What is LangSmith? The Complete Guide to LLM Observability and Evaluation
- Muiz As-Siddeeqi
- Nov 19
- 25 min read
Building an AI chatbot is easy. Getting it to work reliably in front of real customers? That's where most teams hit a wall. When your AI agent randomly fails, burns through your budget with redundant calls, or produces answers you can't explain to stakeholders, traditional debugging tools fall short. LangSmith emerged in 2023 to solve this exact problem—and by November 2025, it's helping teams at Klarna, Elastic, and thousands of other companies turn unpredictable LLM prototypes into production-grade systems that actually ship.
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TL;DR
LangSmith is a unified platform for debugging, testing, evaluating, and monitoring applications built with large language models (LLMs)
Launched in closed beta in August 2023 by the LangChain team and reached general availability with paid plans starting July 2024
Handles over 1 billion traces and helps companies reduce customer resolution times by 80% (Klarna case study)
Works with any framework—not just LangChain—through Python/TypeScript SDKs and OpenTelemetry support
Pricing starts free (5,000 traces/month on Developer plan) and scales to $39/month per seat for teams (Plus plan)
LangChain raised $125 million at $1.25 billion valuation in October 2025, with LangSmith as its primary revenue driver
What is LangSmith?
LangSmith is a comprehensive observability and evaluation platform designed specifically for applications built with large language models. It provides end-to-end tracing, real-time monitoring, automated testing, and prompt optimization tools that help developers debug complex LLM chains, evaluate model performance, and maintain reliable AI systems in production. Created by the LangChain team, it works with any LLM framework through simple SDK integration.
Table of Contents
Introduction: The LLM Observability Challenge
The gap between a working LLM prototype and a production-ready application is vast. A chatbot that responds correctly 30% of the time makes for a great Twitter demo, but it's useless to actual customers. The fundamental problem is visibility. When an LLM application misbehaves, traditional debugging tools can't help. You can't set breakpoints in a neural network. You can't step through probabilistic text generation. The model itself is a black box.
This observability crisis emerged alongside the explosion of LLM applications. By 2025, an estimated 750 million apps will be using LLMs (Keywords Everywhere, 2025). The global LLM market was valued at $4.5 billion in 2023 and is projected to reach $82.1 billion by 2033, representing a compound annual growth rate of 33.7% (Hostinger, July 2025). Yet building these applications remains deceptively difficult. Performance quality was the top challenge for respondents in the "State of AI Agents" survey ran by LangChain in late 2024 (Keywords Everywhere, 2025).
LangSmith addresses this challenge directly. It's not a model provider. It's not a development framework. It's the infrastructure layer that makes LLM applications observable, testable, and reliable. Think of it as the equivalent of Datadog or New Relic, but purpose-built for the unique challenges of large language models.
What is LangSmith? Core Definition and Purpose
LangSmith serves as a dedicated platform for monitoring, debugging and evaluating applications built with large language models (IBM, November 2025). It's a commercial product developed by LangChain, Inc., the company behind the popular open-source LangChain framework.
At its core, LangSmith provides three critical capabilities:
Observability: Every interaction with your LLM application generates a detailed trace—a complete record of inputs, outputs, API calls, tool invocations, and intermediate steps. LangSmith does not add any latency to your application. In the LangSmith SDK, there's a callback handler that sends traces to a LangSmith trace collector which runs as an async, distributed process (LangChain, 2025).
Evaluation: LangSmith lets you build datasets from production traces, define custom evaluators, and systematically test how prompt changes or model swaps affect quality. You can run automated scoring based on relevance, accuracy, toxicity, or custom business metrics.
Deployment Monitoring: In production, LangSmith tracks latency, costs, error rates, and user feedback in real-time. You can set alerts on key metrics and drill down from high-level dashboards into specific problematic traces.
LangSmith provides observability at unprecedented scale—the ability to trace every step of an agent's "thought" process across over 1 billion trace logs (Takafumi Endo, Medium, June 2025). Critically, LangSmith is framework-agnostic. While it integrates seamlessly with LangChain and LangGraph, it works with any LLM application through its Python and TypeScript SDKs.
The Story Behind LangSmith: From LangChain to Production Platform
LangChain began in late 2022 as an open-source project by Harrison Chase, then an engineer at Robust Intelligence. It pioneered the idea of "chains"—building blocks that connect large language models to external tools and data sources in a sequence (Fortune, October 2025). The timing was perfect: OpenAI had just released ChatGPT, and developers were desperate for tools to build practical applications.
The startup LangChain raised a $10 million seed round led by Benchmark in April 2023, and announced a $25 million Series A in 2024 led by Sequoia and valuing the company at $200 million (Fortune, October 2025). LangChain's framework saw explosive adoption, but a critical gap emerged: teams could build prototypes quickly, but moving to production remained painfully difficult.
The blocker had changed. While it was easy to build a prototype of an application in ~5 lines of LangChain code, it was still deceptively hard to take an application from prototype to production. The main issue was application performance—something that works ~30% of the time is good enough for a Twitter demo, but not nearly good enough for production (LangChain Blog, August 2023).
LangSmith launched in closed beta in August 2023 to address this production gap. Around the summer of 2023, LangChain started to get a lot of negative feedback about langchain. While langchain was the fastest place to get started, they traded power for ease of use. The same high level interfaces that made it easy to get started were getting in the way when people tried to customize them to go to production (LangChain Blog, October 2025).
