XScientist

From one idea to a Git-like research history: inspectable, reproducible, and reversible.

Bring one idea—even if you do not know models or API keys. XScientist helps test it without hiding uncertainty, failed attempts, or contrary evidence.

Quick start · Autonomous study · Audit · Install · Docs · Build notes (中文) · 中文

XScientist is a local-first research system and an open scientific protocol. It can explore competing explanations, choose informative experiments, execute them behind an isolation boundary, criticize its own results, and preserve the whole path as typed, machine-readable research objects. A completed run is never presented as a verified scientific claim unless its evidence and review gates actually pass.

[!IMPORTANT] XScientist is alpha research software, not an oracle. Autonomous runs may use paid models. Generated code requires the configured isolated executor. Machine-generated claims remain unverified until their evidence and independent review gates are complete.

This README describes the published 0.1.3 release. Pin the package version or a source commit when an experiment must remain exactly reproducible.

Choose the shortest path

If you are unsure, start with explore. It records what you know and leaves unknown fields honestly incomplete.

Start with your own idea — no API key

Requirements: Python 3.10+ and Git. No API key, model, Docker, or network call is needed after installation.

python -m pip install \
  "xscientist==0.1.3"
xscientist explore ./my-study

The guided flow uses ordinary questions instead of provider or protocol terms:

• What idea do you want to investigate?
• What observable change do you expect?
• What result would make you change your mind?
• What fair comparison or test could you run first?

You may stop after the first answer and run the same command later. XScientist versions the exact state as idea saved, falsifiable, or planned; it never fills a blank with invented science. This path uses no provider, makes no model call, executes no generated code, and creates no evidence or conclusion.

For a scripted start, the same path is explicit:

xscientist explore ./my-study \
  --idea "Does daily walking improve sleep quality?" \
  --expect "Daily walking improves a preregistered sleep score." \
  --disprove "The score is unchanged or worse." \
  --test "Compare walking and usual-activity periods." \
  --non-interactive

The workspace is understandable without reading internal logs:

• question.md is the human-readable research framing;
• research.yaml records local policy and workspace identity;
• .xscientist/objects/ and checkpoints/ preserve typed decisions and history;
• the local Git repository has no remote and never pushes itself.

Use status and history to inspect these records; new users should not need to edit the internal object store directly.

To see what a complete but contested evidence history looks like, run the bundled $0.00 example:

xscientist demo ./first-study --autopilot --open
xscientist status ./first-study

The demo intentionally ends with “more evidence needed”: held-out evidence challenges an over-broad claim. Preserving that conflict is a successful scientific outcome, not a software failure.

Use xscientist status ./first-study --verbose only when you need branch, pipeline, token, or background-run details. Use --json for automation.

Run an autonomous study

Offline guidance can structure user-supplied reasoning, but it cannot honestly invent domain knowledge, data, or findings. For AI-assisted exploration, add a model only after the research question is safely recorded. The same workspace can be upgraded without replacing its history.

First discover usable models. This works before a workspace exists and detects a running local Ollama service before suggesting hosted services.

xscientist provider list

Local model

Install Ollama, download a local model, and make sure its local service is running. The desktop app starts the service; a headless setup can use ollama serve. No hosted API key is needed. The current official CLI reference uses ollama pull to download and ollama ls to list local models:

ollama pull gemma3
ollama ls

python -m pip install \
  "xscientist[research,openai-compatible]==0.1.3"
xscientist provider list
xscientist start ./my-study

The interactive flow asks only for missing choices: question, provider/model, evidence source, local research identity, and optional budget. If one usable provider is detected, it is selected automatically. For an explore workspace, the saved question is reused and existing research files are preserved. A local model removes hosted API cost, but it still uses your machine's compute and does not remove Docker isolation requirements for generated experiment code.

Hosted model

Install the research runtime plus one provider client:

python -m pip install \
  "xscientist[research,openai]==0.1.3"
export OPENAI_API_KEY="..."
xscientist start ./hosted-study

Available client extras are openai, anthropic, zhipu, bedrock, vertex, and openai-compatible. The last covers local Ollama and compatible services such as DeepSeek, Gemini, OpenRouter, and custom endpoints.

For scripts and CI, make every consequential choice explicit:

xscientist start ./ood-study \
  --question "Why does retrieval-guided reflection fail out of distribution?" \
  --provider openai \
  --model openai/gpt-4.1 \
  --user YOUR_NAME \
  --autopilot discovery \
  --data-dir ./data \
  --max-cost-usd 10 \
  --non-interactive

Use --allow-synthetic-data instead of --data-dir only for an explicitly exploratory study. Input data is content-hashed and mounted read-only. Unknown model pricing fails closed when a cost limit is active.

Isolation and readiness

Generated experiment code never runs silently in the host Python process. A model-backed experiment needs Docker and a version-matched executor:

xscientist executor prepare --workspace ./ood-study
xscientist provider check --workspace ./ood-study --max-cost-usd 10
xscientist doctor --workspace ./ood-study --deep

The commands distinguish missing clients, credentials, local models, Docker CLI, Docker daemon, and executor-image mismatches. They print ordered repair commands without making a paid provider request.

Long-running studies

xscientist start ./ood-study \
  --question "Where does the mechanism break?" \
  --allow-synthetic-data --max-cost-usd 10 --detach

xscientist runs list --workspace ./ood-study
xscientist runs watch RUN_ID --workspace ./ood-study
xscientist runs logs RUN_ID --workspace ./ood-study --tail 100
xscientist runs cancel RUN_ID --workspace ./ood-study
xscientist runs resume RUN_ID --workspace ./ood-study

xscientist status ./ood-study shows a failed or active background run before lower-priority scientific follow-ups. A failed state returns a non-zero exit code while preserving a complete JSON report for automation.

