xrtm-forecast 0.1.1

Institutional-grade parallelized agentic reasoning engine.

xrtm-forecast

Institutional-grade parallelized agentic reasoning engine.

Overview

xrtm-forecast is the core intelligence engine originally developed for the CAFE (Computer-Aided Financial Engineering) platform. It provides a domain-agnostic framework for:

• Inference Layer: Standardized provider interfaces for Gemini and OpenAI.
• Reasoning Graph: A pluggable state-machine orchestrator for multi-agent workflows.
• Agent Core: Standardized Agent base class for structured reasoning and parsing.
• Skill Protocol: Composable behaviors (e.g., Search) that agents can dynamically equip.
• Observability: OTel-native structured telemetry and institutional execution reports.
• Evaluation: Built-in harness for backtesting and accuracy metrics (Brier Score).

Architectural Design: "Engine vs. Specialists"

xrtm-forecast is designed for modularity using a "Lego" philosophy:

• The Engine (agents/): Core structural bricks like LLMAgent and ToolAgent.
• The Specialists (agents/specialists/): Pre-built expert roles like the ForecastingAnalyst.
• The Registry: A central exchange for discovering and plugging in agents and tools.

For a deep dive, see Architecture & Design Principles.

Installation

From PyPI (Stable)

pip install xrtm-forecast

# With extras (redis, memory)
pip install "xrtm-forecast[redis,memory]"

From Source (Latest)

pip install git+https://github.com/xrtm-org/forecast.git

Configuration

xrtm-forecast relies on environment variables for API keys and service connections. Create a .env file in your project root:

GEMINI_API_KEY=your_key_here
REDIS_URL=redis://localhost:6379/0  # Optional (fallback to in-memory)

Quick Start: Inference

import asyncio
from forecast import ModelFactory
from forecast.inference.config import GeminiConfig
from pydantic import SecretStr

async def main():
    config = GeminiConfig(
        api_key=SecretStr("your-key"), 
        model_id="gemini-2.0-flash-lite"
    )
    provider = ModelFactory.get_provider(config)
    response = await provider.generate_content_async("What is the causality of inflation?")
    print(response.text)

if __name__ == "__main__":
    asyncio.run(main())

Documentation & Examples

• Architecture: The "Lego" Design
• Agent Registry: Pre-built & Core Agents
• Examples: Check the examples/ directory for structured entry points:
  • core/: Basic library usage.
  • features/: Specialized modules (Skills, Eval, Telemetry).
  • pipelines/: End-to-end multi-agent workflows.

Contributing

We welcome institutional-grade contributions! Please see CONTRIBUTING.md for setup instructions and our development workflow.

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License

xrtm-forecast is open-source software licensed under the Apache-2.0 license. See the LICENSE file for more details.

Copyright © 2026 XRTM Team.