Alternatives to Maxim

Ango Hub is an all-in-one, quality-oriented data annotation platform that AI teams can use. Ango Hub is available on-premise and in the cloud. It allows AI teams and their data annotation workforces to quickly and efficiently annotate their data without compromising quality. Ango Hub is the only data annotation platform that focuses on quality. It features features that enhance the quality of your annotations. These include a centralized labeling system, a real time issue system, review workflows and sample label libraries. There is also consensus up to 30 on the same asset. Ango Hub is versatile as well. It supports all data types that your team might require, including image, audio, text and native PDF. There are nearly twenty different labeling tools that you can use to annotate data. Some of these tools are unique to Ango hub, such as rotated bounding box, unlimited conditional questions, label relations and table-based labels for more complicated labeling tasks.

Vivgrid serves as a comprehensive development platform tailored for AI agents, focusing on critical aspects such as observability, debugging, safety, and a robust global deployment framework. It provides complete transparency into agent activities by logging prompts, memory retrievals, tool interactions, and reasoning processes, allowing developers to identify and address any points of failure or unexpected behavior. Furthermore, it enables the testing and enforcement of safety protocols, including refusal rules and filters, while facilitating human-in-the-loop oversight prior to deployment. Vivgrid also manages the orchestration of multi-agent systems equipped with stateful memory, dynamically assigning tasks across various agent workflows. On the deployment front, it utilizes a globally distributed inference network to guarantee low-latency execution, achieving response times under 50 milliseconds, and offers real-time metrics on latency, costs, and usage. By integrating debugging, evaluation, safety, and deployment into a single coherent framework, Vivgrid aims to streamline the process of delivering resilient AI systems without the need for disparate components in observability, infrastructure, and orchestration, ultimately enhancing efficiency for developers. This holistic approach empowers teams to focus on innovation rather than the complexities of system integration.

Latitude is a comprehensive platform for prompt engineering, helping product teams design, test, and optimize AI prompts for large language models (LLMs). It provides a suite of tools for importing, refining, and evaluating prompts using real-time data and synthetic datasets. The platform integrates with production environments to allow seamless deployment of new prompts, with advanced features like automatic prompt refinement and dataset management. Latitude’s ability to handle evaluations and provide observability makes it a key tool for organizations seeking to improve AI performance and operational efficiency.

AgentHub serves as a dedicated staging platform designed to emulate, trace, and assess AI agents within a secure and private sandbox, allowing for deployment with assurance, agility, and accuracy. Its straightforward setup enables users to onboard agents in mere minutes, complemented by a strong evaluation framework that offers detailed multi-step trace logging, LLM graders, and customizable assessment options. Users can engage in realistic simulations with adjustable personas to replicate varied behaviors and stress-test scenarios, while dataset enhancement techniques artificially increase test set size for thorough evaluation. The system also supports prompt experimentation, facilitating large-scale dynamic testing across multiple prompts, and includes side-by-side trace analysis for comparing decisions, tool usage, and results from different runs. Additionally, an integrated AI Copilot is available to scrutinize traces, interpret outcomes, and respond to inquiries based on the user's specific code and data, transforming agent executions into clear and actionable insights. Furthermore, the platform offers a combination of human-in-the-loop and automated feedback mechanisms, alongside tailored onboarding and expert guidance to ensure best practices are followed throughout the process. This comprehensive approach empowers users to optimize agent performance effectively.

Introducing Ape, the pioneering AI prompt engineer, designed with advanced capabilities such as tracing, dataset curation, batch testing, and evaluations. Achieving a remarkable 93% score on the GSM8K benchmark, Ape outperforms both DSPy, which scores 86%, and traditional LLMs, which only reach 70%. It employs real-world data to continually refine prompts and integrates CI/CD to prevent any decline in performance. By incorporating a human-in-the-loop approach featuring scoring and feedback, Ape enhances its effectiveness. Furthermore, the integration with the Weavel SDK allows for automatic logging and incorporation of LLM outputs into your dataset as you interact with your application. This ensures a smooth integration process and promotes ongoing enhancement tailored to your specific needs. In addition to these features, Ape automatically generates evaluation code and utilizes LLMs as impartial evaluators for intricate tasks, which simplifies your assessment workflow and guarantees precise, detailed performance evaluations. With Ape's reliable functionality, your guidance and feedback help it evolve further, as you can contribute scores and suggestions for improvement. Equipped with comprehensive logging, testing, and evaluation tools for LLM applications, Ape stands out as a vital resource for optimizing AI-driven tasks. Its adaptability and continuous learning mechanism make it an invaluable asset in any AI project.

DeepEval offers an intuitive open-source framework designed for the assessment and testing of large language model systems, similar to what Pytest does but tailored specifically for evaluating LLM outputs. It leverages cutting-edge research to measure various performance metrics, including G-Eval, hallucinations, answer relevancy, and RAGAS, utilizing LLMs and a range of other NLP models that operate directly on your local machine. This tool is versatile enough to support applications developed through methods like RAG, fine-tuning, LangChain, or LlamaIndex. By using DeepEval, you can systematically explore the best hyperparameters to enhance your RAG workflow, mitigate prompt drift, or confidently shift from OpenAI services to self-hosting your Llama2 model. Additionally, the framework features capabilities for synthetic dataset creation using advanced evolutionary techniques and integrates smoothly with well-known frameworks, making it an essential asset for efficient benchmarking and optimization of LLM systems. Its comprehensive nature ensures that developers can maximize the potential of their LLM applications across various contexts.

