15 AI Agent Observability Tools in 2026: AgentOps & Langfuse
AI agent observability tools, 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 agentic monitoring. 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 benchmarked 4 observability tools to measure whether they introduce overhead in production pipelines. We also demonstrated a LangChain observability tutorial using Langfuse.
Agentic monitoring tools overhead benchmark
We integrated each observability platform into our multi-agent travel planning system and ran 100 identical queries to measure their performance overhead compared to a baseline without instrumentation. Read our benchmark methodology.
- LangSmith demonstrated exceptional efficiency with virtually no measurable overhead, making it ideal for performance-critical production environments.
- Laminar introduced minimal overhead at 5%, making it highly suitable for production environments where performance is critical.
- AgentOps and Langfuse showed moderate overhead at 12% and 15% respectively, representing a reasonable trade-off between observability features and performance impact. These platforms still maintain acceptable latency for most production use cases.
Potential reasons behind performance differences
Our benchmark indicates that latency differences are driven by instrumentation depth and execution-path involvement, particularly in multi-agent workflows. Tools offering deeper, step-level observability exhibited higher overhead, while lighter tracing approaches remained closer to the baseline.
1. Instrumentation depth on the execution path
Observability tools add logic to the agent’s execution flow to capture traces and metadata. When this logic runs synchronously during request handling, it directly increases end-to-end latency because the agent must complete this extra work before returning a response.
For example:
- LangSmith added virtually no measurable overhead (~0%), indicating little synchronous work,
- Langfuse’s deeper step-level instrumentation contributed to a higher overhead (~15%).
2. Event amplification across multi-step pipelines
In multi-agent systems, a single user request triggers multiple agent actions. When a tool records detailed data at every step, the total number of events grows quickly, increasing processing and trace-handling overhead as the workflow becomes deeper.
In the benchmark results:
- Langfuse and AgentOps generated noticeably higher overhead (15% and 12%) in our multi-step travel planning workflow
- LangSmith and Laminar emitted fewer events per agent step.
3. Inline evaluation and validation overhead
Some platforms perform additional checks or monitoring while the agent is running. Although each check is lightweight, applying them repeatedly across all agent steps adds measurable latency.
For instance:
- AgentOps’ lifecycle-level monitoring coincided with a 12% overhead
- Laminar showed no evidence of inline evaluation affecting execution, remained at ~5%.
4. Serialization and persistence frequency
Capturing detailed observability data requires serializing traces and writing them to storage or external backends. Higher trace detail increases how often this happens, adding I/O overhead to each request.
In our benchmark:
- Langfuse’s detailed prompt, output, and token tracing resulted in the highest overhead (~15%)
- LangSmith’s lighter trace artifacts remained close to baseline.
5. Integration tightness with the agent framework
How closely a tool integrates with the agent framework affects performance. Tighter integrations reduce translation and orchestration steps, while more generic SDKs add extra processing layers.
For example:
- LangSmith’s tight alignment with agent execution correlated with ~0% overhead
- AgentOps and Langfuse showed higher latency impact, consistent with more decoupled integration paths.
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 case: Debugging failures in multi-agent systems by tracing how errors propagate across agent calls.
Figure 1: Traces dashboard from Weights & Biases Weave.
Weights & Biases Weave records structured execution traces for multi-agent systems, preserving parent–child relationships between agent calls. Inputs, outputs, intermediate states, latency, and token usage are captured per agent and per trace.
Weave monitoring features
- Hierarchical agent tracing rather than flat request logs
- Cost and latency attribution at the agent level
- Native support for evaluation scorers applied directly to traces.
Evaluation capabilities
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.
Best suited for: Teams running multi-step or multi-agent workflows who need trace-level root cause analysis rather than surface metrics.
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.
Langfuse monitoring features
- Visibility into prompt evolution and usage patterns
- Session-based analysis suitable for user-facing applications
- Practical metadata and tagging model for filtering and review
Enterprise-grade features:
Some of these features include:
- 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.
Best suited for: Teams iterating on prompts and monitoring usage in production, especially where user sessions matter.
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 tracks cost, latency, and output quality metrics while applying real-time safety and compliance checks.
The platform combines traditional observability (latency, cost, performance) with AI-powered debugging and evaluation (hallucination detection, factual correctness, coherence, context adherence).