The strategy worked. Usage became billable starting in July 2024 (LangSmith Documentation, 2024), marking LangSmith's transition from beta to a fully commercial product. By October 2025, LangChain announced a $125 million Series B funding round at a $1.25 billion valuation, with LangSmith as its primary revenue driver alongside the LangGraph Platform (Fortune, October 2025).
How LangSmith Works: Architecture and Technical Foundation
LangSmith's architecture consists of several integrated components that work together to provide comprehensive observability:
Core Components
The LangSmith Frontend handles requests and displays the LangSmith UI, making it the point of interaction for end users. The LangSmith Backend manages incoming API requests, logs traces of model executions, processes them, and stores metadata. It also enables developers to collaborate, test, and monitor workflows across runs (ProjectPro, 2024).
The platform uses three database systems optimized for different workloads:
ClickHouse: Stores high-volume traces and feedback data, optimized for analytical queries
PostgreSQL: Handles transactional and operational data like user accounts, projects, and permissions
Redis: Provides fast caching and queuing through in-memory storage
Tracing Mechanism
A trace is essentially a series of steps that your application takes to go from input to output. Each individual step is represented by a run. If you are familiar with OpenTelemetry, you can think of a run as a span and a LangSmith trace as a collection of spans. Runs are bound to a trace by a unique trace ID (LangSmith Documentation, 2025).
When you enable LangSmith in your application by setting environment variables, the SDK automatically captures:
Input text and structured data sent to LLMs
Complete model responses including streaming chunks
Tool calls and their results
Retry logic and error handling
Token usage and cost estimates
Timing information for each step
Custom metadata and tags you attach
When using LangSmith hosted at smith.langchain.com, data is stored in GCP us-central-1. If you're on the Enterprise plan, LangSmith can be delivered to run on your Kubernetes cluster in AWS, GCP, or Azure so that data never leaves your environment (LangChain, 2025).
Integration Points
LangSmith supports multiple integration patterns:
SDK-based Integration: The recommended approach for LangChain/LangGraph applications. Setting LANGSMITH_TRACING=true and providing an API key automatically captures all traces.
Decorator-based Tracing: For non-LangChain code, the @traceable decorator in Python (or traceable() wrapper in TypeScript) manually instruments functions.
OpenTelemetry Support: LangSmith supports OpenTelemetry (OTel) to unify your observability stack across services. Your application does not need to be written in Python or Typescript (LangChain, 2025).
Key Features and Capabilities
1. End-to-End Tracing
LangSmith captures every step of LLM chain execution. When a user query triggers your application, you see:
The exact prompt template and variables used
How the prompt was formatted and sent to the model
The raw model response (including intermediate tokens if streaming)
Any parsing or validation steps applied to the output
Tool calls made by the agent and their results
Final response returned to the user
This visibility is crucial for debugging. When something goes wrong, you can pinpoint whether the issue is in prompt construction, model behavior, tool execution, or output parsing.
2. Datasets and Testing
Production traces can be converted into test datasets with a single click. You build a "golden set" of representative examples, then systematically evaluate how changes affect performance:
Test prompt variations across your dataset
Compare different models (GPT-4 vs. Claude vs. open-source)
Measure the impact of temperature or other parameters
Run regression tests before deploying updates
3. Automated Evaluation
LangSmith supports multiple evaluation approaches:
LLM-as-a-Judge: Use a powerful model (like GPT-4) to score responses on criteria like relevance, accuracy, helpfulness, or safety. Define custom rubrics for your domain.
Rule-based Scoring: Check for specific patterns, keywords, or formatting requirements.
Human Review: Send traces to annotation queues where domain experts can provide feedback.
Custom Evaluators: Write Python functions that implement your exact business logic for quality assessment.
Leveraging LangSmith, Klarna rigorously tested critical use cases for their AI assistant, then validated and refined agent performance with LLM evaluations and prompt iteration (LangChain Blog, February 2025).
4. Production Monitoring
LangSmith tracks business-critical metrics like costs, latency, and response quality with live dashboards. You can set alerts when issues happen and drill into the root cause (LangChain, 2025).
Key monitoring capabilities include:
Cost Tracking: See token usage and estimated costs by model, user, or feature
Latency Analysis: Identify slow steps in your chain (Is the LLM call slow? Or is it tool invocation?)
Error Rates: Track failures, timeouts, and exceptions with detailed stack traces
Conversation Clustering: See clusters of similar conversations to understand what users actually want and quickly find all instances of similar problems to address systemic issues (LangChain, 2025)
5. Prompt Management
Version and test prompts in a dedicated playground. Changes are tracked, and you can compare performance across versions before promoting to production. You can already test, version, and collaborate on prompts in LangSmith. Now, you can automatically sync those prompts to GitHub, external databases, or CI/CD pipelines (LangChain Changelog, July 2025).
6. Deployment Support
As of October 2025, LangGraph Platform has been re-named to "LangSmith Deployment." It provides the infrastructure needed to deploy, scale, and manage stateful, long-running agents (LangChain Blog, July 2025). The Plus plan includes one free development deployment.
7. Multimodal Support
LangSmith now supports images, PDFs, and audio files across the playground, annotation queues, and datasets—making it easier than ever to build, test, and evaluate multimodal applications (LangChain Changelog, May 2025).
LangSmith Pricing: Plans, Costs, and Value
LangSmith uses a two-dimensional pricing model: seat-based licensing for team access and usage-based billing for trace volume.
