Search Results for "task" - Page 4
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Showing 409 open source projects for "task"
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Enterprise-grade ITSM, for every businessFreshservice is an intuitive, AI-powered platform that helps IT, operations, and business teams deliver exceptional service without the usual complexity. Automate repetitive tasks, resolve issues faster, and provide seamless support across the organization. From managing incidents and assets to driving smarter decisions, Freshservice makes it easy to stay efficient and scale with confidence.
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Stop Cyber Threats with VM-Series Next-Gen Firewall on AzureGain integrated visibility across all traffic in a single pass. Deploy Palo Alto Networks VM-Series to determine application identity and content while automating security policy updates via rich APIs.
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Superpowers
An agentic skills framework & software development methodology
...Instead of simply generating code, Superpowers drives an AI through a thoughtful software engineering workflow that starts with clarifying project intent, teasing out a detailed specification, and creating an implementation plan that is readable and actionable. It incorporates test-driven development, task planning, and sub-agent orchestration so that each engineering task is executed, checked, and iterated upon with rigor rather than ad-hoc improvisation. Superpowers includes a rich set of modular “skills” that guide the AI in areas such as brainstorming, planning, debugging, and code review, giving it the tools to function like a disciplined developer rather than a naive auto-completer. -
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Beads
A memory upgrade for your coding agent
Beads is an open-source project providing a distributed, structured memory system for AI coding agents, replacing ad-hoc text plans with a git-backed graph that represents tasks, dependencies, and progress in a persistent, queryable format. Instead of storing plans as unstructured Markdown or ephemeral notes, Beads organizes agent state, task artifacts, and relationships as nodes and edges in a version-controlled graph so that long-horizon projects don’t lose context or coherence as the agent proceeds. This approach helps coding agents — and human collaborators — track which tasks depend on others, what has been done, and where workflows branch or reunify without losing important data. ... -
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CAMEL AI
Finding the Scaling Law of Agents. A multi-agent framework
...Our approach involves using inception prompting to guide chat agents toward task completion while maintaining consistency with human intentions. We showcase how role-playing can be used to generate conversational data for studying the behaviors and capabilities of chat agents, providing a valuable resource for investigating conversational language models. -
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Hermes Desktop
Desktop Companion for Hermes Agent
Hermes Desktop is a native desktop application that simplifies the installation, configuration, and daily use of Hermes Agent, an AI assistant with advanced tool usage and self-improving capabilities. It provides an intuitive graphical interface that eliminates the need to manage Hermes through command-line tools. Users can connect to local or remote Hermes instances while accessing chat, memory management, skills, tools, profiles, and scheduling from a single workspace. The platform... -
Compliant and Reliable File Transfers Backed by Top Security CertificationsStop relying on non-certified, legacy file transfer tools that creak under the weight of modern security demands. Get full audit trails, advanced access controls and more supported by an award-winning team of experts. Start your free 25-day trial today.
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OpenManus
Open-source AI agent framework
OpenManus is an open-source AI agent framework designed to autonomously execute complex, multi-step tasks by combining reasoning, planning, and tool use. It enables developers to build agents that can think, act, and iterate toward goals rather than simply responding to prompts. The platform emphasizes task decomposition, allowing agents to break down objectives into smaller steps and execute them sequentially or recursively. OpenManus supports integration with external tools, APIs, and environments, making it suitable for real-world automation workflows. It is built to be flexible and extensible, enabling customization of agent behaviors, tools, and reasoning strategies. ... -
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Zed
High-performance, multiplayer code editor from the creators of Atom
...Evaluate code inline via Jupyter runtimes and collaboratively edit notebooks. Support for many languages via Tree-sitter, WebAssembly, and the Language Server Protocol. Fast native terminal tightly integrates with Zed's language-aware task runner and AI capabilities. First-class modal editing via Vim bindings, including features like text objects and marks. Zed is built by a global community of thousands of developers. Boost your Zed experience by choosing from hundreds of extensions that broaden language support, offer different themes, and more. -
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PentAGI
Perform penetration testing tasks
PentAGI is a fully autonomous AI agent system designed to perform complex penetration testing tasks by orchestrating multiple intelligent components into a coordinated offensive security workflow. The platform aims to automate significant portions of the penetration testing lifecycle, including reconnaissance, vulnerability discovery, and exploitation planning, reducing the amount of manual effort required from security professionals. It leverages agent-based architecture and AI reasoning to... -
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Qwen3.5
Qwen3.5 is the large language model series developed by Qwen team
Qwen3.5 is part of Alibaba’s Qwen family of large language and multimodal foundation models, designed to power advanced AI applications such as chatbots, coding assistants, and autonomous agents. The project represents a significant step toward “agentic AI,” meaning models that can reason through multi-step tasks and interact with external tools or environments rather than only generating text. Qwen3.5 builds on earlier Qwen generations by improving multilingual understanding, reasoning... -
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BrowserOS
Agentic browser; privacy-first alternative to ChatGPT Atlas
BrowserOS is an open-source, agentic web browser built on a Chromium base that integrates AI agents directly into the browsing experience. Rather than just doing standard browsing, it places AI intelligence at the core: you can connect your own API keys (for e.g., OpenAI, Anthropic, Google Gemini) or run local models (via e.g., Ollama) so that your browsing data and automation stay on your machine — privacy and control are emphasized throughout. The interface remains familiar to users of... -
Auth0 B2B Essentials: SSO, MFA, and RBAC Built InAuth0's B2B Essentials plan gives you everything you need to ship secure multi-tenant apps. Unlimited orgs, enterprise SSO, RBAC, audit log streaming, and higher auth and API limits included. Add on M2M tokens, enterprise MFA, or additional SSO connections as you scale.
