b4ebab65c7
Two follow-up improvements on top of renderDocxFromFile so the docx
pipeline can handle real long-form deliverables instead of just
prose-only memos.
Image embedding (P1).
MarkdownDocxRenderer now recognizes single-line  markdown
and embeds the referenced file via POI's XWPFRun.addPicture():
- PNG / JPG / GIF / BMP read straight from disk
- SVG rasterized via Apache Batik (PNGTranscoder, target width 1400px)
before embedding — OOXML stores raster images, so any vector source
needs conversion. Batik runs in-JVM, no rsvg-convert / cairo on host.
- Pictures are pinned to roughly the printable page width (≈ 5.77 in
for A4 minus default 1800-twip margins) and given a 4:3 height
fallback. Mixing images inline with other paragraph text is not
supported by design — the markdown subset assumes one image per
block paragraph. Inline images would require splitting paragraphs
across runs with explicit positioning, well beyond what this
renderer covers.
- Failure modes (missing file, unsupported format, Batik blowing up)
emit an italicised "[image: alt — reason]" placeholder so the rest
of the document still renders; the agent can read its own log to
see why the picture didn't make it.
- Adds two transitive deps via pom: batik-transcoder + batik-codec at
1.18, ~10 MB combined. Worth it given the alternative is shelling
out to system tooling.
Multi-file render (P2-lite).
New tool renderDocxFromFiles(List<String> filePaths, filename, pageSize)
reads several markdown files in order and renders one combined docx.
Lets the agent split a 30-page proposal into cover.md / ch1.md /
ch2.md / appendix.md and produce a single deliverable in one tool
call. Each path goes through WorkspacePathGuard.validatePath; any
empty or unreadable file aborts with a typed error so the agent
fixes its file list before retrying. Files are joined with a blank
line — no separator markup is injected, headings carry over cleanly.
I deliberately did NOT build the heavier mutable-docx state
("appendDocxChapter / finalizeDocx") flavor of P2: the multi-file
form covers the same workflow with no per-conversation state to
clean up, and the agent can iterate by rewriting the chapter file
and re-running the tool. Stateful append can come later if a
streaming use case actually shows up.
renderDocx and renderDocxFromFile @Tool descriptions updated to point
the agent at renderDocxFromFile for >5 KB markdown and to advertise
the new image-embedding capability.
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| assets | ||
| docker/searxng | ||
| mateclaw-plugin-api | ||
| mateclaw-plugin-sample | ||
| mateclaw-server | ||
| mateclaw-ui | ||
| mateclaw-webchat | ||
| .dockerignore | ||
| .env.example | ||
| .gitignore | ||
| docker-compose.yml | ||
| LICENSE | ||
| README_zh.md | ||
| README.md | ||
Most AI tools die when their vendor has a bad day. Most forget you the moment the tab closes. Most give you a chatbox and call it a product.
MateClaw is the whole widget. One deployment. Reasoning, knowledge, memory, tools, channels — built together, not bolted on. And when your primary model goes down, the next one picks up mid-sentence.
Three things that make it different
1 · Your AI doesn't die when a model does
Primary key expired. Vendor returns 401. Network blip. Quota drained.
Other tools hand you a red error card. MateClaw routes to the next healthy provider — DashScope, OpenAI, Anthropic, Gemini, DeepSeek, Kimi, Ollama, LM Studio, MLX, 14+ in total — and the user sees the reply finish. A provider health tracker parks bad vendors in a cooldown window so they don't waste seconds on every turn.
You don't write a retry script. You drag providers into priority order in Settings → Models and watch the health dashboard fill with green dots as requests route around failures in real time.
2 · Knowledge that links itself
Upload a PDF, a batch of markdown, a scraped page — raw material in.
MateClaw's LLM Wiki digests it into structured pages, builds [[links]] between them, and remembers where every sentence came from. Click a citation, see the exact source chunk. Ask a question, the page you get is stitched from the right chunks — with references you can verify.
This is the difference between a warehouse and a library.
3 · One product, five surfaces
| Surface | What it is |
|---|---|
| Web Console | Full admin — agents, models, tools, skills, knowledge, security, cron |
| Desktop | Electron app with a bundled JRE 21. Double-click, run. No Java install |
| Webchat Widget | One <script> tag embed. Drop it on any site |
| IM Channels | DingTalk · Feishu · WeChat Work · Telegram · Discord · QQ |
| Plugin SDK | Java module for third-party capability packs |
Same brain. Same memory. Same tools. Different doors.
$0 · No tokens metered. No seats billed. Your server. Your data. Your keys.
What's in the box
Agent runtime
ReAct for iterative reasoning. Plan-and-Execute for complex multi-step work. Dynamic context pruning, smart truncation, stale-stream cleanup — the boring stuff that makes long conversations actually work.