Langfuse is a free and open-source LLM engineering platform that helps teams to debug, analyze, and iterate their LLM Applications. Observability: Incorporate Langfuse into your app to start ingesting traces. Langfuse UI : inspect and debug complex logs, user sessions and user sessions Langfuse Prompts: Manage versions, deploy prompts and manage prompts within Langfuse Analytics: Track metrics such as cost, latency and quality (LLM) to gain insights through dashboards & data exports Evals: Calculate and collect scores for your LLM completions Experiments: Track app behavior and test it before deploying new versions Why Langfuse? - Open source - Models and frameworks are agnostic - Built for production - Incrementally adaptable - Start with a single LLM or integration call, then expand to the full tracing for complex chains/agents - Use GET to create downstream use cases and export the data

Dynamiq serves as a comprehensive platform tailored for engineers and data scientists, enabling them to construct, deploy, evaluate, monitor, and refine Large Language Models for various enterprise applications. Notable characteristics include: 🛠️ Workflows: Utilize a low-code interface to design GenAI workflows that streamline tasks on a large scale. 🧠 Knowledge & RAG: Develop personalized RAG knowledge bases and swiftly implement vector databases. 🤖 Agents Ops: Design specialized LLM agents capable of addressing intricate tasks while linking them to your internal APIs. 📈 Observability: Track all interactions and conduct extensive evaluations of LLM quality. 🦺 Guardrails: Ensure accurate and dependable LLM outputs through pre-existing validators, detection of sensitive information, and safeguards against data breaches. 📻 Fine-tuning: Tailor proprietary LLM models to align with your organization's specific needs and preferences. With these features, Dynamiq empowers users to harness the full potential of language models for innovative solutions.

AI engineering can be transparent rather than opaque. With a suite of tools for tracing, assessment, prompt management, and more, HoneyHive emerges as a comprehensive platform for AI observability and evaluation, aimed at helping teams create dependable generative AI applications. This platform equips users with resources for model evaluation, testing, and monitoring, promoting effective collaboration among engineers, product managers, and domain specialists. By measuring quality across extensive test suites, teams can pinpoint enhancements and regressions throughout the development process. Furthermore, it allows for the tracking of usage, feedback, and quality on a large scale, which aids in swiftly identifying problems and fostering ongoing improvements. HoneyHive is designed to seamlessly integrate with various model providers and frameworks, offering the necessary flexibility and scalability to accommodate a wide range of organizational requirements. This makes it an ideal solution for teams focused on maintaining the quality and performance of their AI agents, delivering a holistic platform for evaluation, monitoring, and prompt management, ultimately enhancing the overall effectiveness of AI initiatives. As organizations increasingly rely on AI, tools like HoneyHive become essential for ensuring robust performance and reliability.

Agenta provides a complete open-source LLMOps solution that brings prompt engineering, evaluation, and observability together in one platform. Instead of storing prompts across scattered documents and communication channels, teams get a single source of truth for managing and versioning all prompt iterations. The platform includes a unified playground where users can compare prompts, models, and parameters side-by-side, making experimentation faster and more organized. Agenta supports automated evaluation pipelines that leverage LLM-as-a-judge, human reviewers, and custom evaluators to ensure changes actually improve performance. Its observability stack traces every request and highlights failure points, helping teams debug issues and convert problematic interactions into reusable test cases. Product managers, developers, and domain experts can collaborate through shared test sets, annotations, and interactive evaluations directly from the UI. Agenta integrates seamlessly with LangChain, LlamaIndex, OpenAI APIs, and any model provider, avoiding vendor lock-in. By consolidating collaboration, experimentation, testing, and monitoring, Agenta enables AI teams to move from chaotic workflows to streamlined, reliable LLM development.

Klu.ai, a Generative AI Platform, simplifies the design, deployment, and optimization of AI applications. Klu integrates your Large Language Models and incorporates data from diverse sources to give your applications unique context. Klu accelerates the building of applications using language models such as Anthropic Claude (Azure OpenAI), GPT-4 (Google's GPT-4), and over 15 others. It allows rapid prompt/model experiments, data collection and user feedback and model fine tuning while cost-effectively optimising performance. Ship prompt generation, chat experiences and workflows in minutes. Klu offers SDKs for all capabilities and an API-first strategy to enable developer productivity. Klu automatically provides abstractions to common LLM/GenAI usage cases, such as: LLM connectors and vector storage, prompt templates, observability and evaluation/testing tools.

Literal AI is a collaborative platform crafted to support engineering and product teams in the creation of production-ready Large Language Model (LLM) applications. It features an array of tools focused on observability, evaluation, and analytics, which allows for efficient monitoring, optimization, and integration of different prompt versions. Among its noteworthy functionalities are multimodal logging, which incorporates vision, audio, and video, as well as prompt management that includes versioning and A/B testing features. Additionally, it offers a prompt playground that allows users to experiment with various LLM providers and configurations. Literal AI is designed to integrate effortlessly with a variety of LLM providers and AI frameworks, including OpenAI, LangChain, and LlamaIndex, and comes equipped with SDKs in both Python and TypeScript for straightforward code instrumentation. The platform further facilitates the development of experiments against datasets, promoting ongoing enhancements and minimizing the risk of regressions in LLM applications. With these capabilities, teams can not only streamline their workflows but also foster innovation and ensure high-quality outputs in their projects.

