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Composo vs LangSmith
LangSmith is the official observability and evaluation platform for teams building on LangChain and LangGraph. Composo is framework-agnostic - it works with LangChain, LlamaIndex, custom agents, or anything in between - and focuses on domain-specific failure detection rather than framework-native dev workflows.
LangSmith optimises for LangChain teams. Composo optimises for catching failures regardless of stack.
At a glance
| Dimension | Composo | LangSmith |
|---|---|---|
| Framework | Framework-agnostic (LangChain, LangGraph, LlamaIndex, custom) | LangChain-native; best experience inside LangChain/LangGraph |
| Core strength | Domain-specific quality evaluation and failure detection | Trace inspection, prompt playground, eval tooling for LangChain |
| Evaluation approach | Reward-model calibrated to your domain; learns from corrections | LLM-as-judge, custom evaluators, offline eval sets |
| Pricing | Enterprise contract per deployment | Usage-based; $0.50 per 1,000 base traces, $5 per 1,000 extended traces |
| Deployment model | We deploy it (FDE model, 2 to 4 weeks) | Self-serve SaaS (hours to integrate) |
| Production guardrails | Real-time pass/fail at inference boundary | Not a primary focus |
Where LangSmith is strong
- Native LangChain and LangGraph integration. If you are already building on LangChain, LangSmith is the path of least resistance for tracing and basic evaluation.
- Prompt Playground. Good for rapid prompt iteration against evaluation datasets.
- Offline and online eval support. Eval datasets, regression testing, experiment tracking.
- First-party tooling. Released, maintained, and documented by the LangChain team.
- Ecosystem momentum. The LangChain community is large and growing; LangSmith benefits from that flywheel.
Where Composo is different
- Framework-agnostic. LangSmith is best inside the LangChain ecosystem. Composo works equally well with LangChain, custom agents, LlamaIndex, raw OpenAI SDK calls, or multi-framework systems.
- Domain-specific calibration. LangSmith's evaluators are LLM-as-judge templates you configure. Composo calibrates a reward model to your specific failure modes over 2 to 4 weeks of deployment.
- Catches what LangSmith's evals miss. Composo reaches 90%+ alignment with human domain experts in most production contexts, vs roughly 70% for baseline LLM-as-judge evaluators.
- Production guardrails. Composo can block bad outputs at inference time. One customer rejects 50% of tool calls in real time before they execute.
- Learning from corrections. Domain experts label edge cases, and the evaluation model generalises those corrections across the whole production distribution.
When to pick which
Pick LangSmith if
- · Your stack is LangChain or LangGraph and you want first-party tooling
- · You need trace inspection, prompt playground, and offline eval as your primary workflow
- · Your evaluation needs are simple and you are comfortable configuring LLM-as-judge evaluators
- · SaaS self-serve pricing fits your buying model
Pick Composo if
- · You have multi-framework or custom agent infrastructure (not only LangChain)
- · You need domain-specific quality evaluation, not configurable LLM-as-judge
- · You have production AI where silent failures are unacceptable
- · You want a deployed, calibrated system - not a tool to configure
Frequently asked questions
Does Composo work with LangChain and LangGraph?
Yes. Composo is framework-agnostic and has dedicated integration guides for LangChain and LangGraph agents. You do not have to leave LangSmith to use Composo - Composo reads your existing traces.
Can I use LangSmith and Composo together?
Yes. A common setup is LangSmith for trace visualisation and prompt development, and Composo for production quality evaluation and runtime guardrails. They address different parts of the lifecycle.
Why would I pay for Composo if LangSmith has evaluators built in?
LangSmith's evaluators are LLM-as-judge prompts, which typically plateau around 70% alignment with human domain experts. Composo's domain-calibrated reward model reaches 90%+ alignment with human experts in most production contexts. For teams where that gap translates into missed production failures, Composo is worth the additional investment.
Is Composo only for LangChain users?
No. A large portion of Composo's customer base runs on custom agent frameworks, LlamaIndex, raw OpenAI SDK calls, or multi-framework setups. Composo's value proposition is domain-specific evaluation, not framework integration.
How does pricing compare?
LangSmith is usage-based: $0.50 per 1,000 base traces, $5 per 1,000 extended traces. Composo is an enterprise contract scoped per deployment, typically including calibration, the evaluation model, production guardrails, and ongoing support. Pricing conversations are on a diagnostic call.
See what Composo catches on your own AI.
A clinical-quality failure report on your production AI, delivered in under a week.