Memories - Tess AI Docs

Tess Memories allow you to save, organize, and reuse important information in the form of collections. They work as “knowledge folders” that you activate before each conversation, ensuring the AI responds with context, consistency, and alignment with your work or your team’s work.

What is it?

A Memory Collection is a set of structured information (texts, instructions, data, or rules) that you provide to the AI before starting a conversation. In practice:

You write memories (e.g.: client context, tone of voice, internal rules)
Organize them into thematic collections
Activate these collections in the chat
The AI automatically takes this content into account in its responses

Think of it as a “reusable context package” that you hand over to the agent before each task. Tess Memories allow you to save, organize, and reuse important information in the form of collections. They work as “knowledge folders” that you activate before each conversation, ensuring the AI responds with context, consistency, and alignment with your work or your team’s work.

What is it?

A Memory Collection is a set of structured information (texts, instructions, data, or rules) that you provide to the AI before starting a conversation. In practice:

You write memories (e.g.: client context, tone of voice, internal rules)
Organize them into thematic collections
Activate these collections in the chat
The AI automatically takes this content into account in its responses

Think of it as a “reusable context package” that you hand over to the agent before each task. You can decide, at any time, which memory collections you want to keep active/inactive for each job. It is fully modular. These memories can be used in new conversations, without the user needing to repeat everything every time.

Why is it important?

Real personalization: Tess responds taking history (memory) into account, not just the current message.
Less repetition: The user does not need to provide the same data at each interaction.
Context for the team: Organized memories help both the AI and the human team maintain the same level of service, even when agents change.
Work organization: you can organize your memories by team, by project, by client, or however you prefer, to bring versatility to your way of working with AI.

Configuring and Managing Memories

Open the Chat screen
Locate the Memory Collections icon in the upper right corner\
Select an existing collection or create a new one, according to your needs\
Create memories and a collection\
Remember to activate the collection — a number will appear on the icon and active collections will be marked

In the Memories icon, you can:

Activate or deactivate memory collections
Create new collections segmented by use case
Review, edit, or remove existing memories

After activating a collection, you can start or continue the conversation normally in the chat. Notice whether the LLM is using the memory information to complement responses (when it makes sense). Tess offers 4 sharing options, with different levels of control:

Individual Collection (Private)

Only you access and editWhen you create a collection without sharing, it is entirely yours. Ideal for personal contexts, your own methodologies, or confidential information.

Sharing via Link

Anyone with the link imports a copyGenerate a public link. Whoever accesses it can import a full copy to their own account. Each person has their own independent version after importing.

Sharing with Team

Live access — permanent connectionAll members have real-time access. Any update by the owner appears immediately for everyone in the next chat. Define who can read and who can edit.

Sharing with Workspace

The entire company has accessIdeal for knowledge that everyone should have: brand values, product manual, internal glossary. The admin controls the permissions.

The sharing flow has been improved with more control and clarity:

Organized management modal to view access
Granular permissions (view or edit)
Prevention of duplicate invitations
Active link control
Preservation of the origin of shared collections

To share, simply:

Locate the collection and click the share icon

When you hover over the collection, the buttons will appear — just locate the share button and begin the settings:

Generate a shareable link

If you want to assign a prefix, that is possible, as well as choosing the permission level — view or edit. After configuring this, just generate your link.

If you want to revoke the link, just click on the existing option

If you prefer, choose the sharing level for your team

Best Practices

Clearly define what type of information can become a memory — and what should never be saved (for example, sensitive data, passwords, information that violates privacy or compliance policies).
Use different collections for different contexts (one for support, one for sales, one for internal projects).
Periodically review memories and collections to remove anything outdated or irrelevant, keeping only what truly helps with personalization and service quality.
Prefer shared collections to standardize teams.

Important notes

Active memories consume tokens (they are part of the context sent to the AI)
The more active collections, the higher the potential cost
Avoid storing: Passwords; Sensitive data; or Confidential information without access control
Team/workspace sharing is synchronized in real time

Memories transform Tess into a truly contextual system. With well-structured collections and the new sharing model, you reduce repetition, increase consistency, and scale AI usage with much more control. Tess offers 4 sharing options, with different levels of control:

Individual Collection (Private)

Only you access and editWhen you create a collection without sharing, it is entirely yours. Ideal for personal contexts, your own methodologies, or confidential information.

Sharing via Link

Anyone with the link imports a copyGenerate a public link. Whoever accesses it can import a full copy to their own account. Each person has their own independent version after importing.