Lucidic AI is a dedicated analytics and simulation platform designed specifically for the development of AI agents, enhancing transparency, interpretability, and efficiency in typically complex workflows. This tool equips developers with engaging and interactive insights such as searchable workflow replays, detailed video walkthroughs, and graph-based displays of agent decisions, alongside visual decision trees and comparative simulation analyses, allowing for an in-depth understanding of an agent's reasoning process and the factors behind its successes or failures. By significantly shortening iteration cycles from weeks or days to just minutes, it accelerates debugging and optimization through immediate feedback loops, real-time “time-travel” editing capabilities, extensive simulation options, trajectory clustering, customizable evaluation criteria, and prompt versioning. Furthermore, Lucidic AI offers seamless integration with leading large language models and frameworks, while also providing sophisticated quality assurance and quality control features such as alerts and workflow sandboxing. This comprehensive platform ultimately empowers developers to refine their AI projects with unprecedented speed and clarity.

Athina functions as a collaborative platform for AI development, empowering teams to efficiently create, test, and oversee their AI applications. It includes a variety of features such as prompt management, evaluation tools, dataset management, and observability, all aimed at facilitating the development of dependable AI systems. With the ability to integrate various models and services, including custom solutions, Athina also prioritizes data privacy through detailed access controls and options for self-hosted deployments. Moreover, the platform adheres to SOC-2 Type 2 compliance standards, ensuring a secure setting for AI development activities. Its intuitive interface enables seamless collaboration between both technical and non-technical team members, significantly speeding up the process of deploying AI capabilities. Ultimately, Athina stands out as a versatile solution that helps teams harness the full potential of artificial intelligence.

Automated voice testing, monitoring and more. Test your AI voice agent with 1000s of simulated users within minutes. It's hard to get AI voice agents right. LLM outputs can be affected by a small change in the prompts, function calls or model providers. We are the only platform that can support you from development through to production. Hamming allows you to store, manage, update and sync your prompts with voice infra provider. This is 1000x faster than testing voice agents manually. Use our prompt playground for testing LLM outputs against a dataset of inputs. Our LLM judges quality of generated outputs. Save 80% on manual prompt engineering. Monitor your app in more than one way. We actively track, score and flag cases where you need to pay attention. Convert calls and traces to test cases, and add them to the golden dataset.

Entry Point AI serves as a cutting-edge platform for optimizing both proprietary and open-source language models. It allows users to manage prompts, fine-tune models, and evaluate their performance all from a single interface. Once you hit the ceiling of what prompt engineering can achieve, transitioning to model fine-tuning becomes essential, and our platform simplifies this process. Rather than instructing a model on how to act, fine-tuning teaches it desired behaviors. This process works in tandem with prompt engineering and retrieval-augmented generation (RAG), enabling users to fully harness the capabilities of AI models. Through fine-tuning, you can enhance the quality of your prompts significantly. Consider it an advanced version of few-shot learning where key examples are integrated directly into the model. For more straightforward tasks, you have the option to train a lighter model that can match or exceed the performance of a more complex one, leading to reduced latency and cost. Additionally, you can configure your model to avoid certain responses for safety reasons, which helps safeguard your brand and ensures proper formatting. By incorporating examples into your dataset, you can also address edge cases and guide the behavior of the model, ensuring it meets your specific requirements effectively. This comprehensive approach ensures that you not only optimize performance but also maintain control over the model's responses.

AgentKit offers an all-in-one collection of tools aimed at simplifying the creation, deployment, and enhancement of AI agents. Central to its offerings is Agent Builder, a visual platform that allows developers to easily create multi-agent workflows using drag-and-drop nodes, implement guardrails, preview executions, and manage different workflow versions. The Connector Registry plays a key role in unifying the oversight of data and tool integrations across various workspaces, ensuring effective governance and access management. Additionally, ChatKit facilitates the seamless integration of interactive chat interfaces, which can be tailored to fit specific branding and user experience requirements, into both web and app settings. To ensure high performance and dependability, AgentKit upgrades its evaluation framework with comprehensive datasets, trace grading, automated optimization of prompts, and compatibility with third-party models. Moreover, it offers reinforcement fine-tuning capabilities, further enhancing the potential of agents and their functionalities. This comprehensive suite makes it easier for developers to create sophisticated AI solutions efficiently.

Langtrace is an open-source observability solution designed to gather and evaluate traces and metrics, aiming to enhance your LLM applications. It prioritizes security with its cloud platform being SOC 2 Type II certified, ensuring your data remains highly protected. The tool is compatible with a variety of popular LLMs, frameworks, and vector databases. Additionally, Langtrace offers the option for self-hosting and adheres to the OpenTelemetry standard, allowing traces to be utilized by any observability tool of your preference and thus avoiding vendor lock-in. Gain comprehensive visibility and insights into your complete ML pipeline, whether working with a RAG or a fine-tuned model, as it effectively captures traces and logs across frameworks, vector databases, and LLM requests. Create annotated golden datasets through traced LLM interactions, which can then be leveraged for ongoing testing and improvement of your AI applications. Langtrace comes equipped with heuristic, statistical, and model-based evaluations to facilitate this enhancement process, thereby ensuring that your systems evolve alongside the latest advancements in technology. With its robust features, Langtrace empowers developers to maintain high performance and reliability in their machine learning projects.

