VisionBridge

Give text-only LLMs vision through a tiny OpenAI-compatible proxy.

VisionBridge sits between your chat UI and your models. It lets a reasoning model from LM Studio, Ollama, vLLM, Z.ai, OpenRouter, or any OpenAI-compatible backend inspect images by asking a separate vision model targeted questions.

No training. No weights. Bring your own reasoning model and your own vision model.

How It Works

OpenWebUI / LibreChat / app
        |
        v
VisionBridge at /v1/chat/completions
        |
        +-- reasoning model: LM Studio / Ollama / vLLM / GLM / DeepSeek
        |
        +-- vision model: LLaVA / Qwen-VL / GLM-V / GPT-4o-mini / any VLM

VisionBridge receives an OpenAI-style chat request. If the request contains images, it stores the images, rewrites the prompt for the reasoning model, and lets the reasoning model call tools:

• look(image_id, question) for targeted visual inspection
• ocr(image_id) for text extraction
• scan(image_id) for a tiled full-document text sweep of dense pages
• crop_and_look(image_id, box, question) for zooming into a region (small crops are automatically upscaled before the VLM sees them)
• compare(image_id_a, image_id_b, question) for judging two images together

The vision model does the seeing. The reasoning model does the thinking.

Before the loop starts, every image gets a one-shot scene description from the vision model (in parallel, cached by content), so the reasoning model begins with global context and spends its tool budget on details. Multiple tool calls in one turn run concurrently, and repeated images across a conversation are served from a content-addressed cache instead of being re-fetched and re-decoded.

Tool calling uses the backend's native OpenAI function calling when available. In the default TOOL_MODE=auto, VisionBridge probes the reasoning backend once and transparently falls back to a prompt-JSON protocol for backends and models that don't support tools — so it works with strong hosted models and weak local ones alike.

The loop is built to survive imperfect models: hallucinated image ids, bad crop boxes, malformed JSON, and vision-backend hiccups are fed back to the reasoning model as observations so it can correct itself, and hitting the inspection limit triggers a final best-effort answer instead of an error.

Quick Start With Docker

docker run -p 8080:8080 \
  -e REASONING_BASE_URL=http://host.docker.internal:1234/v1 \
  -e REASONING_API_KEY=lm-studio \
  -e REASONING_MODEL=local-model \
  -e VISION_BASE_URL=http://host.docker.internal:11434/v1 \
  -e VISION_API_KEY=ollama \
  -e VISION_MODEL=llava:13b \
  ghcr.io/eekosystems/visionbridge:latest

Then point your OpenAI-compatible client at:

Base URL: http://localhost:8080/v1
API key: anything
Model: visionbridge

Local Development

cd visionbridge
python -m venv .venv
source .venv/bin/activate    # Windows: .venv\Scripts\activate
pip install -e ".[dev]"
cp .env.example .env         # Windows: copy .env.example .env
visionbridge serve --host 0.0.0.0 --port 8080

Check your backend configuration any time with:

visionbridge doctor

It verifies both backends are reachable, that the configured models exist, and that a test completion succeeds.

Configuration

All settings come from environment variables (or .env):

| Variable | Default | Purpose | | --- | --- | --- | | REASONING_BASE_URL | http://localhost:1234/v1 | Reasoning backend | | REASONING_API_KEY | local | Reasoning API key | | REASONING_MODEL | local-model | Reasoning model id | | VISION_BASE_URL | http://localhost:11434/v1 | Vision backend | | VISION_API_KEY | local | Vision API key | | VISION_MODEL | llava:13b | Vision model id | | BRIDGE_MODEL_NAME | visionbridge | Model name the proxy advertises | | TOOL_MODE | auto | auto, native, or prompt tool calling | | MAX_VISION_CALLS | 8 | Reasoning turns before a forced final answer | | REQUEST_TIMEOUT_SECONDS | 120 | Per-backend-call timeout | | BACKEND_MAX_RETRIES | 2 | Retries on 429/5xx/transport errors | | SCENE_PRIMING | true | Describe each image once before the loop starts | | ALLOW_FILE_URLS | false | Allow file:// image URLs | | ALLOW_PRIVATE_URLS | false | Allow image fetches from private/internal hosts | | MAX_IMAGE_BYTES | 20971520 | Max bytes per image | | MAX_IMAGES_PER_REQUEST | 16 | Max images per request | | IMAGE_CACHE_ENTRIES | 64 | Content-addressed image cache size | | ENABLE_TRACES | true | Keep tool-loop traces for /v1/traces/{id} | | BRIDGE_API_KEYS | (empty) | Comma-separated keys; when set, /v1/* requires one | | EXTRA_MODELS | (empty) | JSON map of extra model names to backend pairs | | LOG_JSON | false | Emit JSON log lines |

