Alternatives to Chainlit

LM-Kit.NET is an enterprise-grade toolkit designed for seamlessly integrating generative AI into your .NET applications, fully supporting Windows, Linux, and macOS. Empower your C# and VB.NET projects with a flexible platform that simplifies the creation and orchestration of dynamic AI agents. Leverage efficient Small Language Models for on‑device inference, reducing computational load, minimizing latency, and enhancing security by processing data locally. Experience the power of Retrieval‑Augmented Generation (RAG) to boost accuracy and relevance, while advanced AI agents simplify complex workflows and accelerate development. Native SDKs ensure smooth integration and high performance across diverse platforms. With robust support for custom AI agent development and multi‑agent orchestration, LM‑Kit.NET streamlines prototyping, deployment, and scalability—enabling you to build smarter, faster, and more secure solutions trusted by professionals worldwide.

Dialogflow by Google Cloud is a natural-language understanding platform that allows you to create and integrate a conversational interface into your mobile, web, or device. It also makes it easy for you to integrate a bot, interactive voice response system, or other type of user interface into your app, web, or mobile application. Dialogflow allows you to create new ways for customers to interact with your product. Dialogflow can analyze input from customers in multiple formats, including text and audio (such as voice or phone calls). Dialogflow can also respond to customers via text or synthetic speech. Dialogflow CX, ES offer virtual agent services for chatbots or contact centers. Agent Assist can be used to assist human agents in contact centers that have them. Agent Assist offers real-time suggestions to human agents, even while they are talking with customers.

Open source platform for machine learning. TensorFlow is a machine learning platform that is open-source and available to all. It offers a flexible, comprehensive ecosystem of tools, libraries, and community resources that allows researchers to push the boundaries of machine learning. Developers can easily create and deploy ML-powered applications using its tools. Easy ML model training and development using high-level APIs such as Keras. This allows for quick model iteration and debugging. No matter what language you choose, you can easily train and deploy models in cloud, browser, on-prem, or on-device. It is a simple and flexible architecture that allows you to quickly take new ideas from concept to code to state-of the-art models and publication. TensorFlow makes it easy to build, deploy, and test.

Fully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case. Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection. Vertex AI Agent Builder empowers developers to design and deploy advanced generative AI applications for enterprise use. It supports both no-code and code-driven development, enabling users to create AI agents through natural language prompts or by integrating with frameworks like LangChain and LlamaIndex.

Flowise is open source and will always be free to use for commercial and private purposes. Build LLMs apps easily with Flowise, an open source UI visual tool to build your customized LLM flow using LangchainJS, written in Node Typescript/Javascript. Open source MIT License, see your LLM applications running live, and manage component integrations. GitHub Q&A using conversational retrieval QA chains. Language translation using LLM chains with a chat model and chat prompt template. Conversational agent for chat model that uses chat-specific prompts.

Lunary is a platform for AI developers that helps AI teams to manage, improve and protect chatbots based on Large Language Models (LLM). It includes features like conversation and feedback tracking as well as analytics on costs and performance. There are also debugging tools and a prompt directory to facilitate team collaboration and versioning. Lunary integrates with various LLMs, frameworks, and languages, including OpenAI, LangChain and JavaScript, and offers SDKs in Python and JavaScript. Guardrails to prevent malicious prompts or sensitive data leaks. Deploy Kubernetes/Docker in your VPC. Your team can judge the responses of your LLMs. Learn what languages your users speak. Experiment with LLM models and prompts. Search and filter everything in milliseconds. Receive notifications when agents do not perform as expected. Lunary's core technology is 100% open source. Start in minutes, whether you want to self-host or use the cloud.

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 an open-source platform that helps engineering and product teams develop production-grade Large Language Model applications. It provides a suite for observability and evaluation, as well as analytics. This allows for efficient tracking, optimization and integration of prompt version. The key features are multimodal logging encompassing audio, video, and vision, prompt management, with versioning and testing capabilities, as well as a prompt playground to test multiple LLM providers. Literal AI integrates seamlessly into various LLM frameworks and AI providers, including OpenAI, LangChain and LlamaIndex. It also provides SDKs for Python and TypeScript to instrument code. The platform supports the creation and execution of experiments against datasets to facilitate continuous improvement in LLM applications.

