💡 bytedance/deer-flow just hit the trending charts — here's why it matters.

🔗 https://github.com/bytedance/deer-flow
📝 An open-source long-horizon SuperAgent harness that researches, codes, and creates. With the help of sandboxes, memories, tools, skill, subagents and message gateway, it handles different levels of tasks that could take minutes to hours.
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DeerFlow is an open-source super agent harness that orchestrates sub-agents, memory, and sandboxes to streamline complex tasks. It's powered by extensible skills and offers a flexible, modular architecture.

To get started, you can use the make setup command to run an interactive wizard that guides you through the configuration process. This includes choosing an LLM provider, optional web search, and execution/safety preferences.

DeerFlow has several key features, including skills & tools, sub-agents, sandbox & file system, context engineering, and long-term memory. It also supports various deployment options, such as local development and Docker.

This project is ideal for developers and researchers looking to build custom AI-powered agents. With its InfoQuest integration and support for multiple LLM providers, DeerFlow offers a powerful platform for intelligent search and crawling.

One-liner takeaway: DeerFlow revolutionizes AI-powered task automation by providing a flexible, modular, and extensible framework for building custom super agents.

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🌟 D4Vinci/Scrapling caught my eye on GitHub Trending today.

🔗 https://github.com/D4Vinci/Scrapling
📝 🕷️ An adaptive Web Scraping framework that handles everything from a single request to a full-scale crawl!
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Scrapling is an adaptive Web Scraping framework that simplifies the process of scraping websites, from a single request to a full-scale crawl. Its key features include automatic element relocation when website structures change, bypassing anti-bot systems like Cloudflare Turnstile, and a spider framework for concurrent, multi-session crawls with pause/resume and automatic proxy rotation.

To use Scrapling, you can start by using its StealthyFetcher to fetch websites under the radar, then scrape data with its css method. For more complex tasks, you can create a custom Spider class to scale up to full crawls.

Scrapling is built for both Web Scrapers and regular users, providing a simple and efficient way to extract data from websites. With its real-time stats and streaming features, you can monitor your crawls and adjust them as needed.

Here's an example of how to use Scrapling:

from scrapling.fetchers import StealthyFetcher
StealthyFetcher.adaptive = True
p = StealthyFetcher.fetch('https://example.com', headless=True, network_idle=True)
products = p.css('.product', auto_save=True)

Overall, Scrapling is a powerful and flexible framework that makes web scraping easier and more efficient. With its advanced features and simple usage, it's a great tool for anyone looking to extract data from websites. Start scraping like a pro with Scrapling - one library, zero compromises!

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🔍 Deep-diving into Hmbown/DeepSeek-TUI — fresh off the trending list.

🔗 https://github.com/Hmbown/DeepSeek-TUI
📝 Coding agent for DeepSeek models that runs in your terminal
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DeepSeek TUI is a terminal-based coding agent that streamlines your workflow by integrating file editing, shell commands, web search, git management, and more. It's built around DeepSeek V4, offering features like auto mode, thinking-mode streaming, and a full tool suite.

To get started, install deepseek-tui using npm, Cargo, or by downloading prebuilt binaries. You can then run deepseek in your terminal to access the TUI.

The app offers three modes: Plan (read-only), Agent (interactive), and YOLO (auto-approve). It also supports session save/resume, workspace rollback, and live cost tracking.

Key technical highlights include a ratatui interface, an async engine, and an OpenAI-compatible streaming client. The tool is designed for developers who want to boost their productivity and is available for Linux, macOS, and Windows.

In short, DeepSeek TUI is a powerful tool that helps you code smarter, not harder - streamline your workflow and take your productivity to the next level.

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💡 anthropics/financial-services just hit the trending charts — here's why it matters.

🔗 https://github.com/anthropics/financial-services
📝 No description.
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The anthropics/financial-services GitHub repository provides a comprehensive set of reference agents, skills, and data connectors for various financial services workflows, including investment banking, equity research, private equity, and wealth management. These agents and skills can be installed as Claude Cowork plugins or deployed through the Claude Managed Agents API for headless execution.

Key features include named agents that run end-to-end workflows, vertical plugins that bundle skills and data connectors by financial services vertical, and MCP integrations with various data providers. The repository is organized into

plugins/

and

managed-agent-cookbooks/

directories, making it easy to navigate and use.

The target audience for this repository includes financial services professionals looking to automate workflows, improve productivity, and enhance decision-making. To get started, users can install the Claude Cowork plugin or deploy the Managed Agents API and begin using the agents and skills to streamline their workflows.

