LangSmith vs Braintrust
ComparisonLangSmith and Braintrust are two of the most prominent platforms in the rapidly maturing AI observability space, both offering tracing, evaluation, and monitoring for LLM-powered applications and AI agents. As agent workflows grow more complex and autonomous, the tooling that watches over them has become mission-critical infrastructure—and the choice between these two platforms increasingly defines how teams ship and maintain AI in production.
LangSmith, built by the team behind LangChain, has expanded aggressively through 2025 and into 2026—launching its Insights Agent for automated production analysis, multi-turn evaluations, the Fleet agent builder for non-technical users, and a managed deployment runtime. Braintrust, meanwhile, closed an $80 million Series B in February 2026 at an $800 million valuation, doubling down on its framework-agnostic approach with support for 13+ integration frameworks, a built-in AI proxy with sub-100ms caching, and CI/CD quality gates that block deploys when evaluation scores drop.
Both platforms solve the same fundamental problem—making AI systems observable and measurable—but they take meaningfully different paths to get there. This comparison breaks down where each excels, where they fall short, and which one fits your team's stack and workflow.
Feature Comparison
| Dimension | LangSmith | Braintrust |
|---|---|---|
| Framework Integration | Deep, zero-config integration with LangChain and LangGraph; other frameworks require manual instrumentation | Framework-agnostic with native SDKs for 13+ frameworks including LangChain, OpenAI Agents SDK, Vercel AI SDK, Google ADK, and more |
| Tracing | Automatic end-to-end tracing of LLM calls, tool invocations, and decision points; strongest within LangChain ecosystem | Exhaustive tracing capturing prompts, tool calls, retrieved context, latency, and cost metadata across any framework |
| Evaluation | Multi-turn evals, pairwise annotation queues, custom evaluator support; Insights Agent runs automated analysis on schedule | 25+ built-in scorers; Loop AI assistant generates custom scorers from natural language; evaluation playground for non-technical users |
| CI/CD Integration | Evaluation results surfaced in dashboards but do not automatically block deploys; manual review required | Native GitHub Action runs evals on every PR and gates releases that would reduce quality scores |
| AI Proxy / Gateway | No built-in proxy; relies on direct model provider connections | Built-in proxy with unified access to OpenAI, Anthropic, Google, AWS, Mistral; automatic caching under 100ms |
| Deployment | LangSmith Deployment offers managed agent runtime with human-in-the-loop, background agents, and exactly-once execution | No managed agent deployment; focused on observability and evaluation layers |
| Non-Technical Access | Fleet agent builder lets non-technical users create and manage agents via natural language | Evaluation playground and collaborative UI designed for cross-functional teams including product managers and domain experts |
| Cost Analytics | Unified cost view across full agent workflows including non-LLM steps | Granular per-request cost breakdown by tokens, model, user, and feature; identifies high-cost request segments |
| Self-Hosting | Available for Enterprise tier; AWS Marketplace deployment option added in 2026 | Enterprise self-hosting with SOC2 and HIPAA compliance options |
| Free Tier | 5,000 traces/month, 14-day retention, 1 seat | 10,000 scores, 1 GB storage, 14-day retention, unlimited users and projects |
| Paid Pricing | Plus at $39/seat/month; 10K traces included, $2.50/1K overage; per-seat billing | Starter at $249/month flat; 50K scores, 5 GB storage, 30-day retention; unlimited users included |
| Language Support | Strongest in Python; TypeScript SDK available but secondary | First-class support for both Python and TypeScript/JavaScript |
Detailed Analysis
Framework Lock-In vs. Framework Freedom
The most consequential difference between LangSmith and Braintrust is their relationship to the broader AI development ecosystem. LangSmith's greatest strength—zero-config tracing for LangChain and LangGraph applications—is also its most significant limitation. If your stack is built on LangChain, LangSmith's automatic instrumentation is unmatched. But teams using OpenAI's Agents SDK, Vercel AI SDK, Google ADK, or other frameworks face additional integration work.
Braintrust takes the opposite approach with native support for 13+ frameworks out of the box. This matters increasingly as teams adopt multi-framework architectures or switch providers as the foundation model landscape evolves. Braintrust's framework-agnostic design means you don't rebuild your observability stack when you change your orchestration layer.
For teams already deep in the LangChain ecosystem with no plans to leave, LangSmith's tight integration is a clear advantage. For everyone else, Braintrust's flexibility reduces long-term risk.
Evaluation Philosophy: Automated Gates vs. Dashboard Reviews
Both platforms offer robust evaluation capabilities, but they differ in how evaluation results flow into the development lifecycle. Braintrust's native GitHub Action runs evaluation suites on every pull request and can block merges when quality scores drop below thresholds. This shifts AI quality assurance left into the CI/CD pipeline, catching regressions before they reach production.
LangSmith surfaces evaluation results in dashboards and recently added the Insights Agent, which runs automated analysis on a configurable schedule. However, these results don't automatically gate deployments—someone must manually review dashboards and intervene. LangSmith's pairwise annotation queues add structured human evaluation, which is valuable for subjective quality assessment but adds latency to the feedback loop.
For teams that want automated quality enforcement in their deploy pipeline, Braintrust's approach is more mature. LangSmith's model is better suited to teams that prefer human-in-the-loop evaluation workflows where automated blocking could be too aggressive.