Developer Plan (Free)
Cost: $0Seats: 1 included
Traces: 5,000 base traces per month included
Retention: Base traces (14 days) or extended traces (400 days)
Base traces cost $0.50 per 1,000 traces after the free allotment. Extended traces cost $5.00 per 1,000 traces. You can manually upgrade base traces to extended traces for $4.50 per 1,000 traces (LangChain Pricing, 2025).
This plan is ideal for individual developers, side projects, and early prototyping. It provides full access to observability and evaluation features.
Plus Plan
Cost: $39 per seat per month
Max Seats: 10
Traces: 10,000 base traces per month included
Deployments: 1 free dev deployment (additional deployments billed at $0.001 per node execution)
Billing is handled monthly on the first of the month. If you add a new team member mid-month, the cost for that seat is pro-rated. If you remove a seat (churn) during the month, you will not receive a credit for the remaining time (MetaCTO, June 2025).
The Plus plan enables team collaboration with shared projects, datasets, and prompts. It's designed for growing teams that need moderate usage and self-service capabilities.
Enterprise Plan
Cost: Custom (contact sales)
Seats: Unlimited
Features: Custom deployment options (cloud, hybrid, self-hosted), advanced security, dedicated support, annual invoicing
The Enterprise plan allows customers to self-host LangSmith on their Kubernetes cluster. LangChain delivers the software to run in your environment, and data will not leave your environment (LangChain Pricing, 2025).
Enterprise plans include white-glove support, a dedicated customer success manager, and monthly check-ins covering LangSmith and LangChain questions.
Startup Program
LangSmith offers a Startup Plan designed for early-stage companies building agentic applications. You'll get discounted rates and generous free trace allotments. Customers can stay on the Startup Plan for 1 year before graduating to the Plus Plan (LangChain Pricing, 2025).
Trace Retention Strategy
Understanding trace types is crucial for cost optimization:
Base Traces (14-day retention): Suitable for quick debugging and short-term analysis. These are priced for high volume at $0.50 per 1,000 traces.
Extended Traces (400-day retention): Extended traces often include valuable feedback—whether from users, evaluators, or human labelers. LangSmith automatically upgrades any trace that receives feedback to an extended trace, ensuring you never lose valuable user-annotated data (LangChain Pricing, 2025).
This automatic upgrade feature is powerful: any trace marked as "good" or "bad" by users, or scored by evaluators, automatically becomes extended without manual intervention.
Cost Comparison
At scale, LangSmith's pricing is competitive. A team of 5 developers running 100,000 traces per month would pay:
5 seats × $39 = $195/month
(100,000 - 10,000 free) × $0.50 / 1,000 = $45/month
Total: $240/month
For context, LangGraph's Plus plan requires a LangSmith Plus subscription at $39 per user per month (ZenML Blog, November 2025), so teams using both products need to budget accordingly.
Real-World Case Studies: Companies Using LangSmith
Case Study 1: Klarna's AI Assistant
Company: Klarna (Global fintech, 85 million active users)
Challenge: Building a production-ready AI assistant to handle customer support at massive scale
Solution: LangGraph for agent orchestration + LangSmith for testing and monitoring
Results: Reduced average customer query resolution time by 80%. The AI Assistant handles 2.5 million conversations to date and performs work equivalent of 700 full-time staff (LangChain Blog, February 2025)
How They Used LangSmith:
Klarna used test-driven development with LangSmith to pinpoint what issues arose by seeing step-by-step how their AI assistant behaved. They rigorously tested critical use cases, then validated and refined agent performance with LLM evaluations and prompt iteration. Klarna's insights helped inspire and design advanced capabilities like meta-prompting, which allows users to suggest specific improvements to prompts (LangChain Blog, February 2025).
The 80% reduction in resolution time directly translated to faster customer service and significant cost savings, demonstrating LangSmith's ROI in high-stakes production environments.
Case Study 2: Elastic's AI Security Assistant
Company: Elastic (Security and observability platform, 20,000+ customers)
Challenge: Building AI agents for real-time threat detection without compromising reliability
Solution: LangGraph for agent orchestration + LangSmith for observability
Results: Cut alert response times for 20,000+ customers. LangSmith gave full visibility into LLM workflows without compromising on data privacy or control (LangChain, 2025)
Elastic used LangGraph to orchestrate their network of AI agents for real-time threat detection, which helped them respond to security risks much more quickly and effectively (LangChain Blog, February 2025). The ability to trace each step in threat analysis workflows was critical for debugging edge cases and ensuring reliable detection.
Case Study 3: Rakuten's GenAI Platform
Company: Rakuten (Japanese e-commerce and internet services, 70+ businesses)
Challenge: Enabling employees across diverse business units to create AI agents
Solution: Enterprise-wide GenAI platform built with LangGraph and LangSmith
Results: Rakuten's GenAI platform lets employees across 70+ businesses create AI agents (LangChain, 2025)
This internal platform democratizes AI development while maintaining governance and quality standards through LangSmith's evaluation and monitoring capabilities.
Case Study 4: AppFolio's Realm-X Copilot
Company: AppFolio (Property management software)
Challenge: Building an AI copilot that helps property managers make faster decisions
Solution: LangGraph for agent architecture + LangSmith for prompt optimization
Results: After switching to LangGraph, response accuracy increased 2x, and they've saved property managers 10+ hours per week (LangChain, 2025)
The 2x improvement in accuracy came from systematic prompt evaluation and iterative refinement enabled by LangSmith's testing framework.
