15 AI Agent Observability Tools: AgentOps, Langfuse & Arize

Observability tools for AI agents, such as Langfuse and Arize, help gather detailed traces (a record of a program or transaction’s execution) and provide dashboards to track metrics in real time. 

Many agent frameworks, like LangChain, use the OpenTelemetry standard to share metadata with observability tools. On top of that, many observability tools provide custom instrumentation for greater flexibility.

We tested 15 observability platforms for LLM applications and AI agents. Each platform was implemented hands-on through setting up workflows, configuring integrations, and running test scenarios. We also demonstrated a LangChain observability tutorial using Langfuse.

AI agent observability platforms

Tier 1: Fine-grained LLM & prompt / output observability

* The capabilities listed in these columns are illustrative examples of what each tool can monitor when extended through integrations or customization. These are not exclusive to a single platform.

Tier 2: Workflow, model & evaluation observability

Tier 3: Agent lifecycle & operations observability

Tier 4: System & infrastructure monitoring (not agent-native)

Datadog (with its LLM Observability module) and Prometheus (via exporters) are increasingly used alongside Langfuse/LangSmith.

Agent development & orchestration platforms:

• Tools like Flowise, Langflow, SuperAGI, and CrewAI enable building, orchestrating, and optimizing agent workflows with no-code/low-code interfaces

Deployment free editions & pricing

The free editions vary by usage limits (e.g., observations, traces, tokens, or units of work). Starting prices are typically for a basic plan, which may have restrictions on features, users, or usage limits.

Weights & Biases (W&B Weave)

Use cases: Debugging multi-agent failures by tracing exactly where errors originate and how they propagate through the agent chain.

Figure 1: Traces dashboard from Weights & Biases Weave.

Weights & Biases Weave is a monitoring and evaluation platform explicitly built for multi-agent LLM systems in production. It addresses the unique challenges of tracking complex agent workflows in which multiple LLMs interact, make decisions, and pass data to one another.

Unlike traditional monitoring tools, Weave understands the hierarchical nature of agent calls, captures the full context of agent interactions, and provides visibility into both individual agent behavior and overall system performance. This enables teams to debug failures, optimize costs, and improve agent quality at scale.

Core monitoring features

• Individual agent performance: Track each agent’s call frequency, success rates, and identify which agents create bottlenecks in your pipeline.
• Input/output tracking: Monitor data flow through the agent chain, viewing inputs, outputs, and transformations across multiple agents.
• Cost tracking: Real-time cost monitoring per trace, identifying expensive agents, and tracking token usage distribution.
• Latency monitoring: Measure execution time per agent, end-to-end latency, and percentile metrics like p95 and p99.
• Success/failure status: Track successful and failed calls, enable error tracking, and perform root cause analysis.
• Time-series analysis: Visualize performance changes over time through cost trends, latency patterns, and throughput metrics.

Weave also provides built-in scorers for evaluation, including:

• HallucinationFreeScorer for detecting hallucinations,
• SummarizationScorer for evaluating summary quality,
• EmbeddingSimilarityScorer for semantic similarity,
• ValidJSONScorer and ValidXMLScorer for format validation,
• PydanticScorer for schema compliance,
• OpenAIModerationScorer for content safety,
• RAGAS scorers like ContextEntityRecallScorer,
• ContextRelevancyScorer for RAG system evaluation.

Langfuse

Use cases: Track LLM interactions, manage prompt versions, and monitor model performance with user sessions.

Figure 2: Langfuse dashboard example showing trace details.¹

Langfuse offers deep visibility into the prompt layer, capturing prompts, responses, costs, and execution traces to help debug, monitor, and optimize LLM applications.

However, Langfuse may not be suitable for teams that prefer Git-based workflows for code and prompt management, as its external prompt management system may not offer the same level of version control and collaboration.

Core monitoring features:

• Sessions: Track individual sessions for specific interactions with the model.
• Users: Monitor and link interactions with user-specific data.
• Environments: Observe and track interactions based on different environments (e.g., development, production).
• Tags: Organize traces using custom labels to improve filtering and tracking.
• Metadata: Capture additional context for traces to enrich data.
• Trace IDs: Unique identifiers for each trace, ensuring accurate tracking and debugging.

