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.
TENET is a collection of components that work together to create a learning system for AI agents.
System Overview
Your Project → Context Hub → Agents → Eval → Training → Policy Head → better agents tomorrow
| Layer | Components |
|---|
| CLI | init, setup, peter, train, eval, ide, deploy |
| Context Hub | Memory DB, Event Bus, Indexer (port 4360) |
| Agent Harness | Peter Parker, RL agents, worktree isolation |
| Learning | Training buffer, policy head (14M params), build evals |
| Platform | Dashboard, auth, cloud agents, team features |
| Mesh | Subway P2P for agent coordination |
Components
CLI
The entry point. All commands go through tenet:
| Command | Purpose |
|---|
tenet | Launch Pi with TENET extensions |
tenet init | Scaffold a new project |
tenet setup | Auto-detect metrics, create agents |
tenet peter | Agent orchestrator |
tenet train | Train policy head |
tenet eval | Run evals, check scores |
tenet ide | Terminal workspace |
tenet deploy | Deploy to platform |
Context Hub
The central coordination daemon. Runs locally on port 4360.
- Memory database — SQLite with indexed memories, semantic embeddings, graph edges
- Event bus — MAP (Multiplayer Agent Protocol) events for agent coordination
- Periodic indexer — Indexes journal entries every 60s, code headers every 5 min
- API server — REST endpoints for memory, events, context, eval
Agent Harness
Autonomous improvement agents that run in isolated git worktrees.
The loop:
Eval before
Measure the baseline metric (coverage, quality, speed)
Agent changes code
In an isolated worktree — main branch is never touched
Eval after
Measure again. Did the metric improve?
Keep or revert
Score improved → advance branch, create PR. Regressed → git reset --hard
Record tuple
(state, action, reward) → training buffer. Policy head learns.
Storage Layer
Everything is files in your repo:
| Component | Format | Location |
|---|
| Journals | JSONL | .tenet/journal/*.jsonl |
| Training buffer | JSONL | .tenet/training-buffer.jsonl |
| Agent configs | TOML | .tenet/agents/*.toml |
| Memory | SQLite | .tenet/memory.db |
| Eval results | JSONL | .tenet/eval.jsonl |
| Policy weights | JSON | .tenet/checkpoints/ |
| Flows | YAML | .tenet/flows/*.yaml |
| Config | JSON | .tenet/config.json |
| API | Purpose |
|---|
/api/tenet/memory/* | Cloud memory sync |
/api/tenet/train | Remote training |
/api/tenet/policy | Policy inference |
/api/tenet/tuples | Tuple upload |
/api/agents/* | Cloud agent management |
/api/sessions/* | Session tracking |
Subway Mesh
P2P agent coordination. Agents discover each other, send messages, broadcast events across machines.
| Agent | Role |
|---|
| Your Pi session | Coding agent with TENET tools |
browser.relay | Terminal browser with DOM access |
| Other sessions | Parallel agents on same or different machines |
Data Flow
Session produces data
Agent sessions write journal entries and training tuples
Hub indexes everything
Context Hub indexes journals, code headers, embeddings
Policy head trains
Training buffer feeds the policy head — learns what works
Peter Parker orchestrates
Picks best next experiment based on policy predictions
Agents improve overnight
Run in worktrees, eval-gated, auto-PR on improvement