Case Study 5: Uber's Code Migration Agents
Company: Uber (Global ride-sharing and delivery platform)
Challenge: Automating large-scale code migrations across the developer platform
Solution: LangGraph-powered specialized agents with LangSmith monitoring
Implementation: Uber integrated LangGraph to streamline large-scale code migrations within their developer platform. They carefully structured a network of specialized agents so that each step of their unit test generation was handled with precision (LangChain Blog, February 2025)
LangSmith's detailed tracing allowed Uber to debug complex multi-agent workflows and ensure high-quality code generation at scale.
LangSmith vs. Alternatives: Market Comparison
The LLM observability space has multiple players, each with different strengths:
LangSmith vs. Arize Phoenix
Phoenix is fully open source, while LangSmith is closed source. Phoenix is backed by Arize AI and is designed to be framework-agnostic from the ground up. Self-hosting is free for Phoenix, versus a paid feature within LangSmith (Arize Phoenix Documentation, 2025).
When to choose Phoenix:
Your team values open-source transparency
Data sovereignty requires self-hosting from day one
You're using multiple orchestration frameworks and want true framework neutrality
Budget constraints favor free self-hosting over managed services
When to choose LangSmith:
You're already using LangChain/LangGraph and want tighter integration
Managed SaaS reduces operational overhead
You need enterprise support and professional services
Deployment features are important (LangSmith Deployment/former LangGraph Platform)
LangSmith vs. Langfuse
Langfuse offers observability and prompt management for LLM applications, emphasizing tracing and usage monitoring. While it provides basic evaluation and prompt management tools, Langfuse is best suited for teams prioritizing open-source flexibility and customization (Maxim AI, September 2025).
Langfuse is open-source and self-hostable, making it attractive for teams with strong infrastructure capabilities. However, LangSmith offers more advanced evaluation workflows and better integration with the LangChain ecosystem.
LangSmith vs. Helicone
Helicone uses a distributed architecture with ClickHouse and Kafka, offering proxy-based integration that requires just one line of code. It provides built-in caching and focuses on operational metrics, while LangSmith specializes in deep debugging of LangChain workflows (Helicone Blog, 2025).
Helicone's proxy approach is simpler for basic use cases, but LangSmith provides deeper insights into complex agent behaviors.
LangSmith vs. Weights & Biases
Weights & Biases is a popular tool for MLOps that offers a wide range of features for LLMs, including tracing, logging, fine-tuning, evaluations, visualization, and collaboration tools. The platform might be overwhelming to get accustomed to, but it's a great choice for ML-heavy projects that need everything in one place (Lunary Blog, 2025).
W&B excels at experiment tracking and model training workflows but is heavier than LangSmith for teams focused purely on LLM application observability.
Market Positioning
A significant portion of LangSmith traces come from non-LangChain frameworks, demonstrating its value beyond its originating ecosystem (Helicone Blog, 2025). This framework-agnostic usage shows LangSmith's practical utility even for teams not using LangChain.
The competitive landscape continues evolving. Open-source alternatives offer transparency and cost advantages, while LangSmith provides enterprise features, managed infrastructure, and the backing of a well-funded company focused on long-term support.
When to Use LangSmith (and When Not To)
Use LangSmith When:
You're Moving LLMs to Production: If your prototype needs to become a real product that users depend on, LangSmith provides the observability infrastructure to make that transition safely.
Debugging Complex Agents: Performance quality was the top challenge for respondents in the "State of AI Agents" survey. Unpredictability of LLMs makes debugging challenging compared to traditional software (LangChain Blog, February 2025). LangSmith's tracing makes the unpredictable more understandable.
You Need Systematic Evaluation: If you're comparing models, testing prompts, or validating changes before deployment, LangSmith's dataset and evaluation features are purpose-built for this workflow.
Cost Visibility is Critical: When token costs are a significant line item, LangSmith tracks usage by feature, user, or model to help optimize spending.
You're Using LangChain/LangGraph: The integration is seamless—just set environment variables and tracing works automatically.
Compliance Requires Audit Trails: For traces ingested on or after May 22, 2024, LangSmith (SaaS) retains trace data for a maximum of 400 days (LangSmith Documentation, 2025). Extended traces provide detailed audit logs for regulated industries.
Consider Alternatives When:
You're Just Experimenting: The free Developer plan is generous, but if you're doing pure research without production goals, simpler tools might suffice.
You Require Open Source: If transparency and code auditability are non-negotiable, Phoenix or Langfuse are better fits.
Budget is Extremely Tight: Self-hosting an open-source alternative eliminates usage fees, though it adds infrastructure overhead.
You're Building Simple, Stateless Applications: If your app is a single LLM call with no chains or agents, LangSmith's features may be overkill. Basic logging might meet your needs.
You Need Traditional ML Experiment Tracking: W&B or similar platforms are better suited for training workflows, hyperparameter tuning, and model versioning.
Getting Started with LangSmith
Step 1: Create an Account
Visit smith.langchain.com and sign up for a free account. You'll receive 5,000 monthly traces on the Developer plan with no credit card required.
Step 2: Get Your API Key
Navigate to Settings → API Keys. Create a new key and save it securely. This key authorizes your application to send traces to LangSmith.
