Cookbooks
Cookbooks are step-by-step guides that demonstrate how to use HEBBS in real-world scenarios. Each cookbook includes complete, runnable code and explains the design decisions behind the implementation.
Getting Started
Section titled “Getting Started”Build foundational skills with HEBBS:
- Your First Memory Agent — connect to HEBBS and build a simple memory-augmented agent from scratch.
- Multi-Strategy Recall — compare all four recall strategies (similarity, temporal, causal, analogical) on the same dataset.
- Entity-Scoped Memory — partition memories by customer, user, or agent and prove isolation.
Use Cases
Section titled “Use Cases”Complete applications demonstrating HEBBS in production scenarios:
- Voice Sales Agent — build an AI sales agent with cross-session memory for discovery calls and objection handling.
- Customer Support Agent — support agent with ticket history, temporal recall, and GDPR-compliant data erasure.
- Research Assistant — cross-domain recall and synthesis for research workflows using analogical recall and reflection.
Patterns
Section titled “Patterns”Reusable patterns for common requirements:
- GDPR Compliance — implement data erasure with
forget()and verify complete deletion. - Real-time Subscribe — surface relevant memories in real-time during conversations.
- Background Learning — use the reflection pipeline to consolidate memories into institutional knowledge.
- Causal Chains — build and query causal relationships between memories.
Infrastructure
Section titled “Infrastructure”Operational guides for running HEBBS:
- Monitoring Stack — set up HEBBS with Prometheus and Grafana using docker-compose.
Prerequisites
Section titled “Prerequisites”All cookbooks assume:
- A running HEBBS server (see Deployment)
- Python: The HEBBS Python SDK installed (
pip install hebbs) with Python 3.10+ - TypeScript: The HEBBS TypeScript SDK installed (
npm install @hebbs/sdk) with Node.js 18+
Most examples show both Python and TypeScript. Pick whichever fits your stack.