GitHub - guregodevo/memdoor: A lightweight coding and automation agent in one CLI binary. Run locally or over SSH, optimize token use, and automate CI and cron with durable workflows.

Five Qwen3.8-27B agents beat Fable 5 on LiveCodeBench Hard, as a Memdoor workflow. GVS5H's harness rebuilt with the authors' exact prompts and loop, on the first ten problems of their list, five passes each: a mean of 9.8 of 10, against 9.6 for the authors' own harness and 8.0 for Fable 5 in a single call, the gap the authors measured on these ten (across their full 100: 92.4 and 90.4). All 50 runs are published: every request, reply, program and grader output. The workflow is YAML and names no model: point it at yours, or at a local one with memdoor connect local (Ollama, llama.cpp, vLLM, LM Studio; how to run it on an open-weight model, no key). examples/workflows/team-solve · the result page · the HN thread

On this machine:

curl -fsSL https://memdoor.ai/install.sh | bash
export OPENROUTER_API_KEY=sk-or-...    # or ANTHROPIC_API_KEY, OPENAI_API_KEY, GEMINI_API_KEY, …
# or no key: memdoor connect local    # an open-weight model on this machine — https://memdoor.ai/docs/open-weight
cd your-project && memdoor             # the first run sets the machine up

Or on a box you can ssh to, with nothing installed there:

curl -fsSL https://memdoor.ai/install.sh | bash          # here, once
memdoor --gateway ssh://you@box                           # installs memdoor on the box, starts its gateway, opens the window on it
memdoor --gateway ssh://you@box connect openrouter        # your key, kept on the box; every turn there runs on it
memdoor --gateway ssh://you@box run "fix the failing test" # the same box from a script or CI

The runs live on the box: close the laptop, the work goes on, and your phone or the next terminal opens the same conversation there.

No account. Telemetry off unless you turn it on. macOS, Linux, Windows, one static binary, Apache 2.0.

In GitHub Actions

One step in a workflow, on your key, with nothing to add to your repo (Marketplace):

on: pull_request
jobs:
  review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v5
      - uses: guregodevo/memdoor-action@v1
        with:
          workflow: pr-review
          openrouter-api-key: ${{ secrets.OPEN_ROUTER_API_KEY }}

Nine are built in:

• pr-triage sorts a pull request for a maintainer: ready for review, needs work, or likely slop, from checks run in the job (the tests, claimed tests that are not there, functions nothing calls).
• pr-review reviews the pull request, verifies each finding by running the code, and fails the job only on a blocker.
• security-review does the same for security only: secrets, injection, path traversal, missing authorization.
• write-tests writes tests for what the change touches, and fails the job when one shows the change is wrong.
• pr-description writes the description: title, What, Why, How to test, Risk.
• explain-failure reads a failed run's logs and the diff: the cause, file:line, and the fix.
• issue-triage suggests labels, the kind, a summary and duplicates for a new issue.
• fix-tests finds the test command, fixes the code until the tests pass, and leaves the fix in the working tree.
• release-notes turns the commits since the last tag into Added / Changed / Fixed.

No key at all: local-model: Qwen3-8B-4bit instead of the key runs any of them on a model inside the runner (janis, one file, the model cached), in one model call. It is a free first pass, not a reviewer: on a free runner it caught an obvious and a subtle bug in about a minute each, and can file a correct change's own fix as a blocker.

Or workflow: <name> for your own, .memdoor/workflows/<name>/ in the repo. The job ends with the run's outcome (0 done, 2 failed, 1 stopped, 3 stopped at an approval gate) and its summary shows the review, the fix or the notes. Trigger on pull_request, never pull_request_target: GitHub gives a fork's pull request no secrets, so a stranger's code never runs beside your key.

On a ChatGPT Plus or Pro plan? Skip the key: memdoor connect chatgpt (or /connect chatgpt in the window) opens Sign in with ChatGPT, and your plan's allowance answers Memdoor's turns. A Claude subscription cannot be used this way (Anthropic's terms); Claude needs an API key.

No key at all? The first memdoor signs you in with OpenRouter in the browser, a free account included (or with ChatGPT), from a gateway on this machine, over ssh or on a box. An account with no credit runs on a free model picked from OpenRouter's list, and the connect says so: 50 requests a day, and the free hosts train on what they are sent, so a trial, not private code. Or no key at all: memdoor connect local finds a model running on this machine (janis, Ollama, llama.cpp, LM Studio), and nothing leaves it. janis is ours: one Go server that runs the open models on a Mac's GPU through MLX or on an NVIDIA box through CUDA, serves chat and the decision model with no key, and runs Qwen3-Coder-30B-A3B on a $0.71-an-hour Google L4; memdoor --gateway ssh://you@box connect local makes that box yours. And that box answers your laptop: memdoor peers add box http://box:18789 --dir ~/app names it, and @box:coder … in any conversation here runs the turn there, on the box's own model, and brings the answer back. Recorded on memdoor.ai: a laptop with no GPU asks, Qwen3.8-27B on an L4 through janis reads, patches and runs a Go program on the box, the window on the laptop shows it land.

Every run is a conversation on a gateway on your machine, whatever door it came in by: a terminal window, memdoor run from a script or CI, a cron schedule, your editor, or the page on your phone. The run a CI job started is the one you open with memdoor resume <id>, watch live, and answer. One gateway, every door (the walk through them, with exit codes).

memdoor run "fix the failing test"        # one turn, no window; the exit code is the receipt of its checks
memdoor workflow run release --wait       # a workflow from a shell or CI, exit code the run's outcome
memdoor resume --last                     # the same run, in a window

memdoor reads your files, writes patches, runs the build and fixes what it broke, in the directory you launch it from. Every model call goes from your machine to the provider you connected, on your account, at their list price.

