LangSmith vs SurrealDB: Which Is Better in 2026?
A side-by-side comparison of LangSmith and SurrealDB, two dev tools tools — what each does, who it's best for, and how to choose between them.
Quick verdict
LangSmith and SurrealDB are both dev tools tools, so it comes down to fit. Pick LangSmith if you want An observability and evaluation platform for LLM apps and AI agents — trace, monitor and debug… Pick SurrealDB if you want A multi-model database unifying documents, graphs, vectors and more in one system — a context layer…
LangSmith
An observability and evaluation platform for LLM apps and AI agents — trace, monitor and debug what they really do.
- Category
- Dev Tools
- Rating
- Not yet rated
- Best for
- observability, llm, agent monitoring
SurrealDB
A multi-model database unifying documents, graphs, vectors and more in one system — a context layer for AI.
- Category
- Dev Tools
- Rating
- Not yet rated
- Best for
- database, multi-model, ai agents
| At a glance | LangSmith | SurrealDB |
|---|---|---|
| What it is | An observability and evaluation platform for LLM apps and AI agents — trace, monitor and debug what they really do. | A multi-model database unifying documents, graphs, vectors and more in one system — a context layer for AI. |
| Category | Dev Tools | Dev Tools |
| Type | Software | Software |
| Best for | observability, llm, agent monitoring, tracing | database, multi-model, ai agents, graph |
What is LangSmith?
LangSmith is an observability, testing and evaluation platform for LLM applications and AI agents, built by the team behind LangChain. As AI apps move from demos to production, LangSmith gives developers the missing visibility layer — showing exactly what an agent did, where it went wrong, what it cost, and whether changes actually made it better.
What is LangSmith?
LangSmith gives you complete visibility into agent and LLM behavior through tracing, monitoring and evaluation. Every run is captured step by step, so you can see the prompts, tool calls, retrievals and model responses that produced an output — and pinpoint what is hurting latency, cost or quality. On top of that sit real-time dashboards, automatic insight clustering, and a rigorous evaluation framework for measuring quality over time.
Who it's for
LangSmith is built for development teams shipping AI agents and LLM applications — from solo builders and startups to large enterprises. Its customers include names like Expedia, Autodesk, Nvidia, Coinbase and ServiceNow, which speaks to how it holds up at serious scale and under real production demands.
Key features
- Tracing: step-by-step visibility into exactly what your agent is doing
- Monitoring: real-time dashboards for token usage, latency, error rates, cost and custom feedback scores
- Insights: automatic clustering to detect usage patterns, common behaviors and failure modes
- Evaluations and datasets for measuring and improving quality
- SmithDB, a purpose-built database for querying nested agent traces with sub-second performance
- SDKs for Python, TypeScript, Go and Java, plus OpenTelemetry support
Framework-agnostic by design
Although it comes from the LangChain team, LangSmith is deliberately framework-agnostic. It works with popular agent frameworks natively and supports OpenTelemetry, so you can instrument an app whether or not it is built on LangChain. That openness matters — it means teams are not locked into one stack to get production-grade observability.
Built specifically for agents
General application-monitoring tools were not designed for the messy, nested, non-deterministic nature of LLM agents. LangSmith was. Its tracing understands multi-step agent runs, SmithDB is optimized for querying those deeply nested traces quickly, and its insight clustering surfaces the failure modes that are unique to AI systems — hallucinations, tool misuse, prompt regressions — rather than just server errors.
Deployment and pricing
LangSmith offers flexible deployment to suit data-residency and compliance needs: fully managed cloud, bring-your-own-cloud (BYOC), and self-hosted. Pricing starts with a free tier for development, then scales with trace volume, with enterprise pricing available on request. That range lets a hobbyist start free and an enterprise run it inside their own infrastructure.
From prototype to production with confidence
The hardest part of building with LLMs is not the demo — it is trusting the system once real users hit it. LangSmith's evaluations and datasets let teams turn subjective "does this feel better?" judgments into measurable scores: you build test sets from real traces, run new prompts or models against them, and see quantitatively whether quality improved or regressed. Paired with live monitoring of cost, latency and error rates, that closes the loop between shipping a change and knowing its true impact, so teams can iterate quickly without breaking what already works.
Why choose LangSmith
For any team taking an LLM app or agent beyond a prototype, LangSmith is close to essential. It turns opaque, unpredictable AI behavior into something you can see, measure and improve — catching regressions before users do and giving you the evaluation data to ship changes with confidence. If you are building agents seriously, purpose-built observability like this is what keeps them reliable in production.
What is SurrealDB?
SurrealDB is a multi-model database that unifies documents, graphs, vectors, relational data and more in a single engine, queried with a SQL-like language called SurrealQL. Positioned as “the unified data layer for AI,” it aims to replace the stack of separate databases and services most modern applications bolt together, and it is trusted by organizations including Samsung, Verizon, Tencent and PolyAI.
What is SurrealDB?
