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Add first-class LM Studio setup

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This commit is contained in:
Advait Paliwal
2026-04-16 15:34:32 -07:00
parent ca559dfd91
commit d2570188f9
11 changed files with 105 additions and 14 deletions
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4
README.md
View File

@@ -25,7 +25,7 @@ curl -fsSL https://feynman.is/install | bash
irm https://feynman.is/install.ps1 | iex
```
The one-line installer fetches the latest tagged release. To pin a version, pass it explicitly, for example `curl -fsSL https://feynman.is/install | bash -s -- 0.2.21`.
The one-line installer fetches the latest tagged release. To pin a version, pass it explicitly, for example `curl -fsSL https://feynman.is/install | bash -s -- 0.2.22`.
The installer downloads a standalone native bundle with its own Node.js runtime.
@@ -33,7 +33,7 @@ To upgrade the standalone app later, rerun the installer. `feynman update` only
To uninstall the standalone app, remove the launcher and runtime bundle, then optionally remove `~/.feynman` if you also want to delete settings, sessions, and installed package state. If you also want to delete alphaXiv login state, remove `~/.ahub`. See the installation guide for platform-specific paths.
Local models are supported through the custom-provider flow. For Ollama, run `feynman setup`, choose `Custom provider (baseUrl + API key)`, use `openai-completions`, and point it at `http://localhost:11434/v1`.
Local models are supported through the setup flow. For LM Studio, run `feynman setup`, choose `LM Studio`, and keep the default `http://localhost:1234/v1` unless you changed the server port. For Ollama or vLLM, choose `Custom provider (baseUrl + API key)`, use `openai-completions`, and point it at the local `/v1` endpoint.
### Skills Only

4
package-lock.json generated
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@@ -1,12 +1,12 @@
{
"name": "@companion-ai/feynman",
"version": "0.2.21",
"version": "0.2.22",
"lockfileVersion": 3,
"requires": true,
"packages": {
"": {
"name": "@companion-ai/feynman",
"version": "0.2.21",
"version": "0.2.22",
"hasInstallScript": true,
"license": "MIT",
"dependencies": {

2
package.json
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@@ -1,6 +1,6 @@
{
"name": "@companion-ai/feynman",
"version": "0.2.21",
"version": "0.2.22",
"description": "Research-first CLI agent built on Pi and alphaXiv",
"license": "MIT",
"type": "module",

2
scripts/install/install.ps1
View File

@@ -110,7 +110,7 @@ This usually means the release exists, but not all platform bundles were uploade
Workarounds:
- try again after the release finishes publishing
- pass the latest published version explicitly, e.g.:
& ([scriptblock]::Create((irm https://feynman.is/install.ps1))) -Version 0.2.21
& ([scriptblock]::Create((irm https://feynman.is/install.ps1))) -Version 0.2.22
"@
}

2
scripts/install/install.sh
View File

@@ -261,7 +261,7 @@ This usually means the release exists, but not all platform bundles were uploade
Workarounds:
- try again after the release finishes publishing
- pass the latest published version explicitly, e.g.:
curl -fsSL https://feynman.is/install | bash -s -- 0.2.21
curl -fsSL https://feynman.is/install | bash -s -- 0.2.22
EOF
exit 1
fi

66
src/model/commands.ts
View File

@@ -83,6 +83,7 @@ const API_KEY_PROVIDERS: ApiKeyProviderInfo[] = [
{ id: "openai", label: "OpenAI Platform API", envVar: "OPENAI_API_KEY" },
{ id: "anthropic", label: "Anthropic API", envVar: "ANTHROPIC_API_KEY" },
{ id: "google", label: "Google Gemini API", envVar: "GEMINI_API_KEY" },
{ id: "lm-studio", label: "LM Studio (local OpenAI-compatible server)" },
{ id: "__custom__", label: "Custom provider (local/self-hosted/proxy)" },
{ id: "amazon-bedrock", label: "Amazon Bedrock (AWS credential chain)" },
{ id: "openrouter", label: "OpenRouter", envVar: "OPENROUTER_API_KEY" },
@@ -132,6 +133,8 @@ async function selectApiKeyProvider(): Promise<ApiKeyProviderInfo | undefined> {
label: provider.label,
hint: provider.id === "__custom__"
? "Ollama, vLLM, LM Studio, proxies"
: provider.id === "lm-studio"
? "http://localhost:1234/v1"
: provider.envVar ?? provider.id,
}));
options.push({ value: "cancel", label: "Cancel" });
@@ -362,6 +365,44 @@ async function promptCustomProviderSetup(): Promise<CustomProviderSetup | undefi
return { providerId, modelIds, baseUrl, api, apiKeyConfig, authHeader };
}
async function promptLmStudioProviderSetup(): Promise<CustomProviderSetup | undefined> {
printSection("LM Studio");
printInfo("Start the LM Studio local server first, then load a model.");
const baseUrlRaw = await promptText("Base URL", "http://localhost:1234/v1");
const { baseUrl } = normalizeCustomProviderBaseUrl("openai-completions", baseUrlRaw);
if (!baseUrl) {
printWarning("Base URL is required.");
return undefined;
}
const detectedModelIds = await bestEffortFetchOpenAiModelIds(baseUrl, "lm-studio", false);
let modelIdsDefault = "local-model";
if (detectedModelIds && detectedModelIds.length > 0) {
