Motorhead is a memory and information retrieval server for LLMs. It provides three simple APIs to assist with memory handling in chat applications using LLMs. The first API, GET /sessions/:id/memory, returns messages up to a maximum window size. The second API, POST /sessions/:id/memory, allows you to send an array of messages to Motorhead for storage. The third API, DELETE /sessions/:id/memory, deletes the session's message list. Motorhead also features incremental summarization, where it processes half of the maximum window size of messages and summarizes them when the maximum is reached. Additionally, it supports searching by text query using vector search. Motorhead is configurable through environment variables, including the maximum window size, whether to enable long-term memory, the model used for incremental summarization, the server port, your OpenAI API key, and the Redis URL.

Toolio is an OpenAI-like HTTP server API implementation that supports structured LLM response generation, making it conform to a JSON schema. It is useful for reliable tool calling and agentic workflows based on schema-driven output. Toolio is based on the MLX framework for Apple Silicon, specifically M1/M2/M3/M4 Macs. It allows users to host MLX-format LLMs for structured output queries and provides a command line client for easier usage of tools. The tool also supports multiple tool calls and the creation of custom tools for specific tasks.

TigerGraph CoPilot is an AI assistant that combines graph databases and generative AI to enhance productivity across various business functions. It includes three core component services: InquiryAI for natural language assistance, SupportAI for knowledge Q&A, and QueryAI for GSQL code generation. Users can interact with CoPilot through a chat interface on TigerGraph Cloud and APIs. CoPilot requires LLM services for beta but will support TigerGraph's LLM in future releases. It aims to improve contextual relevance and accuracy of answers to natural-language questions by building knowledge graphs and using RAG. CoPilot is extensible and can be configured with different LLM providers, graph schemas, and LangChain tools.

ChatMemory is a simple yet powerful long-term memory manager that facilitates communication between AI and users. It organizes conversation data into history, summary, and knowledge entities, enabling quick retrieval of context and generation of clear, concise answers. The tool leverages vector search on summaries/knowledge and detailed history to provide accurate responses. It balances speed and accuracy by using lightweight retrieval and fallback detailed search mechanisms, ensuring efficient memory management and response generation beyond mere data retrieval.

Vinagent is a lightweight and flexible library designed for building smart agent assistants across various industries. It provides a simple yet powerful foundation for creating AI-powered customer service bots, data analysis assistants, or domain-specific automation agents. With its modular tool system, users can easily extend their agent's capabilities by integrating a wide range of tools that are self-contained, well-documented, and can be registered dynamically. Vinagent allows users to scale and adapt their agents to new tasks or environments effortlessly.

Marqo is more than a vector database, it's an end-to-end vector search engine for both text and images. Vector generation, storage and retrieval are handled out of the box through a single API. No need to bring your own embeddings.

Simplemind is an AI library designed to simplify the experience with AI APIs in Python. It provides easy-to-use AI tools with a human-centered design and minimal configuration. Users can tap into powerful AI capabilities through simple interfaces, without needing to be experts. The library supports various APIs from different providers/models and offers features like text completion, streaming text, structured data handling, conversational AI, tool calling, and logging. Simplemind aims to make AI models accessible to all by abstracting away complexity and prioritizing readability and usability.

`npcsh` is a python-based command-line tool designed to integrate Large Language Models (LLMs) and Agents into one's daily workflow by making them available and easily configurable through the command line shell. It leverages the power of LLMs to understand natural language commands and questions, execute tasks, answer queries, and provide relevant information from local files and the web. Users can also build their own tools and call them like macros from the shell. `npcsh` allows users to take advantage of agents (i.e. NPCs) through a managed system, tailoring NPCs to specific tasks and workflows. The tool is extensible with Python, providing useful functions for interacting with LLMs, including explicit coverage for popular providers like ollama, anthropic, openai, gemini, deepseek, and openai-like providers. Users can set up a flask server to expose their NPC team for use as a backend service, run SQL models defined in their project, execute assembly lines, and verify the integrity of their NPC team's interrelations. Users can execute bash commands directly, use favorite command-line tools like VIM, Emacs, ipython, sqlite3, git, pipe the output of these commands to LLMs, or pass LLM results to bash commands.

