Best AI tools for< Llm Frameworks >

SWE Kit is an open-source headless IDE designed for building custom coding agents with state-of-the-art performance. It offers AI-native tools to streamline the coding review process, enhance code quality, and optimize development efficiency. The application supports various agentic frameworks and LLM inference providers, providing a flexible runtime environment for seamless codebase interaction. With features like code analysis, code indexing, and third-party service integrations, SWE Kit empowers developers to create and run coding agents effortlessly.

Langtrace AI is an open-source observability tool powered by Scale3 Labs that helps monitor, evaluate, and improve LLM (Large Language Model) applications. It collects and analyzes traces and metrics to provide insights into the ML pipeline, ensuring security through SOC 2 Type II certification. Langtrace supports popular LLMs, frameworks, and vector databases, offering end-to-end observability and the ability to build and deploy AI applications with confidence.

FineTuneAIs.com is a platform that specializes in custom AI model fine-tuning. Users can fine-tune their AI models to achieve better performance and accuracy. The platform requires JavaScript to be enabled for optimal functionality.

Lakera is the world's most advanced AI security platform that offers cutting-edge solutions to safeguard GenAI applications against various security threats. Lakera provides real-time security controls, stress-testing for AI systems, and protection against prompt attacks, data loss, and insecure content. The platform is powered by a proprietary AI threat database and aligns with global AI security frameworks to ensure top-notch security standards. Lakera is suitable for security teams, product teams, and LLM builders looking to secure their AI applications effectively and efficiently.

Lakera is the world's most advanced AI security platform designed to protect organizations from AI threats. It offers solutions for prompt injection detection, unsafe content identification, PII and data loss prevention, data poisoning prevention, and insecure LLM plugin design. Lakera is recognized for setting global AI security standards and is trusted by leading enterprises, foundation model providers, and startups. The platform is powered by a proprietary AI threat database and aligns with global AI security frameworks.

Portkey is a control panel for production AI applications that offers an AI Gateway, Prompts, Guardrails, and Observability Suite. It enables teams to ship reliable, cost-efficient, and fast apps by providing tools for prompt engineering, enforcing reliable LLM behavior, integrating with major agent frameworks, and building AI agents with access to real-world tools. Portkey also offers seamless AI integrations for smarter decisions, with features like managed hosting, smart caching, and edge compute layers to optimize app performance.

deepset is an AI platform that offers enterprise-level products and solutions for AI teams. It provides deepset Cloud, a platform built with Haystack, enabling fast and accurate prototyping, building, and launching of advanced AI applications. The platform streamlines the AI application development lifecycle, offering processes, tools, and expertise to move from prototype to production efficiently. With deepset Cloud, users can optimize solution accuracy, performance, and cost, and deploy AI applications at any scale with one click. The platform also allows users to explore new models and configurations without limits, extending their team with access to world-class AI engineers for guidance and support.

LlamaIndex is a leading data framework designed for building LLM (Large Language Model) applications. It allows enterprises to turn their data into production-ready applications by providing functionalities such as loading data from various sources, indexing data, orchestrating workflows, and evaluating application performance. The platform offers extensive documentation, community-contributed resources, and integration options to support developers in creating innovative LLM applications.

LlamaIndex is a framework for building context-augmented Large Language Model (LLM) applications. It provides tools to ingest and process data, implement complex query workflows, and build applications like question-answering chatbots, document understanding systems, and autonomous agents. LlamaIndex enables context augmentation by combining LLMs with private or domain-specific data, offering tools for data connectors, data indexes, engines for natural language access, chat engines, agents, and observability/evaluation integrations. It caters to users of all levels, from beginners to advanced developers, and is available in Python and Typescript.

Lyzr AI is a full-stack agent framework designed to build GenAI applications faster. It offers a range of AI agents for various tasks such as chatbots, knowledge search, summarization, content generation, and data analysis. The platform provides features like memory management, human-in-loop interaction, toxicity control, reinforcement learning, and custom RAG prompts. Lyzr AI ensures data privacy by running data locally on cloud servers. Enterprises and developers can easily configure, deploy, and manage AI agents using Lyzr's platform.