Orq.ai stands out as the leading platform tailored for software teams to effectively manage agentic AI systems on a large scale. It allows you to refine prompts, implement various use cases, and track performance meticulously, ensuring no blind spots and eliminating the need for vibe checks. Users can test different prompts and LLM settings prior to launching them into production. Furthermore, it provides the capability to assess agentic AI systems within offline environments. The platform enables the deployment of GenAI features to designated user groups, all while maintaining robust guardrails, prioritizing data privacy, and utilizing advanced RAG pipelines. It also offers the ability to visualize all agent-triggered events, facilitating rapid debugging. Users gain detailed oversight of costs, latency, and overall performance. Additionally, you can connect with your preferred AI models or even integrate your own. Orq.ai accelerates workflow efficiency with readily available components specifically designed for agentic AI systems. It centralizes the management of essential phases in the LLM application lifecycle within a single platform. With options for self-hosted or hybrid deployment, it ensures compliance with SOC 2 and GDPR standards, thereby providing enterprise-level security. This comprehensive approach not only streamlines operations but also empowers teams to innovate and adapt swiftly in a dynamic technological landscape.

Discover a user-friendly yet thorough evaluation platform designed to continuously enhance your AI-powered products. By optimizing the LLMOps workflow, you can foster trust and secure a competitive advantage. EvalsOne serves as your comprehensive toolkit for refining your application evaluation process. Picture it as a versatile Swiss Army knife for AI, ready to handle any evaluation challenge you encounter. It is ideal for developing LLM prompts, fine-tuning RAG methods, and assessing AI agents. You can select between rule-based or LLM-driven strategies for automating evaluations. Moreover, EvalsOne allows for the seamless integration of human evaluations, harnessing expert insights for more accurate outcomes. It is applicable throughout all phases of LLMOps, from initial development to final production stages. With an intuitive interface, EvalsOne empowers teams across the entire AI spectrum, including developers, researchers, and industry specialists. You can easily initiate evaluation runs and categorize them by levels. Furthermore, the platform enables quick iterations and detailed analyses through forked runs, ensuring that your evaluation process remains efficient and effective. EvalsOne is designed to adapt to the evolving needs of AI development, making it a valuable asset for any team striving for excellence.

SRE.ai is an innovative automation platform driven by AI, specifically designed for teams developing on Salesforce. It features AI agents that optimize DevOps processes, facilitating key tasks such as continuous integration, continuous deployment, testing, and managing releases. These agents are customizable to align with unique workflows, which aids in speeding up deployments, resolving errors, and conducting thorough release simulations. By seamlessly integrating with existing tools for communication, ticketing, and version control, SRE.ai boosts productivity and simplifies the complexities associated with releases. Additionally, the platform includes essential functionalities like efficient backups and disaster recovery solutions to protect against potential data loss. With its foundation laid by former engineers from Google Research and DeepMind, SRE.ai enjoys the support of prominent investors and is currently honing its focus on the Salesforce ecosystem. Our agents undergo rigorous quality assessments and include measures to prevent inaccuracies. Moreover, users have the flexibility to configure workflow stages to incorporate human oversight, ensuring a balanced approach between automation and human intervention. This adaptability helps teams to work more efficiently and effectively within their development environments.

Rapidly refine your work and deploy with assurance. To ensure confident deployment, assess your prompts using a comprehensive evaluation toolkit that includes context recall, LLM as a judge, latency metrics, and additional tools. Let us take care of intelligent caching and sophisticated integrations to help you save both time and resources. Engage in swift iterations of your prompts within a collaborative environment that accommodates all leading providers, supports variables, offers automatic versioning, and more. Effortlessly create datasets from actual data utilizing Logs, upload your own as a CSV file, or collaboratively construct and modify within your Adaline workspace. Monitor usage, latency, and other important metrics to keep track of your LLMs' health and your prompts' effectiveness through our APIs. Regularly assess your completions in a live environment, observe how users interact with your prompts, and generate datasets by transmitting logs via our APIs. This is the unified platform designed for iterating, evaluating, and overseeing LLMs. If your performance declines in production, rolling back is straightforward, allowing you to review how your team evolved the prompt over time while maintaining high standards. Moreover, our platform encourages a seamless collaboration experience, which enhances overall productivity across teams.

