A curated, research-driven list of the most relevant AgentOps tools in 2026, covering observability, evaluation, tracing, and cost monitoring for AI agents.
“The future of AI is not models, it is systems of agents.”
Each week this repository highlights:
- New tools
- Real architectures
- Production lessons
Last generated: 2026-05-21
Automation: GitHub Actions + GitHub API
- Why AgentOps
- What is AgentOps?
- AgentOps Landscape (2026)
- Feature Benchmark
- Key Insights
- How to Choose
- Architecture Layers
- TL;DR
- Contributing
- Disclaimer
Building AI agents is relatively easy. Operating them reliably in production is not.
Common failure points include:
- Lack of visibility into agent reasoning
- Prompt failures without traceability
- Uncontrolled token and infrastructure costs
- No measurable evaluation of output quality
AgentOps addresses these challenges by introducing operational discipline around AI agents.
AgentOps is the convergence of DevOps, MLOps, and LLMOps principles into a specialized operational discipline for autonomous LLM-powered agents. While LLMOps manages the lifecycle of individual model calls and prompt pipelines, AgentOps goes further — it governs the entire lifecycle of agents that reason, plan, invoke tools, coordinate with other agents, and evolve over time.
"AgentOps extends conventional DevOps/AIOps principles, introducing specialized stages, metrics, taxonomies, and architectures tailored to the stochastic and semantically rich behaviors of agentic AI systems."
— Wang et al., 2025
| Capability | Description |
|---|---|
| Tracing | Every agent execution is captured as a trace — a structured tree of spans representing individual units of work: LLM calls, tool invocations, retrieval steps, reasoning chains, and inter-agent messages. Built on OpenTelemetry (gen_ai.* semantic conventions), traces make the full decision path visible and reproducible. |
| Monitoring | Real-time tracking of latency, token cost, error rates, tool success rates, and session-level statistics across single and multi-agent deployments. |
| Evaluation | Multi-dimensional assessment of agent behavior: (1) Final response quality, (2) Step-level evaluation of individual tool selections, and (3) Trajectory evaluation — whether the agent followed the expected sequence of actions to reach the goal. |
| Agent-as-Judge (LLM-as-Judge) | An LLM is used as an automated evaluator to score agent outputs and trajectories against defined rubrics — covering correctness, tool usage accuracy, planning quality, parameter extraction, and reflection — scaling evaluation without constant human annotation. |
| Prompt Management | Versioning, A/B testing, and security scanning (injection detection, secret leak prevention) of prompts across agent configurations. |
| Feedback Loops | Collection of explicit (ratings, thumbs up/down) and implicit (click-through, acceptance rates) feedback, attached to execution traces to drive continuous improvement and fine-tuning. |
| Guardrails & Governance | Predefined safety constraints, fallback paths, and escalation rules that limit unsafe tool usage and keep agents aligned with business goals and compliance requirements. |
| Dimension | LLMOps | AgentOps |
|---|---|---|
| Scope | Single LLM calls, prompt pipelines | Multi-step reasoning, tool chains, agent-to-agent workflows |
| Execution | Stateless, short-lived | Stateful, long-running, self-evolving |
| Evaluation unit | Input/output quality | Final response + step correctness + trajectory |
| Observability | Token counts, latency | Traces, spans, reasoning graphs, memory state |
| Accountability | Model provider | Shared: agent owner, FM provider, tool providers |
AgentOps platforms are instrumented via OpenTelemetry, where each agent session produces a trace composed of nested spans — one per LLM call, tool invocation, retrieval operation, or reasoning step. These spans capture token usage, latency, inputs/outputs, and evaluation scores, and are exported to backends like Jaeger, Langfuse, or LangSmith. Agent-as-Judge evaluators run asynchronously over these traces to automatically assess trajectory accuracy and output quality at scale.
- Non-determinism: same input → different outputs across runs
- Autonomy risk: unintended tool selection or unsafe actions
- Complex pipelines: multi-agent orchestration with shared accountability
- Continuous evolution: agents that self-adapt through feedback and fine-tuning
- Cost visibility: token-level spend tracking across long multi-step sessions
Primary reference: Dong, Lu & Zhu — "AgentOps: Enabling Observability of LLM Agents" (CSIRO, 2024)
Also informed by: MLflow LLMOps docs, Arize AI agent observability, OpenTelemetry GenAI SIG semantic conventions, Wang et al. (2025), and industry practice as of 2026.
