Building Earth's Unified Memory System for Artificial General Intelligence
"From Data to Memory: Bridging Artificial Intelligence with Earth's Observable Reality"
memories-dev represents a paradigm shift in grounding artificial intelligence systems through Earth observation data. Current foundation models suffer from hallucinations and limited temporal understanding of real-world physical environments. This framework implements a multi-tiered memory architecture that integrates real-time satellite imagery, geospatial vectors, sensor networks, and environmental metrics to create a comprehensive memory system of Earth's observable state.
Our approach demonstrates significant improvements in AI factuality when reasoning about geographic environments:
These capabilities are achieved through our novel memory management system utilizing specialized Earth analyzers and a hierarchical approach to data acquisition, processing, and retrieval, all designed with scientific rigor and validation protocols.
Current AI systems face fundamental challenges when reasoning about the physical world:
memories-dev addresses these challenges through:
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graph TD
classDef mainProcess fill:#1e293b,stroke:#334155,stroke-width:2px,color:white,font-weight:bold
classDef dataSource fill:#3b82f6,stroke:#2563eb,stroke-width:2px,color:white,font-weight:bold
classDef processing fill:#ef4444,stroke:#dc2626,stroke-width:2px,color:white,font-weight:bold
classDef storage fill:#10b981,stroke:#059669,stroke-width:2px,color:white,font-weight:bold
classDef analysis fill:#8b5cf6,stroke:#7c3aed,stroke-width:2px,color:white,font-weight:bold
classDef integration fill:#f59e0b,stroke:#d97706,stroke-width:2px,color:white,font-weight:bold
A[Data Acquisition] --> B[Multi-modal Processing]
B --> C[Hierarchical Memory System]
C --> D[Earth Analyzers]
D --> E[LLM Integration]
E --> F[Application Layer]
A1[Satellite Imagery] --> A
A2[Vector Databases] --> A
A3[Sensor Networks] --> A
A4[Environmental APIs] --> A
B1[Data Cleaning] --> B
B2[Format Normalization] --> B
B3[Temporal Alignment] --> B
B4[Spatial Registration] --> B
C1[Hot Memory Tier] --> C
C2[Warm Memory Tier] --> C
C3[Cold Memory Tier] --> C
C4[Glacier Storage Tier] --> C
D1[Terrain Analysis] --> D
D2[Climate Modeling] --> D
D3[Environmental Impact] --> D
D4[Urban Development] --> D
A:::dataSource
A1:::dataSource
A2:::dataSource
A3:::dataSource
A4:::dataSource
B:::processing
B1:::processing
B2:::processing
B3:::processing
B4:::processing
C:::storage
C1:::storage
C2:::storage
C3:::storage
C4:::storage
D:::analysis
D1:::analysis
D2:::analysis
D3:::analysis
D4:::analysis
E:::integration
F:::integration
Multi-Tiered Memory Architecture: Hierarchical organization of Earth observation data across hot, warm, cold, and glacier tiers based on access patterns and query relevance
Asynchronous Earth Analyzers: Specialized processing modules that extract contextual understanding from raw observation data in parallel
Temporal Memory Chains: Algorithms for linking observations across time to enable reasoning about environmental changes
Spatiotemporal Query Engine: Advanced retrieval system that handles complex queries with both location and time components
Multi-Modal Data Fusion: Techniques for combining satellite imagery, vector data, and tabular information into unified memory representations
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graph TD
