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Local models

Local is doable, but Equabot expects large context + strong defenses against prompt injection. Small cards truncate context and leak safety. Aim high: ≥2 maxed-out Mac Studios or equivalent GPU rig (~$30k+). A single 24 GB GPU works only for lighter prompts with higher latency. Use the largest / full-size model variant you can run; aggressively quantized or “small” checkpoints raise prompt-injection risk (see Security). Best current local stack. Load MiniMax M2.1 in LM Studio, enable the local server (default http://127.0.0.1:1234), and use Responses API to keep reasoning separate from final text. 
{
  agents: {
    defaults: {
      model: { primary: "lmstudio/minimax-m2.1-gs32" },
      models: {
        "anthropic/claude-opus-4-5": { alias: "Opus" },
        "lmstudio/minimax-m2.1-gs32": { alias: "Minimax" }
      }
    }
  },
  models: {
    mode: "merge",
    providers: {
      lmstudio: {
        baseUrl: "http://127.0.0.1:1234/v1",
        apiKey: "lmstudio",
        api: "openai-responses",
        models: [
          {
            id: "minimax-m2.1-gs32",
            name: "MiniMax M2.1 GS32",
            reasoning: false,
            input: ["text"],
            cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
            contextWindow: 196608,
            maxTokens: 8192
          }
        ]
      }
    }
  }
}
Setup checklist
  • Install LM Studio: https://lmstudio.ai
  • In LM Studio, download the largest MiniMax M2.1 build available (avoid “small”/heavily quantized variants), start the server, confirm http://127.0.0.1:1234/v1/models lists it.
  • Keep the model loaded; cold-load adds startup latency.
  • Adjust contextWindow/maxTokens if your LM Studio build differs.
  • For WhatsApp, stick to Responses API so only final text is sent.
Keep hosted models configured even when running local; use models.mode: "merge" so fallbacks stay available.

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Hybrid config: hosted primary, local fallback

{
  agents: {
    defaults: {
      model: {
        primary: "anthropic/claude-sonnet-4-5",
        fallbacks: ["lmstudio/minimax-m2.1-gs32", "anthropic/claude-opus-4-5"]
      },
      models: {
        "anthropic/claude-sonnet-4-5": { alias: "Sonnet" },
        "lmstudio/minimax-m2.1-gs32": { alias: "MiniMax Local" },
        "anthropic/claude-opus-4-5": { alias: "Opus" }
      }
    }
  },
  models: {
    mode: "merge",
    providers: {
      lmstudio: {
        baseUrl: "http://127.0.0.1:1234/v1",
        apiKey: "lmstudio",
        api: "openai-responses",
        models: [
          {
            id: "minimax-m2.1-gs32",
            name: "MiniMax M2.1 GS32",
            reasoning: false,
            input: ["text"],
            cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
            contextWindow: 196608,
            maxTokens: 8192
          }
        ]
      }
    }
  }
}

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Local-first with hosted safety net

Swap the primary and fallback order; keep the same providers block and models.mode: "merge" so you can fall back to Sonnet or Opus when the local box is down.

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Regional hosting / data routing

  • Hosted MiniMax/Kimi/GLM variants also exist on OpenRouter with region-pinned endpoints (e.g., US-hosted). Pick the regional variant there to keep traffic in your chosen jurisdiction while still using models.mode: "merge" for Anthropic/OpenAI fallbacks.
  • Local-only remains the strongest privacy path; hosted regional routing is the middle ground when you need provider features but want control over data flow.

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Other OpenAI-compatible local proxies

vLLM, LiteLLM, OAI-proxy, or custom gateways work if they expose an OpenAI-style /v1 endpoint. Replace the provider block above with your endpoint and model ID: 
{
  models: {
    mode: "merge",
    providers: {
      local: {
        baseUrl: "http://127.0.0.1:8000/v1",
        apiKey: "sk-local",
        api: "openai-responses",
        models: [
          {
            id: "my-local-model",
            name: "Local Model",
            reasoning: false,
            input: ["text"],
            cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
            contextWindow: 120000,
            maxTokens: 8192
          }
        ]
      }
    }
  }
}
Keep models.mode: "merge" so hosted models stay available as fallbacks.

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Vision / multimodal models

Local models with vision capability (e.g. LLaVA, BakLLaVA, Llama 3.2 Vision) can receive images alongside text. Enable this by setting input to ["text", "image"] in the model config: 
{
  models: {
    mode: "merge",
    providers: {
      lmstudio: {
        baseUrl: "http://127.0.0.1:1234/v1",
        apiKey: "lmstudio",
        api: "openai-responses",
        models: [
          {
            id: "llava-v1.6-34b",
            name: "LLaVA v1.6 34B",
            reasoning: false,
            input: ["text", "image"],
            cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
            contextWindow: 32768,
            maxTokens: 4096
          }
        ]
      }
    }
  }
}
When input includes "image", the gateway automatically detects image file references in prompts (local file paths, ~/ paths, file:// URLs) and injects them into the model request. Image references in earlier conversation turns are also detected and re-injected at their original position so the model retains visual context across a multi-turn session. Limitations:
  • Only local image files are supported; remote HTTP URLs are not fetched for native image injection.
  • Non-image file attachments (PDFs, documents) are not passed to local vision models.
  • If input is set to ["text"] (the default), image references in prompts are silently ignored.

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Troubleshooting

  • Gateway can reach the proxy? curl http://127.0.0.1:1234/v1/models.
  • LM Studio model unloaded? Reload; cold start is a common “hanging” cause.
  • Context errors? Lower contextWindow or raise your server limit.
  • Safety: local models skip provider-side filters; keep agents narrow and compaction on to limit prompt injection blast radius.