The Proxy Advantage
Braintrust's built-in AI proxy is a differentiator that LangSmith simply doesn't match. The proxy provides unified access to models from OpenAI, Anthropic, Google, AWS, and Mistral through a single endpoint, with automatic response caching that delivers sub-100ms latency on cached requests. This means Braintrust can simultaneously serve as your LLM gateway and your observability platform, reducing infrastructure complexity.
LangSmith has no equivalent proxy capability, requiring teams to manage model provider connections separately and integrate a standalone gateway if they want caching or unified routing. For teams running multi-model architectures—increasingly common as specialized models emerge for different tasks—Braintrust's proxy consolidates what would otherwise be a separate infrastructure concern.
Deployment and Runtime: LangSmith's Unique Play
LangSmith has moved beyond pure observability with its Deployment offering—a managed runtime for deploying agents with durable execution, human-in-the-loop approvals, background processing, and multi-agent coordination. This is territory Braintrust hasn't entered; it remains focused on the observability and evaluation layers.
LangSmith Fleet further extends this with a no-code agent builder for non-technical teams, positioning LangSmith as a more complete platform for organizations that want to build and monitor agents in one place. For teams that need managed agent deployment alongside monitoring, LangSmith offers a vertically integrated solution that Braintrust can't match.
However, this vertical integration comes with the same lock-in trade-off: adopting LangSmith Deployment ties your runtime to LangChain's infrastructure, while Braintrust's evaluation-only focus lets you pair it with any deployment strategy.
Pricing and Team Economics
The pricing models reflect fundamentally different philosophies. LangSmith charges $39 per seat per month, which scales linearly with team size. A 10-person team pays $390/month before trace overage. Braintrust charges a flat $249/month for its Starter plan with unlimited users, making it dramatically cheaper for larger teams.
Braintrust's unlimited-users model is particularly advantageous for organizations that want product managers, domain experts, and QA teams involved in AI evaluation—not just engineers. LangSmith's per-seat pricing can create friction around giving non-engineering stakeholders access to observability data. On the other hand, solo developers or very small teams may find LangSmith's $39/seat entry point more accessible than Braintrust's $249 flat fee.
Ecosystem Momentum and Funding
Both companies are well-capitalized and growing. Braintrust's $80M Series B in February 2026, led by Iconiq with participation from Andreessen Horowitz and Greylock at an $800M valuation, signals strong investor confidence in the framework-agnostic observability approach. LangChain, LangSmith's parent company, benefits from the massive LangChain open-source community and its position as the most widely adopted agent framework.
LangSmith's ecosystem advantage is real: many developers encounter it as the natural monitoring solution when they start with LangChain. Braintrust must win teams through product merit rather than ecosystem gravity, but its broader framework support positions it well as the agent framework landscape fragments and diversifies.
Best For
LangChain/LangGraph Production Monitoring
LangSmithZero-config automatic instrumentation for LangChain applications provides the lowest-friction path to production observability. No other platform matches this level of native integration with the LangChain ecosystem.
CI/CD Quality Gates for AI
BraintrustBraintrust's native GitHub Action and deploy-blocking evaluation gates are purpose-built for automated quality enforcement in CI/CD pipelines. LangSmith's dashboard-based review workflow requires manual intervention.
Multi-Framework Agent Monitoring
BraintrustWith native SDKs for 13+ frameworks, Braintrust handles heterogeneous agent stacks without requiring teams to standardize on a single orchestration framework.
Managed Agent Deployment
LangSmithLangSmith Deployment is the only option here—Braintrust doesn't offer agent runtime. If you want observability and deployment in one platform with human-in-the-loop workflows, LangSmith is the choice.
Large Cross-Functional Teams
BraintrustUnlimited users at $249/month flat vs. LangSmith's per-seat pricing makes Braintrust far more economical when product managers, QA, and domain experts need access alongside engineers.
Multi-Model Routing and Caching
BraintrustBraintrust's built-in AI proxy with sub-100ms caching and unified model access eliminates the need for a separate LLM gateway. LangSmith has no equivalent capability.
Non-Technical Agent Building
LangSmithLangSmith Fleet lets non-technical users create agents via natural language descriptions—a capability Braintrust doesn't offer. Ideal for organizations that want business teams to build simple automation agents.
TypeScript-First Teams
BraintrustBraintrust treats TypeScript as a first-class citizen alongside Python. LangSmith's TypeScript support exists but is secondary to its Python-first SDK and documentation.
The Bottom Line
For teams building on LangChain and LangGraph who want a vertically integrated platform spanning observability, evaluation, and managed deployment, LangSmith is the natural choice. Its zero-config tracing, Insights Agent, and Deployment runtime create a cohesive experience that no competitor can match within that ecosystem. If LangChain is your foundation and you plan to keep it that way, LangSmith reduces friction at every step.
For everyone else—and especially for teams running multi-framework stacks, wanting automated CI/CD quality gates, needing a built-in AI proxy, or scaling access across large cross-functional organizations—Braintrust is the stronger platform in 2026. Its framework-agnostic design, deploy-blocking evaluations, generous unlimited-user pricing, and recent $80M Series B funding signal a platform built for the increasingly fragmented reality of production AI. Braintrust doesn't try to own your agent runtime; it focuses on making whatever you build observable and measurable.
The market is heading toward framework diversity, not consolidation. As teams adopt specialized models and orchestration tools for different use cases, the observability layer that works across all of them becomes more valuable than one tightly coupled to a single framework. That trajectory favors Braintrust's approach—but LangSmith's ecosystem gravity and expanding feature set make it a formidable incumbent, particularly for organizations that value vertical integration over flexibility.