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Documentation Index

Fetch the complete documentation index at: https://docs.10et.ai/llms.txt

Use this file to discover all available pages before exploring further.

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Install

npm install -g @10et/cli  # package name during migration
Install the CLI with npm i -g @10et/cli. The tenet command is available globally after install.

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Initialize

New project: 
tenet start my-project
This runs init → setup → launch in one command. Creates the workspace, configures agents for your stack, deploys CI workflows, and opens your editor. Existing project: 
cd your-project
tenet init
tenet setup
init creates the context layer. setup analyzes your codebase and creates personalized RL agents.

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What gets created

your-project/
├── .tenet/
│   ├── config.json          # Project config
│   ├── journal/             # Session journals (JSONL)
│   └── agents/              # Agent configs (TOML)
├── eval/                    # Eval scripts for your stack
├── knowledge/               # VISION, THESIS, NARRATIVE
├── CLAUDE.md                # Agent instructions
└── AGENTS.md                # Pi agent instructions

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Run your first agent

tenet peter agent list

  test-coverage
    Metric: coverage_percent (maximize)

  code-quality
    Metric: quality_score (maximize)
Run one round: 
tenet peter agent test-coverage --rounds 1

  Baseline: 0.1276

  Round 1  +0.0031 KEPT    48 tests added

  Coverage: 12.7% → 13.1%

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Check health

tenet doctor

  [ok] Context Hub (port 4360)
  [ok] Memory: 349 entries, 349 embedded
  [ok] Agents: 5 configured
  All checks passed.

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What’s next

How it works

The RL learning loop, eval system, and agent architecture.

Agent configuration

TOML config reference — metrics, scope, eval scripts.

Memory system

How TENET remembers and learns from history.

Peter Parker

The nightly orchestrator that runs your agents.