Agent setup examples

Use a llmbase_chat_... key with the agent API base URL when an external agent should consume your Pro Chat/Agent subscription budget.

Create a chat agent key

1. Open Dashboard -> Agent Access.
2. In Chat agent keys, create a new key.
3. Copy the key immediately. It is only shown once.

Revoking a key disables it immediately. If a bounded-lifetime key is shown as expired, create a replacement instead of continuing to use the old token.

Configure an OpenAI-compatible agent

Use these settings in any agent that supports a custom OpenAI-compatible API:

Setting	Value
Base URL	https://llmbase.ai/api/v1/agents
API key	Your llmbase_chat_... chat agent key
Models endpoint	GET /models
Chat endpoint	POST /chat/completions

Example Hermes configuration

Hermes should be configured as a named custom provider. A bare custom provider with only OPENAI_API_KEY may not send the key to non-OpenAI hosts. Load the current agent model list first, then choose a returned model with the tool, context, and cost profile your agent loop needs.

model:
  provider: llmbase
  model: <model-id-from-agent-models>
  base_url: https://llmbase.ai/api/v1/agents
  api_key: llmbase_chat_...
  api_mode: chat_completions

custom_providers:
  - name: llmbase
    base_url: https://llmbase.ai/api/v1/agents
    api_key: llmbase_chat_...
    api_mode: chat_completions
    model: <model-id-from-agent-models>

Hermes uses reasoning_effort: medium by default. LLMBase accepts that default for reasoning-capable models. When a model publishes high but no native medium tier, the request uses high automatically; no Hermes override is required. Explicit unsupported values still return a validation error.

Example with the OpenAI SDK

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://llmbase.ai/api/v1/agents",
  apiKey: process.env.LLMBASE_CHAT_AGENT_KEY,
});

const models = await client.models.list();
const model = models.data[0]?.id;

const response = await client.chat.completions.create({
  model,
  messages: [
    { role: "user", content: "Draft a short product update." },
  ],
});

console.log(response.choices[0]?.message?.content);

Example cURL request

curl https://llmbase.ai/api/v1/agents/chat/completions \
  -H "Authorization: Bearer $LLMBASE_CHAT_AGENT_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "<model-id-from-agent-models>",
    "messages": [
      { "role": "user", "content": "Summarize this repo issue." }
    ]
  }'