LlamaIndex, a "dataframework", is designed to help you create LLM apps. Connect semi-structured API data like Slack or Salesforce. LlamaIndex provides a flexible and simple data framework to connect custom data sources with large language models. LlamaIndex is a powerful tool to enhance your LLM applications. Connect your existing data formats and sources (APIs, PDFs, documents, SQL etc.). Use with a large-scale language model application. Store and index data for different uses. Integrate downstream vector stores and database providers. LlamaIndex is a query interface which accepts any input prompts over your data, and returns a knowledge augmented response. Connect unstructured data sources, such as PDFs, raw text files and images. Integrate structured data sources such as Excel, SQL etc. It provides ways to structure data (indices, charts) so that it can be used with LLMs.

Prompt Flow, a set of development tools, is designed to streamline end-to-end AI development cycles based on LLM, from ideation and prototyping to testing and evaluation, to production deployment and monitoring. It simplifies prompt engineering and allows you to create LLM apps of production quality. Prompt flow allows you to create flows that connect LLMs, Python code and other tools in an executable workflow. It is easy to debug and iterate flows, tracing interactions between LLMs in particular. You can evaluate flows, calculate performance and quality metrics with larger datasets and integrate the testing into your CI/CD to ensure quality. It is easy to deploy flows on the platform of your choosing or integrate them into your app code base. The cloud version of Azure AI's Prompt flow facilitates collaboration with your team.

OpenPipe provides fine-tuning for developers. Keep all your models, datasets, and evaluations in one place. New models can be trained with a click of a mouse. Automatically record LLM responses and requests. Create datasets using your captured data. Train multiple base models using the same dataset. We can scale your model to millions of requests on our managed endpoints. Write evaluations and compare outputs of models side by side. You only need to change a few lines of code. OpenPipe API Key can be added to your Python or Javascript OpenAI SDK. Custom tags make your data searchable. Small, specialized models are much cheaper to run than large, multipurpose LLMs. Replace prompts in minutes instead of weeks. Mistral and Llama 2 models that are fine-tuned consistently outperform GPT-4-1106 Turbo, at a fraction the cost. Many of the base models that we use are open-source. You can download your own weights at any time when you fine-tune Mistral or Llama 2.

Dify is an open-source platform that simplifies the creation and management of generative AI applications. It offers a user-friendly orchestration studio for designing workflows, a dedicated Prompt IDE for crafting and testing prompts, and robust LLMOps tools for monitoring and optimizing large language models. Compatible with leading AI models like OpenAI’s GPT series and open-source options such as Llama, Dify provides developers with the flexibility to choose the best models for their projects. Its Backend-as-a-Service (BaaS) capabilities make it easy to integrate AI features into existing systems, enabling the development of intelligent tools like chatbots, document summarizers, and virtual assistants.

The prompt engineering platform allows you to experiment with different prompt versions. You can also evaluate and compare prompts in a series of tests, optimize prompts by one click, share and more. Optimize your AI development workflow. Key features that help you identify and get the best prompts for production use cases. Evaluation allows for a side-by-side comparison between prompts in test cases. Import test cases from CSV and define custom metrics for evaluation. Automatic template and prompt optimization can improve LLM results. View and manage all versions of the prompt and create OpenAI Functions. You can access all your prompts programmatically. This includes observability and analytics. Calculate the cost, latency and effectiveness of each prompt. Parea can help you improve your prompt engineering workflow. Parea helps developers improve the performance of LLM apps by implementing rigorous testing and versioning.

We've already done the hard work of bundling, pre-configuring and integrating all the components that you need to build solutions and integrate LLMs into your business processes. ConfidentialMind allows you to jump into action. Deploy an endpoint for powerful open-source LLMs such as Llama-2 and turn it into an LLM API. Imagine ChatGPT on your own cloud. This is the most secure option available. Connects the rest with the APIs from the largest hosted LLM provider like Azure OpenAI or AWS Bedrock. ConfidentialMind deploys a Streamlit-based playground UI with a selection LLM-powered productivity tool for your company, such as writing assistants or document analysts. Includes a vector data base, which is critical for most LLM applications to efficiently navigate through large knowledge bases with thousands documents. You can control who has access to your team's solutions and what data they have.

