Secure Python Interpreter Service for LLMs and Agents

Boosting Developer Productivity with the OpenAI Agents Python SDK: The OpenAI Agents Python SDK is a lightweight framework for building multi‑agent workflows in Python, with built‑in support for tools, guardrails, sessions, and tracing. It works with OpenAI models and 100+ other LLMs, so teams can stay flexible on model choice while standardizing their agent architecture. Why it matters for productivity? Instead of hand‑rolling custom logic for every AI feature, teams get ready‑made building blocks: - Agents with clear roles, tools, and safety policies. - Sessions that keep conversation and task state without extra wiring. - Tracing that makes debugging and optimization much faster. This turns “glue code” into reusable infrastructure and frees developers to focus on product logic rather than orchestration. Here is a concrete productivity example: Imagine a team maintaining a large Python codebase. With the SDK’s Sandbox Agents, you can spin up an agent in a local Unix sandbox, point it at your Git repo, and let it: - Inspect files (e.g., README, modules, test folders). - Propose changes, apply patches, and run commands in a controlled environment. - For example, you could ask a sandbox agent: “Scan this repository, identify duplicate utility functions, refactor them into a single module, and update imports.” The agent uses the sandbox to read the code, suggest edits, and apply them, turning what used to be a multi‑hour manual refactor into a guided, review‑able change set that takes minutes. Over time, these agent‑driven workflows compound into meaningful time savings across code reviews, documentation updates, and repetitive maintenance tasks. If you’re experimenting with agents or scaling AI features in production, the OpenAI Agents Python SDK is a practical way to turn LLMs into reliable, productive teammates. Please find the GitHub repository here: https://lnkd.in/gpgFvQGj #OpenAI #Agents #Python #MultiAgentSystems #AIEngineering #LLM #MLOps #AgenticAI #AIInfrastructure #SoftwareEngineering

GoScrapy is a high-performance web scraping framework for Go, designed with the familiar architecture of Python's Scrapy. It provides a robust, developer-centric experience for building sophisticated data extraction systems, purposefully crafted for those making the leap from Python to the Go ecosystem. Why GoScrapy? While low-level scraping libraries are powerful, many teams require the high-level architectural framework established by Scrapy. GoScrapy brings this architectural discipline natively to Go, organizing your request callbacks, middlewares, and pipelines into a structured, manageable workflow. Instead of manually orchestrating retries, cookie isolation, or database handoffs, GoScrapy provides the engine that powers your spiders. You focus purely on the extraction logic; the framework manages the high-throughput lifecycle and concurrency in the background.

Eliminate tool schema bloat! Give an AI agent 30+ MCP tools and thousands of tokens of JSON schemas eat the context window every turn. codemode-lite takes a different approach. Instead of flooding the agent with tool schemas, it exposes one tool: run_python. The agent writes Python, calls whatever tools it needs from inside a secure sandbox, and only the final result comes back. No schema bloat. No context growth. Two sandbox options: Podman containers for persistent state with enterprise isolation, or Pyodide WASM via Node.js for lightweight stateless execution. Add new MCP servers by dropping a JSON config. No code changes needed. Blog: https://lnkd.in/eTiBesX9 #AI #LLM #MCP #OpenSource #RedHat

m2cgen lets you export your ML model to multiple languages without taking Python to production 🚀 A new tool, m2cgen, is making waves for data scientists and developers alike by eliminating the need to deploy Python environments in production when integrating machine learning models. This tool is especially useful in situations where your deployment environment does not support Python. Instead of wrapping your model in a Flask API or dealing with complex serialization, m2cgen provides a straightforward solution: it transpiles your machine learning model into several supported languages such as Java, C#, Go, and others. This approach not only reduces potential failures related to network latency and additional services, but also simplifies the deployment process for teams constrained by language-specific infrastructure. • Supported Languages: Transpiles Python models into Java, C#, Go, JavaScript, Haskell, PHP, and even Ruby. • Model Compatibility: Works with several common model types including linear, logistic, SVM, and tree-based models like Random Forests. • No Python Required: Eliminates the need for Python in production, ideal for microservices in non-Python environments. • Latency Reduction: Direct native code execution reduces network latency compared to API-based solutions. • Simple Integration: Exports self-contained source code files, enabling easy integration into existing applications. • Customization: Allows developers to add additional custom code post-generation without impacting the core model logic. For engineers, the introduction of m2cgen means fewer headaches when deploying models into environments that are not Python-friendly. This tool helps in bypassing the traditional workaround of building an additional Flask API which not only adds latency but also increases the system's complexity and potential failure points. By directly converting models into the language of your target deployment environment, you can achieve more seamless integration and simpler maintenance. Teams should consider integrating m2cgen into their workflow when working with diverse tech stacks that do not always align with Python-centric solutions. Additionally, revisiting legacy systems to replace cumbersome Python deployments with m2cgen’s outputs could streamline operations and improve application performance. How might transitioning to language-specific models with m2cgen impact your deployment and maintenance processes compared to traditional methods? #MachineLearning #ModelDeployment #SoftwareEngineering #DataScience #Python #Programming

