Head-to-Head
LangSmith vs AgentOps (2026)
LangSmith
Freemium★ 4.5
Best for: debugging unexpected llm outputs and tracing multi-step agent behaviour, running a/b tests on prompt changes before deploying to production
AgentOps
Freemium★ 4.2
Best for: monitoring production ai agents for loops, failures, and unexpected behaviour, debugging multi-step agent workflows where standard logging is insufficient
LangSmith and AgentOps are both observability platforms for LLM applications, but with different specialisations. LangSmith, built by the LangChain team, covers the full spectrum of LLM application observability - tracing chains, prompts, retrievals, and model calls - with a strong focus on evaluation: systematic testing of prompts and chains against labelled datasets before deployment. AgentOps focuses specifically on agent observability, tracking the session-level behaviour of autonomous agents: tool calls, loop iterations, cost per session, and failure patterns. The tools complement each other more than they compete. For teams using LangChain or LangGraph, LangSmith is the natural choice and integrates with near-zero configuration. For teams building custom agent loops with frameworks like AutoGen, CrewAI, or their own implementations, AgentOps provides session-level insight that generic tracing tools miss. In 2026, as more teams move from simple LLM chains to multi-step autonomous agents, the distinction between chain-level and session-level observability becomes practically important. LangSmith tells you what each call in a chain did. AgentOps tells you what an agent session accomplished, where it went wrong, and how much it cost. For production agent systems, using both in tandem is increasingly common.
Feature Comparison
LLM Chain Tracing
LangSmith traces every step of a LangChain or LangGraph execution automatically - prompts, model calls, retrievals, tool calls. AgentOps also traces LLM calls but is optimised for agent session context rather than chain granularity.
Agent Session Observability
AgentOps provides session-level replay for autonomous agents, tracking each tool call, decision, and iteration within a complete agent run. LangSmith traces individual steps but lacks the session-level structure for non-deterministic agent loops.
Prompt Evaluation and Testing
LangSmith has a structured evaluation framework: create datasets, run chains against them, score outputs, and compare across prompt versions. AgentOps does not have a comparable built-in evaluation system.
Framework Compatibility
AgentOps works with AutoGen, CrewAI, LangChain, LlamaIndex, and custom agent implementations. LangSmith is native to LangChain but also works via OTEL-compatible tracing with other frameworks.
Cost Tracking Per Session
AgentOps tracks total cost, token usage, and latency per agent session, making it easy to see the cost of individual agent runs. LangSmith shows token costs at the chain and call level but without the session aggregation.
Failure Analysis
AgentOps session replay shows exactly which tool call failed and what the agent state was at that point. LangSmith failure traces show the individual step that errored but require manual reconstruction of the session context.
Ease of Integration
Both integrate via a Python SDK with minimal code changes - typically 2-3 lines to initialise. LangSmith has zero-config integration for LangChain. AgentOps is similarly straightforward for supported frameworks.
Verdict
This comparison is context-dependent. LangSmith scores 29/35 and AgentOps scores 31/35. Choose based on your specific workflow needs.
Bottom Line
LangSmith and AgentOps are both observability platforms for LLM and agent applications - they record traces, evaluate output quality, and surface production issues. LangSmith is built by the LangChain team and is the default observability layer for LangChain and LangGraph applications - it integrates with one line of code if you already use those frameworks. AgentOps is framework-agnostic and tends to win on richer agent-specific instrumentation (multi-step trajectory analysis, cost-per-task tracking, eval frameworks for agents specifically). For LangChain shops, LangSmith is the natural pick. For shops running custom agent loops or non-LangChain frameworks, AgentOps is more flexible. Pricing: LangSmith free tier plus $39/dev/mo; AgentOps free tier plus paid tiers from $50/mo.
Pick LangSmith
Your LLM application is built on LangChain or LangGraph. LangSmith is one-line integration, surfaces traces in the same mental model as your code, and handles eval workflows the LangChain team designs around. Best for LangChain shops at any scale.
Pick AgentOps
Your application is custom-built or uses non-LangChain frameworks (LlamaIndex, Haystack, custom agent loops). AgentOps is framework-agnostic and offers richer agent-specific instrumentation - especially for multi-step agent trajectories, cost tracking, and agent-specific eval. Best for engineering teams not committed to LangChain.
Frequently asked
Can either replace generic APM tools like Datadog?
Neither replaces APM for non-LLM workloads. Both are LLM-specific observability tools that complement APM. For full-stack monitoring, run both an APM tool and a dedicated LLM observability platform.
How do evals work on each?
LangSmith ships eval primitives and a UI for running evals against datasets. AgentOps focuses on agent-specific evals (trajectory matching, multi-step reasoning quality). Both let you run human-in-the-loop reviews. For LangChain-specific eval workflows, LangSmith is more native.
Are they self-hostable?
LangSmith offers self-hosting on enterprise tiers. AgentOps is primarily SaaS with self-hosting available on enterprise. For air-gapped or strict data-residency requirements, both have paths but require enterprise contracts.
Which has better cost tracking?
AgentOps has more developed per-task cost attribution out of the box, which matters for agent applications where one user request can trigger 10+ LLM calls. LangSmith tracks costs but the agent-specific aggregation is less mature.