Add the harness, envs, per-bot models and chat attachments
- Harness: providers, model catalog synced hourly, automatic free picks, cooldowns, tool loop with context trimming, MCP computer tools - Bots queue runs replies and tasks through the harness; jobs wait (attempt refunded) while every model is resting - Envs: Settings > Environment, scoped general/workspace/computer, written to computers and redacted from tool output - Models: Settings > Models, per-harness provider select defaulting to OpenRouter, and a models modal on each bot's chat (the coordinator's edits the defaults) - Chat: thinking row while a reply runs, file attachments (upload, paste, drop) that replies read as images, audio and document text - Fix media uploads failing after a user's first file, and private files being served from the public media path Co-Authored-By: Claude Opus 5.5 <[email protected]>
This commit is contained in:
Claude Opus 5.5
@@ -0,0 +1,41 @@
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import { describe, expect, test } from "bun:test";
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import guessModelModalities from "./guess-model-modalities";
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const outputs = (slug: string) =>
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guessModelModalities({ slug }).output_modalities;
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describe("guessModelModalities", () => {
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test("spots speech, transcription, image and embedding models", () => {
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expect(outputs("whisper-large-v3")).toEqual(["transcription"]);
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expect(outputs("gpt-4o-mini-transcribe")).toEqual(["transcription"]);
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expect(outputs("tts-1-hd")).toEqual(["speech"]);
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expect(outputs("gpt-image-1")).toEqual(["image"]);
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expect(outputs("dall-e-3")).toEqual(["image"]);
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expect(outputs("text-embedding-3-small")).toEqual(["embeddings"]);
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});
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test("takes everything else for a tool-calling chat model", () => {
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expect(guessModelModalities({ slug: "deepseek-chat" })).toEqual({
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input_modalities: ["text"],
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output_modalities: ["text"],
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supports_tools: true,
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});
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expect(outputs("mistral-large-latest")).toEqual(["text"]);
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});
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test("knows which chat families see images", () => {
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expect(
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guessModelModalities({ slug: "claude-sonnet-5-5" }).input_modalities,
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).toEqual(["text", "image"]);
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expect(
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guessModelModalities({ slug: "models/gemini-3.5-flash" })
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.input_modalities,
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).toEqual(["text", "image"]);
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expect(
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guessModelModalities({ slug: "qwen2.5-vl:7b" }).input_modalities,
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).toEqual(["text", "image"]);
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expect(
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guessModelModalities({ slug: "deepseek-r1:14b" }).input_modalities,
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).toEqual(["text"]);
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});
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});
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@@ -0,0 +1,65 @@
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import type { HarnessModelInfo } from "../types";
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type Params = {
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slug: string;
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};
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type Guess = Pick<
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HarnessModelInfo,
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"input_modalities" | "output_modalities" | "supports_tools"
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>;
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const Transcription = /whisper|transcri|(?:^|[-_/])(stt|asr)(?=$|[-_:.])|scribe/i;
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const Speech = /(?:^|[-_/])tts(?=$|[-_:.])|speech|voice/i;
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const ImageOut = /dall-e|gpt-image|imagen|flux|stable-diffusion|sdxl|image-gen/i;
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const Embedding = /embed/i;
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/**
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* Chat families that read images.
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*/
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const ImageIn =
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/vision|(?:^|[-_/])vl(?=$|[-_:.])|gpt-4o|gpt-4\.1|gpt-[5-9]|claude|gemini|llava|pixtral|gemma-?[34]|grok-[2-9]|llama-?4|mistral-(small|medium)/i;
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/**
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* What a model takes and makes, guessed from its id, for providers whose
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* model list only has ids. Anything that isn't plainly a speech, image or
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* embedding model is taken for a chat model that can call tools.
