Alternatives to Traceloop
Compare Traceloop alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Traceloop in 2026. Compare features, ratings, user reviews, pricing, and more from Traceloop competitors and alternatives in order to make an informed decision for your business.
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LM-Kit.NET
LM-Kit
LM-Kit.NET is a cutting-edge, high-level inference SDK designed specifically to bring the advanced capabilities of Large Language Models (LLM) into the C# ecosystem. Tailored for developers working within .NET, LM-Kit.NET provides a comprehensive suite of powerful Generative AI tools, making it easier than ever to integrate AI-driven functionality into your applications. The SDK is versatile, offering specialized AI features that cater to a variety of industries. These include text completion, Natural Language Processing (NLP), content retrieval, text summarization, text enhancement, language translation, and much more. Whether you are looking to enhance user interaction, automate content creation, or build intelligent data retrieval systems, LM-Kit.NET offers the flexibility and performance needed to accelerate your project. -
2
Arize AI
Arize AI
Automatically discover issues, diagnose problems, and improve models with Arize’s machine learning observability platform. Machine learning systems address mission critical needs for businesses and their customers every day, yet often fail to perform in the real world. Arize is an end-to-end observability platform to accelerate detecting and resolving issues for your AI models at large. Seamlessly enable observability for any model, from any platform, in any environment. Lightweight SDKs to send training, validation, and production datasets. Link real-time or delayed ground truth to predictions. Gain foresight and confidence that your models will perform as expected once deployed. Proactively catch any performance degradation, data/prediction drift, and quality issues before they spiral. Reduce the time to resolution (MTTR) for even the most complex models with flexible, easy-to-use tools for root cause analysis.Starting Price: $50/month -
3
Braintrust
Braintrust Data
Braintrust is an AI observability and evaluation platform designed to help teams build, monitor, and improve AI systems in production. It enables users to capture and inspect real-time traces of AI interactions, including prompts, responses, and tool usage. The platform allows teams to measure performance using automated and human evaluations to ensure output quality. Braintrust helps identify issues such as hallucinations, regressions, and performance drops before they impact users. It supports prompt and model comparisons, making it easier to optimize AI workflows over time. With scalable trace ingestion and real-time monitoring, teams gain full visibility into how their AI systems behave. The platform integrates with multiple programming languages and tools, allowing developers to work within their existing tech stack. Overall, Braintrust provides a comprehensive solution for maintaining and improving AI quality at scale. -
4
Langfuse
Langfuse
Langfuse is an open source LLM engineering platform to help teams collaboratively debug, analyze and iterate on their LLM Applications. Observability: Instrument your app and start ingesting traces to Langfuse Langfuse UI: Inspect and debug complex logs and user sessions Prompts: Manage, version and deploy prompts from within Langfuse Analytics: Track metrics (LLM cost, latency, quality) and gain insights from dashboards & data exports Evals: Collect and calculate scores for your LLM completions Experiments: Track and test app behavior before deploying a new version Why Langfuse? - Open source - Model and framework agnostic - Built for production - Incrementally adoptable - start with a single LLM call or integration, then expand to full tracing of complex chains/agents - Use GET API to build downstream use cases and export dataStarting Price: $29/month -
5
Maxim
Maxim
Maxim is an agent simulation, evaluation, and observability platform that empowers modern AI teams to deploy agents with quality, reliability, and speed. Maxim's end-to-end evaluation and data management stack covers every stage of the AI lifecycle, from prompt engineering to pre & post release testing and observability, data-set creation & management, and fine-tuning. Use Maxim to simulate and test your multi-turn workflows on a wide variety of scenarios and across different user personas before taking your application to production. Features: Agent Simulation Agent Evaluation Prompt Playground Logging/Tracing Workflows Custom Evaluators- AI, Programmatic and Statistical Dataset Curation Human-in-the-loop Use Case: Simulate and test AI agents Evals for agentic workflows: pre and post-release Tracing and debugging multi-agent workflows Real-time alerts on performance and quality Creating robust datasets for evals and fine-tuning Human-in-the-loop workflowsStarting Price: $29/seat/month -
6
Orq.ai
Orq.ai
Orq.ai is the #1 platform for software teams to operate agentic AI systems at scale. Optimize prompts, deploy use cases, and monitor performance, no blind spots, no vibe checks. Experiment with prompts and LLM configurations before moving to production. Evaluate agentic AI systems in offline environments. Roll out GenAI features to specific user groups with guardrails, data privacy safeguards, and advanced RAG pipelines. Visualize all events triggered by agents for fast debugging. Get granular control on cost, latency, and performance. Connect to your favorite AI models, or bring your own. Speed up your workflow with out-of-the-box components built for agentic AI systems. Manage core stages of the LLM app lifecycle in one central platform. Self-hosted or hybrid deployment with SOC 2 and GDPR compliance for enterprise security. -
7
Weights & Biases
Weights & Biases
