Showing 1209 open source projects for "tasks"

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  • 1
    FireRed-Image-Edit

    FireRed-Image-Edit

    General-purpose image editing model that delivers high-fidelity

    FireRed-Image-Edit is an open-source general-purpose image editing model and toolset designed to deliver high-fidelity, visually coherent edits across a wide range of editing tasks, from simple object modifications to complex enhancements like restoration and style preservation. It is built on a flexible text-to-image foundation model that has been extended with training paradigms including pretraining, supervised fine-tuning, and reinforcement learning to imbue the system with strong instruction following and editing consistency. ...
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  • 2
    Secure OpenClaw

    Secure OpenClaw

    A personal 24x7 AI assistant like OpenClaw

    Secure-OpenClaw is an open-source project designed to transform your everyday messaging apps like WhatsApp, Telegram, Signal, and iMessage into a 24×7 personal AI assistant that can respond to queries, run tasks, remember context, and interact with external services. Unlike a traditional chatbot that lives in a browser or API dashboard, Secure-OpenClaw runs locally or on your chosen infrastructure and listens for messages on channels you already use, giving you an always-on interface to a powerful agent. It leverages Claude and other models to interpret messages and is built to manage persistent memory, scheduled reminders, and integration with hundreds of third-party apps to automate workflows without leaving your chat. ...
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  • 3
    AIPex

    AIPex

    AI browser automation assistant, no migration and privacy first

    AIPex is an AI-augmented development toolkit and workflow platform that aims to accelerate software productivity by integrating intelligent assistants, code generation tools, and customizable automation patterns directly into developer workflows. Rather than treating AI as a separate helper, AIPex embeds AI capabilities into common tasks like scaffolding components, generating tests, analyzing code quality, and performing refactors, allowing developers to stay in flow while benefiting from model-assisted insights. It supports modular plugin architecture so teams can extend or customize how assistants behave based on project conventions, code standards, or tooling preferences. ...
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  • 4
    NagaAgent

    NagaAgent

    A simple yet powerful agent framework for personal assistants

    ...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. Rather than being a simple chatbot, NagaAgent emphasizes persistent thought cycles, context retention, and the ability to decompose complex tasks into smaller executable units, earning it a place in research explorations of agent design. Its architecture facilitates extensibility, allowing developers to plug in different reasoning modules or knowledge sources depending on the domain of use.
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    Train ML Models With SQL You Already Know

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  • 5
    Inkeep

    Inkeep

    Create AI Agents in a No-Code Visual Builder or TypeScript SDK

    ...Agents built with this framework can act as real-time conversational assistants — for example, handling help desk inquiries, providing internal support to teams, or driving in-app experiences — and they can be extended to automate multi-step tasks that interact with external systems like CRMs, knowledge bases, or ticketing systems. The project includes support for designing rich workflows where agents communicate with each other (agent-to-agent communication) and maintain state.
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  • 6
    Dev Janitor

    Dev Janitor

    Your Vibe Coding Toolkit A cross-platform desktop application

    Dev Janitor is an open-source developer productivity tool designed to automatically clean up stale, unused, or poorly maintained code patterns in a codebase, helping teams keep their repositories tidy without consuming engineering time manually pruning technical debt. The tool analyzes project files and identifies opportunities to perform cleanup tasks such as removing dead imports, fixing outdated syntax, simplifying redundant expressions, and consolidating duplicated logic, all while observing established conventions for the languages it supports. Through a pluggable rule system, it allows teams to enforce their own style guides or cleanup policies, enabling tailored automation that fits each codebase’s unique needs. ...
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  • 7
    Context Engineering Template

    Context Engineering Template

    Context engineering is the new vibe coding

    Context Engineering Template is a comprehensive template and workflow repository designed to teach and implement context engineering, a structured approach to preparing and organizing the information necessary for AI coding assistants to complete complex tasks reliably. Instead of relying solely on short prompts, this project encourages developers to create rich, structured context files that include project rules, examples, and validation criteria so that AI systems can act more like informed collaborators and less like general-purpose generators. The repository provides templates such as CLAUDE.md for defining global project rules, INITIAL.md for feature requests, and folders for examples, PRPs, validation scripts, and settings to support systematic prompt generation and execution with tools like Claude Code. ...
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  • 8
    Step3-VL-10B

    Step3-VL-10B

    Multimodal model achieving SOTA performance

    ...Despite having only about 10 billion parameters, it delivers performance that rivals or even surpasses much larger models (10×–20× larger) on a wide range of multimodal benchmarks covering reasoning, perception, and complex tasks, positioning it as one of the most powerful models in its class. It achieves this efficiency and strong performance through unified pre-training on a massive 1.2 trillion-token multimodal corpus that jointly optimizes a language-aligned perception encoder with a powerful decoder, creating deep synergy between image processing and text understanding.
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  • 9
    UCP Python SDK