In summary, the anthropics/financial-services repository provides a powerful toolkit for financial services professionals to automate and optimize their workflows, and with its flexible deployment options and comprehensive feature set, it's an indispensable resource for anyone looking to take their financial services workflow to the next level - Automate your financial workflows with ease.

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📌 Spotted on GitHub Trending: z-lab/dflash — let's break it down.

🔗 https://github.com/z-lab/dflash
📝 DFlash: Block Diffusion for Flash Speculative Decoding
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The DFlash repository on GitHub introduces a lightweight block diffusion model designed for speculative decoding, enabling efficient and high-quality parallel drafting. This model is supported by various backends, including Transformers, SGLang, vLLM, and MLX.

To get started, users can install the required packages using pip install commands and then serve models using vllm serve or python -m sglang.launch_server commands. The repository also provides a quick start guide for each backend and a benchmarking script to evaluate the performance of DFlash.

DFlash has been implemented with several models, including gemma-4-26B-A4B-it, Qwen3.5-27B, and LLaMA3.1-8B-Instruct, which can be found on the Hugging Face model hub.

The key highlight of DFlash is its ability to accelerate language models, making it an exciting development in the field of natural language processing.

In a nutshell, DFlash is a game-changer for language models, and its speculative decoding capabilities make it a must-try for anyone working with large language models.

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🌟 InsForge/InsForge caught my eye on GitHub Trending today.

🔗 https://github.com/InsForge/InsForge
📝 InsForge is a Postgres-based backend with auth, storage, compute, hosting, and AI gateway. Built for coding agents.
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InsForge is a backend development platform designed for AI-native developers, providing a semantic layer between AI coding agents and backend primitives like databases, authentication, and storage. The platform allows agents to understand, operate, and inspect backend systems, enabling efficient development and deployment of applications.

Key features include a semantic layer for backend context engineering, support for multiple backend primitives, and a user-friendly interface for inspecting and configuring backend state.

To get started with InsForge, you can either use the cloud-hosted version at insforge.dev or self-host it using Docker Compose. The platform provides a quickstart guide and comprehensive documentation to help you set up and use the platform.

Technical highlights of InsForge include its support for PostgreSQL, S3-compatible file storage, and OpenAI-compatible API. The platform also provides a model gateway, edge functions, and site deployment capabilities.

InsForge is designed for AI-native developers and provides a unique set of features that cater to their needs. Whether you're building a new application or migrating an existing one, InsForge provides a powerful backend platform to support your development needs.

In summary, InsForge is a powerful backend platform that provides a semantic layer for AI coding agents to interact with backend primitives, making it an ideal choice for AI-native developers. So why wait? Star the repository and start building with InsForge today - revolutionize your backend development with the power of AI!

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🚀 Meet LearningCircuit/local-deep-research: a gem from today's GitHub trending list.

🔗 https://github.com/LearningCircuit/local-deep-research
📝 ~95% on SimpleQA (e.g. Qwen3.6-27B on a 3090). Supports all local and cloud LLMs (llama.cpp, Ollama, Google, ...). 10+ search engines - arXiv, PubMed, your private documents. Everything Local & Encrypted.
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Local Deep Research is an AI-powered research assistant that helps you perform deep, agentic research using multiple LLMs and search engines with proper citations. It's designed to run locally for privacy, allowing you to use any LLM and build your own searchable knowledge base. You own your data and can see exactly how it works.

Key features include:
- Automatic research across web, academic papers, and your own documents
- Synthesis of research into a report with proper citations
- 20+ research strategies for quick facts, deep analysis, or academic research
- Encrypted library for storing and searching your documents
- Support for multiple LLMs and search engines

Usage is straightforward: simply pull and run the Docker image, or install using pip. You can also use Docker Compose for a more streamlined setup.

From a technical standpoint, Local Deep Research uses SQLCipher for encrypted storage, and includes features like in-memory credentials and supply chain security. The project also prioritizes security transparency, with documented scanner suppressions and security alerts.

This project is ideal for researchers, students, and anyone looking for a powerful, private research tool. With its focus on security, privacy, and customization, Local Deep Research is an excellent choice for those who want to take control of their research process.

In short, Local Deep Research is a game-changer for anyone looking to level up their research skills - take control of your research, and unlock new insights with Local Deep Research.