Experiment tracking, hyperparameter optimization, model and dataset versioning with Weights & Biases (WandB). Track, compare, and visualize ML experiments with 5 lines of code. Add a few lines to your script, and each time you train a new version of your model, you'll see a new experiment stream live to your dashboard. Optimize models with our massively scalable hyperparameter search tool. Sweeps are lightweight, fast to set up, and plug in to your existing infrastructure for running models. Save every detail of your end-to-end machine learning pipeline — data preparation, data versioning, training, and evaluation. It's never been easier to share project updates. Quickly and easily implement experiment logging by adding just a few lines to your script and start logging results. Our lightweight integration works with any Python script. W&B Weave is here to help developers build and iterate on their AI applications with confidence. -
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Dynamiq
Dynamiq
Dynamiq is a platform built for engineers and data scientists to build, deploy, test, monitor and fine-tune Large Language Models for any use case the enterprise wants to tackle. Key features: 🛠️ Workflows: Build GenAI workflows in a low-code interface to automate tasks at scale 🧠 Knowledge & RAG: Create custom RAG knowledge bases and deploy vector DBs in minutes 🤖 Agents Ops: Create custom LLM agents to solve complex task and connect them to your internal APIs 📈 Observability: Log all interactions, use large-scale LLM quality evaluations 🦺 Guardrails: Precise and reliable LLM outputs with pre-built validators, detection of sensitive content, and data leak prevention 📻 Fine-tuning: Fine-tune proprietary LLM models to make them your ownStarting Price: $125/month -
9
Athina AI
Athina AI
Athina is a collaborative AI development platform that enables teams to build, test, and monitor AI applications efficiently. It offers features such as prompt management, evaluation tools, dataset handling, and observability, all designed to streamline the development of reliable AI systems. Athina supports integration with various models and services, including custom models, and ensures data privacy through fine-grained access controls and self-hosted deployment options. The platform is SOC-2 Type 2 compliant, providing a secure environment for AI development. Athina's user-friendly interface allows both technical and non-technical team members to collaborate effectively, accelerating the deployment of AI features.Starting Price: Free -
10
Selene 1
atla
Atla's Selene 1 API offers state-of-the-art AI evaluation models, enabling developers to define custom evaluation criteria and obtain precise judgments on their AI applications' performance. Selene outperforms frontier models on commonly used evaluation benchmarks, ensuring accurate and reliable assessments. Users can customize evaluations to their specific use cases through the Alignment Platform, allowing for fine-grained analysis and tailored scoring formats. The API provides actionable critiques alongside accurate evaluation scores, facilitating seamless integration into existing workflows. Pre-built metrics, such as relevance, correctness, helpfulness, faithfulness, logical coherence, and conciseness, are available to address common evaluation scenarios, including detecting hallucinations in retrieval-augmented generation applications or comparing outputs to ground truth data. -
11
Respan
Respan
Respan is a self-driving observability and evaluation platform built specifically for AI agents. It enables teams to trace full execution flows, including messages, tool calls, routing decisions, memory usage, and outcomes. The platform connects observability, evaluations, and optimization into a continuous improvement loop. Metric-first evaluations allow teams to define performance standards such as accuracy, cost, reliability, and safety. Respan also includes capability and regression testing to protect stable behaviors while improving new ones. An AI-powered evaluation agent analyzes failures, identifies root causes, and recommends next steps automatically. With compliance certifications including ISO 27001, SOC 2, GDPR, and HIPAA, Respan supports secure, large-scale AI deployments across industries.Starting Price: $0/month -
12
Arize Phoenix
Arize AI
Phoenix is an open-source observability library designed for experimentation, evaluation, and troubleshooting. It allows AI engineers and data scientists to quickly visualize their data, evaluate performance, track down issues, and export data to improve. Phoenix is built by Arize AI, the company behind the industry-leading AI observability platform, and a set of core contributors. Phoenix works with OpenTelemetry and OpenInference instrumentation. The main Phoenix package is arize-phoenix. We offer several helper packages for specific use cases. Our semantic layer is to add LLM telemetry to OpenTelemetry. Automatically instrumenting popular packages. Phoenix's open-source library supports tracing for AI applications, via manual instrumentation or through integrations with LlamaIndex, Langchain, OpenAI, and others. LLM tracing records the paths taken by requests as they propagate through multiple steps or components of an LLM application.Starting Price: Free -
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Convo
Convo
Kanvo provides a drop‑in JavaScript SDK that adds built‑in memory, observability, and resiliency to LangGraph‑based AI agents with zero infrastructure overhead. Without requiring databases or migrations, it lets you plug in a few lines of code to enable persistent memory (storing facts, preferences, and goals), threaded conversations for multi‑user interactions, and real‑time agent observability that logs every message, tool call, and LLM output. Its time‑travel debugging features let you checkpoint, rewind, and restore any agent run state instantly, making workflows reproducible and errors easy to trace. Designed for speed and simplicity, Convo’s lightweight interface and MIT‑licensed SDK deliver production‑ready, debuggable agents out of the box while keeping full control of your data.Starting Price: $29 per month -
