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🦞 Pynchy (pronounced "Pinchy") — A personal AI assistant like OpenClaw done right. Security first, modular, written in Python.
Why Pynchy?¶
Everyone is writing their own AI assistant. Why write another one? The biggest reason is that I wanted something written in Python, because that's what I'm most comfortable with.
Comparison to Related Projects¶
- ZeroClaw looks great actually, but I don't know how to write in Rust.
- Happy looks great, but ultimately is a remote terminal to Claude Code. I want to add my own security features. Also, I am not fluent in TypeScript.
- NanoClaw is a too minimalist.
- OpenClaw is a massive pile of overcooked spaghetti code. Ain't no way I'm running that security nightmare on my machine.
- pi mono is a less crazy project, which actually OpenClaw built on top of. It doesn't have the security features that I want.
Features¶
- Agents run in containers, providing process, filesystem, and network isolation.
- Built-in plugins ship with the monorepo; third-party plugins are discoverable via Python entry points.
- Uses LiteLLM as the LLM gateway, providing a bunch of features out of the box:
- Automatic load balancing across APIs, to soak up your various allowances from different providers.
- Access to 100+ LLM providers
- Cost tracking and budget management.
- Rate limiting
- MCP gateway — centralized management of external MCP tool servers with per-workspace access control, on-demand Docker lifecycle, and config-driven setup.
- (see the LiteLLM docs for more details)
- Customizable; eight types of plugins are supported — agent cores, skills, channels, service handlers, container runtimes, workspaces, observers, and tunnels.
- Persistent memory with BM25-ranked full-text search — agents save and recall facts across sessions.
- Reoccurring tasks can be scheduled to run at a specific time or interval.
- Policy groups to prevent lethal trifecta prompt injection attacks.
Integrations¶
Built-in plugins provide integrations with external services. All integrations are pluggable — see plugin authoring to add your own.
| Integration | What it does |
|---|---|
| Messaging channel via linked device | |
| Slack | Messaging channel with browser-based token extraction |
| X (Twitter) | Post, like, reply, retweet, and quote via browser automation |
| CalDAV | Calendar access (Nextcloud, etc.) — list, create, delete events |
| Jupyter Notebooks | Per-workspace notebook server with MCP tools |
| Google Drive | File access via OAuth2 MCP server |
Getting Started¶
See the installation guide to get started.
Documentation¶
Full documentation at pynchy.ricardodecal.com.
| Section | What it covers |
|---|---|
| Usage | Day-to-day operation, groups, scheduled tasks |
| Plugin authoring | Writing plugins: channels, skills, MCP servers |
| Architecture & Design | Container isolation, message routing, IPC, security |
| Contributing | How to contribute — plugins, fixes, docs, and more |
FAQ¶
What messaging channels are supported? WhatsApp and Slack have first-party plugins. Channels are pluggable — write a plugin to add new ones.
Why Apple Container instead of Docker? On macOS, Apple Container is lightweight and optimized for Apple silicon. Docker works too and is used as a fallback. On Linux, Docker is the only option.
Is this secure? Agents run in containers, not behind application-level permission checks. They can only access explicitly mounted directories. See the security model for details.
How do I debug issues? Ask Pynchy. "Why isn't the scheduler running?" "What's in the recent logs?" That's the AI-native approach.
Credits¶
Huge thanks to NanoClaw. This project started as a Python port of that project.
License¶
MIT