classDef foundationModels fill:#3498db,stroke:#2980b9,stroke-width:2px,color:white,font-weight:bold
classDef earthMemory fill:#16a085,stroke:#1abc9c,stroke-width:2px,color:white,font-weight:bold
classDef contextNodes fill:#9b59b6,stroke:#8e44ad,stroke-width:2px,color:white
classDef intelligenceNodes fill:#f39c12,stroke:#f1c40f,stroke-width:2px,color:white
classDef memoryNode fill:#e74c3c,stroke:#c0392b,stroke-width:2px,color:white,font-weight:bold
classDef appNode fill:#2c3e50,stroke:#34495e,stroke-width:2px,color:white,font-weight:bold
A[🤖 Foundation Models] -->|Augmented with| B[🌍 Earth Memory]
B -->|Provides| C[📍 Spatial Context]
B -->|Provides| D[⏱️ Temporal Context]
B -->|Provides| E[🌱 Environmental Context]
C -->|Enables| F[📌 Location-Aware Intelligence]
D -->|Enables| G[⏰ Time-Aware Intelligence]
E -->|Enables| H[🌿 Environment-Aware Intelligence]
F --> I[🧠 Collective AGI Memory]
G --> I
H --> I
I -->|Powers| J[🚀 Next-Gen AI Applications]
A:::foundationModels
B:::earthMemory
C:::contextNodes
D:::contextNodes
E:::contextNodes
F:::intelligenceNodes
G:::intelligenceNodes
H:::intelligenceNodes
I:::memoryNode
J:::appNode
linkStyle 0 stroke:#3498db,stroke-width:2px,stroke-dasharray: 5 5
linkStyle 1,2,3 stroke:#16a085,stroke-width:2px
linkStyle 4,5,6 stroke:#9b59b6,stroke-width:2px
The system architecture implements a scientific approach to memory management:
graph TB
classDef primary fill:#2c3e50,stroke:#34495e,stroke-width:2px,color:white,font-weight:bold
classDef secondary fill:#3498db,stroke:#2980b9,stroke-width:2px,color:white
classDef tertiary fill:#1abc9c,stroke:#16a085,stroke-width:2px,color:white
A[Client Application]:::primary --> B[Memory Manager]:::primary
B --> C[Data Acquisition]:::secondary
B --> D[Memory Store]:::secondary
B --> E[Earth Analyzers]:::secondary
B --> F[AI Integration]:::secondary
C --> C1[Satellite Data]:::tertiary
C --> C2[Vector Data]:::tertiary
C --> C3[Sensor Data]:::tertiary
C --> C4[Environmental APIs]:::tertiary
D --> D1[Hot Memory]:::tertiary
D --> D2[Warm Memory]:::tertiary
D --> D3[Cold Memory]:::tertiary
D --> D4[Glacier Storage]:::tertiary
E --> E1[Terrain Analysis]:::tertiary
E --> E2[Climate Analysis]:::tertiary
E --> E3[Environmental Impact]:::tertiary
E --> E4[Urban Development]:::tertiary
F --> F1[Model Connectors]:::tertiary
F --> F2[Context Formation]:::tertiary
F --> F3[Prompt Engineering]:::tertiary
F --> F4[Response Validation]:::tertiary
The scientific processing pipeline ensures data integrity and accessibility:
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sequenceDiagram
participant Client as 📱 Client Application
participant MM as 🧠 Memory Manager
participant DA as 🛰️ Data Acquisition
participant MS as 💾 Memory Store
participant EA as 🔍 Earth Analyzers
participant AI as 🤖 AI Models
Client->>MM: Request Analysis
activate MM
Note over MM: Orchestrates the entire workflow<br/>with parallel processing
par Data Collection Phase
MM->>DA: Fetch Earth Data
activate DA
Note over DA: Multi-source acquisition with<br/>priority-based scheduling
DA-->>MM: Return Raw Data
deactivate DA
and Memory Check Phase
MM->>MS: Query Existing Memories
activate MS
Note over MS: Intelligent caching with<br/>tiered storage strategy
MS-->>MM: Return Cached Results
deactivate MS
end
MM->>EA: Process Earth Data
activate EA
Note over EA: 15+ specialized analyzers<br/>running asynchronously
par Parallel Analysis
EA->>EA: Terrain Analysis
EA->>EA: Climate Analysis