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Lunary
Lunary
Lunary is an AI developer platform designed to help AI teams manage, improve, and protect Large Language Model (LLM) chatbots. It offers features such as conversation and feedback tracking, analytics on costs and performance, debugging tools, and a prompt directory for versioning and team collaboration. Lunary supports integration with various LLMs and frameworks, including OpenAI and LangChain, and provides SDKs for Python and JavaScript. Guardrails to deflect malicious prompts and sensitive data leaks. Deploy in your VPC with Kubernetes or Docker. Allow your team to judge responses from your LLMs. Understand what languages your users are speaking. Experiment with prompts and LLM models. Search and filter anything in milliseconds. Receive notifications when agents are not performing as expected. Lunary's core platform is 100% open-source. Self-host or in the cloud, get started in minutes.Starting Price: $20 per month -
15
ChainForge
ChainForge
ChainForge is an open-source visual programming environment designed for prompt engineering and large language model evaluation. It enables users to assess the robustness of prompts and text-generation models beyond anecdotal evidence. Simultaneously test prompt ideas and variations across multiple LLMs to identify the most effective combinations. Evaluate response quality across different prompts, models, and settings to select the optimal configuration for specific use cases. Set up evaluation metrics and visualize results across prompts, parameters, models, and settings, facilitating data-driven decision-making. Manage multiple conversations simultaneously, template follow-up messages, and inspect outputs at each turn to refine interactions. ChainForge supports various model providers, including OpenAI, HuggingFace, Anthropic, Google PaLM2, Azure OpenAI endpoints, and locally hosted models like Alpaca and Llama. Users can adjust model settings and utilize visualization nodes. -
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Guardrails AI
Guardrails AI
With our dashboard, you are able to go deeper into analytics that will enable you to verify all the necessary information related to entering requests into Guardrails AI. Unlock efficiency with our ready-to-use library of pre-built validators. Optimize your workflow with robust validation for diverse use cases. Empower your projects with a dynamic framework for creating, managing, and reusing custom validators. Where versatility meets ease, catering to a spectrum of innovative applications easily. By verifying and indicating where the error is, you can quickly generate a second output option. Ensures that outcomes are in line with expectations, precision, correctness, and reliability in interactions with LLMs. -
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Deepchecks
Deepchecks
Release high-quality LLM apps quickly without compromising on testing. Never be held back by the complex and subjective nature of LLM interactions. Generative AI produces subjective results. Knowing whether a generated text is good usually requires manual labor by a subject matter expert. If you’re working on an LLM app, you probably know that you can’t release it without addressing countless constraints and edge-cases. Hallucinations, incorrect answers, bias, deviation from policy, harmful content, and more need to be detected, explored, and mitigated before and after your app is live. Deepchecks’ solution enables you to automate the evaluation process, getting “estimated annotations” that you only override when you have to. Used by 1000+ companies, and integrated into 300+ open source projects, the core behind our LLM product is widely tested and robust. Validate machine learning models and data with minimal effort, in both the research and the production phases.Starting Price: $1,000 per month -
18
Opik
Comet
Confidently evaluate, test, and ship LLM applications with a suite of observability tools to calibrate language model outputs across your dev and production lifecycle. Log traces and spans, define and compute evaluation metrics, score LLM outputs, compare performance across app versions, and more. Record, sort, search, and understand each step your LLM app takes to generate a response. Manually annotate, view, and compare LLM responses in a user-friendly table. Log traces during development and in production. Run experiments with different prompts and evaluate against a test set. Choose and run pre-configured evaluation metrics or define your own with our convenient SDK library. Consult built-in LLM judges for complex issues like hallucination detection, factuality, and moderation. Establish reliable performance baselines with Opik's LLM unit tests, built on PyTest. Build comprehensive test suites to evaluate your entire LLM pipeline on every deployment.Starting Price: $39 per month -
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TruLens
TruLens
TruLens is an open-source Python library designed to systematically evaluate and track Large Language Model (LLM) applications. It provides fine-grained instrumentation, feedback functions, and a user interface to compare and iterate on app versions, facilitating rapid development and improvement of LLM-based applications. Programmatic tools that assess the quality of inputs, outputs, and intermediate results from LLM applications, enabling scalable evaluation. Fine-grained, stack-agnostic instrumentation and comprehensive evaluations help identify failure modes and systematically iterate to improve applications. An easy-to-use interface that allows developers to compare different versions of their applications, facilitating informed decision-making and optimization. TruLens supports various use cases, including question-answering, summarization, retrieval-augmented generation, and agent-based applications.Starting Price: Free -