Unexpected outcomes happen all the time. You can pinpoint the source of errors or surprises in real-time with surgical precision when you have full visibility of the entire chain of calls. Unit testing is a key component of software engineering to create production-ready, performant applications. LangSmith offers the same functionality for LLM apps. LangSmith allows you to create test datasets, execute your applications on them, and view results without leaving the application. LangSmith allows mission-critical observability in just a few lines. LangSmith was designed to help developers harness LLMs' power and manage their complexity. We don't just build tools. We are establishing best practices that you can rely upon. Build and deploy LLM apps with confidence. Stats on application-level usage. Feedback collection. Filter traces and cost measurement. Dataset curation - compare chain performance - AI-assisted assessment & embrace best practices.

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.

Gen App Builder is unique because, unlike other generative AI solutions for developers, it provides an orchestration layer which abstracts the complexity involved in combining enterprise systems and generative AI tools, resulting in a smooth and helpful user experience. Gen App Builder offers step-by-step orchestration for search and conversational apps with pre-built workflows to help developers set up and deploy their applications. Gen App Builder allows developers to: Build in minutes or even hours. Google's conversational and search tools powered with foundation models allow organizations to quickly create high-quality experiences which can be integrated into applications and websites.

Voiceflow allows teams to create, test, and ship conversational assistants together faster and at a larger scale. Chat and voice interfaces can be created for any digital product. You can combine conversation design, product development, product copywriting, legal, as well as conversation design and development. All of this is possible with one platform. Eliminate content chaos and functional silos. Voiceflow allows teams to collaborate in an interactive workspace that consolidates all assistant information, conversation flows and intents. It also stores response content, API calls, and other data. 1-click prototyping makes it easy to avoid delays and large dev efforts. Designers can quickly create high-fidelity, shareable prototypes in minutes to help improve the user experience. Voiceflow is the best tool to speed up and scale app delivery. You can speed up your workflow with time-savers such as drag-and-drop design and rapid prototyping. Real-time feedback is also available.

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!

Semantic Kernel, a lightweight open-source development tool, allows you to easily build AI agents, and integrate the latest AI model into your C# or Python codebase. It is a middleware that allows rapid delivery of enterprise grade solutions. Semantic Kernel is flexible, modular and observable, which is why Microsoft and other Fortune 500 firms use it. With security-enhancing features like hooks, filters, and telemetry, you can be confident that you are delivering responsible AI at scale. It's reliable and committed to non-breaking changes. Version 1.0+ is supported across C# Python and Java. Existing chat-based APIs can be easily extended to support other modalities such as voice and video. Semantic Kernel is future-proof and connects your code with the latest AI models that evolve as technology advances.

Instructor is a tool which allows developers to extract structured information from natural language by using Large Language Models. Integrating Python's Pydantic Library allows users to define desired input structures through type hints. This facilitates schema validation and seamless integration. Instructor offers flexibility in implementation by supporting a variety of LLM providers including OpenAI, Anthropic Litellm and Cohere. Its customizability allows for the definition of validators, and custom error messages to enhance data validation processes. Engineers from platforms such as Langflow trust Instructor, highlighting its reliability and effectiveness for managing structured outputs powered LLMs. Instructor is powered Pydantic which is powered type hints. Type annotations control schema validation and prompting. This means less code to write and less learning to do.

Native support for ChatGPT, LangChain and more. Bing, HuggingFace, and more to come. Add your API documentation by hand or import an OpenAPI specification. Access the request prompt and parameters, headers, bodies, and more. View detailed live metrics on how your plugin performs, including latencies and errors. Create your own dashboards to track funnels and aggregate metrics.

