Workspaces & Projects

Workspaces

A workspace is the top-level namespace in MemoryLake. It provides complete data isolation — actors, projects, documents, conversations, and facts within one workspace are invisible to another. You can think of a workspace as a tenant. If you are building a multi-tenant application, each of your customers gets their own workspace. If you are a single team, you might use one workspace for production and another for development.

What a workspace contains

Workspace
  +-- Actors (participant identities bound to this workspace)
  +-- Agents (AI agent definitions bound to this workspace)
  +-- Projects
        +-- Documents
        +-- Conversations + Messages
        +-- Project Facts

Every resource in MemoryLake belongs to a workspace. When you search, you search within a workspace. When an actor accumulates facts, those facts live within the workspace where the conversations happened.

Creating a workspace

curl -X POST https://app.memorylake.ai/openapi/memorylake/api/v3/workspaces \
  -H "Authorization: Bearer $MEMORYLAKE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Production",
    "description": "Production workspace for customer-facing agents"
  }'

See the Workspaces API reference for the full set of workspace operations.

Projects

A project is a knowledge container within a workspace. It holds the three types of content that MemoryLake works with:

Documents — files imported from the Library, indexed for semantic search
Conversations — user-assistant message exchanges
Project Facts — structured knowledge extracted from conversations, scoped to this project

When to create a project

Create a project for each distinct knowledge domain, use case, or application. Some examples:

Scenario	Project structure
A customer support bot	One project per product line or knowledge domain
A personal assistant	One project per user, or one shared project with actor-level memory
A research tool	One project per research topic or dataset
A coding assistant	One project per repository or codebase

Projects are lightweight. It is better to create multiple focused projects than one large project with mixed content. Smaller, focused projects produce better search results.

Creating a project

curl -X POST https://app.memorylake.ai/openapi/memorylake/api/v3/workspaces/{workspace_id}/projects \
  -H "Authorization: Bearer $MEMORYLAKE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Customer Support - Billing",
    "description": "Knowledge base for billing-related support queries"
  }'

See the Projects API reference for the full set of project operations.

Workspaces vs. Projects

Use this decision guide to choose the right level of separation:

Need	Use
Data isolation between tenants	Separate workspaces
Different environments (dev, staging, prod)	Separate workspaces
Different knowledge domains within the same tenant	Separate projects in one workspace
Per-user memory that follows the user across domains	One workspace with multiple projects (actors carry facts across projects)
Shared search across multiple knowledge sources	Multiple projects in one workspace (search is workspace-scoped)

Search operates at the workspace level. If you need to search across multiple knowledge domains in a single query, put those domains in separate projects within the same workspace.

Multi-tenant patterns

For multi-tenant applications, the most common pattern is one workspace per tenant:

Your Application
  +-- Workspace: "Acme Corp"
  |     +-- Project: "Engineering Docs"
  |     +-- Project: "Support Knowledge Base"
  |
  +-- Workspace: "Globex Inc"
        +-- Project: "Product Docs"
        +-- Project: "Internal Wiki"

This guarantees that Acme Corp’s data is never visible to Globex Inc, and vice versa. Each workspace has its own actors, so user memory is also isolated between tenants.

Single-tenant patterns

For single-team or single-application use cases, you typically use one workspace with multiple projects:

Workspace: "My Team"
  +-- Project: "Product Knowledge"
  +-- Project: "Engineering Runbooks"
  +-- Project: "Customer Feedback"

Actors in this workspace can accumulate facts across all three projects. When Jane asks a question in the “Product Knowledge” project and later interacts with the “Engineering Runbooks” project, her actor facts from both interactions are available.

Next Steps

Actors & Memory

Learn how actors provide per-user memory across projects

Memory Pipeline

Understand how conversations become structured facts

Workspaces API

Full API reference for workspace operations

Projects API

Full API reference for project operations

Getting started

Memory Router

Model Router

MemoryLake

Team collaboration

Workspaces

What a workspace contains

Creating a workspace

Projects

When to create a project

Creating a project

Workspaces vs. Projects

Multi-tenant patterns

Single-tenant patterns

Next Steps

Actors & Memory

Memory Pipeline

Workspaces API

Projects API

​Workspaces

​What a workspace contains

​Creating a workspace

​Projects

​When to create a project

​Creating a project

​Workspaces vs. Projects

​Multi-tenant patterns

​Single-tenant patterns

​Next Steps

Actors & Memory

Memory Pipeline

Workspaces API

Projects API

Workspaces

What a workspace contains

Creating a workspace

Projects

When to create a project

Creating a project

Workspaces vs. Projects

Multi-tenant patterns

Single-tenant patterns

Next Steps