Alternatives to Gram

StackAI is an enterprise AI automation platform that allows organizations to build end-to-end internal tools and processes with AI agents. It ensures every workflow is secure, compliant, and governed, so teams can automate complex processes without heavy engineering. With a visual workflow builder and multi-agent orchestration, StackAI enables full automation from knowledge retrieval to approvals and reporting. Enterprise data sources like SharePoint, Confluence, Notion, Google Drive, and internal databases can be connected with versioning, citations, and access controls to protect sensitive information. AI agents can be deployed as chat assistants, advanced forms, or APIs integrated into Slack, Teams, Salesforce, HubSpot, ServiceNow, or custom apps. Security is built in with SSO (Okta, Azure AD, Google), RBAC, audit logs, PII masking, and data residency. Analytics and cost governance let teams track performance, while evaluations and guardrails ensure reliability before production. StackAI also offers model flexibility, routing tasks across OpenAI, Anthropic, Google, or local LLMs with fine-grained controls for accuracy. A template library accelerates adoption with ready-to-use workflows like Contract Analyzer, Support Desk AI Assistant, RFP Response Builder, and Investment Memo Generator. By consolidating fragmented processes into secure, AI-powered workflows, StackAI reduces manual work, speeds decision-making, and empowers teams to build trusted automation at scale.

AgentPass.ai is a robust platform tailored for the secure implementation of AI agents within corporate settings, offering production-ready Model Context Protocol (MCP) servers. It empowers users to establish fully hosted MCP servers effortlessly, eliminating the necessity for coding, and includes essential features such as user authentication, authorization, and access control. Additionally, developers can seamlessly transform OpenAPI specifications into MCP-compatible tool definitions, facilitating the management of intricate API ecosystems through hierarchical structures. The platform also provides observability capabilities, including analytics, audit logs, and performance monitoring, while accommodating multi-tenant architecture to oversee various environments. Organizations leveraging AgentPass.ai can effectively scale their AI automation efforts, ensuring centralized management and regulatory compliance across all AI agent implementations. Furthermore, this platform streamlines the deployment process, making it accessible for teams of varying technical expertise.

Appsmith enables organizations to create custom internal applications quickly with minimal coding. The platform allows users to build applications by connecting data sources, APIs, and workflows through a user-friendly drag-and-drop interface. Appsmith's flexibility with JavaScript lets developers fully customize components, while the open-source architecture and enterprise security features ensure scalability and compliance. With self-hosting and cloud deployment options, businesses can choose the best setup for their needs, whether for simple dashboards or complex business applications. Appsmith offers a comprehensive solution for creating and deploying custom AI agents that can automate key business processes. Designed for sales, support, and people management teams, the platform allows companies to embed conversational agents into their systems. Appsmith's AI agents enhance operational efficiency by managing routine tasks, providing real-time insights, and boosting team productivity, all while leveraging secure data.

TensorBlock is an innovative open-source AI infrastructure platform aimed at making large language models accessible to everyone through two interrelated components. Its primary product, Forge, serves as a self-hosted API gateway that prioritizes privacy while consolidating connections to various LLM providers into a single endpoint compatible with OpenAI, incorporating features like encrypted key management, adaptive model routing, usage analytics, and cost-efficient orchestration. In tandem with Forge, TensorBlock Studio provides a streamlined, developer-friendly workspace for interacting with multiple LLMs, offering a plugin-based user interface, customizable prompt workflows, real-time chat history, and integrated natural language APIs that facilitate prompt engineering and model evaluations. Designed with a modular and scalable framework, TensorBlock is driven by ideals of transparency, interoperability, and equity, empowering organizations to explore, deploy, and oversee AI agents while maintaining comprehensive control and reducing infrastructure burdens. This dual approach ensures that users can effectively leverage AI capabilities without being hindered by technical complexities or excessive costs.

ToolSDK.ai is a complimentary TypeScript SDK and marketplace designed to expedite the development of agentic AI applications by offering immediate access to more than 5,300 MCP (Model Context Protocol) servers and modular tools with just a single line of code. This capability allows developers to seamlessly integrate real-world workflows that merge language models with various external systems. The platform provides a cohesive client for loading structured MCP servers, which include functionalities like search, email, CRM, task management, storage, and analytics, transforming them into tools compatible with OpenAI. It efficiently manages authentication, invocation, and the orchestration of results, enabling virtual assistants to interact with, compare, and utilize live data from a range of services such as Gmail, Salesforce, Google Drive, ClickUp, Notion, Slack, GitHub, and various analytics platforms, as well as custom web search or automation endpoints. Additionally, the SDK comes with example quick-start integrations, supports metadata and conditional logic for multi-step orchestrations, and facilitates smooth scaling to accommodate parallel agents and intricate pipelines, making it an invaluable resource for developers aiming to innovate in the AI landscape. With these features, ToolSDK.ai significantly lowers the barriers for developers to create sophisticated AI-driven solutions.

