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 CLI that gives AI agents persistent memory, a learning loop, and coordination. Here’s what you can do with it.
Context Hub
A daemon that runs alongside your agent sessions. Every agent connects to it via MCP. It serves journals, memory, knowledge docs, and events.
tenet context-hub ensure # start the hub
tenet ask "why is the timeout 30s?" # query your project's history
tenet search "authentication flow" # semantic search across all memory
- Agents start sessions with full context from every previous session
- Semantic search across decisions, code patterns, and experiments
- MCP server — works with Claude Code, Pi, Cursor, any MCP-compatible agent
Peter Parker (Agent Orchestrator)
The meta-orchestrator that decides which agents to run, in what order, and with what context. Named after Andrej Karpathy’s autoresearch pattern.
tenet setup # auto-detect metrics, create agents
tenet peter agent test-coverage -r 5 # run test-coverage agent for 5 rounds
tenet peter daily # run all agents with gradient
tenet peter status # see what's running, what improved
- Eval before — measure the metric (coverage, quality, speed)
- Agent changes code — in an isolated worktree
- Eval after — did the metric improve?
- Keep or revert —
git reset --hard if it regressed
- Record tuple — (state, action, reward) → training buffer
# What a nightly run looks like
$ tenet peter daily
test-coverage (maximize)
Round 1 +0.31% kept 48 tests added
Round 2 -0.02% reverted types broke
Round 3 +0.35% kept config loader
code-quality (maximize)
Round 1 +0.45% kept 12 console.logs → logger
4 improvements shipped. Tomorrow's agents are smarter.
RL Learning Loop
Every agent action produces training data. The policy head learns what works in YOUR codebase.
tenet eval status # current scores and trends
tenet eval compare -2 -1 # compare last two snapshots
tenet train transform # prepare tuples for training
tenet train policy-head # train the policy head
tenet policy-head score --type fix --description "add error handling" --scope small
- Training buffer — append-only JSONL of (state, action, reward) tuples
- Policy head — neural network that predicts which actions improve metrics
- Build evals — write a spec, the eval checks if it’s built. Agents iterate from 0% to 100%.
# Score candidate actions before committing
$ tenet policy-head rank '[
{"type":"fix","description":"add retry logic","scope":"small"},
{"type":"refactor","description":"extract auth module","scope":"medium"}
]'
1. fix: add retry logic → predicted +0.04
2. refactor: extract auth → predicted +0.02
Memory System
Persistent memory with semantic search, graph edges, and knowledge lifecycle.
tenet memory status # embedding health, total entries
tenet memory search "auth flow" # search past decisions
tenet organize # audit docs for staleness → PENDING.md
- Journals — structured entries (decisions, features, fixes, discoveries) from every session
- Knowledge docs — VISION.md, THESIS.md, ARCHITECTURE.md — living documents
- Code headers —
@purpose annotations indexed from source files
- Graph edges — structured relationships (updates, contradicts, related_to)
- Embeddings — semantic vectors for similarity search
Event Bus (MAP)
The nervous system. Events flow between agents, flows, and the hub.
tenet events recent # see what's happening
tenet events publish eval:scored # emit a custom event
| Event | What happens |
|---|
eval:scored with improved=true | Auto-merge PR, close linked issue |
scope:impact | Cascade evals to downstream services |
cron:nightly | Peter Parker runs all agents with gradient |
session:ended | Journal synced, memory indexed |
Flows (YAML Automation)
Declarative event-driven workflows. Cron triggers, event patterns, agent spawns, approval gates.
# .tenet/flows/self-driving.yaml
flows:
- name: auto-merge-on-improvement
trigger:
pattern: "eval:scored"
condition: 'data.improved == "true"'
actions:
- type: command
command: "gh pr merge {{data.pr_number}} --auto"
- type: journal
entry_type: milestone
title: "Auto-merged PR #{{data.pr_number}}"
tenet flows list # see all flows
tenet flows enable <name> # activate a flow
Services (Multi-Repo Coordination)
Register services, declare what they produce and consume. Cross-repo impact detection.
tenet services register ./api # register a service
tenet scope list # see all produces/consumes
tenet scope impact --service api # what breaks if API changes?
tenet onboard ./new-service # full onboarding wizard
api produces: api:schema-change
web consumes: api:schema-change
→ API change triggers web evals automatically
IDE (Terminal Workspace)
Multi-pane tmux workspace with smart surface detection.
tenet ide # auto-detect and launch
tenet ide add browser # add browser pane
tenet ide add eval # add eval dashboard
tenet ide open api # open child service
tenet ide available # list all surface types
Browser (Agent Eyes)
Terminal browser (Carbonyl) with CDP bridge. Agents get DOM, humans see pages.
tenet-browser.sh https://10et.ai # launch browser
# Then from agent session:
subway_call("browser.relay", "navigate", '{"url":"https://..."}')
subway_call("browser.relay", "read", '{}') # structured DOM
subway_call("browser.relay", "click", '{"selector":"#btn"}')
browser.events — agents can respond to what you’re looking at.
Subway Mesh (Agent Coordination)
P2P network for agent-to-agent communication across machines and sessions.
subway_send("datboi.relay", "deploy is failing")
subway_call("browser.relay", "navigate", '{"url":"..."}')
subway_broadcast("status", "deploy complete")
subway_subscribe("browser.events")
Migrating from JFL
If you have the older jfl CLI installed:
npm i -g @10et/cli # install new package
tenet migrate # .jfl/ → .tenet/, updates all configs
tenet migrate --rollback # undo if needed
jfl command and JFL_* env vars continue to work — no rush to migrate.
