Best AI tools for< Debug Llm Calls >

Athina AI is a comprehensive platform designed to monitor, debug, analyze, and improve the performance of Large Language Models (LLMs) in production environments. It provides a suite of tools and features that enable users to detect and fix hallucinations, evaluate output quality, analyze usage patterns, and optimize prompt management. Athina AI supports integration with various LLMs and offers a range of evaluation metrics, including context relevancy, harmfulness, summarization accuracy, and custom evaluations. It also provides a self-hosted solution for complete privacy and control, a GraphQL API for programmatic access to logs and evaluations, and support for multiple users and teams. Athina AI's mission is to empower organizations to harness the full potential of LLMs by ensuring their reliability, accuracy, and alignment with business objectives.

Langtrace AI is an open-source observability tool powered by Scale3 Labs that helps monitor, evaluate, and improve LLM (Large Language Model) applications. It collects and analyzes traces and metrics to provide insights into the ML pipeline, ensuring security through SOC 2 Type II certification. Langtrace supports popular LLMs, frameworks, and vector databases, offering end-to-end observability and the ability to build and deploy AI applications with confidence.

Langtail is a platform that helps developers build, test, and deploy AI-powered applications. It provides a suite of tools to help developers debug prompts, run tests, and monitor the performance of their AI models. Langtail also offers a community forum where developers can share tips and tricks, and get help from other users.

Reprompt is a prompt testing tool that simplifies the process for developers to test their prompts efficiently. It allows users to deploy prompts with confidence, make data-driven decisions, analyze data quickly, debug multiple scenarios simultaneously, and compare changes with previous versions. The tool offers real-time trading, fast operations, no commissions, built-in enterprise encryption and security, 256-bit AES encryption, and advanced security standards.

RagaAI Catalyst is a sophisticated AI observability, monitoring, and evaluation platform designed to help users observe, evaluate, and debug AI agents at all stages of Agentic AI workflows. It offers features like visualizing trace data, instrumenting and monitoring tools and agents, enhancing AI performance, agentic testing, comprehensive trace logging, evaluation for each step of the agent, enterprise-grade experiment management, secure and reliable LLM outputs, finetuning with human feedback integration, defining custom evaluation logic, generating synthetic data, and optimizing LLM testing with speed and precision. The platform is trusted by AI leaders globally and provides a comprehensive suite of tools for AI developers and enterprises.

SWE Kit is an open-source headless IDE designed for building custom coding agents with state-of-the-art performance. It offers AI-native tools to streamline the coding review process, enhance code quality, and optimize development efficiency. The application supports various agentic frameworks and LLM inference providers, providing a flexible runtime environment for seamless codebase interaction. With features like code analysis, code indexing, and third-party service integrations, SWE Kit empowers developers to create and run coding agents effortlessly.

Portkey is a control panel for production AI applications that offers an AI Gateway, Prompts, Guardrails, and Observability Suite. It enables teams to ship reliable, cost-efficient, and fast apps by providing tools for prompt engineering, enforcing reliable LLM behavior, integrating with major agent frameworks, and building AI agents with access to real-world tools. Portkey also offers seamless AI integrations for smarter decisions, with features like managed hosting, smart caching, and edge compute layers to optimize app performance.

LangChain is a framework for developing applications powered by large language models (LLMs). It simplifies every stage of the LLM application lifecycle, including development, productionization, and deployment. LangChain consists of open-source libraries such as langchain-core, langchain-community, and partner packages. It also includes LangGraph for building stateful agents and LangSmith for debugging and monitoring LLM applications.

Haystack is a production-ready open-source AI framework designed to facilitate building AI applications. It offers a flexible components and pipelines architecture, allowing users to customize and build applications according to their specific requirements. With partnerships with leading LLM providers and AI tools, Haystack provides freedom of choice for users. The framework is built for production, with fully serializable pipelines, logging, monitoring integrations, and deployment guides for full-scale deployments on various platforms. Users can build Haystack apps faster using deepset Studio, a platform for drag-and-drop construction of pipelines, testing, debugging, and sharing prototypes.

Wordware is an AI toolkit that empowers cross-functional teams to build reliable high-quality agents through rapid iteration. It combines the best aspects of software with the power of natural language, freeing users from traditional no-code tool constraints. With advanced technical capabilities, multiple LLM providers, one-click API deployment, and multimodal support, Wordware offers a seamless experience for AI app development and deployment.