Haystack is a production-ready open-source AI framework designed to facilitate building AI applications. It offers a flexible components and pipelines architecture, allowing users to customize and build applications according to their specific requirements. With partnerships with leading LLM providers and AI tools, Haystack provides freedom of choice for users. The framework is built for production, with fully serializable pipelines, logging, monitoring integrations, and deployment guides for full-scale deployments on various platforms. Users can build Haystack apps faster using deepset Studio, a platform for drag-and-drop construction of pipelines, testing, debugging, and sharing prototypes.

LangChain is a framework for developing applications powered by large language models (LLMs). It simplifies every stage of the LLM application lifecycle, including development, productionization, and deployment. LangChain consists of open-source libraries such as langchain-core, langchain-community, and partner packages. It also includes LangGraph for building stateful agents and LangSmith for debugging and monitoring LLM applications.

DHTMLX is a JavaScript/HTML5 UI framework that offers a wide range of user-friendly AI chatbot and other UI components. It provides feature-rich libraries for project management, data analysis, content management, and more. DHTMLX is known for its easy customization, simple API, and extensive documentation, making it a popular choice for web developers worldwide.

LangChain is an AI tool that offers a suite of products supporting developers in the LLM application lifecycle. It provides a framework to construct LLM-powered apps easily, visibility into app performance, and a turnkey solution for serving APIs. LangChain enables developers to build context-aware, reasoning applications and future-proof their applications by incorporating vendor optionality. LangSmith, a part of LangChain, helps teams improve accuracy and performance, iterate faster, and ship new AI features efficiently. The tool is designed to drive operational efficiency, increase discovery & personalization, and deliver premium products that generate revenue.

Patched is an open-source workflow automation framework designed for development teams to build AI workflows that automate code reviews, documentation, and patches. It offers ready-to-go patchflows or the ability to create custom ones to accelerate mundane development tasks. Patched integrates seamlessly with popular platforms like Gitlab, GitHub, Jira, and more, allowing users to improve code quality, fix bugs, and create tickets efficiently. The application is privacy-focused, allowing users to deploy it within their own infrastructure for complete privacy. Patched is free and open-source, offering customization options via code or a no-code builder.

Awan LLM is an AI tool that offers an Unlimited Tokens, Unrestricted, and Cost-Effective LLM Inference API Platform for Power Users and Developers. It allows users to generate unlimited tokens, use LLM models without constraints, and pay per month instead of per token. The platform features an AI Assistant, AI Agents, Roleplay with AI companions, Data Processing, Code Completion, and Applications for profitable AI-powered applications.

LLM Clash is a web-based application that allows users to compare the outputs of different large language models (LLMs) on a given task. Users can input a prompt and select which LLMs they want to compare. The application will then display the outputs of the LLMs side-by-side, allowing users to compare their strengths and weaknesses.

LLM Price Check is an AI tool designed to compare and calculate the latest prices for Large Language Models (LLM) APIs from leading providers such as OpenAI, Anthropic, Google, and more. Users can use the streamlined tool to optimize their AI budget efficiently by comparing pricing, sorting by various parameters, and searching for specific models. The tool provides a comprehensive overview of pricing information to help users make informed decisions when selecting an LLM API provider.

The LLM Token Counter is a sophisticated tool designed to help users effectively manage token limits for various Language Models (LLMs) like GPT-3.5, GPT-4, Claude-3, Llama-3, and more. It utilizes Transformers.js, a JavaScript implementation of the Hugging Face Transformers library, to calculate token counts client-side. The tool ensures data privacy by not transmitting prompts to external servers.

LLM Quality Beefer-Upper is an AI tool designed to enhance the quality and productivity of LLM responses by automating critique, reflection, and improvement. Users can generate multi-agent prompt drafts, choose from different quality levels, and upload knowledge text for processing. The application aims to maximize output quality by utilizing the best available LLM models in the market.