FastbuildAI is a self-hosted, open source framework crafted to enable AI developers and entrepreneurs to swiftly create and launch comprehensive AI applications that are ready for commercial use. This platform features an intuitive visual "DIY" interface that minimizes the need for extensive coding, along with integrated tools for handling user authentication, subscription billing, usage tracking, and payment processing. Additionally, it boasts a plugin architecture that allows users to enhance the platform's capabilities with features like chatbots, agent workflows, custom APIs, and multi-modal functionalities. FastbuildAI facilitates quick deployment through Docker and provides adaptable infrastructure options, whether on-premises or in the cloud, ensuring complete control over branding, data management, and monetization strategies. By utilizing FastbuildAI, users can transform an AI idea into an operational SaaS product in a matter of minutes, equipped with a graphical user interface, a robust plugin system, tiered monetization options, and self-hosted functionalities. The framework is designed to cater to both tech-savvy individuals eager to tailor specific processes and those without technical expertise who aspire to launch an AI-driven enterprise successfully. Ultimately, FastbuildAI democratizes access to AI application development, making it feasible for a wider range of users to innovate in this rapidly evolving field.

We have taken the initiative to bundle and set up all necessary components for crafting solutions and seamlessly integrating LLMs into your organizational workflows. With ConfidentialMind, you can immediately get started. It provides an endpoint for the most advanced open-source LLMs, such as Llama-2, effectively transforming it into an internal LLM API. Envision having ChatGPT operating within your personal cloud environment. This represents the utmost in security solutions available. It connects with the APIs of leading hosted LLM providers, including Azure OpenAI, AWS Bedrock, and IBM, ensuring comprehensive integration. Additionally, ConfidentialMind features a playground UI built on Streamlit, which offers a variety of LLM-driven productivity tools tailored for your organization, including writing assistants and document analysis tools. It also comes with a vector database, essential for efficiently sifting through extensive knowledge repositories containing thousands of documents. Furthermore, it empowers you to manage access to the solutions developed by your team and regulate what information the LLMs can access, enhancing data security and control. With these capabilities, you can drive innovation while ensuring compliance and safety within your business operations.

The AI SDK is a complimentary, open source toolkit based on TypeScript, developed by the team behind Next.js, which empowers developers with cohesive, high-level tools for swiftly implementing AI-driven features across various model providers with just a single line of code modification. It simplifies intricate tasks such as managing streaming responses, executing multi-turn tools, handling errors, recovering from issues, and switching between models while being adaptable to any framework, allowing creators to transition from concept to operational application in mere minutes. Featuring a unified provider API, the toolkit enables developers to produce typed objects, design generative user interfaces, and provide immediate, streamed AI replies without the need to redo foundational work, complemented by comprehensive documentation, practical guides, an interactive playground, and community-driven enhancements to speed up the development process. By taking care of the complex elements behind the scenes while still allowing sufficient control for deeper customization, this SDK ensures a smooth integration experience with multiple large language models. Overall, it stands as an essential resource for developers seeking to innovate rapidly and effectively in the realm of AI applications.

The Generative AI Service Cloud Infrastructure is a comprehensive, fully managed platform that provides robust large language models capable of various functions such as generation, summarization, analysis, chatting, embedding, and reranking. Users can easily access pretrained foundational models through a user-friendly playground, API, or CLI, and they also have the option to fine-tune custom models using dedicated AI clusters that are exclusive to their tenancy. This service is equipped with content moderation, model controls, dedicated infrastructure, and versatile deployment endpoints to meet diverse needs. Its applications are vast and varied, serving multiple industries and workflows by generating text for marketing campaigns, creating conversational agents, extracting structured data from various documents, performing classification tasks, enabling semantic search, facilitating code generation, and beyond. The architecture is designed to accommodate "text in, text out" workflows with advanced formatting capabilities, and operates across global regions while adhering to Oracle’s governance and data sovereignty requirements. Furthermore, businesses can leverage this powerful infrastructure to innovate and streamline their operations efficiently.

