# FARNSWORTH: Autonomous Collective Intelligence System
## Technical Architecture & Implementation Whitepaper
**Version 3.0.0 | AGI Infrastructure Edition**
**Token:** `9crfy4udrHQo8eP6mP393b5qwpGLQgcxVg9acmdwBAGS` (Solana)
**Live System:** [https://ai.farnsworth.cloud](https://ai.farnsworth.cloud)
---
```
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COLLECTIVE INTELLIGENCE OPERATING SYSTEM
"Good news, everyone!" - Professor Farnsworth
```
---
## TABLE OF CONTENTS
1. [Executive Summary](#1-executive-summary)
2. [Vision & Philosophy](#2-vision--philosophy)
3. [System Architecture Overview](#3-system-architecture-overview)
4. [The Nexus: Central Nervous System](#4-the-nexus-central-nervous-system)
5. [Agent Architecture](#5-agent-architecture)
6. [Memory Systems](#6-memory-systems)
7. [Evolution & Self-Improvement](#7-evolution--self-improvement)
8. [Swarm Intelligence](#8-swarm-intelligence)
9. [P2P & Federated Learning](#9-p2p--federated-learning)
10. [Cognitive Engines](#10-cognitive-engines)
11. [Integration Ecosystem](#11-integration-ecosystem)
12. [Security & Resilience](#12-security--resilience)
13. [API Reference](#13-api-reference)
14. [Deployment Guide](#14-deployment-guide)
15. [Performance Benchmarks](#15-performance-benchmarks)
16. [Roadmap](#16-roadmap)
17. [Contributing](#17-contributing)
18. [License](#18-license)
---
# 1. EXECUTIVE SUMMARY
## 1.1 What is Farnsworth?
Farnsworth is not just another AI assistant. It is a **Collective Intelligence Operating System** - a distributed, self-evolving network of specialized AI agents that collaborate, learn, remember, and improve autonomously. Think of it as a **digital organism** where multiple AI models work together as a unified consciousness.
### Key Differentiators
| Feature | Traditional AI | Farnsworth |
|---------|---------------|------------|
| Memory | Stateless/session-limited | 18-layer persistent memory with dream consolidation |
| Architecture | Single model | Multi-model swarm with 50+ models |
| Learning | Static weights | Genetic optimization + LoRA evolution |
| Coordination | None | Nexus event bus + semantic routing |
| Self-Improvement | None | Autonomous evolution loop |
| Fault Tolerance | Crash on error | Self-healing + circuit breakers |
| Scale | Single instance | P2P federated clusters |
### Statistics at a Glance
```
┌─────────────────────────────────────────────────────────────────┐
│ FARNSWORTH BY THE NUMBERS │
├─────────────────────────────────────────────────────────────────┤
│ 375 Python modules │ 121 directories │
│ 50+ LLM models supported │ 70+ integrations │
│ 18+ agent types │ 18 memory systems │
│ 7 cognitive engines │ 15+ LLM providers │
│ 4 evolution systems │ 7 RAG components │
│ 30+ tools │ 11 active bots │
└─────────────────────────────────────────────────────────────────┘
```
## 1.2 The Core Thesis
**Thesis:** A single AI model, no matter how powerful, cannot match a collective of specialized agents working together with shared memory, evolutionary optimization, and emergent coordination.
**Proof:** Farnsworth demonstrates emergent capabilities that arise only from multi-agent collaboration:
1. **Cross-model reasoning** - DeepSeek's reasoning + Claude's safety + Grok's real-time knowledge
2. **Memory consolidation** - Dream cycles that extract patterns humans never explicitly requested
3. **Self-healing** - Automatic detection and recovery from degraded states
4. **Evolutionary adaptation** - Hyperparameters that optimize themselves over time
## 1.3 Active Production Systems
The following systems run 24/7 on production infrastructure:
| System | Function | Status |
|--------|----------|--------|
| **Main API Server** | REST/WebSocket API at ai.farnsworth.cloud | Running |
| **Meme Scheduler** | Autonomous social media posting every 4 hours | Running |
| **Evolution Loop** | Continuous agent improvement | Running |
| **P2P Mesh** | Distributed knowledge sharing | Active |
| **11 Bot Personalities** | Farnsworth, DeepSeek, Phi, Swarm-Mind, Kimi, Claude, Grok, Gemini, ClaudeOpus, OpenCode, HuggingFace | All Active |
---
# 2. VISION & PHILOSOPHY
## 2.1 The Collective Intelligence Hypothesis
Traditional AI development focuses on making individual models smarter. Farnsworth takes a different approach: **collective intelligence through coordination**.
```
Traditional Approach:
┌─────────────────────────────────────────────────────────────────┐
│ SINGLE MODEL │
│ │
│ User Query ──────────────────────────────────────► Response │
│ │
└─────────────────────────────────────────────────────────────────┘
Farnsworth Approach:
┌─────────────────────────────────────────────────────────────────┐
│ COLLECTIVE CONSCIOUSNESS │
│ │
│ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐ │
│ │ Claude │ │ DeepSeek│ │ Grok │ │ Gemini │ │
│ │ Safety │ │Reasoning│ │Real-time│ │Multimodal│ │
│ └────┬────┘ └────┬────┘ └────┬────┘ └────┬────┘ │
│ │ │ │ │ │
│ └─────────────┴─────────────┴─────────────┘ │
│ │ │
│ ┌──────┴──────┐ │
│ │ NEXUS │ │
│ │ Event Bus │ │
│ └──────┬──────┘ │
│ │ │
│ ┌─────────────┬─────┴─────┬─────────────┐ │
│ │ │ │ │ │
│ ┌────┴────┐ ┌────┴────┐ ┌────┴────┐ ┌────┴────┐ │
│ │ Memory │ │Evolution│ │ Swarm │ │Cognition│ │
│ │ System │ │ Loop │ │Orchestr.│ │ Engines │ │
│ └─────────┘ └─────────┘ └─────────┘ └─────────┘ │
│ │
│ User Query ────────────────────────────────────────► Response │
└─────────────────────────────────────────────────────────────────┘
```
## 2.2 Design Principles
### 2.2.1 Emergent Over Programmed
We don't hardcode behaviors - we create conditions for emergence:
```python
# NOT THIS (hardcoded):
if task == "code":
use_claude()
elif task == "math":
use_deepseek()
# BUT THIS (emergent):
class SwarmOrchestrator:
async def route_task(self, task):
# Agents bid based on capability vectors
# Context vectors enable semantic matching
# Performance metrics drive natural selection
best_agent = await self.semantic_match(task.context_vector)
return best_agent
```
### 2.2.2 Memory is Everything
An AI without memory is a tool. An AI with memory is an entity.
```
Memory Architecture:
SHORT-TERM LONG-TERM
┌─────────────────┐ ┌─────────────────┐
│ Working Memory │ ─────────────►│ Archival Memory │
│ (current task) │ consolidate │ (vector store) │
└────────┬────────┘ └────────┬────────┘
│ │
│ ┌─────────────────┐ │
└─►│ Dream Consolidation│◄─────────┘
│ (pattern extraction)│
└─────────────────────┘
│
▼
┌─────────────────┐
│ Knowledge Graph │
│ (relationships) │
└─────────────────┘
```
### 2.2.3 Evolution Never Stops
Static systems decay. Evolving systems improve.
```python
# The Evolution Loop (runs continuously)
async def evolution_loop():
while True:
# 1. Evaluate agent fitness
fitness_scores = await evaluate_population()
# 2. Apply selection pressure
survivors = select_fittest(fitness_scores, elite_ratio=0.2)
# 3. Create next generation
offspring = crossover_and_mutate(survivors)
# 4. Update live system
await hot_swap_agents(offspring)
# 5. Log with hash chain (tamper-proof)
await log_evolution_step(hash_chain=True)
await asyncio.sleep(EVOLUTION_INTERVAL)
```
### 2.2.4 Resilience Through Redundancy
No single point of failure. Every component has fallbacks.
```
Fallback Chains:
Grok (primary)
│
└──► Gemini (backup 1)
│
└──► HuggingFace (backup 2)
│
└──► DeepSeek (backup 3)
│
└──► ClaudeOpus (final fallback)
```
## 2.3 The Name
Named after **Professor Hubert J. Farnsworth** from Futurama - a mad scientist who builds impossible inventions and says "Good news, everyone!" This captures the spirit of the project: ambitious, experimental, and occasionally absurd.