Galileo monitoring features
- Failure mode identification beyond surface errors (e.g., hallucinations leading to invalid tool inputs)
- Prescriptive feedback such as suggested prompt changes or few-shot additions
- Tight coupling between evaluation results and recommended fixes.
Best suited for: Organizations prioritizing output quality, safety, and fast iteration cycles with guided remediation.
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 validates LLM inputs and outputs against configurable rules, including toxicity, bias, PII exposure, flag hallucinations, and format compliance.
Guardrails AI monitoring features
- Deterministic validation via RAIL specifications
- Input guards for prompt injection and jailbreak detection
- Automatic retries when validation fails.
Best suited for
Teams that must enforce strict safety, compliance, or formatting guarantees before responses are returned.
LangSmith
Use cases: Agent reasoning and tool-call debugging (LangChain-centric)
Figure 5: LangSmith dashboard showing traces, including their names, inputs, start times, and latencies.
LangSmith captures full reasoning traces for LangChain-based agents, including prompts, retrieved context, tool selection logic, tool inputs/outputs, errors, and exceptions.
LangSmith monitoring features
- Step-by-step inspection of agent decision paths
- Run replay and side-by-side comparison across prompts, models, or tools
- Tight integration with LangChain via callbacks.
Best suited for
Teams building with LangChain who need to debug incorrect reasoning or tool invocation in detail.
Langtrace AI
Use cases: Identifying cost and latency bottlenecks in LLM apps
Figure 6: Langtrace AI trace dashboard.
Langtrace tracks token counts, execution duration, API costs, and request parameters across LLM pipelines using OpenTelemetry-compatible traces.
Langtrace AI monitoring features
- OpenTelemetry alignment for integration with existing backends
- Visibility into cost and latency drivers per step
- Lightweight prompt versioning and testing playground.
Best suited for: Teams optimizing performance and spend across LLM workflows rather than evaluating output quality.
Arize (Phoenix)
Use cases: Monitor model drift, detect bias, and evaluate LLM outputs with comprehensive scoring systems
Figure 7: Arize Phoenix drift monitor dashboard.
Phoenix focuses on behavioral drift, bias detection, and LLM-as-a-judge scoring for relevance, toxicity, and accuracy.
However, it has higher integration overhead compared to lightweight proxies and does not manage prompt versioning as cleanly as dedicated tools.
Phoenix monitoring features
- Open-source core with optional enterprise extensions
- Interactive prompt playground for development
- Drift detection for tracking behavioral changes by time
- Bias checks for identifying response biases,
- LLM-as-a-judge scoring for accuracy, toxicity, and relevance.
Best suited for: Teams monitoring long-term model behavior and regression risk rather than prompt iteration.
Agenta
Use cases: Finding which prompt works best on which model
Figure 8: Image showing various prompt alternatives from Agenta.
Agenta compares model responses across cost, latency, and output quality using shared inputs and controlled context.
Figure 9: Output example from Agenta.
Agenta monitoring features
- Side-by-side model evaluation
- Pre-production decision support.
Best suited for: Early-stage evaluation and model selection.
AgentOps.ai
Use cases: Monitor agent reasoning, track costs, and debug sessions in production
Figure 10: Session replay dashboard example from AgentOps.ai.
AgentOps captures reasoning traces, tool/API calls, session state, caching behavior, and cost metrics for deployed agents.
AgentOps monitoring features
- Session replay for production debugging
- Focus on live agent behavior rather than offline evaluation.
Best suited for: Teams running agents in production who need operational visibility.
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 evaluates prompts, datasets, and models against expected outputs, tracking latency, cost, tool errors, and execution metrics.
Braintrust monitoring features
- Evaluate test datasets with inputs and expected outputs, then compare prompts or models side by side using variables like
{{input}},{{expected}}, and{{metadata}}. - Metric breakdowns including tool execution quality
Best suited for: Teams benchmarking models and prompts prior to rollout.
AgentNeo
Use cases: Debugging multi-agent interactions, tracing tool usage, and evaluating coordination workflows
AgentNeo tracks agent communication, tool usage, execution graphs, and per-agent cost and latency via a Python SDK.
