Configuration

RLMConfig

All configuration is managed through the RLMConfig dataclass. Every field has a sensible default.

from fast_rlm import RLMConfig

config = RLMConfig.default()

Fields

Field	Type	Default	Description
`primary_agent`	`str`	`z-ai/glm-5`	Model used for the root agent
`sub_agent`	`str`	`minimax/minimax-m2.5`	Model used for child subagents
`max_depth`	`int`	`3`	Max recursive subagent depth
`max_calls_per_subagent`	`int`	`20`	Max LLM calls a single subagent can make
`truncate_len`	`int`	`2000`	Output characters shown to the LLM per step
`max_money_spent`	`float`	`1.0`	Hard budget cap in USD — raises an error if exceeded
`max_completion_tokens`	`int`	`50000`	Max total completion tokens across all subagents — raises an error if exceeded
`max_prompt_tokens`	`int`	`200000`	Max total prompt tokens across all subagents — raises an error if exceeded

Modifying config

RLMConfig is a dataclass — just set attributes directly:

config = RLMConfig.default()
config.primary_agent = "openai/gpt-5.2"
config.max_depth = 5
config.max_money_spent = 3.0
config.max_completion_tokens = 100000
config.max_prompt_tokens = 500000

Using a dict

You can also pass a plain dict if you prefer:

from fast_rlm import run

result = run(
    "Summarize this document",
    config={"primary_agent": "openai/gpt-5.2-codex", "max_depth": 5},
)

Dict values are merged on top of the defaults from rlm_config.yaml.

Cost tracking depends on your provider

Not all API providers return cost information in their responses. OpenRouter includes cost data, but OpenAI and most other providers do not. If your provider doesn't return cost, max_money_spent won't be able to enforce a budget and cost will show as "Unknown" in the UI. In that case, use max_completion_tokens and max_prompt_tokens to control spending instead — these work with any provider since token counts are always returned.

How config merging works

Defaults are loaded from the bundled rlm_config.yaml
Your overrides (from RLMConfig or dict) are applied on top
The merged config is written to a temp file and passed to the engine

This means you only need to specify what you want to change — everything else keeps its default value.

Model names

fast-rlm uses OpenRouter by default, so model names follow the OpenRouter format: provider/model-name.

Since agents write and execute Python code in a REPL, choose models that are strong at coding. Recommended models:

Model	Name
GPT-5.2 Codex	`openai/gpt-5.2-codex`
Claude Sonnet 4.6	`anthropic/claude-sonnet-4-6`
Gemini 3.1 Pro Preview	`google/gemini-3.1-pro-preview`
MiniMax M2.5	`minimax/minimax-m2.5`
GLM-5	`z-ai/glm-5`

Browse the full list at openrouter.ai/models.

Using a different provider

If you're pointing RLM_MODEL_BASE_URL at a different API (e.g. OpenAI directly), use that provider's model names instead (e.g. gpt-5.2-codex instead of openai/gpt-5.2-codex).