SurrealDB collapses several kinds of database into one. Instead of running a document store, a graph database, a vector database, an auth service and a cache side by side, you model documents, relationships and vector embeddings in the same engine and query them together with SurrealQL. It is ACID-compliant with distributed write nodes, and it can run embedded inside your application or as a managed cloud service.
Who it's for
SurrealDB is built for developers and engineering teams building data-intensive and AI applications — agents, knowledge graphs, semantic search and real-time apps. It suits teams tired of gluing multiple data systems together and keeping them in sync, as well as enterprises that need one platform to reason over connected, multi-shaped data.
Key features
- Multi-model: documents, graphs, vectors and relational data in one engine
- SurrealQL, a familiar SQL-like query language
- ACID compliance with distributed write nodes
- Built-in authentication and row-level permission models
- Full-text and semantic (vector) search combined in a single query
- An Agent Memory layer for persistent AI-agent context
- Real-time capabilities with roughly 30ms vector-search latency
- Deployable embedded or as a cloud service
Built for the AI era
What makes SurrealDB especially timely is how well its multi-model design fits AI workloads. Retrieval-augmented generation and agents need vector search, structured data and relationships together — usually meaning a vector DB plus a document store plus a graph plus glue code. SurrealDB does all of it in one place, and its Agent Memory layer gives AI agents persistent, queryable context, so the database itself becomes the memory layer instead of yet another bolted-on service.
Fewer systems, less complexity
The core payoff is consolidation. By folding what typically requires five separate systems — vector database, graph database, document store, auth service and cache — into one engine, SurrealDB cuts operational complexity, reduces the latency of hopping between services, and removes whole classes of synchronization bugs. Combined built-in auth and permissions mean access control lives with the data rather than in a separate layer.
Open source and enterprise-ready
SurrealDB is available under open-source licensing, with a free tier accessible through SurrealDB Studio, so developers can start locally at no cost. For production use it brings serious credentials: SOC 2 Type 2, GDPR, ISO 27001 and Cyber Essentials Plus compliance, plus managed cloud hosting — the assurances enterprises need to trust it with real workloads.
Flexible deployment and querying
SurrealDB adapts to how a team wants to work. It can be embedded directly inside an application for local-first or edge scenarios, or run as a managed cloud service for scale, and SurrealQL gives developers a familiar, SQL-like way to express queries that would otherwise require juggling several query languages across different databases. Combining full-text and vector search in a single query — rather than stitching results from separate engines — is the kind of thing that turns complex retrieval code into a few readable lines.
Why choose SurrealDB
For a team building modern, data-intensive or AI-driven applications, SurrealDB offers a genuinely different proposition: one flexible, real-time, multi-model database instead of a fragile constellation of specialized services. Its AI-focused features, open-source availability and enterprise compliance make it a compelling foundation for developers who want power and simplicity at the same time.
Key differences at a glance
- Purpose: LangSmith is An observability and evaluation platform for LLM apps and AI agents — trace, monitor and debug what they really do. SurrealDB, by contrast, is A multi-model database unifying documents, graphs, vectors and more in one system — a context layer for AI.
- Category & type: both sit in Dev Tools, and both are offered as software.
- Best suited for: LangSmith leans toward observability, llm, agent monitoring, whereas SurrealDB leans toward database, multi-model, ai agents.
- Community rating: LangSmith is not yet rated vs SurrealDB is not yet rated. Ratings are community-submitted and change over time.
LangSmith vs SurrealDB: which should you choose?
LangSmith and SurrealDB both serve the dev tools space, so the best choice depends on your priorities. Choose LangSmith if you want An observability and evaluation platform for LLM apps and AI agents — trace, monitor and debug what they… Choose SurrealDB if you want A multi-model database unifying documents, graphs, vectors and more in one system — a context layer for AI.The smartest move is to try each one's free tier or trial on a real task — that's the fastest way to feel the difference and pick the tool you'll actually stick with.
Frequently asked questions
Is LangSmith better than SurrealDB?
It depends on what you need. LangSmith is An observability and evaluation platform for LLM apps and AI agents — trace, monitor and debug what they really do. SurrealDB is A multi-model database unifying documents, graphs, vectors and more in one system — a context layer for AI. Both are dev tools tools, so the right pick comes down to your specific priorities, budget and workflow.
What's the main difference between LangSmith and SurrealDB?
LangSmith focuses on An observability and evaluation platform for LLM apps and AI agents — trace, monitor and debug what they really do. while SurrealDB focuses on A multi-model database unifying documents, graphs, vectors and more in one system — a context layer for AI. Read the full breakdown above and check each tool's site for current features and pricing.
Can I use both LangSmith and SurrealDB?
In many cases, yes — teams often use complementary tools together. Whether it makes sense depends on overlap in functionality and your budget. Try the free tier or trial of each to see how they fit your stack before committing.
Which is cheaper, LangSmith or SurrealDB?
Pricing changes often, so check each tool's pricing page for the latest. Many tools offer a free tier or trial, which is the best way to evaluate value for your specific usage before you pay.