feat(providers): add LiteLLM provider support
Add LiteLLM as a new OpenAI-compatible proxy provider: - Add onboarding flow with API key, base URL, and model selection - Fetch available models from LiteLLM /v1/models endpoint - Auto-detect context window from /model/info endpoint - Set supportsStore: false to avoid "Extra inputs are not permitted" errors with providers that don't support the OpenAI Responses API store parameter - Preserve compat settings through model resolution pipeline - Add provider documentation Closes #2639 Closes #2305 Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Charles-Henri ROBICHE
parent
9688454a30
commit
efd827b526
@ -1014,6 +1014,7 @@
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"providers/vercel-ai-gateway",
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"providers/openrouter",
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"providers/synthetic",
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"providers/litellm",
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"providers/opencode",
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"providers/glm",
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"providers/zai"
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99
docs/providers/litellm.md
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99
docs/providers/litellm.md
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@ -0,0 +1,99 @@
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---
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summary: "Use LiteLLM as an OpenAI-compatible proxy in Clawdbot"
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read_when:
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- You want to use LiteLLM as a model provider
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- You need to connect to a self-hosted LiteLLM proxy
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- You want to use any model through an OpenAI-compatible API
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---
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# LiteLLM
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LiteLLM is an OpenAI-compatible proxy that supports 100+ LLM APIs. Clawdbot
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registers it as the `litellm` provider and uses the OpenAI Completions API.
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## Quick setup
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1) Set up your LiteLLM proxy (see [LiteLLM docs](https://docs.litellm.ai/))
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2) Set environment variables (optional):
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- `LITELLM_API_KEY` - your LiteLLM API key
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- `LITELLM_BASE_URL` - your LiteLLM endpoint (default: `http://localhost:4000`)
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- `LITELLM_MODEL` - default model name (default: `gpt-4`)
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3) Run onboarding:
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```bash
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clawdbot onboard --auth-choice litellm-api-key
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```
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The wizard will prompt for:
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- Base URL (your LiteLLM proxy endpoint)
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- API key
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- Model name (as configured in your LiteLLM proxy)
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## Config example
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```json5
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{
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env: { LITELLM_API_KEY: "sk-..." },
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agents: {
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defaults: {
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model: { primary: "litellm/gpt-4" },
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models: { "litellm/gpt-4": { alias: "GPT-4" } }
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}
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},
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models: {
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mode: "merge",
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providers: {
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litellm: {
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baseUrl: "http://localhost:4000",
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apiKey: "${LITELLM_API_KEY}",
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api: "openai-completions",
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models: [
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{
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id: "gpt-4",
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name: "GPT-4",
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reasoning: false,
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input: ["text"],
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contextWindow: 128000,
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maxTokens: 8192
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}
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]
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}
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}
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}
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}
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```
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## Multiple models
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Add additional models to your config as needed:
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```json5
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{
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models: {
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providers: {
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litellm: {
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baseUrl: "http://localhost:4000",
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apiKey: "${LITELLM_API_KEY}",
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api: "openai-completions",
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models: [
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{ id: "gpt-4", name: "GPT-4", contextWindow: 128000, maxTokens: 8192 },
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{ id: "claude-3-opus", name: "Claude Opus", contextWindow: 200000, maxTokens: 4096 },
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{ id: "gemini-pro", name: "Gemini Pro", contextWindow: 32000, maxTokens: 8192 }
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]
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}
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}
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}
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}
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```
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Then switch models using:
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```bash
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clawdbot config set agents.defaults.model.primary litellm/claude-3-opus
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```
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## Notes
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- Model refs use `litellm/<modelId>` where `modelId` matches your LiteLLM config.
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- The base URL should not include `/v1` - Clawdbot's OpenAI client appends it.
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- Supported LiteLLM models depend on your proxy configuration.
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- See [Model providers](/concepts/model-providers) for provider rules.
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45
src/agents/litellm-models.ts
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45
src/agents/litellm-models.ts
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import type { ModelDefinitionConfig } from "../config/types.js";
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// LiteLLM is a proxy that supports many models, so the base URL and model
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// are user-configurable. We provide sensible defaults for onboarding.