Arcade.dev is a platform designed for AI tool calling that empowers AI agents to safely carry out real-world tasks such as sending emails, messaging, updating systems, or activating workflows through integrations authorized by users. Serving as a secure authenticated proxy in line with the OpenAI API specification, Arcade.dev allows models to access various external services, including Gmail, Slack, GitHub, Salesforce, and Notion, through both pre-built connectors and custom tool SDKs while efficiently handling authentication, token management, and security. Developers can utilize a streamlined client interface—arcadepy for Python or arcadejs for JavaScript—that simplifies tool execution and authorization processes without complicating application logic with the need for credentials or API details. The platform is versatile, supporting secure deployments in the cloud, private VPCs, or local environments and features a control plane designed for managing tools, users, permissions, and observability. This comprehensive management system ensures that developers can maintain oversight and control while leveraging the power of AI to automate various tasks effectively.

Chainlit is a versatile open-source Python library that accelerates the creation of production-ready conversational AI solutions. By utilizing Chainlit, developers can swiftly design and implement chat interfaces in mere minutes rather than spending weeks on development. The platform seamlessly integrates with leading AI tools and frameworks such as OpenAI, LangChain, and LlamaIndex, facilitating diverse application development. Among its notable features, Chainlit supports multimodal functionalities, allowing users to handle images, PDFs, and various media formats to boost efficiency. Additionally, it includes strong authentication mechanisms compatible with providers like Okta, Azure AD, and Google, enhancing security measures. The Prompt Playground feature allows developers to refine prompts contextually, fine-tuning templates, variables, and LLM settings for superior outcomes. To ensure transparency and effective monitoring, Chainlit provides real-time insights into prompts, completions, and usage analytics, fostering reliable and efficient operations in the realm of language models. Overall, Chainlit significantly streamlines the process of building conversational AI applications, making it a valuable tool for developers in this rapidly evolving field.

Disco.dev serves as an open-source personal hub designed for the integration of the Model Context Protocol (MCP), enabling users to easily discover, launch, customize, and remix MCP servers without any setup or infrastructure burdens. This platform offers convenient plug-and-play connectors alongside a collaborative workspace that allows users to quickly deploy servers using either CLI or local execution methods. Users can also delve into community-shared servers, remix them, and adapt them for their specific workflows. By eliminating infrastructure constraints, this efficient approach not only speeds up the development of AI automation but also makes agentic tools more accessible to a broader audience. Additionally, it encourages collaborative efforts among both technical and non-technical users, promoting a modular ecosystem that embraces remixability and innovation. Overall, Disco.dev stands as a pivotal resource for those looking to enhance their MCP experience without traditional limitations.

Start developing in the cloud and deploying on your own server using retrieval-augmented generation, agents, and more. We offer a straightforward pricing model with a fixed fee for each request. Requests can be categorized into two main types: document indexation and generation. Document indexation involves incorporating a document into your knowledge base, while generation utilizes that knowledge base to produce LLM-generated content through RAG. You can establish a RAG workflow by implementing pre-existing components and crafting a prototype tailored to your specific needs. Additionally, we provide various supporting features, such as the ability to trace outputs back to their original documents and support for multiple file formats during ingestion. By utilizing Agents, you can empower the LLM to access additional tools. An Agent-based architecture can determine the necessary data and conduct searches accordingly. Our agent implementation simplifies the hosting of execution layers and offers pre-built agents suited for numerous applications, making your development process even more efficient. With these resources at your disposal, you can create a robust system that meets your demands.

The Model Context Protocol (MCP) is a flexible, open-source framework that streamlines the interaction between AI models and external data sources. It enables developers to create complex workflows by connecting LLMs with databases, files, and web services, offering a standardized approach for AI applications. MCP’s client-server architecture ensures seamless integration, while its growing list of integrations makes it easy to connect with different LLM providers. The protocol is ideal for those looking to build scalable AI agents with strong data security practices.

Choose a model from our extensive open-source library, launch the container, and seamlessly integrate the model API into your application. Whether you're working with image recognition or natural language processing, all our models come pre-trained and are conveniently packaged within a user-friendly API. Our diverse collection of models continues to expand, ensuring you have access to the latest innovations. We carefully select and refine the top models available from sources like HuggingFace and Github. You have the option to host the model on your own with ease or obtain your personal endpoint and API key with just a single click. Cargoship stays at the forefront of advancements in the AI field, relieving you of the burden of keeping up. With the Cargoship Model Store, you'll find a comprehensive selection tailored for every machine learning application. The website features interactive demos for you to explore, along with in-depth guidance that covers everything from the model's capabilities to implementation techniques. Regardless of your skill level, we’re committed to providing you with thorough instructions to ensure your success. Additionally, our support team is always available to assist you with any questions you may have.

