Showing 4644 open source projects for "development"

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  • 1
    Hephaestus

    Hephaestus

    Semi-Structured Agentic Framework. Workflows build themselves

    Hephaestus is an open-source semi-structured agentic framework designed to orchestrate multiple AI agents working together on complex tasks. Instead of relying entirely on predefined workflows, the framework allows agents to dynamically create tasks as they explore a problem space. Developers define high-level phases such as analysis, implementation, and testing, while agents generate specific subtasks within those phases. The system continuously monitors agent behavior and task progression,...
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  • 2
    TokenCost

    TokenCost

    Easy token price estimates for 400+ LLMs. TokenOps

    ...It works by counting tokens in prompts and responses before or after sending requests and then applying pricing information associated with different models. This allows engineers building AI applications, chatbots, or autonomous agents to monitor and predict API expenses during development and production. The library includes pricing information for hundreds of language models and is frequently updated to reflect pricing changes from major AI providers.
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  • 3
    LlamaDeploy

    LlamaDeploy

    Deploy your agentic worfklows to production

    llama_deploy is an open-source framework designed to simplify the deployment and productionization of agent-based AI workflows built with the LlamaIndex ecosystem. The project provides an asynchronous architecture that allows developers to deploy complex multi-agent workflows as scalable microservices. It enables teams to move from experimental prototypes to production systems with minimal changes to existing LlamaIndex code, making it easier to operationalize AI agents. The system supports...
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  • 4
    LMOps

    LMOps

    General technology for enabling AI capabilities w/ LLMs and MLLMs

    LMOps is a research initiative and open-source toolkit focused on the development and operational management of AI applications built with large language models and generative AI systems. The project explores the technologies and methodologies required to move foundation models from research environments into production-grade AI products. It includes experimental tools and frameworks that help developers optimize prompts, design workflows for generative models, and manage the lifecycle of LLM-based systems. ...
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  • 5
    llmware

    llmware

    Unified framework for building enterprise RAG pipelines

    llmware is an open source framework designed to simplify the creation of enterprise-grade applications powered by large language models. The platform focuses on building secure and private AI workflows that can run locally on laptops, edge devices, or self-hosted servers without relying exclusively on cloud APIs. It provides a unified interface for constructing retrieval-augmented generation pipelines, agent workflows, and document intelligence applications. One of the framework’s defining...
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  • 6
    Happy-LLM

    Happy-LLM

    Large Language Model Principles and Practice Tutorial from Scratch

    Happy-LLM is an open-source educational project created by the Datawhale AI community that provides a structured and comprehensive tutorial for understanding and building large language models from scratch. The project guides learners through the entire conceptual and practical pipeline of modern LLM development, starting with foundational natural language processing concepts and gradually progressing to advanced architectures and training techniques. It explains the Transformer architecture, pre-training paradigms, and model scaling strategies while also providing hands-on coding examples so readers can implement and experiment with their own models. ...
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  • 7
    ZAPI

    ZAPI

    ZAPI by Adopt AI is an open-source Python library

    ...Zapi abstracts many repetitive tasks such as validation, authentication flows, and error handling so developers can focus on business logic instead of infrastructure plumbing. It integrates smoothly into modern development stacks, supports hot reloading for rapid iteration, and includes a command-line toolchain for scaffolding new endpoints or services with sensible defaults. The framework also supports plugin extensions that add things like rate limiting, caching layers, and telemetry without cluttering core code.
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  • 8
    Agentic Data Scientist

    Agentic Data Scientist

    An end-to-end Data Scientist

    Agentic Data Scientist is an experimental AI-driven research framework that orchestrates data science workflows through autonomous agents that can reason, plan, and execute complex analytics tasks. Unlike traditional scripted pipelines, this project lets AI agents break down high-level research goals into sub-tasks such as data acquisition, cleaning, modeling, evaluation, and reporting, with minimal human direction. Each agent is designed to independently call functions, interact with data...
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  • 9
    SERA CLI

