# PRAXIST — Autonomous Research System for Executable Experiments

> An autonomous research platform that designs, executes, and evaluates measurable, computer-executable experiments without human intervention.

## Install

Save as a script file and run:

# PRAXIST — Autonomous Research System for Executable Experiments

## Quick Use
```bash
git clone https://github.com/sapientinc/PRAXIST
cd PRAXIST
pip install -e .
praxist run --hypothesis "Compare SGD vs Adam on CIFAR-10 in 10 epochs"
```

## Introduction
PRAXIST is an autonomous research system that turns natural-language hypotheses into fully executable experiments. Given a research question, it designs the experimental protocol, writes the code, runs the experiment, collects results, and produces a structured report — all without manual intervention. It bridges the gap between asking a research question and getting a reproducible, quantitative answer.

## What PRAXIST Does
- Accepts research hypotheses in natural language and generates executable experiment plans
- Writes experiment code automatically, including data loading, training loops, and evaluation
- Runs experiments in isolated environments and captures all metrics and artifacts
- Produces structured reports with tables, charts, and statistical significance tests
- Maintains an experiment log for reproducibility and audit trails

## Architecture Overview
PRAXIST uses a multi-agent pipeline where a planner agent decomposes the hypothesis into sub-experiments, a coder agent generates Python scripts for each, and an executor agent runs them in sandboxed containers. A reviewer agent validates results against the original hypothesis and flags anomalies. The entire pipeline is orchestrated by a coordinator that manages dependencies between experiment stages. Results are stored in a structured database with full provenance tracking.

## Self-Hosting & Configuration
- Install from source with pip and configure the LLM backend (supports multiple providers)
- Set compute resource limits for experiment sandboxes via the configuration file
- Configure artifact storage to local disk or S3-compatible object storage
- Enable GPU access for experiments that require training by setting device flags
- Customize the report template to match your team's publication format

## Key Features
- End-to-end automation from hypothesis to reproducible results
- Sandboxed experiment execution prevents side effects between runs
- Full provenance tracking: every generated file, command, and metric is logged
- Statistical validation layer that checks for significance and common pitfalls
- Extensible agent pipeline: add custom agents for domain-specific analysis

## Comparison with Similar Tools
- **AutoResearch (Karpathy)** — focuses on neural-net training experiments; PRAXIST handles broader research types
- **MLflow** — tracks experiments but does not design or run them; PRAXIST automates the full cycle
- **Weights & Biases** — experiment tracking and visualization; PRAXIST starts from hypothesis, not code
- **DeerFlow** — general-purpose research agent; PRAXIST specializes in measurable, executable experiments

## FAQ
**Q: What kinds of experiments can PRAXIST run?**
A: Any experiment expressible as Python code — ML training, data analysis, simulations, benchmarks.

**Q: Does it require a specific LLM?**
A: No. PRAXIST supports OpenAI, Anthropic, and open-source models via a pluggable backend.

**Q: How does it ensure reproducibility?**
A: Every run records the full environment, random seeds, code, and data versions in a manifest.

**Q: Can I review the generated code before execution?**
A: Yes. A dry-run mode outputs the experiment plan and code for human review before running.

## Sources
- https://github.com/sapientinc/PRAXIST

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Source: https://tokrepo.com/en/workflows/asset-a58e91ca
Author: Script Depot