opencode-model-scout

An opencode plugin that auto-discovers models from OpenAI-compatible providers and enriches them with context window sizes, capability flags, and model metadata. Supports Ollama, oMLX, vLLM, TGI, SGLang, LM Studio, KoboldCpp, llama.cpp, and LocalAI.

Quick Start

opencode plugin opencode-model-scout

Then add "probe": "auto" to any provider in your opencode.json:

{
  "provider": {
    "ollama": {
      "npm": "@ai-sdk/openai-compatible",
      "options": {
        "baseURL": "http://localhost:11434/v1",
        "probe": "auto"
      }
    }
  }
}

Restart opencode. Every model on that provider now has accurate context window sizes, output limits, and capability flags (tool calling, vision, reasoning) discovered automatically.

The Problem

Local AI providers expose models through a generic OpenAI-compatible API that reports almost nothing about what each model can actually do. When you run Ollama, oMLX, LM Studio, or any other inference server, opencode discovers model IDs like qwen3:30b-a3b or gemma3-12b-it — but has no idea whether they support tool calling, vision input, extended thinking, or what their actual context window is. Without this metadata, opencode can't make informed decisions about which model to use, what features to enable, or how much context it can send.

opencode-model-scout fixes this by building a 3-layer metadata enrichment pipeline that runs at startup. It discovers every available model, probes provider-specific APIs for authoritative metadata, and falls back to the models.dev database for anything the probes can't reach. The result: every discovered model gets accurate context limits, capability flags, and modality information — automatically, with zero manual configuration.

How It Works

The plugin runs during opencode's config hook (before any session starts) and enriches model entries through three layers, applied in order:

1. Discovery + keyword categorization — Queries GET /v1/models on every OpenAI-compatible provider to get the raw model list. Model IDs are categorized as chat, embedding, or unknown based on name patterns.

2. Provider-specific probes — When a provider has "probe": "omlx" or "probe": "ollama" in its options, a purpose-built probe calls provider-specific APIs that expose metadata the generic OpenAI API does not. Probes are the most authoritative source and override keyword guesses.

3. models.dev fallback — For any model that still has gaps after probing, the plugin matches the model ID against opencode's built-in models.dev database (~4,000 models) using family + parameter size matching. This fills in capability flags like tool_call, reasoning, and attachment for models the probes don't cover.

Each layer only sets fields that aren't already present — manually configured metadata in your opencode.json is never overwritten, and probes always take priority over models.dev guesses.

Installation

From npm

Using the opencode CLI (installs and patches opencode.json automatically):

opencode plugin opencode-model-scout

Or install with npm directly and add the plugin to your opencode.json manually:

npm install opencode-model-scout

{
  "plugin": ["opencode-model-scout"]
}

After installation, add provider configuration with the probe field — see Configuration below.

From GitHub

opencode plugin github:rmk40/opencode-model-scout

Or in opencode.json:

{
  "plugin": ["github:rmk40/opencode-model-scout"]
}

From Source

For development or modification:

git clone https://github.com/rmk40/opencode-model-scout.git
cd opencode-model-scout
npm install

Then in opencode.json:

{
  "plugin": ["file:///absolute/path/to/opencode-model-scout"]
}

Configuration

Basic: Discovery Only (No Probe)

Any @ai-sdk/openai-compatible provider gets automatic model discovery. Models are enriched with whatever models.dev can match:

{
  "provider": {
    "my-server": {
      "npm": "@ai-sdk/openai-compatible",
      "options": {
        "baseURL": "http://localhost:8000/v1"
      }
    }
  }
}

With a Probe: Full Metadata

Add "probe" to the provider's options to enable provider-specific metadata extraction. The probe field must be inside options, not at the provider top level, because opencode's provider schema rejects unknown top-level fields.

You can specify an explicit probe name or use "auto" to let the plugin detect the server type automatically:

{
  "provider": {
    "omlx": {
      "npm": "@ai-sdk/openai-compatible",
      "options": {
        "baseURL": "http://localhost:8000/v1",
        "apiKey": "your-key",
        "probe": "omlx"
      }
    },
    "ollama": {
      "npm": "@ai-sdk/openai-compatible",
      "options": {
        "baseURL": "http://localhost:11434/v1",
        "probe": "ollama"
      }
    },
    "local": {
      "npm": "@ai-sdk/openai-compatible",
      "options": {
        "baseURL": "http://localhost:5000/v1",
        "probe": "auto"
      }
    }
  }
}

When "probe": "auto" is set, the plugin fingerprints the server using a tiered detection strategy (see Supported Servers) and selects the appropriate probe automatically. If detection fails, the provider still gets discovery + models.dev fallback enrichment.