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📌 Spotted on GitHub Trending: addyosmani/agent-skills — let's break it down.

🔗 https://github.com/addyosmani/agent-skills
📝 Production-grade engineering skills for AI coding agents.
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Production-grade engineering skills for AI coding agents are available in the addyosmani/agent-skills GitHub repository. These skills encode workflows, quality gates, and best practices that senior engineers use when building software, ensuring AI agents follow them consistently across every development phase. The repository includes 7 slash commands that map to the development lifecycle, such as /spec, /plan, /build, /test, /review, /code-simplify, and /ship. Each command activates the right skills automatically, and skills can also be activated based on what you're doing, such as designing an API or building UI. The repository provides a quick start guide for various platforms, including Claude Code, Cursor, Gemini CLI, Windsurf, OpenCode, GitHub Copilot, Kiro IDE & CLI, and Codex. With 20 skills available, developers can ensure their AI coding agents follow best practices and produce high-quality code. These skills cover various aspects of software development, including definition, planning, building, verification, review, and shipping. The repository also includes agent personas and reference checklists to support the skills. Overall, the addyosmani/agent-skills repository provides a valuable resource for developers to improve the quality and consistency of their code. Upgrade your coding game with production-grade engineering skills for AI agents!

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🔍 Deep-diving into VectifyAI/PageIndex — fresh off the trending list.

🔗 https://github.com/VectifyAI/PageIndex
📝 📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG
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PageIndex is a vectorless, reasoning-based RAG system that enables Large Language Models (LLMs) to perform context-aware retrieval over long documents. It builds a hierarchical tree index from documents and uses LLMs to reason over that index for agentic, context-aware retrieval. Key features include: no vector database, no chunking, better explainability and traceability, context-aware retrieval, and human-like retrieval.

To use PageIndex, you can self-host it with this open-source repo, use the cloud service for production-grade results, or integrate it via MCP or API. The PageIndex Framework is available for a deeper dive, and cookbooks and tutorials provide additional usage guides and examples.

Technical highlights include the ability to transform lengthy PDF documents into a semantic tree structure, similar to a "table of contents" but optimized for use with LLMs. PageIndex is suitable for financial reports, regulatory filings, academic textbooks, and more.

The target audience includes developers, researchers, and professionals working with long documents that demand domain expertise and multi-step reasoning.

Try PageIndex today and experience the power of reasoning-based retrieval: pip3 install --upgrade -r requirements.txt and python3 run_pageindex.py --pdf_path /path/to/your/document.pdf.

Takeaway: PageIndex revolutionizes document retrieval with its vectorless, reasoning-based approach, making it an essential tool for anyone working with complex documents.

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⚡ vercel-labs/open-agents is making waves. Here's the full picture.

🔗 https://github.com/vercel-labs/open-agents
📝 An open source template for building cloud agents.
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The Open Agents GitHub repository is an open-source reference application designed to build and run background coding agents on Vercel. It's a three-layer system consisting of a web app, an agent workflow, and a sandbox VM. The key feature is that the agent runs outside the sandbox, interacting with it through tools, allowing for a more flexible and scalable architecture. The repo includes a chat-driven coding agent, durable multi-step execution, and isolated Vercel sandboxes. To use it, you can fork the repository, import it into Vercel, and configure the environment variables. The code is organized into multiple packages, including the agent, sandbox, and shared utilities. With Open Agents, you can build and run custom coding agents on Vercel, enabling automated coding workflows. Automate your coding tasks with Open Agents - the future of coding is here.

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🎯 docusealco/docuseal landed on trending. Worth a proper look.

🔗 https://github.com/docusealco/docuseal
📝 Open source DocuSign alternative. Create, fill, and sign digital documents ✍️
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DocuSeal is an open source platform that provides secure and efficient digital document signing and processing. With DocuSeal, you can create PDF forms to have them filled and signed online on any device with an easy-to-use, mobile-optimized web tool.

Key features include a PDF form fields builder with 12 field types, multiple submitters per document, automated emails, and file storage on disk or cloud services like AWS S3.

To get started, you can try the live demo or deploy DocuSeal in minutes using Docker or Docker Compose.

DocuSeal is perfect for businesses looking to integrate seamless document signing into their web or mobile apps, especially in industries like banking, healthcare, and real estate.

The platform is free and open source, with optional Pro Features available for companies that need more advanced functionality.

In a nutshell, DocuSeal makes it easy to fill, sign, and process documents online - give it a try and streamline your document workflow today!

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