SimpleAI is a self-hosted alternative to the not-so-open AI API, focused on replicating main endpoints for LLM such as text completion, chat, edits, and embeddings. It allows quick experimentation with different models, creating benchmarks, and handling specific use cases without relying on external services. Users can integrate and declare models through gRPC, query endpoints using Swagger UI or API, and resolve common issues like CORS with FastAPI middleware. The project is open for contributions and welcomes PRs, issues, documentation, and more.

The LLM RAG Workshop repository provides a workshop on using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) to generate and understand text in a human-like manner. It includes instructions on setting up the environment, indexing Zoomcamp FAQ documents, creating a Q&A system, and using OpenAI for generation based on retrieved information. The repository focuses on enhancing language model responses with retrieved information from external sources, such as document databases or search engines, to improve factual accuracy and relevance of generated text.

Memobase is a user profile-based memory system designed to enhance Generative AI applications by enabling them to remember, understand, and evolve with users. It provides structured user profiles, scalable profiling, easy integration with existing LLM stacks, batch processing for speed, and is production-ready. Users can manage users, insert data, get memory profiles, and track user preferences and behaviors. Memobase is ideal for applications that require user analysis, tracking, and personalized interactions.

Ragtacts is a Clojure library that allows users to easily interact with Large Language Models (LLMs) such as OpenAI's GPT-4. Users can ask questions to LLMs, create question templates, call Clojure functions in natural language, and utilize vector databases for more accurate answers. Ragtacts also supports RAG (Retrieval-Augmented Generation) method for enhancing LLM output by incorporating external data. Users can use Ragtacts as a CLI tool, API server, or through a RAG Playground for interactive querying.

AwaDB is an AI native database designed for embedding vectors. It simplifies database usage by eliminating the need for schema definition and manual indexing. The system ensures real-time search capabilities with millisecond-level latency. Built on 5 years of production experience with Vearch, AwaDB incorporates best practices from the community to offer stability and efficiency. Users can easily add and search for embedded sentences using the provided client libraries or RESTful API.

Invariant Analyzer is an open-source scanner designed for LLM-based AI agents to find bugs, vulnerabilities, and security threats. It scans agent execution traces to identify issues like looping behavior, data leaks, prompt injections, and unsafe code execution. The tool offers a library of built-in checkers, an expressive policy language, data flow analysis, real-time monitoring, and extensible architecture for custom checkers. It helps developers debug AI agents, scan for security violations, and prevent security issues and data breaches during runtime. The analyzer leverages deep contextual understanding and a purpose-built rule matching engine for security policy enforcement.

Call Center AI is an AI-powered call center solution that leverages Azure and OpenAI GPT. It is a proof of concept demonstrating the integration of Azure Communication Services, Azure Cognitive Services, and Azure OpenAI to build an automated call center solution. The project showcases features like accessing claims on a public website, customer conversation history, language change during conversation, bot interaction via phone number, multiple voice tones, lexicon understanding, todo list creation, customizable prompts, content filtering, GPT-4 Turbo for customer requests, specific data schema for claims, documentation database access, SMS report sending, conversation resumption, and more. The system architecture includes components like RAG AI Search, SMS gateway, call gateway, moderation, Cosmos DB, event broker, GPT-4 Turbo, Redis cache, translation service, and more. The tool can be deployed remotely using GitHub Actions and locally with prerequisites like Azure environment setup, configuration file creation, and resource hosting. Advanced usage includes custom training data with AI Search, prompt customization, language customization, moderation level customization, claim data schema customization, OpenAI compatible model usage for the LLM, and Twilio integration for SMS.

