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Minimal autonomous agent harness with LangGraph Deep Agents

Project description

open-strix

PyPI version

An AI agent that talks to you over Discord.

  • Memory blocks + files, all committed to Github
  • Web fetch/search, files, bash/powershell, subagents
  • Skills

Install uv

Install uv first:

# macOS / Linux
curl -LsSf https://astral.sh/uv/install.sh | sh
# Windows (PowerShell)
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Official install docs (alternate methods like Homebrew, pipx, winget):

Quick start (recommended)

uvx open-strix setup --home my-agent --github
cd my-agent
uv run open-strix

Run setup explicitly first (uvx open-strix setup ...), then run uv run open-strix from inside that home.

open-strix setup bootstraps the target directory with:

  • state/
  • skills/
  • blocks/
  • logs/events.jsonl
  • logs/journal.jsonl
  • scheduler.yaml
  • config.yaml
  • checkpoint.md
  • pyproject.toml
  • uv.lock
  • .env (template)

It also runs:

  • uv init --bare --python 3.11 --vcs none --no-workspace
  • uv add open-strix

It also prints a CLI walkthrough with links and step-by-step setup for:

  • MiniMax M2.5
  • Kimi/Moonshot
  • Discord bot creation + permissions
  • config.yaml values

Then uv run open-strix connects to Discord if a token is present (by default DISCORD_TOKEN). Otherwise it runs in local stdin mode.

Installed mode (optional)

If you prefer a local project install instead of uvx:

uv init --python 3.11
uv add open-strix
uv run open-strix setup --home .
uv run open-strix

Install and auth gh (GitHub CLI)

If you want open-strix setup --github, install and log into gh first.

Install:

# macOS (Homebrew)
brew install gh

# Ubuntu / Debian
sudo apt install gh
# Windows (winget)
winget install --id GitHub.cli

Authenticate:

gh auth login
gh auth status

Official docs:

Create a GitHub repo and set remote

open-strix auto-syncs with git after each turn, so set up a repo + remote early.

Recommended:

uvx open-strix setup --home my-agent --github

Keep this private, since agent memory and logs can contain sensitive context.

Manual fallback with GitHub CLI (gh):

cd my-agent
gh auth login
gh repo create <repo-name> --private --source=. --remote=origin
git add .
git commit -m "Initial commit"
git push -u origin HEAD

Manual fallback with GitHub web UI:

  1. Create a new private empty repo on GitHub (no README, no .gitignore, no license).
  2. In your project directory:
git init
git add .
git commit -m "Initial commit"
git branch -M main
git remote add origin git@github.com:<your-user>/<repo-name>.git
git push -u origin main

If you prefer HTTPS:

git remote add origin https://github.com/<your-user>/<repo-name>.git

Check remote config:

git remote -v

Environment setup

Start from the example env file:

cp .env.example .env

Default model setup in this project expects an Anthropic-compatible endpoint:

  • ANTHROPIC_API_KEY
  • ANTHROPIC_BASE_URL

Discord runtime uses:

  • DISCORD_TOKEN

Optional:

  • DISCORD_TEST_CHANNEL_ID
  • OPEN_STRIX_TEST_MODEL

Models

Default: MiniMax M2.5

This project defaults to:

  • model: MiniMax-M2.5 in config.yaml
  • provider prefix anthropic: internally (so the runtime uses anthropic:MiniMax-M2.5)

Use MiniMax's Anthropic-compatible endpoint in your .env:

  • ANTHROPIC_BASE_URL=https://api.minimax.io/anthropic

MiniMax docs:

Alternative: Kimi K2.5

If you want Kimi instead of MiniMax:

  1. Point Anthropic-compatible env vars at Moonshot:
    • ANTHROPIC_BASE_URL=https://api.moonshot.ai/anthropic
  2. Set model in config.yaml to the current Kimi model ID you want.

Moonshot docs:

Note: the Moonshot update posted on November 8, 2025 references kimi-k2-thinking and kimi-k2-thinking-turbo. If you refer to these as "K2.5", use the exact current model IDs from Moonshot docs/console.

Model config behavior

config.yaml key:

  • model

Behavior:

  • If model has no : (example MiniMax-M2.5), open-strix treats it as Anthropic-provider and uses anthropic:<model>.
  • If model already includes provider:model (example openai:gpt-4o-mini), it is passed through unchanged.

Discord setup

Use Discord's Developer Portal web UI:

  1. General Information: set app/bot name and any basic metadata.
  2. Installation: set Install Link to None, then save.
  3. OAuth2 -> URL Generator:
    • check bot
    • select practical bot permissions (focus on messaging/reactions/history/attachments):
      • View Channels
      • Send Messages
      • Send Messages in Threads
      • Read Message History
      • Add Reactions
      • Attach Files
  4. Bot tab:
    • disable Public Bot
    • enable Message Content Intent
    • (later) set avatar/profile polish
  5. Bot tab -> Reset Token:
    • copy token immediately (it may not be shown again)
    • set .env: DISCORD_TOKEN=<your_discord_bot_token>
  6. Use the generated OAuth2 bot invite URL to add the bot to your server.

Reference docs for the same flow:

Where this is configured in open-strix:

  • Token env var name: config.yaml -> discord_token_env (default DISCORD_TOKEN)
  • Actual token value: your .env
  • Bot allowlist behavior: config.yaml -> always_respond_bot_ids

config.yaml tour

Default:

model: MiniMax-M2.5
journal_entries_in_prompt: 90
discord_messages_in_prompt: 10
discord_token_env: DISCORD_TOKEN
always_respond_bot_ids: []

Key meanings:

  • model: model name (or provider:model)
  • journal_entries_in_prompt: how many journal entries go into each prompt
  • discord_messages_in_prompt: how many recent Discord messages go into each prompt
  • discord_token_env: env var name to read Discord token from
  • always_respond_bot_ids: bot author IDs the agent is allowed to respond to

Related files:

  • scheduler.yaml: cron/time-of-day jobs
  • blocks/*.yaml: memory blocks surfaced in prompt context
  • checkpoint.md: returned by journal tool after a journal write
  • scripts/prediction_review_log.py: helper for structured prediction-accuracy reviews
  • skills/: user-editable local skills
  • /.open_strix_builtin_skills/skill-creator/SKILL.md: packaged built-in skill source mounted as read-only
  • /.open_strix_builtin_skills/prediction-review/SKILL.md: packaged built-in skill for prediction calibration

Runtime behavior note:

  • Git sync (git add -A -> commit -> push) runs automatically after each processed turn.
  • New agent homes are seeded with a twice-daily UTC scheduler job (09:00 and 21:00) for prediction-review calibration.

Personality bootstrap

Creating an agent is less about code, and a whole lot more about the time you spend talking to it. Lily Luo has a great post on forming agent personalities.

You should plan on spending time:

  • Communication patterns — correct the agent to know when and how often it should use the send_message and react tools. Agents often initially find it surprising that their final message is ignored, so they need to use their tools instead.
  • Talk about things you're interested in, see what the agent becomes interested in

Tests

uv run pytest -q

Discord coverage includes:

  • unit tests with mocked boundaries in tests/test_discord.py
  • live integration tests against real Discord in tests/test_discord_live.py

Live test env vars:

  • DISCORD_TOKEN (required for live connect test)
  • DISCORD_TEST_CHANNEL_ID (optional; enables live send-message test)

Safety baseline

  • Agent file writes/edits are blocked outside state/.
  • Reads still use repository scope.
  • This is intentionally simple and should not be treated as production-ready.

License

MIT. See LICENSE.

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