Refact.ai is an open-source AI coding assistant that offers a range of features including code completion, refactoring, and chat. It supports various LLMs such as GPT-4 and Code LLama, allowing users to choose the model that best suits their needs. Refact understands the context of the codebase using a fill-in-the-middle technique, providing relevant suggestions. Users can opt for a self-hosted version or adjust privacy settings for the plugin.

Whybug is an AI tool designed to help developers debug their code by providing explanations for errors. By utilizing a large language model trained on data from StackExchange and other sources, Whybug can predict the causes of errors and suggest fixes. Users can simply paste an error message and receive detailed explanations on how to resolve the issue. The tool aims to streamline the debugging process and improve code quality.

New Relic is an AI monitoring platform that offers an all-in-one observability solution for monitoring, debugging, and improving the entire technology stack. With over 30 capabilities and 750+ integrations, New Relic provides the power of AI to help users gain insights and optimize performance across various aspects of their infrastructure, applications, and digital experiences.

Rerun is an SDK, time-series database, and visualizer for temporal and multimodal data. It is used in fields like robotics, spatial computing, 2D/3D simulation, and finance to verify, debug, and explain data. Rerun allows users to log data like tensors, point clouds, and text to create streams, visualize and interact with live and recorded streams, build layouts, customize visualizations, and extend data and UI functionalities. The application provides a composable data model, dynamic schemas, and custom views for enhanced data visualization and analysis.

Snaplet is a data management tool for developers that provides AI-generated dummy data for local development, end-to-end testing, and debugging. It uses a real programming language (TypeScript) to define and edit data, ensuring type safety and auto-completion. Snaplet understands database structures and relationships, automatically transforming personally identifiable information and seeding data accordingly. It integrates seamlessly into development workflows, providing data where it's needed most: on local machines, for CI/CD testing, and preview environments.

SourceAI is an AI-powered code generator that allows users to generate code in any programming language. It is easy to use, even for non-developers, and has a clear and intuitive interface. SourceAI is powered by GPT-3 and Codex, the most advanced AI technology available. It can be used to generate code for a variety of tasks, including calculating the factorial of a number, finding the roots of a polynomial, and translating text from one language to another.

LogRocket is a session replay, product analytics, and issue detection platform that helps software teams deliver the best web and mobile experiences. With LogRocket, you can see exactly what users experienced on your app, as well as DOM playback, console and network logs, errors, and performance data. You can also surface the most impactful user issues with JavaScript errors, network errors, stack traces, automatic triaging, and alerting. LogRocket also provides product analytics to help you understand how users are interacting with your app, and UX analytics to help you visualize how users experience your app at both the individual and aggregate level.

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CodeMate is an AI pair programmer tool designed to help developers write error-free code faster. It offers features like code navigation, understanding complex codebases, intuitive interface for smarter coding, instant debugging, code refactoring, and AI-powered code reviews. CodeMate supports all programming languages and provides suggestions for code optimizations. The tool ensures the security and privacy of user code and offers different pricing plans for individual developers, teams, and enterprises. Users can interact with their codebase, documentation, and Git repositories using CodeMate Chat. The tool aims to improve code quality and productivity by acting as a co-developer while programming.

Zazzani AI Buddy is an AI-powered platform that empowers users to create, debug code, write articles, and communicate with AI in multiple languages. It enhances productivity by generating ideas, providing context-specific answers, and eliminating monotony through automated tasks. Users can sign up to receive updates and contribute to the platform's growth. Zazzani AI Buddy aims to streamline workflows and inspire creativity through its innovative AI capabilities.

LangWatch is a monitoring and analytics platform designed to track, visualize, and analyze interactions with Large Language Models (LLMs). It offers real-time telemetry to optimize LLM cost and latency, a user-friendly interface for deep insights into LLM behavior, user analytics for engagement metrics, detailed debugging capabilities, and guardrails to monitor LLM outputs for issues like PII leaks and toxic language. The platform supports OpenAI and LangChain integrations, simplifying the process of tracing LLM calls and generating API keys for usage. LangWatch also provides documentation for easy integration and self-hosting options for interested users.

Langtrace is an open source observability software that lets you capture, debug, and analyze traces and metrics from all your applications that leverage LLM APIs, Vector Databases, and LLM-based Frameworks. It supports Open Telemetry Standards (OTEL), and the traces generated adhere to these standards. Langtrace offers both a managed SaaS version (Langtrace Cloud) and a self-hosted option. The SDKs for both Typescript/Javascript and Python are available, making it easy to integrate Langtrace into your applications. Langtrace automatically captures traces from various vendors, including OpenAI, Anthropic, Azure OpenAI, Langchain, LlamaIndex, Pinecone, and ChromaDB.