Metal is a fully-managed, production-ready ML retrieval platform. Metal embeddings can help you find meaning in unstructured data. Metal is a managed services that allows you build AI products without having to worry about managing infrastructure. Integrations with OpenAI and CLIP. Easy processing & chunking of your documents. Profit from our system in production. MetalRetriever is easily pluggable. Simple /search endpoint to run ANN queries. Get started for free. Metal API Keys are required to use our API and SDKs. Authenticate by populating headers with your API Key. Learn how to integrate Metal into your application using our Typescript SDK. You can use this library in JavaScript as well, even though we love TypeScript. Fine-tune spp programmatically. Indexed vector data of your embeddings. Resources that are specific to your ML use case.

A managed PaaS that includes a low-code visual editor, VectorDB and integrations with apps and models to build, test, and deploy high-performance AI applications on the edge. Eliminate costly and error-prone work. Drag and drop agents, apps, data and models to find the best solution. Deployment in less than 60 seconds, and a 50% reduction in latency. Observe, iterate, and test seamlessly. Visibility and tools are essential for accuracy and reliability. Use data-driven decision making with reports on usage, LLM and request. View real-time traces per node. Experiments allow you to optimize embeddings and prompts, models and more. All you need to launch and iterate at large scale. Community of smart-minded builders who share their insights, experiences & feedback. Distilling the most useful tips, tricks and techniques for AI application developers. A platform that allows you to build agentic systems as if you were a 100-person team. A simple and intuitive frontend for managing AI applications and collaborating with them.

Experience JinaChat - a LLM service designed for professionals. JinaChat is a multimodal chat service that goes beyond text and includes images. Enjoy our free short interactions below 100 tokens. Our API allows developers to build complex applications by leveraging long conversation histories. JinaChat is the future of LLM, with multimodal conversations that are long-memory and affordable. Modern LLM applications are often based on long prompts or large memory, which can lead to high costs if the same prompts are sent repeatedly to the server. JinaChat API solves this issue by allowing you to carry forward previous conversations, without having to resend the entire prompt. This is a great way to save both time and money when developing complex applications such as AutoGPT.

Build your RAG system intuitively with fewer abstractions than solutions like LangChain. You can choose from a variety of hosted and remote providers, including vector databases, datasets and Large Language Models. SciPhi allows you to version control and deploy your system from anywhere using Git. SciPhi's platform is used to manage and deploy an embedded semantic search engine that has over 1 billion passages. The team at SciPhi can help you embed and index your initial dataset into a vector database. The vector database will be integrated into your SciPhi workspace along with your chosen LLM provider.

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.

Llama Stack is a flexible framework designed to simplify the development of applications utilizing Meta’s Llama language models. It features a modular client-server architecture that allows developers to customize their setup by integrating different providers for inference, memory, agents, telemetry, and evaluations. With pre-configured distributions optimized for various deployment scenarios, Llama Stack enables a smooth transition from local development to production. It supports multiple programming languages, including Python, Node.js, Swift, and Kotlin, making it accessible across different tech stacks. Additionally, the framework provides extensive documentation and sample applications to help developers efficiently build and deploy Llama-powered solutions.

DeepEval is an open-source, easy-to-use framework for evaluating large-language-model systems. It is similar Pytest, but is specialized for unit-testing LLM outputs. DeepEval incorporates research to evaluate LLM results based on metrics like G-Eval (hallucination), answer relevancy, RAGAS etc. This uses LLMs as well as various other NLP models which run locally on your computer for evaluation. DeepEval can handle any implementation, whether it's RAG, fine-tuning or LangChain or LlamaIndex. It allows you to easily determine the best hyperparameters for your RAG pipeline. You can also prevent drifting and even migrate from OpenAI to your own Llama2 without any worries. The framework integrates seamlessly with popular frameworks and supports synthetic dataset generation using advanced evolution techniques. It also allows for efficient benchmarking and optimizing of LLM systems.

ChatGPT is an OpenAI language model. It can generate human-like responses to a variety prompts, and has been trained on a wide range of internet texts. ChatGPT can be used to perform natural language processing tasks such as conversation, question answering, and text generation. ChatGPT is a pretrained language model that uses deep-learning algorithms to generate text. It was trained using large amounts of text data. This allows it to respond to a wide variety of prompts with human-like ease. It has a transformer architecture that has been proven to be efficient in many NLP tasks. ChatGPT can generate text in addition to answering questions, text classification and language translation. This allows developers to create powerful NLP applications that can do specific tasks more accurately. ChatGPT can also process code and generate it.