OpenPipe offers an efficient platform for developers to fine-tune their models. It allows you to keep your datasets, models, and evaluations organized in a single location. You can train new models effortlessly with just a click. The system automatically logs all LLM requests and responses for easy reference. You can create datasets from the data you've captured, and even train multiple base models using the same dataset simultaneously. Our managed endpoints are designed to handle millions of requests seamlessly. Additionally, you can write evaluations and compare the outputs of different models side by side for better insights. A few simple lines of code can get you started; just swap out your Python or Javascript OpenAI SDK with an OpenPipe API key. Enhance the searchability of your data by using custom tags. Notably, smaller specialized models are significantly cheaper to operate compared to large multipurpose LLMs. Transitioning from prompts to models can be achieved in minutes instead of weeks. Our fine-tuned Mistral and Llama 2 models routinely exceed the performance of GPT-4-1106-Turbo, while also being more cost-effective. With a commitment to open-source, we provide access to many of the base models we utilize. When you fine-tune Mistral and Llama 2, you maintain ownership of your weights and can download them whenever needed. Embrace the future of model training and deployment with OpenPipe's comprehensive tools and features.

Introducing a premier developer platform designed for the testing and debugging of AI agents, we provide the essential tools so you can focus on innovation. With our system, you can visually monitor events like LLM calls, tool usage, and the interactions of multiple agents. Additionally, our rewind and replay feature allows for precise review of agent executions at specific moments. Maintain a comprehensive log of data, encompassing logs, errors, and prompt injection attempts throughout the development cycle from prototype to production. Our platform seamlessly integrates with leading agent frameworks, enabling you to track, save, and oversee every token your agent processes. You can also manage and visualize your agent's expenditures with real-time price updates. Furthermore, our service enables you to fine-tune specialized LLMs at a fraction of the cost, making it up to 25 times more affordable on saved completions. Create your next agent with the benefits of evaluations, observability, and replays at your disposal. With just two simple lines of code, you can liberate yourself from terminal constraints and instead visualize your agents' actions through your AgentOps dashboard. Once AgentOps is configured, every execution of your program is documented as a session, ensuring that all relevant data is captured automatically, allowing for enhanced analysis and optimization. This not only streamlines your workflow but also empowers you to make data-driven decisions to improve your AI agents continuously.

Mistral AI Studio serves as a comprehensive platform for organizations and development teams to create, tailor, deploy, and oversee sophisticated AI agents, models, and workflows, guiding them from initial concepts to full-scale production. This platform includes a variety of reusable components such as agents, tools, connectors, guardrails, datasets, workflows, and evaluation mechanisms, all enhanced by observability and telemetry features that allow users to monitor agent performance, identify root causes, and ensure transparency in AI operations. With capabilities like Agent Runtime for facilitating the repetition and sharing of multi-step AI behaviors, AI Registry for organizing and managing model assets, and Data & Tool Connections that ensure smooth integration with existing enterprise systems, Mistral AI Studio accommodates a wide range of tasks, from refining open-source models to integrating them seamlessly into infrastructure and deploying robust AI solutions at an enterprise level. Furthermore, the platform's modular design promotes flexibility, enabling teams to adapt and scale their AI initiatives as needed.

Phoenix serves as a comprehensive open-source observability toolkit tailored for experimentation, evaluation, and troubleshooting purposes. It empowers AI engineers and data scientists to swiftly visualize their datasets, assess performance metrics, identify problems, and export relevant data for enhancements. Developed by Arize AI, the creators of a leading AI observability platform, alongside a dedicated group of core contributors, Phoenix is compatible with OpenTelemetry and OpenInference instrumentation standards. The primary package is known as arize-phoenix, and several auxiliary packages cater to specialized applications. Furthermore, our semantic layer enhances LLM telemetry within OpenTelemetry, facilitating the automatic instrumentation of widely-used packages. This versatile library supports tracing for AI applications, allowing for both manual instrumentation and seamless integrations with tools like LlamaIndex, Langchain, and OpenAI. By employing LLM tracing, Phoenix meticulously logs the routes taken by requests as they navigate through various stages or components of an LLM application, thus providing a clearer understanding of system performance and potential bottlenecks. Ultimately, Phoenix aims to streamline the development process, enabling users to maximize the efficiency and reliability of their AI solutions.

LMOps is a stack that allows you to launch production-ready applications for monitoring, model management and more. Portkey is a replacement for OpenAI or any other provider APIs. Portkey allows you to manage engines, parameters and versions. Switch, upgrade, and test models with confidence. View aggregate metrics for your app and users to optimize usage and API costs Protect your user data from malicious attacks and accidental exposure. Receive proactive alerts if things go wrong. Test your models in real-world conditions and deploy the best performers. We have been building apps on top of LLM's APIs for over 2 1/2 years. While building a PoC only took a weekend, bringing it to production and managing it was a hassle! We built Portkey to help you successfully deploy large language models APIs into your applications. We're happy to help you, regardless of whether or not you try Portkey!

Okareo is a cutting-edge platform created for AI development, assisting teams in confidently building, testing, and monitoring their AI agents. It features automated simulations that help identify edge cases, system conflicts, and points of failure prior to deployment, thereby ensuring the robustness and reliability of AI functionalities. With capabilities for real-time error tracking and smart safeguards, Okareo works to prevent hallucinations and uphold accuracy in live production scenarios. The platform continuously refines AI by utilizing domain-specific data and insights from live performance, which enhances relevance and effectiveness, ultimately leading to increased user satisfaction. By converting agent behaviors into practical insights, Okareo allows teams to identify successful strategies, recognize areas needing improvement, and determine future focus, significantly enhancing business value beyond simple log analysis. Additionally, Okareo is designed for both collaboration and scalability, accommodating AI projects of all sizes, making it an indispensable resource for teams aiming to deliver high-quality AI applications efficiently and effectively. This adaptability ensures that teams can respond to changing demands and challenges within the AI landscape.