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export const LITELLM_DEFAULT_BASE_URL = "http://localhost:4000";
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export const LITELLM_DEFAULT_MODEL_ID = "gpt-4";
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export const LITELLM_DEFAULT_MODEL_REF = `litellm/${LITELLM_DEFAULT_MODEL_ID}`;
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export const LITELLM_DEFAULT_COST = {
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input: 0,
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output: 0,
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cacheRead: 0,
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cacheWrite: 0,
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};
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export type LitellmModelEntry = {
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id: string;
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name: string;
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reasoning?: boolean;
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input?: readonly ("text" | "image")[];
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contextWindow?: number;
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maxTokens?: number;
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};
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export function buildLitellmModelDefinition(entry: LitellmModelEntry): ModelDefinitionConfig {
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return {
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id: entry.id,
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name: entry.name,
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reasoning: entry.reasoning ?? false,
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input: entry.input ? [...entry.input] : ["text"],
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cost: LITELLM_DEFAULT_COST,
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contextWindow: entry.contextWindow ?? 128000,
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maxTokens: entry.maxTokens ?? 8192,
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// LiteLLM proxies to various providers that may not support the OpenAI Responses API
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// `store` parameter. Disable it by default to avoid "Extra inputs are not permitted" errors.
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compat: { supportsStore: false },
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};
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}
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/**
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* Creates a model reference for a LiteLLM model.
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* The model ID can be any model supported by the LiteLLM proxy.
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*/
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export function litellmModelRef(modelId: string): string {
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return `litellm/${modelId}`;
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}
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@ -285,6 +285,7 @@ export function resolveEnvApiKey(provider: string): EnvApiKeyResult | null {
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venice: "VENICE_API_KEY",
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mistral: "MISTRAL_API_KEY",
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opencode: "OPENCODE_API_KEY",
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litellm: "LITELLM_API_KEY",
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};
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const envVar = envMap[normalized];
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if (!envVar) return null;
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@ -77,17 +77,25 @@ export function resolveModel(
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}
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const providerCfg = providers[provider];
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if (providerCfg || modelId.startsWith("mock-")) {
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// Find the matching model definition from provider config to get compat settings
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const modelDef = providerCfg?.models?.find((m) => m.id === modelId);
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const fallbackModel: Model<Api> = normalizeModelCompat({
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id: modelId,
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name: modelId,
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api: providerCfg?.api ?? "openai-responses",
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name: modelDef?.name ?? modelId,
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api: modelDef?.api ?? providerCfg?.api ?? "openai-responses",
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provider,
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baseUrl: providerCfg?.baseUrl,
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reasoning: false,
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input: ["text"],
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cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
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contextWindow: providerCfg?.models?.[0]?.contextWindow ?? DEFAULT_CONTEXT_TOKENS,
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maxTokens: providerCfg?.models?.[0]?.maxTokens ?? DEFAULT_CONTEXT_TOKENS,
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reasoning: modelDef?.reasoning ?? false,
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input: modelDef?.input ?? ["text"],
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cost: modelDef?.cost ?? { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
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contextWindow:
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modelDef?.contextWindow ??
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providerCfg?.models?.[0]?.contextWindow ??