Wordware allows anyone to create, iterate and deploy useful AI agents. Wordware combines software's best features with the power of language. Remove the constraints of traditional tools that don't require code and empower each team member to iterate on their own. Natural language programming will be around for a long time. Wordware removes prompt from codebases by providing non-technical and technical users with a powerful AI agent creation IDE. Our interface is simple and flexible. With an intuitive design, you can empower your team to collaborate easily, manage prompts and streamline workflows. Loops, branching and structured generation, as well as version control and type safety, help you make the most of LLMs. Custom code execution allows you connect to any API. Switch between large language models with just one click. Optimize your workflows with the best cost-to-latency-to-quality ratios for your application.

Vishwa.ai, an AutoOps Platform for AI and ML Use Cases. It offers expert delivery, fine-tuning and monitoring of Large Language Models. Features: Expert Prompt Delivery : Tailored prompts tailored to various applications. Create LLM Apps without Coding: Create LLM workflows with our drag-and-drop UI. Advanced Fine-Tuning : Customization AI models. LLM Monitoring: Comprehensive monitoring of model performance. Integration and Security Cloud Integration: Supports Google Cloud (AWS, Azure), Azure, and Google Cloud. Secure LLM Integration - Safe connection with LLM providers Automated Observability for efficient LLM Management Managed Self Hosting: Dedicated hosting solutions. Access Control and Audits - Ensure secure and compliant operations.

Athina is a powerful AI development platform designed to help teams build, test, and monitor AI applications with ease. It provides robust tools for prompt management, evaluation, dataset handling, and observability, ensuring the creation of reliable and scalable AI solutions. With seamless integration capabilities for various AI models and services, Athina also prioritizes security with fine-grained access controls and self-hosted deployment options. As a SOC-2 Type 2 compliant platform, it offers a secure and collaborative environment for both technical and non-technical users. By streamlining workflows and enhancing team collaboration, Athina accelerates the development and deployment of AI-driven features.

Oumi, a platform open-source, streamlines the lifecycle of foundational models from data preparation to training and evaluation. It supports training and fine tuning models with parameters ranging from 10 millions to 405 billion using state-of the-art techniques like SFT, LoRA QLoRA and DPO. The platform supports text and multimodal models including architectures such as Llama DeepSeek Qwen and Phi. Oumi provides tools for data curation and synthesis, allowing users to efficiently generate and manage training datasets. It integrates with popular engines such as vLLM and SGLang for deployment, ensuring efficient serving of models. The platform provides comprehensive evaluation capabilities to assess model performance using standard benchmarks. Oumi is designed to be flexible and can run in a variety of environments, including local laptops, cloud infrastructures like AWS, Azure GCP, Lambda, etc.

Discuro is an all-in-one platform that allows developers to quickly build, test and consume complex AI workflows. Our UI makes it easy to define your workflow. When you are ready to execute, just make one API call to Discuro with any inputs and any meta-data. You can use an Orchestrator to feed the generated data back into GPT-3. Integrate with OpenAI to extract the data you need quickly. In minutes, create and consume your own flows. Everything you need to integrate OpenAI at scale has been built by us so that you can concentrate on the product. Integrating with OpenAI is not easy. We'll help you extract the data you need by collecting input/output descriptions. You can easily chain completions together to create large data sets. You can use our iterative input feature for GPT-3 output to feed back in and have us make successive calls to expand your data set. Easy to build and test complex, self-transforming AI workflows and datasets.

ShipGPT is an AI repository that contains a ready-made boilerplate for a variety of AI use cases. It allows you to create your own AI applications or integrate AI into existing tech, without the need to hire full stack developers or AI dev wrappers. ShipGPT allows you to transform your apps into AI apps and create products such as ChatBase, ChatPDF or Jenni AI. The service includes live support as well as continuous updates. The service is designed for developers to create AI apps quickly. It is regularly updated. It does not rely on licensed or third-party libraries or APIs. Instead, it relies on open-source libraries that are easy to maintain.