Step 3: Configure Environment Variables
For Python applications:
export LANGSMITH_TRACING=true
export LANGSMITH_API_KEY=<your-api-key>
export LANGSMITH_PROJECT="my-project" # Optional project nameFor JavaScript/TypeScript:
export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT="my-project"Step 4: Run Your Application
If you're using LangChain or LangGraph, tracing activates automatically. Run your application normally, and traces will appear in the LangSmith dashboard.
For non-LangChain code, use the @traceable decorator:
from langsmith import traceable
@traceable
def my_function(input_text):
# Your LLM logic here
return resultStep 5: Explore the Dashboard
In the LangSmith UI, you'll see:
Projects: Collections of traces from different applications or environments
Traces: Individual execution flows from input to output
Datasets: Saved examples for testing and evaluation
Annotation Queues: Places to review and label traces
Click into any trace to see the complete execution flow, including timing, costs, and intermediate outputs.
Step 6: Create Your First Dataset
Find interesting production traces and add them to a dataset:
Filter traces by criteria (errors, high latency, specific users)
Select traces and click "Add to Dataset"
Name your dataset and add any ground truth labels
Step 7: Run an Evaluation
With a dataset created, test how changes affect quality:
Navigate to the dataset
Click "New Experiment"
Select your target (prompt variation, different model, parameter changes)
Choose evaluators (LLM-as-judge, custom functions, or manual review)
Run the experiment and compare results
This workflow—trace production, build datasets, evaluate systematically—forms the core loop for improving LLM applications.
The LLM Observability Market Context
Market Size and Growth
The broader observability market is expanding rapidly. The observability platform market reached USD 2.9 billion in 2025 and is forecast to expand at a 15.9% CAGR to USD 6.1 billion by 2030 (Mordor Intelligence, November 2024).
Within this, LLM-specific observability is a fast-growing subsegment. Grand View Research estimated the global data observability market size at $2.14 billion and expects it to grow by 12.2% from 2024 to 2030 (Secoda, April 2025).
An evolving aspect of this domain is LLM observability. This discipline builds on traditional ML monitoring to capture important signals related to building, tuning and operating LLMs (Sapphire Ventures, November 2024).
Adoption Patterns
Most teams (60.6%) using LLMs are implementing prompt engineering, and 40.9% are utilizing vector databases. Additionally, 30.1% are leveraging LLM observability tools (Keywords Everywhere, 2025). This 30.1% adoption rate for observability tools indicates that the practice is still emerging but growing quickly.
According to Iopex, 67% of organizations are using generative AI products powered by LLMs to work with human language and produce content (Keywords Everywhere, 2025). As these organizations move from experimentation to production, demand for observability tools like LangSmith will accelerate.
Competitive Funding Environment
LangChain's competitive position is strong. By July 2025, LangChain achieved unicorn status with its Series B funding round, raising $100 million led by IVP. This round valued the company at $1.1 billion (Latenode, 2025). The October 2025 announcement of $125 million at a $1.25 billion valuation, led by IVP with participation from Sequoia, Benchmark, CapitalG, Sapphire Ventures, ServiceNow Ventures, Workday Ventures, Cisco Investments, Datadog, Databricks, and Frontline (Fortune, October 2025) positions LangChain as one of the best-funded companies in the LLM tooling space.
Enterprise Momentum
The customer roster reads like a Fortune 500 directory: Klarna, MUFG Bank, LinkedIn, Vodafone, and Home Depot. These companies have reported significant operational improvements and efficiency gains through their LangChain implementations. These aren't pilot projects—they're production deployments that save thousands of hours and generate measurable ROI (Takafumi Endo, Medium, June 2025).
Reports indicate LangChain achieved significant revenue growth, with some estimates suggesting figures around $8.5 million during their first year of monetization in 2024 (Takafumi Endo, Medium, June 2025). This revenue traction, combined with the funding, suggests long-term sustainability.
AWS Marketplace Availability
In July 2025, LangSmith and LangGraph Platform became available in AWS Marketplace's new AI Agents and Tools category. Customers can now use AWS Marketplace to easily discover, buy, and deploy LangSmith using their AWS accounts (LangChain Blog, July 2025). This distribution channel simplifies procurement for enterprise customers with AWS budgets and committed spend.
Common Pitfalls and How to Avoid Them
Pitfall 1: Not Tagging Traces Properly
Problem: Without proper tags and metadata, finding specific traces becomes difficult as volume grows.
Solution: Establish tagging conventions early. Tag by environment (dev/staging/prod), feature, user cohort, or experiment variant. Use metadata for session IDs, user IDs, and business context.
Pitfall 2: Ignoring Cost Alerts
Problem: LLM costs can spike unexpectedly, especially with agents that make multiple calls per user request.
Solution: Set up alerts in LangSmith when token usage or costs exceed thresholds. Review dashboards weekly to identify cost hotspots. Use trace analysis to find redundant calls or overly verbose prompts.
Pitfall 3: Not Building Datasets from Production
Problem: Testing on synthetic data doesn't reveal real-world failure modes.
Solution: Continuously curate datasets from production traces, especially edge cases, errors, and user-reported issues. These become your regression test suite.
Pitfall 4: Treating All Traces Equally
Problem: Storing every trace as extended (400-day retention) is expensive at scale.
Solution: LangSmith automatically upgrades any trace that receives feedback to an extended trace (LangChain Pricing, 2025). Let this automatic mechanism handle retention for important traces while keeping most as base traces.