EA->>EA: Environmental Impact
EA->>EA: Urban Development
end
EA-->>MM: Return Analysis Results
deactivate EA
MM->>AI: Enhance with AI Models
activate AI
Note over AI: Multiple model integration<br/>with Earth-grounding
AI-->>MM: Return Enhanced Results
deactivate AI
MM->>MS: Store Results
activate MS
Note over MS: Multi-tier storage based on<br/>access patterns & importance
MS-->>MM: Confirm Storage Complete
deactivate MS
MM-->>Client: Return Comprehensive Analysis
deactivate MM
Note over Client,AI: Complete data lifecycle with<br/>adaptive memory management
The memories-dev framework implements a sophisticated data flow architecture that transforms raw Earth observation data into actionable intelligence through a series of optimized processing stages:
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graph LR
classDef ingestion fill:#1d4ed8,stroke:#1e40af,stroke-width:2px,color:white,font-weight:bold
classDef processing fill:#b91c1c,stroke:#991b1b,stroke-width:2px,color:white,font-weight:bold
classDef storage fill:#047857,stroke:#065f46,stroke-width:2px,color:white,font-weight:bold
classDef analytics fill:#7c3aed,stroke:#6d28d9,stroke-width:2px,color:white,font-weight:bold
classDef delivery fill:#9a3412,stroke:#9a3412,stroke-width:2px,color:white,font-weight:bold
%% Data Ingestion Nodes
A1[Satellite Imagery] --> A
A2[Vector Databases] --> A
A3[Sensor Networks] --> A
A4[Environmental APIs] --> A
A[Data Ingestion Engine] --> B
%% Data Processing Nodes
B --> B1[Data Cleaning]
B --> B2[Feature Extraction]
B --> B3[Temporal Alignment]
B --> B4[Spatial Registration]
B[Multi-Modal Processing] --> C
%% Storage Nodes
C --> C1[Hot Memory Cache]
C --> C2[Warm Vector Store]
C --> C3[Cold Object Storage]
C --> C4[Glacier Archive]
C[Adaptive Memory System] --> D
%% Analytics Nodes
D --> D1[Geospatial Analytics]
D --> D2[Time Series Analytics]
D --> D3[Change Detection]
D --> D4[Correlation Engine]
D[Earth Intelligence Suite] --> E
%% Delivery Nodes
E --> E1[AI Model Integration]
E --> E2[Application APIs]
E --> E3[Visualization Tools]
E --> E4[Export Services]
E[Insight Delivery] --> F
F[Decision Intelligence]
%% Classifications
A1:::ingestion
A2:::ingestion
A3:::ingestion
A4:::ingestion
A:::ingestion
B1:::processing
B2:::processing
B3:::processing
B4:::processing
B:::processing
C1:::storage
C2:::storage
C3:::storage
C4:::storage
C:::storage
D1:::analytics
D2:::analytics
D3:::analytics
D4:::analytics
D:::analytics
E1:::delivery
E2:::delivery
E3:::delivery
E4:::delivery
E:::delivery
F:::delivery
| Processing Stage | Capabilities | Benefits |
|---|---|---|
| Data Ingestion | Multi-source acquisition, format normalization, quality filtering | Comprehensive data coverage with quality guarantees |
| Processing Engine | Parallel processing, feature extraction, temporal/spatial alignment | Efficient handling of heterogeneous Earth data |
| Memory System | Tiered storage, adaptive caching, compression, encryption | Optimized performance with cost-efficiency |
| Earth Intelligence | 15+ specialized analyzers, multi-dimensional scoring | Advanced insights across physical environment domains |
| Insight Delivery | Model integration, API exposure, visualization | Actionable intelligence for applications |
This advanced architecture enables memories-dev to process terabytes of Earth observation data with exceptional efficiency, transforming raw data into structured memory that grounds AI systems in physical reality.