AgentBench serves as a comprehensive evaluation framework tailored to measure the effectiveness and performance of autonomous AI agents. It features a uniform set of benchmarks designed to assess various dimensions of an agent's behavior, including their proficiency in task-solving, decision-making, adaptability, and interactions with simulated environments. By conducting evaluations on tasks spanning multiple domains, AgentBench aids developers in pinpointing both the strengths and limitations in the agents' performance, particularly regarding their planning, reasoning, and capacity to learn from feedback. This framework provides valuable insights into an agent's capability to navigate intricate scenarios that mirror real-world challenges, making it beneficial for both academic research and practical applications. Ultimately, AgentBench plays a crucial role in facilitating the ongoing enhancement of autonomous agents, ensuring they achieve the required standards of reliability and efficiency prior to their deployment in broader contexts. This iterative assessment process not only fosters innovation but also builds trust in the performance of these autonomous systems.

Braintrust serves as a robust platform tailored for the development of AI products within enterprises. By streamlining evaluations, providing a prompt playground, and managing data effectively, we eliminate the challenges and monotony associated with integrating AI into business operations. Users can compare various prompts, benchmarks, and the corresponding input/output pairs across different runs. You have the option to experiment in a transient manner or transform your initial draft into a comprehensive experiment for analysis across extensive datasets. Incorporate Braintrust into your continuous integration processes to monitor advancements on your primary branch and automatically juxtapose new experiments with existing live versions prior to deployment. Effortlessly gather rated examples from both staging and production environments, assess them, and integrate these insights into curated “golden” datasets. These datasets are stored in your cloud infrastructure and come with built-in version control, allowing for seamless evolution without jeopardizing the integrity of evaluations that rely on them, ensuring a smooth and efficient workflow as your AI capabilities expand. With Braintrust, businesses can confidently navigate the complexities of AI integration while fostering innovation and reliability.

Prompt Flow is a comprehensive suite of development tools aimed at optimizing the entire development lifecycle of AI applications built on LLMs, encompassing everything from concept creation and prototyping to testing, evaluation, and final deployment. By simplifying the prompt engineering process, it empowers users to develop high-quality LLM applications efficiently. Users can design workflows that seamlessly combine LLMs, prompts, Python scripts, and various other tools into a cohesive executable flow. This platform enhances the debugging and iterative process, particularly by allowing users to easily trace interactions with LLMs. Furthermore, it provides capabilities to assess the performance and quality of flows using extensive datasets, while integrating the evaluation phase into your CI/CD pipeline to maintain high standards. The deployment process is streamlined, enabling users to effortlessly transfer their flows to their preferred serving platform or integrate them directly into their application code. Collaboration among team members is also improved through the utilization of the cloud-based version of Prompt Flow available on Azure AI, making it easier to work together on projects. This holistic approach to development not only enhances efficiency but also fosters innovation in LLM application creation.

Introduce features powered by LLMs into production using tools designed for prompt engineering, semantic search, version control, quantitative testing, and performance tracking, all of which are compatible with the leading LLM providers. Expedite the process of developing a minimum viable product by testing various prompts, parameters, and different LLM providers to quickly find the optimal setup for your specific needs. Vellum serves as a fast, dependable proxy to LLM providers, enabling you to implement version-controlled modifications to your prompts without any coding requirements. Additionally, Vellum gathers model inputs, outputs, and user feedback, utilizing this information to create invaluable testing datasets that can be leveraged to assess future modifications before deployment. Furthermore, you can seamlessly integrate company-specific context into your prompts while avoiding the hassle of managing your own semantic search infrastructure, enhancing the relevance and precision of your interactions.

Coval serves as a robust platform for simulating and evaluating AI agents, aimed at enhancing their reliability across various interaction modes, including chat and voice. It streamlines the testing procedure by allowing engineers to generate thousands of scenarios from just a handful of test cases, thereby ensuring thorough evaluations without the need for manual oversight. Users can effortlessly compile test sets by incorporating customer conversations or articulating user intents using natural language, while Coval manages the formatting seamlessly. The platform accommodates both text and voice simulations, enabling rigorous testing of AI agents based on defined scorecard metrics. Detailed assessments of agent interactions are generated, which not only track performance over time but also facilitate in-depth root cause analysis for specific instances. Additionally, Coval provides workflow metrics that enhance visibility into system processes, which is instrumental in optimizing the performance of AI agents. Ultimately, this comprehensive approach fosters a more efficient development cycle for AI technologies.

At last, a technological solution that profoundly enhances the effectiveness of your customer experience team. Utilize the ASAPP customer experience performance (CXP) platform to not only elevate organizational efficiency but also boost customer satisfaction (CSAT) scores simultaneously. Outdated and rigid infrastructures hinder your capacity to enhance customer experience performance effectively. Simply layering additional technology has failed to decrease the number of agents, lower expenses, or fulfill the promise of happier customers. Our innovative technology is specifically crafted to empower your agents in providing superior service to customers. Contrary to vendor marketing, bots have not supplanted your agents, and merely offering agent assistance falls short. Utilizing machine learning, the platform acquires valuable insights into how your top-performing agents meet customer needs. It’s not merely about the dialogue; it’s the intricate interplay and order of words and actions that constitute Agent Journeys™, leading to a more tailored and effective customer experience. By harnessing these insights, organizations can revolutionize their approach to customer service.