IPEX-LLM is a PyTorch library for running Large Language Models (LLMs) on Intel CPUs and GPUs with very low latency. It provides seamless integration with various LLM frameworks and tools, including llama.cpp, ollama, Text-Generation-WebUI, HuggingFace transformers, and more. IPEX-LLM has been optimized and verified on over 50 LLM models, including LLaMA, Mistral, Mixtral, Gemma, LLaVA, Whisper, ChatGLM, Baichuan, Qwen, and RWKV. It supports a range of low-bit inference formats, including INT4, FP8, FP4, INT8, INT2, FP16, and BF16, as well as finetuning capabilities for LoRA, QLoRA, DPO, QA-LoRA, and ReLoRA. IPEX-LLM is actively maintained and updated with new features and optimizations, making it a valuable tool for researchers, developers, and anyone interested in exploring and utilizing LLMs.

The Awesome-LLM-Post-training repository is a curated collection of influential papers, code implementations, benchmarks, and resources related to Large Language Models (LLMs) Post-Training Methodologies. It covers various aspects of LLMs, including reasoning, decision-making, reinforcement learning, reward learning, policy optimization, explainability, multimodal agents, benchmarks, tutorials, libraries, and implementations. The repository aims to provide a comprehensive overview and resources for researchers and practitioners interested in advancing LLM technologies.

LLM Reasoners is a library that enables LLMs to conduct complex reasoning, with advanced reasoning algorithms. It approaches multi-step reasoning as planning and searches for the optimal reasoning chain, which achieves the best balance of exploration vs exploitation with the idea of "World Model" and "Reward". Given any reasoning problem, simply define the reward function and an optional world model (explained below), and let LLM reasoners take care of the rest, including Reasoning Algorithms, Visualization, LLM calling, and more!

This repository is an awesome list dedicated to resources for using Large Language Models (LLMs) to generate JSON or other structured outputs. It includes terminology explanations, hosted and local models, Python libraries, blog articles, videos, Jupyter notebooks, and leaderboards related to LLMs and JSON generation. The repository covers various aspects such as function calling, JSON mode, guided generation, and tool usage with different providers and models.

This repository is a collection of references to Azure OpenAI, Large Language Models (LLM), and related services and libraries. It provides information on various topics such as RAG, Azure OpenAI, LLM applications, agent design patterns, semantic kernel, prompting, finetuning, challenges & abilities, LLM landscape, surveys & references, AI tools & extensions, datasets, and evaluations. The content covers a wide range of topics related to AI, machine learning, and natural language processing, offering insights into the latest advancements in the field.

The awesome-langchain-zh repository is a collection of resources related to LangChain, a framework for building AI applications using large language models (LLMs). The repository includes sections on the LangChain framework itself, other language ports of LangChain, tools for low-code development, services, agents, templates, platforms, open-source projects related to knowledge management and chatbots, as well as learning resources such as notebooks, videos, and articles. It also covers other LLM frameworks and provides additional resources for exploring and working with LLMs. The repository serves as a comprehensive guide for developers and AI enthusiasts interested in leveraging LangChain and LLMs for various applications.

Kolo is a lightweight tool for fast and efficient data generation, fine-tuning, and testing of Large Language Models (LLMs) on your local machine. It simplifies the fine-tuning and data generation process, runs locally without the need for cloud-based services, and supports popular LLM toolkits. Kolo is built using tools like Unsloth, Torchtune, Llama.cpp, Ollama, Docker, and Open WebUI. It requires Windows 10 OS or higher, Nvidia GPU with CUDA 12.1 capability, and 8GB+ VRAM, and 16GB+ system RAM. Users can join the Discord group for issues or feedback. The tool provides easy setup, training data generation, and integration with major LLM frameworks.

