Search Results for "answer" - Page 2
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Showing 386 open source projects for "answer"
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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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Controllable-RAG-Agent
This repository provides an advanced RAG
...At query time, it anonymizes entities, creates a high-level plan, de-anonymizes and expands that plan into concrete retrieval or reasoning tasks, and executes them in sequence while continuously revising the plan. A key focus is hallucination control: each answer is verified against retrieved context, and responses are reworked when they are not sufficiently grounded in the source documents. -
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agent-browser
Browser automation CLI for AI agents
agent-browser is a toolkit that embeds AI agent capabilities directly into the web browser, enabling agents to interact with web content, scripts, and user actions while maintaining security boundaries that respect user privacy and browser constraints. It effectively provides a sandbox where AI agents can read, scroll, click, and interpret pages in context, allowing them to automate workflows, answer questions about page content, or generate structured summaries directly from the user’s current tab. The project emphasizes standards and safety, defining interfaces that let agents access DOM data, interpret events, and generate actionable insights without exposing sensitive credential-level access or violating policy boundaries. ... -
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Rust Docs MCP Server
Prevents outdated Rust code suggestions from AI assistants
The Rust Docs MCP Server fetches documentation for specified Rust crates, generates embeddings for the content, and provides an MCP tool to answer questions about the crate based on the documentation context. -
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JoyAI-VL-Interaction
An Open Real-time Video-Language Interaction System
...It is designed to watch a webcam or livestream continuously and decide whether to speak, stay silent, or delegate a harder task. Unlike turn-based assistants, it focuses on event-driven interaction where timing matters as much as answer quality. The repository releases the model, training recipe, time-aligned interaction data, and deployable system together. Its system includes inference, WebUI, ASR, TTS, and background-agent services running on vLLM-based infrastructure. It is useful for real-time monitoring, live commentary, cooking guidance, game calling, visual alerts, and other scenarios where an AI should respond at the right moment. -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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LobsterAI
Your 24/7 all-scenario AI agent that gets work done for you
...It also supports remote control through messaging platforms, making it possible to trigger tasks from a phone. LobsterAI is designed for users who want an agent that can actually perform work, not just answer questions. -
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Edpuzzle Answers
A Javascript bookmarklet that can get the answers for Edpuzzle
Edpuzzle Answers is a JavaScript-based bookmarklet and script toolkit designed to interact with Edpuzzle assignments by automating video playback and extracting answer data from embedded questions. It operates directly within the browser, requiring no installation beyond saving the bookmarklet, and injects code into the active Edpuzzle page to modify its behavior in real time. The tool can retrieve answers for multiple-choice questions, automate responses, and adjust playback settings, effectively streamlining the completion of assignments. ... -
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AnySearch Skill
Unified real-time search engine skill for AI agents
...It supports multiple domain-specific search categories, making it useful when general web search is too broad. The skill can also fetch page content after a URL is found, which helps agents answer questions from source material rather than snippets alone. Its main value is turning web search into a reusable, agent-friendly capability with consistent inputs and outputs. -
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zhengxi-views
Zheng Xi (Efonda Fund Manager) Investment Research Agent Skill
...The project organizes a corpus of Zheng Xi’s views from 2012 to 2026, then connects those materials to an extracted investment framework. It also includes real fund data snapshots for managed funds and broader fund comparison workflows. The skill can answer source-grounded questions, explain methodology, compare funds, and score funds against Zheng Xi’s stated framework. It is positioned as a research and learning assistant, not as financial advice. -
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hey
HTTP load generator, ApacheBench (ab) replacement
...It also supports HTTP2 endpoints. It’s simple, efficient, performant, powerful, and was designed as a more modern alternative to Apache’s AB. Why ever look elsewhere one may ask? Well the answer for me is that the summary output, whilst very cool, is a little too simplistic for my use-case. -
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Coco AI App
Search, Connect, Collaborate, Personal AI Search and Assistant
...The application connects services such as Google Workspace, Dropbox, GitHub, and internal knowledge bases into a single searchable interface. It also includes a team-aware generative AI assistant that can answer questions using organization-specific data, effectively acting as a private ChatGPT for workplace knowledge. Built with modern technologies including Rust, Tauri, React, and TypeScript, the app targets high performance and cross-platform usability. The platform emphasizes privacy and supports private deployment so organizations maintain control over their data. ... -
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i-have-adhd
A skill for your coding agent to stop it from burying the answer
i-have-adhd is a compact instruction skill that makes coding assistants produce more direct and ADHD-friendly responses. It asks the agent to lead with the next action instead of opening with background, praise, or conversational filler. Multi-step tasks are numbered, tangents are suppressed, and lists are intentionally kept short. The style also restates the current state, makes completed progress visible, and describes errors without unnecessary drama. Every response should end with one... -
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Antigravity Awesome Skills
The Ultimate Collection of 700+ Agentic Skills for Claude Code
...Because it aims to reduce cognitive overhead, many skills show how to structure intents, handle context, and orchestrate multi-step reasoning without deep technical complexity. It also serves as inspiration for users looking to prototype new use cases — from conversational helpers that answer questions to workflow automators that trigger actions. -
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PaperQA2
High accuracy RAG for answering questions from scientific documents
...In this example we take a folder of research paper PDFs, magically get their metadata - including citation counts and a retraction check, then parse and cache PDFs into a full-text search index, and finally answer the user question with an LLM agent. -
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claude-video
Give Claude the ability to watch any video
Claude Video is an agent skill that gives Claude and compatible coding assistants the ability to analyze video content. It accepts public video URLs or local video files, then extracts the information needed to answer user questions about what happened on screen and in the audio. The workflow checks captions first, downloads only what is necessary, extracts timestamped frames, and produces a transcript through native captions or Whisper fallback. It supports different detail levels so users can trade speed, token cost, and visual coverage depending on the task. ... -
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SurfSense
Connect any LLM to your internal knowledge sources
...Team collaboration is a core focus, with real-time shared chats, role-based access control, and comment threads enabling organized workflows. The platform also supports advanced retrieval augmented generation (RAG) capabilities, enabling powerful search and citation features that help answer questions with contextually relevant data. -
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...It is similar to Pytest but specialized for unit testing LLM outputs. DeepEval incorporates the latest research to evaluate LLM outputs based on metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., which uses LLMs and various other NLP models that run locally on your machine for evaluation. Whether your application is implemented via RAG or fine-tuning, LangChain, or LlamaIndex, DeepEval has you covered. With it, you can easily determine the optimal hyperparameters to improve your RAG pipeline, prevent prompt drifting, or even transition from OpenAI to hosting your own Llama2 with confidence.
