Welcome to the official deployment templates for FastMemory, the horizontal layer of truth for enterprise AI. This repository provides modular concept maps, integration plans, and Python examples for deploying FastMemory across various industries and cloud environments.

• FastBuilder.AI: https://fastbuilder.ai
• Core FastMemory Repo: https://github.com/fastbuilderai/memory
• Superfast (Agent Framework): https://github.com/FastBuilderAI/superfast

Engine Compatibility: Fully compatible with FastMemory v0.2.2+ (Enterprise Telemetry Edition).

FastMemory is an AI memory system that uses the CBFDAE (Component, Block, Function, Data, Access, Event) ontology to build a structured, low-hallucination cognitive graph for AI agents.

Industry-specific concept maps (Mermaid) and Atomic Text Function (ATF) samples.

• SEO | CRM | ERP
• E-commerce | Healthcare | Education
• Finance | Coffee Shop | Restaurant

Architecture maps and step-by-step setup guides for major cloud providers and agentic frameworks.

• Azure OpenAI
• AWS Bedrock
• GCP Vertex AI
• OpenClaw Integration
• Superfast Integration

Runnable Python applications for each case with production features:

• Neo4j/GraphDB Support: Built-in placeholders for persistence.
• Structured Logging: Production-ready logging system.
• Environment Management: Automatic .env support via shared.FastMemoryClient.
• Requirements Specifics: Individual requirements.txt for each example.

1. Clone the Repo:

   git clone https://github.com/FastBuilderAI/memory-template
   cd memory-template

2. Choose an Example (e.g., Coffee Shop):

   cd examples/coffeeshop
   pip install -r requirements.txt

3. Configure Your Environment: Copy .env.example and fill in your credentials:

   cp .env.example .env

4. Run the App:

   python main.py

All examples now inherit from a standardized production template via examples/shared/fastmemory_client.py. This ensures consistent behavior for:

• Connectivity: Graceful handling of Neo4j/LLM drivers.
• Observability: Health checks and structured logging.
• Scalability: Decoupled domain logic from infrastructure.

We welcome contributions! Please map your industry use cases to the CBFDAE framework and submit a PR.