Search Results for "requests" - Page 2
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Showing 64 open source projects for "requests"
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Anthropic SDK Python
Provides convenient access to the Anthropic REST API from any Python 3
The anthropic-sdk-python repository is the official Python client library for interacting with the Anthropic (Claude) REST API. It is designed to provide a user-friendly, type-safe, and asynchronous/synchronous capable interface for making chat/completion requests to models like Claude. The library includes definitions for all request and response parameters using Python typed objects, automatically handles serialization and deserialization, and wraps HTTP logic (timeouts, retries, error mapping) so that developers can call the API in a clean, high-level way. The SDK supports both synchronous and asynchronous usage (via async/await) depending on context. ... -
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oMLX
LLM inference server with continuous batching & SSD caching
...It serves text models, vision-language models, OCR models, embeddings, and rerankers through OpenAI- and Anthropic-compatible APIs. Continuous batching allows concurrent requests, while a tiered KV cache keeps active data in RAM and moves colder blocks to SSD for reuse. Models can be pinned, unloaded manually, or evicted automatically when memory runs low. A web dashboard provides monitoring, chat, downloads, benchmarks, integrations, and per-model settings. It also supports tool calling, structured output, MCP integration, and experimental multi-Mac inference. -
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Amazing-Python-Scripts
Curated collection of Amazing Python scripts
...Its goal is to provide developers with useful coding examples that can solve everyday problems, automate repetitive tasks, or serve as learning exercises. The repository encourages community contributions, allowing developers to add their own scripts and improve existing ones through pull requests. Examples include scripts for sentiment analysis, data scraping, web automation, log analysis, and interactive applications such as games or voice-controlled tools. The project also provides contribution guidelines and documentation so that developers can easily collaborate and expand the collection of scripts. -
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LightLLM
LightLLM is a Python-based LLM (Large Language Model) inference
...LightLLM is designed to handle large-scale model workloads in production environments, supporting efficient batching and GPU utilization for fast inference across multiple requests. Its architecture allows models to be deployed with minimal overhead while maintaining compatibility with popular transformer-based model families such as LLaMA and GPT-style architectures. -
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AI Copywriter
An AI copywriter that uses real copywriting skills + real marketing
...The workflow can generate headlines, subject lines, descriptions, interface microcopy, social posts, and strategic articles. It refuses to invent product facts or unsupported numbers and requests stronger evidence when a claim needs proof. Because the core artifact is a single SKILL.md file, it can run in Claude Code, ChatGPT, Manus, and other instruction-compatible agent systems. -
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Fish Audio Python SDK
The official Python library for the Fish Audio API
...It gives developers a programmatic way to access Fish Audio features such as text-to-speech generation, audio playback, saving output files, and API-based voice workflows. The package is designed for Python applications that need speech generation without manually handling raw HTTP requests. It supports synchronous usage for simple scripts and provides utilities that make generated audio easier to play or export. The SDK is useful for developers building voice products, prototypes, assistants, content tools, or automated audio pipelines. Its main value is simplifying integration with Fish Audio services through a clean Python interface. -
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TokenSpeed
TokenSpeed is a speed-of-light LLM inference engine
...It aims to combine TensorRT-LLM-level speed with vLLM-level usability, making it relevant for teams that need fast generation without sacrificing developer ergonomics. The project is focused on the specific needs of agentic systems, where latency, throughput, and efficient scheduling matter across many short or tool-heavy requests. It builds on ideas and components from the broader open-source inference ecosystem while presenting its own execution stack. TokenSpeed is useful for developers building local or server-side LLM infrastructure for agents, coding systems, and high-volume AI applications. Its main value is providing an inference layer optimized for fast token generation under practical agent workloads. -
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Data Science Interviews
Data science interview questions and answers