Stars updated daily via GitHub Actions.
| Tool | Pricing | Links |
|---|---|---|
| LangSmith | 💰 ~$39/mo | https://smith.langchain.com |
| LangWatch | 💰 €59+ | https://langwatch.ai |
| Braintrust | 💰 ~$249/mo | https://www.braintrustdata.com |
| Datadog LLM | 💰 ~$8 / 10K req | https://www.datadoghq.com |
| Helicone Cloud | 💰 ~$79+ | https://www.helicone.ai |
| Confident AI | 💰 ~$19.99 | https://www.confident-ai.com |
| HoneyHive | 💰 Custom | https://www.honeyhive.ai |
| Langfuse Cloud | 💰 ~$29 | https://langfuse.com |
| Latitude | 💰 Free trial + usage-based | https://latitude.so |
| Maxim AI | 💰 Custom | https://www.getmaxim.ai |
| Galileo | 💰 From $5K/mo (enterprise) | https://www.rungalileo.io |
| Okahu Cloud | 💰 Custom | https://www.okahu.ai |
| Fiddler AI | 💰 Custom | https://www.fiddler.ai |
| Patronus AI | 💰 Custom | https://www.patronus.ai |
| Coval | 💰 Custom | https://www.coval.dev |
| Vellum | 💰 Free (Pro: $25/mo) | https://www.vellum.ai |
| RagaAI Catalyst | 💰 Custom | https://raga.ai |
| Portkey | 💰 Free tier + usage-based | https://portkey.ai |
Coverage of the 8 core AgentOps capabilities per tool.
| Column | Description |
|---|---|
| Tracing | Captures spans/traces of agent execution (LLM calls, tool invocations, reasoning steps) |
| Monitoring | Real-time dashboards — latency, error rates, token usage, session metrics |
| Evaluation | Automated quality scoring of agent outputs and trajectories (LLM-as-Judge, metrics) |
| Prompt Mgmt | Prompt versioning, A/B testing, injection detection |
| Cost Tracking | Token-level spend tracking across providers and sessions |
| Guardrails | Input/output validation and safety constraints at runtime |
| Feedback | Collection of explicit/implicit user feedback attached to traces |
| Multi-agent | Native support for tracing/evaluating multi-agent orchestration |
| Tool | Tracing | Monitoring | Evaluation | Prompt Mgmt | Cost Tracking | Guardrails | Feedback | Multi-agent |
|---|---|---|---|---|---|---|---|---|
| LiteLLM | ✅ | ✅ | ✅ | |||||
| Langfuse | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | |
| Promptfoo | ✅ | ✅ | ✅ | |||||
| Opik (Comet) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | |
| AgentNeo | ✅ | ✅ | ✅ | ✅ | ✅ | |||
| DeepEval | ✅ | |||||||
| RAGAS | ✅ | |||||||
| Phoenix (Arize) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ||
| Evidently AI | ✅ | ✅ | ||||||
| OpenLLMetry | ✅ | ✅ | ✅ | ✅ | ||||
| Guardrails AI | ✅ | |||||||
| Helicone | ✅ | ✅ | ✅ | |||||
| AgentOps SDK | ✅ | ✅ | ✅ | ✅ | ✅ | |||
| Agenta | ✅ | ✅ | ✅ | |||||
| Laminar | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | |
| OpenLIT | ✅ | ✅ | ✅ | ✅ | ||||
| Weave (W&B) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ||
| Monocle2AI | ✅ | ✅ | ✅ | |||||
| Dunetrace | ✅ | ✅ | ✅ | |||||
| Open Bias | ✅ | ✅ | ✅ | ✅ | ||||
| KubeStellar | ✅ | ✅ | ✅ |
| Tool | Tracing | Monitoring | Evaluation | Prompt Mgmt | Cost Tracking | Guardrails | Feedback | Multi-agent |
|---|---|---|---|---|---|---|---|---|
| LangSmith | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | |
| LangWatch | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| Braintrust | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ||
| Datadog LLM | ✅ | ✅ | ✅ | ✅ | ||||
| Helicone Cloud | ✅ | ✅ | ✅ | |||||
| Confident AI | ✅ | |||||||
| HoneyHive | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | |
| Langfuse Cloud | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | |
| Latitude | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | |
| Maxim AI | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ||
| Galileo | ✅ | ✅ | ✅ | ✅ | ✅ | |||
| Okahu Cloud | ✅ | ✅ | ✅ | |||||
| Fiddler AI | ✅ | ✅ | ✅ | ✅ | ✅ | |||
| Patronus AI | ✅ | ✅ | ||||||
| Coval | ✅ | ✅ | ||||||
| Vellum | ✅ | ✅ | ✅ | ✅ | ✅ | |||
| RagaAI Catalyst | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ||
| Portkey | ✅ | ✅ | ✅ | ✅ | ✅ |
“You do not pay for infrastructure, you pay for visibility.”