Our memory system implements a scientifically-validated approach to data organization:
graph LR
classDef memory fill:#2c3e50,stroke:#34495e,stroke-width:2px,color:white,font-weight:bold
classDef storage fill:#3498db,stroke:#2980b9,stroke-width:2px,color:white
classDef features fill:#1abc9c,stroke:#16a085,stroke-width:2px,color:white
A[Memory Manager]:::memory --> B[Hot Memory]:::memory
A --> C[Warm Memory]:::memory
A --> D[Cold Memory]:::memory
A --> E[Glacier Storage]:::memory
subgraph Storage Technologies
B --> F[In-Memory Vector Store]:::storage
C --> G[SSD-based Database]:::storage
D --> H[Object Storage]:::storage
E --> I[Archive Storage]:::storage
end
subgraph Memory Features
A --> J[Auto-Tiering]:::features
A --> K[Compression]:::features
A --> L[Encryption]:::features
A --> M[Analytics]:::features
end
subgraph Scientific Validation
A --> N[Data Integrity]:::features
A --> O[Recall Metrics]:::features
A --> P[Precision Tests]:::features
A --> Q[Latency Analysis]:::features
end
| Memory Tier | Access Time | Storage Medium | Use Case | Data Types |
|---|---|---|---|---|
| Hot Memory | <10ms | RAM-based vector store | Current session data, active location analysis | Embeddings, recent queries, active location context |
| Warm Memory | <100ms | SSD-based database | Recent locations, frequently accessed regions | Recent satellite imagery, vector data for common areas |
| Cold Memory | <1s | Object storage | Historical analysis, less frequent locations | Historical imagery, environmental data series |
| Glacier | <60s | Archive storage | Long-term change detection, baseline data | Baseline measurements, long-term environmental data |
Our specialized Earth analyzers extract scientific insights from raw observation data:
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graph TD
classDef mainSystem fill:#1e293b,stroke:#334155,stroke-width:2px,color:white,font-weight:bold
classDef terrainAnalyzer fill:#3b82f6,stroke:#2563eb,stroke-width:2px,color:white,font-weight:bold
classDef climateAnalyzer fill:#ef4444,stroke:#dc2626,stroke-width:2px,color:white,font-weight:bold
classDef environmentalAnalyzer fill:#10b981,stroke:#059669,stroke-width:2px,color:white,font-weight:bold
classDef landAnalyzer fill:#8b5cf6,stroke:#7c3aed,stroke-width:2px,color:white,font-weight:bold
classDef waterAnalyzer fill:#0ea5e9,stroke:#0284c7,stroke-width:2px,color:white,font-weight:bold
classDef geologicalAnalyzer fill:#f59e0b,stroke:#d97706,stroke-width:2px,color:white,font-weight:bold
classDef urbanAnalyzer fill:#6366f1,stroke:#4f46e5,stroke-width:2px,color:white,font-weight:bold
classDef bioAnalyzer fill:#84cc16,stroke:#65a30d,stroke-width:2px,color:white,font-weight:bold
classDef airAnalyzer fill:#06b6d4,stroke:#0891b2,stroke-width:2px,color:white,font-weight:bold
classDef noiseAnalyzer fill:#ec4899,stroke:#db2777,stroke-width:2px,color:white,font-weight:bold
classDef solarAnalyzer fill:#eab308,stroke:#ca8a04,stroke-width:2px,color:white,font-weight:bold
classDef walkAnalyzer fill:#14b8a6,stroke:#0d9488,stroke-width:2px,color:white,font-weight:bold
classDef viewAnalyzer fill:#8b5cf6,stroke:#7c3aed,stroke-width:2px,color:white,font-weight:bold
classDef microAnalyzer fill:#22c55e,stroke:#16a34a,stroke-width:2px,color:white,font-weight:bold
classDef propertyAnalyzer fill:#f43f5e,stroke:#e11d48,stroke-width:2px,color:white,font-weight:bold
classDef infraAnalyzer fill:#6366f1,stroke:#4f46e5,stroke-width:2px,color:white,font-weight:bold
classDef subAnalyzer fill:#64748b,stroke:#475569,stroke-width:1px,color:white
A[🧠 Earth Memory Analyzers] --> B[🏔️ TerrainAnalyzer]
A --> C[🌡️ ClimateDataFetcher]
A --> D[🌱 EnvironmentalImpactAnalyzer]
A --> E[🏞️ LandUseClassifier]
A --> F[💧 WaterResourceAnalyzer]
A --> G[🪨 GeologicalDataFetcher]
A --> H[🏙️ UrbanDevelopmentAnalyzer]
A --> I[🦋 BiodiversityAnalyzer]
A --> J[💨 AirQualityMonitor]
A --> K[🔊 NoiseAnalyzer]
A --> L[☀️ SolarPotentialCalculator]
