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LocalGemini CLI - Project Scope & Architecture

Project Vision

Fork Google's Gemini CLI to create a local-first AI workflow tool that uses GGUF models (Llama, Phi, Qwen, etc.) instead of cloud APIs, maintaining all the sophisticated features while ensuring complete privacy and offline capability.

Core Architecture Changes

1. Model Backend Replacement

Current: Gemini API calls New: Native Node.js GGUF model inference using node-llama-cpp

import { getLlama, LlamaChatSession } from 'node-llama-cpp';

interface TrustModelClient {
  generateText(prompt: string, options?: GenerationOptions): Promise<string>;
  generateStream(
    prompt: string,
    options?: GenerationOptions,
  ): AsyncIterable<string>;
  loadModel(modelPath: string): Promise<void>;
  unloadModel(): Promise<void>;
  createChatSession(): Promise<LlamaChatSession>;
}

class TrustNodeLlamaClient implements TrustModelClient {
  private llama: any;
  private model: any = null;
  private context: any = null;
  private currentModelPath: string | null = null;

  async loadModel(modelPath: string) {
    if (this.currentModelPath === modelPath && this.model) {
      return; // Model already loaded
    }

    this.llama = await getLlama();
    this.model = await this.llama.loadModel({
      modelPath,
      ...this.getOptimalSettings(),
    });
    this.context = await this.model.createContext();
    this.currentModelPath = modelPath;
  }

  async createChatSession(): Promise<LlamaChatSession> {
    if (!this.context) {
      throw new Error('Model not loaded');
    }
    return new LlamaChatSession({
      contextSequence: this.context.getSequence(),
    });
  }

  async generateText(
    prompt: string,
    options?: GenerationOptions,
  ): Promise<string> {
    const session = await this.createChatSession();
    return session.prompt(prompt, {
      temperature: options?.temperature ?? 0.7,
      maxTokens: options?.maxTokens ?? 2048,
      ...options,
    });
  }

  async *generateStream(
    prompt: string,
    options?: GenerationOptions,
  ): AsyncIterable<string> {
    const session = await this.createChatSession();
    const response = session.promptWithMeta(prompt, {
      temperature: options?.temperature ?? 0.7,
      maxTokens: options?.maxTokens ?? 2048,
      onTextChunk: (chunk: string) => ({ chunk }),
      ...options,
    });

    for await (const result of response) {
      if (result.chunk) {
        yield result.chunk;
      }
    }
  }

  private getOptimalSettings() {
    // Auto-detect optimal settings based on system capabilities
    const totalRAM = process.memoryUsage().heapTotal;
    return {
      threads: Math.min(8, require('os').cpus().length),
      contextSize: 4096,
      // Add GPU detection logic here
    };
  }
}

2. TrustOS Model Management System

interface TrustModelConfig {
  name: string;
  path: string;
  type: 'llama' | 'phi' | 'qwen' | 'mistral';
  quantization: 'Q4_K_M' | 'Q8_0' | 'FP16';
  contextSize: number;
  ramRequirement: string;
  description: string;
  trustScore?: number; // TrustOS feature: community trust rating
  verificationHash?: string; // TrustOS feature: integrity verification
}

class TrustModelManager {
  listAvailableModels(): TrustModelConfig[];
  downloadModel(modelId: string): Promise<void>;
  verifyModel(modelPath: string): Promise<boolean>; // TrustOS integrity check
  switchModel(modelName: string): Promise<void>;
  getCurrentModel(): TrustModelConfig | null;
  getTrustRating(modelId: string): Promise<number>; // Community trust score
}