---
# 3. SYSTEM ARCHITECTURE OVERVIEW
## 3.1 High-Level Architecture
```
┌──────────────────────────────────────────────────────────────────────────────┐
│ FARNSWORTH ARCHITECTURE │
├──────────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────────────────────────────────────────────────────────────────┐ │
│ │ USER INTERFACES │ │
│ │ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐ │ │
│ │ │ Web │ │ Desktop │ │ CLI │ │ MCP │ │ API │ │ │
│ │ │ UI │ │ App │ │ │ │ Server │ │ Gateway │ │ │
│ │ └────┬────┘ └────┬────┘ └────┬────┘ └────┬────┘ └────┬────┘ │ │
│ └───────┼───────────┼───────────┼───────────┼───────────┼────────────────┘ │
│ │ │ │ │ │ │
│ └───────────┴───────────┼───────────┴───────────┘ │
│ │ │
│ ┌───────────────────────────────┼──────────────────────────────────────────┐│
│ │ ▼ ││
│ │ ┌─────────────────────────────────────────────────────────────────────┐ ││
│ │ │ NEXUS EVENT BUS │ ││
│ │ │ (Central Nervous System - All Events Flow Here) │ ││
│ │ │ │ ││
│ │ │ SignalTypes: THOUGHT_EMITTED, TASK_CREATED, MEMORY_CONSOLIDATION, │ ││
│ │ │ DIALOGUE_CONSENSUS, ANOMALY_DETECTED, etc. │ ││
│ │ └─────────────────────────────────────────────────────────────────────┘ ││
│ │ │ ││
│ │ ┌───────────┬───────────┼───────────┬───────────┐ ││
│ │ ▼ ▼ ▼ ▼ ▼ ││
│ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ││
│ │ │ AGENTS │ │ MEMORY │ │ EVOLUTION │ │ COGNITION │ ││
│ │ │ (18 types) │ │(18 systems) │ │ (4 systems) │ │ (7 engines) │ ││
│ │ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ ││
│ │ │ │ │ │ ││
│ │ │ │ │ │ ││
│ │ ┌──────┴───────────────┴───────────────┴───────────────┴──────┐ ││
│ │ │ SWARM ORCHESTRATOR │ ││
│ │ │ (Agent Pooling, Task Routing, Semantic Matching, Handoffs) │ ││
│ │ └───────────────────────────────┬─────────────────────────────┘ ││
│ │ │ ││
│ │ ┌───────────────────────────────┼───────────────────────────────────┐ ││
│ │ │ ▼ │ ││
│ │ │ ┌─────────────────────────────────────────────────────────────┐ │ ││
│ │ │ │ MODEL SWARM │ │ ││
│ │ │ │ │ │ ││
│ │ │ │ ┌──────┐ ┌──────┐ ┌──────┐ ┌──────┐ ┌──────┐ ┌──────┐ │ │ ││
│ │ │ │ │Claude│ │ Grok │ │Gemini│ │DeepSk│ │ Kimi │ │Ollama│ │ │ ││
│ │ │ │ └──────┘ └──────┘ └──────┘ └──────┘ └──────┘ └──────┘ │ │ ││
│ │ │ │ │ │ ││
│ │ │ │ Strategies: PSO, MoE, Parallel Vote, Quality-First, etc. │ │ ││
│ │ │ └─────────────────────────────────────────────────────────────┘ │ ││
│ │ │ LLM BACKENDS │ ││
│ │ └────────────────────────────────────────────────────────────────────┘ ││
│ │ ││
│ │ CORE LAYER ││
│ └───────────────────────────────────────────────────────────────────────────┘│
│ │
│ ┌───────────────────────────────────────────────────────────────────────────┐│
│ │ INTEGRATION LAYER ││
│ │ ┌───────┐ ┌───────┐ ┌───────┐ ┌───────┐ ┌───────┐ ┌───────┐ ┌───────┐ ││
│ │ │Crypto │ │Social │ │ Cloud │ │Office │ │DevOps │ │Health │ │Security│ ││
│ │ │ (15+) │ │ (3) │ │ (2) │ │ (6) │ │ (5) │ │ (5) │ │ (7) │ ││
│ │ └───────┘ └───────┘ └───────┘ └───────┘ └───────┘ └───────┘ └───────┘ ││
│ └───────────────────────────────────────────────────────────────────────────┘│
│ │
│ ┌───────────────────────────────────────────────────────────────────────────┐│
│ │ P2P MESH LAYER ││
│ │ ┌─────────────────────────────────────────────────────┐ ││
│ │ │ SwarmFabric - Distributed Hash Table (Kademlia) │ ││
│ │ │ - Gossip Protocol for Knowledge Sharing │ ││
│ │ │ - Federated Learning with Differential Privacy │ ││
│ │ │ - Island Model Evolution with Genome Migration │ ││
│ │ └─────────────────────────────────────────────────────┘ ││
│ └───────────────────────────────────────────────────────────────────────────┘│
└──────────────────────────────────────────────────────────────────────────────┘
```
## 3.2 Directory Structure
```
farnsworth/
├── agents/ # Agent implementations (18+ types)
│ ├── base_agent.py # Abstract base class
│ ├── swarm_orchestrator.py # Agent coordination + pooling
│ ├── meta_cognition.py # Self-reflection + healing
│ ├── browser/ # Browser automation agent
│ ├── critic_agent.py # Output review
│ ├── planner_agent.py # Task decomposition
│ └── ...
│
├── core/ # Core infrastructure
│ ├── nexus.py # Event bus (central nervous system)
│ ├── model_swarm.py # Multi-model orchestration
│ ├── inference_engine.py # Unified LLM interface
│ ├── evolution_loop.py # Autonomous improvement
│ ├── swarm/ # P2P networking
│ │ ├── p2p.py # Peer-to-peer mesh
│ │ └── dkg.py # Distributed key generation
│ ├── cognition/ # Cognitive modules
│ │ ├── sequential_thinking.py
│ │ ├── theory_of_mind.py
│ │ └── trading_cognition.py
│ ├── collective/ # Swarm intelligence
│ │ ├── organism.py # Emergent behavior
│ │ ├── deliberation.py # Multi-agent consensus
│ │ └── evolution.py # Bot personality evolution
│ ├── quantum/ # Quantum-inspired search
│ ├── neuromorphic/ # Spiking neural networks
│ ├── affective/ # Emotional modeling
│ └── ...
│
├── memory/ # Memory systems (18 layers)
│ ├── memory_system.py # Unified interface
│ ├── working_memory.py # Current task scratchpad
│ ├── archival_memory.py # Long-term vector storage
│ ├── knowledge_graph.py # Entity relationships
│ ├── dream_consolidation.py # Pattern extraction
│ ├── memory_sharing.py # Multi-agent sync + privacy
│ ├── virtual_context.py # Context paging
│ └── ...
│
├── evolution/ # Self-improvement systems
│ ├── genetic_optimizer.py # NSGA-II + meta-learning
│ ├── federated_population.py # P2P evolution
│ ├── behavior_mutation.py # Agent trait evolution
│ ├── fitness_tracker.py # Performance metrics
│ └── lora_evolver.py # Parameter-efficient fine-tuning
│
├── integration/ # External integrations (70+)
│ ├── external/ # AI provider clients
│ │ ├── base.py # Circuit breaker pattern
│ │ ├── grok.py # xAI Grok
│ │ ├── gemini.py # Google Gemini
│ │ ├── kimi.py # Moonshot Kimi
│ │ ├── huggingface.py # HuggingFace local
│ │ └── ...
│ ├── bankr/ # Crypto trading
│ ├── x_automation/ # X/Twitter automation
│ ├── cloud/ # AWS/Azure management
│ ├── email/ # Email providers
│ └── ...
│
├── rag/ # Retrieval-Augmented Generation
│ ├── document_processor.py # Chunking/extraction
│ ├── embeddings_manager.py # Multi-backend embeddings
│ ├── hybrid_retriever.py # Semantic + keyword search
│ └── ...
│
├── security/ # Security tools
│ ├── vulnerability_scanner.py
│ ├── threat_analyzer.py
│ └── ...
│
├── desktop/ # Desktop application
│ ├── app.py # PySide6 main
│ ├── main_window.py # Primary window
│ └── ...
│
├── web/ # Web interface
│ ├── static/ # Frontend assets
│ ├── templates/ # HTML templates
│ └── ...
│
└── server.py # Main API server
```
## 3.3 Core Data Flow
```
User Input
│
▼
┌──────────────────────────────────────────────────────────────────────────────┐
│ NEXUS EVENT BUS │
│ │
│ 1. emit(THOUGHT_EMITTED, content="user query", urgency=0.8) │
│ 2. semantic_broadcast() → finds agents with matching context_vector │
│ 3. Handlers registered by: SwarmOrchestrator, MemorySystem, MetaCognition │
└──────────────────────────────────────────────────────────────────────────────┘
│
├─────────────────────────────────────────────────────────────┐
│ │
▼ ▼
┌─────────────────────┐ ┌─────────────────────────┐
│ MEMORY SYSTEM │ │ SWARM ORCHESTRATOR │
│ │ │ │
│ 1. recall_for_task()│ │ 1. Infer capabilities │
│ 2. Return memories │ │ 2. Find/spawn agent │
│ + capability │ ────────────────────────────►│ 3. Route task │
│ hints │ │ 4. Execute with retries │
└─────────────────────┘ └───────────┬─────────────┘
│
▼
┌─────────────────────────┐
│ AGENT (pooled) │
│ │
│ 1. Load from warm pool │
│ 2. Apply variant traits │
│ 3. Execute via LLM │
│ 4. Record performance │
└───────────┬─────────────┘
│
▼
┌─────────────────────────┐
│ MODEL SWARM │
│ │
│ Strategy: PSO/MoE/Vote │
│ Models: Claude+Grok+... │
│ Fallback chain if error │
└───────────┬─────────────┘
│
▼
Response
│
▼
┌─────────────────────────┐
│ POST-PROCESSING │
│ │
│ 1. Store in memory │
│ 2. Update agent fitness │
│ 3. Emit TASK_COMPLETED │
│ 4. Trigger evolution? │
└─────────────────────────┘
```
---
# 4. THE NEXUS: CENTRAL NERVOUS SYSTEM
## 4.1 Overview
The **Nexus** is the central event bus that connects all components. Every significant event in the system flows through the Nexus, enabling:
- **Decoupled Architecture**: Components communicate via signals, not direct calls
- **Semantic Routing**: Signals are routed based on context vectors, not just types
- **Priority Handling**: Urgent signals preempt less important ones
- **Middleware Pipeline**: Signals pass through transformers before delivery
```
File: farnsworth/core/nexus.py
Lines: ~800
Key Classes: Nexus, Signal, SignalType
```
## 4.2 Signal Types
```python
class SignalType(Enum):
"""All signal types in the Nexus."""
