OpenClaw-py: A Python Port with Improved Security and Scope

 🚀 Excited to announce Claw Code Agent — our open-source Python reimplementation of the Claude Code agent architecture!  💡 Inspired by the reverse-engineering work shared here:  🔗 https://lnkd.in/dzrrj6da  We took that foundation and built a fully working Python agent from it.  🐍 Pure Python. No Rust. No TypeScript. Just Python.  🔧 Full Agent Capabilities:   ✅ Agentic coding loop with tool calling   ✅ File read/write/edit, grep, glob, shell   ✅ Slash commands & context engine   ✅ Session persistence & resume   ✅ Tiered permission system  ⚡ Works with any OpenAI-compatible API:   🟢 vLLM   🟢 Ollama   🟢 LiteLLM Proxy  🐉 run a full coding agent locally, for free.  📬 We're actively developing this — if you have feature requests, ideas, or want to contribute, we'd love to hear from you. Open an issue or submit a PR!  👉 https://lnkd.in/dmuAAYah  ⭐ Star the repo if you find it useful!  #OpenSource #AI #CodingAgent #Python #LLM #vLLM #Qwen #MachineLearning #DevTools #AIAgents #ClaudeCode

🚨 𝟱𝟬𝟬,𝟬𝟬𝟬+ 𝗹𝗶𝗻𝗲𝘀 𝗼𝗳 𝗔𝗜 𝗰𝗼𝗱𝗲... 𝗹𝗲𝗮𝗸𝗲𝗱 𝗯𝘆 𝗺𝗶𝘀𝘁𝗮𝗸𝗲. 𝗔𝗻𝘁𝗵𝗿𝗼𝗽𝗶𝗰 𝗮𝗰𝗰𝗶𝗱𝗲𝗻𝘁𝗮𝗹𝗹𝘆 𝗲𝘅𝗽𝗼𝘀𝗲𝗱 𝗶𝘁𝘀 Claude 𝗖𝗼𝗱𝗲 𝘀𝗼𝘂𝗿𝗰𝗲 𝘃𝗶𝗮 𝗮 `.𝗺𝗮𝗽` 𝗳𝗶𝗹𝗲 𝗶𝗻 𝗮𝗻 𝗻𝗽𝗺 𝗿𝗲𝗹𝗲𝗮𝘀𝗲 — 𝗿𝗲𝘃𝗲𝗮𝗹𝗶𝗻𝗴 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗶𝗻𝘁𝗲𝗿𝗻𝗮𝗹 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲. (Axios) Within hours, the internet did what it does best: → mirrored it on GitHub → analyzed it → rebuilt it One repo stood out: 👉 https://lnkd.in/gEJzYmXx But the real twist? Developers moved beyond copying. They created clean-room reimplementations in Rust & Python (Claw Code) — replicating the architecture without using the original code. (Claw Code) https://lnkd.in/guiUu3Ch This is classic software history repeating itself. 💡 Lesson: It’s not always hacks that break systems — sometimes it’s a single config mistake. And sometimes, that mistake teaches the whole industry how your system works. #AI #DevOps #Security #OpenSource #SoftwareEngineering

From the leaked Claude source code, people are porting the architecture and logic to a different language (python) to avoid legal issues. One example is this one: https://lnkd.in/eVpnWN-n I remember using claude code to reverse engineering it itself, the intention? to enable a custom llm proxy, and it worked! The llm proxy didn't follow the regular authentication mechanism (required extra headers, custom url pattern) plus a custom change in the response. It was the opencode before opencode 😅🤣

I use Tailscale a ton in our post-AI world, mostly to securely host vibecoded slop for myself that is useful for *me* and nobody else. Broadly speaking, you have three options for hosting an app on your tailnet: 1) a port on a host that is on your tailnet; 2) a docker sidecar; and 3) tsnet (tailnet as a library) if you write your app in go. I most often choose #3 and I fall back to #2 if I'm on a non-Go stack. I'm glad to see Tailscale is porting tsnet to rust so I'll have more options (like a python library compiled from rust).