20
HumanSignal
HumanSignal
HumanSignal's Label Studio Enterprise is a comprehensive platform designed for creating high-quality labeled data and evaluating model outputs with human supervision. It supports labeling and evaluating multi-modal data, image, video, audio, text, and time series, all in one place. It offers customizable labeling interfaces with pre-built templates and powerful plugins, allowing users to tailor the UI and workflows to specific use cases. Label Studio Enterprise integrates seamlessly with popular cloud storage providers and ML/AI models, facilitating pre-annotation, AI-assisted labeling, and prediction generation for model evaluation. The Prompts feature enables users to leverage LLMs to swiftly generate accurate predictions, enabling instant labeling of thousands of tasks. It supports various labeling use cases, including text classification, named entity recognition, sentiment analysis, summarization, and image captioning.Starting Price: $99 per month -
21
LLM Council
LLM Council
LLM Council is a lightweight multi-model orchestration tool that enables users to query several large language models simultaneously and synthesize their outputs into a single, higher-confidence response. Instead of relying on one AI system, it routes a prompt to a panel of models, each of which produces an independent answer before anonymously reviewing and ranking the others’ work. A designated “Chairman” model then combines the strongest insights into a unified final output, mimicking the dynamics of a panel of experts reaching consensus. It typically runs as a simple local web interface with a Python backend and React frontend and connects through aggregation services to access models from providers such as OpenAI, Google, and Anthropic. This structured peer-review workflow is designed to surface blind spots, reduce hallucinations, and improve answer reliability by introducing multiple perspectives and cross-model critique.Starting Price: $25 per month -
22
AgentScope
AgentScope
AgentScope is an AI-driven agent observability and operations platform that provides visibility, control, and performance analytics for autonomous AI agents across production workloads. It enables engineering and DevOps teams to monitor, diagnose, and optimize complex multi-agent applications in real time by capturing detailed telemetry on agent actions, decisions, resource usage, and outcome quality. With rich dashboards and timelines, AgentScope helps teams trace execution flows, identify bottlenecks, and understand how agents interact with external systems, APIs, and data sources, improving debugging and reliability for autonomous workflows. It supports customizable alerting, log aggregation, and structured event views so teams can quickly surface anomalous behavior or errors across distributed agent fleets. In addition to real-time monitoring, AgentScope provides historical analysis and reporting that help teams measure performance trends, model drift, etc.Starting Price: Free -
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Scale Evaluation
Scale
Scale Evaluation offers a comprehensive evaluation platform tailored for developers of large language models. This platform addresses current challenges in AI model assessment, such as the scarcity of high-quality, trustworthy evaluation datasets and the lack of consistent model comparisons. By providing proprietary evaluation sets across various domains and capabilities, Scale ensures accurate model assessments without overfitting. The platform features a user-friendly interface for analyzing and reporting model performance, enabling standardized evaluations for true apples-to-apples comparisons. Additionally, Scale's network of expert human raters delivers reliable evaluations, supported by transparent metrics and quality assurance mechanisms. The platform also offers targeted evaluations with custom sets focusing on specific model concerns, facilitating precise improvements through new training data. -
24
Comet
Comet
Manage and optimize models across the entire ML lifecycle, from experiment tracking to monitoring models in production. Achieve your goals faster with the platform built to meet the intense demands of enterprise teams deploying ML at scale. Supports your deployment strategy whether it’s private cloud, on-premise servers, or hybrid. Add two lines of code to your notebook or script and start tracking your experiments. Works wherever you run your code, with any machine learning library, and for any machine learning task. Easily compare experiments—code, hyperparameters, metrics, predictions, dependencies, system metrics, and more—to understand differences in model performance. Monitor your models during every step from training to production. Get alerts when something is amiss, and debug your models to address the issue. Increase productivity, collaboration, and visibility across all teams and stakeholders.Starting Price: $179 per user per month -
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LayerLens
LayerLens
LayerLens is an independent AI model evaluation platform for understanding how models perform through verified results across benchmarks, prompt-level results, agentic benchmarks, and audit-ready comparisons across vendors. It helps teams compare more than 200 AI models side by side, with transparent benchmarks, model comparison tools, and consistent evaluation methods for accuracy, latency, behavior, and real-world applicability. LayerLens is built for deep model analysis through Spaces, where teams can group benchmarks and evaluations, explore task strengths, and track performance patterns in context. It supports continuous evaluation by running ongoing evals across model versions, prompt changes, judge updates, and live traces, helping teams detect quality regressions, drift, silent failures, contamination, and policy issues before they affect production. -
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Laminar