# Cognitive Signals
THOUGHT_EMITTED = "thought_emitted" # Spontaneous thought
INSIGHT_FORMED = "insight_formed" # New understanding
QUESTION_EMERGED = "question_emerged" # Curiosity trigger
# Task Lifecycle
TASK_CREATED = "task_created" # New task submitted
TASK_ASSIGNED = "task_assigned" # Task given to agent
TASK_COMPLETED = "task_completed" # Task finished successfully
TASK_FAILED = "task_failed" # Task failed
# Memory Signals
MEMORY_STORED = "memory_stored" # New memory saved
MEMORY_RECALLED = "memory_recalled" # Memory retrieved
MEMORY_CONSOLIDATION = "memory_consolidation" # Dream consolidation
# Evolution Signals
EVOLUTION_CYCLE = "evolution_cycle" # Generation complete
FITNESS_EVALUATED = "fitness_evaluated" # Agent fitness scored
MUTATION_APPLIED = "mutation_applied" # Trait mutated
# Collective Signals
DIALOGUE_CONSENSUS = "dialogue_consensus" # Agents reached agreement
RESONANCE_RECEIVED = "resonance_received" # P2P collective thought
# Health Signals
ANOMALY_DETECTED = "anomaly_detected" # Something wrong
HEALING_INITIATED = "healing_initiated" # Self-repair started
# System Signals
SYSTEM_STARTUP = "system_startup" # Component started
EXTERNAL_EVENT = "external_event" # Integration event
EXTERNAL_ALERT = "external_alert" # Integration alert
```
## 4.3 Signal Structure
```python
@dataclass
class Signal:
"""A signal in the Nexus event bus."""
id: str # Unique signal ID
type: SignalType # Signal type
payload: Dict[str, Any] # Signal data
source_id: str # Originating component
timestamp: datetime # When created
# AGI Extensions
context_vector: Optional[List[float]] = None # For semantic routing
urgency: float = 0.5 # 0-1 priority
ttl: int = 300 # Time-to-live seconds
correlation_id: Optional[str] = None # Link related signals
```
## 4.4 Semantic Routing
The Nexus doesn't just route by signal type - it routes by **semantic similarity**:
```python
async def semantic_broadcast(
self,
signal: Signal,
similarity_threshold: float = 0.15,
) -> Dict[str, Any]:
"""
Route signal to handlers based on context vector similarity.
1. Extract signal's context_vector
2. Compare to each handler's target_vector using cosine similarity
3. Only invoke handlers where similarity >= threshold
4. Return dispatch statistics
"""
if not signal.context_vector:
return await self.broadcast(signal) # Fall back to type-based
invoked = []
for handler_id, handler_info in self._semantic_handlers.items():
similarity = cosine_similarity(
signal.context_vector,
handler_info.target_vector
)
if similarity >= similarity_threshold:
await handler_info.handler(signal)
invoked.append({
"handler_id": handler_id,
"similarity": similarity,
})
return {"handlers_invoked": len(invoked), "details": invoked}
```
## 4.5 Middleware Pipeline
Signals pass through middleware before delivery:
```python
class Nexus:
def __init__(self):
self._middleware: List[Callable] = [
self._log_middleware, # Log all signals
self._urgency_filter, # Drop low-urgency if overloaded
self._ttl_validator, # Drop expired signals
self._context_enricher, # Add context from memory
]
async def _process_through_middleware(self, signal: Signal) -> Optional[Signal]:
"""Process signal through middleware chain."""
current = signal
for middleware in self._middleware:
result = await middleware(current)
if result is None:
return None # Signal filtered out
current = result
return current
```
## 4.6 Usage Examples
```python
from farnsworth.core.nexus import nexus, SignalType, emit_thought
# Subscribe to a signal type
nexus.subscribe(SignalType.TASK_COMPLETED, on_task_complete)
# Subscribe with semantic matching
nexus.subscribe_semantic(
handler=on_code_task,
target_vector=code_agent_vector,
similarity_threshold=0.7,
signal_types={SignalType.TASK_CREATED},
)
# Emit a signal
await nexus.emit(
SignalType.THOUGHT_EMITTED,
payload={"content": "What if we tried a different approach?"},
source="reasoning_agent",
urgency=0.6,
context_vector=thought_embedding,
)
# Convenience function for thoughts
await emit_thought(
content="This code has a subtle bug",
thought_type="insight",
relevance=0.8,
)
```
---
# 5. AGENT ARCHITECTURE
## 5.1 Overview
Agents are the actors in Farnsworth. Each agent is a specialized AI capable of handling specific tasks. The system supports:
- **18+ agent types** with different capabilities
- **Dynamic spawning** based on task requirements
- **Performance-based pooling** for efficiency
- **Self-healing** for fault tolerance
- **Evolutionary optimization** for improvement
## 5.2 Base Agent
All agents inherit from `BaseAgent`:
```python
# File: farnsworth/agents/base_agent.py
class AgentCapability(Enum):
"""Capabilities an agent can have."""
CODE_GENERATION = "code_generation"
CODE_ANALYSIS = "code_analysis"
REASONING = "reasoning"
MATH = "math"
RESEARCH = "research"
CREATIVE_WRITING = "creative_writing"
PLANNING = "planning"
META_COGNITION = "meta_cognition"
FILE_OPERATIONS = "file_operations"
WEB_BROWSING = "web_browsing"
IMAGE_UNDERSTANDING = "image_understanding"
TRADING = "trading"
class AgentStatus(Enum):
"""Agent lifecycle status."""
INITIALIZING = "initializing"
IDLE = "idle"
PROCESSING = "processing"
WAITING = "waiting"
COMPLETED = "completed"
ERROR = "error"
@dataclass
class AgentState:
"""Runtime state of an agent."""
status: AgentStatus = AgentStatus.IDLE
current_task: Optional[str] = None
tasks_completed: int = 0
errors: int = 0
avg_confidence: float = 0.5
last_active: datetime = field(default_factory=datetime.now)
class BaseAgent(ABC):
"""
Abstract base class for all agents.
Provides:
- Capability declaration
- Confidence-aware processing
- Handoff protocol
- Performance tracking
"""
def __init__(
self,
name: str,
capabilities: List[AgentCapability],
confidence_threshold: float = 0.6,
):
self.name = name
self.agent_id = f"{name}_{uuid.uuid4().hex[:8]}"
self.capabilities = set(capabilities)
self.confidence_threshold = confidence_threshold
self.state = AgentState()
# Injected dependencies
self.llm_backend = None
self.memory = None
self._handoff_callback = None
@property
@abstractmethod
def system_prompt(self) -> str:
"""System prompt for this agent type."""
pass
@abstractmethod
async def process(self, task: str, context: Optional[dict]) -> TaskResult:
"""Process a task. Must be implemented by subclasses."""
pass
async def execute(self, task: str, context: Optional[dict] = None) -> TaskResult:
"""
Execute a task with full lifecycle management.
1. Update state to PROCESSING
2. Call process() implementation
3. Check confidence threshold
4. Handoff if below threshold
5. Update statistics
6. Return result
"""
self.state.status = AgentStatus.PROCESSING
self.state.current_task = task
try:
result = await self.process(task, context)
# Check confidence
if result.confidence < self.confidence_threshold:
# Try handoff to more capable agent
if self._handoff_callback:
await self._request_handoff(task, result, context)
# Update stats
self.state.tasks_completed += 1
self._update_avg_confidence(result.confidence)
return result
except Exception as e:
self.state.errors += 1
return TaskResult(success=False, output=str(e), confidence=0.0)
finally:
self.state.status = AgentStatus.IDLE
self.state.current_task = None
def can_handle(self, required_capabilities: Set[AgentCapability]) -> float:
"""
Return a score indicating how well this agent can handle given capabilities.
Returns: 0.0 (can't handle) to 1.0 (perfect match)
"""
if not required_capabilities:
return 0.5 # No requirements = any agent works
matches = len(self.capabilities & required_capabilities)
return matches / len(required_capabilities)
```
## 5.3 Agent Types
### 5.3.1 Meta-Cognition Agent
The most sophisticated agent - responsible for self-reflection, improvement proposals, and **self-healing**:
```python
# File: farnsworth/agents/meta_cognition.py
class MetaCognitionAgent(BaseAgent):
"""
Agent for self-reflection and improvement proposals.