Production-ready multi-agent orchestration is finally here. With Agent Framework v1.0 introducing some exciting new features. My personal favourites: 1. Agent workflows 2. Middleware hooks 3. Integration with managed agents in Foundry Agent Service 4. Multi-agent orchestration with HITL 5. Skills 6. Foundry Tools, Memory, Observability and Evaluations and many more. Check out the amazing blog post from Shawn Henry detailing the various features with concrete examples in Python and .NET https://lnkd.in/dKBRDujY

Zapier costs $100/month. Make costs $100/month. n8n costs $0 if you self-host — and it can write Python. I've been using n8n for AI automation workflows and the thing that surprised me most: it's not a dumbed-down no-code tool. It's a proper automation platform that happens to have a visual interface. When the GUI isn't enough — you write JavaScript or Python directly in the node. No plugins. No workarounds. 5 things that make it different: ⚡ AI-native: LangChain-based agent nodes built in ⚡ 400+ integrations, 900+ templates ready to use ⚡ Custom code (JS or Python) in any node ⚡ Full self-hosting — your data never leaves your server ⚡ Enterprise SSO + air-gapped deployment for regulated industries 181k GitHub stars. This is what Zapier should have been. Full guide in comments 👇 #n8n #WorkflowAutomation #SelfHosted #AIAgents #NoCode #OpenSource #DevOps

We’ve been shipping across the Chonkie stack. Here’s what’s new: - ChonkieJS just got a major refresh, with out-of-the-box support for 7 chunkers, including CodeChunker, SemanticChunker, TableChunker, and FastChunker. Teams can now build best in class retrieval systems without ever leaving the JS ecosystem. - Chonkie Python now includes the TeraFlopAI Chunker, powered by TeraFlopAI’s segmentation API for stronger semantic splitting. We’re seeing especially strong performance on legal documents. Our open-source ecosystem continues to grow across both libraries, and a big part of that momentum comes from contributors pushing the project forward. If you’re building retrieval or ingestion systems in JS or Python, there’s a lot new to explore. Check us out on GitHub: - ChonkieJS: https://lnkd.in/e4v9fyg7 - Chonkie Python: https://lnkd.in/eEnmgYZR

Excited to share: AgentBudget now has first-party SDKs for Go and TypeScript — not just Python. Same idea across all three: → one line sets a hard dollar limit on any AI agent session → automatic cost tracking → circuit breaking when limits are hit → clear budget reports across OpenAI + Anthropic Python: pip install agentbudget Go: go get github. com/agentbudget/agenbudget/sdks/go TypeScript: npm install @agentbudget/agentbudget All SDKs follow the same session + budget pattern and ship with built-in pricing for 40+ models (GPT-4o, Claude, Gemini, Mistral, Cohere). If you’ve ever had an agent loop and quietly burn $50–$300… this is exactly what AgentBudget is designed to stop. Open source (Apache 2.0). No proxy. No cloud account. No infra. Just a library you drop in. ⭐ https://lnkd.in/e2_tB825 Would love feedback from Go + TypeScript folks building agents — what’s your stack looking like right now? Tags: #AIAgents #OpenSource #Go #TypeScript #Python #LLM #DeveloperTools

Holiday project turned into something more interesting than expected. I started building Canister (https://lnkd.in/dxFqDJMu), a small sandbox for running untrusted code locally. The motivation came from a simple observation: I’m increasingly running AI-generated code (Elixir, Python, Node.js, etc.) that I didn’t fully review. That didn’t feel great. Canister is my attempt to put a lightweight safety boundary around that: - restrict filesystem access - control network calls - allow only a defined syscall surface I wrote a short post about the design, trade-offs, and where this could go: 🔗 https://lnkd.in/d78vNK6X Feedback very welcome — especially from people thinking about AI + security.

Wrap any LangGraph agent in one line and get loop detection, budget guards, and a live local-first dashboard. Open source, no SaaS, both TypeScript and Python. I've been building GraphOS — a small but serious observability + policy layer for LangGraph agents. Three things every team eventually hits in production: • Agents loop silently and burn tokens • Budgets blow before anyone notices • Runs are black boxes until they finish GraphOS gives you: ✓ LoopGuard — halts repeated state OR N visits to the same node ✓ BudgetGuard — hard USD ceiling with a built-in OpenAI/Anthropic price table ✓ MCPGuard — allow/deny MCP servers + per-tool call caps ✓ Local React Flow dashboard with time-travel replay (SQLite-backed) This week, I shipped the Python SDK at full feature parity with the TypeScript one, same wrap shape, same dashboard, both languages stream into the same UI. How I validated it: wrapped a real open-source Python LangGraph agent I didn't write (langchain-ai/retrieval-agent-template) end-to-end. The integration caught a bug that 60 green unit tests had missed — the cost extractor only recognized dict-shaped messages, but real LangChain Python ships AIMessage as a Pydantic model. BudgetGuard always saw $0.00. One-line fix, regression test, shipped 1.0.1. That's exactly the kind of bug that handcrafted demos cannot surface. Full story (TS Act I + Python Act II + the bug-find): https://lnkd.in/esHBTTBw Star, fork, feedback all welcome: https://lnkd.in/e3eVrMG3 #OpenSource #LangGraph #AIAgents #Python #Observability