3. TrustOS Configuration System

# ~/.trustcli/config.yaml
trust:
  privacy_mode: strict # TrustOS: no external calls
  audit_logging: optional
  model_verification: enabled

models:
  default: 'llama-3.2-3b-instruct-q4'
  directory: '~/.trustcli/models'
  auto_verify: true

model_configs:
  llama-3.2-3b-instruct-q4:
    path: 'models/llama-3.2-3b-instruct-q4_k_m.gguf'
    type: 'llama'
    context_size: 4096
    threads: 4
    trust_score: 9.2

  phi-3.5-mini-instruct-q8:
    path: 'models/phi-3.5-mini-instruct-q8_0.gguf'
    type: 'phi'
    context_size: 4096
    threads: 4
    trust_score: 9.5

inference:
  temperature: 0.7
  top_p: 0.9
  max_tokens: 2048
  stream: true

transparency:
  log_prompts: false # TrustOS: user controls logging
  log_responses: false
  show_model_info: true
  show_performance_metrics: true

Feature Parity & Enhancements

Core Features to Maintain

  1. Interactive Chat Mode: Keep the conversational interface
  2. Codebase Analysis: File reading and context building
  3. Project Workflows: Git integration, PR analysis, etc.
  4. MCP Server Support: Plugin architecture for tools
  5. Multimodal Capabilities: Use vision models when available

Local-Specific Enhancements

  1. Model Switching: gemini model switch phi-3.5-mini
  2. Resource Monitoring: Show RAM usage, inference speed
  3. Offline Mode: Complete functionality without internet
  4. Model Recommendations: Suggest models based on task and hardware
  5. Performance Profiling: Benchmark different models/quantizations

Implementation Strategy

Phase 1: Core Replacement (Weeks 1-2)

  • Fork Gemini CLI repository
  • Replace Google auth with local model management
  • Install and configure node-llama-cpp
  • Replace API calls with local inference using node-llama-cpp
  • Basic model loading and text generation
  • Simple configuration system

Phase 2: Model Management (Weeks 3-4)

  • Model download/management system using Hugging Face Hub
  • Support multiple model formats (Llama, Phi, Qwen, Mistral)
  • Model switching functionality (gemini model switch phi-3.5-mini)
  • Performance monitoring and resource usage display
  • Auto-detection of optimal model settings

Phase 3: Feature Parity (Weeks 5-6)

  • Restore all Gemini CLI features with local models
  • Codebase analysis and long context handling
  • Git integration and workflow automation
  • Streaming responses and chat history
  • MCP server compatibility

Phase 4: Local Enhancements (Weeks 7-8)

  • Advanced model management UI in terminal
  • Hardware optimization recommendations
  • Offline documentation and help system
  • Performance benchmarking tools
  • JSON schema enforcement for structured outputs

Technical Challenges & Solutions

Challenge 1: Performance Gap

Problem: Local models are slower than cloud APIs Solution:

  • Implement smart caching for repeated queries
  • Use smaller models for simple tasks, larger for complex ones
  • Parallel processing for batch operations

Challenge 2: Memory Management

Problem: Large models consume significant RAM Solution:

  • Model swapping based on task requirements
  • Quantization recommendations based on available RAM
  • Clear memory management with model unloading

Challenge 3: Model Compatibility

Problem: Different models have different chat formats Solution:

  • Abstraction layer for chat templates
  • Auto-detection of model type and format
  • Fallback to universal formats

Installation & Usage

Installation & Coexistence

# Install TrustCLI alongside Gemini CLI
npm install -g @trustos/cli

# Different binary names - no conflicts
gemini --help          # Google's Gemini CLI
trust --help           # TrustCLI

# Can use in same project
cd my-project/
gemini "Analyze this codebase"     # Uses cloud Gemini
trust "Analyze this codebase"      # Uses local model (private/trusted)

# Hybrid workflows - best of both worlds
gemini "Generate test plan" > plan.md
trust "Review this test plan" --file plan.md --private

Configuration Coexistence

~/.gemini/           # Google Gemini CLI config
~/.trustcli/         # TrustCLI config directory
  ├── config.yaml    # Main configuration
  ├── models/         # Downloaded models
  ├── cache/          # Model cache
  ├── logs/           # Audit logs (optional)
  └── trust/          # Trust scores and verification data

First Run Setup

local-gemini setup
# Guides through:
# 1. Model directory setup
# 2. Hardware detection and recommendations
# 3. First model download (suggests appropriate size)
# 4. Basic configuration

