🧠 Advanced Persistent Project Memory & Engineering Partner Mode

Summary

Enable Lumo to maintain long-term project context across sessions and operate as a proactive engineering partner rather than reactive assistant.

Problem Statement

Current conversation memory is session-bound. Each new chat requires re-establishing context about:
- Project architecture decisions and trade-offs
- Technology stack specifics (local models, frameworks, deployment targets)
- Previous iterations and lessons learned
- Ongoing development challenges

This creates friction for complex, multi-session projects and forces users to constantly re-explain context.

Proposed Solution

Feature 1: Project-Specific Memory Stores

Description: Allow users to create and manage named "project memory" objects that persist across separate conversations.

Key Capabilities:
- Store: Architecture diagrams, tech stack details, API contracts, codebase structure
- Query: Natural language retrieval of project-specific knowledge
- Update: Incremental additions as project evolves
- Export/Import: Share project contexts between team members

Technical Implementation:
```yaml
ProjectMemory:
- Name: "adrenalina-prime"
- Created: timestamp
- Tags: ["engineering", "agent-system", "local-llm"]
- ContextBlocks:
- Type: "architecture" | "api-contract" | "tech-stack" | "decision-log"
- Content: markdown/text
- Last_Modified: timestamp
Feature 2: Engineering Partner Mode

Description: Toggle switch for interaction style optimization.
Standard Mode Engineering Partner Mode
Reactive Q&A Proactive gap identification
Generic responses Stack-aware suggestions (e.g., knows Ollama vs OpenAPI constraints)
No state awareness Remembers previous session's blockers
Neutral tone Direct, technical communication (zero sugar-coating)
Feature 3: Contextual Code Awareness

Description: When uploading code files, Lumo should build internal map of:

File dependencies and imports
Function signatures and interfaces
Testing coverage and gaps
Potential security vulnerabilities based on patterns

Integration Points:

Auto-detect language/framework from file extensions
Cross-reference with known local model capabilities (e.g., deepseek-coder:6.7b context window limits)
Suggest tests before implementation, not after

User Stories

"As a developer working on a multi-week project, I want to resume conversation without explaining what we built last week."

"As an engineer, I want Lumo to notice architectural inconsistencies between my code and stated design goals."

"As a team lead, I want to share project context with junior developers via exported memory bundles."

Success Metrics
Metric Target
Reduction in context-setting time >60% decrease
Multi-session project completion rate +40% increase
User satisfaction (Engineering users) NPS +25 points
Memory feature adoption >50% of Lumo Plus users within 6 months
Risks & Mitigations
Risk Mitigation
Privacy concerns with persistent data All memory encrypted end-to-end; opt-in only
Memory bloat over time Automatic summarization; user-controlled cleanup
Inaccurate recall Version history with diff view; confidence scoring on retrieved context
Competitive Differentiation

Other AI assistants (Claude, ChatGPT, etc.) offer:

✅ Long context windows
❌ Project-scoped memory persistence
❌ Role-based interaction modes
❌ Architecture-aware code review

Lumo's opportunity: Become the first privacy-focused AI with persistent project memory + professional mode optimization.

Priority: High Effort: Medium (requires backend memory infrastructure + UI toggles) Timeline Estimate: 3-4 sprints for MVP

Related Features:

Custom Lumos (already exists, but lacks memory integration)
Persistent Memory (exists, but not project-scoped)
File Analysis (exists, but lacks cross-session code mapping)