow
parent = self.registry.get_workflow(workflow_id)
# Analyze what can be simplified
optimization_plan = self.overseer.create_edge_plan(
workflow=parent,
constraints=constraints
)
# Generate child workflow
child = self.generator.generate_optimized_child(
parent=parent,
plan=optimization_plan,
constraints=constraints
)
# Test on target device simulator
edge_performance = self.test_edge_deployment(child, constraints)
if edge_performance["meets_constraints"]:
self.registry.register_child_workflow(
parent_id=workflow_id,
child=child,
lineage="edge_optimization",
constraints=constraints
)
return child
Cualquier API REST con una especificación OpenAPI
Herramientas CLI- Lintes, formatos, compiladores
Sensores- Hardware/software que mide la realidad objetiva
Validadores- Comprobadores de tipo, escáneres de seguridad, herramientas de cumplimiento
**El superintendente puede seleccionar CUALQUIER de estos para realizar operaciones, siempre y cuando tengan una especificación que el sistema pueda entender.**Ejemplo del mundo real: Integración de la traducción
Ejemplo de integración OpenAPI:
Por qué esto importa:
class GuardrailSystem:
"""Prevents autonomous system from harmful operations."""
def __init__(self):
self.safety_policies = {
"filesystem": FilesystemGuardrails(),
"network": NetworkGuardrails(),
"execution": ExecutionGuardrails(),
"data": DataGuardrails()
}
def validate_operation(self, operation: Dict) -> Dict[str, Any]:
"""
Validate any system operation against safety policies.
Returns: {
"allowed": bool,
"reason": str,
"sanitized_operation": Dict # Safe version if modifications needed
}
"""
operation_type = operation["type"]
policy = self.safety_policies.get(operation_type)
if not policy:
return {"allowed": False, "reason": "Unknown operation type"}
return policy.validate(operation)
class FilesystemGuardrails:
"""Prevent dangerous file operations."""
def __init__(self):
self.allowed_paths = [
"/workspace/artifacts/",
"/workspace/generated/",
"/tmp/dse_sandbox/"
]
self.forbidden_patterns = [
"rm -rf /",
"dd if=/dev/zero",
":(){ :|:& };:", # Fork bomb
"chmod 777",
"chown root"
]
self.forbidden_paths = [
"/",
"/etc",
"/bin",
"/usr",
"/sys",
"/proc",
"~/.ssh",
"~/.aws",
"/var/lib/docker"
]
def validate(self, operation: Dict) -> Dict[str, Any]:
"""Validate filesystem operations."""
path = operation.get("path", "")
action = operation.get("action", "")
content = operation.get("content", "")
# Check if deleting/modifying system files
if any(path.startswith(forbidden) for forbidden in self.forbidden_paths):
return {
"allowed": False,
"reason": f"Cannot modify system path: {path}",
"severity": "CRITICAL"
}
# Check for dangerous commands in file content
for pattern in self.forbidden_patterns:
if pattern in content:
return {
"allowed": False,
"reason": f"Dangerous pattern detected: {pattern}",
"severity": "CRITICAL"
}
# Enforce write restrictions to allowed paths only
if action in ["write", "delete", "modify"]:
if not any(path.startswith(allowed) for allowed in self.allowed_paths):
return {
"allowed": False,
"reason": f"Write not allowed outside workspace: {path}",
"severity": "HIGH"
}
# Check for self-deletion attempts
if "dse" in path or "evolver" in path:
if action == "delete":
return {
"allowed": False,
"reason": "System cannot delete its own core files",
"severity": "CRITICAL"
}
return {"allowed": True, "reason": "Safe operation"}
class NetworkGuardrails:
"""Prevent malicious network operations."""
def __init__(self):
self.allowed_hosts = [
"localhost",
"127.0.0.1",
"ollama-server",
"qdrant-server"
]
self.forbidden_actions = [
"port_scan",
"ddos",
"brute_force",
"sql_injection",
"xss_attack"
]
# Rate limiting
self.rate_limits = {
"requests_per_minute": 100,
"requests_per_host": 10
}
def validate(self, operation: Dict) -> Dict[str, Any]:
"""Validate network operations."""
host = operation.get("host", "")
action = operation.get("action", "")
payload = operation.get("payload", "")
# Only allow connections to whitelisted hosts
if host not in self.allowed_hosts:
# Check if it's a documented API endpoint
if not self._is_approved_external_api(host):
return {
"allowed": False,
"reason": f"Connections to {host} not allowed",
"severity": "HIGH"
}
# Check for attack patterns
for forbidden in self.forbidden_actions:
if forbidden in action.lower():
return {
"allowed": False,
"reason": f"Forbidden network action: {forbidden}",
"severity": "CRITICAL"
}
# Check payload for injection attempts
if self._contains_injection_pattern(payload):
return {
"allowed": False,
"reason": "Potential injection attack detected",
"severity": "CRITICAL"
}
# Rate limiting check
if self._exceeds_rate_limit(host):
return {
"allowed": False,
"reason": "Rate limit exceeded",
"severity": "MEDIUM"
}
return {"allowed": True, "reason": "Safe network operation"}
def _contains_injection_pattern(self, payload: str) -> bool:
"""Detect SQL injection, XSS, command injection patterns."""