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*/
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export default function guessModelModalities({ slug }: Params): Guess {
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if (Transcription.test(slug)) {
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return {
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input_modalities: ["audio"],
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output_modalities: ["transcription"],
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supports_tools: false,
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};
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}
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if (Speech.test(slug)) {
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return {
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input_modalities: ["text"],
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output_modalities: ["speech"],
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supports_tools: false,
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};
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}
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if (ImageOut.test(slug)) {
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return {
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input_modalities: ["text", "image"],
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output_modalities: ["image"],
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supports_tools: false,
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};
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}
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if (Embedding.test(slug)) {
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return {
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input_modalities: ["text"],
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output_modalities: ["embeddings"],
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supports_tools: false,
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};
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}
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return {
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input_modalities: ImageIn.test(slug) ? ["text", "image"] : ["text"],
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output_modalities: ["text"],
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supports_tools: true,
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};
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}
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@@ -0,0 +1,75 @@
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import { describe, expect, test } from "bun:test";
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import modelServesHarness from "./model-serves-harness";
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const chat = {
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input_modalities: ["text"],
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output_modalities: ["text"],
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supports_tools: true,
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};
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describe("modelServesHarness", () => {
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test("text needs tools", () => {
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expect(modelServesHarness({ model: chat, harness: "text" })).toBe(true);
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expect(
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modelServesHarness({
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model: { ...chat, supports_tools: false },
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harness: "text",
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}),
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).toBe(false);
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});
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test("vision needs image input", () => {
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expect(modelServesHarness({ model: chat, harness: "vision" })).toBe(
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false,
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);
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expect(
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modelServesHarness({
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model: { ...chat, input_modalities: ["text", "image"] },
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harness: "vision",
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}),
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).toBe(true);
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});
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test("transcription takes speech-to-text or audio-hearing chat models", () => {
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expect(
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modelServesHarness({
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model: {
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input_modalities: ["audio"],
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output_modalities: ["transcription"],
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supports_tools: false,
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},
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harness: "transcription",
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}),
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).toBe(true);
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expect(
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modelServesHarness({
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model: { ...chat, input_modalities: ["text", "audio"] },
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harness: "transcription",
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}),
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).toBe(true);
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expect(
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modelServesHarness({ model: chat, harness: "transcription" }),
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).toBe(false);
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});
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test("speech and image need that output", () => {
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expect(
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modelServesHarness({
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model: { ...chat, output_modalities: ["speech"] },
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harness: "speech",
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}),
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).toBe(true);
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expect(
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modelServesHarness({
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model: { ...chat, output_modalities: ["text", "audio"] },
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harness: "speech",
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}),
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).toBe(false);
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expect(
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modelServesHarness({
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model: { ...chat, output_modalities: ["image", "text"] },
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harness: "image",
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}),
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).toBe(true);
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});
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});
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@@ -0,0 +1,45 @@
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import type { HarnessKind } from "@/src/dict/harness-dict";
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import type { HarnessModelInfo } from "../types";
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type Params = {
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model: Pick<
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HarnessModelInfo,
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"input_modalities" | "output_modalities" | "supports_tools"
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>;
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harness: HarnessKind;
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};
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/**
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* Whether a model can do a harness's job:
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* - text: reads and writes text, and calls tools (bots work through tools)
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* - vision: reads images, writes text
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* - transcription: a speech-to-text model, or a chat model that hears audio
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* - speech: a text-to-speech model
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* - image: makes images
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*/
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export default function modelServesHarness({ model, harness }: Params) {
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const input = new Set(model.input_modalities);
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const output = new Set(model.output_modalities);
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switch (harness) {
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case "text":
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return (
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input.has("text") && output.has("text") && model.supports_tools
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);
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case "vision":
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return input.has("image") && output.has("text");
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case "transcription":
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return (
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output.has("transcription") ||
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(input.has("audio") && output.has("text"))
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);
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case "speech":
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return output.has("speech");
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case "image":
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return output.has("image");
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}
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}
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@@ -0,0 +1,26 @@
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import { describe, expect, test } from "bun:test";
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import parseModelSize from "./parse-model-size";
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describe("parseModelSize", () => {
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test("reads total parameters, skipping active ones", () => {
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expect(parseModelSize({ slug: "qwen/qwen3-30b-a3b" })).toBe(30);
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expect(
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parseModelSize({ slug: "nvidia/nemotron-3-ultra-550b-a55b:free" }),
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).toBe(550);
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expect(parseModelSize({ slug: "liquid/lfm-2.5-2.6b:free" })).toBe(2.6);
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expect(parseModelSize({ slug: "deepseek-r1:14b" })).toBe(14);
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});
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test("reads trillions", () => {
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expect(parseModelSize({ slug: "qwen/qwen3.8-2.4t-a95b" })).toBe(2400);
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});
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test("is undefined when the slug has no size", () => {
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expect(parseModelSize({ slug: "deepseek/deepseek-v4.1-flash" })).toBe(
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undefined,
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);
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expect(parseModelSize({ slug: "google/gemma-3n-e4b-it" })).toBe(
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undefined,
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);
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});
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});
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@@ -0,0 +1,19 @@
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type Params = {
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slug: string;
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};
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/**
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* Total parameters in billions, read from a model slug
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* (`qwen3-30b-a3b` is 30, `qwen3.8-2.4t-a95b` is 2400). Active-parameter
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* counts (`a3b`) are skipped. Undefined when the slug doesn't say.