Serving multiple model pairs

One instance can serve several reasoning+vision pairs under different model names. Unspecified fields inherit from the default pair:

EXTRA_MODELS={"visionbridge-fast": {"reasoning": {"model": "qwen3:8b"}, "vision": {"model": "llava:7b"}}}

(or point EXTRA_MODELS_FILE at a JSON file). All names appear in GET /v1/models, and clients select a pair with the model field.

LM Studio Backend

1. Open LM Studio.
2. Load your reasoning model.
3. Go to the Developer tab and start the local server.
4. Use the default base URL:

REASONING_BASE_URL=http://localhost:1234/v1
REASONING_API_KEY=lm-studio
REASONING_MODEL=local-model

LM Studio can also be used as the vision backend if you load a vision-capable model and expose it through the same OpenAI-compatible server.

Ollama Backend

REASONING_BASE_URL=http://localhost:11434/v1
REASONING_API_KEY=ollama
REASONING_MODEL=qwen3:32b

VISION_BASE_URL=http://localhost:11434/v1
VISION_API_KEY=ollama
VISION_MODEL=llava:13b

vLLM Backend

REASONING_BASE_URL=http://localhost:8000/v1
REASONING_API_KEY=token-abc123
REASONING_MODEL=Qwen/Qwen3-32B

VISION_BASE_URL=http://localhost:8001/v1
VISION_API_KEY=token-abc123
VISION_MODEL=Qwen/Qwen3-VL-8B-Instruct

OpenWebUI Setup

In OpenWebUI:

Admin Panel -> Settings -> Connections -> OpenAI-compatible
Base URL: http://host.docker.internal:8080/v1
API Key: anything
Model: visionbridge

API Surface

VisionBridge intentionally exposes the small subset most clients need:

• GET /health (add ?probe=true to verify both backends are reachable)
• GET /v1/models
• POST /v1/chat/completions
• GET /v1/traces/{completion_id} — the full tool-loop trace for a recent request: every tool call, its arguments, the vision model's observation, and timings. Invaluable for answering "why did it say that?"

Responses include a usage block aggregated across every reasoning and vision call made for the request, and an X-Request-ID header for correlating logs.

Streaming: image-free requests are streamed token-by-token straight from the reasoning backend. Requests with images stream too: SSE heartbeats keep the connection alive while tools run, and in native tool-calling mode the final answer is forwarded token-by-token as the reasoning model produces it (prompt-JSON mode buffers the loop and streams the answer in chunks).

Errors: backend failures return HTTP 502, invalid inputs 400.

Auth: set BRIDGE_API_KEYS=key1,key2 to require a Bearer key on /v1/* endpoints (/health stays open). For anything internet-facing, still prefer a reverse proxy with TLS in front.

Security Notes

Image URL fetching is hardened by default: hosts resolving to private, loopback, or link-local addresses are rejected (SSRF guard), redirects are validated hop-by-hop, downloads are size-capped, and payloads must decode as real images. Run VisionBridge behind your own reverse proxy / auth layer if you expose it beyond localhost — it deliberately ships without built-in auth.

Benchmarks

benchmarks/ contains a harness that scores VisionBridge against a direct VLM (relaxed accuracy + ANLS) on DocVQA-style JSONL task sets. See benchmarks/README.md.

Status

Actively developed. See CHANGELOG.md for what's new in 0.2.0. Next up:

• published benchmark numbers against caption-only prompting
• persistent on-disk image/primer cache
• OpenWebUI and LibreChat screenshots

Contributions welcome — see CONTRIBUTING.md.

License

MIT