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DEFAULT_CONTEXT_TOKENS,
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maxTokens:
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modelDef?.maxTokens ?? providerCfg?.models?.[0]?.maxTokens ?? DEFAULT_CONTEXT_TOKENS,
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// Preserve compat settings for provider-specific quirks (e.g., supportsStore for LiteLLM)
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compat: modelDef?.compat,
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} as Model<Api>);
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return { model: fallbackModel, authStorage, modelRegistry };
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}
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@ -52,7 +52,7 @@ export function registerOnboardCommand(program: Command) {
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.option("--mode <mode>", "Wizard mode: local|remote")
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.option(
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"--auth-choice <choice>",
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"Auth: setup-token|token|chutes|openai-codex|openai-api-key|openrouter-api-key|ai-gateway-api-key|moonshot-api-key|kimi-code-api-key|synthetic-api-key|venice-api-key|gemini-api-key|zai-api-key|apiKey|minimax-api|minimax-api-lightning|opencode-zen|skip",
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"Auth: setup-token|token|chutes|openai-codex|openai-api-key|openrouter-api-key|ai-gateway-api-key|moonshot-api-key|kimi-code-api-key|synthetic-api-key|venice-api-key|litellm-api-key|gemini-api-key|zai-api-key|apiKey|minimax-api|minimax-api-lightning|opencode-zen|skip",
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)
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.option(
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"--token-provider <id>",
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@ -76,6 +76,9 @@ export function registerOnboardCommand(program: Command) {
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.option("--synthetic-api-key <key>", "Synthetic API key")
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.option("--venice-api-key <key>", "Venice API key")
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.option("--opencode-zen-api-key <key>", "OpenCode Zen API key")
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.option("--litellm-api-key <key>", "LiteLLM API key")
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.option("--litellm-base-url <url>", "LiteLLM base URL (default: http://localhost:4000)")
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.option("--litellm-model <model>", "LiteLLM model name")
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.option("--gateway-port <port>", "Gateway port")
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.option("--gateway-bind <mode>", "Gateway bind: loopback|tailnet|lan|auto|custom")
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.option("--gateway-auth <mode>", "Gateway auth: token|password")
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@ -20,7 +20,8 @@ export type AuthChoiceGroupId =
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| "minimax"
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| "synthetic"
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| "venice"
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| "qwen";
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| "qwen"
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| "litellm";
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export type AuthChoiceGroup = {
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value: AuthChoiceGroupId;
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@ -113,6 +114,12 @@ const AUTH_CHOICE_GROUP_DEFS: {
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hint: "API key",
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choices: ["opencode-zen"],
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},
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{
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value: "litellm",
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label: "LiteLLM",
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hint: "OpenAI-compatible proxy (self-hosted)",
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choices: ["litellm-api-key"],
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},
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];
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export function buildAuthChoiceOptions(params: {
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@ -183,6 +190,11 @@ export function buildAuthChoiceOptions(params: {
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label: "MiniMax M2.1 Lightning",
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hint: "Faster, higher output cost",
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});
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options.push({
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value: "litellm-api-key",
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label: "LiteLLM API key",
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hint: "OpenAI-compatible proxy (any model)",
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});
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if (params.includeSkip) {
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options.push({ value: "skip", label: "Skip for now" });
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}
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@ -15,6 +15,8 @@ import {
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applyAuthProfileConfig,
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applyKimiCodeConfig,
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applyKimiCodeProviderConfig,
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applyLitellmConfig,
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applyLitellmProviderConfig,
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applyMoonshotConfig,
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applyMoonshotProviderConfig,
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applyOpencodeZenConfig,
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@ -36,6 +38,7 @@ import {
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VERCEL_AI_GATEWAY_DEFAULT_MODEL_REF,
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setGeminiApiKey,
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setKimiCodeApiKey,
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setLitellmApiKey,
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setMoonshotApiKey,
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setOpencodeZenApiKey,
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setOpenrouterApiKey,
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@ -85,6 +88,8 @@ export async function applyAuthChoiceApiProviders(
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authChoice = "venice-api-key";
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} else if (params.opts.tokenProvider === "opencode") {
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authChoice = "opencode-zen";
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} else if (params.opts.tokenProvider === "litellm") {
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authChoice = "litellm-api-key";
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}