Laminar
Laminar is an open source all-in-one platform for engineering best-in-class LLM products. Data governs the quality of your LLM application. Laminar helps you collect it, understand it, and use it. When you trace your LLM application, you get a clear picture of every step of execution and simultaneously collect invaluable data. You can use it to set up better evaluations, as dynamic few-shot examples, and for fine-tuning. All traces are sent in the background via gRPC with minimal overhead. Tracing of text and image models is supported, audio models are coming soon. You can set up LLM-as-a-judge or Python script evaluators to run on each received span. Evaluators label spans, which is more scalable than human labeling, and especially helpful for smaller teams. Laminar lets you go beyond a single prompt. You can build and host complex chains, including mixtures of agents or self-reflecting LLM pipelines.Starting Price: $25 per month -
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DeepEval
Confident AI
DeepEval is a simple-to-use, open source LLM evaluation framework, for evaluating and testing large-language model systems. It is similar to Pytest but specialized for unit testing LLM outputs. DeepEval incorporates the latest research to evaluate LLM outputs based on metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., which uses LLMs and various other NLP models that run locally on your machine for evaluation. Whether your application is implemented via RAG or fine-tuning, LangChain, or LlamaIndex, DeepEval has you covered. With it, you can easily determine the optimal hyperparameters to improve your RAG pipeline, prevent prompt drifting, or even transition from OpenAI to hosting your own Llama2 with confidence. The framework supports synthetic dataset generation with advanced evolution techniques and integrates seamlessly with popular frameworks, allowing for efficient benchmarking and optimization of LLM systems.Starting Price: Free -
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Digna
digna GmbH
digna is a data quality and observability platform designed to monitor, analyze, and validate data directly within enterprise data environments. It combines anomaly detection, time-series analytics, and validation into a unified system that helps teams detect issues early and understand how data behaves over time. Core Capabilities * Data Anomaly Detection Identifies changes in data volume, distribution, and behavior using statistical methods and AI-driven models without relying on manually defined rules. * Time-Series Analytics Built-in analytical methods (regression, pattern detection, seasonality analysis) allow users to interpret trends and deviations directly within the platform. * Data Timeliness Monitoring Tracks expected data arrival times and identifies delays across pipelines and data flows. * Data Validation Supports rule-based validation with reusable templates and centralized definitions of allowed values. * Schema Change Tracking Detects structural changes in dat -
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Giskard
Giskard
Giskard provides interfaces for AI & Business teams to evaluate and test ML models through automated tests and collaborative feedback from all stakeholders. Giskard speeds up teamwork to validate ML models and gives you peace of mind to eliminate risks of regression, drift, and bias before deploying ML models to production.Starting Price: $0 -
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Vivgrid
Vivgrid
Vivgrid is a development platform for AI agents that emphasizes observability, debugging, safety, and global deployment infrastructure. It gives you full visibility into agent behavior, logging prompts, memory fetches, tool usage, and reasoning chains, letting developers trace where things break or deviate. You can test, evaluate, and enforce safety policies (like refusal rules or filters), and incorporate human-in-the-loop checks before going live. Vivgrid supports the orchestration of multi-agent systems with stateful memory, routing tasks dynamically across agent workflows. On the deployment side, it operates a globally distributed inference network to ensure low-latency (sub-50 ms) execution and exposes metrics like latency, cost, and usage in real time. It aims to simplify shipping resilient AI systems by combining debugging, evaluation, safety, and deployment into one stack, so you're not stitching together observability, infrastructure, and orchestration.Starting Price: $25 per month -
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Tasq.ai
Tasq.ai
Tasq.ai delivers a powerful, no-code platform for building hybrid AI workflows that combine state-of-the-art machine learning with global, decentralized human guidance, ensuring unmatched scalability, control, and precision. It enables teams to configure AI pipelines visually, breaking tasks into micro-workflows that layer automated inference and quality-assured human review. This decoupled orchestration supports diverse use cases across text, computer vision, audio, video, and structured data, with rapid deployment, adaptive sampling, and consensus-based validation built in. Key capabilities include global deployment of highly screened contributors (“Tasqers”) for unbiased, high-accuracy annotations; granular task routing and judgment aggregation to meet confidence thresholds; and seamless integration into ML ops pipelines via drag-and-drop customization. -
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Ragas
Ragas
Ragas is an open-source framework designed to test and evaluate Large Language Model (LLM) applications. It offers automatic metrics to assess performance and robustness, synthetic test data generation tailored to specific requirements, and workflows to ensure quality during development and production monitoring. Ragas integrates seamlessly with existing stacks, providing insights to enhance LLM applications. The platform is maintained by a team of passionate individuals leveraging cutting-edge research and pragmatic engineering practices to empower visionaries redefining LLM possibilities. Synthetically generate high-quality and diverse evaluation data customized for your requirements. Evaluate and ensure the quality of your LLM application in production. Use insights to improve your application. Automatic metrics that helps you understand the performance and robustness of your LLM application.Starting Price: Free -