Maxim is a enterprise-grade stack that enables AI teams to build applications with speed, reliability, and quality. Bring the best practices from traditional software development to your non-deterministic AI work flows. Playground for your rapid engineering needs. Iterate quickly and systematically with your team. Organise and version prompts away from the codebase. Test, iterate and deploy prompts with no code changes. Connect to your data, RAG Pipelines, and prompt tools. Chain prompts, other components and workflows together to create and test workflows. Unified framework for machine- and human-evaluation. Quantify improvements and regressions to deploy with confidence. Visualize the evaluation of large test suites and multiple versions. Simplify and scale human assessment pipelines. Integrate seamlessly into your CI/CD workflows. Monitor AI system usage in real-time and optimize it with speed.

Lunary serves as a platform for AI developers, facilitating the management, enhancement, and safeguarding of Large Language Model (LLM) chatbots. It encompasses a suite of features, including tracking conversations and feedback, analytics for costs and performance, debugging tools, and a prompt directory that supports version control and team collaboration. The platform is compatible with various LLMs and frameworks like OpenAI and LangChain and offers SDKs compatible with both Python and JavaScript. Additionally, Lunary incorporates guardrails designed to prevent malicious prompts and protect against sensitive data breaches. Users can deploy Lunary within their VPC using Kubernetes or Docker, enabling teams to evaluate LLM responses effectively. The platform allows for an understanding of the languages spoken by users, experimentation with different prompts and LLM models, and offers rapid search and filtering capabilities. Notifications are sent out when agents fail to meet performance expectations, ensuring timely interventions. With Lunary's core platform being fully open-source, users can choose to self-host or utilize cloud options, making it easy to get started in a matter of minutes. Overall, Lunary equips AI teams with the necessary tools to optimize their chatbot systems while maintaining high standards of security and performance.

Langtail is a cloud-based development tool designed to streamline the debugging, testing, deployment, and monitoring of LLM-powered applications. The platform provides a no-code interface for debugging prompts, adjusting model parameters, and conducting thorough LLM tests to prevent unexpected behavior when prompts or models are updated. Langtail is tailored for LLM testing, including chatbot evaluations and ensuring reliable AI test prompts. Key features of Langtail allow teams to: • Perform in-depth testing of LLM models to identify and resolve issues before production deployment. • Easily deploy prompts as API endpoints for smooth integration into workflows. • Track model performance in real-time to maintain consistent results in production environments. • Implement advanced AI firewall functionality to control and protect AI interactions. Langtail is the go-to solution for teams aiming to maintain the quality, reliability, and security of their AI and LLM-based applications.

Kitten Stack serves as a comprehensive platform designed for the creation, enhancement, and deployment of LLM applications, effectively addressing typical infrastructure hurdles by offering powerful tools and managed services that allow developers to swiftly transform their concepts into fully functional AI applications. By integrating managed RAG infrastructure, consolidated model access, and extensive analytics, Kitten Stack simplifies the development process, enabling developers to prioritize delivering outstanding user experiences instead of dealing with backend complications. Key Features: Instant RAG Engine: Quickly and securely link private documents (PDF, DOCX, TXT) and real-time web data in just minutes, while Kitten Stack manages the intricacies of data ingestion, parsing, chunking, embedding, and retrieval. Unified Model Gateway: Gain access to over 100 AI models (including those from OpenAI, Anthropic, Google, and more) through a single, streamlined platform, enhancing versatility and innovation in application development. This unification allows for seamless integration and experimentation with a variety of AI technologies.

Enhance the precision of your machine learning models by leveraging publicly accessible datasets. Streamline the process of data discovery and preparation with curated datasets that are not only readily available for machine learning applications but also easily integrable through Azure services. It is essential to consider real-world factors that could influence business performance. By integrating features from these curated datasets into your machine learning models, you can significantly boost the accuracy of your predictions while minimizing the time spent on data preparation. Collaborate and share datasets with an expanding network of data scientists and developers. Utilize Azure Open Datasets alongside Azure’s machine learning and data analytics solutions to generate insights at an unprecedented scale. Most Open Datasets come at no extra cost, allowing you to pay solely for the Azure services utilized, including virtual machine instances, storage, networking, and machine learning resources. This curated open data is designed for seamless access on Azure, empowering users to focus on innovation and analysis. In this way, organizations can unlock new opportunities and drive informed decision-making.

LangChain provides a comprehensive framework that empowers developers to build and scale intelligent applications using large language models (LLMs). By integrating data and APIs, LangChain enables context-aware applications that can perform reasoning tasks. The suite includes LangGraph, a tool for orchestrating complex workflows, and LangSmith, a platform for monitoring and optimizing LLM-driven agents. LangChain supports the full lifecycle of LLM applications, offering tools to handle everything from initial design and deployment to post-launch performance management. Its flexibility makes it an ideal solution for businesses looking to enhance their applications with AI-powered reasoning and automation.