A --> M[🚶 WalkabilityAnalyzer]
A --> N[👁️ ViewshedAnalyzer]
A --> O[🌤️ MicroclimateAnalyzer]
A --> P[💰 PropertyValuePredictor]
A --> Q[🛣️ InfrastructureAnalyzer]
B --> B1[Elevation Analysis]
B --> B2[Slope Calculation]
B --> B3[Aspect Determination]
B --> B4[Landslide Risk Assessment]
C --> C1[Temperature Trend Analysis]
C --> C2[Precipitation Pattern Recognition]
C --> C3[Climate Change Projection]
C --> C4[Extreme Weather Risk Modeling]
F --> F1[Flood Risk Assessment]
F --> F2[Water Quality Analysis]
F --> F3[Drought Risk Modeling]
F --> F4[Watershed Analysis]
H --> H1[Urban Growth Pattern Analysis]
H --> H2[Development Plan Extraction]
H --> H3[Infrastructure Network Mapping]
H --> H4[Zoning Change Detection]
A:::mainSystem
B:::terrainAnalyzer
C:::climateAnalyzer
D:::environmentalAnalyzer
E:::landAnalyzer
F:::waterAnalyzer
G:::geologicalAnalyzer
H:::urbanAnalyzer
I:::bioAnalyzer
J:::airAnalyzer
K:::noiseAnalyzer
L:::solarAnalyzer
M:::walkAnalyzer
N:::viewAnalyzer
O:::microAnalyzer
P:::propertyAnalyzer
Q:::infraAnalyzer
B1:::subAnalyzer
B2:::subAnalyzer
B3:::subAnalyzer
B4:::subAnalyzer
C1:::subAnalyzer
C2:::subAnalyzer
C3:::subAnalyzer
C4:::subAnalyzer
F1:::subAnalyzer
F2:::subAnalyzer
F3:::subAnalyzer
F4:::subAnalyzer
H1:::subAnalyzer
H2:::subAnalyzer
H3:::subAnalyzer
H4:::subAnalyzer
Each analyzer implements validated scientific methodologies:
| Analyzer | Scientific Method | Data Sources | Validation Approach |
|---|---|---|---|
| TerrainAnalyzer | Digital Elevation Model Analysis | SRTM, ASTER GDEM, LiDAR | Ground truth comparison with surveyed elevations |
| ClimateDataFetcher | Time-series Climatology | CMIP6, ERA5, GLDAS | Cross-validation with meteorological stations |
| WaterResourceAnalyzer | Hydrological Modeling | Sentinel-1/2, Landsat, GRACE | Validation against stream gauges and river monitoring |
| BiodiversityAnalyzer | Ecosystem Assessment | GBIF, iNaturalist, MODIS | Field surveys and expert verification |
| AirQualityMonitor | Atmospheric Science Models | Sentinel-5P, CAMS, AirNow | Correlation with ground station measurements |
The framework seamlessly integrates with leading AI models and platforms:
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graph TD
classDef foundationModels fill:#3498db,stroke:#2980b9,stroke-width:2px,color:white,font-weight:bold
classDef earthMemory fill:#16a085,stroke:#1abc9c,stroke-width:2px,color:white,font-weight:bold
classDef contextNodes fill:#9b59b6,stroke:#8e44ad,stroke-width:2px,color:white,font-weight:bold
classDef intelligenceNodes fill:#f39c12,stroke:#f1c40f,stroke-width:2px,color:white,font-weight:bold
classDef memoryNode fill:#e74c3c,stroke:#c0392b,stroke-width:2px,color:white,font-weight:bold
classDef appNode fill:#2c3e50,stroke:#34495e,stroke-width:2px,color:white,font-weight:bold
A[Foundation Models] -->|Augmented with| B[Earth Memory System]
B -->|Provides| C[Spatial Context Engine]
B -->|Provides| D[Temporal Context Engine]
B -->|Provides| E[Environmental Context Engine]
C -->|Enables| F[Location-Aware Intelligence]
D -->|Enables| G[Temporal Evolution Intelligence]
E -->|Enables| H[Environmental Relationship Intelligence]
F --> I[Collective AGI Memory]
G --> I
H --> I
I -->|Powers| J[Scientific AI Applications]
A:::foundationModels
B:::earthMemory
C:::contextNodes
D:::contextNodes
E:::contextNodes
F:::intelligenceNodes
G:::intelligenceNodes
H:::intelligenceNodes
I:::memoryNode
J:::appNode
linkStyle 0 stroke:#3498db,stroke-width:2px,stroke-dasharray: 5 5
linkStyle 1,2,3 stroke:#16a085,stroke-width:2px
linkStyle 4,5,6 stroke:#9b59b6,stroke-width:2px
| Provider | Models | Key Features | Integration Type |
|---|---|---|---|
| OpenAI | GPT-4/3.5 Family | Function calling, streaming, embeddings | API |
| Anthropic | Claude 3 Family | Streaming, vision, long context | API |
| DeepSeek AI | DeepSeek Coder, Chat | Specialized coding capabilities | API & Local |
| Mistral AI | Mistral Medium/Small | Efficient, high-performance | API & Local |