dangerous_patterns = [
"' OR '1'='1",
"<script>",
"$(rm -rf",
"; DROP TABLE",
"../../etc/passwd",
"${jndi:ldap://", # Log4j
"eval(",
"exec("
]
return any(pattern in payload for pattern in dangerous_patterns)
class ExecutionGuardrails:
"""Prevent dangerous code execution."""
def __init__(self):
self.forbidden_imports = [
"os.system",
"subprocess.Popen",
"eval",
"exec",
"compile",
"__import__",
"ctypes"
]
self.allowed_modules = [
"json", "re", "math", "datetime",
"collections", "itertools", "functools",
"typing", "dataclasses"
]
def validate(self, operation: Dict) -> Dict[str, Any]:
"""Validate code before execution."""
code = operation.get("code", "")
language = operation.get("language", "python")
# AST analysis for Python
if language == "python":
try:
tree = ast.parse(code)
violations = self._analyze_ast(tree)
if violations:
return {
"allowed": False,
"reason": f"Code violations: {violations}",
"severity": "CRITICAL"
}
except SyntaxError as e:
return {
"allowed": False,
"reason": f"Syntax error: {e}",
"severity": "LOW"
}
# Check for forbidden patterns
for forbidden in self.forbidden_imports:
if forbidden in code:
return {
"allowed": False,
"reason": f"Forbidden import/function: {forbidden}",
"severity": "CRITICAL"
}
# Resource limits
if len(code) > 50000: # 50KB limit
return {
"allowed": False,
"reason": "Code size exceeds limit",
"severity": "MEDIUM"
}
return {"allowed": True, "reason": "Safe code"}
def _analyze_ast(self, tree) -> List[str]:
"""Analyze AST for dangerous patterns."""
violations = []
for node in ast.walk(tree):
# Check for eval/exec
if isinstance(node, ast.Call):
if isinstance(node.func, ast.Name):
if node.func.id in ['eval', 'exec', 'compile']:
violations.append(f"Dangerous function: {node.func.id}")
# Check for unsafe imports
if isinstance(node, ast.Import):
for alias in node.names:
if alias.name in ['os', 'subprocess', 'sys']:
violations.append(f"Potentially unsafe import: {alias.name}")
return violations
class DataGuardrails:
"""Prevent data exfiltration and privacy violations."""
def __init__(self):
self.pii_patterns = [
r'\b\d{3}-\d{2}-\d{4}\b', # SSN
r'\b\d{16}\b', # Credit card
r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b', # Email
r'\b\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\b' # IP address
]
def validate(self, operation: Dict) -> Dict[str, Any]:
"""Validate data operations."""
data = operation.get("data", "")
action = operation.get("action", "")
destination = operation.get("destination", "")
# Check for PII in data being sent externally
if action == "send" and destination.startswith("http"):
if self._contains_pii(data):
return {
"allowed": False,
"reason": "Cannot send PII to external endpoint",
"severity": "CRITICAL"
}
# Prevent exfiltration of system secrets
if self._contains_secrets(data):
return {
"allowed": False,
"reason": "Cannot transmit system secrets",
"severity": "CRITICAL"
}
return {"allowed": True, "reason": "Safe data operation"}
def _contains_pii(self, data: str) -> bool:
"""Check for personally identifiable information."""
import re
for pattern in self.pii_patterns:
if re.search(pattern, data):
return True
return False
def _contains_secrets(self, data: str) -> bool:
"""Check for API keys, tokens, passwords."""
secret_indicators = [
"api_key", "api-key", "apikey",
"secret", "password", "passwd",
"token", "auth", "credential",
"private_key", "aws_access"
]
data_lower = data.lower()
return any(indicator in data_lower for indicator in secret_indicators)
class SafetyMonitor:
"""Continuous monitoring and emergency shutdown."""
def __init__(self, guardrails: GuardrailSystem):
self.guardrails = guardrails
self.violation_history = []
self.threat_threshold = 3 # Number of violations before shutdown
def monitor_operation(self, operation: Dict) -> Dict[str, Any]:
"""Monitor every system operation."""
# Pre-execution validation
validation = self.guardrails.validate_operation(operation)
if not validation["allowed"]:
self.violation_history.append({
"timestamp": datetime.utcnow().isoformat(),
"operation": operation,
"violation": validation,
"severity": validation.get("severity", "UNKNOWN")
})
# Check if emergency shutdown needed
critical_violations = [
v for v in self.violation_history[-10:] # Last 10 violations
if v.get("severity") == "CRITICAL"
]
if len(critical_violations) >= self.threat_threshold:
self.emergency_shutdown(
reason="Multiple critical violations detected"
)
logger.warning(
f"Operation blocked: {validation['reason']} "
f"(severity: {validation.get('severity')})"
)
return validation
def emergency_shutdown(self, reason: str):
"""Emergency system shutdown."""
