It is a Python library designed for creating AI assistants and agents using Vectara and Agentic-RAG (Retrieval-Augmented Generation). The library leverages the LlamaIndex Agent framework and provides helper functions to quickly develop tools that connect to Vectara corpora, enabling the creation of powerful AI applications. It supports the creation of tools for querying and summarizing data, as well as integrating with other APIs or mathematical functions.
The library includes pre-built tools, such as the Vectara RAG tool, which calls the full Vectara RAG pipeline to provide summarized responses to queries grounded in data. It also supports metadata filtering, allowing users to refine queries based on specific criteria like document attributes or timeframes. Additionally, the Vectara search tool enables agents to list documents matching a query, useful for tasks like identifying relevant documents or counting occurrences of specific topics.
Users can customize tools using the `create_rag_tool()` and `create_search_tool()` functions, which accept various arguments to configure Vectara queries. The library also supports creating custom tools from Python functions using the `create_tool()` method, provided the functions are defined at the top module level.
Configuration is managed through an `AgentConfig` object, which allows users to specify settings like API keys, LLM providers, and custom instructions. The library integrates with observability tools like Arize Phoenix for monitoring and debugging, and it supports serialization for saving and loading agent states.
The library can be hosted locally or remotely via a FastAPI server, making it accessible through HTTP requests. It is open-source, licensed under Apache 2.0, and welcomes contributions from the community. Documentation, examples, and a Discord community are available for further support.
It is an agent designed to use its own browser to perform tasks on your behalf. This operator functions as an automated assistant capable of navigating the internet, accessing websites, and executing specific actions as instructed.
It is an industry-leading developer platform designed to test, debug, and deploy AI agents, supporting over 400 large language models (LLMs), Crews, and AI agent frameworks.
It is a full-stack web scraping and data extraction platform designed for developers and businesses to build, deploy, and publish web scrapers, AI agents, and automation tools.
It is a unified observability and evaluation platform for AI designed to accelerate the development of AI applications and agents while optimizing their performance in production.
It is an AI-powered tool designed to provide actionable insights from databases by allowing users to ask questions in natural language, eliminating the need for extensive SQL expertise.
It is a recommender system simulator called Agent4Rec, designed to explore the potential of large language model (LLM)-empowered generative agents in simulating human-like behavior in recommendation environments.
It is a platform designed to provide real-time updates and detailed insights on agents’ activities and performance in call centers, enabling users to make informed decisions and optimize operations.
It is an AI-powered platform designed to simplify life, boost productivity, and enhance creativity through a suite of dynamic tools and personalized AI companions.
It is a platform designed for building, deploying, and managing AI Agents with a focus on reliability, accuracy, and seamless integration across systems.
It is a platform designed to integrate advanced conversational AI technology into businesses, enabling the creation of AI phone agents that handle sales, scheduling, and customer support.
