vs LangSmith LangSmith debugs. Prefactor governs.
LangSmith traces LLM calls, runs evaluations, and catches regressions. Prefactor enforces policies, scores risk, and routes decisions to human approvers.
- LLM tracing: detailed traces of every LLM call — inputs, outputs, latency, token usage, and chain execution visualised end-to-end.
- Evaluation framework: run evaluations against datasets, compare prompt versions, and detect quality regressions systematically.
- Dataset management: curate test datasets, collect production examples, and build evaluation pipelines around real-world inputs.
- Prompt playground: iterate on prompts interactively, test variations, and compare outputs side-by-side.
- Monitoring: track latency, error rates, and usage patterns across your LLM applications in production.
- LangChain integration: automatic tracing and deep visibility for applications built on the LangChain framework.
Best for: development teams using LangChain who need to evaluate, debug, and iterate on LLM application quality.
What we do - Outcome quality assessment: did the agent produce the right result for the task — not just avoid errors or match a test dataset?
- Cost efficiency assessment: was the spend proportionate to the result? Enforce cost caps and prevent overspend at runtime.
- Scope adherence: did the agent stay within its approved boundaries, tools, and actions — or did it drift out of scope?
- Composite risk score combining outcome, cost, and scope signals with customer-set thresholds.
- Inline blocking and approval routing when risk thresholds are crossed — enforce governance in real time.
- Agent registry and lifecycle governance from registration through retirement with role-based controls.
- Immutable audit trail for regulatory compliance and incident investigation.
Best for: AI leadership, compliance, and governance teams that need to enforce policies and control agent behaviour in production.
LangSmith: evaluation and debugging
- LLM tracing and chain visualisation
- Dataset-driven evaluation
- Prompt iteration and regression detection
- LangChain-native tooling
Prefactor: governance and enforcement
- Risk scoring and assessment
- Outcome quality evaluation
- Real-time policy enforcement
- Approval routing and blocking
LangSmith helps you build better agents during development. Prefactor helps you run them responsibly in production. Evaluation and governance are complementary disciplines.
Evaluation measures quality. Governance enforces boundaries.
Evaluation platforms like LangSmith help teams measure and improve LLM output quality — running test suites, comparing prompt versions, and detecting regressions. Governance platforms like Prefactor help teams enforce rules about what agents are allowed to do in production — setting cost caps, defining scope boundaries, scoring risk in real time, and taking action when thresholds are crossed. LangSmith tells you if an output changed. Prefactor tells you if an agent crossed a boundary and decides what to do about it. Teams that care about both quality and control need both evaluation and governance.
| Capability | LangSmith | |
|---|---|---|
| Evaluation and tracing | ||
| Primary use case | Evaluate and debug LLM applications | Govern agent behaviour at runtime |
| LLM call tracing | ✓ | — |
| Dataset-driven evaluation | ✓ | — |
| Prompt playground | ✓ | — |
| Regression detection | ✓ | — |
| Production monitoring | ✓ | ✓ |
| Framework-agnostic | ◔ | ✓ |
| Agent assessment | ||
| Outcome quality assessment | — | ✓ |
| Cost efficiency assessment | — | ✓ |
| Scope adherence evaluation | — | ✓ |
| Composite risk scoring | — | ✓ |
| Governance and enforcement | ||
| Policy enforcement | — | ✓ |
| Inline blocking of agent execution | — | ✓ |
| Approval routing | — | ✓ |
| Cost cap enforcement | — | ✓ |
| Scope enforcement | — | ✓ |
| Enterprise readiness | ||
| Agent registry | — | ✓ |
| Lifecycle governance | — | ✓ |
| Role-based access control | ✓ | ✓ |
| Immutable audit trail | ◔ | ✓ |
| Regulatory compliance support | — | ✓ |
Evaluation and runtime governance
Use LangSmith to evaluate and iterate on LLM quality during development. Use Prefactor to enforce governance policies in production. Evaluation and governance are complementary.
Book a demo View all comparisonsFrequently asked questions
What is the difference between LLM evaluation and agent governance?
LLM evaluation — what LangSmith provides — focuses on tracing LLM calls, running evaluations against datasets, detecting prompt regressions, and helping developers iterate on quality during development. Agent governance — what Prefactor provides — focuses on enforcing policies at runtime: scoring risk, blocking agents that exceed cost or scope boundaries, routing decisions to human approvers, and maintaining audit trails. Evaluation tells you whether outputs changed. Governance tells you whether agents crossed a boundary and takes action.
Is LangSmith only for LangChain users?
LangSmith was built by the LangChain team and is deeply integrated with the LangChain ecosystem. While it does support tracing from non-LangChain applications, its strongest capabilities — automatic tracing, prompt hub integration, and chain visualisation — are designed around LangChain primitives. Prefactor is framework-agnostic by design. It works with any agent framework, any LLM provider, and any orchestration layer because governance needs to apply uniformly across your entire agent fleet.
Can I use LangSmith and Prefactor together?
Yes. Many teams use LangSmith during development and testing to evaluate prompt quality, detect regressions, and iterate on agent behaviour. They then use Prefactor in production to enforce governance policies — risk scoring, cost caps, scope enforcement, and approval routing. LangSmith helps you build better agents. Prefactor helps you run them responsibly. They address different stages of the agent lifecycle.