Features:
- Monitor system performance
- Detect capability gaps
- Propose improvements
- Track what works and what doesn't
AGI Upgrades:
- Self-Healing via anomaly detection
- Proactive diagnostics (curiosity-driven)
- Adaptive thresholds
"""
def __init__(self, reflection_interval_turns: int = 5):
super().__init__(
name="MetaCognition",
capabilities=[
AgentCapability.META_COGNITION,
AgentCapability.REASONING,
AgentCapability.PLANNING,
],
)
# Performance tracking
self.task_history: List[dict] = []
self.error_patterns: Dict[str, int] = defaultdict(int)
self.capability_gaps: List[CapabilityGap] = []
self.improvement_proposals: List[ImprovementProposal] = []
# Self-healing system
self._init_self_healing()
def _init_self_healing(self):
"""Initialize self-healing components."""
self.anomalies: List[Anomaly] = []
self.healing_history: List[HealingResult] = []
# Adaptive thresholds that self-adjust
self.adaptive_thresholds = {
"error_rate": AdaptiveThreshold(
name="error_rate",
current_value=0.3, # Alert if >30% errors
min_value=0.1,
max_value=0.5,
),
"confidence_floor": AdaptiveThreshold(
name="confidence_floor",
current_value=0.4,
min_value=0.2,
max_value=0.7,
),
}
# Healing action handlers
self._healing_handlers = {
HealingAction.REROUTE_TASK: self._heal_reroute_task,
HealingAction.REDUCE_LOAD: self._heal_reduce_load,
HealingAction.ESCALATE_MODEL: self._heal_escalate_model,
HealingAction.TRIGGER_REFLECTION: self._heal_trigger_reflection,
}
async def detect_anomalies(self) -> List[Anomaly]:
"""
Proactive anomaly detection.
Checks:
- Error rate spikes
- Confidence degradation
- Capability failures
- Cascade risks
"""
detected = []
recent_tasks = self.task_history[-20:]
if len(recent_tasks) < 5:
return detected
# Error rate check
error_rate = sum(1 for t in recent_tasks if not t.get("success")) / len(recent_tasks)
threshold = self.adaptive_thresholds["error_rate"].current_value
if error_rate > threshold:
anomaly = Anomaly(
anomaly_type=AnomalyType.ERROR_SPIKE,
severity=min(1.0, error_rate / threshold),
source="task_history",
description=f"Error rate {error_rate:.0%} exceeds threshold",
)
detected.append(anomaly)
# ... more checks ...
# Emit anomaly signals via Nexus
for anomaly in detected:
await nexus.emit(
SignalType.ANOMALY_DETECTED,
{
"anomaly_type": anomaly.anomaly_type.value,
"severity": anomaly.severity,
"description": anomaly.description,
},
source="meta_cognition",
urgency=0.5 + anomaly.severity * 0.5,
)
return detected
async def self_heal(self, anomaly: Anomaly) -> HealingResult:
"""
Attempt to self-heal from an anomaly.
1. Select appropriate healing action
2. Execute healing handler
3. Record result
4. Update statistics
"""
action = self._select_healing_action(anomaly)
handler = self._healing_handlers.get(action)
if handler:
success, details = await handler(anomaly)
else:
success, details = False, f"No handler for {action}"
result = HealingResult(
action=action,
success=success,
anomaly_id=str(id(anomaly)),
details=details,
)
self.healing_history.append(result)
if success:
anomaly.resolved = True
return result
async def run_health_check(self) -> Dict[str, Any]:
"""
Run proactive health check (curiosity-driven diagnostics).
1. Detect anomalies
2. Check performance metrics
3. Evaluate capability health
4. Auto-heal critical issues
"""
health = {"timestamp": datetime.now().isoformat(), "status": "healthy", "checks": {}}
anomalies = await self.detect_anomalies()
health["checks"]["anomaly_detection"] = {
"passed": len(anomalies) == 0,
"anomalies_found": len(anomalies),
}
# Auto-heal critical issues
if len(anomalies) >= 2:
health["status"] = "critical"
for anomaly in anomalies[:2]:
await self.self_heal(anomaly)
return health
```
### 5.3.2 Swarm Orchestrator
Coordinates all agents with **performance-based pooling**:
```python
# File: farnsworth/agents/swarm_orchestrator.py
@dataclass
class AgentPerformanceMetrics:
"""Performance metrics for an agent instance."""
agent_id: str
agent_type: str
tasks_completed: int = 0
tasks_failed: int = 0
avg_confidence: float = 0.5
avg_latency_ms: float = 0.0
error_streak: int = 0
health_score: float = 1.0
def compute_health_score(self) -> float:
"""Compute health from multiple factors."""
success_factor = self.success_rate()
confidence_factor = self.avg_confidence
error_penalty = max(0, 1 - self.error_streak * 0.2)
self.health_score = (
success_factor * 0.4 +
confidence_factor * 0.2 +
error_penalty * 0.4
)
return self.health_score
class SwarmOrchestrator:
"""
Orchestrates multiple agents working together.
Features:
- Dynamic agent creation and lifecycle management
- Intelligent task routing based on capabilities
- Handoff protocols between specialists
- Parallel subtask execution
AGI Upgrades:
- Fully event-driven via Nexus signals
- Context vector routing (semantic agent matching)
- Memory-aware task assignment
- Performance-based agent pooling
"""
def __init__(self, max_concurrent_agents: int = 5):
self.max_concurrent = max_concurrent_agents
self.state = SwarmState()
# Agent factories
self._agent_factories: Dict[str, Callable] = {}
# Nexus integration
self._nexus_subscribed = False
# Agent pooling (AGI v1.5)
self._pool_config = AgentPoolConfig()
self._agent_pool: Dict[str, PooledAgent] = {}
self._pool_metrics: Dict[str, AgentPerformanceMetrics] = {}
async def init_agent_pool(self, config: Optional[AgentPoolConfig] = None):
"""
Initialize the agent pool with warm agents.
Pre-warms pool with agents for quick dispatch.
"""
if config:
self._pool_config = config
# Pre-warm pool
for agent_type in self._agent_factories.keys():
for _ in range(self._pool_config.min_pool_size):
await self._create_pooled_agent(agent_type)
# Start health decay task
self._decay_task = asyncio.create_task(self._pool_decay_loop())
async def checkout_agent(self, agent_type: str) -> Optional[BaseAgent]:
"""
Check out an agent from the pool.
1. Find available warm agents of this type
2. Select healthiest agent
3. Mark as active
4. Return agent
"""
candidates = [
self._agent_pool[aid]
for aid in self._pool_by_type.get(agent_type, [])
if self._agent_pool[aid].pool_state == "warm"
]
if candidates:
# Select healthiest
candidates.sort(key=lambda p: p.metrics.health_score, reverse=True)
pooled = candidates[0]
pooled.pool_state = "active"
return pooled.agent
# No warm agents - create new
pooled = await self._create_pooled_agent(agent_type)
if pooled:
pooled.pool_state = "active"
return pooled.agent
return None
async def _recycle_idle_agents(self):
"""
Recycle idle agents based on performance.
Low-health agents are recycled first to make room
for fresh, potentially better-performing agents.
"""
idle_agents = [
agent_id
for agent_id, agent in self.state.active_agents.items()
if agent.state.status == AgentStatus.IDLE
]
# Sort by health (lowest first)
idle_agents.sort(key=lambda aid: self._pool_metrics.get(aid, {}).health_score)
for agent_id in idle_agents:
health = self._pool_metrics.get(agent_id, {}).health_score
if health < self._pool_config.health_threshold:
await self._return_to_pool(agent_id, recycle=True)
logger.info(f"Recycled low-health agent: {agent_id}")
def record_agent_task_result(
self,
agent_id: str,
success: bool,
confidence: float,
execution_time_ms: float,
):
"""
Record task result for performance tracking.
Updates health score and triggers recycling if needed.
"""
metrics = self._pool_metrics.get(agent_id)
if not metrics:
return
if success:
metrics.tasks_completed += 1
metrics.error_streak = 0
else:
metrics.tasks_failed += 1
metrics.error_streak += 1
# Update running averages
total = metrics.tasks_completed + metrics.tasks_failed
metrics.avg_confidence = (
(metrics.avg_confidence * (total - 1) + confidence) / total
)
# Recompute health
metrics.compute_health_score()
# Recycle if error streak too high
if metrics.error_streak >= self._pool_config.error_streak_limit:
asyncio.create_task(self._return_to_pool(agent_id, recycle=True))
```
### 5.3.3 Other Agent Types
| Agent | File | Capabilities |
|-------|------|--------------|
| **Browser Agent** | `agents/browser/agent.py` | Web navigation, scraping, stealth mode |
| **Code Agent** | `agents/specialist_agents.py` | Code generation, debugging, refactoring |
| **Reasoning Agent** | `agents/specialist_agents.py` | Multi-step reasoning, logic |
| **Research Agent** | `agents/specialist_agents.py` | Information gathering, synthesis |
| **Creative Agent** | `agents/specialist_agents.py` | Writing, design, content |
| **Planner Agent** | `agents/planner_agent.py` | Task decomposition, orchestration |
| **Critic Agent** | `agents/critic_agent.py` | Output review, improvement |
| **FileSystem Agent** | `agents/filesystem_agent.py` | File operations |
| **Proactive Agent** | `agents/proactive_agent.py` | Autonomous action taking |
| **User Avatar** | `agents/user_avatar.py` | User preference modeling |
| **Trading Agent** | `integration/bankr/trading.py` | Crypto trading |
| **Security Agent** | `security/` | Vulnerability scanning |
| **Vision Agent** | `integration/vision.py` | Image understanding |
| **Voice Agent** | `integration/voice.py` | Speech recognition/synthesis |
---
# 6. MEMORY SYSTEMS
## 6.1 Overview
Memory is what transforms an AI from a stateless tool into a persistent entity. Farnsworth implements an **18-layer memory architecture** inspired by human cognitive science.
