Best 17 AgentOps Tools: AgentNeo, Langfuse & more
AgentOps is emerging as a key discipline in IT operations, focusing on the deployment, management, and optimization of autonomous agents.
Today, I will introduce AgentOps and the leading tools shaping it as a new discipline within IT operations.
I will also outline the challenges of operating agents and explain how the conceptual AgentOps automation pipeline can address them through observability, metrics, issue detection, etc.
How to think about AgentOps
One of the hard parts of operating reliable agentic systems is making sure system behavior is observable and traceable at every step. This means tracking what inputs went into the agent, what tools it used, what outputs it generated, and why it made certain decisions.
AgentOps covers the entire lifecycle of agents, from single-step actions to complex multi-agent workflows. Unlike standard monitoring tools, which capture metrics without context, it makes visible the reasoning steps, decisions, and execution paths that agents follow.
This transparency can make it easier to debug failures and optimize costs in production.
Best 17 AgentOps tools
*For the remainder of this discussion, the term “agent” refers specifically to LLM-based agents.
Core AgentOps platforms
Agent-first tools for agent lifecycle management: session replays, tracing, monitoring, debugging, optimization.
Name | GitHub repo URL | Stars | Focus area |
|---|---|---|---|
AgentOps | 4.8k | Agents | |
Agenta | 3.1k | LLM applications | |
AgentNeo | 16.1k | Agents | |
AGIFlow | 24 | Agents | |
Agent-Panel | N/A | N/A | Agents |
Azure AI Foundry Agent Service | N/A | N/A | AgentOps (cloud-native) |
Hybrid observability + AgentOps
These tools, originally designed for LLMOps, are now expanding into AgentOps. In addition to core LLMOps features, they offer workflow tracing, evaluation, feedback, and limited agent monitoring.
Name | GitHub repo URL | Stars | Scope |
|---|---|---|---|
Arize Phoenix | 6.9k | LLM applications | |
Dify | 113k | LLM applications | |
Langfuse | 15.7k | LLM applications | |
LangSmith | 630 | LLM applications | |
LangTrace AI | 1k | LLM applications | |
Lunary | 1.4k | LLM applications | |
Trulens | 2.7k | LLM applications | |
Helicone | 4.4k | LLM applications | |
PortKey | 9.3k | LLM applications | |
Laminar | 2.3k | LLM applications |
Adapted from1
Most of the tools listed above are open source and available on GitHub. A few exceptions exist, such as Azure AI Foundry Agent Service, Agent-Panel, and the LangSmith platform, which are commercial or cloud-native services.
For more on agent observability, see: agentic monitoring.
Core AgentOps features
Here are applications of core features listed in our table (above):
Data integration
Tools with data integration are central to AgentOps. They connect to codebases, company documents, system logs, and performance metrics to give a complete view of the IT environment.
Customization
Extend agent capabilities by adding toolkits, connecting to multiple knowledge bases, or integrating fine-tuned models for specific business needs.
Prompt management
The prompt management feature in agentops tools allows you to efficiently manage, retrieve, and use prompts in your projects. With tools offering prompt management, developers can compare prompts across models, run A/B tests, and monitor for issues such as prompt injection or secret leaks.
Here’s a real-world example of prompt management with library details using RagaAI-Catalyst.3
Evaluation
Evaluation tools go beyond simply checking final outputs by validating the entire reasoning process. They support benchmarking agent performance, evaluating individual steps, and analyzing the agent’s overall decision path.
With these tools, teams can create and manage detailed metric evaluations for RAG applications, tracking performance at every stage of the execution process.
Feedback
AgentOps tools that provide feedback enable teams to capture both explicit signals (ratings, likes, dislikes, comments) and implicit signals (time spent, clicks, acceptance or rejection).
Monitoring
AgentOps tools with monitoring capabilities give teams real-time visibility into agent performance. They track critical metrics such as latency, cost, and error rates.
