Agent Memory

curl -X PUT http://localhost:4200/api/memory/settings \
  -H "Content-Type: application/json" \
  -H "X-Gaia-UI: 1" \
  -d '{"memory_enabled": true}'

curl http://localhost:4200/api/memory/settings
# → {"memory_enabled": true, ...}

You: "Remember that Sarah's email is [email protected]"
Agent: "Saved -- I've linked that to Sarah's contact profile."

You: "I have a course starting on March 25th"
Agent: "Noted. I'll remind you as it gets closer."

You: "I always want dark mode in code examples"
Agent: "Got it, I'll apply that preference going forward."

You: "What do you know about my deployment process?"
Agent: [recalls skill entries about deployment workflow]

You: "What do you know about Sarah?"
Agent: [recalls all entries linked to entity person:sarah_chen]

You: "What did we discuss about deployment last week?"
Agent: "Last week you pushed the staging deploy and hit a kubectl
        permissions error. You resolved it by adding the service
        account role."

You: "What errors have I seen this week?"
Agent: "Two errors this week: a missing pytest conftest.py on Monday,
        and a Docker build failure yesterday due to a stale cache."

You: "What meetings did I have in March?"
Agent: "Four standups in March: API migration (Sarah), two design
        reviews, and the Q2 planning kickoff on the 28th."

You: "Actually, we switched from React to Vue"
Agent: "Updated -- I've changed your project's framework from React to Vue 3."
       [agent recalls the React fact, updates it in place, marks old as superseded]

You: "What's today looking like?"
Agent: "You have a busy afternoon. By the way, your online course
        starts in 3 days -- just a heads-up."
       [agent internally marks reminded_at so it won't surface this again]

You: "Forget what you know about the old deployment process"
Agent: [recalls the entry, deletes it]

You: "What did we discuss about database migration?"
Agent: "Found 3 conversations mentioning database migration:
        two sessions last month about the Postgres schema, and
        one this week where you decided to add a new index."

You: "What did we talk about in the last 7 days?"
Agent: "This week's conversations covered: code review for the
        auth module, debugging the CI pipeline, and planning
        the Q2 dashboard."

You: "What were we working on in March?"
Agent: [searches conversation turns from March 1-31 and summarizes]

You: "Log today's work: finished the memory spec, reviewed analysis docs,
      pushed the feature branch. Still blocked on the CI lint step."

Agent: "Logged. I'll remember this when you ask about your work history."

— Two weeks later —

You: "What did I work on the first week of April?"
Agent: "On April 1st, you finished the memory spec, reviewed analysis docs,
        and pushed the feature branch. You were blocked on CI lint."

You: "When did I push the memory branch?"
Agent: "April 1st -- you pushed feature/agent-memory that day."

You: "In today's standup: Sarah said the API migration is done.
      John is blocked waiting for design review.
      Deadline for Q2 report moved to April 15."

Agent: Stored 3 items:
  - Note: standup summary (domain=meeting:standup)
  - Reminder: Q2 report due April 15 (with due_at)
  - Fact: John blocked on design review (linked to person:john)

— Next week —

You: "Is John still blocked?"
Agent: "Last I heard (April 1st standup), John was blocked
        waiting for design review."

You: "What's the Q2 report deadline?"
Agent: "April 15th -- the deadline was moved from the original date."

You: "Save this: the Hindsight paper shows that cross-encoder reranking
      after RRF fusion gives 91.4% accuracy on LongMemEval. Key insight:
      independent encoders miss fine-grained relevance signals."

Agent: "Saved. I'll surface that when you ask about retrieval or search accuracy."

— Later —

You: "What do I know about improving retrieval accuracy?"
Agent: "The Hindsight paper found that cross-encoder reranking after
        RRF fusion reaches 91.4% on LongMemEval. The key insight is
        that independent encoders miss fine-grained relevance signals."

You: "Remind me to submit the expense report by Friday"
Agent: "Done -- I'll make sure to mention it before Friday."

— Thursday afternoon, talking about code —

You: "Can you refactor this function?"
Agent: "Sure, here's the refactored version. By the way,
        your expense report is due tomorrow -- don't forget to submit it."

You: "Remind me to do a weekly review every Friday at 5pm"
Agent: "Set. I'll remind you every Friday at 5pm and roll it
        forward automatically after each reminder."

You: "Sarah Chen is the VP of Engineering, her email is [email protected]"
Agent: "Got it -- I've saved Sarah's info. Her email is stored privately."

— Another conversation —

You: "Sarah prefers morning meetings and likes detailed agendas"
Agent: "Added to Sarah's profile."

— Later —

You: "I need to set up a meeting with Sarah about the roadmap"
Agent: "Sarah prefers morning meetings with detailed agendas.
        Her email is [email protected]. Want me to draft an agenda?"

Session 1:
Agent runs: import torch
Error: ModuleNotFoundError: No module named 'torch'
[agent silently stores this as an error pattern to avoid in future]

Session 2:
You: "Can you test this PyTorch model?"
Agent: "I know from past experience that torch isn't installed
        on this machine. Let me install it first with
        pip install torch --index-url ..."

You: "Remember as a skill: to set up a new React project,
      run npx create-next-app, then install tailwind, then configure eslint"

You: "Switch to my personal context"
Agent: "Switched to your personal context."

You: "Remember that my dentist appointment is Thursday at 2pm"
Agent: "Done -- dentist appointment Thursday at 2pm, stored in your personal context."

You: "Remember Sarah's email is [email protected], mark it as sensitive"
Agent: "Saved -- I've marked Sarah's email as private and linked it to her profile."

Day 1:
You: "I have a course starting next Tuesday"
Agent: "Noted -- I'll remind you as it gets closer."

Day 5 (3 days before):
You: "What's the weather like?"
Agent: "It's sunny. By the way, your online course starts in 3 days."

Day 8 (the day):
You: "Good morning"
Agent: "Good morning! Your course starts today. How's it going?"

You: "I committed to exercising 3 times this week"
Agent: "Holding you to it! I'll check in at the end of the week."

Friday evening:
You: "Let's plan the weekend"
Agent: "Before we plan -- it's the end of the week. How did
        the exercise commitment go? Did you hit 3 times?"

You: "I've been using the new AMD Ryzen 9 9950X for the project,
      and it's much faster for local LLM inference. The team
      decided to standardize on Python 3.12 going forward."

Agent silently extracts:
  - fact: "User has AMD Ryzen 9 9950X" (new)
  - fact: "Team standardized on Python 3.12" (update if version changed)
  - No action on "much faster" (subjective, ephemeral)

gaia chat --ui

March: "Project uses React 18"
  -> superseded by: "Project uses React 19" (April)
    -> superseded by: "Project uses Vue 3" (May)

Agent runs: import torch
Error: ModuleNotFoundError: No module named 'torch'

Next session system prompt:
Known errors to avoid:
  - execute_code: import torch fails -- torch not installed on this machine

gaia memory bootstrap --reset

rm ~/.gaia/memory.db