Knowledge & memory
- LLM Wiki — raw materials digest into linked pages with citations
- Workspace memory —
AGENTS.md,SOUL.md,PROFILE.md,MEMORY.md, daily notes - Memory lifecycle — post-conversation extraction, scheduled consolidation, dreaming workflows
Tools, skills, MCP
Built-in tools for web search, files, memory, date/time. MCP over stdio / SSE / Streamable HTTP. SKILL.md packages from the ClawHub marketplace. A Tool Guard layer with RBAC, approval flows, and path protection — capability needs boundaries.
Multimodal creation
Text-to-speech · Speech-to-text · Image · Music · Video. First-class, not add-ons.
Enterprise-ready
RBAC + JWT. Full audit trail. Flyway-managed schema that auto-heals on upgrade. One JAR to ship. MySQL in production, H2 for dev — nothing to change in your code.
AI is becoming infrastructure
On March 2, 2026, Claude went dark for 4 hours across API, web, and mobile. Three weeks later, another 5 hours. Every company that bet their AI strategy on a single vendor spent those outages staring at red error cards.
This is the same shift databases went through around 2010 and cloud went through around 2018: the winning layer stops being tied to one supplier. 57% of companies now run AI agents in production. None of them want one vendor's bad day to become their bad day.
MateClaw is that layer — built the Spring Boot way.
Why MateClaw
| MateClaw | OpenClaw | Hermes Agent | Claude Code | Cursor | |
|---|---|---|---|---|---|
| Multi-vendor failover | Chain + health tracker + cooldown | Swap providers via config | Orchestration w/ retry | Anthropic only | One model |
| Knowledge digestion | LLM Wiki + page-level citations | Canvas + memory | Skills Hub + memory | — | Code index |
| Multi-user admin | RBAC + approval flow + audit | Config-file first | Single-user CLI | Enterprise tier | Teams plan |
| Surfaces | Web admin + Desktop + Widget + SDK + 6 IM | 25+ chat channels | 15+ channels (CLI-led) | 3 IM preview | IDE only |
| Stack | Java (Spring Boot) | TypeScript | Python | TypeScript | Electron/TS |
| License / Price | Apache 2.0 · Free | MIT · Free | MIT · Free | Proprietary · $20–200/mo | Proprietary · $0–200/mo |
OpenClaw and Hermes Agent are excellent personal AI platforms — pick either if you're running one user on one laptop, building your own agent from CLI, and treating everything as config files to hand-tune. Both have bigger communities than MateClaw today.
MateClaw is the version built for teams. RBAC per agent, per model, per tool. An approval flow that pauses risky actions for review. Full audit trail. A web admin dashboard where one operator manages 50 agents across 14 vendors. Spring Boot inside — drop-in for any Java shop already running production services.
Same "whole widget" philosophy. Different center of gravity.
Quick start
# Backend
cd mateclaw-server
mvn spring-boot:run # http://localhost:18088
# Frontend
cd mateclaw-ui
pnpm install && pnpm dev # http://localhost:5173
Login: admin / admin123
Docker
cp .env.example .env
docker compose up -d # http://localhost:18080
Desktop
Download from GitHub Releases. Bundles JRE 21. No Java install needed.
Architecture
Technical architecture
Project structure
mateclaw/
├── mateclaw-server/ Spring Boot 3.5 backend (Spring AI Alibaba, StateGraph runtime)
├── mateclaw-ui/ Vue 3 + TypeScript admin SPA (built into the server JAR)
├── mateclaw-webchat/ Embeddable chat widget (UMD / ES bundles)
├── mateclaw-plugin-api/ Java SDK for third-party capability plugins
├── mateclaw-plugin-sample/ Reference plugin implementation
├── docker-compose.yml
└── .env.example
Desktop binaries ship via GitHub Releases with a bundled JRE 21 — no Java install needed.
Tech stack
| Layer | Technology |
|---|---|
| Backend | Spring Boot 3.5 · Spring AI Alibaba 1.1 · MyBatis Plus · Flyway |
| Agent | StateGraph runtime · ReAct + Plan-Execute |
| Database | H2 (dev) · MySQL 8.0+ (prod) |
| Auth | Spring Security + JWT |
| Frontend | Vue 3 · TypeScript · Vite · Element Plus · TailwindCSS 4 |
| Desktop | Electron · electron-updater · JRE 21 (bundled) |
| Widget | Vite library mode · UMD + ES bundles |
Documentation
Full docs at claw.mate.vip/docs — setup, architecture, each subsystem, API reference.
Roadmap
Sharper multi-agent collaboration · Smarter model routing · Deeper multimodal understanding · Longer-lived memory · A richer ClawHub.
Contributing
git clone https://github.com/matevip/mateclaw.git
cd mateclaw
cd mateclaw-server && mvn clean compile
cd ../mateclaw-ui && pnpm install && pnpm dev
Why the name
Mate is companion. Claw is capability.
Something that stays with you — and grabs work and moves it.
License
Apache License 2.0. No asterisks.