Usage Examples

# Basic usage - same patterns as Gemini CLI
trust "Explain this error in main.py"

# Model management
trust model list
trust model download microsoft/Phi-3.5-mini-instruct-gguf
trust model switch phi-3.5-mini
trust model verify phi-3.5-mini  # TrustOS: verify model integrity
trust model benchmark

# Advanced features
trust --model llama-3.2-3b "Code review this PR"
trust --offline "Help with this script"
trust --format json "Extract data from this log"
trust --temperature 0.1 "Write unit tests"
trust --private "Analyze sensitive code"  # Explicit privacy mode

# Server mode for integrations
trust serve --port 8080  # OpenAI-compatible API
trust serve --unix-socket /tmp/trust.sock

# Performance monitoring & transparency
trust stats
trust benchmark --all-models
trust recommend-model --task coding --ram 16gb
trust audit --show-logs  # TrustOS: transparency features
trust trust-score llama-3.2-3b  # Community trust rating

Recommended Models for Different Use Cases

Lightweight Development (< 8GB RAM)

  • Phi-3.5-mini-instruct (3.8B params, Q4_K_M): Fast coding assistance
  • Qwen2.5-1.5B-Instruct (1.5B params, Q8_0): Quick questions

Standard Development (8-16GB RAM)

  • Llama-3.2-3B-Instruct (3B params, Q8_0): Balanced performance
  • CodeQwen1.5-7B-Chat (7B params, Q4_K_M): Code-specialized

Power Users (16GB+ RAM)

  • Llama-3.1-8B-Instruct (8B params, Q8_0): High-quality responses
  • DeepSeek-Coder-7B-Instruct (7B params, Q8_0): Advanced coding

Additional Strategic Goals

7. Community & Ecosystem Building

  • Model sharing: Community-curated model recommendations
  • Plugin marketplace: Extensible tool integrations
  • Educational content: Guides for different use cases
  • Enterprise adoption: Clear migration paths from cloud CLIs

8. Competitive Differentiation

  • Better than Ollama CLI: More sophisticated workflows and integrations
  • Better than cloud CLIs: Privacy, cost, and offline capabilities
  • Better than basic LLM tools: Rich feature set and professional tooling
  • Unique hybrid approach: Best of both local and cloud workflows

9. Technical Excellence

  • Zero-config startup: Works out of the box with sensible defaults
  • Graceful degradation: Fallback strategies when resources are limited
  • Error recovery: Robust handling of model loading failures
  • Observability: Rich logging and debugging capabilities

10. Long-term Sustainability

  • Model agnostic: Support for multiple inference backends
  • Format flexibility: Adapt to new model formats as they emerge
  • Cloud bridge: Optional hybrid mode with cloud APIs as fallback
  • Enterprise support: Clear path to commercial support if needed

Contributing Guidelines

  • TypeScript/Node.js: Pure Node.js stack for CLI framework and model inference
  • node-llama-cpp: Native Node.js bindings for GGUF model inference (no Python)
  • Comprehensive testing: Multi-model testing across different hardware configurations
  • Documentation: Model compatibility guides and TrustOS integration docs
  • Performance benchmarking: Automated benchmarking suite for model comparison
  • TrustOS standards: Follow TrustOS framework guidelines for transparency and trust

Success Metrics & KPIs

Adoption Metrics

  1. GitHub Stars: 10K+ within first year
  2. Weekly Downloads: 50K+ npm downloads
  3. Community Engagement: 500+ contributors, active Discord/forum
  4. Enterprise Adoption: 100+ companies using in production

Technical Metrics

  1. Feature Parity: 95%+ of Gemini CLI features working locally
  2. Performance: <10s response time for typical queries
  3. Resource Efficiency: Runs smoothly on 8GB+ laptops
  4. Setup Experience: Complete setup in <5 minutes
  5. Model Ecosystem: Support for 25+ popular models

Quality Metrics

  1. Reliability: 99.9% uptime for local inference
  2. User Satisfaction: 4.5+ stars on npm/GitHub
  3. Documentation: Comprehensive guides for all use cases
  4. Support Response: <24h response time for issues