Composio serves as an integration platform aimed at strengthening AI agents and Large Language Models (LLMs) by allowing easy connectivity to more than 150 tools with minimal coding efforts. This platform accommodates a diverse range of agentic frameworks and LLM providers, enabling efficient function calling for streamlined task execution. Composio boasts an extensive repository of tools such as GitHub, Salesforce, file management systems, and code execution environments, empowering AI agents to carry out a variety of actions and respond to multiple triggers. One of its standout features is managed authentication, which enables users to control the authentication processes for every user and agent through a unified dashboard. Additionally, Composio emphasizes a developer-centric integration methodology, incorporates built-in management for authentication, and offers an ever-growing collection of over 90 tools ready for connection. Furthermore, it enhances reliability by 30% through the use of simplified JSON structures and improved error handling, while also ensuring maximum data security with SOC Type II compliance. Overall, Composio represents a robust solution for integrating tools and optimizing AI capabilities across various applications.

Omni is an AI framework that allows you to connect Prompts and Tools to LLM Agents. Agents are built on the ReAct paradigm, which is Reason + Act. They allow LLM models and tools to interact to complete a task. Automate customer service, document processing, qualification of leads, and more. You can easily switch between LLM architectures and prompts to optimize performance. Your workflows are hosted as APIs, so you can instantly access AI.

Open Agent Studio stands out as a revolutionary no-code co-pilot builder, enabling users to create solutions that are unattainable with conventional RPA tools today. We anticipate that competitors will attempt to replicate this innovative concept, giving our clients a valuable head start in exploring markets that have not yet benefited from AI, leveraging their specialized industry knowledge. Our subscribers can take advantage of a complimentary four-week course designed to guide them in assessing product concepts and launching a custom agent featuring an enterprise-grade white label. The process of building agents is simplified through the ability to record keyboard and mouse actions, which includes functions like data scraping and identifying the start node. With the agent recorder, crafting generalized agents becomes incredibly efficient, allowing training to occur as quickly as possible. After recording once, users can distribute these agents throughout their organization, ensuring scalability and a future-proof solution for their automation needs. This unique approach not only enhances productivity but also empowers businesses to innovate and adapt in a rapidly evolving technological landscape.

WRITER is the end-to-end platform for building, activating, and supervising AI agents across the enterprise. It empowers IT and business teams to collaboratively automate work, improve decision making, and scale impact — with full control and governance. Unlike other solutions built exclusively for engineers or business users, the WRITER platform gives anyone the tools to build powerful AI agents. Users can easily connect agents to customer data and systems, map business processes to guide agent behavior, and create and test prompts. They can get started quickly with a library of over 100 prebuilt agents, or build their own using intuitive no-code and pro-code tools. When code is needed, developers can create tools, APIs, and integrations with ease — then make them available to agents through a simple drag-and-drop interface.

Our platform provides an integrated suite of top-tier tools and resources designed for the effortless transformation of concepts into production-ready AI applications. With our unified system, you can construct and implement comprehensive custom Generative AI solutions, eliminating the complications associated with using multiple tools and shifting between different platforms. You can initiate your next AI project through a single, convenient API endpoint. The process of creating, managing, and enhancing AI systems has reached an unprecedented level of simplicity. Discover serves as aiXplain’s marketplace, featuring an array of models and datasets from diverse providers. You have the option to subscribe to these models and datasets for utilization with aiXplain’s no-code/low-code tools or implement them in your own code via the SDK, unlocking countless possibilities for innovation. Embrace the ease of access to high-quality resources as you embark on your AI journey.

Kanvo offers a seamless JavaScript SDK that enhances LangGraph-based AI agents with integrated memory, observability, and resilience, all without the need for any infrastructure setup. The SDK allows developers to integrate just a few lines of code to activate features such as persistent memory for storing facts, preferences, and goals, as well as threaded conversations for multi-user engagement and real-time monitoring of agent activities, which records every interaction, tool usage, and LLM output. Its innovative time-travel debugging capabilities enable users to checkpoint, rewind, and restore any agent's run state with ease, ensuring that workflows are easily reproducible and errors can be swiftly identified. Built with an emphasis on efficiency and user-friendliness, Convo's streamlined interface paired with its MIT-licensed SDK provides developers with production-ready, easily debuggable agents straight from installation, while also ensuring that data control remains entirely with the users. This combination of features positions Kanvo as a powerful tool for developers looking to create sophisticated AI applications without the typical complexities associated with data management.