AgentNeo monitoring features
- Open-source and locally runnable
- Interactive local dashboard (
localhost:3000) for real-time monitoring of multi-agent workflows. - Integration using decorators (e.g.,
@tracer.trace_agent,@tracer.trace_tool)
Best suited for: Engineering teams experimenting with multi-agent systems.
Laminar
Use case: Track performance across different LLM frameworks and models.
Figure 12: Traces dashboard example from Laminar.
Laminar tracks execution spans, costs, token usage, and latency percentiles across LLM frameworks and models.
Laminar monitoring features
- Framework-agnostic performance analysis
- Fine-grained span inspection.
Best suited for: Comparative performance analysis across heterogeneous stacks.
Helicone
Use cases: Track multi-step agent workflows and analyze user session patterns.
Figure 12: Image showing 3 months of changes in requests, costs, errors, and latency.
Helicone captures request volumes, costs, errors, latency trends, and session-level agent workflows.
Helicone monitoring features
- User journey visibility
- Historical trend analysis.
Best suited for: Product teams monitoring usage patterns and user-level behavior.
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 simulates thousands of conversations to measure task completion, correctness, and tool-call effectiveness.
Coval monitoring features
- Simulation-based agent testing
- Automatic regression detection
- Voice and text agent support.
Best suited for: Pre-deployment validation and regression detection.
Datadog
Use cases: Infrastructure and application observability with LLM signal correlation.
Datadog collects infrastructure metrics (CPU, memory, network), application performance data (latency, error rates, throughput), and logs. For LLM applications, it can ingest token usage, cost per request, model latency, and security-related signals such as prompt injection attempts.
Datadog monitoring features
- Broad, system-wide observability across infrastructure, applications, and AI workloads
- Large integration ecosystem (900+ integrations) enabling correlation between AI behavior and infrastructure health
Best suited for: Organizations that want to correlate LLM behavior with underlying infrastructure and application performance rather than inspect agent reasoning or prompt
Prometheus
Use cases: Monitor system performance, track application metrics, and set up alerting for infrastructure issues.
Prometheus is an open-source monitoring system that scrapes time-series metrics from HTTP endpoints at regular intervals to track infrastructure, application, database, container, and custom business metrics.
Prometheus monitoring features
- Time-series metrics collection via pull-based scraping
- PromQL for querying, aggregation, and alert conditions
- Exporter ecosystem (e.g., Node Exporter) for broad system coverage
Best suited for: Infrastructure and application monitoring with rule-based alerting.
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 integrates with data sources such as Prometheus, OpenTelemetry, and Datadog to provide unified observability dashboards.
Grafana monitoring features
- Dashboards across metrics, logs, and traces
- Cross-system correlation for LLM, agent, and infrastructure signals
- Alert routing and notification management.
Best suited for: Centralized 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
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:
- 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
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 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 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.
Benchmark methodology
To evaluate the performance overhead of observability platforms in production LLM applications, we developed a systematic benchmarking approach using a real-world agentic workflow.
Test application
We built a sequential multi-agent travel planning system using LangChain that processes natural language travel requests through five stages:
- Parser agent: Extracts structured data (origin, destination, dates, duration) from user input
- Flight finder agent: Retrieves available flights via Amadeus API
- Weather reporter agent: Fetches destination weather forecasts using WeatherAPI
- Activity recommender Agent: Suggests activities based on weather conditions
- Travel planner agent: Synthesizes all outputs into a comprehensive itinerary
The system uses Claude 4 Haiku via OpenRouter for all LLM calls and integrates external APIs for real-time data.
Benchmark design
Baseline establishment: We first measured the application’s performance without any observability instrumentation, running 100 identical queries to establish a baseline for comparison.
Platform integration: We then integrated five leading observability platforms (LangSmith, Laminar, AgentOps, Langfuse) one at a time, instrumenting the same tracing points across all platforms for consistency.
Sequential execution: Each platform was tested independently by running all 100 queries consecutively before moving to the next platform. This approach minimizes variability from external factors like network conditions or API rate limits.
Controlled Environment: All tests were executed on the same server infrastructure with identical query sets to ensure fair comparison. To isolate overhead from LLM-induced latency variations, we configured the model with temperature=0 and structured prompts to minimize response variability across runs.
Metrics collected
For each platform, we measured average latency and calculated overhead as the additional latency introduced compared to the baseline: ((Platform Latency - Base Latency) / Base Latency) × 100
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.
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