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}
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@ -579,5 +584,234 @@ export async function applyAuthChoiceApiProviders(
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return { config: nextConfig, agentModelOverride };
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}
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if (authChoice === "litellm-api-key") {
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let hasCredential = false;
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let apiKey: string | undefined;
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// Check for pre-provided credentials via CLI options
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if (!hasCredential && params.opts?.token && params.opts?.tokenProvider === "litellm") {
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apiKey = normalizeApiKeyInput(params.opts.token);
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await setLitellmApiKey(apiKey, params.agentDir);
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hasCredential = true;
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}
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if (!hasCredential) {
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await params.prompter.note(
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[
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"LiteLLM is an OpenAI-compatible proxy that supports many models.",
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"You'll need to provide:",
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" 1. Base URL (e.g., http://localhost:4000)",
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" 2. API key",
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" 3. Model selection (fetched from your LiteLLM instance)",
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].join("\n"),
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"LiteLLM",
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);
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}
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// Check for existing env key
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const envKey = resolveEnvApiKey("litellm");
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if (envKey) {
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const useExisting = await params.prompter.confirm({
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message: `Use existing LITELLM_API_KEY (${envKey.source}, ${formatApiKeyPreview(envKey.apiKey)})?`,
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initialValue: true,
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});
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if (useExisting) {
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apiKey = envKey.apiKey;
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await setLitellmApiKey(apiKey, params.agentDir);
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hasCredential = true;
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}
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}
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if (!hasCredential) {
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const key = await params.prompter.text({
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message: "Enter LiteLLM API key",
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validate: validateApiKeyInput,
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});
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apiKey = normalizeApiKeyInput(String(key));
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await setLitellmApiKey(apiKey, params.agentDir);
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}
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// Prompt for base URL
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const defaultBaseUrl = process.env.LITELLM_BASE_URL ?? "http://localhost:4000";
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const baseUrl = await params.prompter.text({
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message: "Enter LiteLLM base URL",
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initialValue: defaultBaseUrl,
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placeholder: defaultBaseUrl,
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validate: (value) => {
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if (!value?.trim()) return "Base URL is required";
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try {
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new URL(value);
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return undefined;
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} catch {
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return "Invalid URL format";
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}
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},
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});
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const normalizedBaseUrl = String(baseUrl).trim();
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// Try to fetch available models from LiteLLM
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type LitellmModelInfo = { id: string; maxInputTokens?: number; maxOutputTokens?: number };
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let availableModels: LitellmModelInfo[] = [];
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const authHeaders: Record<string, string> = apiKey ? { Authorization: `Bearer ${apiKey}` } : {};
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// First fetch model list from /v1/models
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try {
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const modelsUrl = new URL("/v1/models", normalizedBaseUrl).toString();
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const response = await fetch(modelsUrl, {
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headers: authHeaders,
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signal: AbortSignal.timeout(10000),
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});
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if (response.ok) {
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const data = (await response.json()) as {
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data?: Array<{ id: string }>;
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};
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if (data.data && Array.isArray(data.data)) {
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availableModels = data.data.map((m) => ({ id: m.id }));
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}
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}
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} catch {
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// Fetching models failed - will fall back to manual entry
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}
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// Then fetch detailed model info from /model/info (LiteLLM-specific endpoint)
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// This provides context window and max tokens info
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type ModelInfoEntry = {
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model_name: string;
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model_info?: {
