It is a contract-based development toolkit designed to define, inspect, and verify the behavior of AI agents using natural language. Relari enables teams to collaboratively create natural language contracts that capture an AI agent’s expected behavior and reasoning process across critical scenarios. These contracts are transformed into automated tests, rigorously verifying the agent’s complete behavior across thousands of diverse scenarios through powerful simulation and synthetic test generation. The platform provides immediate insights into how agents execute complex tasks via comprehensive trace analysis, helping teams rapidly identify and resolve issues before deployment.
Relari addresses challenges in AI development by offering synthetic golden datasets and tailored evaluation metrics, enabling data-driven decisions on parameters like similarity thresholds, chunk sizes, and retrieval strategies. This approach significantly improves iteration speed, helping teams achieve production-grade performance for multiple large language model (LLM) products quickly. It also overcomes the limitations of traditional LLM-as-a-judge evaluations, which are expensive and unstable, by providing deterministic evaluation and domain-specific synthetic datasets.
The platform is ideal for individual developers, researchers, and AI teams aiming to deploy reliable agentic applications at scale. It supports various agent frameworks, including LangGraph, LlamaIndex, CrewAI, and AutoGen, and is platform-agnostic. Relari’s synthetic datasets and custom simulators allow teams to stress-test agents across diverse scenarios, ensuring robust performance. Additionally, enterprise plans offer self-hosting options for data security.
Relari’s framework goes beyond traditional metrics like correctness and faithfulness by analyzing complex execution traces, ensuring each step aligns with contract requirements. This comprehensive approach helps teams systematically improve AI performance, enabling faster iteration and deployment of reliable AI agents. Trusted by AI pioneers, Relari empowers teams to build confidence in their AI agents through contract-driven development.
It is a terminal-based platform designed for experimenting with AI-driven software engineering, specifically focusing on code generation and improvement.
It is an open-source platform designed to enhance AI development by providing tools for tracing, evaluating, and optimizing large language model (LLM) applications in real time.
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 a platform designed to build and deploy AI agents that address trust barriers in adopting agentic AI by embedding data protection, policy enforcement, and validation into every agent, ensuring business success.
It is a GitHub-native tool designed to automate and enhance the pull request (PR) workflow by running multiple AI agents in parallel directly on your codebase.
It is an AI-powered software testing platform designed to automate API and UI testing with no human intervention, enabling developers to achieve enterprise-level QA efficiency.
It is an open-source framework called Internet of Agents (IoA) designed to enable diverse, distributed AI agents to collaborate and solve complex tasks through internet-like connectivity.
It is a platform that replaces queues, state management, and scheduling with durable functions, enabling developers to build reliable, AI-ready step functions faster without managing infrastructure.
It is a Chrome extension called Qodo Merge that integrates AI-powered chat and code review tools directly into GitHub to analyze pull requests, automate reviews, highlight changes, suggest improvements, and ensure code changes adhere to best practices.
It is an AI-driven observability platform designed to monitor, analyze, and optimize GitHub Actions workflows by detecting anomalies, identifying root causes, and providing actionable fixes to improve CI pipeline performance and developer productivity.
It is a barebones library called **smolagents** designed to enable the creation and orchestration of agents that write Python code to call tools and manage other agents.
It is an AI-driven platform designed to automate and streamline employee support processes, enabling organizations to enhance productivity and reduce repetitive tasks.
It is a Python framework called Langroid, developed by researchers from Carnegie Mellon University (CMU) and the University of Wisconsin-Madison (UW-Madison), designed to build lightweight, extensible, and intuitive LLM-powered applications.
It is a platform that enables businesses to build and deploy private AI models that they fully control, ensuring data privacy and security through advanced encryption and flexible deployment options.
It is a memory system designed to enhance AI agents by enabling them to retain and utilize knowledge effectively for completing tasks, ranging from simple to complex.
It is a conversational AI platform that enables users to create, customize, and deploy AI bots and agents tailored to specific needs, allowing seamless communication with users across apps, websites, and messaging platforms like Slack, Discord, WhatsApp, and Telegram.
It is an advanced AI platform designed to automate and optimize complex computer systems by orchestrating hundreds of AI models tailored to specific tasks, file types, and architectures.
It is a platform that enables creators to monetize their brand by creating an AI Twin, which interacts with audiences like a real person through text, video calls, live streaming, and gaming experiences while maintaining 100% ownership and earning 80% of the revenue.