Capture production data. Evaluate outputs together and fine-tune the performance of your LLM. A detailed log overview will help you understand what is happening in production. Work with domain experts, product managers and engineers to create reliable model outputs. Track AI metrics, such as speed, token usage, and quality scores. Copilot automates model evaluations and improvements for your use cases. Create, manage, or optimize prompts for precise and relevant interactions between AI models and users. Compare fine-tuned models and foundation models to improve prompt performance. Build a fine-tuning dataset with your team. Create custom fine-tuning data to optimize model performance.

The Oracle AI Agent Platform is a comprehensive service designed for the development, implementation, and oversight of sophisticated virtual agents that utilize large language models along with integrated AI technologies. Setting up these agents involves a straightforward multi-step process, allowing them to utilize various tools such as converting natural language into SQL queries, enhancing responses with information from enterprise knowledge repositories, invoking custom functions or APIs, and managing interactions with sub-agents. These agents are capable of engaging in multi-turn conversations while maintaining context, which allows them to address follow-up inquiries and provide personalized, coherent exchanges. To ensure quality and safety, the platform includes built-in guardrails for content moderation, prevention of prompt injection attacks, and safeguarding of personally identifiable information (PII). Additionally, the system offers optional human-in-the-loop mechanisms that enable real-time oversight and the ability to escalate issues when necessary, ensuring a balance between automation and human control. This combination of features positions the Oracle AI Agent Platform as a robust solution for businesses looking to enhance customer interactions through intelligent automation.

doteval serves as an AI-driven evaluation workspace that streamlines the development of effective evaluations, aligns LLM judges, and establishes reinforcement learning rewards, all integrated into one platform. This tool provides an experience similar to Cursor, allowing users to edit evaluations-as-code using a YAML schema, which makes it possible to version evaluations through various checkpoints, substitute manual tasks with AI-generated differences, and assess evaluation runs in tight execution loops to ensure alignment with proprietary datasets. Additionally, doteval enables the creation of detailed rubrics and aligned graders, promoting quick iterations and the generation of high-quality evaluation datasets. Users can make informed decisions regarding model updates or prompt enhancements, as well as export specifications for reinforcement learning training purposes. By drastically speeding up the evaluation and reward creation process by a factor of 10 to 100, doteval proves to be an essential resource for advanced AI teams working on intricate model tasks. In summary, doteval not only enhances efficiency but also empowers teams to achieve superior evaluation outcomes with ease.

Parea is a prompt engineering platform designed to allow users to experiment with various prompt iterations, assess and contrast these prompts through multiple testing scenarios, and streamline the optimization process with a single click, in addition to offering sharing capabilities and more. Enhance your AI development process by leveraging key functionalities that enable you to discover and pinpoint the most effective prompts for your specific production needs. The platform facilitates side-by-side comparisons of prompts across different test cases, complete with evaluations, and allows for CSV imports of test cases, along with the creation of custom evaluation metrics. By automating the optimization of prompts and templates, Parea improves the outcomes of large language models, while also providing users the ability to view and manage all prompt versions, including the creation of OpenAI functions. Gain programmatic access to your prompts, which includes comprehensive observability and analytics features, helping you determine the costs, latency, and overall effectiveness of each prompt. Embark on the journey to refine your prompt engineering workflow with Parea today, as it empowers developers to significantly enhance the performance of their LLM applications through thorough testing and effective version control, ultimately fostering innovation in AI solutions.

Introducing the inaugural platform designed specifically for prompt engineers, where you can log OpenAI requests, review usage history, monitor performance, and easily manage your prompt templates. With this tool, you’ll never lose track of that perfect prompt again, ensuring GPT operates seamlessly in production. More than 1,000 engineers have placed their trust in this platform to version their prompts and oversee API utilization effectively. Begin integrating your prompts into production by creating an account on PromptLayer; just click “log in” to get started. Once you’ve logged in, generate an API key and make sure to store it securely. After you’ve executed a few requests, you’ll find them displayed on the PromptLayer dashboard! Additionally, you can leverage PromptLayer alongside LangChain, a widely used Python library that facilitates the development of LLM applications with a suite of useful features like chains, agents, and memory capabilities. Currently, the main method to access PromptLayer is via our Python wrapper library, which you can install effortlessly using pip. This streamlined approach enhances your workflow and maximizes the efficiency of your prompt engineering endeavors.

Teammately is an innovative AI agent designed to transform the landscape of AI development by autonomously iterating on AI products, models, and agents to achieve goals that surpass human abilities. Utilizing a scientific methodology, it fine-tunes and selects the best combinations of prompts, foundational models, and methods for knowledge organization. To guarantee dependability, Teammately creates unbiased test datasets and develops adaptive LLM-as-a-judge systems customized for specific projects, effectively measuring AI performance and reducing instances of hallucinations. The platform is tailored to align with your objectives through Product Requirement Docs (PRD), facilitating targeted iterations towards the intended results. Among its notable features are multi-step prompting, serverless vector search capabilities, and thorough iteration processes that consistently enhance AI until the set goals are met. Furthermore, Teammately prioritizes efficiency by focusing on identifying the most compact models, which leads to cost reductions and improved overall performance. This approach not only streamlines the development process but also empowers users to leverage AI technology more effectively in achieving their aspirations.