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max_input_tokens?: number;
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max_tokens?: number;
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max_output_tokens?: number;
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};
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};
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const modelInfoMap = new Map<string, { maxInputTokens?: number; maxOutputTokens?: number }>();
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try {
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const modelInfoUrl = new URL("/model/info", normalizedBaseUrl).toString();
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const response = await fetch(modelInfoUrl, {
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headers: authHeaders,
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signal: AbortSignal.timeout(10000),
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});
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if (response.ok) {
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const data = (await response.json()) as { data?: ModelInfoEntry[] };
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if (data.data && Array.isArray(data.data)) {
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for (const entry of data.data) {
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if (entry.model_name && entry.model_info) {
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modelInfoMap.set(entry.model_name, {
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maxInputTokens: entry.model_info.max_input_tokens,
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maxOutputTokens: entry.model_info.max_output_tokens ?? entry.model_info.max_tokens,
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});
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}
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}
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}
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}
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} catch {
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// Model info fetch failed - context window will need manual entry
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}
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// Merge model info into available models
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availableModels = availableModels.map((m) => {
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const info = modelInfoMap.get(m.id);
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return {
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id: m.id,
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maxInputTokens: info?.maxInputTokens,
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maxOutputTokens: info?.maxOutputTokens,
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};
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});
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let normalizedModelId: string;
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let contextWindow: number | undefined;
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let maxTokens: number | undefined;
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if (availableModels.length > 0) {
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// Let user select from available models
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type SelectOption = { value: string; label: string; hint?: string };
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const modelOptions: SelectOption[] = availableModels.map((m) => ({
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value: m.id,
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label: m.id,
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hint: m.maxInputTokens ? `${Math.round(m.maxInputTokens / 1000)}k context` : undefined,
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}));
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modelOptions.push({ value: "__custom__", label: "Enter custom model name", hint: undefined });
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const selectedModel = await params.prompter.select({
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message: `Select model (${availableModels.length} available)`,
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options: modelOptions,
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});
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if (selectedModel === "__custom__") {
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const customModel = await params.prompter.text({
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message: "Enter model name",
|
||||
validate: (value) => (value?.trim() ? undefined : "Model name is required"),
|
||||
});
|
||||
normalizedModelId = String(customModel).trim();
|
||||
} else {
|
||||
normalizedModelId = String(selectedModel);
|
||||
const modelInfo = availableModels.find((m) => m.id === normalizedModelId);
|
||||
if (modelInfo?.maxInputTokens) {
|
||||
contextWindow = modelInfo.maxInputTokens;
|
||||
}
|
||||
if (modelInfo?.maxOutputTokens) {
|
||||
maxTokens = modelInfo.maxOutputTokens;
|
||||
}
|
||||
}
|
||||
} else {
|
||||
// Fall back to manual model entry
|
||||
const defaultModel = process.env.LITELLM_MODEL ?? "gpt-4";
|
||||
const modelId = await params.prompter.text({
|
||||
message: "Enter model name (as configured in LiteLLM)",
|
||||
initialValue: defaultModel,
|
||||
placeholder: defaultModel,
|
||||
validate: (value) => (value?.trim() ? undefined : "Model name is required"),
|
||||
});
|
||||
normalizedModelId = String(modelId).trim();
|
||||
}
|
||||
|
||||
// If context window wasn't auto-detected, prompt for it
|
||||
if (!contextWindow) {
|
||||
const contextInput = await params.prompter.text({
|
||||
message: "Enter context window size (tokens)",
|
||||
initialValue: "128000",
|
||||
placeholder: "128000",
|
||||
validate: (value) => {
|
||||
const num = Number(value);
|
||||
if (Number.isNaN(num) || num <= 0) return "Must be a positive number";
|
||||
return undefined;
|
||||
},
|
||||
});
|
||||
contextWindow = Number(contextInput);
|
||||
}
|
||||
|
||||
const modelRef = `litellm/${normalizedModelId}`;
|
||||
|
||||
nextConfig = applyAuthProfileConfig(nextConfig, {
|
||||
profileId: "litellm:default",
|
||||
provider: "litellm",
|
||||
mode: "api_key",
|
||||
});
|
||||
|
||||
if (params.setDefaultModel) {
|
||||
nextConfig = applyLitellmConfig(nextConfig, {
|
||||
baseUrl: normalizedBaseUrl,
|
||||
modelId: normalizedModelId,
|
||||
contextWindow,
|
||||
maxTokens,
|
||||
});
|
||||
await params.prompter.note(
|
||||
`Default model set to ${modelRef}${contextWindow ? ` (${Math.round(contextWindow / 1000)}k context)` : ""}`,
|
||||
"Model configured",
|
||||
);
|
||||
} else {
|
||||
nextConfig = applyLitellmProviderConfig(nextConfig, {
|
||||
baseUrl: normalizedBaseUrl,
|
||||
modelId: normalizedModelId,
|
||||
contextWindow,
|
||||
maxTokens,
|
||||
});
|
||||
agentModelOverride = modelRef;
|
||||
await noteAgentModel(modelRef);
|
||||
}
|
||||
|
||||
return { config: nextConfig, agentModelOverride };
|
||||
}
|
||||
|
||||
return null;
|
||||
}
|
||||
|
||||
@ -411,6 +411,111 @@ export function applyVeniceConfig(cfg: MoltbotConfig): MoltbotConfig {
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Apply LiteLLM provider configuration without changing the default model.
