---
title: "LangSmith"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# LangSmith

LangSmith is a tool developed by LangChain, designed to streamline the development, debugging, and deployment of applications that rely on large language models (LLMs) and multi-agent systems. It provides an integrated suite of features for monitoring, tracing, and evaluating language model-powered applications, making it easier for developers to track the performance of their LLMs, detect issues, and optimize the behavior of agents.

Here’s how LangSmith is typically used:

Key Features of LangSmith:

	1.	Evaluation and Metrics Tracking: LangSmith allows developers to run tests on their LLM-powered applications and track the performance metrics. This includes tracing the output of LLMs and agents, identifying which prompts or actions led to failures, and monitoring overall application performance.
	2.	Debugging LLMs: Since building applications on top of LLMs often involves a trial-and-error approach with prompts, LangSmith provides tools to log and visualize the interaction between the application and the language models. This helps developers pinpoint what went wrong in a specific interaction or how the LLM generated its output.
	3.	Version Control for Prompts: LangSmith allows for managing and versioning the prompts and chains of agents used in LLM applications, helping track changes and their effects on the performance of the application over time.
	4.	Tracing and Agent Monitoring: When working with multi-agent systems, LangSmith helps visualize and track how the agents interact, what decisions they make, and how effectively they achieve their tasks. This helps in optimizing the logic behind agent interactions.
	5.	Deployment Management: LangSmith integrates with the LangChain framework and makes it easier to manage and deploy production-level applications, offering tools to monitor production deployments.

How It’s Used in LLM Applications:

	•	LLM Development: Developers working with complex LLM applications can use LangSmith to track their application’s performance, trace interactions, debug errors, and continuously improve their language models.
	•	Agent-Based Systems: Since LangSmith supports multi-agent architectures, developers can utilize it to track the behavior of agents and improve the decision-making processes in AI agents.
	•	A/B Testing of Prompts: LangSmith facilitates version control of prompts and provides a controlled environment to test how variations in prompts or configurations affect the application’s behavior.


In summary, LangSmith is specialized for monitoring and debugging LLM and agent-based systems.

## Langfuse

Langfuse is an open-source alternative to LangSmith that provides a similar set of features for monitoring, debugging, and improving LLM-based applications and agent workflows. It is designed to track and visualize the interactions between language models, agents, and their environments, enabling developers to optimize the performance of their LLM-powered systems.

Langfuse is widely considered the best open-source alternative to Langsmith, as it provides similar functionalities for LLM engineering and debugging, while being completely free to use and self-host due to its open-source nature, unlike Langsmith which requires a paid subscription for full access to features.

Key Features of Langfuse:

	1.	Tracing and Monitoring:
	•	Call Tracing: Langfuse allows developers to trace all interactions with language models and other components within an LLM-powered application. This helps identify how different inputs are processed and what outputs are generated, making debugging and optimization easier.
	•	Detailed Execution Flows: Similar to LangSmith, Langfuse provides detailed views of the execution flows within the system, allowing developers to analyze how prompts or agents lead to specific outcomes.
	•	Real-time Monitoring: Langfuse offers real-time monitoring of LLM applications in production environments. Developers can track the performance of their language models and agents in real time, observing latency, success rates, and potential issues.
	2.	Metrics and Evaluation:
	•	Custom Metrics: Langfuse allows developers to define custom metrics to measure the performance of their LLM applications. This includes tracking the success of individual tasks, agent behaviors, and response quality.
	•	Prompt Performance Evaluation: Langfuse makes it easy to evaluate how effective different prompts or prompt variations are, enabling A/B testing and iterative improvements.
	•	User Feedback Integration: Developers can integrate feedback loops where users can rate or interact with the outputs of language models, allowing for continuous learning and improvements.
	3.	Agent Support:
	•	Multi-Agent System Tracking: Like LangSmith, Langfuse supports multi-agent workflows, where developers can monitor how multiple AI agents interact with each other, make decisions, and work together to complete complex tasks.
	•	Agent State Monitoring: Langfuse tracks the internal states of agents, helping developers understand how decisions are made and how agents collaborate or compete to achieve goals.
	4.	Event-Driven Architecture:
	•	Event Logging: Langfuse logs all relevant events in the LLM or agent workflow, allowing for precise debugging. This includes events like API calls, agent decisions, model responses, and any external interactions.
	•	Error Tracking: Langfuse makes it easy to track errors in the flow of prompts, agent decisions, or external interactions, making debugging faster and more efficient.
	5.	Visualization Tools:
	•	Interactive Dashboards: Langfuse provides interactive dashboards where developers can visualize the traces of their LLM-powered applications. These dashboards can display prompt-response cycles, agent decisions, performance metrics, and error logs.
	•	Decision Flow Diagrams: For agent-based systems, Langfuse offers decision flow diagrams that show how agents arrive at specific decisions based on their input and internal logic.
	6.	Version Control and Experiment Tracking:
	•	Prompt and Model Versioning: Langfuse allows for versioning of prompts and models, similar to LangSmith. Developers can track how changes to prompts, model parameters, or agent logic affect performance over time.
	•	Experiment Tracking: Developers can run experiments, compare different versions of prompts or models, and analyze the results within Langfuse. This helps in optimizing and evolving LLM-powered applications continuously.
	7.	Deployment and Integration:
	•	Deployment Agnostic: Langfuse can be integrated into various deployment environments, whether cloud-based or on-premises. It supports both centralized and distributed architectures.
	•	Integration with Existing LLM Frameworks: Langfuse can be integrated with popular LLM frameworks like LangChain and other agent-based systems. It can also be used alongside custom-built systems to provide tracing and monitoring.

Use Cases for Langfuse:

	1.	LLM Application Development: Developers building applications on top of LLMs (e.g., chatbots, content generators) can use Langfuse to monitor performance, trace interactions, and identify areas of improvement.
	2.	Multi-Agent Systems: Teams working on agent-based systems, where multiple agents interact with each other or with users, can track the agents’ decision-making processes and optimize their workflows.
	3.	Debugging and Optimization: For applications that rely on language models or complex prompts, Langfuse can help developers debug why certain prompts lead to undesirable outcomes and optimize the prompts for better results.
	4.	Monitoring in Production: Langfuse can be used in production environments to monitor LLM applications in real time, track latency, identify failures, and ensure that models and agents are performing as expected.

Open Source and Customization:

One of the major advantages of Langfuse over proprietary tools like LangSmith is that it is open-source. This allows for full customization and control over the platform. Teams can modify Langfuse to suit their specific needs, integrate it with internal tools, and deploy it in private environments without relying on external services.

Comparison to LangSmith:

	•	Cost: Langfuse is open-source and free to use, whereas LangSmith may come with usage-based costs or require a paid subscription for advanced features.
	•	Customization: Langfuse can be customized and extended based on specific project needs, making it more flexible than LangSmith, which might be more rigid in its feature set.
	•	Community: Being open-source, Langfuse benefits from contributions from the developer community, which means it may evolve faster and support a wider range of use cases over time.
	•	Integration: LangSmith is tightly integrated with the LangChain ecosystem, which may offer more seamless integration if you’re already using LangChain. However, Langfuse can be more easily integrated with a broader set of frameworks and custom workflows.

Conclusion:

Langfuse is a robust open-source alternative to LangSmith, offering many of the same capabilities for monitoring, debugging, and optimizing LLM and agent-based applications. Its flexibility, open-source nature, and integration capabilities make it a compelling choice for developers looking for a customizable solution for tracking and improving their language model-based workflows.