Data Science Interviews is an open-source repository that collects common data science interview questions along with community-provided answers and explanations. The project serves as a preparation resource for students, job seekers, and professionals who want to review the technical knowledge required for data science roles. The repository organizes questions into different categories including theoretical machine learning concepts, technical programming questions, and probability or... -
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Integuru v0
The first AI agent that builds permissionless integrations
...Instead of relying on official developer documentation or publicly available APIs, the system analyzes network traffic generated by user interactions within a web application. Developers capture browser requests and authentication data, which the agent then uses to infer the structure of the platform’s internal API endpoints. Based on this information, the system generates executable code that can replicate the original action programmatically. This approach allows developers to automate workflows and build integrations with services that do not provide official APIs or developer tools. ... -
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Semantic Router
Superfast AI decision making and processing of multi-modal data
Semantic Router is a superfast decision-making layer for your LLMs and agents. Rather than waiting for slow, unreliable LLM generations to make tool-use or safety decisions, we use the magic of semantic vector space — routing our requests using semantic meaning. Combining LLMs with deterministic rules means we can be confident that our AI systems behave as intended. Cramming agent tools into the limited context window is expensive, slow, and fundamentally limited. Semantic Router enables lightning-fast and cheap tool usage that can scale to many thousands of tools. ... -
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Mosec
A high-performance ML model serving framework, offers dynamic batching
Mosec is a high-performance and flexible model-serving framework for building ML model-enabled backend and microservices. It bridges the gap between any machine learning models you just trained and the efficient online service API. -
12
BentoML
Unified Model Serving Framework
...Integrate with any training pipeline or ML experimentation platform. Parallelize compute-intense model inference workloads to scale separately from the serving logic. Adaptive batching dynamically groups inference requests for optimal performance. Orchestrate distributed inference graph with multiple models via Yatai on Kubernetes. Easily configure CUDA dependencies for running inference with GPU. Automatically generate docker images for production deployment. -
13
Shell-AI
LangChain powered shell command generator and runner CLI
...Instead of requiring users to remember complex command syntax, the tool lets them describe their intent in plain English and automatically suggests commands that accomplish the task. The system is powered by large language models and integrates with frameworks such as LangChain to interpret user requests and translate them into executable shell instructions. Users interact with the program through an interactive terminal interface where multiple command suggestions are presented for review before execution. This approach improves productivity for developers and system administrators who frequently use terminal environments but may not recall every command variation. ... -
14
Context Engineering Template
Context engineering is the new vibe coding
...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. By using this template, teams can ensure consistency across AI outputs, reduce errors that stem from contextual misunderstandings, and build reusable patterns. -
15
ArXiv MCP Server
A Model Context Protocol server for searching and analyzing arXiv
...With simple tools like “search” and “fetch,” an agent can find papers, pull abstracts, and download PDFs for downstream summarization or analysis. The project includes packaging and CI to publish to PyPI, plus tests and linting for reliability. Issue threads show feature requests such as extracting embedded LaTeX and improving markdown conversion, reflecting active community use in research flows. It’s designed to be drop-in for MCP clients, giving them typed inputs/outputs and predictable errors around a well-known academic corpus. For developers building research copilots, it removes the glue work of wiring arXiv APIs into an agent toolchain. -
16
Instructor
Structured outputs for llms
Instructor is a tool that enables developers to extract structured data from natural language using Large Language Models (LLMs). Integrating with Python's Pydantic library allows users to define desired output structures through type hints, facilitating schema validation and seamless integration with IDEs. Instructor supports various LLM providers, including OpenAI, Anthropic, Litellm, and Cohere, offering flexibility in implementation. Its customizable nature permits the definition of... -
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Open SWE
Open source async coding agent that plans, codes, and opens PRs
