000 compare

AgentPing vs LangSmith.

Both tools watch AI work in production, but the unit of analysis is different. LangSmith is built around the trace and the offline eval; AgentPing is built around the agent run and the live production stream. This page is an honest read on where each one fits.

001 what langsmith does well

Deep LangChain integration, mature offline evals, a big ecosystem.

LangSmith ships first-class instrumentation for LangChain and LangGraph: full trace trees, prompt playgrounds, dataset-driven evals, side-by-side prompt comparison. If you're inside the LangChain ecosystem and your primary workflow is offline evaluation at development time, LangSmith is the obvious fit.

002 where agentping differs

Framework-agnostic, per-customer attribution, schedule freshness, live drift.

AgentPing's design starts from a different place. The unit of analysis is the agent run, not the trace. The runtime priority is the production stream, not the offline batch. Cost attribution is per-agent, per-customer, per-feature out of the box. Schedule freshness pages on a missed cron within the grace window. Drift detection runs continuously on the score distribution, not as a periodic eval job.

Side-by-side

          capabilities
          honest read
        
              Capability
              LangSmith
              AgentPing
            
              LangChain instrumentation
              First-class
              Framework-agnostic (any LLM client)
            
              Offline eval suite
              Mature
              Not a focus
            
              Cost attribution by customer / feature
              Limited
              First-class, tag at run start
            
              Schedule freshness (missed cron alerts)
              Not a focus
              Per-agent cron + tolerance window
            
              Live drift detection on production scores
              Batch-oriented
              Continuous z-score on 14-day baseline
            
              LLM-as-judge on production sample
              Yes
              Yes, with calibration anchors and a hard spend cap
            
              SDK contract (non-blocking, bounded queue)
              Variable
              2s hard timeout, bounded queue, never blocks
            
              Anomaly detection on per-agent spend
              Not a focus
              14-day baseline, alert routes per agent

Capability	LangSmith	AgentPing
LangChain instrumentation	First-class	Framework-agnostic (any LLM client)
Offline eval suite	Mature	Not a focus
Cost attribution by customer / feature	Limited	First-class, tag at run start
Schedule freshness (missed cron alerts)	Not a focus	Per-agent cron + tolerance window
Live drift detection on production scores	Batch-oriented	Continuous z-score on 14-day baseline
LLM-as-judge on production sample	Yes	Yes, with calibration anchors and a hard spend cap
SDK contract (non-blocking, bounded queue)	Variable	2s hard timeout, bounded queue, never blocks
Anomaly detection on per-agent spend	Not a focus	14-day baseline, alert routes per agent

003 when to pick which

These tools have different jobs.

The right answer is often "both, for different reasons". LangSmith is the offline / dev-time tool. AgentPing is the production runtime tool. If you can only have one, the question is which problem hurts more right now.

Pick LangSmith if

Your stack is LangChain or LangGraph and you want the deepest possible framework integration.
Your primary workflow is offline evaluation on curated datasets before shipping.
Your team needs prompt playgrounds and dataset-driven A/B comparison as a first-class workflow.

Pick AgentPing if

Your agents span multiple frameworks (or no framework) and you want one SDK contract for all of them.
You need per-customer or per-feature cost attribution from day one.
You run scheduled agents and need missed-cron alerts.
You want continuous drift detection on the production stream, not a batch eval cadence.
You want one tool for cost, monitoring, and quality, with shared alert routing.

004 frequently asked

Is AgentPing trying to replace LangSmith?

No. LangSmith is the natural fit for LangChain-heavy stacks that want deep offline eval workflows. AgentPing is the natural fit for framework-agnostic production agents (Python, TypeScript, Go, Laravel) that need cost attribution per customer, schedule freshness, and continuous quality scoring on the production stream rather than offline batches.

Does AgentPing work with LangChain?

Yes. The SDK auto-instruments any LLM client (Anthropic, OpenAI) regardless of framework. LangChain calls flow through the same client wrappers and land in AgentPing as run records. You can run AgentPing alongside LangSmith if you want both production attribution and the LangChain-specific eval ergonomics.

Can I keep my LangSmith evals and add production monitoring?

Yes, and that's a sensible setup. Use LangSmith for the offline eval suite and dev-time trace inspection. Use AgentPing for production cost attribution, schedule freshness, drift detection on the live stream, and per-customer rollups. The two have different jobs.

How does pricing compare?

LangSmith's pricing is per-trace at usage scale; AgentPing's is a flat tier (Starter £99, Team £249, Business £499) with monthly event allowances. The flat tier maps better to predictable budgets; the per-trace model maps better to bursty workloads. Annual billing saves 20% on every tier.

What about LangSmith's eval datasets?

LangSmith's offline eval datasets are mature; AgentPing doesn't replicate that surface. AgentPing's production scoring uses rubrics applied to live runs (with deterministic checks plus LLM-as-judge), which is a different problem from offline batch evaluation. Many teams run both.

005 read next

How AgentPing implements cost, monitoring, and quality.

Features → What is AI agent observability? → Docs →