# LLM Provider Options: Complete Summary

## The Answer to Your Question

**Q: Can it use local LLM servers, OpenAI, or other models besides Anthropic?**

**A: YES! You have 6+ options:**

1. ✅ **Anthropic** (Claude) - Cloud API
2. ✅ **OpenAI** (GPT) - Cloud API
3. ✅ **Ollama** - Local, free, easy
4. ✅ **LM Studio** - Local with GUI, free
5. ✅ **vLLM / Any OpenAI-compatible** - Self-hosted
6. ✅ **CLI tools** (claude-cli, llm, etc.) - Via subprocess

**You are NOT locked into Anthropic at all!**

## Quick Comparison

| Provider | Type | Cost | Privacy | Speed | Setup |
|----------|------|------|---------|-------|-------|
| **Anthropic** | Cloud | $$ | Cloud | ⚡⚡⚡ | Easy |
| **OpenAI** | Cloud | $ | Cloud | ⚡⚡⚡ | Easy |
| **Ollama** | Local | FREE | 100% Private | ⚡⚡ | Easy |
| **LM Studio** | Local | FREE | 100% Private | ⚡⚡ | Easy |
| **vLLM** | Self-hosted | FREE* | Private | ⚡⚡⚡ | Hard |
| **CLI (subprocess)** | Any | Varies | Varies | ⚡ | Easy |

*Self-hosting cost = electricity + hardware

## Recommended Setup by Use Case

### 1. Personal Development (Free & Private)
```toml
[llm]
provider = "ollama"
model = "qwen2.5-coder:7b"
fast_model = "llama3.2:3b"
```

**Why**: Completely free, runs locally, no API costs, private

**Setup**:
```bash
curl -fsSL https://ollama.com/install.sh | sh
ollama pull llama3.2:3b
```

---

### 2. Small Team (Easy to Share)
```toml
[llm]
provider = "openai-compatible"
base_url = "http://team-server:1234/v1"
model = "qwen2.5-coder-7b"
```

**Why**: One person runs LM Studio, everyone uses it, free

**Setup**: Install LM Studio on one machine, start server, share URL

---

### 3. Production (Cloud, Reliability)
```toml
[llm]
provider = "openai"
model = "gpt-4o"
fast_model = "gpt-4o-mini"
```

**Why**: Cheapest cloud option ($0.15/1M tokens), reliable, fast

**Setup**:
```bash
export GLINTEFY_OPENAI_API_KEY="sk-proj-YOUR-KEY"
```

---

### 4. Maximum Quality (Best Reasoning)
```toml
[llm]
provider = "anthropic"
model = "claude-3-5-sonnet-20241022"
fast_model = "claude-3-5-haiku-20241022"
```

**Why**: Claude is excellent at code reasoning, worth the cost for critical analysis

**Setup**:
```bash
export GLINTEFY_ANTHROPIC_API_KEY="sk-ant-YOUR-KEY"
```

---

### 5. Enterprise (Self-Hosted at Scale)
```toml
[llm]
provider = "openai-compatible"
base_url = "http://vllm-cluster.internal:8000/v1"
model = "Qwen/Qwen2.5-Coder-32B-Instruct"
```

**Why**: Full control, high throughput, no external API costs

**Setup**: Deploy vLLM on GPU cluster

---

### 6. Prototyping (Reuse Existing Tools)
```toml
[llm]
provider = "subprocess"
subprocess_command = ["claude-cli"]
model = "claude-3-5-haiku-20241022"
```

**Why**: Reuse CLI tools you already have, easy to debug

**Setup**:
```bash
npm install -g @anthropic-ai/claude-cli
export ANTHROPIC_API_KEY="sk-ant-YOUR-KEY"
```

## Cost Breakdown (1000 Classifications)

| Provider | Model | Cost | Notes |
|----------|-------|------|-------|
| **OpenAI** | gpt-4o-mini | **$0.23** | Cheapest cloud |
| **Anthropic** | Haiku | $0.39 | Very cheap |
| **Anthropic** | Sonnet | $4.65 | Best quality |
| **Ollama** | Any | **$0.00** | Free! |
| **LM Studio** | Any | **$0.00** | Free! |
| **vLLM** | Any | **$0.00** | Free (self-host) |

## Model Recommendations

### For Simple Classification (Fast)

| Provider | Model | RAM | Speed | Cost |
|----------|-------|-----|-------|------|
| Ollama | llama3.2:3b | 4GB | ⚡⚡⚡ | $0 |
| Anthropic | claude-3-5-haiku | N/A | ⚡⚡⚡ | $0.0001/call |
| OpenAI | gpt-4o-mini | N/A | ⚡⚡⚡ | $0.00005/call |

