Agent Context Management Best Practices: How to Make AI Remember 100-Turn Conversations
Making AI remember context isn't hard. What's hard is remembering what matters, forgetting what doesn't, and recalling the right information at the right time. This is the most underrated challenge in Agent engineering.
The Core Problem
Large model context windows are finite — even 200K tokens have limits. Plus:
- Longer context = slower inference, higher cost
- More irrelevant information = more diluted attention
- Valuable context is often scattered across multiple sessions
The key question isn't "how to cram more in," but "how to select better."
Layered Memory Architecture
YingClaw uses a four-tier memory model:
┌─────────────────────────────────┐
│ Session Context (current chat) │ ← Fully preserved
├─────────────────────────────────┤
│ Short-term Memory (7-day TTL) │ ← Daily notes
├─────────────────────────────────┤
│ Long-term Memory (permanent) │ ← User preferences, project conventions
├─────────────────────────────────┤
│ Core Memory (permanent) │ ← Immutable facts
└─────────────────────────────────┘
Key Principles
| Tier | What to store | What NOT to store |
|---|---|---|
| Session | Entire current conversation | — |
| Short-term | Progress milestones, debug logs | API keys, passwords |
| Long-term | "User prefers TypeScript" | What you ate today |
| Core | "Project MSRV is 1.75" | Temporary config |
Three Practical Techniques
1. Compress, Don't Truncate
Don't simply discard old messages. Use an Agent to summarize conversation history, preserving key decisions and context:
Original (500 tokens):
"I tried approach A, it didn't work. Then tried B, tweaked param x=3.
The results look good, but y still needs optimization..."
Compressed (50 tokens):
"Confirmed: approach B works, x=3. Pending: optimize y."
2. Keyword-Triggered Memory Recall
Don't load all memories for every interaction. Use keywords from the current conversation to recall relevant memory fragments:
User: "Help me review the auth module"
→ Triggers recall keywords: "auth", "security", "code review"
→ Recalls: "Project uses JWT + refresh token (decided in May)"
3. Regular Review and Cleanup
Have the Agent auto-run a weekly memory cleanup:
"Review this week's memories. Merge duplicates. Delete stale information. Preserve the 5 most important findings."
Impact Data
After adopting layered memory, YingClaw's internal testing showed:
| Metric | Before | After |
|---|---|---|
| Context relevance | 64% | 93% |
| Avg. token consumption | 12K | 5K |
| User satisfaction | 3.2/5 | 4.5/5 |
Context management isn't bells and whistles — it's the engineering challenge that turns an Agent from "functional" into "great."