ActionWeaver is an AI application framework designed for simplicity, relying on OpenAI and Pydantic. It supports both OpenAI API and Azure OpenAI service. The framework allows for function calling as a core feature, extensibility to integrate any Python code, function orchestration for building complex call hierarchies, and telemetry and observability integration. Users can easily install ActionWeaver using pip and leverage its capabilities to create, invoke, and orchestrate actions with the language model. The framework also provides structured extraction using Pydantic models and allows for exception handling customization. Contributions to the project are welcome, and users are encouraged to cite ActionWeaver if found useful.

Motorhead is a memory and information retrieval server for LLMs. It provides three simple APIs to assist with memory handling in chat applications using LLMs. The first API, GET /sessions/:id/memory, returns messages up to a maximum window size. The second API, POST /sessions/:id/memory, allows you to send an array of messages to Motorhead for storage. The third API, DELETE /sessions/:id/memory, deletes the session's message list. Motorhead also features incremental summarization, where it processes half of the maximum window size of messages and summarizes them when the maximum is reached. Additionally, it supports searching by text query using vector search. Motorhead is configurable through environment variables, including the maximum window size, whether to enable long-term memory, the model used for incremental summarization, the server port, your OpenAI API key, and the Redis URL.

MemGPT is a system that intelligently manages different memory tiers in LLMs in order to effectively provide extended context within the LLM's limited context window. For example, MemGPT knows when to push critical information to a vector database and when to retrieve it later in the chat, enabling perpetual conversations. MemGPT can be used to create perpetual chatbots with self-editing memory, chat with your data by talking to your local files or SQL database, and more.

Polyfire is an all-in-one managed backend for AI apps that allows users to build AI applications directly from the frontend, eliminating the need for a separate backend. It simplifies the process by providing most backend services in just a few lines of code. With Polyfire, users can easily create chatbots, transcribe audio files, generate simple text, manage long-term memory, and generate images. The tool also offers starter guides and tutorials to help users get started quickly and efficiently.

GPTSwarm is a graph-based framework for LLM-based agents that enables the creation of LLM-based agents from graphs and facilitates the customized and automatic self-organization of agent swarms with self-improvement capabilities. The library includes components for domain-specific operations, graph-related functions, LLM backend selection, memory management, and optimization algorithms to enhance agent performance and swarm efficiency. Users can quickly run predefined swarms or utilize tools like the file analyzer. GPTSwarm supports local LM inference via LM Studio, allowing users to run with a local LLM model. The framework has been accepted by ICML2024 and offers advanced features for experimentation and customization.

DistServe improves the performance of large language models serving by disaggregating the prefill and decoding computation. It allows setting parallelism configs and scheduling strategies for the two phases independently, handling KV-Cache communication and memory management automatically. Utilizes a high-performance C++ Transformer inference library SwiftTransformer with features like model/pipeline parallelism, FlashAttention, Continuous Batching, and PagedAttention. Supports GPT-2, OPT, and LLaMA2 models.

Since the emergence of chatGPT in 2022, the acceleration of Large Language Model has become increasingly important. Here is a list of papers on LLMs inference and serving.

Bosquet is a tool designed for LLMOps in large language model-based applications. It simplifies building AI applications by managing LLM and tool services, integrating with Selmer templating library for prompt templating, enabling prompt chaining and composition with Pathom graph processing, defining agents and tools for external API interactions, handling LLM memory, and providing features like call response caching. The tool aims to streamline the development process for AI applications that require complex prompt templates, memory management, and interaction with external systems.

LangChain-Udemy-Course is a comprehensive course directory focusing on LangChain, a framework for generative AI applications. The course covers various aspects such as OpenAI API usage, prompt templates, Chains exploration, callback functions, memory techniques, RAG implementation, autonomous agents, hybrid search, LangSmith utilization, microservice architecture, and LangChain Expression Language. Learners gain theoretical knowledge and practical insights to understand and apply LangChain effectively in generative AI scenarios.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.