🚀 Python for DevOps – Real-Time Error Monitoring Practiced building a simple real-time log monitoring script using Python to detect errors instantly. 📂 Use Case: In production, logs are continuously generated. We need a way to detect errors in real time instead of manually checking files. 💻 Python Script: import time with open("app.log", "r") as f: f.seek(0, 2) # move to end of file while True: line = f.readline() if not line: time.sleep(1) continue if "ERROR" in line: print("Alert:", line.strip()) Output: ubuntu@satheesha:~/python$ python3 real-time_log-montr.py Alert ERROR: Test Wed Apr 22 08:05:25 UTC 2026 Alert ERROR: Test Wed Apr 22 08:05:27 UTC 2026 Alert ERROR: Test Wed Apr 22 08:05:29 UTC 2026 Alert ERROR: Test Wed Apr 22 08:05:31 UTC 2026 🔍 What this does: Reads log file in real time Starts from end (like tail -f) Continuously checks for new entries Prints alert when ERROR is detected 🔥 Why this matters: Helps detect issues instantly Reduces downtime Automates monitoring tasks 💡 Key Learning: Python can be used to build lightweight monitoring tools, similar to real-world DevOps systems. 📈 Next Step: Add timestamps Handle multiple log levels Send alerts (email/Slack) Auto-restart services on failure #Python #DevOps #Automation #Monitoring #Logging #Scripting #Learning #100DaysOfCode

🚀 Built an Enterprise RAG-Based Knowledge Retrieval System I recently worked on designing and implementing a Retrieval-Augmented Generation (RAG) based chatbot to improve enterprise knowledge access. 🔍 Problem: Teams were spending significant time searching across scattered documents and knowledge bases. 💡 Solution: Developed a secure, scalable system using: • LLM embeddings for semantic understanding • Vector database for efficient similarity search • Cloud-native architecture for scalability and high availability 📈 Impact: • Improved information retrieval efficiency by ~20% • Reduced manual search effort across teams • Enabled faster decision-making with contextual responses ⚙️ Tech Stack: Java, Python, Vector DB, LLMs, Microservices, GCP This project reflects how AI/ML (RAG + LLMs) can be integrated into real-world enterprise systems to drive productivity and efficiency. 🔗 GitHub: https://lnkd.in/dW-bhYNv #EngineeringManager #AI #MachineLearning #LLM #RAG #SystemDesign #Microservices #Cloud #Java #CPlusPlus

Shipping Python code shouldn’t feel like rolling dice in production. Modern tooling has quietly changed the game — not by adding complexity, but by removing entire classes of bugs before they ever exist In my latest Towards Data Science article I break down how a lightweight but powerful toolchain can turn your dev pipeline into a safety net: black → zero-effort format consistency ruff → lightning-fast linting pytest → confidence through real, maintainable tests mypy → catching type-related bugs before runtime py-spy → understanding performance without touching code pre-commit → enforcing all of the above automatically The real takeaway isn’t the tools themselves — it’s how combining them creates a feedback loop that catches issues early, standardizes quality, and speeds up development instead of slowing it down. If your pipeline still relies on “we’ll catch it in review” or “we’ll fix it later”… this is worth your time. Read the full breakdown and setup guide: https://lnkd.in/ewuXn6NF

OpenAI updates Agents SDK, adds sandbox for safer code execution: OpenAI’s updated Agents SDK helps developers build agents that inspect files, run commands, edit code, and handle tasks within controlled sandbox environments. The update provides standardized infrastructure for OpenAI models, a model-native harness that lets agents work with files and tools on a computer, and native sandbox execution for running tasks safely. The new harness and sandbox capabilities launch first in Python, with TypeScript support planned for a future release. Additional features, including code mode … More → The post OpenAI updates Agents SDK, adds sandbox for safer code execution appeared first on Help Net Security.

Built Pyre Agents: An Elixir-Orchestrated Runtime for Python Agents Pyre lets you build agents in Python without relying on Python to orchestrate them. Elixir/BEAM handles concurrency and fault tolerance, which makes large-scale, reliable agent systems much more practical. Read here: https://lnkd.in/e6cw_mT6