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Label Sleuth
Open source no-code system for text annotation and building of text
An open-source no-code system for text annotation and building text classifiers. No AI knowledge needed. From task definition to working model in just a few hours! While domain experts label their data, Label Sleuth automatically trains in the background-appropriate machine learning models. To avoid wasted labeling effort, Label Sleuth employs active learning techniques to guide the user in what they should be labeled next. Domain experts can quickly start labeling their data through an intuitive user interface. ... -
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Conversational Health Agents (CHA)
A Personalized LLM-powered Agent Frameworks
CHA, or Conversational Health Agents, is an open-source framework designed to build intelligent healthcare assistants powered by large language models and external data sources. The system enables developers to create personalized AI agents that can interact with users through natural language while performing multi-step reasoning and task execution. It integrates orchestration capabilities that allow the agent to gather information from APIs, knowledge bases, and external services in order to generate more accurate and context-aware responses. The framework supports modular components such as planning, tool execution, and multimodal input processing, which makes it suitable for complex healthcare applications. ... -
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Ploomber
The fastest way to build data pipelines
...The system integrates with common development environments such as Jupyter Notebook, VS Code, and PyCharm, enabling data scientists to continue working with familiar tools while building scalable workflows. Ploomber automatically manages task dependencies and execution order, allowing complex pipelines with multiple stages to run reliably. The framework can deploy pipelines across different computing environments including Kubernetes, Airflow, AWS Batch, and high-performance computing clusters. It also helps teams maintain reproducibility by tracking changes in code and rerunning only outdated pipeline tasks. -
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Transfer Learning Repo
Transfer learning / domain adaptation / domain generalization
...The project functions as a large knowledge hub that organizes papers, tutorials, datasets, and software implementations across topics such as domain adaptation, domain generalization, multi-task learning, and few-shot learning. The repository includes surveys and theoretical explanations that help readers understand how transfer learning methods allow models trained in one domain to adapt to new tasks or datasets. In addition to academic references, the project provides practical code implementations of many transfer learning algorithms so that researchers can reproduce experiments or build their own applications. ... -
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SGR Agent Core
Schema-Guided Reasoning (SGR) has agentic system design
SGR Agent Core is an open-source framework for building intelligent AI research agents based on a methodology known as Schema-Guided Reasoning (SGR). The framework provides a core library that allows developers to design autonomous agents capable of structured reasoning and complex task execution. Instead of relying solely on free-form prompts, the system organizes reasoning processes around schemas that guide how agents analyze problems, gather information, and generate outputs. This architecture enables agents to follow structured reasoning workflows while still benefiting from the flexibility of large language models. ... -
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AI Agents From Scratch
Demystify AI agents by building them yourself. Local LLMs
...It focuses on explaining the architecture of agent systems rather than simply providing finished code, making it useful for developers who want to understand how AI agents actually work internally. By building agents incrementally, the project helps learners grasp concepts such as decision loops, task decomposition, and environment interaction. -
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NagaAgent
A simple yet powerful agent framework for personal assistants
NagaAgent is an experimental framework for building interactive virtual agents capable of autonomous reasoning, dialog, and task execution using components that mirror human cognitive patterns. It provides abstractions for representing goals, context, and state so that agents can plan sequences of actions, evaluate outcomes, and adjust behavior over time. The project includes mechanisms for semantic memory, reasoning pipelines, and integration points with external data sources and language models so that agents can interpret natural language instructions and produce coherent multi-step outputs. ... -
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ENScan Go
ENScan_GO is an enterprise information reconnaissance tool