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Beaconcure
Beaconcure
Intelligent clinical data analysis. Accelerate and de-risk regulatory approvals with our automated analytics and validation platform. The clinical data analytics software unlike no other improve data quality & de-risk your submission. Automate your manual quality process, Avoid QC rerun, Eliminate data duplication or inconsistencies, Ensure data traceability, Transparent quality process. Accelerate time to market & generate revenue earlier. reduce data validation time. free up resources, reduce cost. Accelerate regulatory approval. The need for accurate clinical data validation and high-quality data output has never been more urgent. Verify manages and analyzes clinical data to mitigate risk and expedite approval of a new drug and vaccine. Ensure quality, speed & success. -
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Instructor
Instructor
Instructor is a tool that enables developers to extract structured data from natural language using Large Language Models (LLMs). Integrating with Python's Pydantic library allows users to define desired output structures through type hints, facilitating schema validation and seamless integration with IDEs. Instructor supports various LLM providers, including OpenAI, Anthropic, Litellm, and Cohere, offering flexibility in implementation. Its customizable nature permits the definition of validators and custom error messages, enhancing data validation processes. Instructor is trusted by engineers from platforms like Langflow, underscoring its reliability and effectiveness in managing structured outputs powered by LLMs. Instructor is powered by Pydantic, which is powered by type hints. Schema validation and prompting are controlled by type annotations; less to learn, and less code to write, and it integrates with your IDE.Starting Price: Free -
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Atla
Atla
Atla is the agent observability and evaluation platform that dives deeper to help you find and fix AI agent failures. It provides real‑time visibility into every thought, tool call, and interaction so you can trace each agent run, understand step‑level errors, and identify root causes of failures. Atla automatically surfaces recurring issues across thousands of traces, stops you from manually combing through logs, and delivers specific, actionable suggestions for improvement based on detected error patterns. You can experiment with models and prompts side by side to compare performance, implement recommended fixes, and measure how changes affect completion rates. Individual traces are summarized into clean, readable narratives for granular inspection, while aggregated patterns give you clarity on systemic problems rather than isolated bugs. Designed to integrate with tools you already use, OpenAI, LangChain, Autogen AI, Pydantic AI, and more. -
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Sup AI
Sup AI
Sup AI is a multi-LLM platform that merges outputs from several top large language models, such as GPT, Claude, Llama, and more, to generate richer, more accurate, and better-validated answers than any single model could provide. It applies real-time “logprob confidence scoring,” analyzing each token’s probability to detect uncertainty or hallucination; when a model’s confidence falls below a threshold, the response is halted, helping ensure that delivered answers remain high-quality and trustworthy. Sup’s “multi-model fusion” then compares, contrasts, and consolidates outputs from different models, cross-verifying and synthesizing the best parts into a final result. Sup also supports “multimodal RAG” (retrieval-augmented generation) to incorporate external data (text, PDFs, images) into context-aware responses, giving the AI access to factual sources and helping it “never forget” relevant information.Starting Price: $20 per month -
37
BenchLLM
BenchLLM
Use BenchLLM to evaluate your code on the fly. Build test suites for your models and generate quality reports. Choose between automated, interactive or custom evaluation strategies. We are a team of engineers who love building AI products. We don't want to compromise between the power and flexibility of AI and predictable results. We have built the open and flexible LLM evaluation tool that we have always wished we had. Run and evaluate models with simple and elegant CLI commands. Use the CLI as a testing tool for your CI/CD pipeline. Monitor models performance and detect regressions in production. Test your code on the fly. BenchLLM supports OpenAI, Langchain, and any other API out of the box. Use multiple evaluation strategies and visualize insightful reports. -
38
Prompt flow
Microsoft
Prompt Flow is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, and evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality. With Prompt Flow, you can create flows that link LLMs, prompts, Python code, and other tools together in an executable workflow. It allows for debugging and iteration of flows, especially tracing interactions with LLMs with ease. You can evaluate your flows, calculate quality and performance metrics with larger datasets, and integrate the testing and evaluation into your CI/CD system to ensure quality. Deployment of flows to the serving platform of your choice or integration into your app’s code base is made easy. Additionally, collaboration with your team is facilitated by leveraging the cloud version of Prompt Flow in Azure AI. -
39
Codeanywhere
Codeanywhere