    UCP Python SDK

    The official Python SDK for UCP

    ...This SDK provides Pydantic models for UCP schemas, making it easy for Python developers to construct, validate, and serialize protocol messages and data structures according to the UCP specification. By adhering to the official protocol standards, applications built on this SDK can participate in tasks like capability discovery, checkout flows, order management, and more, while remaining interoperable across different UCP implementations and surfaces.
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  • 10
    Agently 4

    Agently 4

    Build GenAI application quick and easy

    Agently is a Python framework for building generative-AI (“GenAI”) applications; it focuses on enabling developers to orchestrate AI agents, workflows, and event-driven logic in a robust, reusable way. With Agently, one can define agents that call different models, chain tasks, trigger workflows based on events, and switch models with minimal code changes. It abstracts away boilerplate around model API calls, tool usage, prompt management, and workflow state. The project aims at production-grade GenAI application development rather than just one-off scripts — you’ll find examples of news gathering, agentic workflows, control systems, etc. ...
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  • 11
    HunyuanWorld-Mirror

    HunyuanWorld-Mirror

    Fast and Universal 3D reconstruction model for versatile tasks

    HunyuanWorld-Mirror focuses on fast, universal 3D reconstruction that can ingest varied inputs and produce multiple kinds of 3D outputs. The model accepts combinations of images, camera intrinsics and poses, or even depth cues, then reconstructs consistent 3D geometry suitable for downstream rendering or editing. The pipeline emphasizes both speed and flexibility so creators can go from casual captures to assets without elaborate capture rigs. Outputs can include point clouds, estimated...
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  • 12
    Vision Transformer Pytorch

    Vision Transformer Pytorch

    Implementation of Vision Transformer, a simple way to achieve SOTA

    ...It’s widely used as an educational reference for people learning transformers in vision and as a lightweight baseline for research prototypes. The project encourages experimentation—swap optimizers, change augmentations, or plug the transformer backbone into downstream tasks.
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  • 13
    nanochat

    nanochat

    The best ChatGPT that $100 can buy

    ...The repository stitches together every stage of the lifecycle: tokenizer training, pretraining a Transformer on a large web corpus, mid-training on dialogue and multiple-choice tasks, supervised fine-tuning, optional reinforcement learning for alignment, and finally efficient inference with caching. Its north star is approachability and speed: you can boot a fresh GPU box and drive the whole pipeline via a single script, producing a usable chat model in hours and a clear markdown report of what happened. The code is written to be read—concise training loops, transparent configs, and minimal wrappers—so you can audit each step, tweak it, and rerun without getting lost in framework indirection.
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  • 14
    FastbuildAI

    FastbuildAI

    An open-source AI framework for developers and entrepreneurs

    FastbuildAI is a pragmatic framework for building agentic applications that can plan, call tools, and produce reliable outputs without forcing you into a heavy platform. It emphasizes fast iteration: you describe tasks declaratively, wire up tools with typed schemas, and let the runtime handle planning, retries, and result aggregation. The project leans into reproducibility with run records, seed control, and structured traces so you can compare behaviors across versions and inputs. Prompt and memory management are treated as first-class concerns, enabling short-lived scratchpads for reasoning as well as long-horizon state when an agent operates over multiple sessions. ...
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  • 15
    ML Ferret

    ML Ferret

    Refer and Ground Anything Anywhere at Any Granularity

    ...The repo presents the vision-language pipeline, model assets, and paper resources that show how Ferret answers questions, follows instructions, and returns grounded outputs rather than just text. In practice, this enables tasks like “find that small red icon next to the chart and describe it” where both the linguistic reference and the visual region are ambiguous without fine spatial reasoning.
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  • 16
    Granite 3.0 Language Models

    Granite 3.0 Language Models

    New set of lightweight state-of-the-art, open foundation models

    This repository introduces Granite 3.0 language models as lightweight, state-of-the-art open foundation models built to natively support multilinguality, coding, reasoning, and tool usage. A central goal is efficient deployment, including the potential to run on constrained compute resources while remaining useful for a broad span of enterprise tasks. The repo positions the models for both research and commercial use under an Apache-2.0 license, signaling permissive adoption paths. Documentation highlights the capability mix (reasoning, tool use, code) and points to model artifacts and guidance for evaluation. Activity on the project shows an evolving codebase with open pull requests and standard GitHub project structure for issues and security visibility. ...
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  • 17
    Granite TSFM