    SERA CLI

    A tool to use the Ai2 Open Coding Agents Soft-Verified Agents

    ...The project is targeted at practitioners and researchers in the AI space who need a flexible but powerful CLI interface for model invocation, endpoint configuration, and integration with development pipelines.
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  • 10
    Claude Cognitive

    Claude Cognitive

    Persistent context and multi-instance coordination

    Claude Cognitive is an advanced memory and context-management extension designed to address the stateless limitations of Claude Code by giving the model a form of persistent “working memory” and multi-instance coordination. It introduces an attention-based context router that prioritizes files and content relevant to the current development discussion — tagging them as HOT, WARM, or COLD based on recency and keyword activation — so Claude Code doesn’t waste token budget rereading irrelevant code. This context routing dramatically reduces redundant token usage and accelerates large codebase interactions by focusing only on what truly matters to the current task. Additionally, Claude-Cognitive includes a pool coordinator to share state across multiple Claude Code instances, preserving what’s been learned or completed and preventing repetitive debugging or redundant exploration.
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  • 11
    MiniMind-V

    MiniMind-V

    "Big Model" trains a visual multimodal VLM with 26M parameters

    MiniMind-V is an experimental open-source project that aims to train a very small multimodal vision–language model (VLM) from scratch with extremely low compute and cost, making research and experimentation accessible to more people. The repository showcases training workflows and code designed to produce a 26-million parameter model—including both image and text capabilities—using minimal resources in very little time, reflecting a trend toward democratizing AI research. MiniMind-V combines...
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  • 12
    RamaLama

    RamaLama

    Simplifies the local serving of AI models from any source

    ...Developers can use familiar container commands to pull, run, and interact with AI models from any source, treating models similarly to how container images are handled in OCI workflows. RamaLama supports multiple model registries and offers a REST API or chatbot interface for interacting with running models, making it flexible for local development, testing, or integration into larger systems.
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  • 13
    MedGemma

    MedGemma

    Collection of Gemma 3 variants that are trained for performance

    MedGemma is a collection of specialized open-source AI models created by Google as part of its Health AI Developer Foundations initiative, built on the Gemma 3 family of transformer models and trained for medical text and image comprehension tasks that help accelerate the development of healthcare-focused AI applications. It includes multiple variants such as a 4 billion-parameter multimodal model that can process both medical images and text and a 27 billion-parameter text-only (and multimodal) model that offers deeper clinical reasoning and understanding at higher capacity, making it suitable for complex tasks like medical question answering, summarization of clinical notes, or generating reports from radiology images. ...
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  • 14
    Ling

    Ling

    Ling is a MoE LLM provided and open-sourced by InclusionAI

    ...As more developers and researchers engage with the platform, we can expect rapid advancements and improvements, leading to even more sophisticated applications. Model inference and API code (e.g. integration with Transformers). This collaborative approach accelerates development and ensures that the models remain at the forefront of technology, addressing emerging challenges in various fields.
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  • 15
    AReal

    AReal

    Lightning-Fast RL for LLM Reasoning and Agents. Made Simple & Flexible

    ...It is intended to facilitate reproducible RL training on reasoning / agentic tasks, supporting scaling from single nodes to large GPU clusters. It can streamline the development of AI agents and reasoning systems. Support for algorithm and system co-design optimizations (to improve efficiency and stability).
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  • 16
    Agno

    Agno

    Lightweight framework for building Agents with memory, knowledge, etc.

    Agno is a modular, open-source artificial general intelligence (AGI) research platform that allows developers to build, evaluate, and experiment with cognitive architectures in a composable way. It provides a flexible framework for modeling reasoning, memory, decision-making, and planning, aimed at long-term AI research beyond narrow learning. Agno embraces multi-agent environments and symbolic reasoning as part of its core design, enabling experiments with structured knowledge,...
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  • 17
    Taipy

    Taipy

    Turns Data and AI algorithms into production-ready web applications

    From simple pilots to production-ready web applications in no time. No more compromise on performance, customization, and scalability. Taipy enhances performance with caching control of graphical events, optimizing rendering by selectively updating graphical components only upon interaction. Effortlessly manage massive datasets with Taipy's built-in decimator for charts, intelligently reducing the number of data points to save time and memory without losing the essence of your data's shape....
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  • 18
    AWS Secrets Manager Python caching

