The short answer: LangSmith is LangChain's developer observability, tracing, and evaluation platform for LLM applications that engineers build and maintain in code. Gravity is an AI agent platform where non-developers run finished, expert-built agents in 60 seconds, with no code required. They operate at opposite ends of the spectrum: LangSmith instruments agents you build; Gravity runs agents for you.

What LangSmith actually is

LangSmith is a commercial product from LangChain Inc., the company behind the LangChain framework and LangGraph runtime. It was released in 2023 as a companion product to those open-source tools, giving engineering teams a structured way to observe, debug, test, and evaluate the LLM applications they build on top of the LangChain stack.

The core product is an instrumentation and evaluation layer. You add the LangSmith SDK to your Python or JavaScript agent codebase, point it at a project, and it begins capturing traces: every call to the LLM, every tool invocation, every intermediate step in the agent loop, token counts, latencies, errors, and the full prompt and completion text. Those traces appear in LangSmith's browser UI, where engineers can inspect them, filter by time or status, replay specific runs, and compare prompt versions.

Tracing and debugging

The tracing surface is LangSmith's strongest feature. When a LangChain or LangGraph agent produces a wrong answer or hits an unexpected error, the trace shows exactly where in the execution graph the failure happened: which tool call returned bad data, which LLM completion went off the rails, which retry loop spun out. For teams building complex multi-step agents in code, this visibility is genuinely valuable. See AI agent monitoring and observability for the broader landscape of what good observability looks like.

Evaluation tooling

LangSmith also offers a structured evaluation workflow. Engineers can define datasets of input-output pairs, run their agent against those datasets, and score the results with LLM-as-judge evaluators or custom scoring functions. This lets teams track quality regressions when they change prompts or swap model versions. The evaluation metrics that matter for production agents, including faithfulness, task completion rate, and tool accuracy, can be tracked in LangSmith's dashboards across versions. That said, you need to write the evaluation harness yourself; LangSmith provides the infrastructure, not the evaluators.

Prompt Hub and playground

LangSmith includes a prompt hub for storing and versioning prompts, and a playground for testing them interactively against different model providers. Teams can share prompts across projects, track which prompt version shipped with which model, and roll back if a version underperforms.

What LangSmith is not

LangSmith does not run agents. It does not contain any finished agents you can deploy. It does not provide a user interface for non-developers. It does not replace a platform. It is an observability and evaluation layer that assumes you already have an agent codebase and want to see inside it more clearly. Without that underlying codebase, LangSmith has nothing to observe.

What Gravity does

Gravity is an AI agent platform at the opposite end of the build-vs-run spectrum. You do not write code. You do not instrument anything. You describe what you need in plain words, and an expert-built agent runs it end to end, typically in about 60 seconds.

The agents on Gravity are built and maintained by specialists. The platform handles model routing, tool integrations, error recovery, retries, and the infrastructure that keeps agents running reliably in production. From the user's perspective, the interface is an outcome description: "Summarise last week's support tickets by category and flag anything with a CSAT below 7." The agent handles everything else.

Gravity is pay-per-use: $1 equals 1,000 credits, and you pay only when an agent runs. There is no subscription to maintain an idle codebase. See AI agent platform pricing comparison 2026 for how this stacks up against other options.

If you are coming from the LangChain ecosystem and wondering how Gravity fits relative to the whole stack, the Gravity vs LangChain comparison covers the framework side, and Gravity vs LangGraph covers the runtime side. LangSmith is the observability layer that sits on top of both.

For readers comparing Gravity against other agent-building tools rather than LangChain-adjacent tools, Gravity vs CrewAI and best LangChain alternatives for non-developers are useful starting points.

Side-by-side comparison

The table below maps the two products across dimensions that matter for someone choosing between "build and instrument my own agent stack" versus "run finished agents on a platform."

Dimension LangSmith Gravity
Who it is for Software engineers building LLM apps and agents in code Anyone who needs agents to do work: founders, ops teams, non-developers
Code required Yes. You must instrument an existing codebase with the LangSmith SDK. No. You describe an outcome in plain words and the agent runs.
Time to first working agent Weeks to months (build the agent first, then instrument with LangSmith) Under 60 seconds to run a finished agent
What you actually get Observability and evaluation layer for agents you built yourself Finished, expert-built agents ready to run
Tracing and debugging Yes: full trace capture, step-level inspection, error replay Platform-managed; internal observability is not exposed to users
Evaluation tooling Yes: dataset-driven eval, LLM-as-judge, custom scorers, version comparison Not applicable; agents are maintained and evaluated by the platform
Maintenance burden On your team: you own the agent code, model versions, SDK upgrades, and infra On the platform: Gravity maintains agents, swaps models, handles uptime
Hosting and infra Cloud-hosted SaaS (with self-hosted Enterprise option); your agent runs on your infra Fully managed; no infra to provision or maintain
Pricing model Tiered SaaS subscription (free tier available; paid tiers by traces and seats) Pay-per-use; $1 = 1,000 credits; no baseline subscription fee
Best for Engineering teams that built LLM applications and need structured visibility into agent behaviour Non-engineering buyers who want agents running immediately with no build investment

Who should use LangSmith

LangSmith is the right tool for engineering teams in a specific situation: they are actively building and maintaining LLM applications or multi-step agents in code, and they need structured visibility into what those agents are doing.

You are building agents in LangChain or LangGraph

LangSmith integrates most tightly with LangChain and LangGraph. If your team's agent runtime is LangGraph and you need to inspect the state transitions, tool calls, and LLM responses across runs, LangSmith is the natural observability companion. The observability dashboards it provides are purpose-built for this stack.

