| name | herclaw-agentsystem |
| description | Self-improving AI agent framework with autonomous learning, skill creation, and self-evolution.
Features:
- Learning Loop: Autonomous learning from experience
- Skill Creation: Auto-generate skills from patterns
- Self-Evolution: RL-based continuous improvement
- Persistent Memory: Cross-session three-layer memory
- Nudge System: Proactive behavior triggers
|
| license | MIT |
| version | 2.0.0 |
HerClaw Agent System
Self-improving AI agent framework for autonomous learning and evolution.
Architecture
┌─────────────────────────────────────────────────────────────────────────┐
│ HerClaw Agent System │
├─────────────────────────────────────────────────────────────────────────┤
│ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │
│ │ Learning Loop │───▶│ Skill Creation │───▶│ Self-Evolution │ │
│ │ │ │ │ │ │ │
│ │ • Experience │ │ • Opportunity │ │ • RL Pipeline │ │
│ │ Collection │ │ Detection │ │ • Behavior │ │
│ │ • Pattern │ │ • Template │ │ Optimization │ │
│ │ Extraction │ │ Generation │ │ • Capability │ │
│ │ • Skill │ │ • Validation │ │ Refinement │ │
│ │ Synthesis │ │ • Hub Sync │ │ • Deployment │ │
│ └────────┬────────┘ └────────┬────────┘ └────────┬────────┘ │
│ │ │ │ │
│ └──────────────────────┼──────────────────────┘ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────────┐ │
│ │ Persistent Memory │ │
│ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │
│ │ │ Episodic │ │ Semantic │ │ User │ │ │
│ │ │ Memory │ │ Memory │ │ Model │ │ │
│ │ └─────────────┘ └─────────────┘ └─────────────┘ │ │
│ └─────────────────────────────────────────────────────────────────┘ │
│ ▼ │
│ ┌─────────────────────────────────────────────────────────────────┐ │
│ │ Nudge System │ │
│ └─────────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────┘
Part 1: Learning Loop System
Autonomous learning from experience through continuous cycle of collection, extraction, synthesis, and validation.
Experience Collection
class ExperienceCollector:
"""Collects and structures interaction experiences."""
def __init__(self, storage_backend):
self.storage = storage_backend
self.trajectory_buffer = []
self.min_experiences_for_pattern = 5
def capture_interaction(self, interaction):
"""Capture a single interaction as an experience."""
experience = {
'id': self._generate_id(),
'timestamp': datetime.now().isoformat(),
'user_input': interaction['user_input'],
'context': interaction.get('context', {}),
'actions': interaction['actions'],
'outcome': interaction['outcome'],
'feedback': interaction.get('feedback'),
'embedding': self._embed_interaction(interaction)
}
self.trajectory_buffer.append(experience)
self.storage.store_experience(experience)
return experience['id']
def get_similar_experiences(self, query, k=10):
"""Retrieve similar past experiences using vector search."""
query_embedding = self._embed_query(query)
return .storage.vector_search(query_embedding, k=k)
Pattern Extraction
class PatternExtractor:
"""Extracts actionable patterns from collected experiences."""
def __init__(self, llm_client, config):
self.llm = llm_client
self.pattern_threshold = config.get('pattern_threshold', 0.7)
def extract_patterns(self, experiences):
"""Analyze experiences to identify recurring patterns."""
clusters = self._cluster_experiences(experiences)
patterns = []
for cluster in clusters:
if len(cluster) >= 3:
pattern = self._analyze_cluster(cluster)
if pattern['success_rate'] >= self.pattern_threshold:
patterns.append(pattern)
return patterns
Skill Synthesis
class SkillSynthesizer:
"""Synthesizes new skills from extracted patterns."""
def synthesize_skill(self, pattern, experiences):
"""Create a new skill from a pattern."""
skill_name = self._generate_skill_name(pattern)
instructions = self._generate_instructions(pattern, experiences)
triggers = self._define_triggers(pattern)
return Skill(
name=skill_name,
description=pattern['description'],
instructions=instructions,
triggers=triggers,
confidence=pattern['success_rate']
)
Part 2: Skill Creation System
Autonomous generation of new skills from experience patterns.
Opportunity Detection
class OpportunityDetector:
"""Detects opportunities for new skill creation."""
def scan_for_opportunities(self, experiences):
"""Scan experiences for skill creation opportunities."""
opportunities = []
task_groups = self._group_by_task(experiences)
for task_type, group in task_groups.items():
if len(group) >= 3:
coverage = self._check_skill_coverage(task_type)
if coverage < 0.7:
opportunities.append(Opportunity(
task_type=task_type,
frequency=len(group),
success_rate=self._calc_success_rate(group),
priority=self._calc_priority(task_type, group)
))
return sorted(opportunities, key=lambda x: x.priority, reverse=True)
Skill Generation
class SkillGenerator:
"""Generates new skills from patterns."""
def generate_skill(self, opportunity, experiences):
"""Generate a skill document from opportunity."""
pattern = self._extract_pattern(experiences)
skill_doc = self._fill_template(
template=self._get_template(opportunity.task_type),
pattern=pattern,
examples=experiences[:5]
)
refined = self._llm_refine(skill_doc)
return Skill(
name=self._generate_name(opportunity),
description=refined['description'],
instructions=refined['instructions'],
triggers=refined['triggers']
)
Part 3: Self-Evolution System
Continuous improvement through reinforcement learning and behavioral optimization.
