xrtm-forecast 0.2.2

Institutional-grade modular engine for generative forecasting and agentic reasoning.

xrtm-forecast

Professional engine for generative forecasting and agentic reasoning

xrtm-forecast acts as the rigorous backbone for state-of-the-art agentic workflows, bridging the gap between rapid prototyping and mission-critical deployment.

It centralizes the "Reasoning Graph" definition so that agent behaviors are deterministic and auditable. forecast is the pivot across the ecosystem: if a provider is supported, it can be plugged into any agent topology (Orchestrator, Debate, Consensus) without changing business logic.

We pledge to uphold research-grade transparency: strict typing, zero-tolerance verification, and rigorous double-trace auditability for every decision made by an AI.

Installation

Standard Installation (Cloud + Core)

pip install xrtm-forecast

Hardware-Specific Local Inference

pip install "xrtm-forecast[transformers]"  # PyTorch + HuggingFace
pip install "xrtm-forecast[vllm]"          # High-throughput serving
pip install "xrtm-forecast[llama-cpp]"     # CPU-optimized GGUF
pip install "xrtm-forecast[xlm]"           # Local Encoder specialists

Quickstart

Get started with xrtm-forecast right away with the Analyst API. The Analyst is a high-level reasoning class that supports research, search, and probability estimation.

import asyncio
from forecast import create_forecasting_analyst

async def main():
    # 1. Instantiate the analyst (API keys injected from env)
    agent = create_forecasting_analyst(model_id="gemini")
    
    # 2. Execute reasoning loop
    result = await agent.run(
        "Will a general-purpose AI (AGI) be publicly announced before 2030?"
    )
    
    # 3. Inspect the rigorous output
    print(f"Confidence: {result.confidence}")
    print(f"Reasoning: {result.reasoning}")

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

Why should I use xrtm-forecast?

1. Temporal Integrity (The Time Machine):

   • Most agent frameworks leak future data during backtests. xrtm-forecast has a Temporal Sandboxing engine that rigidly enforces cut-off dates for search and memory.
   • Verify your strategies against past market events with zero look-ahead bias.

2. Probabilistic Rigor:

   • Agents are treated as calibrated instruments, not just chatbots. We support native Brier Score calculation, Reliability Diagrams, and Confidence Interval estimation out of the box.

3. Hybrid "Quant-Qual" Intelligence:

   • Seamlessly mix fast statistical models (e.g., ARIMA, XGBoost) with slow, deliberative LLM Agents in the same graph.
   • Orchestrate complex "Consensus" topologies where multiple agents debate to reduce variance.

4. Double-Trace Auditability:

   • Forecasting requires accountability. We provide a dual-layer audit trail: Structural (OTel traces of execution flow) and Logical (reasoning snapshots) for every prediction.

Why shouldn't I use xrtm-forecast?

• You need a generic "Chat with PDF" or "Customer Support" bot. We are hyper-focused on Forecasting and Research workflows.
• You want "magic" autoscaling or loose typing. We prioritize correctness, repeatability, and type-safety over ease of prototyping.
• You don't care about backtesting or time-travel debugging.

Example Components

xrtm-forecast comes with a comprehensive Kit of pre-built instruments. Expand the categories below to see examples.

Agents (Personas)

• Minimal Agent: The "Hello World" of reasoning.
• Forecasting Analyst: A specialized researcher for binary market questions.
• Adversary (Red Team): An agent trained to find flaws in arguments.

Topologies (Interaction Patterns)

• Debate: Two agents arguing for opposing sides before a judge.
• Consensus: Multiple agents varying in temperature converging on a decision.
• Orchestrator Basics: Building a custom state machine from scratch.

Capabilities (Skills)

• Discovery (Search): Automated information retrieval.
• Streaming: Real-time token streaming for UIs.
• Calibration: Adjusting confidence intervals to match reality.
• Trace Replay: Re-running a saved execution for debugging.