| Cohere | Command/Embed | Advanced embeddings, multilingual | API |
| Meta | Llama 3 Family | Open weights, fine-tuning support | Local |
| Local Models | Quantized & GGUF | Offline operation, customization | Local |
from memories.models.load_model import LoadModel
from memories.models.multi_model import MultiModelInference
# Initialize multiple models for ensemble analysis
models = {
"openai": LoadModel(model_provider="openai", model_name="gpt-4"),
"anthropic": LoadModel(model_provider="anthropic", model_name="claude-3-opus"),
"deepseek": LoadModel(model_provider="deepseek-ai", model_name="deepseek-coder")
}
# Create multi-model inference engine
multi_model = MultiModelInference(models=models)
# Analyze property with Earth memory integration
responses = multi_model.get_responses_with_earth_memory(
query="Analyze environmental risks for this property",
location={"lat": 37.7749, "lon": -122.4194},
earth_memory_analyzers=["terrain", "climate", "water"]
)
# Compare model assessments
for provider, response in responses.items():
print(f"\n--- {provider.upper()} ASSESSMENT ---")
print(response["analysis"])
memories-dev supports three scientifically-validated deployment architectures:
Optimized for research environments and single-instance deployments:
graph TD
Client[Client Applications] --> API[API Gateway]
API --> Server[Memories Server]
Server --> Models[Model System]
Server --> DataAcq[Data Acquisition]
Models --> LocalModels[Local Models]
Models --> APIModels[API-based Models]
DataAcq --> VectorData[Vector Data Sources]
DataAcq --> SatelliteData[Satellite Data]
Server --> Storage[Persistent Storage]
Designed for high-reliability scientific computing environments:
graph TD
Client[Client Applications] --> LB[Load Balancer]
LB --> Node1[Node 1]
LB --> Node2[Node 2]
LB --> Node3[Node 3]
subgraph "Consensus Group"
Node1 <--> Node2
Node2 <--> Node3
Node3 <--> Node1
end
Node1 --> Models1[Model System]
Node2 --> Models2[Model System]
Node3 --> Models3[Model System]
Node1 --> DataAcq1[Data Acquisition]
Node2 --> DataAcq2[Data Acquisition]
Node3 --> DataAcq3[Data Acquisition]
subgraph "Shared Storage"
Storage[Distributed Storage]
end
Node1 --> Storage
Node2 --> Storage
Node3 --> Storage
For large-scale scientific computing and production environments:
graph TD
Client[Client Applications] --> LB[Load Balancer]
LB --> API1[API Gateway 1]
LB --> API2[API Gateway 2]
LB --> API3[API Gateway 3]
subgraph "Manager Nodes"
Manager1[Manager 1]
Manager2[Manager 2]
Manager3[Manager 3]
Manager1 <--> Manager2
Manager2 <--> Manager3
Manager3 <--> Manager1
end
API1 --> Manager1
API2 --> Manager2
API3 --> Manager3
subgraph "Worker Nodes"
Worker1[Worker 1]
Worker2[Worker 2]
Worker3[Worker 3]
Worker4[Worker 4]
Worker5[Worker 5]
end
Manager1 --> Worker1
Manager1 --> Worker2
Manager2 --> Worker3
Manager2 --> Worker4
Manager3 --> Worker5
subgraph "Shared Services"
Registry[Container Registry]
Config[Configuration Store]
Secrets[Secrets Management]
Monitoring[Monitoring & Logging]
end
Manager1 --> Registry
Manager1 --> Config
Manager1 --> Secrets
Manager1 --> Monitoring
| Cloud Provider | Features | Deployment Models | Hardware Support |
|---|---|---|---|
| AWS | Auto-scaling, S3 integration, Lambda functions | All | NVIDIA GPUs, Graviton (ARM) |
| GCP | Kubernetes, TPU support, Cloud Storage | All | NVIDIA GPUs, TPUs |
| Azure | AKS, Container Apps, Blob Storage | All | NVIDIA GPUs, AMD MI |
| On-premises | Custom hardware support, airgapped operation | All | NVIDIA GPUs, AMD MI, Intel GPUs |
memories-dev has been rigorously benchmarked across multiple dimensions:
The framework undergoes continuous performance testing across different deployment architectures:
Performance testing focuses on key metrics including query latency, memory throughput, data ingestion rates, and concurrent user capacity. Full benchmark reports are available in the documentation.