Enterprise-grade features:

• Log levels: Adjust the verbosity of logs for more granular insights.
• Multi-modality: Supports text, images, audio, and other formats for multi-modal LLM applications.
• Releases & versioning: Track version history and see how new releases affect the model’s performance.
• Trace URLs: Access detailed traces via unique URLs for further inspection and debugging.
• Agent graphs: Visualize agent interactions and dependencies for a better understanding of agent behavior.
• Sampling: Collect representative data from interactions to analyze without overwhelming the system.
• Token & cost tracking: Track token usage and costs for each model call, ensuring efficient resource management.
• Masking: Protect sensitive data by masking it in traces, ensuring privacy and compliance

Galileo

Use cases: Monitor cost/latency, evaluate output quality, block unsafe responses, and provide actionable fixes

Figure 3: Graphs showing tool selection quality, context adherence, agent action compilation, and time to first token.

Galileo monitors standard metrics such as cost, latency, and performance while simultaneously enforcing safety checks to block harmful or non-compliant responses in real time.

It goes beyond surface-level monitoring by identifying specific failure modes (e.g., hallucination leading to incorrect tool inputs), tracing root causes across workflows, and recommending concrete improvements (e.g., adding a few-shot example to demonstrate correct tool input).

The platform combines traditional observability (latency, cost, performance) with AI-powered debugging and evaluation (hallucination detection, factual correctness, coherence, context adherence), offering actionable insights to improve agent and LLM behavior.

Guardrails AI

Use cases: Prevent harmful outputs, validate LLM responses, and ensure compliance with safety policies

Figure 4: Guard behavior dashboard showing the differences in guard run duration and guard failures.

Guardrails AI enforces safety and compliance by validating every LLM interaction through configurable input and output validators. It can measure toxicity scores, detect bias patterns, identify PII exposure, and flag hallucinations.

Input guards protect against prompt injection attacks and jailbreaking attempts, while output guards validate response quality and enforce policy adherence.

The platform supports the RAIL specification for defining custom validation rules and automatically retries generation when violations occur. This ensures LLMs and agents produce outputs that are safe, compliant, and aligned with organizational standards.

LangSmith

Use cases: Debugging the reasoning chain for the agent making incorrect tool calls

Figure 5: LangSmith dashboard showing traces, including their names, inputs, start times, and latencies.

LangSmith is a strong platform for debugging. It is natively integrated with LangChain, so if you are building with LangChain, you can send traces automatically with minimal setup.

You can step through the agent’s decision path to pinpoint where reasoning diverged: see the prompt/template used, retrieved context, tool selection logic, input parameters sent to tools, the results returned, and any errors/exceptions.

Built-in metrics expose token consumption, latency, and cost per step, and prompt/version history helps identify templates that correlate with poor decisions.

You can replay and compare runs (e.g., alternate prompts/models/tools) and attach evaluators to flag failure modes such as incorrect tool invocation, missing context, or brittle prompts.

Integrates natively via LangChain callbacks (minimal code), with options to export traces (e.g., OpenTelemetry) into your broader observability stack.

Langtrace AI 

Use cases: Identifying cost and latency bottlenecks in LLM apps

Figure 6: Langtrace AI trace dashboard.

Langtrace provides granular tracing for LLM pipelines to uncover performance bottlenecks and cost inefficiencies.

It tracks input/output token counts, execution duration, and API costs for each model call, surfacing the specific steps that drive up latency or spend.

The trace view captures request attributes (model parameters, prompt content) and events (logs, errors, execution steps) across the workflow, offering clear visibility into where failures or inefficiencies occur.

This enables pinpointing bottlenecks in prompts, tool calls, or model selection and making targeted optimizations to reduce latency and control costs. It includes prompt lifecycle features such as version control and a playground for testing prompt variants.

It focuses on workflow and pipeline-level tracing, giving you visibility into prompts, model calls, and agent steps. Traces follow OpenTelemetry standards, allowing them to flow into existing backends and be explored in Langtrace’s dashboard.

Arize (Phoenix)

Use cases: Monitor model drift, detect bias, and evaluate LLM outputs with comprehensive scoring systems

Figure 7: Arize Phoenix drift monitor dashboard.