You need systematic evaluation across prompt versions

When a prompt change might degrade quality across a large class of inputs, LangSmith's dataset-driven evaluation harness lets teams run the old and new versions against the same inputs and score the delta. This is a meaningful time-saver compared to manually inspecting outputs. For a broader view of what to measure, see AI agent evaluation metrics.

You are debugging production failures in complex agent loops

When a multi-step agent in production produces a wrong result and your logs are a flat stream of JSON, LangSmith's trace view reconstructs the full execution tree. Engineers can identify the exact node where the agent diverged from the correct path, check the inputs and outputs at that node, and reproduce the failure in the playground.

Your team operates a mature MLOps or AI engineering function

LangSmith fits into an established engineering workflow. Teams that already have CI/CD pipelines for their agent code, that run evaluation suites before shipping prompt changes, and that have dedicated time to instrument and maintain the observability layer will get the most from the product.

Who should use Gravity

Gravity is the right choice when the goal is getting agents running quickly, without building or maintaining anything.

You do not have an engineering team dedicated to AI agent development

Most teams that need agents are not AI engineering teams. A growth team that wants an agent to qualify inbound leads, a finance team that wants an agent to reconcile invoices, or a founder who wants an agent to monitor competitors does not have the bandwidth to build, instrument, and maintain a LangChain-based system. Setting up your first AI agent covers what a no-code path looks like in practice.

Speed to value is the constraint

Building a production-ready agent in code takes weeks at minimum: designing the agent loop, wiring tool integrations, writing evaluation datasets, standing up LangSmith, debugging edge cases in traces, and shipping. If you need the agent running this week, a finished platform is the right answer.

Standard operations work

The majority of agent use cases are recurring, well-understood tasks: summarising reports, triaging inboxes, following up with leads, extracting structured data from documents, monitoring dashboards. These are not novel primitives requiring custom engineering. They are standard work, and a platform handles them without any code investment from the buyer.

You want predictable, per-use costs

The full cost of the LangSmith path includes the LangSmith subscription, model API costs, infrastructure costs, and engineering time. For buyers without a dedicated AI engineering team, that total is high relative to the value delivered. Gravity's pay-per-use model prices the outcome directly.

Can you use Gravity and LangSmith together

Technically, yes, though they do not share a direct integration surface. The practical scenario is a team that runs standard-ops agents on Gravity (zero engineering required) while also maintaining a separate internal codebase for more custom or novel agent work instrumented with LangSmith. The two tools cover different layers and different use cases within the same organisation.

There is no scenario where a LangSmith user would point LangSmith at Gravity's infrastructure to trace Gravity-managed agents. Gravity's observability is internal to the platform. What LangSmith observes is the code you own and run.

The cleaner framing: if you are evaluating whether to build a custom agent stack (and instrument it with LangSmith) versus run a finished agent on Gravity, that is a build-vs-buy decision. Most teams should buy for standard work and build only where custom primitives are genuinely required.

Pricing and cost model

LangSmith uses a tiered SaaS subscription model. As of 2026, a free tier is available with limits on traces and seats; paid tiers unlock higher trace volumes, additional seats, longer data retention, SSO, and Enterprise features including self-hosting options (LangSmith product page, retrieved 2026). The LangSmith subscription does not include the cost of the LLM API calls or the infrastructure your agent runs on; those are billed separately by the model provider and your cloud host.

Gravity is pay-per-use with no baseline subscription. $1 equals 1,000 credits; you pay only when an agent runs. Infrastructure, model routing, and maintenance are included. There is no charge for idle time or unused capacity. This makes the cost structure directly comparable to the value delivered: you pay per outcome, not per seat or per trace.

For teams comparing total cost across the build-vs-buy options, the honest accounting on the build side includes: LangSmith subscription, model API costs (per token, billed by the provider), cloud infrastructure, and engineering time to build, evaluate, and maintain the agent codebase. On the Gravity side, it is the per-run credit cost, full stop.

Frequently asked questions

Is LangSmith an AI agent platform?

No. LangSmith is an observability, tracing, and evaluation platform built by LangChain for engineers who are already building LLM and agent applications in code. It shows you what your agents are doing internally and helps you measure quality, but it does not run agents for you. If you want to run finished agents without writing code, you need a platform like Gravity.

Do I need to code to use LangSmith?

Yes. LangSmith is a developer tool. You instrument your agent code with the LangSmith SDK, which adds tracing to your LangChain or LangGraph application. There is no way to use LangSmith without first writing the agent code it is meant to observe. It is built for software engineers, not for business users or ops teams who want to run agents without coding.

Can I use Gravity and LangSmith together?

They serve different layers, so direct integration is unlikely to be necessary. If your team uses Gravity to run production agents, Gravity handles the observability layer internally. If you are also maintaining a separate in-house agent codebase built on LangChain or LangGraph, you could instrument that codebase with LangSmith independently. They do not compete; they sit at different levels of the stack.

Which is cheaper, Gravity or LangSmith?

They price different things. Gravity charges per agent run: $1 equals 1,000 credits, and you only pay when an agent actually executes. LangSmith charges for the observability and evaluation layer on top of an LLM application you build and host yourself, meaning you also pay separately for the underlying model calls, infrastructure, and engineering time. Total cost on the LangSmith path is almost always higher because you are assembling more pieces.

Who should use LangSmith vs Gravity?

Use LangSmith if you have a software engineering team actively building and maintaining LLM applications or agents in code, and you need structured visibility into traces, prompts, latencies, and evaluation scores. Use Gravity if you want to run expert-built agents in 60 seconds without writing or maintaining code. The two tools address opposite ends of the build-vs-run spectrum.

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