Reference

This page is a lookup reference for everything configurable: the full config.json schema with its validation rules, and a comparison of the supported LLM providers.

Configuration schema

The config file is loaded by ModelConfigFromJsonFile in internal/core/config.go.

Field	Type	Required	Description
`provider`	string	yes	LLM provider: `Gemini`, `OpenAi`, `Anthropic`, `Ollama`, `DeepSeek`, `Kimi`, `Qwen`, or `Grok`. Values are case sensitive
`model-name`	string	yes	Provider specific model ID, for example `googleai/gemini-2.5-flash-lite`
`api-key`	string	no	API key. When empty, the agent falls back to the provider’s environment variable
`temperature`	float32	no	Sampling temperature, for example `0.7`
`max-tokens`	uint32	no	Maximum tokens generated per model response
`max-iterations`	int	no	Cap on plan and execute loop iterations per user request

Example

{
  "provider": "Anthropic",
  "model-name": "claude-sonnet-4-5",
  "temperature": 0.5,
  "max-tokens": 2048,
  "max-iterations": 15
}

Validation errors

The loader refuses to start under the following conditions:

Condition	Error message
Empty `model-name`	`model name can't be empty`
Unrecognized `provider` value	`provider <x> is not supported`
Negative `max-iterations`	`max iterations can't be negative`
No key found in file or environment	`api key not found`
Missing file and no fallback present	`config file not found`

File resolution

The config path resolves in this order:

The explicit path passed via -c / --config, if that file exists.
Otherwise config.json in the current working directory as a fallback.

API key resolution order

The api-key field in the JSON file.
The environment variable matching the configured provider:

Provider	Environment variable
`Gemini`	`GEMINI_API_KEY`
`OpenAi`	`OPENAI_API_KEY`
`Anthropic`	`ANTHROPIC_API_KEY`
`Ollama`	N/A (uses `server-address`)
`DeepSeek`	`DEEPSEEK_API_KEY`
`Kimi`	`KIMI_API_KEY`
`Qwen`	`QWEN_API_KEY`
`Grok`	`GROK_API_KEY`

LLM providers

The agent uses Firebase Genkit to talk to multiple providers. Configure one in config.json and supply its key; you never need more than one at a time.

Google Gemini (default)

Provider value: "Gemini"
Key variable: GEMINI_API_KEY
Model prefix: googleai/ (auto-applied if omitted)
Get key: Google AI Studio
Notes: Free tier available for flash models

OpenAI

Provider value: "OpenAi"
Key variable: OPENAI_API_KEY
Model prefix: openai/ (auto-applied if omitted)
Get key: OpenAI Platform
Notes: Paid API, requires billing credit

Anthropic

Provider value: "Anthropic"
Key variable: ANTHROPIC_API_KEY
Model prefix: anthropic/ (auto-applied if omitted)
Get key: Anthropic Console
Notes: Paid API with usage dashboard

Ollama (local)

Provider value: "Ollama"
Key variable: None (uses server-address field)
Model prefix: None (local models)
Get key: No API key needed, runs locally
Notes: Requires local Ollama server running. Set server-address in config (default: http://localhost:11434)

DeepSeek

Provider value: "DeepSeek"
Key variable: DEEPSEEK_API_KEY
Model prefix: deepseek/ (auto-applied if omitted)
Get key: DeepSeek Platform
Notes: Paid API

Kimi (Moonshot AI)

Provider value: "Kimi"
Key variable: KIMI_API_KEY
Model prefix: moonshotai/ (auto-applied if omitted)
Get key: Moonshot AI Console
Notes: Paid API

Qwen (Alibaba)

Provider value: "Qwen"
Key variable: QWEN_API_KEY
Model prefix: alibaba/ (auto-applied if omitted)
Get key: Alibaba Cloud DashScope
Notes: Paid API

Grok (xAI)

Provider value: "Grok"
Key variable: GROK_API_KEY
Model prefix: xai/ (auto-applied if omitted)
Get key: xAI Console
Notes: Paid API

Switching providers

Change provider and model-name together and export the matching environment variable:

{
  "provider": "OpenAi",
  "model-name": "gpt-4o-mini",
  "max-iterations": 10
}

export OPENAI_API_KEY="sk-..."
go run . agent-chat

For Ollama, set the server address instead:

{
  "provider": "Ollama",
  "model-name": "llama3",
  "server-address": "http://localhost:11434",
  "max-iterations": 10
}

go run . agent-chat

A single user request triggers several LLM calls: one for intent classification plus one or more loop iterations. Lighter and faster models such as Gemini Flash, GPT-4o mini, or Ollama models keep total latency low, which matters because each iteration waits on a network round trip. If you pick a model with smaller output limits, raise max-tokens; if you routinely ask for complex multi step tasks, raise max-iterations so the agent has room to finish.

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