Arize Phoenix specializes in LLM and model observability with strong evaluation tooling, including:

• Drift detection for tracking behavioral changes over time
• Bias checks for identifying response biases,
• LLM-as-a-judge scoring for accuracy, toxicity, and relevance.

It also provides an interactive prompt playground for testing and comparing prompts during development.

The open-source package can be self-hosted and integrates with Arize’s cloud platform for enterprise features.

However, it has higher integration overhead compared to lightweight proxies and does not manage prompt versioning as cleanly as dedicated tools, making it better suited for evaluation and monitoring rather than prompt lifecycle management.

Agenta

Use cases: Finding which prompt works best on which model

Figure 8: Image showing various prompt alternatives from Agenta.

Agenta enables teams to input specific context (such as customer service policies or FAQ content) and test how different models respond to the same queries.

Figure 9: Output example from Agenta.

It supports side-by-side comparisons of models across response speed, API costs, and output quality, helping determine which model is the best fit for a given use case before production deployment.

AgentOps.ai

Use cases: Monitor agent reasoning, track costs, and debug sessions in production

Figure 10: Session replay dashboard example from AgentOps.ai.

AgentOps.ai provides observability for agents. It captures reasoning traces, tool/API calls, session state, and caching behavior, while tracking metrics like token usage, latency, and cost per interaction.

Best for understanding and optimizing agent behavior in production.

Braintrust

Use cases: Finding which prompt, dataset, or model performs better with detailed evaluation and error analysis

Figure 11: Customer support agent dashboard from Braintrust.

Braintrust allows you to create test datasets with inputs and expected outputs, then compare prompts or models side by side using variables like {{input}}, {{expected}}, and {{metadata}}.

After connecting your model APIs and running tests, you can monitor rich performance metrics on the Monitor page, including latency, spans, total cost, token count, time to first token, tool error rate, and tool execution duration.

This enables precise evaluation of model behavior and helps identify the most effective prompt, dataset, or model configuration for production use.

AgentNeo

Use cases: Debugging multi-agent interactions, tracing tool usage, and evaluating coordination workflows

AgentNeo is an open-source Python SDK built for monitoring multi-agent systems.

It tracks how agents communicate, which tools they invoke, and visualizes the entire conversation flow through execution graphs.

Key metrics include token consumption per agent, execution duration, cost per interaction, and tool usage patterns.

Integration is straightforward using decorators (e.g., @tracer.trace_agent, @tracer.trace_tool), and the SDK provides an interactive local dashboard (localhost:3000) for real-time monitoring of multi-agent workflows.

Laminar

Best use case: Track performance across different LLM frameworks and models.

Laminar dashboard shows your agent execution with detailed metrics, including duration, cost, and token usage.

You can track trace status, latency percentiles, model-level cost breakdowns, and token consumption patterns over time through interactive charts and filtering options.

You can drill down into detailed execution breakdowns showing individual task latencies, costs, and performance metrics for each component in your LLM workflow.

Each span reveals duration, input/output data, and request parameters, enabling precise bottleneck identification and debugging across your entire application stack.

Figure 12: Traces dashboard example from Laminar.

Helicone

Use cases: Track multi-step agent workflows and analyze user session patterns.

Helicone provides observability for LLM agents through two main views. The Dashboard highlights high-level metrics, including total requests, costs, error rates by type, top model usage, geographical distribution, and latency trends.

Figure 12: Image showing 3 months of changes in requests, costs, errors, and latency.

The Sessions view reveals detailed agent workflow execution, showing how agents process requests through multi-step API calls with traces, success rates, and session durations. This allows teams to track complete user journeys from initial prompt to final output, monitor each tool interaction, and identify bottlenecks in complex workflows.

Coval

Use cases: Simulate thousands of agent conversations, test voice/chat interactions, and validate behavior before deployment.

Figure 13: Coval’s evaluation dashboard showing the percentages of achieved goals, verified identity, correct repetition, agent clarity, and incorrect information.

Coval automates agent testing through large-scale conversation simulations.