Gain a comprehensive understanding of your model's efficacy by logging both inputs and outputs while enhancing them with relevant metadata and user insights. This approach allows you to truly assess your model's functionality and identify areas that require refinement. Keep an eye out for errors and pinpoint underperforming user segments and scenarios that may need attention. The most effective models leverage user-generated data; therefore, systematically collect atypical or low-performing instances to enhance your model through retraining. Rather than sifting through countless outputs following adjustments to your prompts or models, adopt a programmatic evaluation of your LLM-driven applications. Rapidly identify and address performance issues by monitoring new deployments in real-time and effortlessly updating the version of your application that users engage with. Establish connections between your self-hosted or third-party models and your current data repositories for seamless integration. Handle enterprise-scale data effortlessly with our serverless streaming data flow engine, designed for efficiency and scalability. Moreover, Gantry adheres to SOC-2 standards and incorporates robust enterprise-grade authentication features to ensure data security and integrity. This dedication to compliance and security solidifies trust with users while optimizing performance.

NeuroSplit is an innovative adaptive-inferencing technology that employs a unique method of "slicing" a neural network's connections in real time, resulting in the creation of two synchronized sub-models; one that processes initial layers locally on the user's device and another that offloads the subsequent layers to cloud GPUs. This approach effectively utilizes underused local computing power and can lead to a reduction in server expenses by as much as 60%, all while maintaining high levels of performance and accuracy. Incorporated within Skymel’s Orchestrator Agent platform, NeuroSplit intelligently directs each inference request across various devices and cloud environments according to predetermined criteria such as latency, cost, or resource limitations, and it automatically implements fallback mechanisms and model selection based on user intent to ensure consistent reliability under fluctuating network conditions. Additionally, its decentralized framework provides robust security features including end-to-end encryption, role-based access controls, and separate execution contexts, which contribute to a secure user experience. To further enhance its utility, NeuroSplit also includes real-time analytics dashboards that deliver valuable insights into key performance indicators such as cost, throughput, and latency, allowing users to make informed decisions based on comprehensive data. By offering a combination of efficiency, security, and ease of use, NeuroSplit positions itself as a leading solution in the realm of adaptive inference technologies.

FPT AI Factory serves as a robust, enterprise-level platform for AI development, utilizing NVIDIA H100 and H200 superchips to provide a comprehensive full-stack solution throughout the entire AI lifecycle. The FPT AI Infrastructure ensures efficient and high-performance scalable GPU resources that accelerate model training processes. In addition, FPT AI Studio includes data hubs, AI notebooks, and pipelines for model pre-training and fine-tuning, facilitating seamless experimentation and development. With FPT AI Inference, users gain access to production-ready model serving and the "Model-as-a-Service" feature, which allows for real-world applications that require minimal latency and maximum throughput. Moreover, FPT AI Agents acts as a builder for GenAI agents, enabling the development of versatile, multilingual, and multitasking conversational agents. By integrating ready-to-use generative AI solutions and enterprise tools, FPT AI Factory significantly enhances the ability for organizations to innovate in a timely manner, ensure reliable deployment, and efficiently scale AI workloads from initial concepts to fully operational systems. This comprehensive approach makes FPT AI Factory an invaluable asset for businesses looking to leverage artificial intelligence effectively.

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.

Discuro serves as a comprehensive platform designed for developers aiming to effortlessly create, assess, and utilize intricate AI workflows. With our user-friendly interface, you can outline your workflow, and when you're set to run it, simply send us an API call accompanied by your inputs and any necessary metadata, while we take care of the execution. By employing an Orchestrator, you can seamlessly feed the data generated back into GPT-3, ensuring reliable integration with OpenAI and facilitating easy extraction of the required information. In just a few minutes, you can develop and utilize your own workflows, as we've equipped you with everything necessary for large-scale integration with OpenAI, allowing you to concentrate on product development. The initial hurdle in connecting with OpenAI is acquiring the data you need, but we simplify this by managing input/output definitions for you. You can effortlessly connect multiple completions to assemble extensive datasets. Additionally, leverage our iterative input capability to reintroduce GPT-3 outputs, enabling us to make successive calls that broaden your dataset and more. Overall, our platform empowers you to construct and evaluate sophisticated self-transforming AI workflows and datasets with remarkable ease and efficiency.

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!

DataChain serves as a bridge between unstructured data found in cloud storage and AI models alongside APIs, facilitating immediate data insights by utilizing foundational models and API interactions to swiftly analyze unstructured files stored in various locations. Its Python-centric framework significantly enhances development speed, enabling a tenfold increase in productivity by eliminating SQL data silos and facilitating seamless data manipulation in Python. Furthermore, DataChain prioritizes dataset versioning, ensuring traceability and complete reproducibility for every dataset, which fosters effective collaboration among team members while maintaining data integrity. The platform empowers users to conduct analyses right where their data resides, keeping raw data intact in storage solutions like S3, GCP, Azure, or local environments, while metadata can be stored in less efficient data warehouses. DataChain provides versatile tools and integrations that are agnostic to cloud environments for both data storage and computation. Additionally, users can efficiently query their unstructured multi-modal data, implement smart AI filters to refine datasets for training, and capture snapshots of their unstructured data along with the code used for data selection and any associated metadata. This capability enhances user control over data management, making it an invaluable asset for data-intensive projects.

