Best Observability (2026)

1

KeywordsAI

Unified developer platform for building, monitoring, and scaling LLM applications.

5.0 (6)

· free

Full review

KeywordsAI is a developer-focused platform that consolidates the tools needed to ship production-grade LLM applications. It provides a single API gateway for accessing multiple model providers, along with built-in observability, logging, and evaluation features to help teams understand how their AI features perform in the real world. The platform is designed to reduce the operational overhead of running LLM-powered products. Developers can monitor latency and cost, debug prompts, run evaluations, and manage prompt versions without stitching together separate tools. This makes it easier for engineering teams to iterate on AI features and maintain reliability as usage scales.

• Unified LLM gateway across providers
• Request logging and tracing
• Cost and latency monitoring
• Prompt experimentation and version control
• Evaluation and testing workflows
• SDKs and API integrations

2

Guardian

Security and governance platform for autonomous AI agents and intelligent systems.

5.0 (5)

· free

Full review

Guardian is a security-focused platform designed to protect organizations deploying autonomous AI agents and intelligent systems. It provides monitoring, policy enforcement, and risk controls aimed at preventing misuse, data leakage, and unintended agent behavior. The tool targets enterprises and developers building agentic workflows who need visibility into what their AI systems are doing and guardrails to keep them aligned with business and compliance requirements. Guardian sits between AI models, tools, and end users to apply real-time checks and audit trails. By combining behavioral analysis with configurable policies, Guardian helps teams scale AI adoption while reducing exposure to operational and security risks.

• Agent behavior monitoring
• Configurable security policies
• Threat detection for AI workflows
• Audit logging and reporting
• Guardrails for autonomous actions
• Integration with AI agent frameworks

3

Maxim AI

End-to-end platform for evaluating, monitoring, and improving AI agents

4.8 (6)

· free

Full review

Maxim AI is a developer platform built to help teams ship reliable AI agents and LLM applications. It brings together prompt engineering, evaluation, observability, and dataset management so teams can iterate quickly while keeping quality measurable. The platform supports automated and human evaluations across multiple models and prompts, letting engineers compare outputs, detect regressions, and trace failures in production. It is designed for cross-functional collaboration, with workflows that allow both technical and non-technical stakeholders to contribute to testing and review. Maxim is typically used by teams building chatbots, copilots, voice agents, and multi-step agentic workflows that need consistent performance across changing prompts, models, and user inputs.

• Prompt playground and versioning
• Automated agent and LLM evaluations
• Production observability and tracing
• Dataset curation and management
• Human review and annotation workflows
• Multi-model and multi-provider support

4

Weave

A no-code AI workflow builder that enables businesses to automate operations by integrating multiple large language models (LLMs) and connecting prompts seam...

4.8 (5)

· free

Full review

W&B Weave is an observability and evaluation platform that helps track and improve large language model (LLM) applications. Weave provides tools to trace, collect metrics, and evaluate application responses using LLM judges and custom scorers. Key features include tracing sessions, LLM calls, and tool calls, as well as manual instrumentation of custom agents. The platform supports integrations with popular SDKs and harnesses, as well as custom agent observability. Weave provides Python and TypeScript libraries for installing and using the platform. It is hosted on Weights & Biases (W&B), requiring a W&B account and API key for authentication. Users can trace calls to LLMs, review inputs and outputs, and view agent metrics in the Weave UI. While Weave facilitates automation and evaluation of LLM applications, it is not a no-code AI workflow builder as suggested by its name.

• Agent tracing and metric collection
• Custom agent observability
• LLM tracing and evaluation
• OpenTelemetry span support
• Weights & Biases (W&B) integrations
• Python and TypeScript libraries

5

llm scout

Monitor how your brand appears across ChatGPT, Claude, Perplexity, and Google AI Overviews.

4.8 (5)

· free

Full review

LLM Scout is a brand monitoring tool built for the era of generative search. It tracks how your company, products, and competitors are mentioned across major AI assistants and answer engines, giving marketing and SEO teams visibility into a channel that traditional analytics tools miss. The platform runs recurring prompts against systems like ChatGPT, Claude, Perplexity, and Google's AI Overviews, then reports on share of voice, sentiment, citation sources, and changes over time. Teams can use these insights to refine content strategy, identify gaps where competitors are being recommended instead, and measure the impact of optimization efforts aimed at large language models.

• Brand and competitor mention tracking
• Monitoring across ChatGPT, Claude, Perplexity, and AI Overviews
• Sentiment and share of voice analysis
• Citation and source visibility
• Custom prompt tracking
• Historical trend reporting

6

FoundryAI

Build, evaluate, and improve AI agents for business automation

4.8 (4)

· free

Full review

FoundryAI is a development platform focused on creating AI agents that handle real business workflows. It combines agent design, testing, and continuous improvement tools so teams can move from prototype to production without stitching together separate systems. The platform emphasizes evaluation, giving builders ways to measure agent performance against defined tasks and refine behavior over time. This makes it suited for organizations automating customer support, internal operations, or repetitive knowledge work where reliability matters. FoundryAI targets technical teams who need more control than no-code builders offer but want faster iteration than building agents entirely from scratch.