We believe that the most effective and differentiated applications won't only call out via an API to a language model. LangChain supports several modules. We provide examples, how-to guides and reference docs for each module. Memory is the concept that a chain/agent calls can persist in its state. LangChain provides a standard interface to memory, a collection memory implementations and examples of agents/chains that use it. This module outlines best practices for combining language models with your own text data. Language models can often be more powerful than they are alone.

DataChain connects your unstructured cloud files with AI models, APIs and foundational models to enable instant data insights. Its Pythonic stack accelerates the development by tenfold when switching to Python-based data wrangling, without SQL data islands. DataChain provides dataset versioning to ensure full reproducibility and traceability for each dataset. This helps streamline team collaboration while ensuring data integrity. It allows you analyze your data wherever it is stored, storing raw data (S3, GCP or Azure) and metadata in inefficient datawarehouses. DataChain provides tools and integrations which are cloud-agnostic in terms of both storage and computing. DataChain allows you to query your multi-modal unstructured data. You can also apply intelligent AI filters for training data and snapshot your unstructured dataset, the code used for data selection and any stored or computed meta data.

No one wants to have their AI respond to a client with outdated information. Neum AI provides accurate and current context for AI applications. Set up your data pipelines quickly by using built-in connectors. These include data sources such as Amazon S3 and Azure Blob Storage and vector stores such as Pinecone and Weaviate. Transform and embed your data using built-in connectors to embed models like OpenAI, Replicate and serverless functions such as Azure Functions and AWS Lambda. Use role-based controls to ensure that only the right people have access to specific vectors. Bring your own embedding model, vector stores, and sources. Ask us how you can run Neum AI on your own cloud.

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.

Create and manage AI applications without the need to move data to complex vector databases and pipelines. Integrate AI, vector search and real-time inference directly with your database. Python is all you need. All your AI models can be deployed in a single, scalable deployment. The AI models and APIs are automatically updated as new data is processed. You don't need to duplicate your data or create an additional database to use vector searching and build on it. SuperDuperDB allows vector search within your existing database. Integrate and combine models such as those from Sklearn PyTorch HuggingFace, with AI APIs like OpenAI, to build even the most complicated AI applications and workflows. With simple Python commands, deploy all your AI models in one environment to automatically compute outputs in your datastore (inference).

Laminar is a platform that allows you to create the best LLM products. The quality of your LLM application is determined by the data you collect. Laminar helps collect, understand, and use this data. You can collect valuable data and get a clear view of the execution of your LLM application by tracing it. You can use this data to create better evaluations, dynamic examples and fine-tune your application. All traces are sent via gRPC in the background with minimal overhead. Audio models will be supported soon. Tracing text and image models are supported. You can use LLM-as a judge or Python script evaluators on each span. Evaluators can label spans. This is more scalable than manual labeling and is especially useful for smaller teams. Laminar allows you to go beyond a simple prompt. You can create and host complex chains including mixtures of agents, or self-reflecting LLM pipes.

Martian outperforms GPT-4 across OpenAI's evals (open/evals). Martian outperforms GPT-4 in all OpenAI's evaluations (open/evals). We transform opaque black boxes into interpretable visual representations. Our router is our first tool built using our model mapping method. Model mapping is being used in many other applications, including transforming transformers from unintelligible matrices to human-readable programs. Automatically reroute your customers to other providers if a company has an outage or a high latency period. Calculate how much money you could save using the Martian Model Router by using our interactive cost calculator. Enter the number of users and tokens per session. Also, specify how you want to trade off between cost and quality.

Powerful language models a click away. Join over 8,000 developers in building the next wave world-changing applications. Fine-tune GPT-J and deploy Codegen, FLAN-T5, GPT NeoX and GPT NeoX. There are multiple models with different capabilities and prices. GPT-J has the fastest speed, while GPT NeoX is the most powerful. And more models are coming. These models can be used for classification, entity extracting, code generation and chatbots. They can also be used for content generation, summarizations, paraphrasings, sentiment analysis and more. These models have already been pre-trained using a large amount of text taken from the internet. The fine-tuning process improves this for specific tasks, by training on more examples than are possible in a prompt. This allows you to achieve better results across a range of tasks.