Log10 is a one-line Python integration to manage your LLM data. It helps you log both closed and open-source LLM calls, compare and identify the best models and prompts, store feedback for fine-tuning, collect performance metrics such as latency and usage, and perform analytics and monitor compliance for LLM powered applications. Log10 offers various integration methods, including a python LLM library wrapper, the Log10 LLM abstraction, and callbacks, to facilitate its use in both existing production environments and new projects. Pick the one that works best for you. Log10 also provides a copilot that can help you with suggestions on how to optimize your prompt, and a feedback feature that allows you to add feedback to your completions. Additionally, Log10 provides prompt provenance, session tracking and call stack functionality to help debug prompt chains. With Log10, you can use your data and feedback from users to fine-tune custom models with RLHF, and build and deploy more reliable, accurate and efficient self-hosted models. Log10 also supports collaboration, allowing you to create flexible groups to share and collaborate over all of the above features.

AgentNeo is an advanced, open-source Agentic AI Application Observability, Monitoring, and Evaluation Framework designed to provide deep insights into AI agents, Large Language Model (LLM) calls, and tool interactions. It offers robust logging, visualization, and evaluation capabilities to help debug and optimize AI applications with ease. With features like tracing LLM calls, monitoring agents and tools, tracking interactions, detailed metrics collection, flexible data storage, simple instrumentation, interactive dashboard, project management, execution graph visualization, and evaluation tools, AgentNeo empowers users to build efficient, cost-effective, and high-quality AI-driven solutions.

Langfuse is a powerful tool that helps you develop, monitor, and test your LLM applications. With Langfuse, you can: * **Develop:** Instrument your app and start ingesting traces to Langfuse, inspect and debug complex logs, and manage, version, and deploy prompts from within Langfuse. * **Monitor:** Track metrics (cost, latency, quality) and gain insights from dashboards & data exports, collect and calculate scores for your LLM completions, run model-based evaluations, collect user feedback, and manually score observations in Langfuse. * **Test:** Track and test app behaviour before deploying a new version, test expected in and output pairs and benchmark performance before deploying, and track versions and releases in your application. Langfuse is easy to get started with and offers a generous free tier. You can sign up for Langfuse Cloud or deploy Langfuse locally or on your own infrastructure. Langfuse also offers a variety of integrations to make it easy to connect to your LLM applications.

LLMClient is a JavaScript/TypeScript library that simplifies working with large language models (LLMs) by providing an easy-to-use interface for building and composing efficient prompts using prompt signatures. These signatures enable the automatic generation of typed prompts, allowing developers to leverage advanced capabilities like reasoning, function calling, RAG, ReAcT, and Chain of Thought. The library supports various LLMs and vector databases, making it a versatile tool for a wide range of applications.

TapeAgents is a framework that leverages a structured, replayable log of the agent session to facilitate all stages of the LLM Agent development lifecycle. The agent reasons by processing the tape and the LLM output to produce new thoughts, actions, control flow steps, and append them to the tape. Key features include building agents as low-level state machines or high-level multi-agent team configurations, debugging agents with TapeAgent studio or TapeBrowser apps, serving agents with response streaming, and optimizing agent configurations using successful tapes. The Tape-centric design of TapeAgents provides ultimate flexibility in project development, allowing access to tapes for making prompts, generating next steps, and controlling agent behavior.

QuantaLogic is a ReAct framework for building advanced AI agents that seamlessly integrates large language models with a robust tool system. It aims to bridge the gap between advanced AI models and practical implementation in business processes by enabling agents to understand, reason about, and execute complex tasks through natural language interaction. The framework includes features such as ReAct Framework, Universal LLM Support, Secure Tool System, Real-time Monitoring, Memory Management, and Enterprise Ready components.

AgentNeo is a Python package that provides functionalities for project, trace, dataset, experiment management. It allows users to authenticate, create projects, trace agents and LangGraph graphs, manage datasets, and run experiments with metrics. The tool aims to streamline AI project management and analysis by offering a comprehensive set of features.