|
||||
* LiteLLM is a flexible proxy that supports many models, so base URL and model
|
||||
* are user-configurable.
|
||||
*/
|
||||
export function applyLitellmProviderConfig(
|
||||
cfg: ClawdbotConfig,
|
||||
params: {
|
||||
baseUrl: string;
|
||||
modelId: string;
|
||||
modelName?: string;
|
||||
contextWindow?: number;
|
||||
maxTokens?: number;
|
||||
},
|
||||
): ClawdbotConfig {
|
||||
const modelRef = `litellm/${params.modelId}`;
|
||||
const models = { ...cfg.agents?.defaults?.models };
|
||||
models[modelRef] = {
|
||||
...models[modelRef],
|
||||
alias: models[modelRef]?.alias ?? params.modelName ?? params.modelId,
|
||||
};
|
||||
|
||||
const providers = { ...cfg.models?.providers };
|
||||
const existingProvider = providers.litellm;
|
||||
const existingModels = Array.isArray(existingProvider?.models) ? existingProvider.models : [];
|
||||
const newModel = {
|
||||
id: params.modelId,
|
||||
name: params.modelName ?? params.modelId,
|
||||
reasoning: false,
|
||||
input: ["text"] as ("text" | "image")[],
|
||||
cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
|
||||
contextWindow: params.contextWindow ?? 128000,
|
||||
maxTokens: params.maxTokens ?? 8192,
|
||||
// LiteLLM proxies to various providers that may not support the OpenAI Responses API
|
||||
// `store` parameter. Disable it to avoid "Extra inputs are not permitted" errors.
|
||||
compat: { supportsStore: false },
|
||||
};
|
||||
const hasModel = existingModels.some((model) => model.id === params.modelId);
|
||||
const mergedModels = hasModel ? existingModels : [...existingModels, newModel];
|
||||
const { apiKey: existingApiKey, ...existingProviderRest } = (existingProvider ?? {}) as Record<
|
||||
string,
|
||||
unknown
|
||||
> as { apiKey?: string };
|
||||
const resolvedApiKey = typeof existingApiKey === "string" ? existingApiKey : undefined;
|
||||
const normalizedApiKey = resolvedApiKey?.trim();
|
||||
providers.litellm = {
|
||||
...existingProviderRest,
|
||||
baseUrl: params.baseUrl,
|
||||
api: "openai-completions",
|
||||
...(normalizedApiKey ? { apiKey: normalizedApiKey } : {}),
|
||||
models: mergedModels.length > 0 ? mergedModels : [newModel],
|
||||
};
|
||||
|
||||
return {
|
||||
...cfg,
|
||||
agents: {
|
||||
...cfg.agents,
|
||||
defaults: {
|
||||
...cfg.agents?.defaults,
|
||||
models,
|
||||
},
|
||||
},
|
||||
models: {
|
||||
mode: cfg.models?.mode ?? "merge",
|
||||
providers,
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Apply LiteLLM provider configuration AND set LiteLLM as the default model.
|
||||
* Use this when LiteLLM is the primary provider choice during onboarding.