Two more things set it apart.

A decision model in front of the chat model. Jev (TypeSafe) answers a typed question such as is this hit relevant to the task? with a calibrated probability in under half a second, and the harness acts on it: search, file and log reads return only what counts; only the tools the turn needs are sent; a turn that stops making progress ends instead of looping to a cap. Measured over 44 paired runs, decisions off and on: 22 of 22 passed each way; a question about the codebase went from 54,661 to 14,367 input tokens (−74%); an edit with a test and ten one-file fixes read the same files either way (within ±11%) (docs/features/DECIDE.md).

Workflows. Say the steps and the coder writes them as task files, one YAML per step, run as a graph by mario. Independent steps run in parallel; a step is done when its check passes, never because the model says so; a gate waits for your approval with the diff in front of you; a failed run resumes at the failed step. The files live in your repo, so a workflow is kept, shared and run again, by hand or on your local cron (docs/features/WORKFLOWS.md). Why workflows rather than skills: the write-up. Worked examples, each run before it was added: examples/workflows/. One of them, the step that grades a trading rule (examples/workflows/nightly-rule/steps/grade.yaml), as it runs nightly; no code in it:

type: agent
prompt: |
  Today's partition is {{.partition}}.
  Read rules/{{.partition}}.md and grade today's trading rule: assess it
  briefly (clarity, risk profile, plausibility) and write a JSON object
  {"score": <0-100 integer>, "verdict": "<one sentence>"} into
  grades/{{.partition}}.json (create grades/ if needed).
requires:
  - table_pattern: build
target:
  # run by the engine, not by the agent: the grade must exist and be newer than step 1's start stamp
  command: test "grades/{{.partition}}.json" -nt "rules/{{.partition}}.start" && test -s "grades/{{.partition}}.json"
timeout: 10m

A recorded session of the review loop (findings verified by running, a gate, the fixes applied) plays at memdoor.ai/workflows.

Install

curl -fsSL https://memdoor.ai/install.sh | bash
export OPENROUTER_API_KEY=sk-or-...    # or ANTHROPIC_API_KEY, OPENAI_API_KEY, GEMINI_API_KEY, … or /connect inside
cd your-project && memdoor             # the first run sets the machine up

macOS (Apple Silicon and Intel), Linux (amd64, arm64; static), Windows (irm https://memdoor.ai/install.ps1 | iex; the agent runs commands in Git Bash when it is installed, otherwise PowerShell). One binary, installed to ~/.local/bin, no sudo, no Docker, no Python. The installer (scripts/install.sh) verifies the SHA-256 of what it downloads.

From a clone, with Go 1.25:

git clone https://github.com/guregodevo/memdoor && cd memdoor
make build                             # ./memdoor, stamped with the tag it was built from
cp memdoor ~/.local/bin/               # or anywhere on your PATH

In the window

Esc interrupts a turn, ctrl+f opens the files panel, ctrl+g the turn as a graph, ctrl+o expands tool frames, Shift+Tab sets the reasoning effort, @path mentions a file. MEMDOOR_APPROVE=changes asks before every command and write, for machines whose policy requires it.

In your editor

memdoor acp serves the same coder over the Agent Client Protocol: Zed and JetBrains host it natively; VS Code through an ACP client extension (for example "ACP Client"), with one setting:

"acp.agents": { "Memdoor": { "command": "memdoor", "args": ["acp"] } }

Each editor session is a conversation on your local gateway; a prompt is a turn in the editor's workspace folder, and the turn's text, tool calls and approvals show as the editor's own. The steps: memdoor.ai/docs/editor.

From a script

memdoor run "make the retry wait configurable and add a test"     # a headless turn, no window
git diff | memdoor run "review this diff; one line per finding"    # stdin is the input
memdoor run --json "fix the failing test" | jq 'select(.event=="done")'
memdoor workflow run release --wait                                 # progress on stderr, results on stdout
memdoor remote --last                                               # the link /remote gives, on stdout; the gateway holds the run for your phone
memdoor remote --view --last                                        # the watch-only link: an audience, or a pair of eyes

The exit code is the turn's receipt: 0 checked, 2 unverified or a failing check, 3 a question nobody answered (--yes answers it), 1 an error, 130 interrupted. The gateway starts if none is running; every run is a conversation memdoor resume opens. Headless, then attached.

A workflow in GitHub Actions is one step: In GitHub Actions above.

Where your data goes

Files, sessions and commands stay on your machine. What leaves is what the model reads, sent to the provider you connected; on OpenRouter every request carries data_collection: deny and hosts that train on paid inputs are kept out of the ladders. The one exception is a :free model, one you pin or the one an OpenRouter account with no credit answers on: its hosts train on what they are sent, the request says so (allow), and Memdoor says so at the pin or when the key is connected; use one for a trial, not on private code. On a vendor key or a company gateway nothing else is contacted: docs/SECURITY.md lists every host the binary can name and a test fails the build on a new one.

Documentation

• Getting started
• Workflows · Scheduled checks · MCP servers
• Providers · Security · Decisions
• CLI reference · Architecture · Skills · ADRs
• The served docs: memdoor.ai/docs

Repository layout

cmd/cli/       the CLI (cobra): tui, model, workflow, cron, account, …
gateway/       the local gateway: agent runtime, tools, providers, workflows
pkg/           the domain: decision, metering, plan, workflow (the mario task factory), …
web/           the memdoor.ai site and the served docs (React, Vite)
scripts/       install.sh, install.ps1, deploy.sh
docs/          features, reference, ADRs
examples/      workflows built by the coder and run, to copy into a project

License

Apache License 2.0 — LICENSE, NOTICE. The public repository is github.com/guregodevo/memdoor; issues there.