Search Results for "self-host" - Page 4
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Showing 179 open source projects for "self-host"
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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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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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cognee
Deterministic LLMs Outputs for AI Applications and AI Agents
Cognee implements scalable, modular data pipelines that allow for creating the LLM-enriched data layer using graph and vector stores. Cognee acts a semantic memory layer, unveiling hidden connections within your data and infusing it with your company's language and principles. This self-optimizing process ensures ultra-relevant, personalized, and contextually aware LLM retrievals. Any kind of data works; unstructured text or raw media files, PDFs, tables, presentations, JSON files, and so many more. Add small or large files, or many files at once. We map out a knowledge graph from all the facts and relationships we extract from your data. ... -
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Hindsight
Hindsight: Agent Memory That Learns
Hindsight is an advanced, open-source memory system for AI agents designed to enable long-term learning, reasoning, and consistency across interactions by treating memory as a first-class component of intelligence rather than a simple retrieval layer. It addresses one of the core limitations of modern AI agents, which is their inability to retain and meaningfully use past experiences over time, by introducing a structured, biomimetic memory architecture inspired by how human memory works.... -
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ChatGPT Academic
ChatGPT extension for scientific research work
ChatGPT extension for scientific research work, specially optimized academic paper polishing experience, supports custom shortcut buttons, supports custom function plug-ins, supports markdown table display, double display of Tex formulas, complete code display function, new local Python/C++/Go project tree Analysis function/Project source code self-translation ability, newly added PDF and Word document batch summary function/PDF paper full-text translation function. All buttons are dynamically generated by reading functional.py, you can add custom functions at will, and liberate the pasteboard. Support for markdown tables output by GPT. If the output contains a formula, it will be displayed in tex form and rendered form at the same time, which is convenient for copying and reading. -
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claude-obsidian
Claude + Obsidian knowledge companion
claude-obsidian is an AI-powered knowledge engine that transforms an Obsidian vault into a self-organizing, continuously evolving wiki. Instead of acting as a simple chat assistant, it autonomously creates, links, and maintains structured knowledge based on user inputs and external sources. The system follows the LLM Wiki pattern, where information is stored as persistent markdown files that grow richer over time through cross-referencing and synthesis. -
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
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E2B Desktop Sandbox
E2B Desktop Sandbox for LLMs. E2B Sandbox
E2B Desktop is an open-source sandboxed virtual desktop environment designed to enable secure “computer use” by large language models and automated agents. The platform provides isolated virtual machines where applications can be executed safely without affecting the host system. Each sandbox runs independently and can be configured with custom dependencies or tools required by an AI agent or automation workflow. The system allows developers to programmatically create and control these virtual desktops through SDKs available in languages such as Python and JavaScript. Within a sandbox, developers can launch applications like browsers, editors, or other software that an AI agent may need to interact with. ... -
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Habit Tracker
Habit Tracker for the AI Coding Workshop
...It runs locally with a FastAPI backend (Python) and a React frontend, storing all data in a lightweight SQLite database so there’s no need for user accounts or cloud storage, which keeps habit data fully private and self-contained. The app provides streak tracking and completion rates for each habit, giving users feedback on consistency and motivation by showing how often habits are completed and where they may be lagging. A calendar view lets users see a monthly grid of their habit history with color-coded days to highlight patterns and encourage daily engagement. ... -
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mcp-use
A solution to build and deploy MCP agents and applications
...Developers can create custom agents (via MCPAgent) that dynamically select the most appropriate server for each task using configurable pipelines or a built-in server manager. It simplifies authentication, access control, audit logging, observability, sandboxed runtime environments, and deployment workflows, whether self-hosted or managed, making MCP development production-ready. With integrations for popular frameworks like LangChain (Python) and LangChain.js (TypeScript), mcp-use accelerates the creation of tool-enabled AI agents. -