Our Cloud IDE saves you time by deploying a development environment in seconds, enabling you to code, learn, build, and collaborate on your projects. With our amazing web-based code editor in Codeanywhere, you will forget you ever used any other code editor. All major programming languages fully supported, including JavaScript/TypeScript, PHP, Python, Ruby, Go, Java, C/C++, C# any many other. Intelligent editing features such as auto-complete, code refactor, go to definition, rename symbol and many others. Debug code with break points, call stacks and interactive console. Fully featured Git client. Expandable with vast amount of existing extensions. You can also spin up powerful containers in seconds, that can be fully preconfigured for the programming environment of your choice. Develop and run your code on our infrastructure with full sudo access. Prebuilt development environments for all major programming languages, packed with tools and database preinstalled.Starting Price: $2.50 per user per month -
40
Actian Data Observability
Actian
Actian Data Observability is an AI-powered platform designed to continuously monitor, validate, and manage the health, quality, and reliability of data across modern data environments. It uses automated Data Observability Agents that validate data as it arrives in data lakehouses or warehouses, detecting anomalies, explaining root causes, and coordinating resolution before issues impact dashboards, reports, or AI systems. It provides real-time visibility into data pipelines, ensuring that data remains accurate, complete, and trustworthy throughout its lifecycle. It eliminates blind spots by monitoring 100% of data rather than relying on sampling, allowing organizations to identify hidden errors that could otherwise corrupt analytics or machine learning outcomes. With built-in anomaly detection powered by AI and machine learning, it proactively identifies irregularities such as schema changes, missing data, or unexpected distributions, enabling faster diagnosis and resolution. -
41
OpenPipe
OpenPipe
OpenPipe provides fine-tuning for developers. Keep your datasets, models, and evaluations all in one place. Train new models with the click of a button. Automatically record LLM requests and responses. Create datasets from your captured data. Train multiple base models on the same dataset. We serve your model on our managed endpoints that scale to millions of requests. Write evaluations and compare model outputs side by side. Change a couple of lines of code, and you're good to go. Simply replace your Python or Javascript OpenAI SDK and add an OpenPipe API key. Make your data searchable with custom tags. Small specialized models cost much less to run than large multipurpose LLMs. Replace prompts with models in minutes, not weeks. Fine-tuned Mistral and Llama 2 models consistently outperform GPT-4-1106-Turbo, at a fraction of the cost. We're open-source, and so are many of the base models we use. Own your own weights when you fine-tune Mistral and Llama 2, and download them at any time.Starting Price: $1.20 per 1M tokens -
42
Plurai
Plurai
Plurai is the real-world trust platform for AI agents, built for simulation-driven evaluation, protection, and optimization that turns agents into trusted, continuously improving production systems. It helps teams train evals and guardrails tailored to their use case, bridging the gap from prototype to reliable production at scale. Plurai’s simulation platform prepares agents for the real world, not the lab, with hyper-realistic, product-tailored experimentation and evaluation that covers production complexity. It generates authentic multi-turn scenarios, personas, required artifacts, and tool mocking, using organizational PRDs, relevant sources, and policies to build a knowledge graph and expand edge-case coverage. Instead of relying on static datasets, manual test creation, or inconsistent LLM-as-a-judge methods, Plurai groups evaluations into structured, runnable experiments so teams can test new versions, measure regressions, and validate improvements before release.Starting Price: Free -
43
Lucidic AI
Lucidic AI
Lucidic AI is a specialized analytics and simulation platform built for AI agent development that brings much-needed transparency, interpretability, and efficiency to often opaque workflows. It provides developers with visual, interactive insights, including searchable workflow replays, step-by-step video, and graph-based replays of agent decisions, decision tree visualizations, and side‑by‑side simulation comparisons, that enable you to observe exactly how your agent reasons and why it succeeds or fails. The tool dramatically reduces iteration time from weeks or days to mere minutes by streamlining debugging and optimization through instant feedback loops, real‑time “time‑travel” editing, mass simulations, trajectory clustering, customizable evaluation rubrics, and prompt versioning. Lucidic AI integrates seamlessly with major LLMs and frameworks and offers advanced QA/QC mechanisms like alerts, workflow sandboxing, and more. -
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Galileo AI
Galileo AI
Galileo AI creates delightful, editable UI designs from a simple text description. It empowers you to design faster than ever. Our technology learns from thousands of top user experience designs and builds the UI that meets your needs in lightning speed. Populate your designs with our carefully curated AI-generated illustrations and images to match your vision and style. By leveraging large language models, our AI understands the complex context and fills the end-to-end product copy accurately. Spend less time on tedious tasks such as creating repetitive UI patterns and making small visual tweaks. Instead, focus your efforts on landing bigger impact: designing creative solutions. -
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Scribens
Scribens