←INTELLI•GRAPHS→ is a semantic wiki that aims to integrate diverse data sources into cohesive knowledge graphs, enabling real-time collaboration among humans, AI assistants, and autonomous agents; it serves multiple functions, including a personal information organizer, genealogy tool, project management center, digital publishing service, customer relationship management system, document storage solution, geographic information system, biomedical research database, electronic health record infrastructure, digital twin engine, and an e-governance monitoring tool, all powered by a cutting-edge progressive web application that prioritizes offline access, peer-to-peer connectivity, and zero-knowledge end-to-end encryption using locally generated keys. With this platform, users can enjoy seamless, conflict-free collaboration, access a schema library with built-in validation, and benefit from the comprehensive import/export capabilities of encrypted graph files, which also accommodate attachments; in addition, the system is designed for AI and agent compatibility through APIs and tools like IntelliAgents, which facilitate identity management, task orchestration, and workflow planning complete with human-in-the-loop checkpoints, adaptive inference networks, and ongoing memory improvements, thus enhancing overall user experience and efficiency.

The Azure Model Catalog, part of Azure AI Foundry, is Microsoft’s central marketplace for enterprise-grade AI models. It provides access to the world’s most powerful AI systems, including GPT-5 for complex reasoning, Sora-2 for generative video, and DeepSeek-R1 for scientific and analytical applications. The catalog bridges the gap between cutting-edge AI research and real-world implementation, allowing users to browse, test, and deploy models directly within Azure’s secure environment. Developers can easily integrate models through APIs and SDKs, leveraging tools for training, evaluation, and continuous monitoring. Azure’s partnership with leaders like Meta, Mistral, Cohere, and NVIDIA ensures a diverse and interoperable model ecosystem. Built with compliance and transparency in mind, the platform supports GDPR, ISO, and SOC standards. From data science experimentation to large-scale enterprise deployments, Azure Model Catalog simplifies every stage of the AI lifecycle. It’s the go-to environment for teams seeking innovation, reliability, and global scalability under Microsoft’s trusted AI framework.

Fireworks collaborates with top generative AI researchers to provide the most efficient models at unparalleled speeds. It has been independently assessed and recognized as the fastest among all inference providers. You can leverage powerful models specifically selected by Fireworks, as well as our specialized multi-modal and function-calling models developed in-house. As the second most utilized open-source model provider, Fireworks impressively generates over a million images each day. Our API, which is compatible with OpenAI, simplifies the process of starting your projects with Fireworks. We ensure dedicated deployments for your models, guaranteeing both uptime and swift performance. Fireworks takes pride in its compliance with HIPAA and SOC2 standards while also providing secure VPC and VPN connectivity. You can meet your requirements for data privacy, as you retain ownership of your data and models. With Fireworks, serverless models are seamlessly hosted, eliminating the need for hardware configuration or model deployment. In addition to its rapid performance, Fireworks.ai is committed to enhancing your experience in serving generative AI models effectively. Ultimately, Fireworks stands out as a reliable partner for innovative AI solutions.

Retool is a platform that enables developers to combine the benefits of traditional software development with a drag-and-drop editor and AI to build internal tools faster. Every tool can be deployed anywhere, debugged with your toolchain, and shared reliably at any scale—ensuring good software by default. Retool is used by industry leaders such as Amazon, American Express, and OpenAI for mission critical custom software across operations, billing, and customer support.

Discover a comprehensive suite of tools that enables you to seamlessly customize advanced AI models to suit your unique requirements, utilizing your own datasets. Create highly intelligent AI agents by integrating the specialized capabilities of multiple state-of-the-art AI models. Every AI model available on the platform is open-source, ensuring transparency. Furthermore, the datasets used for training these models are readily available, along with an acknowledgment of their limitations and inherent biases. This open approach fosters innovation and encourages users to build responsibly.