|
||||
*/
|
||||
export function applyLitellmConfig(
|
||||
cfg: ClawdbotConfig,
|
||||
params: {
|
||||
baseUrl: string;
|
||||
modelId: string;
|
||||
modelName?: string;
|
||||
contextWindow?: number;
|
||||
maxTokens?: number;
|
||||
},
|
||||
): ClawdbotConfig {
|
||||
const next = applyLitellmProviderConfig(cfg, params);
|
||||
const modelRef = `litellm/${params.modelId}`;
|
||||
const existingModel = next.agents?.defaults?.model;
|
||||
return {
|
||||
...next,
|
||||
agents: {
|
||||
...next.agents,
|
||||
defaults: {
|
||||
...next.agents?.defaults,
|
||||
model: {
|
||||
...(existingModel && "fallbacks" in (existingModel as Record<string, unknown>)
|
||||
? {
|
||||
fallbacks: (existingModel as { fallbacks?: string[] }).fallbacks,
|
||||
}
|
||||
: undefined),
|
||||
primary: modelRef,
|
||||
},
|
||||
},
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
export function applyAuthProfileConfig(
|
||||
cfg: MoltbotConfig,
|
||||
params: {
|
||||
|
||||
@ -164,3 +164,17 @@ export async function setOpencodeZenApiKey(key: string, agentDir?: string) {
|
||||
agentDir: resolveAuthAgentDir(agentDir),
|
||||
});
|
||||
}
|
||||
|
||||
export const LITELLM_DEFAULT_MODEL_REF = "litellm/gpt-4";
|
||||
|
||||
export async function setLitellmApiKey(key: string, agentDir?: string) {
|
||||
upsertAuthProfile({
|
||||
profileId: "litellm:default",
|
||||
credential: {
|
||||
type: "api_key",
|
||||
provider: "litellm",
|
||||
key,
|
||||
},
|
||||
agentDir: resolveAuthAgentDir(agentDir),
|
||||
});
|
||||
}
|
||||
|
||||
@ -7,6 +7,8 @@ export {
|
||||
applyAuthProfileConfig,
|
||||
applyKimiCodeConfig,
|
||||
applyKimiCodeProviderConfig,
|
||||
applyLitellmConfig,
|
||||
applyLitellmProviderConfig,
|
||||
applyMoonshotConfig,
|
||||
applyMoonshotProviderConfig,
|
||||
applyOpenrouterConfig,
|
||||
@ -33,10 +35,12 @@ export {
|
||||
applyOpencodeZenProviderConfig,
|
||||
} from "./onboard-auth.config-opencode.js";
|
||||
export {
|
||||
LITELLM_DEFAULT_MODEL_REF,
|
||||
OPENROUTER_DEFAULT_MODEL_REF,
|
||||
setAnthropicApiKey,
|
||||
setGeminiApiKey,
|
||||
setKimiCodeApiKey,
|
||||
setLitellmApiKey,
|
||||
setMinimaxApiKey,
|
||||
setMoonshotApiKey,
|
||||
setOpencodeZenApiKey,
|
||||
|
||||
@ -17,6 +17,7 @@ export type AuthChoice =
|
||||
| "kimi-code-api-key"
|
||||
| "synthetic-api-key"
|
||||
| "venice-api-key"
|
||||
| "litellm-api-key"
|
||||
| "codex-cli"
|
||||
| "apiKey"
|
||||
| "gemini-api-key"
|
||||
@ -71,6 +72,9 @@ export type OnboardOptions = {
|
||||
syntheticApiKey?: string;
|
||||
veniceApiKey?: string;
|
||||
opencodeZenApiKey?: string;
|
||||
litellmApiKey?: string;
|
||||
litellmBaseUrl?: string;
|
||||
litellmModel?: string;
|
||||
gatewayPort?: number;
|
||||
gatewayBind?: GatewayBind;
|
||||
gatewayAuth?: GatewayAuthChoice;
|
||||
|
||||
@ -48,6 +48,7 @@ const SHELL_ENV_EXPECTED_KEYS = [
|
||||
"AI_GATEWAY_API_KEY",
|
||||
"MINIMAX_API_KEY",
|
||||
"SYNTHETIC_API_KEY",
|
||||
"LITELLM_API_KEY",
|
||||
"ELEVENLABS_API_KEY",
|
||||
"TELEGRAM_BOT_TOKEN",
|
||||
"DISCORD_BOT_TOKEN",
|
||||
|
||||
Loading…
Reference in New Issue
Block a user