Add jan.ai provider documentation
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@ -262,7 +262,26 @@ ollama pull llama3.3
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Ollama is automatically detected when running locally at `http://127.0.0.1:11434/v1`. See [/providers/ollama](/providers/ollama) for model recommendations and custom configuration.
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### Local proxies (LM Studio, vLLM, LiteLLM, etc.)
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### jan.ai
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jan.ai is a local LLM runtime built on llama.cpp with OpenAI-compatible API:
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- Provider: `jan`
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- Auth: None required (local server), but needs `JAN_API_KEY` set to any value for auto-discovery
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- Example model: `jan/<model-name>`
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- Installation: https://jan.ai
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```json5
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{
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agents: {
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defaults: { model: { primary: "jan/llama-3.3-70b" } }
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}
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}
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```
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jan.ai is automatically detected when running locally at `http://127.0.0.1:1337/v1`. See [/providers/jan](/providers/jan) for detailed setup and configuration.
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### Local proxies (LM Studio, vLLM, jan.ai, etc.)
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Example (OpenAI‑compatible):
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@ -1883,16 +1883,16 @@ injection and unsafe behavior. See [Security](/gateway/security).
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More context: [Models](/concepts/models).
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### Can I use selfhosted models llamacpp vLLM Ollama
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### Can I use selfhosted models llamacpp vLLM Ollama jan.ai
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Yes. If your local server exposes an OpenAI-compatible API, you can point a
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custom provider at it. Ollama is supported directly and is the easiest path.
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custom provider at it. Ollama and jan.ai are supported directly with auto-discovery and are the easiest paths.
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Security note: smaller or heavily quantized models are more vulnerable to prompt
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injection. We strongly recommend **large models** for any bot that can use tools.
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If you still want small models, enable sandboxing and strict tool allowlists.
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Docs: [Ollama](/providers/ollama), [Local models](/gateway/local-models),
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Docs: [Ollama](/providers/ollama), [jan.ai](/providers/jan), [Local models](/gateway/local-models),
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[Model providers](/concepts/model-providers), [Security](/gateway/security),
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[Sandboxing](/gateway/sandboxing).
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193
docs/providers/jan.md
Normal file
193
docs/providers/jan.md
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@ -0,0 +1,193 @@
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---
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summary: "Run Clawdbot with jan.ai (local LLM runtime using llama.cpp)"
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read_when:
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- You want to run Clawdbot with local models via jan.ai
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- You need jan.ai setup and configuration guidance
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---
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# jan.ai
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jan.ai is a local LLM runtime built on llama.cpp with OpenAI-compatible API. Clawdbot integrates with jan.ai and can **auto-discover available models** when you opt in with `JAN_API_KEY` (or an auth profile) and do not define an explicit `models.providers.jan` entry.
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## Quick start
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1) Install jan.ai: https://jan.ai
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2) Download models using jan.ai's UI or CLI
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3) Enable jan.ai for Clawdbot (any value works; jan.ai doesn't require a real key):
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```bash
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# Set environment variable
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export JAN_API_KEY="jan-local"
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# Or configure in your config file
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clawdbot config set models.providers.jan.apiKey "jan-local"
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```
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4) Use jan.ai models:
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```json5
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{
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agents: {
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defaults: {
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model: { primary: "jan/llama-3.3-70b" }
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}
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}
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}
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```
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## Model discovery (implicit provider)
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When you set `JAN_API_KEY` (or an auth profile) and **do not** define `models.providers.jan`, Clawdbot discovers models from the local jan.ai instance at `http://127.0.0.1:1337/v1`:
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- Queries `/v1/models` endpoint
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- Includes all models from jan.ai
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- Marks `reasoning` when model ID contains "r1" or "reasoning" (case-insensitive)
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- Sets `input: ["text"]` for all models (jan.ai primarily supports text models)
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- Sets `contextWindow` to 128000
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- Sets `maxTokens` to 8192
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- Sets all costs to `0` (local provider)
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This avoids manual model entries while keeping the catalog aligned with your jan.ai installation.
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To see what models are available:
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```bash
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clawdbot models list
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```
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If you set `models.providers.jan` explicitly, auto-discovery is skipped and you must define models manually (see below).
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## Configuration
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### Basic setup (implicit discovery)
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The simplest way to enable jan.ai is via environment variable:
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```bash
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export JAN_API_KEY="jan-local"
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```
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### Explicit setup (manual models)
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Use explicit config when:
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- jan.ai runs on another host/port.
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- You want to force specific context windows or model lists.
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- You want to override default model settings.
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```json5
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{
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models: {
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providers: {
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jan: {
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baseUrl: "http://127.0.0.1:1337/v1",
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apiKey: "jan-local",
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api: "openai-completions",
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models: [
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{
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id: "llama-3.3-70b",
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name: "Llama 3.3 70B",
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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: 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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If `JAN_API_KEY` is set, you can omit `apiKey` in the provider entry and Clawdbot will fill it for availability checks.
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### Custom base URL (explicit config)
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If jan.ai is running on a different host or port (explicit config disables auto-discovery, so define models manually):
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```json5
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{
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models: {
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providers: {
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jan: {
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apiKey: "jan-local",
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baseUrl: "http://jan-host:1337/v1",
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api: "openai-completions"
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}
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}
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}
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}
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```
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### Model selection
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Once configured, all your jan.ai models are available:
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```json5
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{
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agents: {
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defaults: {
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model: {
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primary: "jan/llama-3.3-70b",
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fallback: ["jan/qwen2.5-coder-32b"]
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}
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}
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}
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}
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```
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## Advanced
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### Reasoning models
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Clawdbot marks models as reasoning-capable when the model ID contains "r1" or "reasoning" (case-insensitive). This includes models like DeepSeek-R1 and other reasoning models.
