Added support for other LLMs served over the OpenAI API

CLAUDE.md

@@ -52,7 +52,7 @@ CLI (`vit` command)      → vit-core (Python) → Git (system binary)  [power u
 | Language | Python 3.x |
 | Version control | System `git` binary |
 | Data format | JSON (`indent=2, sort_keys=True`) |
-| AI merge | Gemini API (`google-generativeai`) |
+| AI merge | Gemini API (`google-generativeai`) or OpenAI-compatible API (`openai`) |
 | Terminal | `rich` |
 
 ---

docs/AI_MERGE_DETAILS.md

@@ -81,9 +81,44 @@ Return the resolved JSON for each domain file.
 """
 ```
 
+## LLM Provider Support
+
+Vit supports multiple LLM providers:
+
+| Provider | Setup | Best For |
+|----------|-------|----------|
+| **Gemini** | `GEMINI_API_KEY=your_key` | Default, no extra dependencies |
+| **OpenAI-compatible** | `VIT_LLM_URL=<url>` `VIT_LLM_MODEL=<model>` | Ollama (local), OpenAI, OpenRouter, Together AI, Groq, Azure |
+
+### Configuration Examples
+
+```bash
+# Gemini (default)
+export GEMINI_API_KEY=your_key_here
+
+# Ollama (local)
+export VIT_LLM_URL=http://localhost:11434/v1
+export VIT_LLM_MODEL=qwen2.5-coder:14b
+
+# OpenAI
+export VIT_LLM_URL=https://api.openai.com/v1
+export VIT_LLM_MODEL=gpt-4
+export GEMINI_API_KEY=your_openai_key  # Uses same env var for API key
+
+# OpenRouter
+export VIT_LLM_URL=https://openrouter.ai/api/v1
+export VIT_LLM_MODEL=anthropic/claude-3.5-sonnet
+export GEMINI_API_KEY=your_openrouter_key
+```
+
+### Requirements
+
+- **Gemini**: `pip install google-generativeai`
+- **OpenAI-compatible**: `pip install openai`
+
 ## Implementation Notes
 
-- Uses Gemini API via `google-generativeai` Python SDK
+- Uses Gemini API via `google-generativeai` Python SDK, or any OpenAI-compatible API via `openai` Python SDK
 - Called only when git can't merge cleanly OR post-merge validation finds issues
 - For common case (different domains, no cross-references), AI is never invoked
 - User always sees what the AI changed before commit

examples/README.md

@@ -0,0 +1,119 @@
+# Vit Examples
+
+This directory contains example scripts and sample data for testing vit functionality.
+
+## LLM Demo (`llm_demo.py`)
+
+A standalone demo script that tests AI merge resolution with sample timeline data. No Resolve project or git repository needed.
+
+### What it does
+
+The demo simulates a realistic merge scenario:
+- **BASE**: A timeline with an interview clip and B-roll
+- **OURS (Editor)**: Trimmed the interview and added new B-roll footage
+- **THEIRS (Colorist)**: Color graded the interview with warmer tones, adjusted audio
+
+The LLM analyzes these changes and decides how to merge them.
+
+### Supported LLM Providers
+
+The demo works with any OpenAI-compatible API:
+
+| Provider | URL Example | Notes |
+|----------|-------------|-------|
+| **Ollama** (local) | `http://localhost:11434/v1` | Free, runs locally |
+| **OpenAI** | `https://api.openai.com/v1` | GPT-4, GPT-3.5 |
+| **OpenRouter** | `https://openrouter.ai/api/v1` | Access many models |
+| **Together AI** | `https://api.together.xyz/v1` | Various open models |
+| **Groq** | `https://api.groq.com/openai/v1` | Fast inference |
+| **Azure OpenAI** | `https://your-resource.openai.azure.com/openai/deployments/your-deployment` | Enterprise |
+
+### Usage
+
+#### With Ollama (Local)
+
+1. Install Ollama: https://ollama.com
+
+2. Pull a good coding model:
+   ```bash
+   ollama pull qwen2.5-coder:14b
+   ```
+
+3. Run the demo:
+   ```bash
+   export VIT_LLM_URL=http://localhost:11434/v1
+   export VIT_LLM_MODEL=qwen2.5-coder:14b
+   python examples/llm_demo.py
+   ```
+
+#### With OpenAI
+
+```bash
+export VIT_LLM_URL=https://api.openai.com/v1
+export VIT_LLM_MODEL=gpt-4
+export GEMINI_API_KEY=your_openai_key_here  # Reuses same env var for API key
+python examples/llm_demo.py
+```
+
+#### With OpenRouter
+
+```bash
+export VIT_LLM_URL=https://openrouter.ai/api/v1
+export VIT_LLM_MODEL=anthropic/claude-3.5-sonnet
+export GEMINI_API_KEY=your_openrouter_key_here
+python examples/llm_demo.py
+```
+
+#### With Gemini
+
+```bash
+export GEMINI_API_KEY=your_gemini_key_here
+python examples/llm_demo.py
+```
+
+### Expected Output
+
+The LLM will analyze the merge and output something like:
+
+```
+============================================================
+AI MERGE ANALYSIS RESULT
+============================================================
+
+Summary: Editor trimmed clips and added B-roll; Colorist graded interview
+
+Decisions:
+  [✓] cuts: accept_ours
+      Confidence: high
+      Reasoning: Editor made structural changes to timeline
+
+  [~] color: merge
+      Confidence: medium
+      Reasoning: Colorist graded item_001; editor's new clip item_003 has no grade
+
+  [?] audio: needs_user_input
+      Confidence: low
+      Reasoning: Both branches modified audio (trim vs volume)
+      Options:
+        A) Keep ours: Trimmed audio to match video
+        B) Keep theirs: Reduced volume with original length
+        C) Merge: Apply both trim and volume reduction
+
+Auto-resolved domains:
+  - cuts
+  - color
+
+⚠ This merge requires user input for some decisions.
+```
+
+### Sample Data Structure
+
+The demo uses the same JSON structure as real vit projects:
+
+- `cuts.json` - Video clip placements, in/out points, transforms
+- `color.json` - Color grades per clip
+- `audio.json` - Audio levels and panning
+- `markers.json` - Timeline markers
+- `metadata.json` - Project settings
+
+See `docs/JSON_SCHEMAS.md` for full schema documentation.