User Memory

User Memory enables your agent to remember important facts about users across conversations. Instead of starting fresh every time, your agent can recall preferences, past tickets, and relevant context.

User Memory automatically extracts and stores durable facts from conversations:

User: "I'm the lead programmer at Acme Integration and we just took over service
       for the Foley Building."
           |
           v
    [Conversation ends]
           |
           v
    [Memory extraction runs]
           |
           v
    Memories stored:
    - Role: Lead programmer
    - Company: Acme Integration
    - Account: Foley Building service contract
           |
           v
    [Next conversation]
           |
           v
AI: "Welcome back. Last time we worked on the Foley Building handoff. What can
     I help with today?"

1. Open Memory Settings.

Go to your agent's Build page and find Memory Settings in the sidebar.

2. Enable Memory Extraction.

Toggle Enable User Memory on. This activates automatic memory extraction from conversations.

3. Configure categories (optional).

Choose which types of information to remember:

• Saved Facts: concrete information (names, companies, roles).
• Preferences: how users like to interact.
• Topic Highlights: key discussion topics.
• User Insights: inferred characteristics.
• Recent Queries: common questions asked.

4. Set custom instructions (optional).

Add specific extraction guidance:

Focus on:
- Integrator firm name and the user's role
- Active projects and accounts the user is responsible for
- Manufacturer mix the user works in (Crestron, Q-SYS, Extron, Biamp, Shure)
- Open service tickets and dispatch context

Do NOT remember:
- Personal contact information
- Specific contract dollar values
- Confidential client business details

Saved Facts#

Concrete, verifiable information:

• Names and roles.
• Company / integrator firm information.
• Manufacturer mix and standards.
• Account and site details.

Preferences#

How users like to interact:

• Communication style (formal vs casual).
• Response length preference.
• Preferred formats (bullets vs paragraphs).
• Language preferences.

Topic Highlights#

Key subjects from conversations:

• Features or systems they are interested in.
• Problems they are trying to solve.
• Questions they have asked.
• Projects or rooms they have discussed.

User Insights#

Inferred characteristics:

• Technical expertise level (junior programmer vs senior commissioning engineer).
• Decision-making authority.
• Industry segment (corporate AV, higher-ed, broadcast, hospitality).
• Use case patterns.

Go to Chats > select a user > Memories to see:

• All memories for that user.
• When each memory was created.
• Which conversation it came from.
• Confidence score.

Editing memories#

Click any memory to edit or correct it:

1. Update the content.
2. Change the category.
3. Adjust the confidence score.

Deleting memories#

Remove memories that are:

• Incorrect.
• Outdated (the user changed firms, the project closed out).
• No longer relevant.

Go to Memories > click the memory > Delete.

Bulk management#

For compliance or cleanup:

• Delete all memories for a user: User profile > Clear Memory.
• Export memories: download as CSV for review.

What to remember#

Do remember:

• Professional context (firm, role, manufacturer mix).
• Stated preferences.
• Key topics discussed.
• Problems they are solving.

Do not remember:

• Sensitive personal information.
• Temporary states ("having a rough day on a closeout").
• Information they asked you to forget.
• Confidential client business data without consent.

Privacy considerations#

Warning: Always tell users your agent has memory capabilities. Consider adding to your system prompt: "I can remember relevant details from our conversations to provide better assistance. Let me know if you'd prefer I not remember something."

Memory in your system prompt#

Help your agent use memories well:

You have access to memories about this user from past conversations.
Use these to:
- Personalize greetings and responses
- Avoid asking questions they've already answered
- Reference their specific context (firm, projects, manufacturer mix)

Don't:
- Mention that you "remember" things in a creepy way
- Assume memories are 100% accurate
- Reference memories if they seem outdated

Memory extraction runs automatically after conversations:

1. Conversation ends: user leaves or 10 minutes of inactivity.
2. Queued for processing: the conversation enters the memory queue.
3. AI analysis: GPT-4 analyzes the conversation for memorable facts.
4. Structured extraction: facts get categorized and scored for confidence.
5. Storage: memories are encrypted and stored with the user profile.

Extraction timing#

Confidence scores#

Each memory has a confidence score (0 to 1):

• 0.9 to 1.0: explicitly stated by the user.
• 0.7 to 0.9: strongly implied.
• 0.5 to 0.7: reasonable inference.
• Below 0.5: uncertain, may need verification.

Field service desk#

Remember:

• Sites and rooms the tech works on.
• Past tickets the tech has reported.
• Preferred contact method.
• Technical expertise level.

Pre-sales / RFP assistant#

Remember:

• Firm size and segment.
• Active opportunities and project context.
• Decision timeline.
• Manufacturer preferences.

Internal AV operations agent#

Remember:

• The user's role on the team (PM, programmer, lead engineer).
• Active project assignments.
• Communication preferences.
• Recurring tasks (weekly reports, gear pulls).

Programmer onboarding agent#

Remember:

• Learning progress on Crestron SIMPL or Q-SYS Lua.
• Topics mastered.
• Areas needing work.
• Preferred learning style.

Memories not being extracted#

1. Verify memory is enabled in settings.
2. Check that conversations are ending properly.
3. Allow 10 to 15 minutes for extraction to process.
4. Verify the user is identified (not anonymous).

Incorrect memories#

1. Review and edit incorrect memories.
2. Adjust custom extraction instructions.
3. Add negative examples to your instructions.

Too many memories#

1. Focus extraction on specific categories.
2. Add instructions to prioritize durable facts.
3. Periodically clean up outdated memories.

Current limitations to keep in mind:

• Extraction delay: memories are not available instantly (10+ minute delay).
• No real-time recall: the current implementation extracts memories but does not automatically inject them into prompts.
• Token limits: very long conversations may be truncated for extraction.

Note: We are actively improving the memory system. Future updates will include real-time memory recall and more sophisticated memory management.

You can access user memories through code using the AVCodex MCP Server.

Available tools#

Example prompts#

"Show me user memories for my service desk agent"
"What facts has my agent learned about users?"
"List memories with high confidence scores"

Workflow: reviewing user insights#

You: Show me the memories my pre-sales agent has extracted.

AI: [Calls get_user_memories]
Found 47 user memories:

High confidence (0.9+):
- User "john@acmeintegration.com": Lead programmer at Acme Integration, 50-person firm.
- User "sarah@bigcorp.com": Director of corporate IT, evaluating a 40-room refresh, budget approved.

Medium confidence (0.7-0.9):
- User "mike@startupav.io": Technical background, comparing Crestron and Q-SYS for a new HQ build.
...

Note: MCP server access requires a Builder plan or higher. See the MCP Setup Guide to get started.

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