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Homemade Machine Learning
Python examples of popular machine learning algorithms
homemade-machine-learning is a repository by Oleksii Trekhleb containing Python implementations of classic machine-learning algorithms done “from scratch”, meaning you don’t rely heavily on high-level libraries but instead write the logic yourself to deepen understanding. Each algorithm is accompanied by mathematical explanations, visualizations (often via Jupyter notebooks), and interactive demos so you can tweak parameters, data, and observe outcomes in real time. The purpose is... -
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yourself-skill
Instead of distilling others, it is better to distil yourself
yourself-skill is an AI skill framework focused on self-reflection and personalization, enabling agents to adapt their behavior based on user context and interaction history. It encourages systems to maintain awareness of user preferences, goals, and communication styles. The project emphasizes building more human-aligned interactions by incorporating memory and contextual reasoning. It can be integrated into broader AI systems to improve personalization and continuity across sessions. ... -
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Agents 2.0
An Open-source Framework for Data-centric Language Agents
Agents is an open-source framework designed to build and train autonomous language agents through a data-centric and learning-oriented architecture. The project introduces a concept known as agent symbolic learning, which treats an agent pipeline similarly to a neural network computational graph. In this framework, each node in the pipeline represents a step in the reasoning or action process, while prompts and tools act as adjustable parameters analogous to neural network weights. During... -
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SkillOpt
Text-space optimizer that trains reusable natural-language skills
...The project is focused on making agents more effective through text-space optimization rather than traditional fine-tuning. It is most useful for AI researchers and agent developers studying self-improving workflows, skill libraries, and evaluation-driven prompt refinement. -
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AWorld
Build, evaluate and train General Multi-Agent Assistance with ease
AWorld (Agent World) is an agent runtime/framework. It supports building, evaluating, and training self-improving intelligent agents and multi-agent systems (MAS). It is designed to provide infrastructure for agent orchestration, iterative learning, and environment interaction at scale. Scalable training across environments and distributed setups. Support for multi-agent collaboration/orchestration (MAS). The system is intended to help agents evolve via experience. -
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Hamilton DAGWorks
Helps scientists define testable, modular, self-documenting dataflow
Hamilton is a lightweight Python library for directed acyclic graphs (DAGs) of data transformations. Your DAG is portable; it runs anywhere Python runs, whether it's a script, notebook, Airflow pipeline, FastAPI server, etc. Your DAG is expressive; Hamilton has extensive features to define and modify the execution of a DAG (e.g., data validation, experiment tracking, remote execution). To create a DAG, write regular Python functions that specify their dependencies with their parameters. As... -
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Aim
An easy-to-use & supercharged open-source experiment tracker
...The Aim standard package comes with all integrations. If you'd like to modify the integration and make it custom, create a new integration package and share with others. Aim is an open-source, self-hosted AI Metadata tracking tool designed to handle 100,000s of tracked metadata sequences. The two most famous AI metadata applications are: experiment tracking and prompt engineering. Aim provides a performant and beautiful UI for exploring and comparing training runs, and prompt sessions. -
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Scikit-LLM
Seamlessly integrate LLMs into scikit-learn
...Note: unlike in a typical supervised setting, the performance of a zero-shot classifier greatly depends on how the label itself is structured. It has to be expressed in natural language, descriptive, and self-explanatory. -
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x-transformers
A simple but complete full-attention transformer
A simple but complete full-attention transformer with a set of promising experimental features from various papers. Proposes adding learned memory key/values prior to attending. They were able to remove feedforwards altogether and attain a similar performance to the original transformers. I have found that keeping the feedforwards and adding the memory key/values leads to even better performance. Proposes adding learned tokens, akin to CLS tokens, named memory tokens, that is passed through... -
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deep-q-learning
Minimal Deep Q Learning (DQN & DDQN) implementations in Keras