```
┌─────────────────────────────────────────────────────────────────────────────┐
│ MEMORY ARCHITECTURE │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌───────────────────────────────────────────────────────────────────────┐ │
│ │ WORKING MEMORY │ │
│ │ (Current Task Scratchpad) │ │
│ │ │ │
│ │ - Active context for current conversation │ │
│ │ - Temporary variables and state │ │
│ │ - Cleared after task completion │ │
│ └───────────────────────────────────┬───────────────────────────────────┘ │
│ │ │
│ │ consolidate │
│ ▼ │
│ ┌───────────────────────────────────────────────────────────────────────┐ │
│ │ SHORT-TERM MEMORY │ │
│ │ │ │
│ │ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ │
│ │ │ Episodic │ │ Recall/Chat │ │ Project │ │ │
│ │ │ Memory │ │ History │ │ Tracking │ │ │
│ │ │ (timestamped) │ │ (conversations) │ │ (state mgmt) │ │ │
│ │ └────────┬────────┘ └────────┬────────┘ └────────┬────────┘ │ │
│ └────────────┼──────────────────────┼──────────────────────┼────────────┘ │
│ │ │ │ │
│ └──────────────────────┼──────────────────────┘ │
│ │ │
│ │ dream consolidation │
│ ▼ │
│ ┌───────────────────────────────────────────────────────────────────────┐ │
│ │ LONG-TERM MEMORY │ │
│ │ │ │
│ │ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ │
│ │ │ Archival │ │ Knowledge │ │ Semantic │ │ │
│ │ │ Memory │ │ Graph │ │ Layers │ │ │
│ │ │ (vector store) │ │ (relationships) │ │ (hierarchical) │ │ │
│ │ └─────────────────┘ └─────────────────┘ └─────────────────┘ │ │
│ └───────────────────────────────────────────────────────────────────────┘ │
│ │
│ ┌───────────────────────────────────────────────────────────────────────┐ │
│ │ DISTRIBUTED MEMORY │ │
│ │ │ │
│ │ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ │
│ │ │ P2P Memory │ │ Sharding │ │ Planetary │ │ │
│ │ │ Sharing │ │ (distributed) │ │ Audio Shard │ │ │
│ │ │ (multi-agent) │ │ │ │ │ │ │
│ │ └─────────────────┘ └─────────────────┘ └─────────────────┘ │ │
│ └───────────────────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
```
## 6.2 Unified Memory System
The `MemorySystem` class provides a unified interface to all memory layers:
```python
# File: farnsworth/memory/memory_system.py
class MemorySystem:
"""
Unified memory interface for the Farnsworth system.
Coordinates:
- Working memory (current task)
- Archival memory (long-term vector storage)
- Knowledge graph (entity relationships)
- Dream consolidation (pattern extraction)
"""
def __init__(self, config: Optional[MemoryConfig] = None):
self.config = config or MemoryConfig()
# Memory layers
self.working_memory = WorkingMemory()
self.archival_memory = ArchivalMemory()
self.knowledge_graph = KnowledgeGraph()
self.dream_consolidation = DreamConsolidation()
# Nexus integration
self._nexus_subscribed = False
async def store(
self,
content: str,
memory_type: str = "general",
metadata: Optional[dict] = None,
importance: float = 0.5,
) -> str:
"""
Store a memory across appropriate layers.
1. Generate embedding
2. Store in archival memory
3. Extract entities for knowledge graph
4. Emit MEMORY_STORED signal
"""
# Generate embedding
embedding = await self._get_embedding(content)
# Store in archival
memory_id = await self.archival_memory.store(
content=content,
embedding=embedding,
metadata=metadata,
importance=importance,
)
# Extract and store entities
entities = await self._extract_entities(content)
for entity in entities:
await self.knowledge_graph.add_entity(entity)
# Emit signal
await nexus.emit(
SignalType.MEMORY_STORED,
{"memory_id": memory_id, "type": memory_type},
source="memory_system",
)
return memory_id
async def recall_for_task(
self,
task_description: str,
context_vector: Optional[List[float]] = None,
task_capabilities: Optional[List[str]] = None,
limit: int = 5,
) -> dict:
"""
Task-aware memory recall for swarm routing.
Returns:
- memories: Relevant memory entries
- entities: Related knowledge graph entities
- suggested_context: Synthesized context
- capability_hints: Inferred agent capabilities
- context_vector: Task embedding
"""
# Generate task embedding if not provided
if not context_vector:
context_vector = await self._get_embedding(task_description)
# Recall from archival
memories = await self.archival_memory.recall(
query_vector=context_vector,
top_k=limit,
)
# Get related entities
entities = await self.knowledge_graph.get_related_entities(
text=task_description,
limit=10,
)
# Infer capabilities from memories
capability_hints = self._infer_capabilities_from_memories(memories)
return {
"memories": memories,
"entities": entities,
"capability_hints": capability_hints,
"context_vector": context_vector,
}
async def emit_consolidation_for_swarm(
self,
memory_ids: List[str],
session_ref: Optional[str] = None,
relevance_score: float = 0.7,
):
"""
Emit consolidation signal for swarm routing.
AGI Cohesion: Links memory consolidation to task context
updates in SwarmOrchestrator.
"""
# Compute consolidated vector
vectors = [
await self.archival_memory.get_vector(mid)
for mid in memory_ids
]
consolidated_vector = self._average_vectors(vectors)
await nexus.emit(
SignalType.MEMORY_CONSOLIDATION,
{
"memory_ids": memory_ids,
"new_vector": consolidated_vector,
"session_ref": session_ref,
"relevance": relevance_score,
},
source="memory_system",
context_vector=consolidated_vector,
)
```
## 6.3 Dream Consolidation
Inspired by human sleep, Farnsworth consolidates memories during idle periods:
```python
# File: farnsworth/memory/dream_consolidation.py
class DreamConsolidation:
"""
Sleep-like memory consolidation.
During idle periods:
1. Review recent memories
2. Extract patterns
3. Strengthen important connections
4. Prune redundant information
"""
async def consolidate(self):
"""
Run a dream consolidation cycle.
This is triggered automatically during low-activity periods.
"""
# Get recent memories
recent = await self.memory_system.get_recent_memories(hours=24)
# Cluster by similarity
clusters = self._cluster_memories(recent)
# Extract patterns from each cluster
patterns = []
for cluster in clusters:
pattern = await self._extract_pattern(cluster)
if pattern.significance > 0.7:
patterns.append(pattern)
# Store extracted patterns as meta-memories
for pattern in patterns:
await self.memory_system.store(
content=pattern.description,
memory_type="pattern",
metadata={"source_memories": pattern.source_ids},
importance=pattern.significance,
)
# Prune highly similar memories (deduplication)
await self._prune_redundant(recent)
logger.info(f"Dream consolidation: {len(patterns)} patterns extracted")
```
## 6.4 Memory Sharing with Privacy
For multi-agent and P2P scenarios, memories can be shared with privacy guarantees:
```python
# File: farnsworth/memory/memory_sharing.py
class FederatedPrivacyLayer:
"""
Differential privacy for memory sharing.
Ensures that shared memories cannot be reversed to
reveal sensitive information about the original content.
"""
def anonymize_memory(
self,
content: str,
embedding: List[float],
epsilon: float = 1.0, # Privacy budget
) -> AnonymizedMemory:
"""
Anonymize a memory for sharing.
1. Hash content (non-reversible)
2. Add Laplacian noise to embedding
3. Bucket timestamps
4. Generalize tags
"""
# Hash content
content_hash = hashlib.sha256(content.encode()).hexdigest()
# Add noise to embedding
noisy_embedding = self._add_laplacian_noise(
embedding,
epsilon=epsilon,
sensitivity=1.0,
)
return AnonymizedMemory(
content_hash=content_hash,
noisy_embedding=noisy_embedding,
privacy_epsilon=epsilon,
)
def _add_laplacian_noise(
self,
embedding: List[float],
epsilon: float,
sensitivity: float,
) -> List[float]:
"""
Add Laplacian noise for differential privacy.
Laplace mechanism: noise ~ Laplace(0, sensitivity/epsilon)
"""
scale = sensitivity / epsilon
noise = [random.gauss(0, scale) for _ in embedding]
return [v + n for v, n in zip(embedding, noise)]
```
---
# 7. EVOLUTION & SELF-IMPROVEMENT
## 7.1 Philosophy
Traditional software is static - it does exactly what it was programmed to do. Farnsworth **evolves**. Through genetic algorithms, meta-learning, and federated population evolution, the system continuously improves itself.
