Long Horizon Agents

Build long horizon agents that don't lose the thread

Long horizon agents fail in silence—losing context mid-task, making poor decisions across hundreds of steps, and drifting from their goals.

LangSmith gives you full visibility into every step, tools to debug context-folding and retrieval failures, and evals to validate reinforcement learning reward signals across the full task horizon.

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LangSmith tracing interface showing long-horizon agent execution across multiple steps

How LangSmith powers long horizon agents

Trace the full run

Capture every step, tool call, and context-folding operation across the entire long horizon agent lifecycle. See exactly how context accumulates, compresses, and influences decisions over hundreds of steps.

Evaluate end-to-end

Run evals that score long horizon agent behavior across the complete task—not just individual steps. Test reinforcement learning reward signals for interactive LLM agents and catch goal drift before it reaches users.

Scale via context-folding

Ship long horizon agents with stateful session management and context-folding support. Monitor context growth, flag anomalous behavior, and keep extended agent runs reliable across your entire user base.

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LangSmith powers top engineering teams, from AI startups to global enterprises

Built for Long Horizon Agents

Teams trust LangSmith to develop, evaluate, and deploy their most complex long horizon agent applications—from context-folding pipelines to reinforcement learning workflows

50M+

LLM Calls Traced

1B+

Events Ingested per Day

100K+

Monthly active orgs in LangSmith SaaS

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LangSmith for Long Horizon Agents

Debug, evaluate, and scale long horizon agents that reason and act across hundreds of steps

Trace every decision, tool call, and context-folding operation across long multi-step runs. Understand exactly where your long horizon agent lost the thread, made a wrong turn, or ran out of useful context—no more guessing from final outputs alone.

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LangSmith Observability interface showing long-horizon agent traces

Built for Enterprise

Security and compliance at scale

LangSmith meets the demanding security, performance, and collaboration requirements of large organizations building AI applications at scale.

Granular permissions

Role-based access control with org-level permissions and project isolation to meet your security and compliance requirements.

SOC 2 Type II

Third-party security certification with comprehensive security controls.

Trust center

Self-hosted deployment

Self-hosting options to maintain full control over your AI data and meet strict compliance requirements.

Why top AI teams choose LangSmith for long horizon agents

Debug context-folding failures

Pinpoint exactly where a long horizon agent went off track—which step, which tool call, which context-folding decision caused the failure—without replaying the entire run from scratch.

Validate reinforcement learning reward signals

Test long horizon interactive LLM agents end-to-end with evals that score behavior across the complete task. Validate RL reward signals, measure goal completion rates, and catch drift before shipping.

Scale via context-folding and stateful backends

Deploy long horizon agents designed to run for hours or days. LangSmith handles session management, context scaling via folding, and production monitoring so extended workloads stay reliable at any scale.

How leading teams build long horizon agents with LangSmith

"Working with LangSmith on the Elastic AI Assistant had a significant positive impact on the overall pace and quality of our development and shipping experience. We couldn't have delivered the product experience our customers now have without LangSmith—and we couldn't have done it at the same pace without it."

James Spiteri, Director of Security Product Management at Elastic

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"What we really needed was a more structured way to test new approaches, something better than just shipping and seeing what happened. LangSmith gave us a more scientific, structured way to understand what was actually working, whether that meant running pairwise evaluations or digging into why accuracy jumped from 70% to 80%. Our engineers especially love the intuitive debugging experience, it's saved us a lot of time."

Yusuke Kaji, General Manager of AI for Business Development at Rakuten