Scribens checks the grammar of your texts and finds spelling mistakes. Avoid copy-pasting and keep the formatting of your original texts. Correct your texts on Gmail, Hotmail, Yahoo, Facebook, Twitter, LinkedIn, forums, blogs, etc. Use Scribens as a seamless extension of Microsoft Word, Outlook, PowerPoint, Excel, OpenOffice, or LibreOffice. Scribens corrects over 250 types of common grammar and spelling mistakes, including verbs, nouns, pronouns, prepositions, homonyms, punctuation, typography, and more. Online corrections are included with explanations in order to help the user progress his or her English writing skills. Scribens employs a sophisticated syntactical recognition algorithm that detects even the most subtle errors in a text. In offering you advanced correction software, Scribens allows you to significantly improve the quality of your writing. Scribens detects stylistic elements such as repetitions, run-on sentences, redundancies, and more.Starting Price: €9.90 per month -
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AgentOps
AgentOps
Industry-leading developer platform to test and debug AI agents. We built the tools so you don't have to. Visually track events such as LLM calls, tools, and multi-agent interactions. Rewind and replay agent runs with point-in-time precision. Keep a full data trail of logs, errors, and prompt injection attacks from prototype to production. Native integrations with the top agent frameworks. Track, save, and monitor every token your agent sees. Manage and visualize agent spending with up-to-date price monitoring. Fine-tune specialized LLMs up to 25x cheaper on saved completions. Build your next agent with evals, observability, and replays. With just two lines of code, you can free yourself from the chains of the terminal and instead visualize your agents’ behavior in your AgentOps dashboard. After setting up AgentOps, each execution of your program is recorded as a session and the data is automatically recorded for you.Starting Price: $40 per month -
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Fluq
Fluq
Fluq is an AI agent observability and orchestration platform designed to give teams full visibility and control over how their AI agents operate in real time. It acts as a centralized “single pane of glass” where every agent action, LLM calls, tool usage, file operations, token consumption, and associated costs are tracked and visualized through detailed waterfall traces. By routing all agent requests through a lightweight proxy, Fluq requires minimal setup and works with any LLM provider or agent framework, allowing organizations to integrate it into existing systems without modifying code. It enables teams to inspect each decision an agent makes, drill into execution steps, and understand exactly how outcomes are generated, improving transparency and debuggability. It also includes governance features such as policy enforcement, spend limits, approval gates, and access controls, helping prevent issues like runaway costs, misuse of tools, or inaccurate outputs.Starting Price: $29 per month -
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MLflow
MLflow
MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. MLflow currently offers four components. Record and query experiments: code, data, config, and results. Package data science code in a format to reproduce runs on any platform. Deploy machine learning models in diverse serving environments. Store, annotate, discover, and manage models in a central repository. The MLflow Tracking component is an API and UI for logging parameters, code versions, metrics, and output files when running your machine learning code and for later visualizing the results. MLflow Tracking lets you log and query experiments using Python, REST, R API, and Java API APIs. An MLflow Project is a format for packaging data science code in a reusable and reproducible way, based primarily on conventions. In addition, the Projects component includes an API and command-line tools for running projects. -
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doteval
doteval
doteval is an AI-assisted evaluation workspace that simplifies the creation of high-signal evaluations, alignment of LLM judges, and definition of rewards for reinforcement learning, all within a single platform. It offers a Cursor-like experience to edit evaluations-as-code against a YAML schema, enabling users to version evaluations across checkpoints, replace manual effort with AI-generated diffs, and compare evaluation runs on tight execution loops to align them with proprietary data. doteval supports the specification of fine-grained rubrics and aligned graders, facilitating rapid iteration and high-quality evaluation datasets. Users can confidently determine model upgrades or prompt improvements and export specifications for reinforcement learning training. It is designed to accelerate the evaluation and reward creation process by 10 to 100 times, making it a valuable tool for frontier AI teams benchmarking complex model tasks. -
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Sift
Sift
Sift is a unified observability platform purpose-built for modern, mission-critical hardware systems that provides engineers with infrastructure and tooling to ingest, store, normalize, and explore high-frequency, high-cardinality telemetry and event data from design, validation, manufacturing, and operations in a single source of truth rather than fragmented dashboards and scripts; it centralizes diverse data types, aligns signals across subsystems, and structures information for fast search, visual review, and traceability so teams can detect anomalies, perform root-cause analysis, automate verification and validation, and debug hardware with real-time precision. It supports automated data review, no-code visualization and querying of massive datasets, continuous anomaly detection, and integration with engineering workflows, including CI/CD pipelines and tooling, while enabling telemetry governance, collaboration, reporting, and knowledge capture across siloed teams.
