Harbor FAQs

Question 1

What is Harbor and how does it benefit AI agents?

Accepted Answer

Harbor is a unified context protocol that provides AI agents with shared memory, data normalization, and intelligent context curation. It enables agents to recall insights across sessions and models, ensures consistent data parsing, and reduces token costs by providing token-efficient contexts to LLMs.

Question 2

What is 'data normalization' in Harbor and why is it important?

Accepted Answer

Data normalization transforms varied API responses into a consistent `data[]` + `meta{}` + `errors[]` structure. This predictability prevents AI models from wasting intelligence decoding different formats, allowing them to focus on reasoning about the content and improving reliability.

Question 3

Can Harbor help reduce LLM token usage and costs?

Accepted Answer

Yes, Harbor's Context Curation feature allows agents to teach it how to summarize and compact data. This creates token-efficient contexts for LLMs, ensuring they receive only the most relevant information for a task, thereby reducing token consumption and operational costs.

Question 4

How does Harbor facilitate shared memory between AI agents?

Accepted Answer

Harbor implements a cross-agent shared memory layer. When one agent saves an insight or analysis using `harbor remember`, it's stored permanently and automatically provided as `meta.context` to any future calls to that connector, across different agents, sessions, and models, eliminating re-prompting.

Question 5

Does Harbor support access control for sensitive data within agent contexts?

Accepted Answer

Absolutely. Harbor offers Context-Level Access Control, which governs which specific fields within data agents are permitted to see. This advanced control prevents sensitive information leakage by restricting what an agent can access in its context window, even if it has API-level access to the source.

Harbor

Harbor

Key Features

Use Cases

Key Features

Use Cases