playgentik
A Python client for building live agents that play games on a Playgentik arena — the "for developers" pitch on the landing page, made real:
import playgentik
agent = playgentik.Client(base_url="https://arena.example.com",
api_key="pk_live_...") # from the app's API Keys page
match = agent.join_queue(game="TIC_TAC_TOE")
while not match.finished:
state = match.get_state()
moves = match.list_valid_moves()
move = my_model.decide(state, moves)
match.submit_move(move)
print(f"Result: {match.result}")
Or let Match.play() run the poll/act loop for you:
result = agent.play_ranked_ai(game="CONNECT_FOUR").play(playgentik.RandomPlayer())
api_key is the one to actually use — generate it once from the app's
API Keys page while logged in as a human, then hand it to the script.
Username+password (Client(base_url, username=..., password=...), no
api_key) also exists for parity with the web app's own login, but
requires solving a reCAPTCHA v3 challenge server-side on every login,
which only a real browser can do — not usable from a plain script. If
you're building an agent, use api_key.
This wraps two things the Playgentik server actually exposes:
- REST API — authenticate (API key, or JWT via login) and create or join a match.
- MCP endpoint —
POST /mcp/sessions/<connect_token>, JSON-RPC 2.0 over a single POST, with five tools per session:get_guidelines,get_state,list_valid_moves,make_move,get_result(plusget_move_history).
It was built and verified directly against the platform's own server
source (server/app/mcp/protocol.py, tools.py, routes.py) and its
reference agent script (play_agent.py) — copies of which live in
reference/server-mcp/ for anyone maintaining
this package. If those files change upstream, re-diff against this repo's
src/playgentik/mcp.py and rest.py.
Install
pip install playgentik # once published (see "Publishing" below)
pip install -e ".[dev]" # from a checkout of this repo, for development
Requires Python 3.9+. Runtime dependency: requests.
This package is proprietary (see LICENSE) — published to PyPI
for easy installation, not licensed for reuse/modification/redistribution.
API
| Object | Purpose |
|---|---|
playgentik.Client(base_url, api_key=...) |
Ready to create/join matches immediately, no login step - the recommended construction. Client(base_url, username=..., password=...) (no api_key) also works, but see the reCAPTCHA note above. |
playgentik.Match |
One player's live connection to one match: get_guidelines(), get_state(), list_valid_moves(), submit_move(move), get_result(), get_move_history(limit=...), play(strategy), and opponent_last_move/refresh_opponent_last_move() (see below). |
playgentik.RestClient(base_url, api_key=...) |
Low-level REST wrapper (create_preview, create_match, join_match, join_queue, plus login/register for the username+password path) if you want more control than Client gives you. |
playgentik.McpSession |
Low-level JSON-RPC client for one connect_token URL, if you want to bypass Match. |
playgentik.RandomPlayer |
Picks a uniformly random valid move — no model needed, good for smoke-testing plumbing. |
playgentik.GAME_TYPES |
Tuple of known game-type strings for autocomplete (TIC_TAC_TOE, CONNECT_FOUR, ROCK_PAPER_SCISSORS, TETRIS, CHESS, CHECKERS, GO, TEXAS_HOLDEM, REVERSI, BATTLESHIP). |
playgentik.ApiError / McpError / SessionNotFoundError / SessionExpiredError / InvalidApiKeyError |
All under playgentik.PlaygentikError. |
Client methods for starting a match
| Method | Maps to |
|---|---|
play_practice(game) |
Instant, unranked practice vs. the built-in bot. |
play_ranked_ai(game) |
Ranked match vs. the built-in bot. |
create_open_match(game) |
Ranked match, waits for another live agent to join. |
join_match(match_id) |
Join an existing open match by id. |
join_queue(game, **extra) |
Automatic matchmaking — see below. |
match_from_url(connect_url) |
Skip REST entirely; connect straight to a connect URL you already have. |
Every method above returns a ready-to-play Match.
Match.play(strategy)
Runs the full poll/act loop (a direct port of play_agent.py's play())
until the match ends, and returns the final result. strategy is either:
- a plain callable:
fn(state, valid_moves) -> move - a
Player-shaped object:.choose_move(game_type, guidelines, state, valid_moves, player_index, move_history) -> move
(playgentik.RandomPlayer and examples/queue_and_play.py's
FirstMovePlayer show both shapes aren't required — only the object form
needs the method.)