    AWS Secrets Manager Python caching

    Enables in-process caching of secrets for Python applications

    The AWS Secrets Manager Python caching client enables in-process caching of secrets for Python applications. To use this client you must have Python 3.6 or newer. Use of Python versions 3.5 or older are not supported. An Amazon Web Services (AWS) account to access secrets stored in AWS Secrets Manager. To create an AWS account, go to Sign In or Create an AWS Account and then choose I am a new user. Follow the instructions to create an AWS account. To create a secret in AWS Secrets Manager,...
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  • 19
    HTTPie Desktop

    HTTPie Desktop

    Cross-platform API testing client for humans

    ...It also includes AI-assisted features to help streamline request creation and improve productivity. Overall, HTTPie Desktop provides a clean, intuitive, and powerful environment for API development across devices.
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  • 20
    MiniStack

    MiniStack

    Ministack: Free, open-source local AWS emulator

    ...The system is designed to be drop-in compatible with existing AWS tooling, including boto3, Terraform, AWS CLI, and other SDKs, making migration seamless for existing projects. MiniStack also prioritizes efficiency, with a significantly smaller footprint and faster startup time compared to alternatives, making it ideal for local development environments.
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  • 21
    Colab-MCP

    Colab-MCP

    An MCP server for interacting with Google Colab

    ...Instead of relying on manual notebook usage, the system allows MCP-compatible agents to execute code, manage files, install dependencies, and orchestrate entire development workflows within Colab’s cloud infrastructure. This approach bridges the gap between local AI agents and remote high-performance compute environments, allowing users to offload heavy workloads such as machine learning training, data analysis, and dependency-heavy tasks to Colab’s GPU and TPU resources. By exposing Colab as an MCP server, the tool enables seamless integration with a wide range of AI assistants and agent frameworks, creating a standardized interface for tool use and execution.
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  • 22
    Wanwu AI Agent Platform

    Wanwu AI Agent Platform

    Enterprise AI agent platform for workflows, models, and RAG apps

    Wanwu is an enterprise-grade AI agent development platform designed to help organizations build and deploy intelligent applications at scale. It provides a multi-tenant environment that enables teams to create AI agents, orchestrate workflows, and implement retrieval-augmented generation systems within a unified framework. Wanwu integrates large language models with business process automation, allowing developers to design complex, production-ready AI solutions tailored to enterprise needs. ...
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  • 23
    Nexent

    Nexent

    Zero-code platform for building AI agents from natural language input

    ...It focuses on a zero-code approach, allowing users to define workflows and agent behavior purely through language prompts, significantly lowering the barrier to entry for AI development. Built on the MCP ecosystem, Nexent integrates a wide range of tools, models, and data sources into a unified environment for agent creation and execution. Nexent supports multi-agent collaboration, enabling multiple intelligent agents to interact and coordinate tasks within complex workflows. It also includes capabilities for data processing, knowledge tracing, and multimodal interaction, allowing agents to work with different input and output formats. ...
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  • 24
    TypeChat

    TypeChat

    Library for building type-safe natural language interfaces with LLMs

    TypeChat is an open source library developed by Microsoft that simplifies the creation of natural language interfaces by using type definitions to structure interactions with large language models. Traditional natural language interfaces often relied on complex decision trees to interpret user intent and gather required inputs. With the rise of large language models, developers can interpret user requests more easily, but they still face challenges related to output reliability, safety, and...
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  • 25
    Astron Agent

    Astron Agent

    Enterprise platform for building and orchestrating AI agent workflows

    Astron Agent is an enterprise-grade platform designed for building and managing intelligent AI agent workflows in production environments. It provides a development environment that combines workflow orchestration, model management, and integration with various AI tools and services. Astron Agent enables organizations to design complex agent-driven processes that coordinate models, automation tools, and enterprise systems. It also integrates robotic process automation capabilities so agents can execute tasks across digital systems instead of only generating responses. ...
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