The dashboard will display LLM events for each message sent by each agent, including those made by the human user:
Tracing
Tracing capabilities provide deep visibility into AI agent systems by capturing the full flow of execution. This allows teams to track critical aspects of agent behavior, including:
- LLM interactions and token usage
- Tool utilization and execution patterns
- Network activities and API calls
- User interactions and feedback
- Agent decision-making processes
In another example, you can view your run in real time at app.agentops.ai. The AgentOps dashboard displays details such as agents interacting with one another, every use of the calculator tool, and each OpenAI call for LLM processing:
Guardrails
Guardrails in AgentOps set rules and safety checks to prevent harmful or unintended actions. They enforce compliance, protect sensitive data, and provide fallback paths when risks arise, ensuring agents remain secure and reliable.
Challenges of operating agents
LLM-based agents (sometimes called agentic systems) are no longer just prototypes and are being deployed in customer support, software engineering, trading, and other business-critical domains.
Unlike traditional software, agents act with a high degree of autonomy, interact with external tools, and adapt over time.
This introduces new operational challenges that existing Ops frameworks (DevOps, MLOps, SecOps) only partially address:
- Complex artifacts and pipelines: Agents are compound systems made up of multiple components, such as context managers, planning modules, and external tools.
- These systems generate both static artifacts (e.g., workflows and goals) and runtime outputs (e.g., plans and decisions).
- Managing these evolving pipelines requires visibility across many moving parts.
- High autonomy: Agents interact dynamically with external environments, shifting contexts, and third-party tools. Since these interactions are not always predefined, there is a risk of unintended behaviors, such as selecting an insecure external API.
- Unbounded API consumption: Because agents rely heavily on external APIs, usage can quickly spiral.
- For example, a lead-generation agent scraping LinkedIn and repeatedly calling enrichment APIs. If left unchecked, this could rack up thousands of dollars in API fees in a single day.
- Non-deterministic behavior: Because LLMs are probabilistic, agents may produce different outputs even with identical inputs.
- For example, a sales agent that adjusts its outreach messages based on response rates. This adaptability makes versioning and reproducibility difficult, since two runs of the “same” agent may yield very different results.
- Continuous evolution: Agents often adapt over time in response to user feedback or runtime performance. While this adaptability can improve functionality, it also makes it harder to ensure alignment with intended quality standards throughout the agent’s lifecycle.
- Shared accountability: Responsibility for an agent’s actions is spread across several parties: the agent’s owner, the LLM provider, and external tool vendors.
- Because many stakeholders are involved, it can be challenging to pinpoint the origin of a failure or determine who should be held accountable when something goes wrong.
To address the challenges faced by developers, testers, operators, business users and put AgentOps in context, we can dive into a conceptual AI AgentOps Automation Pipeline. This six-stage process spans from capturing raw behavior to enabling self-healing:
Bridging the AI agent gap: AgentOps automation pipeline (Conceptual)
The AgentOps automation pipeline is a continuous loop that keeps agents observable, reliable, and adaptable in production. It works through six interconnected stages:
- Observe behavior: AgentOps monitors real-time agent actions, including LLM calls, tool usage, DB queries, and inter-agent communication, visualized as task graphs and execution paths.
- Collect metrics: Raw data is turned into metrics, tracking usage, task success, performance, and quality to provide insights into costs, compliance, etc.
- Detect issues: AgentOps flags failures, categorizes errors like timeouts or guardrail violations, and triggers alerts before escalation.
- Identify root cause: It links issues to causes, such as ambiguous prompts or coordination failures, with tools to trace workflows and answer queries like “Why did this fail?
- Optimize recommendations: Based on root cause, AgentOps suggests fixes like refining prompts, restructuring workflows, or choosing better tools.
- Automate operations: The system applies fixes automatically, adjusting prompts or workflows and making agents self-healing without redeployment.
The evolution of Ops landscape
Pre-2010s: Dedicated Ops teams managed infrastructure in silos, leading to slow response times, communication breakdowns, and limited visibility across systems.
Late 2000s: Popularized by companies like Amazon, DevOps emerged to combine development and operations, enabling faster and more reliable releases through practices such as CI/CD, Infrastructure as Code, and automation.
2016–2024: AIOps was introduced to bring AI into IT operations, offering automated anomaly detection, predictive analytics, and root cause analysis assistance. Despite its strengths, AIOps still required significant human intervention for complex incidents.
Now: AgentOps, driven by the rise of generative AI and autonomous agents, is being shaped by companies such as Anthropic, OpenAI, and emerging startups.
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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