Best Agent Observability Tools (2026)

1

ClawWatcher

Sledenje OpenClaw v realnem času, ki razčleni potrošitev žetonov, dejanja in stroške na nalogo, da lahko zaznate odpadke in optimizirate povpraševanja.

4.8 (6)

· freemium

Polna ocena

ClawWatcher is a Agent Observability Tools tool listed on Agent Pantheon.

2

Trent AI

Agentic AI security platform that continuously scans, judges, and mitigates risks across AI systems.

4.8 (4)

· contact

Polna ocena

Trent AI is an AI security platform built around specialized agents that work together to safeguard machine learning models and AI applications. Each agent handles a distinct role in the security lifecycle, from scanning for vulnerabilities to judging severity, mitigating issues, and evaluating outcomes. The platform is designed for continuous operation, providing ongoing assurance rather than point-in-time audits. By coordinating multiple agents, Trent AI aims to catch emerging threats, model weaknesses, and policy violations as AI systems evolve in production. It targets security teams, ML engineers, and compliance leads who need automated coverage across increasingly complex AI deployments.

• Continuous AI system scanning
• Severity judgment agent
• Automated mitigation workflows
• Post-mitigation evaluation
• Multi-agent orchestration
• Coverage across the AI security lifecycle

3

Wayfound AI

An AI agent supervision platform designed for business teams to monitor, align, and optimize agent performance and compliance.

4.5 (4)

· paid

Polna ocena

Wayfound AI is an AI agent supervision platform, categorized as a "Guardian Agent" solution, that focuses on the business-led oversight of AI agents and agentic workflows. It addresses the common challenge that traditional technical observability tools only confirm an AI agent's operational status, but do not provide insight into its actual business performance, adherence to goals, or compliance with organizational policies. The platform is primarily designed for business leaders, governance teams, and non-technical users, enabling them to oversee and improve AI agent performance without requiring coding expertise. It operates through a "Supervisor Agent" that continuously monitors agent activities, including real-time analysis of 100% of interaction transcripts, to assess performance, identify issues, and ensure alignment with business objectives. Key capabilities of Wayfound AI include providing agent scorecards, real-time alerts for errors, performance drift, and compliance risks, along with concrete recommendations for improvement. It offers AI compliance monitoring through intuitive rule enforcement, performance optimization based on clear insights, and features like "Supervised Self-Healing" for real-time agent adjustments. The platform also manages complex multi-agent applications and human-in-the-loop steps within broader agentic processes. Wayfound AI extends beyond basic technical monitoring to offer actionable AI explainability, enforcement capabilities, and continuous improvement loops. It aims to help organizations scale their AI initiatives safely and efficiently by ensuring AI agents deliver brand-safe, compliant, and consistently high-performing experiences. Reported benefits include reducing monitoring costs, accelerating agent deployment, and achieving AI agent ROI within a short timeframe. The platform also mentions integration flexibility, including an "MCP server" and a "Salesforce Agentforce partnership."

• Real-time AI agent supervision and performance monitoring
• Agent scorecards, alerts, and improvement recommendations
• AI compliance monitoring with intuitive rule enforcement
• Transcript analysis of agent interactions
• Supervised self-healing capabilities for AI agents
• Optimization for multi-agent workflows and human-in-the-loop processes

4

CICube

AI DevOps agent, ki spremlja delovne tokove GitHub Actions, zaznava anomalije in ponuja izvedljive popravke.

4.5 (4)

