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Quickstart

PurrCat is under rapid development. Deploying from source is recommended so you can follow updates closely and get the latest features & fixes as soon as they land.

Windows users can also grab the installer from the latest Release for a quick start. macOS/Linux installers are not yet tested due to limited manpower, so please build from source on those platforms.

Requirements

What you need depends on how you deploy:

• Installer: only Docker is required (the sandboxed Bash tool and file isolation rely on it) — no extra tools needed

• From source: Docker + uv (required — it manages Python versions and dependencies) + Node.js 18+; Git is used to fetch the source code (or download the ZIP)

• Alternatively, run purrcat setup to initialize the environment (uv, Docker, embedding model, Playwright) in one step

Option 1: Electron desktop (recommended)

git clone https://github.com/PurrPod/purrcat.git
cd purrcat
uv sync                    # Python dependencies
npm install                # Root dependencies (Electron, etc.)
npm install --prefix ui    # Frontend dependencies
npm run dev                # Starts backend + frontend + Electron

Option 2: Web UI (lightweight)

uv sync
npm install --prefix ui
npm run build:ui                             # Build frontend assets
uv run python main.py --api --headless       # Open http://localhost:8000 in a browser

Note: several features (local file access, terminal, etc.) depend on the Electron runtime and may misbehave in a plain browser. The desktop client is recommended for full functionality.

Architecture

01 Hybrid Memory and Knowledge Graph

• Short-term working memory: in-memory memo variables retain condensed summaries of the last 10 interactions across session switches.

• Core memory: MEMORY.md stores user profiles and work experience, injected into the system prompt at initialization.

• Long-term structured memory (PurrMemo): episodic engine (SQLite + FTS5) and semantic engine (ChromaDB + NetworkX), with dynamic relation strengthening/weakening and HTML graph export.

• Hybrid retrieval: RRF fusion of BM25 and vector search, executed concurrently through a global thread pool.

• Asynchronous digestion: new cognitions are buffered in pending and converted to triples by a background daemon; a decay mechanism cleans up long-unused memories.

02 Harness DAG Workflow Engine

• Multi-agent concurrency under a single persona, avoiding natural-language inter-agent chatter and its token cost.

• Per-task tool binding and stage-specific prompt injection.

• Polymorphic node matrix: LLM-vision image generation, conditional routing (if/else, switch), human intervention, and more.

• Safe rollback: inject commands at any node; downstream states are cleared for precise breakpoint recovery.

• Workflows load from a single JSON file and hot-update at runtime.

03 Secure Toolchain

• Sandboxed Bash: commands run in isolated Docker containers, with optional directory mounts for external access.

• FileSystem suite: read / edit / write / search / glob, with PDF/DOCX/XLSX converted to Markdown via MarkItDown.

• Boundary control: physical black/white lists; imports are checked against a 30MB limit and path traversal, exports trigger Git snapshots.

• Extension tools: CallMCP, hybrid Search, Fetch, Memo, Cron, Task, ComputerUse, BrainStorm, KernelUpgrade; external MCP servers supported.

04 Agent Hub and Session Management

• Git-style session branching: new, branch, and switch sessions; trial-and-error without losing the main trunk.

• Automatic repair: malformed tool calls are intercepted and rolled back to a safe state.

• Context truncation: when token limits are exceeded, older history is replaced by memo summaries at safe cut points.

• Persona and vitality: SOUL.md defines values; a heartbeat-driven mechanism lets the agent patrol, clean up, and report during idle time.

• Declarative execution paradigm (PARADIGM): PARADIGM.yaml defines triggers, lifecycle hooks, tool-use checks, and loop exit conditions in near-natural-language rules — rewrite the Agent loop by editing config.

• A dedicated vision consultant isolates image processing from the main session to improve signal-to-noise ratio.

05 Proactive Perception and Event Gateway

• Sensors run as independent subprocesses with PEP 723 inline dependencies managed by uv; a single sensor crash does not affect the main process.

• Stdio JSON-RPC communication over pipes; no network ports involved.

• Built-in sensors: System (heartbeat/polling), Feishu (WebSocket), RSS, and Audio (Whisper + pyttsx3).

06 Model Scheduling and Concurrency

• API key load balancing: idle-first key allocation under a thread lock, preventing single-key rate limits.

• Semaphore queuing and jittered exponential backoff (up to 8 retries) for high-concurrency availability.

07 KV Cache and Token Economics

• Stable KV cache hit rates across session switches, via strong key-to-session binding in the API key manager.

• DAG execution removes inter-agent token redundancy; task executors produce summaries for background digestion instead of full-history reads.

08 Configuration-Driven Extension

• Zero-code MCP integration: paste standard JSON into mcp_config.json; the tool tree hot-updates after handshake.

• purrcat install skill <url> downloads community skills and loads them into the retrieval tree.

• Visual DAG editing in the UI, with one-click JSON import/export.

• Sensors toggle on/off in the UI; missing sensor scripts are fetched automatically at startup.

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Acknowledgments

• zhenghuanle tested the installation flow from scratch.

• Gaeulczy tested the one-click setup and run scripts.

• JohnWJ-co helped test the Mac build and troubleshoot error causes.

• Sponsored by the Smart Agent Development Competition hosted by the Sun Yat-sen University OpenHarmony Technology Club.

License

Released under the MIT license. You are free to use, modify, and distribute this project, including for commercial purposes.