It measures success rates, response accuracy, task completion rates, and tool-call effectiveness across varied scenarios. The platform can simulate thousands of conversations from minimal test cases.

Supports both voice and text interactions, and provides audio replay for voice agents. With built-in CI/CD integration

Coval also enables automatic regression detection when agent behavior changes, helping teams validate and ship reliable agents more quickly.

Datadog

Use cases: Monitor the entire infrastructure stack, track application performance, and correlate system-wide metrics for extensive observability.

Datadog provides observability across infrastructure, applications, and AI workloads.

It monitors CPU, memory, network metrics, along with application response times, error rates, and throughput.

For LLM applications, Datadog tracks token usage, cost per request, model latency, and prompt injection attempts.

The platform also offers 900+ integrations, which help organizations correlate AI performance with underlying infrastructure health.

Prometheus

Use cases: Monitor system performance, track application metrics, and set up alerting for infrastructure issues.

Prometheus is an open-source monitoring system that collects time-series metrics from HTTP endpoints (/metrics) at defined intervals. It tracks system metrics (CPU, memory, disk, network), application metrics (request rates, error rates, response times), database performance, container metrics, and custom business metrics.

With the PromQL query language, teams can analyze data, build dashboards, and configure alerts. Monitoring capabilities are extended through exporters (e.g., Node Exporter) that support a wide range of systems and services.

Grafana

Use cases: Visualize metrics, build dashboards, and route alerts across LLM, agent, and infrastructure data.

Figure 14: Traces dashboard showing the change in request rate, total usage tokens, average usage cost, and total usage cost.

Grafana is an open-source visualization and analytics platform that connects to backends such as Prometheus, OpenTelemetry, and Datadog.

It provides unified dashboards for monitoring LLM, agent, and infrastructure metrics, enabling teams to correlate signals across different systems.

Grafana also supports alert routing and notifications, making it a central hub for observability visualization and incident response.

Tutorial: LangChain observability with Langfuse

We built a multi-step LangChain pipeline with three stages:

1. question analysis
2. answer generation
3. answer verification

After setting up the pipeline, we connected it to Langfuse to monitor and track the execution in real-time. By doing this, we were able to explore how Langfuse helps us gather detailed insights into AI application performance, costs, and behavior.

Here’s what we observed through Langfuse:

Dashboard overview

Figure 15: Langfuse’s cost, usage management, and latency dashboards.

Langfuse provided us with several dashboards that give us visibility into different aspects of the pipeline’s performance:

1. Cost Dashboard: This tracks the spending across all API calls, with detailed breakdowns per model and time period.
2. Usage Management: It monitors execution metrics, such as observation counts and resource allocation, helping us track how resources are used during execution.
3. Latency Dashboard: This dashboard helped us analyze response times, detect bottlenecks, and visualize performance trends.

Usage metrics

Figure 16: Image showing Langfuse’s usage metrics, including total trace count, total observation count, and total score count (both numeric and categorical).

The usage metrics dashboard gave us the following insights into how the system performed:

• Total trace count: We tracked eight traces, each representing a full question-answer cycle in the pipeline.
• Total observation count: On average, each trace had 16 observations, reflecting the multi-step nature of the process.

On top of that, Langfuse enables us to track usage patterns, resource allocation, and peak times over the last 7 days, helping us understand when the system is most active and how resources are distributed across time.

Trace inspection

Figure 17: Langfuse’s traces dashboard showing input, output, observability levels, latency, and tokens.

When drilling into an individual trace, we were able to see detailed execution information:

• Trace rows: Each row represents one complete pipeline execution with a unique trace ID.
• Latency metrics: The execution time varied, ranging from 0.00s to 34.08s.
• Token counts: The dashboard tracked input/output token usage, which helps in cost and efficiency management.
• Environment filtering: We could filter traces based on deployment environments (e.g., development, production).

Individual trace details

Figure 18: Langfuse’s sequential chain architecture.