AgentKit is a framework for creating and orchestrating AI Agents, enabling developers to build, test, and deploy reliable AI applications at scale. It allows for creating networked agents with separate tasks and instructions to solve specific tasks, as well as simple agents for tasks like writing content. The framework requires the Inngest TypeScript SDK as a dependency and provides documentation on agents, tools, network, state, and routing. Example projects showcase AgentKit in action, such as the Test Writing Network demo using Workflow Kit, Supabase, and OpenAI.

Skyvern automates browser-based workflows using LLMs and computer vision. It provides a simple API endpoint to fully automate manual workflows, replacing brittle or unreliable automation solutions. Traditional approaches to browser automations required writing custom scripts for websites, often relying on DOM parsing and XPath-based interactions which would break whenever the website layouts changed. Instead of only relying on code-defined XPath interactions, Skyvern adds computer vision and LLMs to the mix to parse items in the viewport in real-time, create a plan for interaction and interact with them. This approach gives us a few advantages: 1. Skyvern can operate on websites it’s never seen before, as it’s able to map visual elements to actions necessary to complete a workflow, without any customized code 2. Skyvern is resistant to website layout changes, as there are no pre-determined XPaths or other selectors our system is looking for while trying to navigate 3. Skyvern leverages LLMs to reason through interactions to ensure we can cover complex situations. Examples include: 1. If you wanted to get an auto insurance quote from Geico, the answer to a common question “Were you eligible to drive at 18?” could be inferred from the driver receiving their license at age 16 2. If you were doing competitor analysis, it’s understanding that an Arnold Palmer 22 oz can at 7/11 is almost definitely the same product as a 23 oz can at Gopuff (even though the sizes are slightly different, which could be a rounding error!) Want to see examples of Skyvern in action? Jump to #real-world-examples-of- skyvern

BambooAI is a lightweight library utilizing Large Language Models (LLMs) to provide natural language interaction capabilities, much like a research and data analysis assistant enabling conversation with your data. You can either provide your own data sets, or allow the library to locate and fetch data for you. It supports Internet searches and external API interactions.

OpenLLMetry-JS is a set of extensions built on top of OpenTelemetry that gives you complete observability over your LLM application. Because it uses OpenTelemetry under the hood, it can be connected to your existing observability solutions - Datadog, Honeycomb, and others. It's built and maintained by Traceloop under the Apache 2.0 license. The repo contains standard OpenTelemetry instrumentations for LLM providers and Vector DBs, as well as a Traceloop SDK that makes it easy to get started with OpenLLMetry-JS, while still outputting standard OpenTelemetry data that can be connected to your observability stack. If you already have OpenTelemetry instrumented, you can just add any of our instrumentations directly.

OpenLLMetry is a set of extensions built on top of OpenTelemetry that gives you complete observability over your LLM application. Because it uses OpenTelemetry under the hood, it can be connected to your existing observability solutions - Datadog, Honeycomb, and others. It's built and maintained by Traceloop under the Apache 2.0 license. The repo contains standard OpenTelemetry instrumentations for LLM providers and Vector DBs, as well as a Traceloop SDK that makes it easy to get started with OpenLLMetry, while still outputting standard OpenTelemetry data that can be connected to your observability stack. If you already have OpenTelemetry instrumented, you can just add any of our instrumentations directly.

AgentOps is a toolkit for evaluating and developing robust and reliable AI agents. It provides benchmarks, observability, and replay analytics to help developers build better agents. AgentOps is open beta and can be signed up for here. Key features of AgentOps include: - Session replays in 3 lines of code: Initialize the AgentOps client and automatically get analytics on every LLM call. - Time travel debugging: (coming soon!) - Agent Arena: (coming soon!) - Callback handlers: AgentOps works seamlessly with applications built using Langchain and LlamaIndex.

Easily integrate Large Language Models into your Python code. Simply use the `@prompt` and `@chatprompt` decorators to create functions that return structured output from the LLM. Mix LLM queries and function calling with regular Python code to create complex logic.

Experts.js is a tool that simplifies the creation and deployment of OpenAI's Assistants, allowing users to link them together as Tools to create a Panel of Experts system with expanded memory and attention to detail. It leverages the new Assistants API from OpenAI, which offers advanced features such as referencing attached files & images as knowledge sources, supporting instructions up to 256,000 characters, integrating with 128 tools, and utilizing the Vector Store API for efficient file search. Experts.js introduces Assistants as Tools, enabling the creation of Multi AI Agent Systems where each Tool is an LLM-backed Assistant that can take on specialized roles or fulfill complex tasks.

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