Sharing with Team

Live access — permanent connectionAll members have real-time access. Any update by the owner appears immediately for everyone in the next chat. Define who can read and who can edit.

Sharing with Workspace

The entire company has accessIdeal for knowledge that everyone should have: brand values, product manual, internal glossary. The admin controls the permissions.

The sharing flow has been improved with more control and clarity:

Organized management modal to view access
Granular permissions (view or edit)
Prevention of duplicate invitations
Active link control
Preservation of the origin of shared collections

To share, simply:

Locate the collection and click the share icon

When you hover over the collection, the buttons will appear — just locate the share button and begin the settings:

Generate a shareable link

If you want to assign a prefix, that is possible, as well as choosing the permission level — view or edit. After configuring this, just generate your link.

If you want to revoke the link, just click on the existing option

If you prefer, choose the sharing level for your team

Best Practices

Clearly define what type of information can become a memory — and what should never be saved (for example, sensitive data, passwords, information that violates privacy or compliance policies).
Use different collections for different contexts (one for support, one for sales, one for internal projects).
Periodically review memories and collections to remove anything outdated or irrelevant, keeping only what truly helps with personalization and service quality.
Prefer shared collections to standardize teams.

Important notes

Active memories consume tokens (they are part of the context sent to the AI)
The more active collections, the higher the potential cost
Avoid storing: Passwords; Sensitive data; or Confidential information without access control
Team/workspace sharing is synchronized in real time

Using Memories via API

Authenticated memory API calls must include x-workspace-id (required as of 2026-09-01). Until then, omitting it falls back to the selected workspace (deprecated). After the cutoff, missing header → 422. See API Overview.

You can manage memories and apply them to agent executions directly through the API. The full flow involves three steps:

Step 1 — Create a memory collection

curl --request POST \
  --url 'https://api.tess.im/memory-collections' \
  --header 'Authorization: Bearer YOUR_API_KEY' \
  --header 'x-workspace-id: YOUR_WORKSPACE_ID' \
  --header 'Content-Type: application/json' \
  --data '{
    "name": "Customer data",
    "description": "Profile information and preferences"
  }'

Save the id from the response — you will use it in the next steps.

Step 2 — Create memories in the collection

Add each memory record by passing the collection_id obtained in the previous step:

curl --request POST \
  --url 'https://api.tess.im/memories' \
  --header 'Authorization: Bearer YOUR_API_KEY' \
  --header 'x-workspace-id: YOUR_WORKSPACE_ID' \
  --header 'Content-Type: application/json' \
  --data '{
    "memory": "The customer prefers formal communication and works in the financial sector.",
    "collection_id": 123
  }'

The created memory goes through an embedding process (semantic indexing). The embedding_status field starts as pending and changes to succeeded when it is ready to be queried. Wait for succeeded status before executing the agent to ensure the memory is found.

Step 3 — Execute the agent with memory collections

When executing the agent, pass the memory_collections parameter with the IDs of the collections you want to activate:

curl --request POST \
  --url 'https://api.tess.im/agents/YOUR_AGENT_ID/execute' \
  --header 'Authorization: Bearer YOUR_API_KEY' \
  --header 'x-workspace-id: YOUR_WORKSPACE_ID' \
  --header 'Content-Type: application/json' \
  --data '{
    "memory_collections": [123],
    "messages": [
      { "role": "user", "content": "How do you prefer me to communicate with you?" }
    ]
  }'

How memory injection works: Tess uses semantic search (RAG) to automatically select the most relevant memories from the collection based on the conversation content. Only memories with high semantic similarity are injected into the agent context — you do not need to specify which memories to use, just which collection to activate.

​What is it?

​What is it?

​Why is it important?

​Configuring and Managing Memories

​Collection Sharing

Individual Collection (Private)

Sharing via Link

Sharing with Team

Sharing with Workspace

​Sharing management

​Important notes

​Collection Sharing

Individual Collection (Private)

Sharing via Link

Sharing with Team

Sharing with Workspace

​Sharing management

​Important notes

​Using Memories via API

​Step 1 — Create a memory collection

​Step 2 — Create memories in the collection

​Step 3 — Execute the agent with memory collections

What is it?

What is it?

Why is it important?

Configuring and Managing Memories

Collection Sharing

Sharing management

Important notes

Collection Sharing

Sharing management

Important notes

Using Memories via API

Step 1 — Create a memory collection

Step 2 — Create memories in the collection

Step 3 — Execute the agent with memory collections