relaypipe 0.1.1

Chain any AI coding CLIs (Claude, Codex, Gemini, Aider, ...) into an approve-once pipeline.

agent-relay

Chain any AI coding CLIs into an approve-once pipeline.

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You give a target. One agent plans, another reviews the plan, another implements it — automatically, with no copy-pasting between them. The only time it stops for you is the final gate, right before anything is committed.

  target ─▶ ┌──────┐   PLAN.md   ┌────────┐  review.md  ┌──────────┐   ⏸ you
            │ plan │ ──────────▶ │ review │ ──────────▶ │ implement│ ─────▶ commit?
            └──────┘             └────────┘             └──────────┘
            (claude)              (codex)                 (codex)

Each arrow is an automatic hand-off; files on disk are the medium. Swap any box for a different agent by changing one line of YAML.

Why

Running a multi-agent workflow by hand means copy-pasting one tool's output into the next, over and over. Add a third agent and the transfers multiply. agent-relay makes the hand-offs automatic and gates only the decision that actually needs a human.

Supported agents

Built-in adapters: claude (Claude Code), codex (OpenAI Codex), gemini (Gemini CLI), aider, and generic — wrap any CLI from YAML with no code:

- name: implement
  agent: generic
  command: ["mytool", "run", "--prompt", "{prompt}", "--out", "{output}"]

Placeholders: {prompt} {output} {workspace} {model} {role}.

Install

pipx install relaypipe         # once published
# or, from source:
pip install -e ".[dev]"

Quick start: idea → project (zero config)

The fastest path. Give an idea and a folder; agent-relay plans, loops the plan through review until it's solid, shows you the final plan, and — once you approve — builds the project into that folder. No YAML, no prompt files:

relaypipe new "a todo-list CLI in Rust with JSON persistence" -d ./todo-cli

What happens:

1. plan — an agent drafts a build plan
2. review loop — a reviewer critiques it; the plan is revised and re-reviewed until VERDICT: APPROVED or the loop cap (default 3) is hit
3. ⏸ you approve — the final plan is printed; answer y to build
4. build — the project is created in ./todo-cli

Useful options:

relaypipe new "..." -d ./app --agent codex          # use Codex to plan+review+build
relaypipe new "..." -d ./app --agent claude --build-agent codex   # mix agents
relaypipe new "..." -d ./app --max-iterations 5     # allow more review rounds
relaypipe new "..." -d ./app --dry-run              # preview the commands
relaypipe new "..." -d ./app --yes                  # CI: skip the approval prompt

For full control over steps, agents, and gates, write a pipeline (below) and use relaypipe run.

Use

# list available agents
relaypipe agents

# run the example pipeline
relaypipe run "Add a --json flag to the CLI" -p examples/pipeline.yaml

# preview commands without running anything
relaypipe run "..." -p examples/pipeline.yaml --dry-run

# resume an interrupted run
relaypipe run --resume -p examples/pipeline.yaml

# CI mode: auto-approve every gate
relaypipe run "..." -p pipeline.yaml --yes

# override the loop cap for this run (beats max_iterations in the YAML)
relaypipe run "..." -p pipeline.yaml --max-iterations 5

Example: agent-relay improving itself

The repo ships a self-hosted pipeline that points agent-relay at its own codebase. It reads this project's PLAN.md, has one agent review a roadmap item (agent-relay init), and has another implement it — pausing once for your approval before you commit:

# preview the chain (reads PLAN.md, no API calls)
relaypipe run -p examples/self-hosted/pipeline.yaml --dry-run

# run it for real
relaypipe run -p examples/self-hosted/pipeline.yaml

The goal is baked into the prompts, so no positional target is needed — the plan file is the input. This is the best worked example of the "review an existing plan → implement" flow: see examples/self-hosted/.

Pipeline config

name: plan-review-implement
workspace: .
steps:
  - name: plan
    agent: claude
    role: plan                  # plan | review | implement | generic
    prompt_file: prompts/plan.md
    output: PLAN.md             # what this agent writes

  - name: review-plan
    agent: codex
    role: review
    inputs: [PLAN.md]           # files fed into the prompt ({inputs})
    output: .agent-relay/plan-review.md

  - name: implement
    agent: codex
    role: implement
    inputs: [PLAN.md, .agent-relay/plan-review.md]
    approve_after: true         # the single human gate

Review loops (iterate up to a cap)

One plan→review pass is rarely enough. A loops: block repeats a contiguous group of steps until a reviewer approves — or a hard iteration cap is hit, so it never spins forever:

steps:
  - name: plan
    agent: claude
    role: plan
    inputs: [PLAN.md, .agent-relay/review.md]   # sees last round's feedback
    output: PLAN.md
  - name: review
    agent: codex
    role: review
    inputs: [PLAN.md]
    output: .agent-relay/review.md
  - name: implement
    agent: codex
    role: implement
    inputs: [PLAN.md]
    approve_after: true

loops:
  - name: plan-review
    steps: [plan, review]            # must be contiguous & in pipeline order
    until_step: review               # whose output holds the verdict
    max_iterations: 3                # the cap (override per-run with --max-iterations)
    approved_marker: "VERDICT: APPROVED"

How it stops:

• The review prompt is told to end its output with VERDICT: APPROVED or VERDICT: NEEDS_WORK. The runner reads the verdict step's output: approved → exit the loop early; otherwise → re-run the group.
• The feedback path is the normal file hand-off: list the review's output as an input of the first step, so the next pass sees the last review.
• If the cap is reached without approval, the run logs 🛑 hit max_iterations and proceeds to the next step anyway (the human gate still backstops it).

The self-hosted example uses exactly this — see examples/self-hosted/pipeline.yaml.

Adding a new agent

Two ways:

1. No code — use the generic adapter with a command: template (above).

2. An adapter class — ~20 lines:

   from agent_relay.adapters.base import Agent, AgentContext, register
   
   @register
   class MyToolAgent(Agent):
       name = "mytool"
       binary = "mytool"
   
       def build_command(self, ctx: AgentContext) -> list[str]:
           cmd = [self.binary, "--prompt", ctx.prompt]
           if ctx.output_file:
               cmd += ["--out", str(ctx.output_file)]
           return cmd
   

   Ship it as a separate package by registering the agent_relay.adapters entry point — no fork needed.

Development

pip install -e ".[dev]"
pytest          # tests use mocked agents — no API keys needed
ruff check .

License

MIT — see LICENSE.