StableVicuna, the first large-scale chatbot to be trained using RHLF (reinforced learning from human feedback), is an open-source chatbot that has been fine-tuned and RLHF trained. StableVicuna, a further fine-tuned and RLHF-trained version of Vicuna v0-13b is a fine-tuned LLaMA model. To achieve StableVicuna’s strong performance we use Vicuna as a base model and follow Steinnon et. al.’s typical three-stage RLHF pipeline. and Ouyang et al. Concretely, using a combination of three datasets, we further train the Vicuna base model with supervised refinement (SFT). OpenAssistant Dataset (OASST1) is a corpus of human-generated and human-annotated assistant style conversation data, comprising 161,443 message distributed across 66 497 conversation trees in 35 different languages. GPT4All Generating Prompts, a dataset containing 437,605 prompts generated by GPT-3.5; Alpaca is a dataset of over 52,000 instructions and demos generated by OpenAI’s text-davinci 003.

The Xilinx AI development platform for AI Inference on Xilinx hardware platforms consists optimized IP, tools and libraries, models, examples, and models. It was designed to be efficient and easy-to-use, allowing AI acceleration on Xilinx FPGA or ACAP. Supports mainstream frameworks as well as the most recent models that can perform diverse deep learning tasks. A comprehensive collection of pre-optimized models is available for deployment on Xilinx devices. Find the closest model to your application and begin retraining! This powerful open-source quantizer supports model calibration, quantization, and fine tuning. The AI profiler allows you to analyze layers in order to identify bottlenecks. The AI library provides open-source high-level Python and C++ APIs that allow maximum portability from the edge to the cloud. You can customize the IP cores to meet your specific needs for many different applications.

AI Crypto-Kit enables developers to create crypto agents by seamlessly integrating Web3 platforms such as Coinbase, OpenSea and more, to automate real world crypto/DeFi workflows. Developers can create AI-powered crypto automation within minutes, including applications like trading agents, community rewards systems, Coinbase wallets, portfolio tracking, market analyses, and yield farming. The platform includes features designed for crypto agents. These include fully managed agent authentication, with support for API keys, JWT and automatic token refreshing; optimization for LLM call-outs to provide enterprise-grade reliability; integration with over 30 Web3 platforms including Binance Aave OpenSea and Chainlink, as well as SDKs and TypeScript APIs for agentic apps interactions.

Track the latency and cost of each prompt. Track the cost and latency of each prompt. Create dynamic variables for your prompts. Call them using API and insert variables in the prompt. Combine the power of multiple LLMs in your application. Track latency and costs of requests to optimize the best options. Test different prompts, and save the ones you like.

LangWatch is a vital part of AI maintenance. It protects you and your company from exposing sensitive information, prevents prompt injection, and keeps your AI on track, preventing unforeseen damage to the brand. Businesses with integrated AI can find it difficult to understand the behaviour of AI and users. Maintaining quality by monitoring will ensure accurate and appropriate responses. LangWatch's safety check and guardrails help prevent common AI problems, such as jailbreaking, exposing sensitive information, and off-topic discussions. Real-time metrics allow you to track conversion rates, output, user feedback, and knowledge base gaps. Gain constant insights for continuous improvements. Data evaluation tools allow you to test new models and prompts and run simulations.

Basalt is a platform for AI development that allows teams to quickly build, test and launch better AI features. Basalt allows you to prototype quickly with our no-code playground. You can draft prompts that include co-pilot guidance, and you can also structure sections. You can iterate quickly by switching between models and versions, saving them and switching back and forth. Our co-pilot can help you improve your prompts by providing recommendations. Test and iterate your prompts by using realistic cases. Upload your dataset or let Basalt create it for you. Run your prompt on multiple test cases at scale and gain confidence from evaluators. Basalt SDK abstracts and deploys prompts within your codebase. Monitor production by capturing logs, and optimizing by staying informed about new errors and edge-cases.