...The tool targets analysts who need one-click collection and normalized output to reduce manual lookups across registries and platforms. Recent releases added a reworked task model with queueing, resumable searches via cached progress, export format options, and a public API surface for custom keyword strategies. Documentation and issues discuss operational concerns such as rate limits, verification challenges, and use of proxies to reduce bans. The project is maintained under Apache-2.0 and is positioned for both single-shot queries and batch investigations. -
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4M
4M: Massively Multimodal Masked Modeling
...Training/inference configs and issues discuss things like depth tokenizers, input masks for generation, and CUDA build questions, signaling active research iteration. The design leans into flexibility and steerability, so prompts and masks can shape behavior without bespoke heads per task. In short, 4M provides a unified recipe to pretrain large multimodal models that generalize broadly while remaining practical to fine-tune. -
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VGGT
[CVPR 2025 Best Paper Award] VGGT
VGGT is a transformer-based framework aimed at unifying classic visual geometry tasks—such as depth estimation, camera pose recovery, point tracking, and correspondence—under a single model. Rather than training separate networks per task, it shares an encoder and leverages geometric heads/decoders to infer structure and motion from images or short clips. The design emphasizes consistent geometric reasoning: outputs from one head (e.g., correspondences or tracks) reinforce others (e.g., pose or depth), making the system more robust to challenging viewpoints and textures. ... -
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Eko
Build Production-ready Agentic Workflow with Natural Language
...With a focus on high development efficiency, Eko simplifies the creation of multi-step workflows, enabling users to integrate and automate tasks across platforms. It provides a unified interface for managing agents, offering features such as web resource access and high task complexity handling. Eko is open-source and can be used to execute tasks like browser automation, system operations, and software testing. -
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OpenCompass
OpenCompass is an LLM evaluation platform
...Pre-support for 20+ HuggingFace and API models, a model evaluation scheme of 50+ datasets with about 300,000 questions, comprehensively evaluating the capabilities of the models in five dimensions. One line command to implement task division and distributed evaluation, completing the full evaluation of billion-scale models in just a few hours. Support for zero-shot, few-shot, and chain-of-thought evaluations, combined with standard or dialogue type prompt templates, to easily stimulate the maximum performance of various models. -
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node-red-contrib-custom-chatgpt
Node-RED ChatGPT
...Create a new secret key" then copy and paste the "API key" into the node API_KEY property value. msg.payload should be a well-written prompt that provides enough information for the model to know what you want and how it should respond. Its success generally depends on the complexity of the task and quality of your prompt. A good rule of thumb is to think about how you would write a word problem for a middle schooler to solve. msg.payload should be a well-written prompt that provides enough information for the model to know what you want and how it should respond. -
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DeepCTR
Package of deep-learning based CTR models
...Provide tensorflow estimator interface for large scale data and distributed training. It is compatible with both tf 1.x and tf 2.x. With the great success of deep learning,DNN-based techniques have been widely used in CTR prediction task. The data in CTR estimation task usually includes high sparse,high cardinality categorical features and some dense numerical features. Since DNN are good at handling dense numerical features,we usually map the sparse categorical features to dense numerical through embedding technique. -
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MiniMax-M2.5
State of the art LLM and coding model
MiniMax-M2.5 is a state-of-the-art foundation model extensively trained with reinforcement learning across hundreds of thousands of real-world environments. It delivers leading performance in coding, agentic tool use, search, and complex office workflows, achieving top benchmark scores such as 80.2% on SWE-Bench Verified and 76.3% on BrowseComp. Designed to reason efficiently and decompose tasks like an experienced architect, M2.5 plans features, structure, and system design before... -
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Oh My codeX (OMX)
Your codex is not alone. Add hooks, agent teams, HUDs
Oh My codeX (OMX) is a multi-agent orchestration layer designed to extend the capabilities of OpenAI Codex CLI by introducing structured teamwork, automation, and advanced workflow management. It addresses limitations in the base Codex environment, such as the lack of hooks, agent coordination, and persistent execution, by layering a shell-based system that enables richer interaction patterns. The project transforms a single AI coding assistant into a coordinated system of specialized agents...
