TENET — Documentation

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

What it enables:

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

Each round:

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

The loop:

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

What’s stored:

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

Events trigger flows automatically:

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)

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

Scopes enable cascade:

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"}')

Human interactions broadcast on 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

The jfl command and JFL_* env vars continue to work — no rush to migrate.

Getting Started

Agent Harness

Learning System

Memory

Context Hub

Integrations

Guides

API Reference

Capabilities

Context Hub

Peter Parker (Agent Orchestrator)

RL Learning Loop

Memory System

Event Bus (MAP)

Flows (YAML Automation)

Services (Multi-Repo Coordination)

IDE (Terminal Workspace)

Browser (Agent Eyes)

Subway Mesh (Agent Coordination)

Migrating from JFL

​Context Hub

​Peter Parker (Agent Orchestrator)

​RL Learning Loop

​Memory System

​Event Bus (MAP)

​Flows (YAML Automation)

​Services (Multi-Repo Coordination)

​IDE (Terminal Workspace)

​Browser (Agent Eyes)

​Subway Mesh (Agent Coordination)

​Migrating from JFL

Context Hub

Peter Parker (Agent Orchestrator)

RL Learning Loop

Memory System

Event Bus (MAP)

Flows (YAML Automation)

Services (Multi-Repo Coordination)

IDE (Terminal Workspace)

Browser (Agent Eyes)

Subway Mesh (Agent Coordination)

Migrating from JFL