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*/
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export default function parseModelSize({ slug }: Params): number | undefined {
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const name = slug.split("/").pop() || slug;
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const match = name.match(/(?<![a-z0-9.])(\d+(?:\.\d+)?)([bt])(?![a-z])/i);
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if (!match?.[1] || !match[2]) return undefined;
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const size = Number(match[1]);
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return match[2].toLowerCase() == "t" ? size * 1000 : size;
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}
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@@ -0,0 +1,165 @@
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import { describe, expect, test } from "bun:test";
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import type { HarnessCatalogModel } from "../types";
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import rankAutoModels from "./rank-auto-models";
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const day = 24 * 60 * 60 * 1000;
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function model(
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slug: string,
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extra: Partial<HarnessCatalogModel> = {},
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): HarnessCatalogModel {
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return {
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slug,
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free: true,
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input_modalities: ["text"],
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output_modalities: ["text"],
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supports_tools: true,
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context_length: 262144,
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released_at: 0,
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...extra,
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};
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}
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/**
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* OpenRouter's free list on 2026-10-18 (no DeepSeek or Qwen that day).
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*/
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const live = [
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model("xingchen-agi/xing4.0-29b-a4b:free", { released_at: 20 * day }),
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model("nvidia/nemotron-3.5-super-vl:free", {
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input_modalities: ["text", "image", "video"],
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intelligence_score: 26.5,
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}),
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model("liquid/lfm-2.5-2.6b:free", {
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context_length: 65536,
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intelligence_score: 8.4,
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}),
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model("nvidia/nemotron-3.5-lightning:free", { intelligence_score: 12.9 }),
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model("thinkingmachines/inkling-small:free", {
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input_modalities: ["text", "image", "audio"],
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intelligence_score: 25.7,
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agentic_score: 23.5,
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}),
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model("cohere/north-mini-code:free", { intelligence_score: 9.9 }),
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model("nvidia/nemotron-3.5-content-safety:free", {
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supports_tools: false,
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}),
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model("nvidia/nemotron-3-ultra-550b-a55b:free", {
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intelligence_score: 22.9,
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}),
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model("google/gemma-4-31b-it:free", {
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input_modalities: ["image", "text", "video"],
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intelligence_score: 14.7,
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}),
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model("openrouter/free", { input_modalities: ["text", "image"] }),
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model("anthropic/claude-paid", { free: false }),
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];
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const slugs = (models: HarnessCatalogModel[]) => models.map((m) => m.slug);
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describe("rankAutoModels", () => {
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test("follows the vendor preference, then competent others, then routers", () => {
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expect(slugs(rankAutoModels({ models: live, harness: "text" }))).toEqual(
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[
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"nvidia/nemotron-3.5-super-vl:free",
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"nvidia/nemotron-3-ultra-550b-a55b:free",
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"nvidia/nemotron-3.5-lightning:free",
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"liquid/lfm-2.5-2.6b:free",
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"thinkingmachines/inkling-small:free",
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"google/gemma-4-31b-it:free",
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"xingchen-agi/xing4.0-29b-a4b:free",
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"openrouter/free",
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],
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);
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});
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test("puts DeepSeek, then Qwen, ahead of everything once they're free", () => {
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const ranked = rankAutoModels({
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models: [
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...live,
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model("qwen/qwen3.8-flash:free"),
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model("deepseek/deepseek-v4.1-flash:free"),
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],
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harness: "text",