· paid

Polna ocena

CICube deluje kot platforma za opazovanje, poganjana s pomočjo umetne inteligence, posebej zasnovana za delovne tokove GitHub Actions. Rešuje pogost izziv, saj CI/CD pipeline pogosto delujejo kot „črne škatle“ brez podrobnih vpogledov, kar vodi v časovno zahtevno odpravljanje napak in neučinkovito delovanje. Orodje si prizadeva CI pipeline narediti transparentne ter DevOps ekipi zagotavlja inteligenco za zmanjšanje stroškov, odpravo neučinkovitosti in izboljšanje zmogljivosti. Platforma uporablja AI agente za stalno spremljanje GitHub Actions, zaznavanje anomalij in identifikacijo osnovnih vzrokov neuspehov. Ključna zmožnost je AI Root Cause Analysis, ki samodejno določa težave in predlaga inteligentne popravke, kar zmanjša potrebo po ročnem preiskovanju. Poleg tega vključuje pogovorno vmesnik, ki ga poganja large language models (LLMs), kar uporabnikom omogoča, da postavljajo vprašanja v naravnem jeziku o svojih CI podatkih, na primer "Zakaj je moj build tako počasni?", in prejmejo takojšnje odgovore. CICube presega tradicionalne kazalnike CI, saj poudarja optimizacijo stroškov, zlasti z izračunom in zmanjševanjem skritih stroškov, povezanih s preklapljanjem konteksta razvijalcev. Trdi, da pogoste prekinjave zaradi neuspešnih gradnje ali obvestil CI bistveno vplivajo na produktivnost razvijalcev. Platforma ponuja podrobne vpoglede v stroške CI in zagotavlja tedenske poročila, ki razvijalskim ekipam pomagajo slediti in optimizirati svoje izdatke. Orodje CICube uporablja "CubeScore™" za ocenjevanje zmogljivosti življenjskega cikla CI v primerjavi z North Star Metrics, kot so Mean Time To Recovery (MTTR), stopnja uspeha, Throughput in trajanje. Ponudba vključuje AI-podprte vpoglede in opozorila, ki pomagajo odpraviti težave, kot so padanje stopnje uspeha ali naraščajoče trajanje pipeline, z namenom zmanjšanja MTTR. Integracija je zasnovana z varnostjo v mislih in uporablja dovoljenja za branje samo za podatke GitHub Actions.

• AI analiza vzrokov
• Razgovorni vmesnik za CI podatke, pogojen z LLM
• AI vodeni vpogledi in opozorila za CI
• CubeScore™ z metriki North Star (MTTR, stopnja uspeha, pretok, trajanje)
• Optimizacija stroškov CI in poročanje
• Sledenje GitHub Actions v realnem času

5

Crawl4AI

Odprtokodni spletni crawler in scraper, ki proizvaja čisto, LLM-pripravljeno izhodno vsebino za AI agente in pipelines

4.4 (5)

· free

Polna ocena

Crawl4AI je odprtokodna knjižnica v Pythonu za krmarjenje in pajkanje spletnih strani z izhodom, prilagojenim za velike jezikovne modele in AI delovne tokove. Namesto da vrača surovo HTML, se osredotoča na ustvarjanje čiste, strukturirane vsebine – najbolj znano v obliki Markdown – ki jo je mogoče neposredno vnesti v LLM pozive, retrieval pipeline-e ali v podatkovne zbirke za trening in fino nastavitev. Distribuirana je pod odprtokodno licenco na GitHubu, kjer je pridobila znaten odziv v skupnosti AI razvijalcev. Orodje je namenjeno razvijalcem, inženirjem podatkov in graditeljem AI agentov, ki potrebujejo zbirati spletno vsebino programatično, ne da bi morali plačevati ali biti omejeni s kvotami komercialnih scraping API-jev. Predstavljeno je kot samopostrežna, brezplačna alternativa gostovanim storitvam, ki uporabnikom daje popoln nadzor nad tem, kako se strani pridobivajo, upodabljajo in pretvarjajo. V ozadju Crawl4AI uporablja headless brskalnik (zgrajen na Playwright), ki upodablja JavaScript‑intenzivne strani, nato pa uporabi strategije za ekstrakcijo in filtriranje, da pretvori upodobljeni DOM v uporabno vsebino. Podpira generiranje Markdowna s možnostmi za odstranitev boilerplate‑a in šuma ter strukturirano ekstrakcijo z uporabo bodisi CSS/XPath selektorjev ali LLM‑temeljnih strategij ekstrakcije, ki vrnejo podatke po shemi. Asinhrono delovanje omogoča sočasno indeksiranje številnih URL‑jev. Med izstopajoče zmogljivosti spadajo nastavljivo filtriranje vsebine za zmanjšanje nepomembnega besedila, možnost izločanja strukturiranega JSON‑a preko shem, upravljanje sej in brskalnika za obravnavo prijav ali dinamičnih interakcij, podpora za hook‑e in izvajanje po meri napisanega JavaScript‑a ter izločanje medijev/povezav. Uporabiti ga je mogoče kot knjižnica v Python aplikaciji ali ga namestiti prek Dockerja za uporabo v obliki storitve. V običajnem delovnem toku Crawl4AI deluje v fazi zajema v RAG ali agentskem pipelineu: pridobiva in očisti strani, rezultat v obliki Markdown ali strukturiranih podatkov pa razdeli na koščke, vgradi ali posreduje LLM‑ju. Njegov LLM‑prijazen izhod zmanjša predobdelavo, ki je običajno potrebna pri spletnem strganju za AI primere uporabe. Njen glavni prednosti so, da je brezplačen, samogostujoč, aktivno razvijan in zasnovan posebej za AI porabo, namesto za splošno strganje. Kompromisi vključujejo operativne stroške obratovanja brezglavnih brskalnikov v velikem obsegu, inherentno krhkost strganja zaradi spreminjajočih se struktur spletnih strani in anti-bot ukrepov ter krivuljo učenja pri konfiguracijskih možnostih. V primerjavi s ponudbami v oblaku, kot sta Firecrawl ali Apify, prenaša stroške in vzdrževanje na uporabnika v zameno za več nadzora in brez stroškov uporabe.

