Overview

Qyclaw is a platformized intelligent agent task platform (智能体任务平台) targeted at multi-tenant scenarios. It goes beyond simple chat interfaces or black-box agent containers by providing a complete layered "agent operating system" that ensures agents are runnable, isolatable, and auditable.

The architecture is divided into three layers:

Upper layer: Multi-user, multi-session, multi-skill, and multi-connector platform capabilities.
Middle layer: Runtime orchestration including queues, scheduling, memory, permissions, and auditing.
Lower layer: Containerized tool execution sandbox (applied only to high-risk operations).

Key Features

Multi-Tenancy & User System: Built-in user login, session management, permission controls, and admin panel. Supports conversation-level workspaces, skill publishing/review, and user isolation.
Layered Memory Model:
- Long-term user memory (persistent across sessions)
- Session-private memory
- Memory candidates and full audit logs for traceability
- Integration with Hindsight for async retain/recall and low-frequency reflection.
Tools, Skills & Connectors:
- System tools (terminal, web_search, fetch_url, etc.)
- Skills: Encapsulated behaviors/workflows with draft/publish/group/share/copy features. Scopes include global, group, user, or conversation.
- MCP Connectors: User-private external integrations (GitHub, Postgres, Notion, custom HTTP) with session-level binding and isolation.
Secure Execution: High-risk operations (shell commands, file I/O, skill scripts, Office/PDF handling) run in isolated Docker containers while core state management remains lightweight and recoverable.
Multi-Backend Support: Dynamically switch between backends like deepagents and Claude per session. Platform assets (sessions, memory, skills, audit logs) stay independent for easy gray-scale updates or fallbacks.
Queue & Scheduling: Per-session serial queues, global concurrency control, task retries, timing tasks, long-running tasks, and human-in-the-loop approval workflows.

Technical Stack

Backend: Python (primary)
Frontend: Vue.js
Deployment: Docker Compose with PostgreSQL and sandbox containers
Configuration: YAML-based (config.yaml / config-docker.yaml)

Deployment Options

Docker (Recommended for Production)

git clone https://github.com/760485464/qyclaw.git
cd qyclaw
# Edit config-docker.yaml for backend routing, API keys, etc.
sh docker_certs.sh
docker compose -f docker-compose-docker.yaml up -d --build

Access frontend at http://localhost:8080/frontend/ and backend API docs at http://localhost:8000/docs.

Local Development

Separate backend (FastAPI) and frontend (Vue) startup with PostgreSQL + sandbox containers via Docker Compose.

Use Cases

Building internal AI workbenches for teams
Secure multi-user agent automation platforms
Auditable enterprise AI task orchestration
Environments requiring strong isolation for tool execution while maintaining persistent memory and skill sharing

Qyclaw emphasizes security boundaries, auditability, and operational reliability, making it suitable for production-grade multi-tenant AI agent deployments.

qyclaw