Top 16 AI Agent Observability Tools: Langfuse, Arize & More

After diving into documentation, reading user reviews, and spending hours with demos of AI popular observability tools for AI agents, I categorized these platforms into three tiers based on their primary functions:

How AI agent observability tools work?

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.

Tier 1: Core (end-to-end traceability and debugging of AI agents)

Purpose-built for deep observability, tracing, evaluation, and optimization of LLMs and agents.

• Langfuse → Workflow-level observability: Focuses on workflow-level observability, meaning it tracks each step an agent takes within its workflow. It connects prompts to their outputs and provides detailed debugging capabilities, especially in production environments.

• Arize → Model-level observability: embeds evaluations and tracing into agents for holistic performance insights.
  
• Langtrace AI → Workflow & pipeline observability: Offers step-by-step debugging and visibility into complex agent workflows, particularly for troubleshooting and monitoring in production.

• LangSmith → LangChain-integrated tracing: Delivers detailed prompt and workflow evaluation built directly into LangChain applications, ensuring seamless observability from development to production.

• Laminarn → Real-time analytics: Features live dashboards and continuous monitoring of agent execution, allowing for instant identification of performance issues.

• Braintrust → Evaluation pipelines: Implements structured evaluation frameworks to assess agent behavior against key performance indicators (KPIs) and expected outcomes, ensuring consistent performance.

• Galileo → Outcome analysis & optimization: Focuses on identifying errors, analyzing agent outputs, and optimizing agent reliability and robustness for better performance.

• Coval → Tracking & fine-tuning: Enables ongoing optimization and refinement of deployed agents to ensure they evolve and improve in response to real-world usage.

Tier 2: Observability & AgentOps (dual-focus)

Blend agent lifecycle management with observability, supporting both operational control and performance insight.

• AgentOps.ai → Lifecycle monitoring: Tracks agent uptime, reliability, and performance in production, ensuring continuous operation and health.

• Agenta → Agent development & deployment: rovides workflows for building and deploying agents with integrated observability hooks for monitoring during the development and deployment process.

• AgentNeo → Orchestration & coordination: Manages multi-agent systems while adding monitoring capabilities to ensure smooth collaboration between agents.

• Agent-Panel → Management dashboards: Offers a centralized interface for overseeing agents.

• Datadog → Agent monitoring & governance: Integrates deep observability (traces, experiments, debugging) with operational oversight through its AI Agents Console for usage, ROI, security, and compliance.

Tier 3: Adjacent (enhance agent observability & safety)

Tools that support observability indirectly, improving usage efficiency and output reliability.

• Helicone → Usage metrics & optimization: Tracks API calls, token consumption, latency, and costs for usage-level observability, optimizing performance and expenses.

• Guardrails AI → Output validation: Enforces safety, structure, and compliance rules on agent outputs before delivery.

Core observability features

• Tracing: Tracks the flow of data and actions throughout an agent’s workflow for detailed debugging and performance monitoring.
  • Limited system-level tracing: Tracking is restricted to basic system actions, without deep visibility into the agent’s full workflow or performance.
  • Eval-based tracing: Tracing that is based on evaluation metrics, offering insights into agent performance during assessments.
  • Scenario-based workflow tracing: Tracing specific to predefined scenarios, focusing on how agents handle particular situations or tasks.
• Evaluations: Assesses agent performance and behavior based on predefined metrics.

Arize

Arize Phoenix is designed for LLM and model observability. 

It brings strong evaluation tooling, including

• drift detection for monitoring changes in agent behavior or model performance
• bias checks for evaluating the agent’s outputs to identify potential biases in its responses
• “LLM-as-a-judge” scoring for accuracy, toxicity, or relevance. 

It also provides an interactive prompt playground for testing and comparing prompts, which is particularly useful during early iterations.

The open-source Phoenix package is fully self-hostable and integrates with Arize’s cloud platform for more enterprise features.

The downside for earlier teams is the integration overhead and the fact that Arize is heavier than a caching/logging proxy (like Helicone). It doesn’t manage prompt versioning as cleanly as Langfuse either. It’s better for evaluations and monitoring than prompt lifecycle management.

Langfuse

Source: Langfuse¹

From a production perspective, Langfuse is best thought of as a core observability and prompt management layer for LLM and agent applications.

Unlike lightweight proxies that focus on logging and caching, Langfuse is designed to provide zero-latency tracing, versioned prompt management, and debugging tools that don’t compromise uptime or introduce overhead.

However, as projects grow in complexity, Langfuse is not efficient at providing the deep full-stack observability.

It focuses primarily on LLM interactions, which means it cannot trace system-wide interactions or error reports beyond the LLM layer. This limitation becomes more apparent when managing entire workflows or integrating with external systems..

Also, 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.

Langfuse works well for smaller projects, but if your project requires full-stack monitoring or infrastructure observability, Datadog or Prometheus are more suitable alternatives. 

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

Langtrace AI 

Langtrace AI is an open-source observability tool for LLM applications.

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.

Key strengths include:

• Step-level observability of LLM workflows and agents.
  
• Evaluation support, with dataset-based tests and correctness checks.
  
• Prompt lifecycle features, including version control and a playground for testing prompt variants.
  
• Deployment flexibility, with both a hosted option and full self-host support via open source.

Langtrace is also newer and still maturing, so its ecosystem and integrations aren’t yet as broad as those of Langfuse or LangSmith.

Agent development & orchestration platforms

1. No-code low-code agent builders:

No-code platforms are valuable for prototyping and developing AI agents without programming expertise.

Flowise

Flowise is a no-code builder that allows creating customized LLM workflows through a drag-and-drop interface. It supports integrations that enable analysis, monitoring, and iterative improvement.

Langflow

Langflow is a graphical interface for LangChain built with react-flow. It provides an environment to design and prototype LLM workflows and can be connected to external tools for monitoring and debugging.

Dify

Dify is an open-source platform for building LLM applications. It includes an Agent Builder and templates for quickly creating AI agents, which can be expanded into more complex systems using workflows. It also supports integrations for managing and improving applications.

2. Agent frameworks:

SuperAGI

SuperAGI is an open-source framework for building autonomous agents with a focus on multi-agent orchestration.

By integrating with observability tools (e.g., Langfuse, OpenTelemetry) into SuperAGI, you can:

• Trace task execution paths step by step.
  
• Monitor resource consumption and scaling decisions.
  
• Capture and analyze intermediate outputs to detect drift or unexpected behaviors.

CrewAI

CrewAI is designed for coordinating multiple specialized agents (“crew members”) to collaborate on tasks. It emphasizes collaboration, task distribution, and workflow execution.

Provides visibility into task assignments, inter-agent communication, and execution outcomes, which can be traced for workflow debugging and optimization.

LangChain

LangChain is the most widely used developer framework for LLM applications, offering primitives like chains, memory, and tools to connect models with APIs and data sources.

LangChain becomes significantly more powerful when paired with LangSmith (its companion observability tool). When integrated, LangChain and  LangSmith can provide:

• Step-level tracing of chains and agent workflows.
  Prompt evaluation (automated, human, or LLM-as-a-judge) to measure correctness, relevance, or safety.
• Debugging in production by replaying traces from live applications.

llama-agents

llama-agents is a lightweight framework for creating modular AI agents, offering libraries and abstractions for flexible agent orchestration.

Integrates with tools like Langfuse for step-level trace capture and performance metrics, helping debug modular agent workflows.

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.

External Links

• 1. https://langfuse.com/docs/observability/features/token-and-cost-tracking