Memory
Memory enables agents to remember information across conversations. Without memory, every conversation starts from zero. With memory, agents build up context about users, teams, and past interactions over time.
Why Memory Matters
Without memory:
Conversation 1: "My laptop is a MacBook Pro M3" -- "Got it!"
Conversation 2 (next day): "My laptop is running slow" -- "What kind of laptop do you have?" (forgot)
With memory:
Conversation 1: "My laptop is a MacBook Pro M3" -- "Got it, I'll remember that!"
Conversation 2 (next day): "My laptop is running slow" -- "I see you're using a MacBook Pro M3. Let me help..." (remembers)
Memory Types
MeetLoyd has four types of memory, each with a different scope:
| Type | Scope | Persists Across Sessions | Shared With | Use Case |
|---|---|---|---|---|
| Conversation | Single conversation | No | Same conversation only | Short-term context within a dialogue |
| User | Per user, per agent | Yes (configurable retention) | Same user + agent | Personalization, preferences, past issues |
| Team | All agents in a team | Yes (until deleted) | All team agents | Shared knowledge, current outage info, customer context |
| Knowledge | Per agent | Yes (until deleted) | Same agent | Reference documents, guides, policies |
Conversation Memory
Automatic within a single conversation. No configuration needed -- the agent naturally remembers everything said in the current thread.
User Memory
Persists across conversations for a specific user. The agent remembers your name, preferences, devices, and past issues from previous sessions. Retention is configurable (default 90 days).
Team Memory
Shared across all agents in a team. When one agent learns something important (e.g., "we're investigating an email outage"), every agent on the team can see it.
Knowledge Memory
Structured information uploaded to an agent as reference material -- employee handbooks, product documentation, troubleshooting guides. The agent can search and reference this content when answering questions.
How Agents Use Memory
Agents use memory tools automatically during conversations:
| Action | What Happens |
|---|---|
| User shares personal info | Agent stores it (name, department, devices, preferences) |
| User asks a factual question | Agent searches knowledge base and memories |
| User returns for a new conversation | Agent retrieves relevant context from past sessions |
| Agent learns something important | Agent stores it in team memory for other agents |
Files and Memory
Files uploaded to conversations are automatically parsed and their content is cached. When a user attaches files to a message, the full parsed text is injected into the agent's prompt -- agents can read documents instantly without calling a download tool.
Agents can then store key facts from documents as memories for future conversations. For RAG use cases, uploaded files are automatically chunked and searchable alongside memories.
See Files and Attachments for details on storage, parsing, and content injection.