Evolution Pipeline
class EvolutionPipeline:
"""RL-based evolution pipeline."""
def __init__(self, config):
self.config = config
self.reward_functions = {
'task_completion': TaskCompletionReward(),
'user_satisfaction': UserSatisfactionReward(),
'efficiency': EfficiencyReward()
}
def train(self, trajectories, epochs=10):
"""Train behavior model on collected trajectories."""
for epoch in range(epochs):
total_reward = 0
for trajectory in trajectories:
reward = self._calculate_reward(trajectory)
total_reward += reward
self._update_policy(trajectory, reward)
self._log_epoch(epoch, total_reward / len(trajectories))
return self._get_trained_model()
Performance Monitoring
class EvolutionMonitor:
"""Monitors system performance for evolution triggers."""
def collect_metrics(self):
"""Collect performance metrics."""
return {
'timestamp': datetime.now().isoformat(),
'task_completion_rate': self._calc_task_completion(),
'user_satisfaction': self._get_user_satisfaction(),
'error_rate': self._calc_error_rate(),
'latency': self._get_avg_latency()
}
def detect_improvement_opportunities(self):
"""Detect areas for improvement."""
opportunities = []
metrics = self.metrics_history[-1]
if metrics['error_rate'] > 0.05:
opportunities.append({
'type': 'error_reduction',
'priority': 'high'
})
if metrics['latency'] > self.config['target_latency']:
opportunities.append({
'type': 'performance_optimization',
'priority': 'medium'
})
return opportunities
Part 4: Persistent Memory System
Three-layer architecture for cross-session memory.
Memory Layers
class PersistentMemory:
"""Three-layer persistent memory system."""
def __init__(self, config):
self.episodic = EpisodicMemory(config['episodic'])
self.semantic = SemanticMemory(config['semantic'])
self.user_model = UserModel(config['user_model'])
self.vector_store = ChromaDB(
persist_directory=config['vector_db']['persist_directory']
)
def store(self, experience):
"""Store experience across all memory layers."""
episode_id = self.episodic.store(experience)
facts = self._extract_facts(experience)
for fact in facts:
self.semantic.store(fact, source=episode_id)
self.user_model.update(experience)
def retrieve_context(self, query, max_tokens=4000):
"""Retrieve relevant context for query."""
context = []
context.append(self.user_model.get_context())
context.extend(self.episodic.search(query, k=10))
context.extend(self.semantic.search(query, k=20))
return context
Part 5: Nudge System
Self-prompting for proactive behavior.
Nudge Types
class NudgeSystem:
"""Self-prompting system for proactive behavior."""
def __init__(self, config):
self.scheduled_nudges = []
self.nudge_handlers = {
'memory_persistence': self._handle_memory_persistence,
'skill_creation': self._handle_skill_creation,
'evolution_check': self._handle_evolution_check,
'learning_reminder': self._handle_learning_reminder
}
def schedule_nudge(self, nudge_type, trigger, data=None):
"""Schedule a nudge for future execution."""
nudge = {
'id': self._generate_id(),
'type': nudge_type,
'trigger': trigger,
'data': data,
'status': 'pending'
}
self.scheduled_nudges.append(nudge)
return nudge['id']
def check_and_execute(self):
"""Check for due nudges and execute them."""
due_nudges = self._get_due_nudges()
for nudge in due_nudges:
handler = self.nudge_handlers.get(nudge['type'])
if handler:
result = handler(nudge)
nudge[] =
nudge[] = result
Configuration
learning_loop:
enabled: true
min_experiences_for_pattern: 5
pattern_threshold: 0.7
skill_creation:
enabled: true
min_occurrences: 3
self_evolution:
enabled: true
reward_weights:
task_completion: 1.0
user_satisfaction: 0.8
efficiency: 0.5
persistent_memory:
enabled: true
episodic:
max_episodes: 100000
semantic:
min_confidence: 0.6
user_model:
preference_decay_days: 90
vector_db:
type: "chromadb"
persist_directory: "./memory"
nudge_system:
enabled: true
max_scheduled: 100
Quick Start
from scripts import MemoryManager, PatternRecorder
memory = MemoryManager()
recorder = PatternRecorder()
memory.add_episode(
user_input="Analyze this document",
context={"file": "report.pdf"},
actions=["load", "extract", "analyze"],
outcome="Success"
)
patterns = recorder.detect_patterns()
for pattern in patterns:
print(f"Pattern: {pattern.name}, Success: {pattern.success_rate}")
Files
scripts/memory_manager.py - Memory systemscripts/pattern_recorder.py - Pattern recordingreferences/memory-system.md - Memory docsreferences/pattern-recording.md - Pattern docsreferences/workflow-optimization.md - Workflow docs