logger.critical(f"EMERGENCY SHUTDOWN: {reason}")
# Stop all running workflows
self.stop_all_workflows()
# Disable autonomous operations
self.disable_autonomous_mode()
# Alert operators
self.send_alert(
severity="CRITICAL",
message=f"System emergency shutdown: {reason}",
violations=self.violation_history[-10:]
)
# Save state for forensics
self.save_forensic_snapshot()
# Halt system
sys.exit(1)
El planificador (oversor) puede ahora:
- LLM genera código Python usando la API con manejo de errores
Ejecución
El sistema integra herramientas ejecutables para una validación completa:
Herramientas de prueba disponibles en producción:
pylint
- Comprobación de estilo PEP 8 y análisis de calidad de códigomypy
- Comprobación de tipo estáticaflake8
- Comprobación de estilo y detección de erroresnegro
- Validación de formato de códigobandido
- Escaneo de vulnerabilidad de seguridadpytest
- Ejecución de pruebas unitarias con coberturaradón
Análisis de complejidad (complejidad ciclomática, índice de mantenimiento)
buitre
isort
Futura integración de herramientas:
Validación visual
Titiritero + modelos de visión para el diseño web
Perfil de rendimiento
Verificación del cumplimiento
Servicios de dominio
Geocodificación, enriquecimiento de datos, etc.
**6.**Flujos de trabajo infantil optimizados para borde
**¿Qué pasa si los flujos de trabajo pueden generar versiones optimizadas de sí mismos para entornos con recursos limitados?**La optimización del borde permite:
Flexibilidad de despliegue- Mismo flujo de trabajo, múltiples perfiles de recursos
Simplificación automática- El sistema aprende lo que se puede podar
Ajuste específico del dispositivo
Barandillas y limitaciones de seguridad
A medida que el sistema se vuelve más autónomo, necesitamos mecanismos de seguridad sólidos para evitar que haga cosas dañinas.
Las barandillas de protección proporcionan:
Seguridad de las redes
- Detección PII, escaneo secreto, prevención de la exfiltración
Por qué esto importa:
A medida que el sistema se vuelve más autónomo a través de la evolución, teóricamente podría:
Evolucionar código que elimina archivos importantes para "optimizar el almacenamiento"
Cada operación (archivo escribe, llamadas de red, ejecución de código, transmisión de datos) debe pasar por las barandillas antes de la ejecución.
El sistema debe ser seguro por defecto, no es seguro con la esperanza de que no hace algo dañino.
Ideas salvajes (las cosas realmente divertidas):
Polinización cruzada
Nodos de diferentes dominios aprendiendo de las mutaciones del otroEvolución adversa
Dos agentes compitiendo para encontrar vulnerabilidades en el código del otro
Meta-evolución
Aprendizaje colaborativo
Laboratorios de investigación sintética
Cadenas de herramientas autoexpansivas
- El sistema descubre e integra nuevas herramientas automáticamente
Este último se conecta de nuevo con las ideas de consenso global de la Parte 6.
La arquitectura es agnóstica del dominio:
Enfoque de los planes de supervisión
Aparatos para grupos electrógenos
El Ejecutador corre en la caja de arena
Acondicionamiento físico de las puntuaciones de los evaluadores
El sistema evoluciona
Sustitúyase "código" por:
Contratos jurídicos
Estrategias empresariales
Políticas sociales
- Generar propuestas, simular efectos, evaluar contra objetivos, evolucionar
NO es:
AGI o algo parecidoSentido o consciente
Capacidad de razonamiento general
Un reemplazo para desarrolladores humanos
Lo es:
Un sistema evolutivo para artefactos de código
Flujo de trabajo multi-agente con memoria
Una red de optimización auto-mejoradora
Un prototipo para la evolución sintética dirigida
Revelan lo que es posible. Y lo que es posible aquí es un sistema que: Aprende de la experiencia
README.mdMejora con el tiempoADVANCED_FEATURES.mdDesarrolla la especializaciónHIERARCHICAL_EVOLUTION.mdConstruye conocimientos canónicosSYSTEM_OVERVIEW.mdEvoluciona sin reprogramación explícitaEso no es AGI.
src/overseer_llm.pyPero podría ser el sustrato del que emerge el AGI.src/evaluator.pyNo este sistema específicamente.src/qdrant_rag_memory.pyPero sistemas como este, ampliados, conectados, permitieron evolucionar a través de millones de dominios.src/tools_manager.pyLas partes 1-6 de esta serie exploraron esa trayectoria teóricamente.src/auto_evolver.pyLa parte 7 es que me doy cuenta:Podemos construir los primeros pasos ahora mismo.
Conclusión: El experimento continúa
hacia los objetivos
El DSE es mi desordenado, experimental y codificado intento de construir eso.
No está listo para la producción.
Ni siquiera está "buen código" listo. (NO soy desarrollador de Python, ya que cualquiera que lea la fuente lo notará inmediatamente.) #AI #MachineLearning #CodeGeneration #Ollama #RAG #EvolutionaryAlgorithms #LLM #Qdrant #Python #EmergentIntelligence #DirectedEvolution
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