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GPT Discord
A robust, all-in-one GPT interface for Discord
GPTDiscord is an all-in-one AI bot that brings ChatGPT-style workflows directly into Discord. It supports long-running conversations, model settings, multimodal image understanding, and generated images. The bot can answer questions using custom documents, websites, PDFs, and Discord channel history as context. It also includes internet-connected chat, AI moderation, translation, usage tracking, and automatic retries when upstream requests fail. More advanced setups can use code interpretation, data analysis, vector search, and document indexes for richer server workflows. ... -
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Vue CLI
Standard Tooling for Vue.js Development
...Vue CLI provides a great number of features including out-of-the-box support for TypeScript, Babel, PostCSS, ESLint, PWA, as well as Unit Testing and End-to-end Testing. It comes with its own graphical user interface and a plugin system that allows for the building and sharing of reusable solutions that can answer common needs. Vue CLI provides instant prototyping with a single Vue file, and is fully configurable with no need for ejecting. Vue CLI is future-ready, allowing you to ship native ES2015 code for modern browsers, or build your Vue components as native web components. -
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uqlm
Uncertainty Quantification for Language Models, is a Python package
...The system implements a variety of uncertainty quantification techniques that assign confidence scores to model responses. These scores help developers determine how likely a generated answer is to contain errors or fabricated information. The library includes both black-box and white-box approaches to uncertainty estimation. Black-box methods evaluate model outputs through multiple generations or comparative analysis, while white-box methods rely on token probabilities produced during inference. UQLM also supports ensemble strategies and model-as-judge approaches for evaluating responses. ... -
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Make It heavy
A Python framework that emulates Grok Heavy functionality
...It is designed to emulate the style of Grok Heavy by splitting a user query into several specialized research angles. The system runs four agents in parallel so each one can explore the problem from a different perspective. It then combines their outputs into one unified answer through an intelligent synthesis step. The framework uses OpenRouter for model access and can also run in single-agent mode for simpler tasks. Overall, it is useful for users who want broader research coverage, richer reasoning diversity, and structured multi-agent responses from one command-line workflow. -
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PyTensor
Python library for defining and optimizing mathematical expressions
PyTensor is a fork of Aesara, a Python library for defining, optimizing, and efficiently evaluating mathematical expressions involving multi-dimensional arrays. PyTensor is based on Theano, which has been powering large-scale computationally intensive scientific investigations since 2007. A hackable, pure-Python codebase. Extensible graph framework is suitable for rapid development of custom operators and symbolic optimizations. Implements an extensible graph transpilation framework that... -
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Grounded Docs
Open-Source Alternative to Context7, Nia, and Ref.Tools
...By acting as an intermediary layer between documentation sources and AI tools, the server enables models to access structured documentation in a consistent and machine-readable format. This makes it easier for AI systems to answer technical questions, generate code examples, or retrieve reference material without requiring developers to manually integrate documentation into prompts. The architecture follows the MCP specification, which allows AI assistants and agent frameworks to connect to external tools through standardized protocols. -
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PandasAI
PandasAI is a Python library that integrates generative AI
PandasAI is a Python library that adds Generative AI capabilities to pandas, the popular data analysis and manipulation tool. It is designed to be used in conjunction with pandas, and is not a replacement for it. PandasAI makes pandas (and all the most used data analyst libraries) conversational, allowing you to ask questions to your data in natural language. For example, you can ask PandasAI to find all the rows in a DataFrame where the value of a column is greater than 5, and it will... -
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TreeQuest
A Tree Search Library with Flexible API for LLM Inference-Time Scaling
TreeQuest, developed by SakanaAI, is a versatile Python library implementing adaptive tree search algorithms—such as AB‑MCTS—for enhancing inference-time performance of large language models (LLMs). It allows developers to define custom state-generation and scoring functions (e.g., via LLMs), and then efficiently explores possible answer trees during runtime. With support for multi-LLM collaboration, checkpointing, and mixed policies, TreeQuest enables smarter, trial‑and‑error question answering by leveraging both breadth (multiple attempts) and depth (iterative refinement) strategies to find better outputs dynamically -
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Kernel Memory
Research project. A Memory solution for users, teams, and applications
...The project focuses on enabling applications to store, index, and retrieve information so that AI systems can incorporate external knowledge when generating responses. It supports scenarios such as document ingestion, semantic search, and retrieval-augmented generation, allowing language models to answer questions using contextual information from private or enterprise datasets. Kernel Memory can ingest documents in multiple formats, process them into embeddings, and store them in searchable indexes. Applications can then query these indexed data sources to retrieve relevant information and include it as context for AI responses.