With a suite observability tools, you can confidently evaluate, test and ship LLM apps across your development and production lifecycle. Log traces and spans. Define and compute evaluation metrics. Score LLM outputs. Compare performance between app versions. Record, sort, find, and understand every step that your LLM app makes to generate a result. You can manually annotate and compare LLM results in a table. Log traces in development and production. Run experiments using different prompts, and evaluate them against a test collection. You can choose and run preconfigured evaluation metrics, or create your own using our SDK library. Consult the built-in LLM judges to help you with complex issues such as hallucination detection, factuality and moderation. Opik LLM unit tests built on PyTest provide reliable performance baselines. Build comprehensive test suites for every deployment to evaluate your entire LLM pipe-line.

Xpander.ai serves as a backend-as-a-service platform specifically designed for the deployment of production-level AI agents, providing developers with a comprehensive infrastructure that manages various essential components such as memory, tools, connectors, multi-agent workflows, triggering, state management, observability, and CI/CD pipelines without necessitating any infrastructure setup. The platform features a visual AI agent workbench that allows users to design, configure, simulate, test, and deploy agents in an interactive manner, while also facilitating collaboration among multiple agents, integrating various tools, implementing role-based access, and ensuring runtime governance. Developers are empowered to link their agents to SaaS or enterprise systems using AI-optimized connectors, create workflows compatible with tools, and observe agent performance through integrated observability and lifecycle management tools. Furthermore, it offers deployment options on both hosted cloud infrastructure and private VPCs, balancing agility with secure enterprise integration, thus streamlining the process of transforming ideas into production-ready agents. With its advanced features, Xpander.ai not only enhances the development experience but also fosters innovation in the AI agent landscape.

DataOcean AI stands out as a premier provider of meticulously labeled training data and extensive AI data solutions, featuring an impressive array of over 1,600 pre-made datasets along with countless tailored datasets specifically designed for machine learning and artificial intelligence applications. Their diverse offerings encompass various modalities, including speech, text, images, audio, video, and multimodal data, effectively catering to tasks such as automatic speech recognition (ASR), text-to-speech (TTS), natural language processing (NLP), optical character recognition (OCR), computer vision, content moderation, machine translation, lexicon development, autonomous driving, and fine-tuning of large language models (LLMs). By integrating AI-driven methodologies with human-in-the-loop (HITL) processes through their innovative DOTS platform, DataOcean AI provides a suite of over 200 data-processing algorithms and numerous labeling tools to facilitate automation, assisted labeling, data collection, cleaning, annotation, training, and model evaluation. With nearly two decades of industry experience and a presence in over 70 countries, DataOcean AI is committed to upholding rigorous standards of quality, security, and compliance, effectively serving more than 1,000 enterprises and academic institutions across the globe. Their ongoing commitment to excellence and innovation continues to shape the future of AI data solutions.

Laminar is a comprehensive open-source platform designed to facilitate the creation of top-tier LLM products. The quality of your LLM application is heavily dependent on the data you manage. With Laminar, you can efficiently gather, analyze, and leverage this data. By tracing your LLM application, you gain insight into each execution phase while simultaneously gathering critical information. This data can be utilized to enhance evaluations through the use of dynamic few-shot examples and for the purpose of fine-tuning your models. Tracing occurs seamlessly in the background via gRPC, ensuring minimal impact on performance. Currently, both text and image models can be traced, with audio model tracing expected to be available soon. You have the option to implement LLM-as-a-judge or Python script evaluators that operate on each data span received. These evaluators provide labeling for spans, offering a more scalable solution than relying solely on human labeling, which is particularly beneficial for smaller teams. Laminar empowers users to go beyond the constraints of a single prompt, allowing for the creation and hosting of intricate chains that may include various agents or self-reflective LLM pipelines, thus enhancing overall functionality and versatility. This capability opens up new avenues for experimentation and innovation in LLM development.

Start customizing LLMs and prompts right away without needing a PhD, as everything you need is provided in Starter Kits tailored to your specific use case, including model, prompt, and dataset recommendations. With these resources, you can immediately begin testing, assessing, and fine-tuning model outputs. You have the freedom to explore various models, both proprietary and open-source, along with different prompts and techniques all at once, which accelerates the iteration process. The platform also incorporates automated testing and evaluation, along with AI-driven prompt and enhancement suggestions, allowing you to conduct numerous experiments simultaneously and achieve high-quality results in a shorter time frame. Verta’s user-friendly interface is designed to support individuals of all technical backgrounds in swiftly obtaining superior model outputs. By utilizing a human-in-the-loop evaluation method, Verta ensures that human insights are prioritized during critical phases of the iteration cycle, helping to capture expertise and foster the development of intellectual property that sets your GenAI products apart. You can effortlessly monitor your top-performing options through Verta’s Leaderboard, making it easier to refine your approach and maximize efficiency. This comprehensive system not only streamlines the customization process but also enhances your ability to innovate in artificial intelligence.