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The pattern

Use evolving memory when facts can change over time and you need both the current truth and historical trail. Good candidates:
  • Content strategy (what is working right now)
  • Active product priorities
  • Temporary experiments and their outcomes
  • Current customer state or lifecycle stage

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Example: LinkedIn content strategy

A LinkedIn post generator that adapts to changing strategy: Step 1 - Save initial strategy: 
curl -X POST https://api.memcontext.in/api/memories \
  -H "Content-Type: application/json" \
  -H "X-API-Key: mc_your_key" \
  -d '{
    "content": "Long-form LinkedIn posts with personal stories are performing best right now",
    "category": "fact",
    "scope": "user_123",
    "project": "linkedin-generator"
  }'
Step 2 - Strategy evolves, save new memory: 
curl -X POST https://api.memcontext.in/api/memories \
  -H "Content-Type: application/json" \
  -H "X-API-Key: mc_your_key" \
  -d '{
    "content": "Short hook-first posts are now outperforming long-form content on LinkedIn",
    "category": "fact",
    "scope": "user_123",
    "project": "linkedin-generator"
  }'
MemContext automatically classifies the second memory as an update to the first, creating a version chain. Search now returns only the current truth. Step 3 - Save a lesson: 
curl -X POST https://api.memcontext.in/api/memories \
  -H "Content-Type: application/json" \
  -H "X-API-Key: mc_your_key" \
  -d '{
    "content": "Posts with a question hook in the first line get 3x more engagement",
    "category": "decision",
    "scope": "user_123",
    "project": "linkedin-generator"
  }'

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Temporal awareness - automatic and explicit

MemContext automatically detects time-sensitive content during save. For example:
  • “LinkedIn algorithm favors short posts right now” → auto-classified as medium-term, expires in 30 days
  • “Meeting with client tomorrow at 3pm” → auto-classified as short-term, expires in 7 days
  • “User prefers TypeScript” → auto-classified as permanent, no expiry
You can also explicitly set validUntil to override the auto-detection: 
{
  "content": "Running a 2-week A/B test on carousel vs single-image posts",
  "category": "context",
  "scope": "user_123",
  "project": "linkedin-generator",
  "validUntil": "2026-04-25T00:00:00Z"
}
Use the same scope for all memories that belong to the same end user or tenant. The project groups the LinkedIn generator context inside that isolated lane. If you pass validUntil, auto-detection is skipped entirely. After April 25, this memory will not appear in search results.
  1. Save current working strategy as a fact or decision
  2. Generate content by combining profile context and search results
  3. Observe outcomes
  4. Save lessons as new memories (decision or context)
  5. Submit feedback on memories that were useful or stale
  6. When strategy changes, save a new memory - the system handles supersession automatically

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Feedback-driven ranking

When you use memory_feedback to mark memories as helpful, outdated, or wrong, search results are re-ranked accordingly:
  • “Wrong” memories are pushed to the bottom (0.3x score)
  • “Outdated” memories are demoted (0.5x score)
  • “Helpful” memories get a small boost (1.1x score)
This means the system learns from your explicit signals over time.

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Why this works

You do not need fine-tuning or retraining for this class of problem. You need:
  • Current truth (served by isCurrent filtering)
  • Auto-expiry of temporal content (auto-TTL classification)
  • Historical trail (accessible via the history endpoint)
  • Retrieval that handles both semantic and exact-match queries (hybrid search)
  • A feedback loop that directly affects ranking (via memory_feedback)