What remains autonomous

The simple entry point does not reduce the research loop. Depending on the selected profile, XScientist can:

1. propose rival and null hypotheses instead of defending the first idea;
2. lock predictions and rank experiments by expected information value;
3. execute bounded experiments and retain failed or negative attempts;
4. scan anomalies, contradictions, evidence quality, and transfer boundaries;
5. run independent review roles, repair bounded defects, and stop at hard gates;
6. package the paper, evidence DAG, provenance, and exact continuation context.

Autonomy does not bypass scientific authority. XScientist does not silently invent missing user answers, label synthetic data as empirical, run generated code on the host, promote an unreviewed claim, publish research, or push a workspace remote.

Profiles expose one meaningful trade-off:

Deep strategy commands remain available under xscientist research, but new users do not need to learn them before the first result. See the deep-research protocol and method-discovery protocol.

Inspect and reproduce

Research Git versions scientific objects rather than asking users to infer meaning from a folder of logs. Git is the current local storage adapter; no GitHub account or remote is required, and XScientist never pushes research by itself.

If you know GitHub, the mental model is deliberately familiar:

xscientist status ./first-study
xscientist history list ./first-study
xscientist history show ./first-study --commit HEAD
xscientist history diff ./first-study
xscientist audit ./first-study --level trace
xscientist audit ./first-study --level replay
xscientist audit ./first-study --level verify

Audit answers three different questions and never conflates them:

• trace: can every claim be traced to recorded evidence and decisions?
• replay: are code, data, environment, seed, and command sufficient to rerun it?
• verify: was the result independently checked under the required gates?

These levels form a one-way ladder: a recorded claim may be traceable without being replayable, and replayable without being independently verified. A blocked audit is an actionable scientific gap, not necessarily a software failure.

Save a meaningful manual change before trying a risky alternative. Rollback is preview-only unless --apply is explicit. Applying it appends a reversal checkpoint: it never deletes or rewrites the original result.

xscientist history save ./first-study -m "record corrected measurement rule"
xscientist history rollback ./first-study --commit HEAD
# Review the target, impact, blockers, and generated apply command first.
xscientist history rollback ./first-study --commit HEAD --apply

Unsaved tracked, staged, selected, or research-eligible changes and the first checkpoint block rollback. Policy-excluded generated views are preserved and do not block it; after a reversal, status marks an older DAG as stale and prints the exact refresh command. Reverting an older checkpoint can still conflict with newer work, in which case --apply stops without discarding current history.

For reproduction, bundles, object inspection, context snapshots, deep diffs, and branches, use the advanced protocol surface:

xscientist research reproduce HEAD --repo ./first-study --execute --record \
  --reproduces @latest:claim --verifier human:REPRODUCER

xscientist research bundle --repo ./first-study --dest ./study-backup
xscientist research export --repo ./first-study --dest ./exchange

A generated DAG is a disposable view, not scientific source data. Regenerating it does not dirty a research checkpoint or prevent a bundle. Eligible research changes, tracked edits, or staged changes still block bundling until reviewed.

To challenge a conclusion without erasing its history:

xscientist research branch challenge/boundary --repo ./first-study --switch
xscientist research plan @latest:hypothesis --repo ./first-study \
  "Search for a counterexample" \
  --test "A reproducible failure refutes the current mechanism"
xscientist research switch main --repo ./first-study
xscientist research merge challenge/boundary --repo ./first-study --preview

A small surface over inspectable layers

flowchart TB
  U["explore · start · status"] --> O["Autonomous research loop"]
  O --> E["Isolated experiments and providers"]
  O --> R["Typed Research Git history"]
  E --> D["Evidence DAG and ARA artifacts"]
  R --> D
  D --> A["audit · history · reproduce"]

The everyday surface stays small: explore, start, status, audit, and history. Readiness repair lives under doctor, detached execution under runs, and the complete scientific protocol under research. The first table in this README is the only decision tree a new user needs.

The public orchestration surface lives in xscientist/, the experiment workflow in ai_scientist/, and versioned schemas in ai_scientist/protocol/. See Architecture.

Installation

Pin a commit rather than main for an exactly repeatable experiment.

Install optional capabilities only when a study needs them:

Core protocol and CLI support Python 3.10–3.13. Autonomous execution also depends on the selected provider, Docker, and the study's experiment stack.

Outputs and boundaries

An autonomous project keeps configuration, ideas, experiments, papers, logs, and ARA handoff artifacts separate. The exact layout is documented in Output directories.

For sensitive domains, use XScientist as research infrastructure—not as a substitute for domain experts, ethics review, or regulated validation.

SDK and documentation

from xscientist import ProjectRequest, XScientist

client = XScientist(output_root="./research-output")
result = client.run_project(
    ProjectRequest(
        project="retrieval-study",
        question="When does retrieval-guided reflection fail?",
        autopilot="discovery",
        allow_synthetic_data=True,
        max_cost_usd=10,
    )
)
print(result.returncode)

Run xscientist --help for the small everyday command set and xscientist research --help for the complete scientific protocol surface.

Project status

XScientist is under active alpha development. Contributions should include a test, preserve protocol/schema compatibility, and avoid weakening provenance, isolation, cost, or scientific gates. Read CONTRIBUTING.md and CHANGELOG.md.

Paper: XScientist: Towards an AI-Driven Scientific Research Ecosystem.

Apache-2.0 licensed. See LICENSE.