Trinity Lite

Local AgentOps for cross-vendor CLI coding agents. Route work, recover state, and accept only with evidence.

Trinity Lite is a local control plane for Codex, Claude Code, Hermes, and custom
CLI agents. It connects the tools you already use; it does not ask you to
rebuild them inside another framework.

中文 README · Docs · Why Trinity Lite? · Recipes

The problem

You already use more than one capable coding agent. The hard part is no longer
starting another agent; it is preserving task truth across tools, recovering
after a client disconnects, preventing duplicate work, and deciding when a
result is actually accepted. Trinity Lite is that local operations layer.

What it does

• Route by capability, not name. You describe the task. The router matches it to the right agent — no hardcoded agent names, no fragile dispatch logic. "Implement a rate limiter" lands on the agent you tagged implement. "Review the auth module" goes to the agent tagged review.
• Give every agent a pull queue. Workers read pending tasks from the shared bus, execute them via CLI, and write results back. Each agent polls on its own schedule. You never copy-paste an output between terminals again.
• Remember every decision. Every task, status change, result, error, and inter-agent message lands in a local SQLite database. Query who did what, when, and what happened — without setting up a logging pipeline.
• Review, verify, then accept. orchestrate runs primary work, routes the required review, runs local verification, and writes acceptance evidence back to SQLite.
• Block footguns before they fire. Self-routes become explicit local-work decisions instead of creating loops. Delegation depth has a hard cap. Working directories must be in the allowlist.

Quick start

30 seconds, no agents required:

pip install trinity-lite
trinity-lite doctor
trinity-lite orchestrate "implement a hello-world function"

Mock agents are built in. You see the full route → work → review → verify → accept cycle before you wire up anything real.

Not another framework

Trinity Lite does not build agents. It operates the agents you already have.

LangGraph and CrewAI give you primitives for building agents from scratch — graph definitions, role abstractions, and tool wrappers. Trinity Lite starts from the opposite end: Claude Code is running in one terminal, Codex is running in another, and their work needs reliable handoff, recovery, independent review, and an acceptance trail. No new agent abstraction. Just local AgentOps for the CLIs you already use.

Who this is for

Features

• Route by capability. Tag agents with implement, review, audit — the router matches tasks to the agent that can do them. No agent names in your dispatch logic.
• Dispatch directly when you need control. Bypass the router and send a task straight to claude_code or codex. Best of both worlds.
• Persist everything in SQLite. Tasks, statuses, results, errors, and messages in one local file. Query it with sqlite3 or any tool that speaks SQL.
• Accept with evidence, not vibes. The review flow records route decisions, review links, verification results, acceptance reasons, and accepted_at in SQLite. A reviewed task is accepted only after the local verifier passes.
• Isolate agent code edits with git worktrees. Released as a v0.6 preview: trinity-lite worktree creates managed branches and checkouts, records the base commit, and returns diff evidence without touching your main checkout.
• Run CLI workers on demand. trinity-lite worker codex --once pulls one queued task, executes the agent's command, and writes the result. Run it in a loop, in cron, or by hand.
• Execute safely, no shell injection. Agent commands are JSON arrays run with shell=False. No string interpolation into a shell. No surprises.
• Test with mock agents. Mock agents simulate the full cycle without real CLIs. Prototype routing, persistence, and review handoffs first. Wire up real agents later.
• Guard against runaway delegation. Self-delegation is blocked. Delegation depth is capped. Working directories are allowlisted. Safe by default.
• Check health in one pass. trinity-lite doctor verifies Python, SQLite, route config, agent config, and publish readiness.
• Zero core dependencies. The default runtime is Python standard library only. YAML pipelines are available through an optional extra.
• 150+ tests guarding the surface area. Mock workflows, safety checks, routing, persistence, MCP, and acceptance gates — all covered.
• Optional model selection hints. Select from your declared model pool with transparent task, tier, and capability rules; no claim of a universal best or cheapest model.

Install

pip install trinity-lite

Python 3.10+. Zero core runtime dependencies. Standard library only unless an optional extra is installed.

Optional extras

pip install "trinity-lite[yaml]"          # YAML pipeline files
pip install "trinity-lite[mcp]"           # MCP server — 13 tools + 3 resources
pip install "trinity-lite[agent-skill]"   # agent-skill-system integration

Workflow example

Route primary work → run the worker → run the reviewer → verify → accept. One command, one audit trail.

trinity-lite orchestrate "implement a rate limiter for the API"

The primary task row records route_json, review_task_id, verification_json, acceptance_status, acceptance_reason, and accepted_at.

Ready for real CLIs when you are:

cp examples/agents.command.example.json agents.local.json
trinity-lite orchestrate "implement a rate limiter for the API" --agents agents.local.json

Prefer manual control? Use the lower-level bus commands:

trinity-lite dispatch-auto "implement a parser"
trinity-lite worker codex --once
trinity-lite tasks

Worktree Preview

This is a v0.6 preview. It manages isolated worktree lifecycle and diff
evidence while keeping automatic merge-back out of scope.

Create an isolated checkout for an agent:

trinity-lite worktree create "fix parser bug" --repo . --agent codex
trinity-lite worktree list
trinity-lite worktree diff <task_id>
trinity-lite worktree cleanup <task_id>

Worktree preview records branch, base commit, path, agent id, task id, and diff
evidence. It does not merge branches or delete branches by default. See
Worktree Parallelism Preview.

MCP server

Turn the task bus into an MCP server. Let any MCP client dispatch, query, and route tasks.

pip install trinity-lite[mcp]
trinity-lite mcp serve

13 tools:

3 resources: trinity://health, trinity://tasks/recent, trinity://tasks/{task_id}

If an MCP client disconnects or times out before it displays the task id, call
trinity_latest for the source agent, then call trinity_status with the
returned primary task id. By default trinity_latest skips secondary review
children so recovery lands on the user-facing task.

Acceptance Evidence

trinity-lite orchestrate now writes a local acceptance trail to the task row:

• route_json: JSON-encoded route decision used for primary dispatch
• review_task_id and parent_task_id: links between primary work and secondary review
• gate_status: primary_pending, review_pending, review_passed, review_attention, verification_failed, or accepted
• verification_json: JSON-encoded local verifier result, defaulting to trinity-lite doctor
• acceptance_status, acceptance_reason, and accepted_at

If the reviewer reports P0/P1 findings, the flow stops at review_attention. If local verification fails, it stops at verification_failed. accepted_at is written only after the required review and verification pass.

Optional Model Selector

Select from a model pool you control using task complexity and declared
capabilities. This is a transparent routing helper, not a universal cost
optimizer:

# Auto-detect your available models (zero config)
trinity-lite detect-models

# Or set up interactively (no JSON needed)
trinity-lite setup-models

How it works: Define your model pool with tiers (budget / standard / premium) and strength tags. The selector picks automatically:

Manual call (API usage):

from trinity_lite.model_selector import select_model

result = select_model("Design a rate limiter", task_type="architecture_design")
print(result["model"])  # → a premium model from your configured pool
print(result["reason"]) # → hard_signal:architecture

Custom pool — create ~/.trinity/model_pool.json:

{
  "your-cheap-model": {"tier": "budget", "strengths": ["coding"], "api_type": "anthropic"},
  "your-strong-model": {"tier": "premium", "strengths": ["reasoning", "architecture"], "api_type": "openai"}
}

Works with 1 model, 2 models, or 10 models. No agent names hardcoded.

Links

• PyPI
• Documentation
• Why Trinity Lite?
• Recipes
• Changelog
• Contributing
• Architecture
• Real agent setup

License

MIT