Top 15 AI Agent Observability Tools: Langfuse, Arize & More
Observability tools for AI agents, like Langfuse and Arize, help gather detailed traces (a record of the processing of a program or transaction) 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 build custom instrumentations for added flexibility.
We tested 15 observability platforms for LLM applications and AI agents. Each platform was implemented hands-on through setting up workflows, configuring integrations, 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 this 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.
Langfuse
Use cases: Track LLM interactions, manage prompt versions, and monitor model performance with user sessions
Source: Langfuse1
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 managing code and prompts, as its external prompt management system may not offer the same level of version control and collaboration.
What does Langfuse offer?
Note that observability is the broader concept of understanding what is happening under the hood of your LLM application. Traces are the Langfuse objects used to achieve deep observability.
Core 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
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 (for example, hallucination leading to incorrect tool inputs), tracing root causes across workflows, and recommending concrete improvements (for example, adding few-shot examples 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
Guardrails AI enforces safety and compliance through configurable input and output validators that check every LLM interaction. 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 can automatically retry generations when violations occur. This ensures LLMs and agents produce outputs that are safe, compliant, and aligned with organizational standards.
LangSmith
Best use case: Debugging the reasoning chain for agent making incorrect tool calls
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
Best use case: Identifying cost and latency bottlenecks in LLM apps
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 to pinpoint bottlenecks in prompts, tool calls, or model selection and make targeted optimizations to reduce latency and control costs. It includes prompt lifecycle features, including 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 while also being explored in Langtrace’s dashboard.
Arize (Phoenix)
Use cases: Monitor model drift, detect bias, and evaluate LLM outputs with comprehensive scoring systems
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
Best use case: Finding which prompt works best on which model
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.
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
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.
Besr for understanding and optimizing agent behavior in production.
You can debug why an agent chose a specific action, compare session outcomes across users, and monitor performance trends. An interactive dashboard supports real-time monitoring, making
Braintrust
Best use case: Finding which prompt, dataset, or model performs better with detailed evaluation and error analysis
Braintrust allows you to create test datasets with inputs and expected outputs, then compare prompts or models side by side using variables like
,, and.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.,
,), and the SDK provides an interactive local dashboard () 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, cost breakdowns by model, 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.
Helicone
Best case: 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 such as total requests, costs, error rates by type, top model usage, geographical distribution, and latency trends.
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 sases: Simulate thousands of agent conversations, test voice/chat interactions, and validate behavior before deployment
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 supports offers 900+ integrations this integrations help organizations to 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 (
) 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
Best use case: Visualize metrics, build dashboards, and route alerts across LLM, agent, and infrastructure data.
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:
- question analysis
- answer generation
- 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
Langfuse provided us with several dashboards that give us visibility into different aspects of the pipeline’s performance:
- Cost Dashboard: This tracks the spending across all API calls, with detailed breakdowns per model and time period.
- Usage Management: It monitors execution metrics, such as observation counts and resource allocation, helping us track how resources are used during execution.
- Latency Dashboard: This dashboard helped us analyze response times, detect bottlenecks, and visualize performance trends.
Usage metrics
The usage metrics dashboard gave us the following insights into how the system performed:
- Total trace count: We tracked 8 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
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
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
Langfuse also provided automated evaluations of our responses:
- Quality assessment: The system evaluated the structure, coherence, and completeness of the responses, highlighting areas that were well-organized, but suggesting that the response 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 clearer and more efficient.
- Performance insights: The system gave feedback on token usage and response relevance, helping us optimize efficiency while ensuring the output remains useful 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
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.
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 at scale with multiple models, agents, or chains in production, 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 necessary visibility and control.
- 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
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.
Be the first to comment
Your email address will not be published. All fields are required.