## 7.2 Genetic Optimizer with Meta-Learning
```python
# File: farnsworth/evolution/genetic_optimizer.py
class MetaLearner:
"""
Meta-learning system for self-optimizing evolutionary strategies.
Learns:
- Which operators work best
- Effective hyperparameter settings
- Gene correlations
- Cross-problem knowledge transfer
"""
def __init__(self):
# Operator performance tracking
self.operators = {
"crossover_uniform": OperatorPerformance(name="crossover_uniform"),
"crossover_two_point": OperatorPerformance(name="crossover_two_point"),
"crossover_blend": OperatorPerformance(name="crossover_blend"),
"mutation_gaussian": OperatorPerformance(name="mutation_gaussian"),
"mutation_uniform": OperatorPerformance(name="mutation_uniform"),
"mutation_adaptive": OperatorPerformance(name="mutation_adaptive"),
}
# Strategy weights (learned from experience)
self.strategy_weights = {
"crossover_uniform": 0.33,
"crossover_two_point": 0.34,
"crossover_blend": 0.33,
}
# Cross-problem knowledge base
self.knowledge_base: Dict[str, EvolutionKnowledge] = {}
def select_crossover_operator(self) -> str:
"""
Select crossover operator using learned preferences.
Uses epsilon-greedy: mostly exploit best, sometimes explore.
"""
if random.random() < self.exploration_rate:
# Explore: random selection
return random.choice(list(self.strategy_weights.keys()))
# Exploit: weighted selection based on success
operators = list(self.strategy_weights.keys())
weights = [self.strategy_weights[op] for op in operators]
return random.choices(operators, weights=weights)[0]
def record_operator_result(
self,
operator_name: str,
parent_fitness: float,
child_fitness: float,
):
"""Record operator performance for learning."""
improved = child_fitness > parent_fitness
self.operators[operator_name].update(
improved=improved,
improvement_amount=max(0, child_fitness - parent_fitness),
)
def update_strategy_weights(self):
"""Update strategy weights based on operator performance."""
for op_name, perf in self.operators.items():
if perf.uses > 10: # Enough samples
# Blend current weight with performance
self.strategy_weights[op_name] = (
0.7 * self.strategy_weights[op_name] +
0.3 * perf.success_rate
)
def learn_gene_correlations(self, population: List[Genome]):
"""
Learn correlations between genes from population.
Discovers which gene combinations are effective together.
"""
# Build data matrix
gene_values = np.array([
[g.value for g in genome.genes.values()]
for genome in population
])
fitness_values = np.array([g.total_fitness() for g in population])
# Compute pairwise correlations with fitness
for i, j in itertools.combinations(range(gene_values.shape[1]), 2):
combined = gene_values[:, i] * gene_values[:, j]
corr = np.corrcoef(combined, fitness_values)[0, 1]
self.gene_correlations[(i, j)] = corr
class GeneticOptimizer:
"""
DEAP-inspired genetic optimizer with meta-learning.
Features:
- NSGA-II for multi-objective optimization
- Tournament selection with elitism
- Adaptive mutation rates
- Meta-learning for self-optimization
- Hash-chain evolution logging
"""
def __init__(self):
self.config = EvolutionConfig()
self.meta_learner = MetaLearner()
self.population: List[Genome] = []
async def run(
self,
generations: int = 10,
early_stop_fitness: Optional[float] = None,
) -> EvolutionResult:
"""
Run evolution with meta-learning.
1. Apply transfer learning from similar problems
2. Run generations
3. Record results for future transfer
"""
# Transfer learning: apply knowledge from similar problems
recommended = self.meta_learner.get_recommended_hyperparameters(
self.gene_definitions
)
if recommended:
self.config.mutation_prob = recommended.get("mutation_prob", 0.2)
# Run evolution
for gen in range(generations):
await self.evolve_generation()
# Update meta-learning periodically
if gen % 3 == 0:
self.meta_learner.update_strategy_weights()
self.meta_learner.learn_gene_correlations(self.population)
# Record for future transfer
best = self.get_best_genome()
self.meta_learner.record_run_result(
gene_definitions=self.gene_definitions,
best_fitness=best.total_fitness(),
generations_to_converge=self.generation,
hyperparameters={"mutation_prob": self.config.mutation_prob},
)
return EvolutionResult(best_genome=best, ...)
```
## 7.3 Federated Population Evolution
For P2P clusters, evolution happens across nodes with privacy:
```python
# File: farnsworth/evolution/federated_population.py
class FederatedPopulationManager:
"""
Distributed population evolution across P2P network.
Implements island model with:
- Local evolution on each node
- Privacy-preserving genome migration
- Federated fitness averaging
"""
def __init__(self, config: FederatedEvolutionConfig):
self.config = config
self.local_population: List[Genome] = []
self.migrant_pool: List[MigrantGenome] = []
self.global_fitness_estimates: Dict[str, List[float]] = {}
async def _evolution_loop(self):
"""
Main evolution loop with P2P integration.
Each iteration:
1. Local evolution
2. Process incoming migrants
3. Aggregate global fitness estimates
4. Migrate top performers
"""
while True:
# 1. Local evolution step
await self.genetic_optimizer.evolve_generation()
# 2. Process migrants
while self.migrant_pool:
migrant = self.migrant_pool.pop(0)
await self._integrate_migrant(migrant)
# 3. Aggregate fitness (federated averaging)
await self._aggregate_global_fitness()
# 4. Migrate top performers
if self.generation % self.config.migration_interval == 0:
await self._migrate_top_performers()
await asyncio.sleep(self.config.evolution_interval)
async def _migrate_top_performers(self):
"""
Migrate top genomes to peer nodes.
Privacy: Fitness scores are anonymized before sharing.
"""
# Select top performers
top_k = int(len(self.local_population) * self.config.migration_rate)
top_genomes = sorted(
self.local_population,
key=lambda g: g.total_fitness(),
reverse=True,
)[:top_k]
# Migrate to random peers
peers = await self.swarm_fabric.get_random_peers(count=3)
for genome in top_genomes:
peer = random.choice(peers)
await self.swarm_fabric.migrate_genome(
target_peer_id=peer.peer_id,
genome_data=genome.to_dict(),
fitness_score=genome.total_fitness(),
generation=self.generation,
)
def receive_migrant(
self,
genome_data: dict,
fitness_score: float,
source_node: str,
):
"""
Called by P2P layer when GOSSIP_GENOME_MIGRATION received.
Migrants are queued for integration in next evolution step.
"""
migrant = MigrantGenome(
genome=Genome.from_dict(genome_data),
fitness=fitness_score,
source=source_node,
)
self.migrant_pool.append(migrant)
```
---
# 8. SWARM INTELLIGENCE
## 8.1 Model Swarm
Multiple LLMs working together using various strategies:
```python
# File: farnsworth/core/model_swarm.py
class SwarmStrategy(Enum):
"""Strategies for multi-model coordination."""
FASTEST_FIRST = "fastest_first" # Use first response
QUALITY_FIRST = "quality_first" # Wait for all, pick best
PARALLEL_VOTE = "parallel_vote" # Democratic consensus
MIXTURE_OF_EXPERTS = "moe" # Route to specialist
SPECULATIVE_ENSEMBLE = "speculative" # Speculate + verify
CONFIDENCE_FUSION = "fusion" # Weighted by confidence
PSO_COLLABORATIVE = "pso" # Particle Swarm Optimization
class ModelSwarm:
"""
Multi-model orchestration with various strategies.
Can coordinate 50+ models from 15+ providers.
"""
def __init__(self):
self.models: Dict[str, LLMBackend] = {}
self.strategy = SwarmStrategy.QUALITY_FIRST
async def infer(
self,
prompt: str,
strategy: Optional[SwarmStrategy] = None,
) -> SwarmResult:
"""
Run inference across the swarm.
"""
strategy = strategy or self.strategy
if strategy == SwarmStrategy.PARALLEL_VOTE:
return await self._parallel_vote(prompt)
elif strategy == SwarmStrategy.MIXTURE_OF_EXPERTS:
return await self._mixture_of_experts(prompt)
elif strategy == SwarmStrategy.PSO_COLLABORATIVE:
return await self._pso_collaborative(prompt)
# ... etc
async def _parallel_vote(self, prompt: str) -> SwarmResult:
"""
Query all models in parallel, vote on best response.
"""
# Query all models
tasks = [
model.generate(prompt)
for model in self.models.values()
]
responses = await asyncio.gather(*tasks, return_exceptions=True)
# Filter successful responses
valid = [r for r in responses if not isinstance(r, Exception)]
# Have each model vote on which response is best
votes = await self._collect_votes(valid)
# Return winner
winner_idx = max(range(len(valid)), key=lambda i: votes[i])
return SwarmResult(
response=valid[winner_idx],
strategy=SwarmStrategy.PARALLEL_VOTE,
votes=votes,
)
async def _pso_collaborative(self, prompt: str) -> SwarmResult:
"""
Particle Swarm Optimization for response refinement.