Multiple Providers

The plugin processes every provider in your config independently. You can mix probed and unprobed providers freely:

{
  "provider": {
    "omlx": {
      "npm": "@ai-sdk/openai-compatible",
      "options": {
        "baseURL": "http://localhost:8000/v1",
        "apiKey": "strata",
        "probe": "omlx"
      }
    },
    "ollama": {
      "npm": "@ai-sdk/openai-compatible",
      "options": {
        "baseURL": "http://localhost:11434/v1",
        "probe": "ollama"
      }
    },
    "lmstudio": {
      "npm": "@ai-sdk/openai-compatible",
      "options": {
        "baseURL": "http://localhost:1234/v1"
      }
    }
  }
}

In this example, oMLX and Ollama get full probe enrichment. LM Studio gets discovery + models.dev fallback. You could also use "probe": "auto" on any of these to let the plugin detect the server type automatically.

Supported Servers

Server	Probe	Status	What It Extracts
oMLX	omlx	Tested	Context, output limit, model type, load state, size
Ollama	ollama	Tested	Context, tools, vision, thinking, family, quantization
llama.cpp	ollama	Expected	Partial Ollama metadata (API compat unverified against live instance)
LocalAI	ollama	Expected	Partial Ollama metadata (API compat unverified against live instance)
LM Studio	lmstudio	Untested	Context, vision, tool use, architecture, quantization, size, load state
TGI	tgi	Untested	Context, output limit
SGLang	sglang	Untested	Context, model type, vision
vLLM	vllm	Untested	Context
KoboldCpp	koboldcpp	Untested	Context, vision

Support Tiers

• Tested -- verified against a live instance
• Expected -- API-compatible based on server source code review; not yet tested against a live instance
• Untested -- probe implemented based on API documentation; needs community verification

models.dev Fallback

When no probe is configured (or for capability gaps that probes don't cover), the plugin matches discovered model IDs against opencode's built-in models.dev database using a 3-tier matching strategy:

1. Exact normalized match — qwen3-30b-a3b matches directly
2. Family + size match — qwen3:0.6b → family "qwen" + size "0.6b" → matches a qwen model with the same parameter count
3. Family-only match — qwen3:14b → family "qwen" → inherits capabilities from any known qwen model

The fallback only applies capability flags (tool_call, reasoning, attachment, temperature, modalities, family). It does not apply context or output limits — those vary too much across quantization levels and providers to be guessed reliably.

/modelscout Command

Inspect what was discovered during startup:

/modelscout           Show discovered models with metadata
/modelscout --json    Show as JSON (for debugging or scripting)

Example output:

Models Discovery

omlx (probe: omlx) — 7 models
──────────────────────────────────────────────────
  qwen3-coder-next
    Context: 262,144 | Output: 32,768 | Type: llm | Temp | Size: 43.9 GB

  Qwen3.5-35B-MLX-mxfp4
    Context: 262,144 | Output: 32,768 | Type: vlm | Vision, Temp | Size: 19.2 GB

ollama (probe: ollama) — 3 models
──────────────────────────────────────────────────
  qwen3:0.6b
    Context: 40,960 | Type: llm | Tools, Reasoning, Temp | Family: qwen3 | Params: 0.6B | Quant: Q4_K_M | Size: 480.5 MB

  smollm2:135m
    Type: llm | Temp | Params: 135M | Size: 98.8 MB

  nomic-embed-text
    Temp | Family: nomic | Params: 137M

local (auto → vllm) — 2 models
──────────────────────────────────────────────────
  Qwen/Qwen3-8B
    Context: 32,768 | Type: llm | Temp

Timeouts and Resilience

The plugin is designed to never block opencode startup:

• Individual fetch calls use a 3-second timeout (model list) or 2-second timeout (probes), all via the shared probeFetch() wrapper
• The entire config hook has a 5-second abort timeout — if discovery takes longer, in-flight HTTP requests are cancelled via abort signal propagation and opencode starts normally
• Per-provider isolation — each provider is wrapped in its own try-catch, so a failing provider never prevents discovery for other providers
• All errors are caught and logged — a failing probe, an offline provider, or a malformed response never crashes the plugin or prevents opencode from starting
• Capability flags are only set to true, never false — if a probe can't determine a capability, the field is left undefined (unknown) rather than incorrectly denied

Manually Configured Models

Models you explicitly configure in opencode.json are never modified by the plugin. If you have:

{
  "provider": {
    "ollama": {
      "npm": "@ai-sdk/openai-compatible",
      "options": {
        "baseURL": "http://localhost:11434/v1",
        "probe": "ollama"
      },
      "models": {
        "qwen3:30b": {
          "name": "My Custom Qwen",
          "limit": { "context": 8192, "output": 2048 }
        }
      }
    }
  }
}

The plugin will discover any other models Ollama has available and enrich them, but qwen3:30b keeps your custom name and limits untouched.

License

MIT

Contributing

See CONTRIBUTING.md for architecture details, how to build new probes, and development workflow.