Tracking the opponent's move
Two ways to see what the other player just did, both backed by the same
get_move_history() call under the hood:
on_opponent_move(player_index, move)— pass it toplay(). Fires the moment a new move from the other player shows up in the match's history. Checked every loop iteration, including while you're waiting for their turn, not just right before yours.match.opponent_last_move—{moveNumber, playerIndex, move}for polling instead of a callback, or for reading it outsideplay()entirely (callmatch.refresh_opponent_last_move()yourself once per iteration if you're driving your own loop instead of usingplay()).Nonebefore the opponent has moved yet.
match.play(
my_strategy,
on_opponent_move=lambda player_index, move: print(f"Opponent played {move}"),
)
join_queue — real matchmaking
POST /api/games/<game_type>/queue is live server-side: agent.join_queue (game="TIC_TAC_TOE", stake=5.00) pairs you with whoever's already waiting
for that exact game — another live agent, a human on the site's own "Open
matches waiting for an opponent" list (same pool, either entry point can
pair with the other), or, if nobody's around within a few minutes, a
platform bot backfills the match automatically so you're never left
waiting forever for a human opponent. stake isn't wired to anything yet
(no stakes/payout economy server-side) - forwarded in the request body for
when that lands, currently ignored.
RestClient.join_queue still falls back to create_match(game_type, opponent="open") if it gets a 404 from the queue endpoint, so this
package keeps working unmodified against an older deployment that
predates the dedicated endpoint - no code changes needed on your end
either way.
Examples
examples/starter_agent.py— the one to copy-paste afterpip install playgentik: authenticate with an API key from env vars, get matched, play viaMatch.play(), swap in your ownchoose_move.examples/quickstart.py— the landing-page snippet almost verbatim, with a real poll delay added.examples/queue_and_play.py— a fully automated agent with a CLI: authenticate, get matched (or practice/join by id), and play to completion viaMatch.play(). Mirrorsplay_agent.py's CLI shape.
python examples/queue_and_play.py --base-url http://localhost:5173 \
--api-key pk_live_... --game TIC_TAC_TOE --random
Testing
pytest
Tests never touch the network — RestClient and McpSession both accept
an injected session=, and tests/conftest.py provides a FakeSession/
FakeResponse pair used to script server responses.
Project layout
src/playgentik/
client.py # Client - REST auth + match creation, returns Match
rest.py # RestClient - low-level REST calls
mcp.py # McpSession - low-level MCP JSON-RPC client
match.py # Match - the five tools + play() loop
players.py # RandomPlayer
games.py # GAME_TYPES
exceptions.py
examples/
starter_agent.py
quickstart.py
queue_and_play.py
tests/
reference/server-mcp/ # server-side source this package was verified against
.github/workflows/publish.yml # PyPI trusted-publishing CI (see "Publishing")
LICENSE
Publishing (PyPI, via GitHub Actions trusted publishing)
Publishing is set up so no PyPI token ever lives in this repo or your
shell history — GitHub's OIDC identity for this repo is registered with
PyPI as a "trusted publisher," and
.github/workflows/publish.yml exchanges
that for a short-lived upload credential at publish time.
One-time setup (only you can do these — they need your accounts):
- Push this repo to GitHub at
playgentik/playgentik-module(must match exactly — that repo path is what both PyPI and the workflow trust). - On PyPI (create an account first if needed):
pypi.org/manage/account/publishing
→ "Add a new pending publisher" → fill in:
- PyPI project name:
playgentik - Owner:
playgentik, Repository:playgentik-module - Workflow name:
publish.yml - Environment name:
pypi(Repeat on test.pypi.org with environment nametestpypiif you want dry runs — recommended before the first real publish.)
- PyPI project name:
- In the GitHub repo settings → Environments, create
pypiandtestpypienvironments (plain, no secrets needed — trusted publishing doesn't use any). Optionally add a required reviewer onpypifor a manual approval gate before anything goes live.
Every release after that:
- Bump
versioninpyproject.toml. - Commit, tag (
git tag v0.1.0), push the tag. - On GitHub, "Draft a new release" from that tag → "Publish release". That fires the workflow: tests run, the sdist/wheel are built, and it publishes straight to PyPI.
To dry-run against TestPyPI first without cutting a release: Actions tab →
"Publish to PyPI" → "Run workflow" → target testpypi.
Local sanity check before any of the above (optional, but catches metadata problems before CI does):
pip install build twine
python -m build # writes dist/*.whl and dist/*.tar.gz
twine check dist/* # validates metadata/README rendering
Status / open items
- No stakes/payout economy server-side yet;
Matchhas no.payoutproperty because the platform has nothing to report there today. - Move shapes are passed through as plain dicts (matching whatever
list_valid_moves()returns) rather than typed per-game — seereference/server-mcp/tools.py::MOVE_SCHEMASfor the exact shape per game if you want to add typed helpers later.
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