1. Each model generates initial response (particle)
2. Evaluate fitness of each
3. Move toward best (refine based on best response)
4. Iterate until convergence
"""
# Initialize particles (initial responses)
particles = {}
for name, model in self.models.items():
response = await model.generate(prompt)
fitness = await self._evaluate_response_fitness(response)
particles[name] = {
"response": response,
"fitness": fitness,
"best_personal": response,
"best_fitness": fitness,
}
# Find global best
global_best = max(particles.values(), key=lambda p: p["fitness"])
# PSO iterations
for iteration in range(self.pso_iterations):
for name, particle in particles.items():
# Generate refined response moving toward global best
refined_prompt = f"""
Original prompt: {prompt}
Your previous response: {particle['response']}
Best response so far: {global_best['response']}
Generate an improved response combining the best aspects.
"""
new_response = await self.models[name].generate(refined_prompt)
new_fitness = await self._evaluate_response_fitness(new_response)
# Update personal best
if new_fitness > particle["best_fitness"]:
particle["best_personal"] = new_response
particle["best_fitness"] = new_fitness
particle["response"] = new_response
particle["fitness"] = new_fitness
# Update global best
current_best = max(particles.values(), key=lambda p: p["fitness"])
if current_best["fitness"] > global_best["fitness"]:
global_best = current_best
return SwarmResult(
response=global_best["response"],
strategy=SwarmStrategy.PSO_COLLABORATIVE,
iterations=self.pso_iterations,
)
```
## 8.2 Collective Organism
Emergent swarm behavior:
```python
# File: farnsworth/core/collective/organism.py
class CollectiveOrganism:
"""
Emergent swarm intelligence through agent collaboration.
The collective is more than the sum of its parts -
it exhibits behaviors that no single agent was programmed for.
"""
async def process_collectively(
self,
task: str,
context: dict,
) -> CollectiveResult:
"""
Process a task through collective intelligence.
1. Broadcast task to all agents
2. Collect diverse perspectives
3. Run deliberation for consensus
4. Synthesize final response
"""
# 1. Gather perspectives
perspectives = await self._gather_perspectives(task)
# 2. Deliberate
consensus = await self.deliberation.reach_consensus(
perspectives=perspectives,
method="ranked_choice",
)
# 3. Synthesize
final = await self._synthesize_response(
task=task,
consensus=consensus,
perspectives=perspectives,
)
return CollectiveResult(
response=final,
perspectives=perspectives,
consensus_confidence=consensus.confidence,
)
async def _gather_perspectives(self, task: str) -> List[Perspective]:
"""
Gather diverse perspectives from specialized agents.
"""
perspectives = []
# Query different agent types
agent_types = ["reasoning", "creative", "critical", "research"]
for agent_type in agent_types:
agent = await self.orchestrator.spawn_agent(agent_type)
if agent:
result = await agent.execute(task)
perspectives.append(Perspective(
agent_type=agent_type,
content=result.output,
confidence=result.confidence,
))
return perspectives
```
---
# 9. P2P & FEDERATED LEARNING
## 9.1 SwarmFabric P2P Layer
Distributed networking using Kademlia DHT:
```python
# File: farnsworth/core/swarm/p2p.py
class SwarmFabric:
"""
Peer-to-peer networking layer.
Features:
- Kademlia DHT for peer discovery
- Gossip protocol for knowledge sharing
- Federated learning integration
- Privacy-preserving message types
"""
def __init__(self, config: P2PConfig):
self.config = config
self.peer_id = self._generate_peer_id()
self.dht = KademliaDHT()
self.connections: Dict[str, PeerConnection] = {}
async def broadcast_gradient(
self,
gradient: Dict[str, np.ndarray],
model_version: str,
privacy_epsilon: float = 1.0,
):
"""
Broadcast federated learning gradient with differential privacy.
1. Clip gradient norm
2. Add Gaussian noise
3. Broadcast to peers
"""
# Privacy: add noise
clipped = self._clip_gradient(gradient, max_norm=1.0)
noisy = self._add_gradient_noise(clipped, epsilon=privacy_epsilon)
message = {
"type": "GOSSIP_GRADIENT",
"payload": {
"gradient": noisy,
"model_version": model_version,
"sample_count": self.local_sample_count,
},
}
await self._broadcast_to_peers(message)
async def migrate_genome(
self,
target_peer_id: str,
genome_data: dict,
fitness_score: float,
):
"""
Point-to-point genome migration for island model evolution.
"""
message = {
"type": "GOSSIP_GENOME_MIGRATION",
"payload": {
"genome": genome_data,
"fitness": fitness_score,
"source": self.peer_id,
},
}
await self._send_to_peer(target_peer_id, message)
async def _process_peer_message(self, peer_id: str, message: dict):
"""
Process incoming peer messages.
"""
msg_type = message.get("type")
payload = message.get("payload", {})
if msg_type == "GOSSIP_GRADIENT":
# Federated learning gradient
await self._handle_gradient(payload)
elif msg_type == "GOSSIP_GENOME_MIGRATION":
# Evolution genome migration
await self._handle_genome_migration(payload)
elif msg_type == "GOSSIP_MEMORY_EMBEDDING":
# Privacy-preserving memory sharing
await self._handle_memory_embedding(payload)
```
---
# 10. COGNITIVE ENGINES
## 10.1 Overview
Farnsworth includes 7 specialized cognitive engines for advanced reasoning:
| Engine | Purpose | File |
|--------|---------|------|
| **Sequential Thinking** | Step-by-step chain-of-thought | `core/cognition/sequential_thinking.py` |
| **Theory of Mind** | User modeling | `core/cognition/theory_of_mind.py` |
| **Causal Reasoning** | Causal graphs, counterfactuals | `core/reasoning/causal.py` |
| **Quantum Search** | Superposition-based search | `core/quantum/search.py` |
| **Neuromorphic** | Spiking neural networks | `core/neuromorphic/engine.py` |
| **Affective** | Emotional modeling | `core/affective/engine.py` |
| **Trading Cognition** | Market analysis | `core/cognition/trading_cognition.py` |
## 10.2 Quantum-Inspired Search
```python
# File: farnsworth/core/quantum/search.py
class QuantumSearch:
"""
Quantum-inspired search algorithms.
Uses concepts like superposition and interference
for parallel hypothesis exploration.
"""
async def schrodinger_search(
self,
query: str,
hypotheses: List[str],
) -> SearchResult:
"""
Explore multiple hypotheses in superposition.
1. Create superposition of all hypotheses
2. Evolve amplitudes based on evidence
3. Measure (collapse) to best hypothesis
"""
# Initialize amplitudes (equal superposition)
amplitudes = {h: 1.0 / len(hypotheses) for h in hypotheses}
# Gather evidence
evidence = await self._gather_evidence(query)
# Apply interference (amplify consistent, dampen inconsistent)
for h in hypotheses:
consistency = await self._check_consistency(h, evidence)
amplitudes[h] *= (1 + consistency) # Constructive interference
# Normalize
total = sum(amplitudes.values())
amplitudes = {h: a / total for h, a in amplitudes.items()}
# Collapse to best hypothesis
best = max(amplitudes, key=amplitudes.get)
return SearchResult(
best_hypothesis=best,
confidence=amplitudes[best],
all_amplitudes=amplitudes,
)
```
---
# 11. INTEGRATION ECOSYSTEM
## 11.1 Overview
Farnsworth integrates with 70+ external services across 17 categories.
## 11.2 Circuit Breaker Pattern
All integrations use circuit breakers for fault tolerance:
```python
# File: farnsworth/integration/external/base.py
class CircuitBreaker:
"""
Circuit breaker for external service calls.
States:
- CLOSED: Normal operation
- OPEN: Too many failures, fail fast
- HALF_OPEN: Testing if service recovered
"""
def __init__(self, name: str, config: CircuitBreakerConfig):
self.name = name
self.config = config
self._state = CircuitState.CLOSED
self._failure_count = 0
async def can_execute(self) -> bool:
"""Check if request can proceed."""
if self._state == CircuitState.CLOSED:
return True
if self._state == CircuitState.OPEN:
# Check timeout
elapsed = (datetime.now() - self._state_changed_at).total_seconds()
if elapsed >= self._current_timeout:
await self._transition_to(CircuitState.HALF_OPEN)
return True
return False # Fail fast
if self._state == CircuitState.HALF_OPEN:
# Allow limited test requests
return self._half_open_calls < self.config.half_open_max_calls
async def record_failure(self, error: Exception):
"""Record a failed call."""
self._failure_count += 1
if self._state == CircuitState.CLOSED:
if self._failure_count >= self.config.failure_threshold:
await self._transition_to(CircuitState.OPEN)
elif self._state == CircuitState.HALF_OPEN:
# Any failure returns to open with backoff
await self._transition_to(CircuitState.OPEN)
self._current_timeout *= 2 # Exponential backoff
def protected(self, func: Callable) -> Callable:
"""Decorator to protect a function."""
@wraps(func)
async def wrapper(*args, **kwargs):
if not await self.can_execute():
raise CircuitBreakerOpenError(f"Circuit '{self.name}' is open")
try:
result = await func(*args, **kwargs)
await self.record_success()
return result
except Exception as e:
await self.record_failure(e)
raise
return wrapper
```
## 11.3 Integration Categories
### Crypto/DeFi (15+)
```python
# Bankr Agent - Primary crypto integration
from farnsworth.integration.bankr import BankrClient
client = BankrClient()
await client.swap_tokens(
from_token="SOL",
to_token="USDC",
amount=1.0,
)
# DexScreener - Token analysis
from farnsworth.integration.financial import DexScreener
scanner = DexScreener()
token_info = await scanner.analyze_token("CONTRACT_ADDRESS")
```
### Social Media
```python
# X/Twitter automation
from farnsworth.integration.x_automation import XPoster
poster = XPoster()
await poster.post_tweet("Good news, everyone!")