...For learners and researchers interested in reinforcement learning, this repo offers a concrete, runnable example bridging theory and practice: you can execute the code, play with hyperparameters, observe convergence behavior, and see how deep Q-learning learns policies over time in standard environments. Because it’s self-contained and Python-based, it's well-suited for experimentation, modifications, or extension — for instance adapting to custom Gym environments, tweaking network architecture, or combining with other RL techniques. -
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Diplomacy Cicero
Code for Cicero, an AI agent that plays the game of Diplomacy
...The codebase is implemented primarily in Python with performance-critical components in C++ (via pybind11 bindings) and is configured to run in a high‐GPU cluster environment. Configuration is managed via protobuf files to define tasks such as self-play, benchmark agent comparisons, and RL training. The project is now archived and read-only, reflecting that it is no longer actively developed but remains publicly available for research use. -
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dm_control
DeepMind's software stack for physics-based simulation
DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo. DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo physics. The MuJoCo Python bindings support three different OpenGL rendering backends: EGL (headless, hardware-accelerated), GLFW (windowed, hardware-accelerated), and OSMesa (purely software-based). At least one of these three backends must be available in order render... -
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AWS MCP Servers
Helping you get the most out of AWS, wherever you use MCP
AWS MCP Servers are a collection of remotely hosted, fully-managed Model Context Protocol (MCP) servers by AWS, providing AI applications with real-time access to AWS documentation, API references, best practices, and infrastructure-management capabilities via natural-language workflows. An MCP Server is a lightweight program that exposes specific capabilities through the standardized Model Context Protocol. Host applications (such as chatbots, IDEs, and other AI tools) have MCP clients that maintain 1:1 connections with MCP servers. Common MCP clients include agentic AI coding assistants (like Q Developer, Cline, Cursor, Windsurf) as well as chatbot applications like Claude Desktop, with more clients coming soon. MCP servers can access local data sources and remote services to provide additional context that improves the generated outputs from the models. -
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hCaptcha Challenger
Gracefully face hCaptcha challenge with multimodal llms
hCaptcha Challenger is an open-source automation framework designed to solve hCaptcha verification challenges using computer vision models and multimodal reasoning techniques. The project integrates machine learning models capable of analyzing visual captcha tasks and identifying the correct responses required to pass the verification process. Instead of relying on third-party captcha-solving services or browser scripts, the system operates independently by using pretrained neural networks... -
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FastKoko
Dockerized FastAPI wrapper for Kokoro-82M text-to-speech model
FastKoko is a self-hosted text-to-speech server built around the Kokoro-82M model and exposed through a FastAPI backend. It is designed to be easy to deploy via Docker, with separate CPU and GPU images so that users can choose between pure CPU inference and NVIDIA GPU acceleration. The project exposes an OpenAI-compatible speech endpoint, which means existing code that talks to the OpenAI audio API can often be pointed at a Kokoro-FastAPI instance with minimal changes. -
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MaxText
A simple, performant and scalable Jax LLM
MaxText is a high-performance, highly scalable open-source framework designed to train and fine-tune large language models using the JAX ecosystem. The project acts as both a reference implementation and a practical training library that demonstrates best practices for building and scaling transformer-based language models on modern accelerator hardware. It is optimized to run efficiently on Google Cloud TPUs and GPUs, enabling researchers and engineers to train models ranging from small... -
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FATE
An industrial grade federated learning framework
...It implements secure computation protocols based on homomorphic encryption and multi-party computation (MPC). Supporting various federated learning scenarios, FATE now provides a host of federated learning algorithms, including logistic regression, tree-based algorithms, deep learning and transfer learning. FATE became open-source in February 2019. FATE TSC was established to lead FATE open-source community, with members from major domestic cloud computing and financial service enterprises. FedAI is a community that helps businesses and organizations build AI models effectively and collaboratively, by using data in accordance with user privacy protection, data security, data confidentiality and government regulations. -
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Ollama RAG Chatbot
Chat with multiple PDFs locally
...With Docker support, script-based setup, optional ngrok exposure, and a clear local run path, it serves as a compact starter project for people who want a hands-on, self-hosted PDF chat system.