Ragas is a framework that helps you evaluate your Retrieval Augmented Generation (RAG) pipelines. RAG denotes a class of LLM applications that use external data to augment the LLM’s context. There are existing tools and frameworks that help you build these pipelines but evaluating it and quantifying your pipeline performance can be hard. This is where Ragas (RAG Assessment) comes in. Ragas provides you with the tools based on the latest research for evaluating LLM-generated text to give you insights about your RAG pipeline. Ragas can be integrated with your CI/CD to provide continuous checks to ensure performance.

Awesome-AI-Agents is a curated list of projects, frameworks, benchmarks, platforms, and related resources focused on autonomous AI agents powered by Large Language Models (LLMs). The repository showcases a wide range of applications, multi-agent task solver projects, agent society simulations, and advanced components for building and customizing AI agents. It also includes frameworks for orchestrating role-playing, evaluating LLM-as-Agent performance, and connecting LLMs with real-world applications through platforms and APIs. Additionally, the repository features surveys, paper lists, and blogs related to LLM-based autonomous agents, making it a valuable resource for researchers, developers, and enthusiasts in the field of AI.

Langtrace is an open source observability software that lets you capture, debug, and analyze traces and metrics from all your applications that leverage LLM APIs, Vector Databases, and LLM-based Frameworks. It supports Open Telemetry Standards (OTEL), and the traces generated adhere to these standards. Langtrace offers both a managed SaaS version (Langtrace Cloud) and a self-hosted option. The SDKs for both Typescript/Javascript and Python are available, making it easy to integrate Langtrace into your applications. Langtrace automatically captures traces from various vendors, including OpenAI, Anthropic, Azure OpenAI, Langchain, LlamaIndex, Pinecone, and ChromaDB.

SalesGPT is an open-source AI agent designed for sales, utilizing context-awareness and LLMs to work across various communication channels like voice, email, and texting. It aims to enhance sales conversations by understanding the stage of the conversation and providing tools like product knowledge base to reduce errors. The agent can autonomously generate payment links, handle objections, and close sales. It also offers features like automated email communication, meeting scheduling, and integration with various LLMs for customization. SalesGPT is optimized for low latency in voice channels and ensures human supervision where necessary. The tool provides enterprise-grade security and supports LangSmith tracing for monitoring and evaluation of intelligent agents built on LLM frameworks.

AdaSociety is a multi-agent environment designed for simulating social structures and decision-making processes. It offers built-in resources, events, and player interactions. Users can customize the environment through JSON configuration or custom Python code. The environment supports training agents using RLlib and LLM frameworks. It provides a platform for studying multi-agent systems and social dynamics.

This repository provides a comprehensive list of academic papers, articles, tutorials, slides, and projects related to Large Language Model (LLM) systems. It covers various aspects of LLM research, including pre-training, serving, system efficiency optimization, multi-model systems, image generation systems, LLM applications in systems, ML systems, survey papers, LLM benchmarks and leaderboards, and other relevant resources. The repository is regularly updated to include the latest developments in this rapidly evolving field, making it a valuable resource for researchers, practitioners, and anyone interested in staying abreast of the advancements in LLM technology.

Mini LLM Flow is a 100-line minimalist LLM framework designed for agents, task decomposition, RAG, etc. It aims to be the framework used by LLMs, focusing on high-level programming paradigms while stripping away low-level implementation details. It serves as a learning resource and allows LLMs to design, build, and maintain projects themselves.

Pocket Flow is a 100-line minimalist LLM framework designed for (Multi-)Agents, Task Decomposition, RAG, etc. It aims to be the framework used by LLMs, focusing on stripping away low-level implementation details and emphasizing high-level programming paradigms. Pocket Flow serves as a learning resource and provides a core abstraction of a nested directed graph for breaking down tasks into multiple steps.

Pocket Flow is a 100-line minimalist LLM framework designed for (Multi-)Agents, Workflow, RAG, etc. It provides a core abstraction for LLM projects by focusing on computation and communication through a graph structure and shared store. The framework aims to support the development of LLM Agents, such as Cursor AI, by offering a minimal and low-level approach that is well-suited for understanding and usage. Users can install Pocket Flow via pip or by copying the source code, and detailed documentation is available on the project website.

