xrtm-forecast v0.12.0
The Runtime Engine for XRTM.
xrtm-forecast provides the agents, providers, topologies, and orchestration to build AI forecasting systems. It's a composable framework — import the pieces you need and wire them together.
What's new in v0.12
- Baselines:
MarketImpliedBaseline(the honest benchmark for prediction markets),BaseRateBaseline, andConstantBaseline. - Lexical retrieval: zero-dependency BM25
LexicalRetrieverfor archive search/dedup without embedding models.
What's new in v0.11
- Search-result cache:
CachedSearchToolcaches tool results with a TTL, cutting external API calls and making backtests reproducible.
What's new in v0.10
- Reasoning-model ready: thinking-mode toggle,
reasoning_contenthandling, and explicitEmptyContentError(plus a larger-max_tokensretry) instead of silent parse failures. - Structured output + telemetry: provider-enforced JSON,
parse_status, token usage (including cached/reasoning tokens), and model/prompt provenance on every forecast. - Typed decisions:
DecisionProvider(LLM and TypeSafe Jev) with an escalation router for cheap System-One gating. - Cost policies: currency-aware pricing (DeepSeek CNY preset), cost ledgers, hard budgets, and off-peak scheduling.
- Reliability: token-bucket rate limiting, jittered retries, cache TTL, and deterministic-only caching by default.
Installation
pip install xrtm-forecast
Quick Start
from xrtm.forecast.kit.agents.specialists.analyst import ForecastingAnalyst
from xrtm.forecast.core.config.inference import OpenAIConfig
from xrtm.forecast.providers.inference.factory import ModelFactory
# Use any OpenAI-compatible endpoint
config = OpenAIConfig(
model_id="your-model",
base_url="$OPENAI_BASE_URL",
api_key="your-api-key",
)
provider = ModelFactory.get_provider(config)
analyst = ForecastingAnalyst(model=provider, name="forecaster")
# Ask a forecasting question
from xrtm.data import ForecastQuestion
question = ForecastQuestion(
id="fed-march-2025",
title="Will the Fed raise rates in March 2025?",
)
forecast = await analyst.run(question)
print(f"Probability: {forecast.probability:.2f}")
What's Included
OpenAI-compatible Provider
One provider that works with any OpenAI-compatible API: any OpenAI-compatible endpoint. Set base_url to point anywhere.
Agents
ForecastingAnalyst— Full forecast agent: structured prompts, causal reasoning traces, JSON output parsingLLMAgent— Base agent with tool retrieval, output parsingRoutingAgent— FAST/SMART tiered routing for cost optimizationToolAgent/GraphAgent— Wrap functions or sub-graphs as agents
Topologies
Composable multi-agent patterns in kit/topologies/:
RecursiveConsensus— Parallel analysts → aggregate → supervisor → loopcreate_debate_graph()— Pro/Con/Judge debate patterncreate_fanout_graph()— Parallel workers → aggregator
Orchestration
Orchestrator— DAG state machine for workflow graphsAsyncRuntime— Chronos-aware async runtime
Infrastructure
InferenceCache— SQLite cache for LLM responses (enabled by default)- Retry with backoff — 2 retries on API errors
LeakageGuardian— Temporal leakage prevention for backtests
XRTM Ecosystem
Part of the XRTM forecasting stack:
| Package | Role | Version |
|---|---|---|
xrtm-data |
Schemas & question sources | 0.3.0 |
xrtm-eval |
Scoring (Brier, ECE, LogScore) | 0.3.0 |
xrtm-forecast |
Runtime engine (this package) | 0.7.0 |
xrtm-train |
Backtesting & optimization | 0.3.0 |
xrtm |
Product CLI | 0.9.0 |
License
Apache 2.0
1 maintainer
Metadata
1 maintainer
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