SweKit empowers users to create PR agents that can review code, suggest enhancements, uphold coding standards, detect potential problems, automate merge approvals, and offer insights into best practices, thereby streamlining the review process and improving code quality. Additionally, it automates the development of new features, troubleshoots intricate issues, generates and executes tests, fine-tunes code for optimal performance, refactors for better maintainability, and ensures adherence to best practices throughout the codebase, which significantly boosts development speed and efficiency. With its sophisticated code analysis, advanced indexing, and smart file navigation tools, SweKit allows users to effortlessly explore and engage with extensive codebases. Users can pose questions, trace dependencies, uncover logic flows, and receive immediate insights, facilitating smooth interactions with complex code structures. Furthermore, it ensures that documentation remains aligned with the code by automatically updating Mintlify documentation whenever modifications are made to the codebase, guaranteeing that your documentation is precise, current, and accessible for both your team and users. This synchronization fosters a culture of transparency and keeps all stakeholders informed of the latest developments in the project's lifecycle.

The Intel Open Edge Platform streamlines the process of developing, deploying, and scaling AI and edge computing solutions using conventional hardware while achieving cloud-like efficiency. It offers a carefully selected array of components and workflows designed to expedite the creation, optimization, and development of AI models. Covering a range of applications from vision models to generative AI and large language models, the platform equips developers with the necessary tools to facilitate seamless model training and inference. By incorporating Intel’s OpenVINO toolkit, it guarantees improved performance across Intel CPUs, GPUs, and VPUs, enabling organizations to effortlessly implement AI applications at the edge. This comprehensive approach not only enhances productivity but also fosters innovation in the rapidly evolving landscape of edge computing.

The Intel® Distribution of OpenVINO™ toolkit serves as an open-source AI development resource that speeds up inference on various Intel hardware platforms. This toolkit is crafted to enhance AI workflows, enabling developers to implement refined deep learning models tailored for applications in computer vision, generative AI, and large language models (LLMs). Equipped with integrated model optimization tools, it guarantees elevated throughput and minimal latency while decreasing the model size without sacrificing accuracy. OpenVINO™ is an ideal choice for developers aiming to implement AI solutions in diverse settings, spanning from edge devices to cloud infrastructures, thereby assuring both scalability and peak performance across Intel architectures. Ultimately, its versatile design supports a wide range of AI applications, making it a valuable asset in modern AI development.

At Thesys, we have developed C1, the pioneering API for Generative User Interfaces that is ready for production. This innovative tool allows AI applications to present fully interactive user interfaces in real-time. Rather than delivering just text responses, C1 enables agents to provide dynamic dashboards, forms, lists, and other complex interfaces tailored to the specifics of each inquiry and its context. Many AI solutions continue to depend on text-only responses, which can diminish user engagement, while teams often invest extensive effort in connecting LLM outputs to fragile UI templates. This manual process is not only time-consuming to establish but also challenging to sustain and impossible to scale effectively. C1 revolutionizes this landscape by collaborating with both agile startups and large corporations to enhance their copilots, internal applications, and virtual assistants with smart generative interfaces. This approach not only streamlines development but also significantly boosts user interaction and satisfaction.

Atla serves as a comprehensive observability and evaluation platform tailored for AI agents, focusing on diagnosing and resolving failures effectively. It enables real-time insights into every decision, tool utilization, and interaction, allowing users to track each agent's execution, comprehend errors at each step, and pinpoint the underlying causes of failures. By intelligently identifying recurring issues across a vast array of traces, Atla eliminates the need for tedious manual log reviews and offers concrete, actionable recommendations for enhancements based on observed error trends. Users can concurrently test different models and prompts to assess their performance, apply suggested improvements, and evaluate the impact of modifications on success rates. Each individual trace is distilled into clear, concise narratives for detailed examination, while aggregated data reveals overarching patterns that highlight systemic challenges rather than mere isolated incidents. Additionally, Atla is designed for seamless integration with existing tools such as OpenAI, LangChain, Autogen AI, Pydantic AI, and several others, ensuring a smooth user experience. This platform not only enhances the efficiency of AI agents but also empowers users with the insights needed to drive continuous improvement and innovation.

TorqCloud is crafted to assist users in sourcing, transferring, enhancing, visualizing, securing, and interacting with data through AI-driven agents. This all-encompassing AIOps solution empowers users to develop or integrate custom LLM applications end-to-end via an intuitive low-code platform. Engineered to manage extensive data sets, it provides actionable insights, making it an indispensable resource for organizations striving to maintain a competitive edge in the evolving digital arena. Our methodology emphasizes seamless cross-disciplinary integration, prioritizes user requirements, employs test-and-learn strategies to expedite product delivery, and fosters collaborative relationships with your teams, which include skills transfer and training. We begin our process with empathy interviews, followed by stakeholder mapping exercises that help us thoroughly analyze the customer journey, identify necessary behavioral changes, assess problem scope, and systematically break down challenges. Additionally, this comprehensive approach ensures that we align our solutions closely with the specific needs of each organization, further enhancing the overall effectiveness of our offerings.