Praxist

Praxist coordinates parallel research agents, experiments, evidence retention, and synthesis across generations. It supplies the reusable research process; your task project supplies the science.

Read the technical paper, Praxist: From Experimental Artifacts to Solution Lineages, for the system design and evaluation.

A generation inherits frontier, agenda, and Gems; allocates parallel peers that build artifacts; evaluates those artifacts into typed findings; and synthesizes findings into a frontier update and the next agenda, which the following generation inherits. Artifacts, findings, decisions, and agendas accumulate into a lineage DAG that explains the final artifact.

Parallel work becomes measured evidence, durable research state, and a focused agenda for the next generation.

Research Infrastructure, Explicit Science¶

A coding agent is the recommended interface between the researcher and two deliberately separate systems.

Praxist owns	The task project owns
Peers, generations, orchestration, resource scheduling, and lifecycle	Objective, constraints, baseline, environment, and permitted changes
Agent runtimes, evidence transport, durable state, replay, and synthesis	Evaluator, metrics, protocol, evidence maturity, prompts, and roles

The boundary keeps Praxist reusable across fields and makes the task project the sole source of domain meaning. Architecture defines the software boundary; Task Projects defines the scientific contract.

Install Praxist¶

Install and configure Praxist with one command:

python3 -m pip install --index-url https://pypi.org/simple "praxist[agents,codex]" && praxist setup --interactive --install-skills codex

Or let an agent install from PyPI and follow the packaged setup runbook:

codex --yolo
# or: claude --dangerously-skip-permissions
Install and configure Praxist using its packaged OOBE runbook. Stop after readiness checks.

Installation never selects a project or starts research. Before the separate takeover step, read the Quickstart and Your First Task. The selected project must already contain the code and local resources needed to run its baseline; Praxist does not invent missing data, simulators, credentials, or measurements.

Choose A Runtime Profile¶

Use an existing Codex login through Codex-native mode.

Codex-native profile
Prefer a high-cache-hit-rate open-source model API after a representative quality and cache check.

Model API selection

Continue After Setup¶

Define the research brief, prerequisites, and launch gates.

Your first task
Use the direct CLI for lifecycle and monitoring operations.

Direct CLI operations

The Research Brief Is the Control Surface¶

Takeover can inspect code and measure an existing baseline, but it cannot infer the researcher's priorities. The brief should identify the objective, credibility standard, allowed resources, exploration policy, and practical run budget. Those decisions shape research direction, experiment throughput, and retention from the first generation onward.

Your First Task provides a complete example and explains how takeover turns that brief into a validated task project.

Find a Specific Answer¶

You want to...	Start here
Install and configure Praxist	Installation
Complete setup and hand off a project	Quickstart
Understand project prerequisites	Your First Task
Use agent workflows	Agent Skills
Diagnose a failure or stall	Troubleshooting
Understand the research loop	Research Loop
Configure a task harness	Task Projects
Choose a scaffold or complete reference	Examples And Templates
Inspect a complete Python/JAX project	Rocket Booster Recovery
Inspect a complete native Rust project	Rocket Booster Recovery (Rust)
Extend Praxist	Developer Guide
Look up an exact command	CLI Reference

The Documentation Policy identifies the sole owner of each contract.