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});
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expect(slugs(ranked).slice(0, 2)).toEqual([
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"deepseek/deepseek-v4.1-flash:free",
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"qwen/qwen3.8-flash:free",
|
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]);
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});
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test("recognises vendors in local model names", () => {
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const ranked = rankAutoModels({
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models: [model("llama3.3:70b"), model("qwen3:32b")],
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harness: "text",
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});
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expect(slugs(ranked)).toEqual(["qwen3:32b", "llama3.3:70b"]);
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});
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test("filters by what the harness needs", () => {
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expect(
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slugs(rankAutoModels({ models: live, harness: "vision" })),
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).toEqual([
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"nvidia/nemotron-3.5-super-vl:free",
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"thinkingmachines/inkling-small:free",
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"google/gemma-4-31b-it:free",
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"openrouter/free",
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]);
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expect(
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slugs(rankAutoModels({ models: live, harness: "transcription" })),
|
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).toEqual(["thinkingmachines/inkling-small:free"]);
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expect(rankAutoModels({ models: live, harness: "speech" })).toEqual(
|
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[],
|
||||
);
|
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});
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test("puts speech-to-text models ahead of chat models that hear audio", () => {
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const ranked = rankAutoModels({
|
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models: [
|
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model("deepseek/omni", { input_modalities: ["text", "audio"] }),
|
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model("acme/scribe", {
|
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input_modalities: ["audio"],
|
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output_modalities: ["transcription"],
|
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supports_tools: false,
|
||||
}),
|
||||
],
|
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harness: "transcription",
|
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});
|
||||
|
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expect(slugs(ranked)).toEqual(["acme/scribe", "deepseek/omni"]);
|
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});
|
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|
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test("drops retired and unscored small models from other vendors", () => {
|
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const ranked = rankAutoModels({
|
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models: [
|
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model("acme/tiny-7b"),
|
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model("acme/big-mini"),
|
||||
model("acme/gone", { expires_at: 10 }),
|
||||
model("acme/short", { context_length: 8000 }),
|
||||
model("acme/solid-70b"),
|
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],
|
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harness: "text",
|
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now: 100,
|
||||
});
|
||||
|
||||
expect(slugs(ranked)).toEqual(["acme/solid-70b"]);
|
||||
});
|
||||
|
||||
test("within a vendor, unscored full-size models beat small ones, newest first", () => {
|
||||
const ranked = rankAutoModels({
|
||||
models: [
|
||||
model("deepseek/deepseek-lite", { released_at: 3 }),
|
||||
model("deepseek/deepseek-old", { released_at: 1 }),
|
||||
model("deepseek/deepseek-new", { released_at: 2 }),
|
||||
],
|
||||
harness: "text",
|
||||
});
|
||||
|
||||
expect(slugs(ranked)).toEqual([
|
||||
"deepseek/deepseek-new",
|
||||
"deepseek/deepseek-old",
|
||||
"deepseek/deepseek-lite",
|
||||
]);
|
||||
});
|
||||
});
|
||||
@@ -0,0 +1,163 @@
|
||||
import { AppData } from "@/src/data/app-data";
|
||||
import {
|
||||
AutoModelRouterSlugs,
|
||||
AutoModelVendorPreference,
|
||||
SmallModelPattern,
|
||||
SpecialistModelPattern,
|
||||
type HarnessKind,
|
||||
} from "@/src/dict/harness-dict";
|
||||
import type { HarnessCatalogModel } from "../types";
|
||||
import modelServesHarness from "./model-serves-harness";
|
||||
import parseModelSize from "./parse-model-size";
|
||||
|
||||
type Params<T extends HarnessCatalogModel> = {
|
||||
models: T[];
|
||||
harness: HarnessKind;
|
||||
/**
|
||||
* Unix ms, for retired models. Defaults to now.
|
||||
*/
|
||||
now?: number;
|
||||
};
|
||||
|
||||
/**
|
||||
* The free models a harness picks automatically, best first:
|
||||
* 1. `AutoModelVendorPreference` in order (DeepSeek, Qwen, NVIDIA, Liquid)
|
||||
* 2. any other vendor's model that looks competent (text and vision only)
|
||||
* 3. routers such as `openrouter/free`, last
|
||||
*
|
||||
* Within a group, benchmarked models come first by intelligence index,
|
||||
* then the rest: full-size before small variants, newest first.
|
||||
*/
|
||||
export default function rankAutoModels<T extends HarnessCatalogModel>({
|
||||
models,
|
||||
harness,
|
||||
now = Date.now(),
|
||||
}: Params<T>): T[] {
|
||||
const ranked = models
|
||||
.filter(
|
||||
(model) =>
|
||||
model.free &&
|
||||
!(model.expires_at && model.expires_at <= now) &&
|
||||
!SpecialistModelPattern.test(model.slug) &&
|
||||
modelServesHarness({ model, harness }),
|
||||
)
|
||||
.map((model) => ({ model, tier: grabTier({ model, harness }) }))
|
||||
.filter(
|
||||
(entry): entry is { model: T; tier: number } =>
|
||||
entry.tier !== undefined,
|
||||
);
|
||||
|
||||
return ranked
|
||||
.sort(
|
||||
(a, b) =>
|
||||
(harness == "transcription"
|
||||
? Number(!isSpeechToText(a.model)) -
|
||||
Number(!isSpeechToText(b.model))
|
||||
: 0) ||
|
||||
a.tier - b.tier ||
|
||||
compareInTier(a.model, b.model),
|
||||
)
|
||||
.map((entry) => entry.model);
|
||||
}
|
||||
|
||||
/**
|
||||
* Dedicated speech-to-text models transcribe before chat models that hear
|
||||
* audio: some free chat endpoints list audio input but drop it.
