| --- |
| license: apache-2.0 |
| pretty_name: Parley computer-use environment from a one-line request (REA) |
| tags: |
| - computer-use |
| - cua |
| - gui-agent |
| - rl-environment |
| - agentenv |
| - desktop |
| - linux |
| - rea |
| - language-learning |
| - vocabulary |
| - flashcards |
| - from-request |
| --- |
| |
| # Parley: a computer-use environment built from a one-line request |
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|  |
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| **The real Parley 23.08 vocabulary trainer running on an Ubuntu 24.04 desktop, as an [agentenv](https://pypi.org/project/agentenv-framework/) computer environment. An agent completes the task only through screenshots, mouse and keyboard, and a verifier grades the saved files with no model.** |
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| Claude Code built this environment from a one-line request in about 25 minutes, with only REA's build skill and MCP tools; [How it was made](#how-it-was-made) has the request and the steps. Parley opens a small vocabulary collection, `Documents/spanish.kvtml`, at launch: Spanish Basics, with one lesson of six English–Spanish word pairs, dog/perro, cat/gato, house/casa, water/agua, book/libro and apple/manzana. The agent has to add the pair library/biblioteca and save the collection. It sees only the screen and acts only with the mouse and keyboard. |
|
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| ## Watch |
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| - Agent runs (Claude Code with Claude Sonnet 5.5): [run 1](media/solve-1.mp4), [run 2](media/solve-2.mp4), [run 3](media/solve-3.mp4) |
| - Witness, which proves the task can be done through the computer: [witness.mp4](media/witness.mp4) |
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| Each video is the computer's own screen recording at 10 frames per second. |
|
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| ## The task |
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| > In Parley, add a new word pair to my Spanish Basics vocabulary collection in Documents/spanish.kvtml: the English word "library" with the Spanish "biblioteca". Then save the collection. |
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| `bundle/artifacts/add-library-word-pair-check/verify.py` reads the app's files through the computer's `data/get` view and passes only if every check holds: |
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| | Check | Kind | File | |
| | --- | --- | --- | |
| | `pair-spanish` | `text_equals` | `Documents/spanish.kvtml` | |
| | `pair-english` | `text_equals` | `Documents/spanish.kvtml` | |
| | `one-more-entry` | `member_count` | `Documents/spanish.kvtml` | |
| | `apple-kept` | `text_equals` | `Documents/spanish.kvtml` | |
| | `book-kept` | `text_equals` | `Documents/spanish.kvtml` | |
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| A `.kvtml` file is XML, with one `entry` per word pair and a `translation` per language (id 0 English, id 1 Spanish). Parley numbers entries in save order, so the checks find entries by their translations (`[>translation=library]`, a selector on a child element's text): the library entry's Spanish must read "biblioteca" and the biblioteca entry's English "library", the collection must hold seven entries (`member_count`), and book and apple must keep libro and manzana. REA's draft keyed the new entry by its id; Claude Code re-keyed it and the two guards by their translations, kept the entry count as REA wrote it, and discarded two suggestions on the lesson's list of entry references. |
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| ## Results |
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| 3 of 3 runs passed, about $0.57 per run. |
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| | Run | Outcome | Turns | Cost | Video | |
| | --- | --- | --- | --- | --- | |
| | 1 | passed | 9 | $0.99 | [video](media/solve-1.mp4) | |
| | 2 | passed | 9 | $0.50 | [video](media/solve-2.mp4) | |
| | 3 | passed | 9 | $0.23 | [video](media/solve-3.mp4) | |
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| ## Run it |
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| You need Docker. `agentenv-framework` builds the computer: Ubuntu 24.04 with the app installed from Ubuntu's archive (`parley` 23.08.5). |
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| ```bash |
| pip install agentenv-framework==0.9.1431 |
| hf download earakely-scale/ParleyFromRequestComputerEnv-REA --repo-type dataset --local-dir parley-request |
| agent-env run parley-request/bundle --task add-library-word-pair --agent claude-code --model <model> |
| ``` |
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| The agent needs a model provider configured for agentenv. `--task smoke` only boots the computer, with no agent. `bundle/README.md` describes the bundle in detail. |
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| ## How it was made |
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| Claude Code built this environment from a one-line request, given only [REA](https://github.com/morluto/rea)'s build skill and REA's MCP tools: |
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| > Build me a Parley environment where the agent adds a new word pair to a Spanish vocabulary collection and saves it. |
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| It asked only the skill's upfront questions, wrote no app-specific scripts, and finished in about 25 minutes. The steps were REA's usual ones: |
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| 1. **Capture.** REA launched Parley on a Linux desktop and recorded its package, windows and opened files while the edit was made. It also recorded the app's state folders before and after. |
| 2. **Export.** REA turned the capture into an agentenv `computer_app`. It installs the app, seeds the starting files, starts the app, and waits for its main window. It also serves typed views of the app's files for verifiers. |
| 3. **Task.** The task's checks were validated against the capture: they fail on the starting state and pass on the captured end state. |
| 4. **Witness.** In a fresh computer, the pair was added through computer actions alone, and the checks were read at four stages. They held on the first try: |
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| | Stage | Checks | |
| | --- | --- | |
| | `seed` | fail, as expected: there is no library pair and six entries; the kept checks pass | |
| | `after_edit` | pass | |
| | `near_miss` | fail, as expected: the Spanish was retyped as "bibloteca" | |
| | `after_reset` | fail, as expected: `data/reset` restored the seed | |
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| The agent runs came after the build: Claude Code with Claude Sonnet 5.5 (`--model anthropic/claude-sonnet-5-5 --no-emulate`, on an amd64 host) solved the task 3 times, each run recorded. |
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| ## What's inside |
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| ``` |
| bundle/ the agentenv bundle: envs/ (app module, seed files), tasks/, artifacts/ (verifier), evals/ |
| media/ witness and agent-run recordings (MP4) with short GIF previews |
| results.json the runs above, with each check's result |
| ``` |
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| ## Limits |
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| - The new pair is found by its words, which must be exact and in the right columns, "library" in English and "biblioteca" in Spanish. Whether it belongs to Lesson 1 is not checked. |
| - Only book and apple are checked as kept; the other four pairs are not. |
| - The computer installs whatever `parley` Ubuntu 24.04's archive holds at build time (23.08.5 for these runs); it is not pinned. |
| - Three runs is a small sample: the solve rate shows the task is solvable, not how hard it is. |
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| ## Licences |
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| The bundle code, the task and the synthetic seed files are Apache-2.0. Parley itself is not included: the computer installs it from Ubuntu's archive (`parley` 23.08.5) at build time, under its own licence (GPL-2.0-or-later). |
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