...It integrates directly with development workflows by responding to triggers from tools like GitHub, enabling users to initiate tasks through issues or comments. Open SWE is capable of creating commits and automatically opening pull requests once implementation is complete, effectively closing the loop on development tasks. It also supports interactive feedback during execution, allowing users to guide or adjust the process mid-task. Despite its advanced capabilities, the project has been officially marked as deprecated. -
18
SageMaker Hugging Face Inference Toolkit
Library for serving Transformers models on Amazon SageMaker
...This library provides default pre-processing, predict and postprocessing for certain Transformers models and tasks. It utilizes the SageMaker Inference Toolkit for starting up the model server, which is responsible for handling inference requests. For the Dockerfiles used for building SageMaker Hugging Face Containers, see AWS Deep Learning Containers. The SageMaker Hugging Face Inference Toolkit implements various additional environment variables to simplify your deployment experience. The Hugging Face Inference Toolkit allows user to override the default methods of the HuggingFaceHandlerService. ... -
19
AI Runner
Offline inference engine for art, real-time voice conversations
...At the core of its LLM stack is a mode-based architecture with specialized “modes” such as Author, Code, Research, QA and General, and a workflow manager that automatically routes user requests to the right agent based on the task. The project has a strong focus on developer ergonomics, with thorough development guidelines, environment configuration using .env variables, and a clear structure for tests, tools and agents. -
20
GPTCache
Semantic cache for LLMs. Fully integrated with LangChain
...However, as your application grows in popularity and encounters higher traffic levels, the expenses related to LLM API calls can become substantial. Additionally, LLM services might exhibit slow response times, especially when dealing with a significant number of requests. To tackle this challenge, we have created GPTCache, a project dedicated to building a semantic cache for storing LLM responses. This project is undergoing swift development, and as such, the API may be subject to change at any time. -
21
Kaleidoscope-SDK
User toolkit for analyzing and interfacing with Large Language Models
...Users must authenticate using their Vector Institute cluster credentials. This can be done interactively instantiating a client object. This will generate an authentication token that will be used for all subsequent requests. The token will expire after 30 days, at which point the user will be prompted to re-authenticate. -
22
SPPAS
SPPAS - the automatic annotation and analyses of speech
...SPPAS is able to produce automatically speech annotations from a recorded speech sound and its orthographic transcription. SPPAS is helpful for the analysis of any annotated data: estimate statistical distributions, make requests, manage files, visualize annotations. SPPAS offers a file converter from/to a wide range of formats: xra, TextGrid, eaf, trs... <https://sppas.org> -
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High-Level Training Utilities Pytorch
High-level training, data augmentation, and utilities for Pytorch
...A high-level module for Keras-like training with callbacks, constraints, and regularizers. Comprehensive data augmentation, transforms, sampling, and loading. Utility tensor and variable functions so you don't need numpy as often. Have any feature requests? Submit an issue! I'll make it happen. Specifically, any data augmentation, data loading, or sampling functions. ModuleTrainer. The ModuleTrainer class provides a high-level training interface that abstracts away the training loop while providing callbacks, constraints, initializers, regularizers, and more. You also have access to the standard evaluation and prediction functions. ... -
24
OpenaiBot
Refractoring ChatBot+LLM, Gpt-3.5-turbo, ChatGPT Bot/Voice Assistant
...Compatibility with multiple LLMs and integration with GPT and third-party systems is handled by our llm-kira project on GitHub. It can accurately limit billing, with limits and ID binding. Supports asynchronous operations and can handle multiple requests simultaneously. Allows for private and group chats, catering to different scenarios. Implements chat rate limiting to avoid overly frequent requests. Provides entertainment and interactive features, allowing for proactive engagement with users. Includes blacklists, whitelists, and quota systems to control conversation partners. ... -
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ChatGPT-Reviewer
Automated pull requests reviewing and issues triaging with ChatGPT
Automated pull requests reviewing and issues triaging with ChatGPT. Create an OpenAI API key here, and then set the key as an action secret in your repository named OPENAI_API_KEY. The ChatGPT reviewer PRs are also getting reviewed by ChatGPT, refer the pull requests for the sample review comments. In order to protect public repositories for malicious users, Github runs all pull request workflows raised from repository forks with a read-only token and no access to secrets.