const sample = detectedModelIds.slice(0, 10).join(", ");
printInfo(`Detected LM Studio models: ${sample}${detectedModelIds.length > 10 ? ", ..." : ""}`);
modelIdsDefault = detectedModelIds[0]!;
} else {
printInfo("No models detected from /models. Enter the exact model id shown in LM Studio.");
}
const modelIdsRaw = await promptText("Model id(s) (comma-separated)", modelIdsDefault);
const modelIds = normalizeModelIds(modelIdsRaw);
if (modelIds.length === 0) {
printWarning("At least one model id is required.");
return undefined;
}
return {
providerId: "lm-studio",
modelIds,
baseUrl,
api: "openai-completions",
apiKeyConfig: "lm-studio",
authHeader: false,
};
}
async function verifyCustomProvider(setup: CustomProviderSetup, authPath: string): Promise<void> {
const registry = createModelRegistry(authPath);
const modelsError = registry.getError();
@@ -548,6 +589,31 @@ async function configureApiKeyProvider(authPath: string, providerId?: string): P
return configureBedrockProvider(authPath);
}
if (provider.id === "lm-studio") {
const setup = await promptLmStudioProviderSetup();
if (!setup) {
printInfo("LM Studio setup cancelled.");
return false;
}
const modelsJsonPath = getModelsJsonPath(authPath);
const result = upsertProviderConfig(modelsJsonPath, setup.providerId, {
baseUrl: setup.baseUrl,
apiKey: setup.apiKeyConfig,
api: setup.api,
authHeader: setup.authHeader,
models: setup.modelIds.map((id) => ({ id })),
});
if (!result.ok) {
printWarning(result.error);
return false;
}
printSuccess("Saved LM Studio provider.");
await verifyCustomProvider(setup, authPath);
return true;
}
if (provider.id === "__custom__") {
const setup = await promptCustomProviderSetup();
if (!setup) {

9
tests/model-harness.test.ts
View File

@@ -79,6 +79,15 @@ test("resolveModelProviderForCommand falls back to API-key providers when OAuth
assert.equal(resolved?.id, "google");
});
test("resolveModelProviderForCommand supports LM Studio as a first-class local provider", () => {
const authPath = createAuthPath({});
const resolved = resolveModelProviderForCommand(authPath, "lm-studio");
assert.equal(resolved?.kind, "api-key");
assert.equal(resolved?.id, "lm-studio");
});
test("resolveModelProviderForCommand prefers OAuth when a provider supports both auth modes", () => {
const authPath = createAuthPath({});

2
website/public/install
View File

@@ -261,7 +261,7 @@ This usually means the release exists, but not all platform bundles were uploade
Workarounds:
- try again after the release finishes publishing
- pass the latest published version explicitly, e.g.:
curl -fsSL https://feynman.is/install | bash -s -- 0.2.21
curl -fsSL https://feynman.is/install | bash -s -- 0.2.22
EOF
exit 1
fi

2
website/public/install.ps1
View File

@@ -110,7 +110,7 @@ This usually means the release exists, but not all platform bundles were uploade
Workarounds:
- try again after the release finishes publishing
- pass the latest published version explicitly, e.g.:
& ([scriptblock]::Create((irm https://feynman.is/install.ps1))) -Version 0.2.21
& ([scriptblock]::Create((irm https://feynman.is/install.ps1))) -Version 0.2.22
"@
}

4
website/src/content/docs/getting-started/installation.md
View File

@@ -117,13 +117,13 @@ These installers download the bundled `skills/` and `prompts/` trees plus the re
The one-line installer already targets the latest tagged release. To pin an exact version, pass it explicitly:
```bash
curl -fsSL https://feynman.is/install | bash -s -- 0.2.21
curl -fsSL https://feynman.is/install | bash -s -- 0.2.22
```
On Windows:
```powershell
& ([scriptblock]::Create((irm https://feynman.is/install.ps1))) -Version 0.2.21
& ([scriptblock]::Create((irm https://feynman.is/install.ps1))) -Version 0.2.22
```
## Post-install setup

22
website/src/content/docs/getting-started/setup.md
View File

@@ -52,9 +52,25 @@ Amazon Bedrock (AWS credential chain)
Feynman verifies the same AWS credential chain Pi uses at runtime, including `AWS_PROFILE`, `~/.aws` credentials/config, SSO, ECS/IRSA, and EC2 instance roles. Once that check passes, Bedrock models become available in `feynman model list` without needing a traditional API key.
### Local models: Ollama, LM Studio, vLLM
### Local models: LM Studio, Ollama, vLLM
If you want to use a model running locally, choose the API-key flow and then select:
If you want to use LM Studio, start the LM Studio local server, load a model, choose the API-key flow, and then select:
```text
LM Studio (local OpenAI-compatible server)
```
The default settings are:
```text
Base URL: http://localhost:1234/v1
Authorization header: No
API key: lm-studio
```
Feynman attempts to read LM Studio's `/models` endpoint and prefill the loaded model id.
For Ollama, vLLM, or another OpenAI-compatible local server, choose:
```text
Custom provider (baseUrl + API key)
@@ -70,7 +86,7 @@ Model ids: llama3.1:8b
API key: local
```
That same custom-provider flow also works for other OpenAI-compatible local servers such as LM Studio or vLLM. After saving the provider, run:
After saving the provider, run:
```bash
feynman model list