Reading
This is a production-grade OpenClaw plugin that enables Moss AI agents to run in Open WebUI, providing secure multi-agent routing, strict access control, and channel-based workflows suitable for editing, development, and operation scenarios.
Section 01
This production-grade OpenClaw plugin integrates Moss AI agents into OpenWebUI, enabling safe multi-agent routing, strict access control, and channel-based workflows. It supports editing, development, and operations scenarios, with OpenAI compatibility and self-hosting capabilities.
Section 02
OpenWebUI is a popular self-hosted LLM web interface with a ChatGPT-like user experience, running on private servers. OpenClaw is an emerging AI agent platform for creating and managing Moss agents (each with unique identities and capabilities). This plugin connects the two, allowing seamless use of Moss agents in OpenWebUI.
Section 03
The plugin uses an OpenAI-compatible API layer. Workflow:
Model discovery: Dynamic from ~/.openclaw/workspace/moss-models/ — each folder is a model (folder name as model.id, IDENTITY.md required; loads .md/.txt files as context).
Section 04
Prompt Construction: Loads model folder → uses IDENTITY.md as system message → adds context files → combines with chat history. Optional Config: config.json in model folders sets agentId (default main) or maxContextBytes. Installation: Local dev (pnpm install/build, env vars like OPENCLAW_API_URL; Docker (compose up -d --build); embed in OpenClaw. OpenWebUI Integration: Add OpenAI connection (API URL to provider port, any API key) → Moss agents appear as models.
Section 05
Section 06
Security mechanisms:
Section 07
Limitations: No streaming chat.completions; uses last user message only; simple context file concatenation; no RAG/vector search; no tools/attachments. Future: Add streaming support; better long-dialogue context management; vector DB integration; tool calling.
Section 08
openclaw-openwebui-moss is a well-designed bridge between OpenWebUI and OpenClaw ecosystems. Its file-driven model discovery, OpenAI-compatible layer, and flexible config make it a powerful AI agent management platform for teams using both tools.
Keep going with more reads from the same topic.
Splinter is a minimalist, high-performance key-value (KV) and vector storage system enabling zero-latency inter-process communication via shared memory and atomic operations. With only 766 lines of core code, it supports millions of operations per second and 768-dimensional vector storage, offering a new architectural approach for local LLM inference and data-intensive applications.
Folkering OS is the world's first AI-native bare-metal operating system, entirely written in Rust no_std without relying on Linux, POSIX, or libc. It can generate commands from scratch, compile them into WASM, and run them in 10 seconds, achieving true self-evolution.
This project explores how to use Large Language Models (LLMs) to detect logical vulnerabilities in Ethereum smart contracts, especially business logic flaws that are difficult for traditional static analysis tools to capture, such as economic manipulation and execution order errors.
Cross-generational hardware testing based on NVIDIA A10G and H100 GPUs, comparing and analyzing the throughput, latency, and concurrent scalability of three mainstream LLM inference frameworks. SGLang achieves a 3.4x performance advantage over vLLM on H100, while Ollama faces architectural bottlenecks in high-concurrency scenarios.