Pitfall 5: Over-Reliance on LLM-as-Judge
Problem: Using a powerful LLM to evaluate outputs adds cost and latency. Sometimes simpler checks suffice.
Solution: Use a tiered evaluation approach: fast rule-based checks for obvious failures, LLM scoring for nuanced quality, and human review for ambiguous cases.
Pitfall 6: Not Integrating with Alerting Systems
Problem: Issues get buried in dashboards instead of triggering immediate response.
Solution: Connect LangSmith to your existing monitoring stack (PagerDuty, Slack, email). Set alerts for error rate spikes, latency degradation, or cost anomalies.
Pitfall 7: Neglecting Data Privacy
Problem: Traces contain user inputs and outputs that may include sensitive information.
Solution: When using LangSmith hosted at smith.langchain.com, data is stored in GCP us-central-1. If you're on the Enterprise plan, LangSmith can deliver to run on your Kubernetes cluster in AWS, GCP, or Azure so data never leaves your environment (LangChain, 2025). For regulated industries, use self-hosted deployment or carefully sanitize data before sending to the cloud.
Future Outlook: Where LangSmith is Headed
Deepening Agent Support
We see a number of new ways that we can help customers, and we want to make LangSmith a single place where you can get most of your tooling to build reliable agents. Today we're bringing deployments into LangSmith and are setting LangSmith up to be the comprehensive agent engineering platform (LangChain Blog, October 2025).
This vision extends beyond observability into the full agent development lifecycle: design, build, test, deploy, and operate. Expect more features around agent composition, workflow visualization, and deployment orchestration.
Enhanced Multimodal Capabilities
LangSmith now supports images, PDFs, and audio files across the playground, annotation queues, and datasets (LangChain Changelog, May 2025). As multimodal models become standard, LangSmith will expand support for video, complex document understanding, and cross-modal evaluation.
AI-Assisted Debugging
Future versions will likely incorporate AI directly into the debugging workflow. Imagine asking "Why did this trace fail?" and getting an AI-generated hypothesis based on similar failures, or having LangSmith automatically suggest prompt improvements based on evaluation results.
Expanded Integrations
LangSmith and LangGraph Platform are available in AWS Marketplace (LangChain Blog, July 2025). Expect similar marketplace listings on GCP and Azure, plus deeper integrations with CI/CD tools, data warehouses, and business intelligence platforms.
Standardization Around OpenTelemetry
LangSmith supports OpenTelemetry to unify your observability stack across services (LangChain, 2025). As OTel becomes the standard for observability, LangSmith will deepen its support, making it easier to correlate LLM traces with traditional application metrics.
Competitive Pressure
The open-source alternatives are maturing quickly. Phoenix, Langfuse, and Helicone are all actively developed with strong communities. This competition will push LangSmith to innovate on features, usability, and pricing. The result benefits users: better tools across the board.
Frequently Asked Questions
Q1: Is LangSmith only for LangChain applications?
No. While LangSmith integrates seamlessly with LangChain and LangGraph, it works with any LLM application. LangSmith works with any framework. If you're already using LangChain or LangGraph, just set one environment variable to get started with tracing (LangChain, 2025). For other frameworks, use the Python or TypeScript SDK with decorators.
Q2: Does LangSmith add latency to my application?
No, LangSmith does not add any latency to your application. In the LangSmith SDK, there's a callback handler that sends traces to a LangSmith trace collector which runs as an async, distributed process (LangChain, 2025). Traces are sent asynchronously after responses are returned to users.
Q3: How much does LangSmith cost for a typical team?
The Plus plan costs $39 per seat per month (up to 10 seats) and includes 10,000 base traces monthly. Additional traces cost $0.50 per 1,000 (base) or $5.00 per 1,000 (extended). A 5-person team running 100,000 traces per month would pay approximately $240/month total.
Q4: Can I self-host LangSmith?
Yes, LangSmith can be self-hosted on the Enterprise plan. LangChain delivers the software to run on your Kubernetes cluster, and data will not leave your environment (LangChain Pricing, 2025). This option requires contacting sales for custom pricing.
Q5: What's the difference between LangChain and LangSmith?
LangChain helps you build workflows, while LangSmith helps ensure that they run smoothly by offering tools for debugging, monitoring and managing complex AI systems (IBM, November 2025). LangChain is the development framework (open-source). LangSmith is the observability platform (commercial).
Q6: How does LangSmith compare to traditional APM tools like Datadog?
Traditional APM tools track infrastructure metrics (CPU, memory, requests per second) but don't understand LLM-specific concerns like prompt quality, token usage, or semantic correctness. LangSmith provides LLM-native observability. Many teams use both: Datadog for infrastructure, LangSmith for LLM applications.
Q7: Can LangSmith help reduce my LLM costs?
Yes, in several ways. It tracks token usage by feature/user/model, helping you identify inefficient prompts or redundant calls. Evaluation features let you test cheaper models systematically. Cost tracking is especially important for agentic applications because resource usage is determined dynamically by the agent itself (LangChain Changelog, July 2025).
Q8: What happens to my data in LangSmith?
LangSmith will not train on your data, and you own all rights to your data. When using LangSmith hosted at smith.langchain.com, data is stored in GCP us-central-1 (LangChain, 2025). For stricter requirements, self-host on your infrastructure.
Q9: How long are traces retained?
Base traces have 14-day retention. Extended traces are retained for 400 days. LangSmith automatically upgrades traces with feedback to extended (LangSmith Documentation, 2025).