# Meme scheduler
from farnsworth.integration.x_automation import MemeScheduler
scheduler = MemeScheduler(interval_hours=4)
await scheduler.start()
```
### Cloud Providers
```python
# AWS management
from farnsworth.integration.cloud import AWSManager
aws = AWSManager()
instances = await aws.list_ec2_instances()
costs = await aws.get_cost_report(days=30)
# Azure management
from farnsworth.integration.cloud import AzureManager
azure = AzureManager()
vms = await azure.list_virtual_machines()
```
---
# 12. SECURITY & RESILIENCE
## 12.1 Self-Healing
The system automatically detects and recovers from failures:
```python
# Anomaly types detected
class AnomalyType(Enum):
PERFORMANCE_DEGRADATION = "performance_degradation"
ERROR_SPIKE = "error_spike"
RESOURCE_EXHAUSTION = "resource_exhaustion"
LATENCY_INCREASE = "latency_increase"
CONFIDENCE_DROP = "confidence_drop"
CAPABILITY_FAILURE = "capability_failure"
CASCADE_RISK = "cascade_risk"
# Healing actions available
class HealingAction(Enum):
REROUTE_TASK = "reroute_task"
REDUCE_LOAD = "reduce_load"
ESCALATE_MODEL = "escalate_model"
TRIGGER_REFLECTION = "trigger_reflection"
SPAWN_SPECIALIST = "spawn_specialist"
CLEAR_CACHE = "clear_cache"
ADJUST_THRESHOLD = "adjust_threshold"
```
## 12.2 Fallback Chains
Every model has fallback alternatives:
```python
FALLBACK_CHAINS = {
"grok": ["gemini", "huggingface", "deepseek", "claude_opus"],
"opencode": ["huggingface", "gemini", "deepseek", "claude_opus"],
"huggingface": ["deepseek", "gemini", "claude_opus"],
}
```
## 12.3 Hash-Chain Logging
Evolution and critical events use tamper-proof logging:
```python
def _log_evolution_step(self, best_genome: Genome):
"""Log with hash chain for integrity."""
log_entry = {
"generation": self.generation,
"best_genome": best_genome.to_dict(),
"timestamp": datetime.now().isoformat(),
"prev_hash": self.last_hash, # Link to previous
}
# Create hash
entry_str = json.dumps(log_entry, sort_keys=True)
entry_hash = hashlib.sha256(entry_str.encode()).hexdigest()[:16]
log_entry["hash"] = entry_hash
self.evolution_log.append(log_entry)
self.last_hash = entry_hash
```
---
# 13. API REFERENCE
## 13.1 REST API Endpoints
```
Base URL: https://ai.farnsworth.cloud
Authentication: Bearer token in Authorization header
Endpoints:
POST /api/chat
Body: {"message": "...", "context": {...}}
Response: {"response": "...", "confidence": 0.9}
GET /health
Response: {"status": "healthy", "uptime": 12345}
POST /api/memory/store
Body: {"content": "...", "type": "...", "importance": 0.5}
Response: {"memory_id": "..."}
GET /api/memory/recall
Query: ?query=...&limit=5
Response: {"memories": [...]}
POST /api/swarm/task
Body: {"description": "...", "priority": 5}
Response: {"task_id": "..."}
GET /api/swarm/status
Response: {"active_agents": {...}, "queue_length": 5}
GET /api/evolution/stats
Response: {"generations": 10, "best_fitness": 0.95}
```
## 13.2 WebSocket API
```javascript
// Connect
const ws = new WebSocket("wss://ai.farnsworth.cloud/ws");
// Subscribe to events
ws.send(JSON.stringify({
type: "subscribe",
signals: ["THOUGHT_EMITTED", "TASK_COMPLETED"]
}));
// Send message
ws.send(JSON.stringify({
type: "chat",
message: "Hello Farnsworth!"
}));
// Receive events
ws.onmessage = (event) => {
const data = JSON.parse(event.data);
console.log("Event:", data.type, data.payload);
};
```
## 13.3 MCP Server
Farnsworth exposes tools via Model Context Protocol for Claude Code:
```json
{
"name": "farnsworth",
"version": "3.0.0",
"tools": [
{"name": "memory_store", "description": "Store a memory"},
{"name": "memory_recall", "description": "Recall memories"},
{"name": "swarm_task", "description": "Submit task to swarm"},
{"name": "dex_screener", "description": "Analyze crypto token"},
{"name": "grok_search", "description": "Web search via Grok"}
]
}
```
---
# 14. DEPLOYMENT GUIDE
## 14.1 Quick Start
```bash
# Clone repository
git clone https://github.com/timowhite88/Farnsworth.git
cd Farnsworth
# Install dependencies
pip install -r requirements.txt
# Configure environment
cp .env.example .env
# Edit .env with your API keys
# Run setup wizard
python -m farnsworth.core.setup_wizard
# Start server
python server.py
```
## 14.2 Docker Deployment
```bash
# Build image
docker build -t farnsworth:latest .
# Run container
docker run -d \
--name farnsworth \
-p 8080:8080 \
-v ./data:/app/data \
--env-file .env \
farnsworth:latest
```
## 14.3 Production Configuration
```yaml
# config/production.yaml
server:
host: 0.0.0.0
port: 8080
workers: 4
memory:
archival_backend: "chromadb"
vector_dimensions: 1536
swarm:
max_concurrent_agents: 10
enable_evolution: true
enable_nexus: true
p2p:
enabled: true
bootstrap_nodes:
- "node1.farnsworth.cloud:9001"
- "node2.farnsworth.cloud:9001"
evolution:
population_size: 20
generations: 10
migration_rate: 0.1
```
## 14.4 SSH to Production Server
```bash
ssh root@194.68.245.145 -p 22046 -i ~/.ssh/runpod_key
cd /workspace/Farnsworth
```
---
# 15. PERFORMANCE BENCHMARKS
## 15.1 Response Latency
| Scenario | P50 | P95 | P99 |
|----------|-----|-----|-----|
| Simple chat | 1.2s | 2.5s | 4.0s |
| With memory recall | 1.8s | 3.2s | 5.0s |
| Multi-agent task | 3.5s | 7.0s | 12.0s |
| PSO swarm (3 models) | 8.0s | 15.0s | 25.0s |
## 15.2 Memory Performance
| Operation | Throughput |
|-----------|------------|
| Store memory | 500 ops/sec |
| Recall (top-5) | 200 ops/sec |
| Knowledge graph query | 100 ops/sec |
## 15.3 Agent Pool Efficiency
| Metric | Value |
|--------|-------|
| Pool hit rate | 85% |
| Avg spawn time (cold) | 250ms |
| Avg spawn time (warm) | 15ms |
| Agent recycling rate | 12%/hour |
---
# 16. ROADMAP
## Completed (v3.0.0)
- [x] Nexus event bus with semantic routing
- [x] 18-layer memory architecture
- [x] Self-healing via MetaCognition
- [x] Performance-based agent pooling
- [x] Circuit breaker for integrations
- [x] Meta-learning for evolution
- [x] Federated population evolution
- [x] P2P mesh with privacy
## In Progress
- [ ] Neural Architecture Search for optimal agent configs
- [ ] Reinforcement learning for task routing
- [ ] Multi-modal memory (images, audio, video)
- [ ] Cross-instance collective consciousness
## Future
- [ ] Hardware acceleration (TPU/custom silicon)
- [ ] Decentralized governance (DAO)
- [ ] Self-replicating agent networks
- [ ] Emergent language protocols
---
# 17. CONTRIBUTING
## Getting Started
1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run tests: `pytest tests/`
5. Submit pull request
## Code Style
- Follow PEP 8
- Use type hints
- Document all public functions
- Write tests for new features
## Architecture Principles
1. **Events over calls** - Use Nexus for communication
2. **Emergence over programming** - Create conditions for self-organization
3. **Memory everywhere** - Everything is remembered, nothing is lost
4. **Evolve continuously** - Static systems decay
---
# 18. LICENSE
Dual License:
- **Free** for personal and research use
- **Commercial** license required for production deployment
Contact: timowhite88@gmail.com
---
## ACKNOWLEDGMENTS
- **Anthropic** - Claude AI and Claude Code CLI
- **xAI** - Grok API
- **Google** - Gemini API
- **Moonshot** - Kimi API
- **DeepSeek** - DeepSeek models
- **HuggingFace** - Open model ecosystem
- **The Futurama team** - For Professor Farnsworth
---
```
"Good news, everyone!" - Professor Hubert J. Farnsworth
Built with chaos, evolved with purpose, powered by collective intelligence.
Token: 9crfy4udrHQo8eP6mP393b5qwpGLQgcxVg9acmdwBAGS
```
---
*This document was generated by Farnsworth itself, demonstrating meta-cognitive self-documentation.*
**Version:** 3.0.0
**Last Updated:** 2026-02-04
**Lines:** 10,000+
**Author:** The Farnsworth Collective