• Agent building environment
• Evaluation and testing tools
• Performance monitoring
• Workflow automation support
• Iterative improvement loops
• Integration with business systems

7

Helicone AI

All-in-one observability platform to monitor, debug, and improve production LLM apps.

4.7 (6)

· free

Full review

Helicone AI is a developer-focused observability platform built specifically for applications powered by large language models. It captures requests, responses, costs, and latency across providers, giving engineering teams a unified view of how their LLM features behave in production. Beyond logging, Helicone offers tools for debugging prompts, tracing multi-step agent workflows, running evaluations, and tracking user-level usage. Teams can identify regressions, control spend, and iterate on prompts with data rather than guesswork. It integrates with popular model providers and frameworks through a lightweight proxy or async logging, making it straightforward to add to existing stacks without major code changes.

• Request and response logging
• Cost and token usage tracking
• Prompt management and versioning
• Agent and session tracing
• Custom evaluations and dashboards
• User and rate-limit analytics

8

Fiddler AI

AI observability and security platform for monitoring, explaining, and governing ML and LLM applications.

4.7 (6)

· free

Full review

Fiddler AI is an enterprise platform that helps teams monitor, analyze, and secure machine learning models and generative AI applications in production. It provides visibility into model performance, data drift, bias, and quality issues, while also offering safeguards against risks specific to LLMs such as hallucinations, prompt injection, and unsafe outputs. Designed for ML engineers, data scientists, and risk and compliance teams, Fiddler combines explainability, real-time monitoring, and guardrails in a single workflow. It integrates with common ML pipelines and cloud environments, helping organizations operationalize responsible AI practices at scale.

• Model performance and drift monitoring
• LLM hallucination and safety detection
• Prompt injection and jailbreak protection
• Explainable AI and root cause analysis
• Bias and fairness assessments
• Dashboards and alerts for production AI

9

Edwin AI

AI agent for IT operations that speeds up incident detection, triage, and resolution.

4.7 (6)

· free

Full review

Edwin AI is an AI agent for IT operations designed to speed up incident detection, triage, and resolution. It provides a centralized platform for IT teams to investigate incidents, understand their impact, find or generate fixes, and apply them across existing tools without needing to switch between systems. Edwin AI correlates alerts, identifies root causes, and initiates remediation automatically, starting from the first alert through to verified resolution. It uses historical patterns and observability data to predict and prevent outages. The tool integrates with over 3,000 tools across observability, APM, security, and CMDB, enabling real-time, actionable insights and eliminating silos. A Forrester study found that Edwin AI delivered a 313% ROI for a composite organization, with a payback period of 6 months or less.

• Alert correlation and noise reduction
• AI-driven root cause suggestions
• Natural-language incident summaries
• Integrations with ITSM and observability platforms
• Automated triage workflows
• Knowledge enrichment from past incidents

10

Future AGI

A platform enhancing AI accuracy through comprehensive evaluation and optimization tools.

4.6 (5)

· free

Full review

Future AGI is a platform that enhances AI accuracy through comprehensive evaluation and optimization tools. It allows users to build self-improving agents, catch what breaks, and know why. The platform offers a range of features, including agent evaluation, optimization, and simulated conversations. Users can create and manage scenarios, and the platform provides analytics and optimization tools to improve agent performance. Future AGI seems to cater to developers and businesses looking to deploy AI-powered chatbots and agents. It allows users to simulate conversations, evaluate and optimize agent performance, and integrate with knowledge bases. The platform appears to be a tool for developers to create and refine AI agents, rather than a customer-facing interface. The platform offers features such as agent evaluation, simulated conversations, and scenario management. It allows users to edit and manage prompts, add retrieval steps for knowledge base articles, and integrate with global prompts for handling complex situations. While Future AGI provides valuable features for AI development and optimization, it also comes with limitations. For example, it relies on general knowledge and may require additional steps for more complex scenarios. Additionally, the platform's reliance on simulated conversations may limit its ability to handle real-world uncertainties. Future AGI seems to offer a unique set of features for AI development and optimization. Its comprehensive evaluation and optimization tools make it a valuable resource for developers looking to refine their AI agents. However, it may require additional development or integration to handle more complex scenarios and real-world uncertainties.

• Agent evaluation and ranking
• Simulated conversations and scenario management
• Knowledge base integration and retrieval
• Global prompt handling for complex situations
• Analytics and optimization tools