The AI stack that serves
forecasting models and picks the best one.
Tollama AI combines time series model serving with rigorous evaluation — powered by autonomous agents that forecast, benchmark, and select the right model for production.
Teams waste weeks wiring up incompatible model runtimes, writing one-off evaluation notebooks, and guessing which model works best. There is no standard way to serve, evaluate, or select time series models.
Every TSFM ships its own install, APIs, and deployment patterns. Serving multiple models means managing multiple stacks with conflicting dependencies.
One-off notebooks with random splits, inconsistent metrics, and no reproducibility. No standardized way to compare models on your data.
Without rigorous backtesting, teams deploy the wrong model — or never deploy at all. Model selection stays manual and error-prone.
tollama serves the models. tollama-eval finds the best one. Together, they are the complete forecasting stack.
Unifies 7 TSFMs and 4 neural baselines behind one daemon, one SDK, one REST API, and first-class agent pathways. Pull, serve, and query Chronos, TimesFM, Moirai, and more through a single interface.
Benchmarks 36+ models with expanding-window cross-validation, AutoML, leaderboard scoring, and reproducible HTML/PDF reports. CLI, SDK, REST API, and campaign mode.
10-layer Decision Trust OS. L0 Control Plane, Conformal Prediction, SHAP, Constitutional Guard, Bayesian Trust Aggregation. EU AI Act + Korean AI Basic Act ready.
Specialized forecasting and calibration tools built on the Tollama stack.
Spline + LSTM/GRU forecasting pipeline with rolling-window CV, FastAPI server, React dashboard, and agent-ready integrations.
Trust score for prediction markets. Calibration analysis, statistical metrics, and TSFM inference on Polymarket probabilities.
The next layer of the Tollama AI stack — expanding from local runtimes to cloud orchestration, continuous monitoring, and multi-agent memory.
Hosted daemon with managed API gateway, model serving, and usage metering — no local setup required.
Scheduled benchmark runs, regression checks, and release gating for forecasting systems.
MCP Server, OpenAI Agents SDK, Claude Agent SDK, LangGraph integration wrappers for trust pipeline.
Value-based guardrails beyond formal rules. Fairness, transparency, and human oversight verification.
Cross-agent planning, shared memory, and task lifecycle management — connecting forecasting, calibration, and coding agents into coherent workflows.
Start building with the Tollama AI stack. Deploy forecasting agents with confidence.