Ask
Pose a binary, numeric, date, categorical, thresholded, or conditional question with an explicit resolution rule.
search the future
OpenSuperforecaster
Turn a hard question into cited probabilities you can audit.
A self-hosted forecasting appliance. It fans your question out to parallel research agents, aggregates their findings into an inspectable probability estimate, and keeps every source, attempt, trace, and benchmark score on your own machine.
06 Forecast modes
03× Parallel agents
04 Local start steps
100% Local trust chain
01
Trust Chain
Don't trust a score — inspect the chain that produced it. Every run keeps its work.
Fan out
Parallel Smithers agents gather evidence independently, so disagreement stays visible instead of averaged away.
Synthesize
Findings aggregate into a probability or distribution with rationale, caveats, and inline source citations.
Audit
Sources, attempts, trace bundles, and benchmark scores persist as inspectable local records you can replay.
02
Architecture
One local pipeline: question in, cited probability out, with every intermediate step persisted.
A question fans out to parallel agents that research sources, then synthesis emits a probability and forecast artifacts.
Agents pull from the open web, arXiv, GitHub, reference data, and more — every citation is captured in the source bank.
Traces, benchmark cases, and a local store back the answer so any run can be replayed and re-scored.
03
Cockpit
The real product surface: compose a question, launch a source-backed run, inspect the record.
local appliance
Forecasts are records, not chat replies.
Ask a question and watch researchers gather evidence in real time. Every run becomes a local object: the question, its sources, individual attempts, the aggregate answer, artifacts, and the full workflow trace.
run detail
Every answer opens up.
Each run detail page surfaces the emitted probability, the artifact rows behind it, the source bank, and a forecast ledger of attempts, aggregates, and Brier + log scores. Export the trace bundle, CSV, or Parquet for deeper review.
CSVParquetTrace bundlesArtifactsDuckDB tables
04
Forecast Modes
Six typed forecast modes, each with a dedicated renderer instead of a wall of freeform text.
Binary
P(event) as an explicit yes/no probability.
Numeric
A point estimate with a percentile range over a continuous quantity.
Date
When an event resolves, expressed as a distribution over time.
Categorical
Probabilities across a set of mutually exclusive outcomes.
Thresholded
The odds a tracked metric crosses a specific threshold.
Conditional
How one event shifts the probability of another.
05
Evaluation
Forecasting systems fail when their assumptions, evidence, or scoring setup stay hidden. This one makes them boringly visible.
benchmark lab
Score the workflow before you trust it.
Run benchmark and pastcasting loops against fixed evidence from the same operational lab that tracks diagnostics, recent runs, pending resolutions, and workflow health before a forecast workflow gets trusted.
Question
What exactly was forecast, and how does it resolve?
Sources
Which evidence moved the probability, and where did it come from?
Disagreement
Where did individual researchers diverge before synthesis?
Trace
Can the run be replayed from prompt to artifact bundle?
Score
Did the workflow survive local benchmark and pastcasting checks?
Action
What should be re-run before anyone trusts this in a real decision?
06
Run Locally
Open forecasting infrastructure should be inspectable, forkable, and runnable by default.
clone and start
Bring up the appliance on your machine.
Postgres, object storage, the web cockpit, and the worker come up from the repo with Docker Compose, so the full trust chain stays local and inspectable.
Agent subscriptions are mounted through one local auth root. Keep Codex as the default, or configure Claude Code, Pi, Kimi, and other Smithers CLI providers from the setup page after their profiles are logged in.
Next.js Local cockpit UI Postgres Product ledger MinIO Artifact + trace storage DuckDB Local analytics Smithers Durable workflows Smithers agents Configurable fan-out
local start
git clone https://github.com/ralfboltshauser/open-superforecaster.git
cd open-superforecaster
cp .env.example .env
mkdir -p data/agent-auth/codex/default
docker compose up --build