Configuration

All runtime settings live in AmritaConfig — a single object you create once and pass to create_agent() / ChatObject (or set globally).

The Config Tree

Field	Purpose
llm (LLMConfig)	Model settings: stream, temperature, memory abstraction, thinking
function_config (FunctionConfig)	Tool calling: limit, minimal context, middle messages
builtin (BuiltinAgentConfig)	Agent behavior: tool calling mode, thought mode, stall trigger
cookie (CookieConfig)	Cookie security detection

Global vs Per-Call

from amrita_core import minimal_init
from amrita_core.config import AmritaConfig, FunctionConfig, LLMConfig

config = AmritaConfig(
    function_config=FunctionConfig(agent_tool_call_limit=15),
    llm=LLMConfig(stream=True),
)
await minimal_init(config)  # global default

agent = create_agent(..., config=config)  # or per-agent

get_config() returns the global config; set_config() replaces it.

Key Settings for Agent Behavior

Setting	Default	Effect
function_config.agent_tool_call_limit	—	Hard cap on tool rounds per run
builtin.tool_calling_mode	"agent"	"agent" / "rag" / "none"
builtin.agent_thought_mode	—	"reasoning" / "reasoning-required" (explicit reasoning)
builtin.loop_reasoning_trigger	—	Stall detection: N identical tool signatures → give up
llm.enable_memory_abstract	False	Auto-summarize long history
llm.memory_abstract_threshold	—	Token threshold for summarization

Presets

A ModelPreset bundles endpoint + model + ThinkingConfig + tools, and is loaded from the data backend per session. create_agent() builds one from your base_url / api_key / model arguments; advanced setups use MultiPresetManager to serve different presets per session (see Data Layer).

Next

Event System — hooks into the processing pipeline.