figureout 0.1.1

FigureOut - a lightweight and resilient multi-LLM orchestrator

FigureOut - a lightweight and resilient multi-LLM orchestrator

FigureOut is a lightweight, modular orchestrator for developers who want to build LLM workflows without the framework bloat. Unlike heavy frameworks that hide logic behind abstractions, FigureOut keeps your code clean, predictable, and easy to debug. Perfect for prototyping and production-grade apps where you need full control.

Overview

FigureOut has no hardcoded domain knowledge. You supply your own roles, system prompts, output schemas, and classifier guidelines via RoleDefinition. The package classifies incoming queries and dispatches them to the appropriate role, returning a structured JSON response.

Installation

Install the base package plus the extra for your LLM provider:

pip install figureout[openai]   # OpenAI, Meta (Llama), Mistral, or Groq
pip install figureout[gemini]   # Google Gemini
pip install figureout[claude]   # Anthropic Claude
pip install figureout[all]      # All providers

Supported LLM Providers

Provider	Enum	API Key Env Var	Install Extra
OpenAI	LLM.OPENAI	OPENAI_API_KEY	pip install figureout[openai]
Google Gemini	LLM.GEMINI	GEMINI_API_KEY	pip install figureout[gemini]
Anthropic Claude	LLM.CLAUDE	ANTHROPIC_API_KEY	pip install figureout[claude]
Meta (Llama)	LLM.META	META_API_KEY	pip install figureout[openai]
Mistral	LLM.MISTRAL	MISTRAL_API_KEY	pip install figureout[openai]
Groq	LLM.GROQ	GROQ_API_KEY	pip install figureout[openai]

Only install the extra for the provider(s) you intend to use.

Quick Start

import asyncio
from figureout import FigureOut, RoleDefinition

concierge = FigureOut(
    llm="openai",
    llm_version="gpt-4o",
    roles={
        "product_search": RoleDefinition(
            prompt="You are a product search specialist. Help users find products.",
            schema='{"results": [{"id": int, "name": str, "price": float}], "summary": str}',
            guideline="queries about finding or searching for products",
        ),
        "off_topic": RoleDefinition(
            prompt="Politely decline and explain you can only help with product searches.",
            schema='{"message": str}',
            guideline="anything unrelated to products",
        ),
    },
)

result = asyncio.run(concierge.run("Find me a blue running shoe under $100"))
print(result)
# {"results": [...], "summary": "..."}

API

FigureOut Constructor

FigureOut(
    llm: str | LLM,
    llm_version: str,
    roles: dict[str, RoleDefinition],
    lite_llm_version: str | None = None,
    verbose: bool = False,
    max_roles: int = 1,
    max_output_tokens: int = 16384,
    max_retries: int = 2,
    max_tool_rounds: int = 3,
    interpret_tool_response: bool | None = None,
    cache_enabled: bool = True,
    cache_size: int = 128,
    inject_date: bool = True,
    api_key: str | None = None,
    mcp_server=None,
)

All constructor params can also be set via environment variables:

Param	Env Var
llm	FIGUREOUT_LLM
llm_version	FIGUREOUT_LLM_VERSION
lite_llm_version	FIGUREOUT_LITE_LLM_VERSION
verbose	FIGUREOUT_VERBOSE
max_roles	FIGUREOUT_MAX_ROLES
max_output_tokens	FIGUREOUT_MAX_OUTPUT_TOKENS
max_retries	FIGUREOUT_MAX_RETRIES
max_tool_rounds	FIGUREOUT_MAX_TOOL_ROUNDS
interpret_tool_response	FIGUREOUT_INTERPRET_TOOL_RESPONSE
cache_enabled	FIGUREOUT_CACHE_ENABLED
cache_size	FIGUREOUT_CACHE_SIZE
inject_date	FIGUREOUT_INJECT_DATE

RoleDefinition

RoleDefinition(
    prompt: str,      # System prompt for this role
    schema: str,      # JSON schema string defining the output shape
    guideline: str,   # Short description used by the classifier to select this role
)

run() Method

await concierge.run(
    user_query: str,
    role: str | None = None,         # Skip classification, use this role directly
    context: str | None = None,      # Prepended to user_query as additional context
    output_schema: str | None = None # Override the role's default schema
)

Returns a dict matching the role's output schema.

Multi-Role Queries

Set max_roles > 1 to allow multiple roles to handle a single query in parallel:

concierge = FigureOut(llm="openai", llm_version="gpt-4o", roles=ROLES, max_roles=3)
result = await concierge.run("Find concerts and sports events this weekend")
# {"concert_discovery": {...}, "sports_discovery": {...}}

When a single role is selected, the result is returned directly (not wrapped in a role-keyed dict).

off_topic Role

Define an "off_topic" role to control fallback behavior. The classifier never selects it directly — it is used automatically when no other role matches:

"off_topic": RoleDefinition(
    prompt="Politely decline.",
    schema='{"message": str}',
    guideline="anything unrelated",
)

Tool Use (FastMCP)

Pass a FastMCP server to enable tool calling:

from fastmcp import FastMCP

mcp = FastMCP("my-server")

@mcp.tool()
def search_products(query: str) -> list:
    ...

concierge = FigureOut(llm="gemini", llm_version="gemini-2.0-flash", roles=ROLES, mcp_server=mcp)

interpret_tool_response

Controls how tool results are handled after tool execution:

Value	Behavior
None (default)	Normal loop — LLM responds naturally after tool calls
True	Bridge messages appended after tool calls + forced JSON mode round; ensures nested fields are populated
False	Return raw tool output without sending back to the LLM

Use interpret_tool_response=True for roles that return complex nested JSON structures.

Verbose Mode

With verbose=True, the response is wrapped under a "response" key and a "debug" key is added with token usage and timing information:

concierge = FigureOut(..., verbose=True)
result = await concierge.run("Find flights to Tokyo")
# {"response": {"flights": [...]}, "debug": {"input_tokens": 512, "output_tokens": 256, ...}}

Examples

The examples/ directory contains complete domain-specific backends with React+Vite frontends:

Example	Domain
event-sports-booking/	Sports event discovery and booking
flight-booking/	Flight search and booking
restaurant-reservation/	Restaurant discovery and reservations
things-to-do/	Activity and places discovery
customer-support-chat/	FAQ-based customer support chat

To run an example:

cd examples/<example-name>/mcp-server
python app.py

Development

pip install -e ".[dev]"
pytest
pytest tests/test_figureout.py::test_name  # Run a single test