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### Model Costs
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jan.ai runs locally, so all model costs are set to $0.
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### Context windows
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For auto-discovered models, Clawdbot defaults to a context window of 128000 and maxTokens of 8192. You can override these values in explicit provider config.
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## Troubleshooting
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### jan.ai not detected
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Make sure jan.ai is running and that you set `JAN_API_KEY` (or an auth profile), and that you did **not** define an explicit `models.providers.jan` entry.
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And that the API is accessible:
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```bash
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curl http://localhost:1337/v1/models
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```
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### No models available
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Make sure jan.ai has models downloaded and available. Check the jan.ai UI to ensure models are installed, or download models through jan.ai's interface.
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To verify API endpoint accessibility:
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```bash
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curl http://localhost:1337/v1/models
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```
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### Connection refused
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Check that jan.ai is running on the correct port (default 1337):
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```bash
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# Check if jan.ai is running on port 1337
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netstat -an | grep 1337
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# Or restart jan.ai
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# Restart through the jan.ai application or service
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```
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## See Also
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- [Model Providers](/concepts/model-providers) - Overview of all providers
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- [Model Selection](/concepts/models) - How to choose models
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- [Gateway Configuration](/gateway/configuration) - Full config reference
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- [Ollama Provider](/providers/ollama) - Similar local provider for comparison
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@ -75,6 +75,17 @@ const OLLAMA_DEFAULT_COST = {
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cacheWrite: 0,
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};
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const JAN_BASE_URL = "http://127.0.0.1:1337/v1";
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const JAN_API_BASE_URL = "http://127.0.0.1:1337";
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const JAN_DEFAULT_CONTEXT_WINDOW = 128000;
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const JAN_DEFAULT_MAX_TOKENS = 8192;
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const JAN_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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interface OllamaModel {
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name: string;
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modified_at: string;
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@ -90,6 +101,18 @@ interface OllamaTagsResponse {
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models: OllamaModel[];
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}
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interface JanModel {
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id: string;
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object: string;
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created: number;
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owned_by: string;
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}
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interface JanModelsResponse {
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object: string;
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data: JanModel[];
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}
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async function discoverOllamaModels(): Promise<ModelDefinitionConfig[]> {
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// Skip Ollama discovery in test environments
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if (process.env.VITEST || process.env.NODE_ENV === "test") {
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@ -128,6 +151,44 @@ async function discoverOllamaModels(): Promise<ModelDefinitionConfig[]> {
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}
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}
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async function discoverJanModels(): Promise<ModelDefinitionConfig[]> {
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// Skip jan.ai discovery in test environments
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if (process.env.VITEST || process.env.NODE_ENV === "test") {
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return [];
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}
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try {
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const response = await fetch(`${JAN_API_BASE_URL}/v1/models`, {
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signal: AbortSignal.timeout(5000),
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});
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if (!response.ok) {
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console.warn(`Failed to discover jan.ai models: ${response.status}`);
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return [];
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}
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const data = (await response.json()) as JanModelsResponse;
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if (!data.data || data.data.length === 0) {
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console.warn("No jan.ai models found on local instance");
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return [];
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}
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return data.data.map((model) => {
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const modelId = model.id;
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const isReasoning =
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modelId.toLowerCase().includes("r1") || modelId.toLowerCase().includes("reasoning");
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return {
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id: modelId,
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name: modelId,
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reasoning: isReasoning,
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input: ["text"],
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cost: JAN_DEFAULT_COST,
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contextWindow: JAN_DEFAULT_CONTEXT_WINDOW,
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maxTokens: JAN_DEFAULT_MAX_TOKENS,
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};
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});
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} catch (error) {
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console.warn(`Failed to discover jan.ai models: ${String(error)}`);
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return [];
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}
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}
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function normalizeApiKeyConfig(value: string): string {
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const trimmed = value.trim();
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const match = /^\$\{([A-Z0-9_]+)\}$/.exec(trimmed);
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@ -359,6 +420,15 @@ async function buildOllamaProvider(): Promise<ProviderConfig> {
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};
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}
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async function buildJanProvider(): Promise<ProviderConfig> {
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const models = await discoverJanModels();
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return {
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baseUrl: JAN_BASE_URL,
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api: "openai-completions",
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models,
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};
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}
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export async function resolveImplicitProviders(params: {
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agentDir: string;
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}): Promise<ModelsConfig["providers"]> {
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@ -418,6 +488,14 @@ export async function resolveImplicitProviders(params: {
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providers.ollama = { ...(await buildOllamaProvider()), apiKey: ollamaKey };
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}
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// jan.ai provider - only add if explicitly configured
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const janKey =
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resolveEnvApiKeyVarName("jan") ??
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resolveApiKeyFromProfiles({ provider: "jan", store: authStore });
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if (janKey) {
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providers.jan = { ...(await buildJanProvider()), apiKey: janKey };
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}
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return providers;
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}
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@ -11,7 +11,8 @@
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"skipLibCheck": true,
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"resolveJsonModule": true,
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"noEmitOnError": true,
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"allowSyntheticDefaultImports": true
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"allowSyntheticDefaultImports": true,
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"types": ["node"]
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},
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"include": ["src/**/*"],
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"exclude": [
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