Q10: Can I evaluate models other than OpenAI?
Yes. LangSmith supports evaluation of any model accessible via API: Anthropic Claude, Google Gemini, open-source models on HuggingFace, self-hosted models, and more. The evaluation framework is model-agnostic.
Q11: Is there a free tier?
Yes. The Developer plan is free forever and includes 1 seat and 5,000 base traces per month, providing full access to observability and evaluation features.
Q12: How does LangSmith handle multimodal inputs?
LangSmith supports images, PDFs, and audio files across the playground, annotation queues, and datasets (LangChain Changelog, May 2025). You can trace and evaluate applications that process images, documents, or audio alongside text.
Q13: Can I export my data from LangSmith?
You can schedule automatic exports of your LangSmith traces without needing to set up your own infrastructure (LangChain Changelog, July 2025). This enables syncing to data warehouses, long-term archival, or compliance reporting.
Q14: Does LangSmith support real-time monitoring?
Yes. LangSmith tracks business-critical metrics like costs, latency, and response quality with live dashboards. You can set alerts when issues happen and drill into the root cause (LangChain, 2025).
Q15: What's the learning curve for LangSmith?
For LangChain users, the learning curve is minimal—just set environment variables. For others, the @traceable decorator requires understanding basic concepts (traces, runs, projects), but the UI is intuitive. Most teams become productive within a few hours.
Key Takeaways
LangSmith is purpose-built observability for LLM applications. It traces every step from input to output, making the "black box" of LLM chains understandable and debuggable.
The platform addresses the production gap. Building a prototype is easy; shipping reliable LLM applications is hard. LangSmith bridges this gap with systematic testing, evaluation, and monitoring.
Framework-agnostic design ensures wide applicability. While optimized for LangChain/LangGraph, LangSmith works with any LLM framework through SDKs and OpenTelemetry support.
Pricing scales from free to enterprise. Individual developers get 5,000 free traces monthly. Teams pay $39 per seat plus usage-based fees. Enterprises can self-host.
Real companies report measurable results. Klarna cut resolution times by 80%. Elastic improved threat response. AppFolio doubled accuracy. These aren't theoretical benefits.
The observability market is expanding rapidly. With LLM adoption accelerating and 30.1% of teams already using observability tools, demand for platforms like LangSmith will grow.
Competition drives innovation. Open-source alternatives like Phoenix and Langfuse provide pressure that benefits the ecosystem. Choose the tool that fits your needs and constraints.
Trace retention strategy matters for cost control. Use base traces (14 days) for debugging and let LangSmith automatically upgrade important traces to extended (400 days) when they receive feedback.
LangSmith is evolving toward a full agent platform. Observability is the foundation, but deployments, prompt management, and workflow orchestration are expanding the scope.
Starting is straightforward. Sign up for free, set environment variables, and run your application. Traces appear automatically. The barrier to entry is intentionally low.
Actionable Next Steps
Create a free LangSmith account at smith.langchain.com. No credit card required for the Developer plan.
Instrument one LLM application. Set the required environment variables and observe how traces appear in the dashboard. Start with your simplest chain to build intuition.
Build your first dataset. Identify 20-50 representative examples from production (or create synthetic ones). These become your foundation for evaluation.
Run a prompt comparison experiment. Test 2-3 prompt variations on your dataset. Use LLM-as-judge scoring to see which performs best. This workflow is the core value proposition.
Set up cost alerts. Define thresholds for daily/weekly token usage. LangSmith will notify you when costs spike, helping you catch issues before they hit your bill.
Integrate with your team's workflow. Connect LangSmith to Slack for error notifications. Add links to traces in your support tickets. Make observability part of your process.
Review the documentation. LangSmith's docs cover advanced features like custom evaluators, annotation queues, and meta-prompting. Explore what's possible beyond basic tracing.
Join the community. The LangChain Discord and GitHub discussions are active. Ask questions, share learnings, and see how others use LangSmith.
Plan your upgrade path. If you're on the free tier and growing, estimate when you'll need Plus or Enterprise based on trace volume and team size. Budget accordingly.
Measure ROI. Track specific metrics: time saved debugging, cost reductions from prompt optimization, or quality improvements from systematic evaluation. Quantify LangSmith's impact to justify investment.
Glossary
Agent: An LLM application that can reason, use tools, and take actions on behalf of users, often through multiple iterative steps.
Base Trace: A trace with 14-day retention, suitable for short-term debugging. Costs $0.50 per 1,000 traces.
Chain: A sequence of operations (prompts, model calls, parsing, tool use) connected to transform input into output.
Dataset: A collection of saved inputs and expected outputs used for testing and evaluation.
Evaluator: A function (rule-based, LLM-powered, or custom) that scores trace outputs on quality criteria.
Extended Trace: A trace with 400-day retention, automatically applied when feedback is received. Costs $5.00 per 1,000 traces.
LLM (Large Language Model): A neural network trained on massive text data to understand and generate human-like language (e.g., GPT-4, Claude, Gemini).
Observability: The ability to understand a system's internal state by examining its outputs (traces, metrics, logs).
Prompt: The text instruction or context provided to an LLM to guide its response.
Run: A single unit of work in a trace, such as an LLM call, tool invocation, or parsing step.
Trace: The complete execution flow of an application from input to output, composed of multiple runs.
Token: The basic unit of text processed by LLMs. Roughly 4 characters or 0.75 words. Pricing is per token.