Our Earth analyzers are validated against scientific ground truth data:
Detailed methodology and validation reports are available in our scientific documentation.
The multi-tiered memory architecture is designed for optimal performance:
Each tier is continuously optimized for access patterns, compression ratios, and storage efficiency.
For researchers interested in the technical foundations of the framework:
Our work intersects with several active research domains:
For comprehensive documentation, visit memories-dev.readthedocs.io, which includes:
# Basic installation
pip install memories-dev
# With GPU support
pip install memories-dev[gpu]
# Full installation with all features
pip install memories-dev[all]
# Clone repository
git clone https://github.com/Vortx-AI/memories-dev.git
cd memories-dev
# Install development dependencies
pip install -e ".[dev]"
# Install documentation tools
pip install -e ".[docs]"
# Pull the official Docker image
docker pull vortx/memories-dev:2.0.3
# Run with GPU support
docker run --gpus all -p 8000:8000 -v ./data:/app/data vortx/memories-dev:2.0.3
from memories.earth import OvertureClient, SentinelClient
import os
# Initialize clients
overture_client = OvertureClient(
api_key=os.getenv("OVERTURE_API_KEY")
)
sentinel_client = SentinelClient(
username=os.getenv("SENTINEL_USER"),
password=os.getenv("SENTINEL_PASSWORD")
)
# Configure memory system
from memories import MemoryStore, Config
memory_config = Config(
storage_path="./earth_memory",
hot_memory_size=50, # GB
warm_memory_size=200, # GB
cold_memory_size=1000, # GB
vector_store="milvus",
embedding_model="text-embedding-3-small"
)
memory_store = MemoryStore(memory_config)
from examples.real_estate_agent import RealEstateAgent
from memories import MemoryStore, Config
# Initialize memory store
config = Config(
storage_path="./real_estate_data",
hot_memory_size=50,
warm_memory_size=200,
cold_memory_size=1000
)
memory_store = MemoryStore(config)
# Initialize agent with earth memory
agent = RealEstateAgent(
memory_store,
enable_earth_memory=True,
analyzers=["terrain", "climate", "water", "environmental"]
)
# Add property and analyze
property_id = await agent.add_property(property_data)
analysis = await agent.analyze_property_environment(property_id)
print(f"Property added: {property_id}")
print(f"Environmental analysis: {analysis}")
from memories.analyzers import ChangeDetector
from datetime import datetime, timedelta
# Initialize change detector
detector = ChangeDetector(
baseline_date=datetime(2020, 1, 1),
comparison_dates=[
datetime(2021, 1, 1),
datetime(2022, 1, 1),
datetime(2023, 1, 1),
datetime(2024, 1, 1)
]
)
# Detect environmental changes
changes = await detector.analyze_changes(
location={"lat": 37.7749, "lon": -122.4194, "radius": 5000},
indicators=["vegetation", "water_bodies", "urban_development"],
visualization=True
)
# Present findings
detector.visualize_changes(changes)
detector.generate_report(changes, format="pdf")
We welcome contributions from the scientific community! See CONTRIBUTING.md for guidelines.
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
Built with 💜 by the memories-dev team
FAQs
Collective Memory Infrastructure for AGI
We found that memories-dev demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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