|
||||
*/
|
||||
function isSpeechToText(model: HarnessCatalogModel) {
|
||||
return model.output_modalities.includes("transcription");
|
||||
}
|
||||
|
||||
const OtherTier = AutoModelVendorPreference.length;
|
||||
const RouterTier = OtherTier + 1;
|
||||
|
||||
/**
|
||||
* Undefined drops the model.
|
||||
*/
|
||||
function grabTier({
|
||||
model,
|
||||
harness,
|
||||
}: {
|
||||
model: HarnessCatalogModel;
|
||||
harness: HarnessKind;
|
||||
}) {
|
||||
const slug = model.slug.toLowerCase();
|
||||
|
||||
if (AutoModelRouterSlugs.includes(slug)) {
|
||||
return RouterTier;
|
||||
}
|
||||
|
||||
const name = slug.split("/").pop() || slug;
|
||||
const vendor_tier = AutoModelVendorPreference.findIndex(
|
||||
(vendor) => slug.startsWith(`${vendor}/`) || name.startsWith(vendor),
|
||||
);
|
||||
|
||||
if (vendor_tier >= 0) {
|
||||
return vendor_tier;
|
||||
}
|
||||
|
||||
if (harness != "text" && harness != "vision") {
|
||||
return OtherTier;
|
||||
}
|
||||
|
||||
return isCompetent({ model }) ? OtherTier : undefined;
|
||||
}
|
||||
|
||||
function isCompetent({ model }: { model: HarnessCatalogModel }) {
|
||||
if (
|
||||
model.context_length &&
|
||||
model.context_length < AppData.HarnessAutoMinContext
|
||||
) {
|
||||
return false;
|
||||
}
|
||||
|
||||
if (model.intelligence_score !== undefined) {
|
||||
return model.intelligence_score >= AppData.HarnessAutoMinScore;
|
||||
}
|
||||
|
||||
const size = parseModelSize({ slug: model.slug });
|
||||
|
||||
return (
|
||||
!SmallModelPattern.test(model.slug) &&
|
||||
(size === undefined || size >= AppData.HarnessAutoMinSizeB)
|
||||
);
|
||||
}
|
||||
|
||||
function compareInTier(a: HarnessCatalogModel, b: HarnessCatalogModel) {
|
||||
const a_scored = a.intelligence_score !== undefined;
|
||||
const b_scored = b.intelligence_score !== undefined;
|
||||
|
||||
if (a_scored != b_scored) {
|
||||
return a_scored ? -1 : 1;
|
||||
}
|
||||
|
||||
if (a_scored && b_scored) {
|
||||
const by_score =
|
||||
(b.intelligence_score || 0) - (a.intelligence_score || 0) ||
|
||||
(b.agentic_score || 0) - (a.agentic_score || 0);
|
||||
|
||||
if (by_score) return by_score;
|
||||
}
|
||||
|
||||
const a_small = isSmall(a);
|
||||
const b_small = isSmall(b);
|
||||
|
||||
if (a_small != b_small) {
|
||||
return a_small ? 1 : -1;
|
||||
}
|
||||
|
||||
return (
|
||||
(b.released_at || 0) - (a.released_at || 0) ||
|
||||
(b.context_length || 0) - (a.context_length || 0) ||
|
||||
a.slug.localeCompare(b.slug)
|
||||
);
|
||||
}
|
||||
|
||||
function isSmall(model: HarnessCatalogModel) {
|
||||
const size = parseModelSize({ slug: model.slug });
|
||||
|
||||
return (
|
||||
SmallModelPattern.test(model.slug) ||
|
||||
(size !== undefined && size < AppData.HarnessAutoMinSizeB)
|
||||
);
|
||||
}
|
||||
Reference in New Issue
Block a user