• Ustvarjanje Markdowna s filtriranjem vsebine
• Strukturirano izvlečenje na osnovi CSS/XPath in LLM
• Renderiranje brezglavnega brskalnika na osnovi Playwright
• Asinhrono vzporedno crawlanje
• Podpora za seje, hooke in prilagojeni JavaScript
• Docker uvajanje za uporabo kot storitev

6

Manifest

Real-time cost observability and routing for AI agents and applications, enabling multi-provider LLM inference optimization.

4.4 (5)

· free

Polna ocena

Manifest is an open-source platform designed to help users manage and optimize their AI inference costs by providing a routing layer between AI agents or applications and various large language model (LLM) providers. It addresses the challenge of high AI bills and the complexity of efficiently using multiple LLM services by putting users in control of their model consumption and expenditure. The tool functions by allowing users to connect their autonomous agents, applications, or third-party harnesses to Manifest. They then add their preferred LLM providers, which can include API key-based services (like OpenAI, Anthropic, Mistral), existing monthly subscriptions (e.g., Anthropic, GitHub Copilot), custom OpenAI- or Anthropic-compatible endpoints, and even local models running on personal infrastructure via Ollama, LM Studio, or llama.cpp. Once connected, Manifest enables users to define routing rules, select specific models and providers for different queries, and set up fallbacks. This allows for dynamic model selection based on cost, performance, or availability. For instance, it can prioritize using quotas from a pre-paid subscription and automatically fall back to pay-as-you-go models when limits are exceeded. The platform also offers real-time visualization of spending, helping users track every dollar spent across their AI operations. A standout capability is Manifest's "AUTO-FIX" feature, which attempts to remediate common LLM request failures before they reach the agent. This includes fixing issues like deprecated or not-found models, wrong parameters, malformed requests, and exceeded context windows, aiming to prevent downtime and improve request success rates. Manifest is built with flexibility in mind, supporting a wide array of AI applications, personal agents, and workflows. It is available as a cloud version for ease of onboarding or a self-hosted Docker deployment, reflecting its open-source nature. This approach aims to make AI more affordable and accessible, from individual developers to established enterprises, by offering tools to reduce costs without compromising quality or locking users into a single provider.

• LLM call routing and optimization
• Multi-provider integration (OpenAI, Anthropic, custom, local)
• Subscription and pay-as-you-go model management
• Real-time cost observability and visualization
• Automated LLM request failure fixing
• Self-hosted deployment option via Docker