We further explored the trace in more detail to understand the execution breakdown:

• Sequential chain architecture: The trace displayed a visual flow showing each step, starting from SequentialChain → LLMChain → ChatOpenAI, with clear hierarchical structure.
• Input/output tracking: The original question, “What are the benefits of using Langfuse for AI agent observability?” was tracked at each stage, along with the respective outputs produced by the AI at each step.
• Token analysis: We observed that 1,203 tokens were used for input and 1,516 tokens for output, which has cost implications related to token usage and helps optimize resource management.
• Timing data: The total latency for the full trace was 34.08s, broken down across each component:
  • SequentialChain → 14.02s
  • LLMChain → 10.25s
  • ChatOpenAI → 9.81s
• Model information: Langfuse confirmed the usage of the Anthropic Claude-Sonnet-4 model, with details on the specific settings, including temperature configuration.
• Formatted output: Both Preview and JSON views were provided for debugging, giving insights into the model’s response in human-readable form and machine-readable format.

Automated analysis

Figure 19: Langfuse automated evaluations example.

Langfuse also provided automated evaluations of our responses:

• Quality assessment: The system evaluated the structure, coherence, and completeness of the responses, highlighting well-organized sections but suggesting the responses could be more concise.
• Improvement suggestions: It identified sections with redundancy, suggesting where phrasing could be improved, and combined related points to make the response more transparent and more efficient.
• Performance insights: The system gave feedback on token usage and response relevance, helping us optimize efficiency while ensuring the output remains helpful and on-topic.
• Structured feedback: The feedback was organized into clear categories, allowing us to address specific areas for improvement in a targeted manner.

User analytics

Figure 20: The image shows anonymized user activity, showing each user’s first and last interactions, event volumes, token consumption, and associated costs to help analyze engagement, resource usage, and budget allocation.

Langfuse tracks detailed interactions between users and the AI agent:

• User activity timeline: Displays the first and last interaction for each user, helping identify active versus dormant users. We can see when users engaged with the system for the first and last time.
• Event volume tracking: Tracks the number of events each user triggered. For example, some users generated over 2,000 events, showing their level of engagement with the system.
• Token consumption analysis: Monitors the total number of tokens consumed by each user. Token usage ranged from 6.59K to 357K tokens, providing insights into resource usage.
• Cost attribution: Breaks down the costs associated with each user, making it easier to track spending and optimize budget allocation for resource use.
• User identification: Uses anonymized user IDs to maintain privacy while tracking individual user interactions, helping with usage analysis without compromising user confidentiality.

Figure 21: An example of the session view, showing the whole conversation flow alongside the executed Python code, correlating user inputs with system outputs and displaying session metadata to give a complete picture of how the interaction was processed.

The session view allows us to track granular details of user interactions:

• Complete conversation flow: Shows the full question-answer interaction, making it easy to follow the entire conversation from start to finish.
• Implementation visibility: Displays the actual Python code used during the session, providing insight into the technical implementation.
• Input/output correlation: Links user questions to the corresponding system responses, helping us troubleshoot and identify where issues may have occurred in the conversation.
• Session metadata: Includes technical details such as timing, user context, and specific implementation data, offering a comprehensive view of the session’s execution.

When not to use observability tools

• Early-stage development: If you’re still validating product-market fit or building out your first agent workflows, the focus should be on core functionality rather than on extensive observability.
  
• API bottlenecks: If your primary issues are API costs, latency, or caching, the immediate priority should be optimizing these areas, not tracking system-level metrics.
  
• Model optimization: If improvements are mainly driven by model selection, fine-tuning, or prompt engineering, observability tools for drift and bias may not yet be necessary.

When to use observability tools

• Production at scale: When you’re operating across multiple models, agents, or chains, observability tools are essential for monitoring performance and ensuring system health.
• Enterprise or customer-facing applications: For applications where reliability, safety, and compliance are non-negotiable, observability tools provide the visibility and control needed.
• Continuous monitoring: When you need to monitor drift, bias, performance, and safety issues over time, which cannot be easily captured with basic scripts or manual checks, observability tools are crucial.
• High-risk scenarios: In environments where the cost of failure (e.g., hallucinations, unsafe outputs) is significant, observability ensures risks are minimized and issues are detected early.

FAQ

Reference Links

1.

Model Usage & Cost Tracking for LLM applications (open source) - Langfuse

Principal Analyst

Cem Dilmegani

Principal Analyst

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Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 55% of Fortune 500 every month.

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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