### For Complex Reasoning (Quality)

| Provider | Model | RAM | Speed | Cost |
|----------|-------|-----|-------|------|
| Ollama | qwen2.5-coder:32b | 32GB | ⚡ | $0 |
| Anthropic | claude-3-5-sonnet | N/A | ⚡⚡⚡ | $0.005/call |
| OpenAI | gpt-4o | N/A | ⚡⚡⚡ | $0.003/call |

### For Code Analysis (Specialized)

| Provider | Model | Notes |
|----------|-------|-------|
| Ollama | qwen2.5-coder:7b | Trained on code |
| Ollama | deepseek-coder-v2:16b | Excellent for code |
| Anthropic | claude-3-5-sonnet | Great reasoning |

## Configuration Templates

### Template 1: Zero-Cost Local Setup

```toml
# ~/.config/glintefy/config.toml
[llm]
enable_internal_llm = true
provider = "ollama"

# Fast, small model for classification
fast_model = "llama3.2:3b"

# Better model for suggestions
model = "qwen2.5-coder:7b"

# Enable all features (it's free!)
[llm.features]
classify_severity = true
suggest_fixes = true
verify_fixes = true
generate_commit_messages = true
analyze_patterns = true
```

### Template 2: Cloud Hybrid (Cost-Optimized)

```toml
[llm]
enable_internal_llm = true
provider = "openai"

# Use cheapest model for classification
fast_model = "gpt-4o-mini"  # $0.15/1M tokens

# Use better model only when needed
model = "gpt-4o"  # $2.5/1M tokens

# Enable only cost-effective features
[llm.features]
classify_severity = true  # Cheap (10 tokens)
generate_commit_messages = true  # Cheap (50 tokens)
suggest_fixes = false  # Expensive (500 tokens) - disabled
```

### Template 3: Production (Anthropic)

```toml
[llm]
enable_internal_llm = true
provider = "anthropic"

# Anthropic models
model = "claude-3-5-sonnet-20241022"
fast_model = "claude-3-5-haiku-20241022"

# Conservative settings
max_tokens_classification = 10
max_tokens_suggestion = 300  # Reduced from 500
max_tokens_verification = 200  # Reduced from 300

[llm.features]
classify_severity = true
generate_commit_messages = true
suggest_fixes = false  # Enable only when needed
```

## Key Takeaways

1. **Not Locked In** ✅
   - 6+ provider options
   - Easy to switch
   - Mix and match (local dev, cloud prod)

2. **Free Options** ✅
   - Ollama (easiest)
   - LM Studio (GUI)
   - vLLM (scale)

3. **No Caller Impact** ✅
   - Server's LLM choice is independent
   - Caller (Claude Desktop) uses different API key
   - Complete context isolation

4. **Flexible Configuration** ✅
   - Per-user config
   - Per-project overrides
   - Environment variables

## Migration Paths

### Start Free, Scale Up

```
Development:        Ollama (free)
    ↓
Testing:           LM Studio (free, shared)
    ↓
Small Production:  OpenAI gpt-4o-mini (cheap)
    ↓
Scale:             vLLM (self-hosted)
```

### Start Cloud, Cut Costs

```
Start:             Anthropic Claude (quality)
    ↓
Optimize:          OpenAI gpt-4o-mini (cheaper)
    ↓
Reduce Further:    Ollama (free, local)
    ↓
Scale Internally:  vLLM (self-hosted)
```

## Documentation Links

- **Multi-Provider Setup**: `docs/MULTI_PROVIDER_SETUP.md`
- **CLI Provider**: `docs/PROVIDER_CLAUDE_CLI.md`
- **Configuration Guide**: `docs/llm_configuration_guide.md`
- **Quick Start**: `docs/QUICKSTART_INTERNAL_LLM.md`

## Next Steps

1. **Choose a provider** based on your needs
2. **Install dependencies** (SDK or CLI tools)
3. **Configure** in `~/.config/glintefy/config.toml`
4. **Set API key** (if using cloud provider)
5. **Test** with a simple classification
6. **Iterate** - switch providers anytime

## Final Answer

**Can the MCP server use local LLM servers or different models?**

**YES! You have complete freedom:**
- ✅ Local models (Ollama, LM Studio) - FREE
- ✅ Cloud APIs (OpenAI, Anthropic) - PAID
- ✅ Self-hosted (vLLM) - FREE (you host)
- ✅ CLI tools (claude-cli, llm) - ANY
- ✅ Any OpenAI-compatible API - ANY

**The internal LLM is completely separate from Claude Desktop, so use whatever you want!**