    Granite TSFM

    Foundation Models for Time Series

    ...It documents the currently supported Python versions and points users to where the core TSFM models are hosted and how to wire up service components. Issues and examples in the tracker illustrate common tasks such as slicing inference windows or using pipeline helpers that return pandas DataFrames, grounding the library in day-to-day time-series operations. The ecosystem around TSFM also includes a community cookbook of “recipes” that showcase capabilities and patterns. Overall, the repo is designed as a hands-on companion for teams adopting time-series foundation models in production-leaning settings.
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  • 18
    Granite Code Models

    Granite Code Models

    A Family of Open Foundation Models for Code Intelligence

    Granite Code Models are IBM’s open-source, decoder-only models tailored for code tasks such as fixing bugs, explaining and documenting code, and modernizing codebases. Trained on code from 116 programming languages, the family targets strong performance across diverse benchmarks while remaining accessible to the community. The repository introduces the model lineup, intended uses, and evaluation highlights, and it complements IBM’s broader Granite initiative spanning multiple modalities. ...
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  • 19
    MetaCLIP

    MetaCLIP

    ICLR2024 Spotlight: curation/training code, metadata, distribution

    MetaCLIP is a research codebase that extends the CLIP framework into a meta-learning / continual learning regime, aiming to adapt CLIP-style models to new tasks or domains efficiently. The goal is to preserve CLIP’s strong zero-shot transfer capability while enabling fast adaptation to domain shifts or novel class sets with minimal data and without catastrophic forgetting. The repository provides training logic, adaptation strategies (e.g. prompt tuning, adapter modules), and evaluation across base and target domains to measure how well the model retains its general knowledge while specializing as needed. ...
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  • 20
    Kodu

    Kodu

    Kodu is an autonomous coding agent that lives in your IDE

    ...Claude Coder supports large-context interactions, enabling the AI to process entire repositories or multi-file structures rather than isolated snippets. It includes conversation history, diff previews, and code-generation templates for repetitive tasks. The project also focuses on openness—developers can extend it with plugins, API configurations, and custom model backends to use Anthropic’s Claude or other compatible LLM APIs.
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  • 21
    Seamless Communication

    Seamless Communication

    Foundational Models for State-of-the-Art Speech and Text Translation

    Seamless Communication is a research project focused on building more integrated, low-latency multimodal communication between humans and AI agents. The motivation is to move beyond “text in, text out” and enable direct, live, multi-turn exchange involving language, gesture, gaze, vision, and modality switching without user friction. The system architecture includes a real-time multimodal signal pipeline for audio, video, and sensor data, a dialog manager that can decide when to act (speak,...
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  • 22
    Courses (Anthropic)

    Courses (Anthropic)

    Anthropic's educational courses

    ...Each course mixes short readings with runnable notebooks and exercises, guiding you through concepts like model parameters, streaming, multimodal prompts, structured outputs, and evaluation. Assignments emphasize realistic tasks such as building small utilities, testing prompts against edge cases, and measuring quality so you learn to ship things that work. The materials are written for developers but remain friendly to newcomers, with clear setup instructions and minimal boilerplate. Because the repo is live and maintained, lessons are updated as the SDK and models evolve, and issues are used to track fixes, clarifications, and new modules.
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  • 23
    ChatGLM3

    ChatGLM3

    ChatGLM3 series: Open Bilingual Chat LLMs | Open Source Bilingual Chat

    ChatGLM3 is ZhipuAI & Tsinghua KEG’s third-gen conversational model suite centered on the 6B-parameter ChatGLM3-6B. It keeps the series’ smooth dialog and low deployment cost while adding native tool use (function calling), a built-in code interpreter, and agent-style workflows. The family includes base and long-context variants (8K/32K/128K). The repo ships Python APIs, CLI and web demos (Gradio/Streamlit), an OpenAI-format API server, and a compact fine-tuning kit. Quantization (4/8-bit),...
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  • 24
    MiniCPM-o

    MiniCPM-o

    A GPT-4o Level MLLM for Vision, Speech and Multimodal Live Streaming

    MiniCPM-o 2.6 is a cutting-edge multimodal large language model (MLLM) designed for high-performance tasks across vision, speech, and video. Capable of running on end-side devices such as smartphones and tablets, it provides powerful features like real-time speech conversation, video understanding, and multimodal live streaming. With 8 billion parameters, MiniCPM-o 2.6 surpasses its predecessors in versatility and efficiency, making it one of the most robust models available.
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  • 25
    Marvin

    Marvin

    A batteries-included library for building AI-powered software

    ...These functions differ from conventional ones in that they don’t rely on source code, but instead generate their outputs on-demand through AI. With AI functions, you don't have to write complex code for tasks like extracting entities from web pages, scoring sentiment, or categorizing items in your database. Just describe your needs, call the function, and you're done. AI functions work with native data types, so you can seamlessly integrate them into any codebase and chain them into sophisticated pipelines. In addition to AI functions, Marvin also introduces more flexible bots. ...
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