Observed patterns:
- Open source tools dominate early-stage development
- SaaS tools dominate production and scale
- Evaluation is becoming the primary differentiator
Getting started (OSS, free):
- Langfuse — tracing + analytics out of the box
- Phoenix — debugging & evaluation with a local UI
- AgentOps SDK — drop-in agent tracking with minimal setup
Evaluation-focused:
- DeepEval / RAGAS — open source, 50+ metrics, great for CI pipelines
- Braintrust / LangWatch — managed, strong eval workflow tooling
- Opik — evaluation + tracing in one OSS package
- Promptfoo — red teaming & regression testing via CLI
Production & enterprise:
- LangSmith — full-stack tracing & evaluation for LangChain-heavy teams
- Datadog LLM — enterprise-grade, existing Datadog customers
- Galileo — hallucination detection + real-time guardrails
- Fiddler AI — AI governance and compliance at scale
Gateway, routing & cost control:
- LiteLLM — unified OSS gateway, 100+ providers, cost tracking
- Helicone — OSS proxy with caching and analytics
- Portkey — multi-provider routing + guardrails + observability
Guardrails & safety:
- Guardrails AI — OSS input/output validation framework
- Open Bias — OSS policy enforcement & active defense
- Galileo — real-time production guardrails
- Patronus AI — automated hallucination detection
ML monitoring & drift:
- Evidently AI — OSS data/model drift detection
- Weave (W&B) — ML experiment tracking extended to LLMs
The AgentOps ecosystem spans five layers:
| Layer | Purpose | Tools |
|---|---|---|
| Gateway & Routing | Proxy, cost control, multi-provider | LiteLLM, Helicone, Portkey |
| Tracing & Observability | Spans, traces, session replay | Langfuse, Phoenix, OpenLIT, OpenLLMetry, Laminar, AgentOps SDK, Monocle2AI |
| Evaluation | Quality scoring, regression, red-teaming | DeepEval, RAGAS, Promptfoo, Opik, Braintrust, Agenta |
| Guardrails & safety | Input/output validation, policy enforcement | Guardrails AI, Open Bias, Galileo, Patronus AI |
| Monitoring & Drift | Model drift, cost tracking, data quality | Evidently AI, Weave (W&B), Datadog LLM |
- Open source tools provide flexibility and lower cost
- SaaS tools provide scalability and operational maturity
- Evaluation capabilities are the main competitive advantage
This repository is a curated list based on research as of 2026.
Pricing, features, and adoption metrics may change over time.
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Pull requests are welcome.
The README tables are generated automatically every day from data/tools.json.
To add a new tool, open a PR that appends an entry to that file — the pipeline will pick it up and update the lists on the next scheduled run.
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{
"name": "Tool Name",
"category": "open_source",
"focus": "One-line description of what it does",
"official_url": "https://...",
"github_repo": "owner/repo",
"docs_url": "https://...",
"pricing_label": "",
"pricing_url": "",
"display_link": "https://github.com/owner/repo"
}-
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OpenClaw Monitor - Real-time monitoring dashboard for OpenClaw AI agents. Track token usage, session status, and message trends.