# Product Requirements Document: Conversation Memory System
## Introduction/Overview
The Conversation Memory System is a local storage and retrieval solution that enables continuity across Claude chat sessions. The system addresses the core problem that Claude cannot access previous conversations, limiting the ability to build context over time and reference past solutions or decisions.
**Goal:** Create a searchable local repository of Claude conversations that can be referenced during current sessions to provide context and continuity.
## Goals
1. **Enable Session Continuity**: Allow Claude to reference previous conversations when explicitly requested
2. **Solution Discovery**: Quickly find past discussions about specific topics, technologies, or problems
3. **Context Management**: Give users control over when and how previous context is included in current conversations
4. **Weekly Insights**: Generate summaries of conversation topics and completed tasks for reflection and planning
5. **Cost-Effective Design**: Only search/load context when specifically requested to manage API costs
## User Stories
**Primary Use Case - Context Retrieval:**
- As a developer, I want to use a `/mem` command followed by a topic search so that Claude can find and optionally include relevant previous discussions in our current conversation
- As a user, I want to control whether the retrieved context is included in the session so that I can manage conversation length and API costs
**Weekly Review:**
- As a user, I want to generate weekly summaries of my Claude conversations that include topics explored and coding tasks completed so that I can track my learning progress and project development
**Content Management:**
- As a user, I want to import my historical Claude conversations into the system so that I can access past solutions and decisions
- As a user, I want new conversations to be automatically added to the system so that the knowledge base stays current
## Functional Requirements
1. **Search Interface**
1.1. The system must respond to `/mem [search_term]` commands during conversations
1.2. The system must return relevant conversation excerpts with metadata (date, topics, relevance score)
1.3. The system must allow users to choose whether to include found context in the current conversation
2. **Storage Management**
2.1. The system must store conversations in organized markdown files with date-based folder structure
2.2. The system must maintain searchable indexes for topics, dates, and keywords
2.3. The system must support importing conversations from Claude export formats
2.4. The system must automatically process and store new conversations
3. **Weekly Reporting**
3.1. The system must generate weekly summaries including topics discussed and coding tasks completed
3.2. The system must identify recurring themes and progression in learning/project development
3.3. The system must output summaries in markdown format for easy review
4. **Technical Implementation**
4.1. The system must provide both file system script and MCP server implementation options
4.2. The system must store all data locally on Ubuntu/WSL side for simplicity
4.3. The system must integrate with existing VSCode development workflow
4.4. The system must handle conversation text only (no file uploads or artifacts in v1)
## Non-Goals (Out of Scope)
- **Multi-AI Integration**: Support for Perplexity Pro or other AI conversation imports (future release)
- **File Upload Handling**: Processing of uploaded files or code artifacts within conversations (future release)
- **Cloud Synchronization**: Remote backup or cross-device sync functionality
- **Real-time Integration**: Automatic context loading without explicit user request
- **Conversation Editing**: Modifying or updating stored conversations after import
- **Advanced Analytics**: Complex conversation analysis beyond basic topic and task identification
## Design Considerations
**File Structure:**
```
claude-memory/
├── conversations/
│ ├── 2025/
│ │ ├── 01-january/
│ │ └── 02-february/
│ ├── index.json
│ └── topics.json
├── summaries/
│ ├── weekly/
│ └── monthly/
├── scripts/ (File System Option)
└── mcp-server/ (MCP Server Option)
```
**User Interface:**
- Command-based interaction: `/mem terraform setup`
- Clear feedback on search results with relevance indicators
- Simple yes/no prompts for including context in conversation
## Technical Considerations
**Implementation Options:**
1. **File System Scripts**: Python scripts for export, search, and indexing with direct file manipulation
2. **MCP Server**: Custom MCP server with standardized API for conversation management
**WSL Integration:**
- Store all files on Ubuntu side (`/home/username/claude-memory/`)
- Run processing scripts in Ubuntu environment
- Access via WSL from Windows VSCode when needed
**Search Strategy:**
- Text-based keyword matching for initial implementation
- Topic indexing for improved relevance
- Date-range filtering for temporal context
**Dependencies:**
- Python 3.8+ for processing scripts
- Standard file system tools
- MCP server framework (if MCP option chosen)
## Success Metrics
**Immediate Success (3 months):**
- Weekly usage of `/mem` command for context retrieval
- Successful generation of 12 weekly summaries
- Ability to find and reference solutions from past conversations
**Long-term Success (6 months):**
- Measurable improvement in conversation continuity
- Reduced time spent re-explaining context or repeating solutions
- Regular use of weekly summaries for project planning
**Technical Success:**
- Sub-5 second search response times
- 95%+ successful conversation imports
- Zero data loss during processing
## Open Questions
1. **Integration Path**: What's the best way to integrate the `/mem` command with Claude's existing interface? Should this be a separate tool or built into conversation flow?
2. **Search Sophistication**: Should the initial version implement simple keyword matching, or invest in more sophisticated relevance scoring?
3. **Context Window Management**: How should the system handle large amounts of retrieved context that might exceed conversation limits?
4. **Privacy/Security**: What measures should be implemented to protect locally stored conversation data?
5. **Migration Strategy**: If starting with file system scripts, what's the cleanest path to migrate to MCP server later?
6. **Weekly Summary Format**: What specific elements should be included in weekly summaries beyond topics and coding tasks?
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**Next Steps:**
1. Choose implementation approach (file system vs MCP server)
2. Create basic file structure and conversation template
3. Implement core search functionality
4. Test with sample conversation imports
5. Develop weekly summary generation
