Project 2: Conversation Memory Manager
Build a memory manager that summarizes, stores, and retrieves conversation context across long sessions.
Quick Reference
| Attribute | Value |
|---|---|
| Difficulty | Level 2: Intermediate |
| Time Estimate | 8-12 hours |
| Language | Python or JavaScript |
| Prerequisites | Token counting, basic storage |
| Key Topics | memory buffers, summarization, retrieval |
1. Learning Objectives
By completing this project, you will:
- Implement conversation buffers with size limits.
- Summarize older messages into memory notes.
- Retrieve relevant memory for new prompts.
- Compare buffer vs summary strategies.
- Evaluate response consistency over long chats.
2. Theoretical Foundation
2.1 Memory vs Context
Context is what fits in the window. Memory is what persists beyond it.
3. Project Specification
3.1 What You Will Build
A memory manager that stores conversation history and injects summaries when the window is full.
3.2 Functional Requirements
- Buffer memory with size limits.
- Summary memory for older messages.
- Retrieval of relevant summaries.
- Configurable strategies (keep recent vs summarize).
- Evaluation on long conversation tasks.
3.3 Non-Functional Requirements
- Deterministic summarization for testing.
- Trace logs of memory updates.
- Safe fallbacks if summary fails.
4. Solution Architecture
4.1 Components
| Component | Responsibility |
|---|---|
| Buffer | Store recent messages |
| Summarizer | Compress older context |
| Memory Store | Persist summaries |
| Retriever | Fetch relevant memory |
5. Implementation Guide
5.1 Project Structure
LEARN_LLM_MEMORY/P02-conversation-memory/
├── src/
│ ├── buffer.py
│ ├── summarize.py
│ ├── store.py
│ └── retrieve.py
5.2 Implementation Phases
Phase 1: Buffer (3-4h)
- Implement rolling buffer with max tokens.
- Checkpoint: buffer drops oldest messages.
Phase 2: Summarization (3-4h)
- Summarize dropped content.
- Checkpoint: summaries capture key facts.
Phase 3: Retrieval (2-4h)
- Inject summaries into new prompts.
- Checkpoint: responses stay consistent.
6. Testing Strategy
6.1 Test Categories
| Category | Purpose | Examples |
|---|---|---|
| Unit | buffer logic | max size enforcement |
| Integration | summarizer | summary inserted |
| Regression | retrieval | relevant memory included |
6.2 Critical Test Cases
- Summary is added when buffer overflows.
- Retrieved memory improves consistency.
- Summaries do not exceed token budget.
7. Common Pitfalls & Debugging
| Pitfall | Symptom | Fix |
|---|---|---|
| Over-summarization | lost details | keep key facts list |
| Summary drift | incorrect memory | add validation step |
| Too much memory | context overflow | cap summary length |
8. Extensions & Challenges
Beginner
- Add manual summary edits.
- Add memory export.
Intermediate
- Add topic-based memory buckets.
- Add memory expiration rules.
Advanced
- Add vector search for memory retrieval.
- Add personalization embeddings.
9. Real-World Connections
- Support agents need persistent context over long chats.
- Personal assistants rely on long-term memory.
10. Resources
- LangChain memory docs
- LLM summarization guides
11. Self-Assessment Checklist
- I can manage buffer and summary memory.
- I can retrieve relevant memories.
- I can keep memory within token limits.
12. Submission / Completion Criteria
Minimum Completion:
- Buffer + summary memory
Full Completion:
- Retrieval of summaries
- Evaluation on long chats
Excellence:
- Vector-based memory retrieval
- Memory expiration rules
This guide was generated from project_based_ideas/AI_AGENTS_LLM_RAG/LEARN_LLM_MEMORY.md.