Log10 is a one-line Python integration to manage your LLM data. It helps you log both closed and open-source LLM calls, compare and identify the best models and prompts, store feedback for fine-tuning, collect performance metrics such as latency and usage, and perform analytics and monitor compliance for LLM powered applications. Log10 offers various integration methods, including a python LLM library wrapper, the Log10 LLM abstraction, and callbacks, to facilitate its use in both existing production environments and new projects. Pick the one that works best for you. Log10 also provides a copilot that can help you with suggestions on how to optimize your prompt, and a feedback feature that allows you to add feedback to your completions. Additionally, Log10 provides prompt provenance, session tracking and call stack functionality to help debug prompt chains. With Log10, you can use your data and feedback from users to fine-tune custom models with RLHF, and build and deploy more reliable, accurate and efficient self-hosted models. Log10 also supports collaboration, allowing you to create flexible groups to share and collaborate over all of the above features.

cassIO is a framework-agnostic Python library that seamlessly integrates Apache Cassandra with ML/LLM/genAI workloads. It provides an easy-to-use interface for developers to connect their Cassandra databases to machine learning models, allowing them to perform complex data analysis and AI-powered tasks directly on their Cassandra data. cassIO is designed to be flexible and extensible, making it suitable for a wide range of use cases, from data exploration and visualization to predictive modeling and natural language processing.

LangChain is an amazing framework to get LLM projects done in a matter of no time, and the ecosystem is growing fast. Here is an attempt to keep track of the initiatives around LangChain. Subscribe to the newsletter to stay informed about the Awesome LangChain. We send a couple of emails per month about the articles, videos, projects, and tools that grabbed our attention Contributions welcome. Add links through pull requests or create an issue to start a discussion. Please read the contribution guidelines before contributing.

PySpur is a graph-based editor designed for LLM workflows, offering modular building blocks for easy workflow creation and debugging at node level. It allows users to evaluate final performance and promises self-improvement features in the future. PySpur is easy-to-hack, supports JSON configs for workflow graphs, and is lightweight with minimal dependencies, making it a versatile tool for workflow management in the field of AI and machine learning.

Expert on LLM security risks, providing detailed, accurate advice.

Specializes in summarizing LLM news and papers from the past week.

An LLM specialist aiding in learning and understanding LLMs.

Daily updates on LLM news and trends

I explain AI papers in Chinese.

Advisor for safe LLM integration using OWASP guidelines

Translates and provides responses in Comanche

Download and Run Open Source LLMs Locally.

This GPT generates the best possible LLM-agents for your system prompts. You can also specify the model size, like 3B, 33B, 70B, etc.

Specialized LLM in computer security, acting as a CISO with 20 years of experience, providing precise, data-driven technical responses to enhance organizational security.

I imitate GPT-5 LLM, with advanced reasoning, personalization, and higher emotional intelligence

A guide for ML & LLM bootcamp with a bioengineering background.

Expert in hacking and programming queries on LLM V 1.1

Using OpenLLMLeaderboard data to answer your questions about LLM. For Currently!

Premier AI Prompt Engineer for Advanced LLM Optimization, Enhancing AI-to-AI Interaction and Comprehension. Create -> Optimize -> Revise iteratively

专门优化和翻译不同LLM的提示词的专家，基于用户选择和LLM特性。

EmotionPrompt手法に基づいて作成していますが、本来の理論とは反対に人間に対してLLMがPromptを投げます。本来の手法の詳細：https://ai-data-base.com/archives/58158

Hack Me if you can - I can only talk to you about computer security, software security and LLM security @JacquesGariepy

Helps you build logic security into your GPTs custom instructions. Documentation: https://github.com/infotrix/SSLLMs---Semantic-Secuirty-for-LLM-GPTs

🪄Prompt一键强化,快速、精准对齐需求，与AI对话更高效。 🧙‍♂️解锁LLM潜力，让ChatGPT、Claude更懂你，工作快人一步。 🧸你的AI对话伙伴，定制专属需求，轻松开启高品质对话体验