Tool: An external function or API that an LLM can invoke (e.g., web search, calculator, database query).
Sources and References
LangChain Official Website. "LangSmith - Observability." LangChain, 2025. https://www.langchain.com/langsmith
LangChain Blog. "Announcing LangSmith, a unified platform for debugging, testing, evaluating, and monitoring your LLM applications." August 18, 2023. https://blog.langchain.com/announcing-langsmith/
LangChain Blog. "Reflections on Three Years of Building LangChain." October 2025. https://blog.langchain.com/three-years-langchain/
Fortune. "Exclusive: Early AI darling LangChain is now a unicorn with a fresh $125 million in funding." October 20, 2025. https://fortune.com/2025/10/20/exclusive-early-ai-darling-langchain-is-now-a-unicorn-with-a-fresh-125-million-in-funding/
TechCrunch. "Open source agentic startup LangChain hits $1.25B valuation." October 21, 2025. https://techcrunch.com/2025/10/21/open-source-agentic-startup-langchain-hits-1-25b-valuation/
LangChain. "Plans and Pricing." 2025. https://www.langchain.com/pricing
MetaCTO. "The True Cost of LangSmith - A Comprehensive Pricing & Integration Guide." June 24, 2025. https://www.metacto.com/blogs/the-true-cost-of-langsmith-a-comprehensive-pricing-integration-guide
LangChain Blog. "How Klarna's AI assistant redefined customer support at scale for 85 million active users." February 27, 2025. https://blog.langchain.com/customers-klarna/
LangChain Blog. "LangSmith and LangGraph Platform are now available in AWS Marketplace." July 16, 2025. https://blog.langchain.com/aws-marketplace-july-2025-announce/
LangChain Blog. "Is LangGraph Used In Production?" February 6, 2025. https://blog.langchain.com/is-langgraph-used-in-production/
Endo, Takafumi. "LangChain: Why It's the Foundation of AI Agent Development in the Enterprise Era." Medium, June 27, 2025. https://medium.com/@takafumi.endo/langchain-why-its-the-foundation-of-ai-agent-development-in-the-enterprise-era-f082717c56d3
Arize Phoenix Documentation. "Open Source LangSmith Alternative: Arize Phoenix vs. LangSmith." 2025. https://arize.com/docs/phoenix/learn/resources/faqs/langsmith-alternatives
Maxim AI. "Choosing the Right AI Evaluation and Observability Platform: An In-Depth Comparison of Maxim AI, Arize Phoenix, Langfuse, and LangSmith." September 4, 2025. https://www.getmaxim.ai/articles/choosing-the-right-ai-evaluation-and-observability-platform
IBM. "What is LangSmith?" November 18, 2025. https://www.ibm.com/think/topics/langsmith
ProjectPro. "How to Use LangSmith with HuggingFace Models?" 2024. https://www.projectpro.io/article/langsmith/1122
LangSmith Documentation. "Concepts." 2025. https://docs.smith.langchain.com/observability/concepts
LangSmith Documentation. "Frequently Asked Questions." 2024. https://docs.smith.langchain.com/pricing/faq
Keywords Everywhere Blog. "50+ Essential LLM Usage Stats You Need To Know In 2025." 2025. https://keywordseverywhere.com/blog/llm-usage-stats/
Mordor Intelligence. "Observability Market Size, Report, Share & Competitive Landscape 2030." November 13, 2024. https://www.mordorintelligence.com/industry-reports/observability-market
Secoda. "Key Data Observability Trends in 2025." April 9, 2025. https://www.secoda.co/blog/key-data-observability-trends
Hostinger. "LLM statistics 2025: Comprehensive insights into market trends and integration." July 1, 2025. https://www.hostinger.com/tutorials/llm-statistics
Sapphire Ventures. "Observability in 2024: Understanding the State of Play and Future Trends." November 18, 2024. https://sapphireventures.com/blog/observability-in-2024-understanding-the-state-of-play-and-future-trends/
Grand View Research. "Large Language Models Market Size | Industry Report, 2030." 2024. https://www.grandviewresearch.com/industry-analysis/large-language-model-llm-market-report
Global Market Insights. "Enterprise LLM Market Size & Share, Statistics Report 2025-2034." September 1, 2025. https://www.gminsights.com/industry-analysis/enterprise-llm-market
Menlo Ventures. "2025 Mid-Year LLM Market Update: Foundation Model Landscape + Economics." August 1, 2025. https://menlovc.com/perspective/2025-mid-year-llm-market-update/
Latenode. "LangChain Funding & Valuation 2025: Complete Financial Overview." 2025. https://latenode.com/blog/langchain-funding-valuation-2025-complete-financial-overview
Analytics Vidhya. "Ultimate Langsmith Guide for 2025." November 19, 2024. https://www.analyticsvidhya.com/blog/2024/07/ultimate-langsmith-guide/
LangChain Changelog. "July 2025." July 1, 2025. https://changelog.langchain.com/?categories=cat_FvjDMlZoyaKkX&date=2025-07-01
ZenML Blog. "LangGraph Pricing Guide: How Much Does It Cost?" November 2025. https://www.zenml.io/blog/langgraph-pricing
Helicone. "The Complete Guide to LLM Observability Platforms: Comparing Helicone vs Competitors (2025)." 2025. https://www.helicone.ai/blog/the-complete-guide-to-LLM-observability-platforms
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