We built a tool to stress test AI agents with simulated conversations

Hi everyone,

A common challenge when building AI agents is anticipating how real users will interact with them. Agents might work perfectly in local tests but still break once they’re in production. Small variations in human behavior can easily expose edge cases that are hard to catch during development.

So we built ArkSim, an open-source framework that simulates conversations with synthetic users and stress-tests AI agents to help catch these issues earlier.

What does ArkSim do:

ArkSim simulates multi-turn conversations between synthetic users and your agent so you can see how it behaves across longer interactions.

This can help surface issues like:

• Agents losing context during longer interactions

• Unexpected conversation paths

• Failures that only appear after several turns

The idea is to test conversation flows more like real interactions, instead of just single prompts.

Integration / Examples

There are example integrations available for:

• OpenAI Agents SDK

• Claude Agent SDK

• Google ADK

• LangChain / LangGraph

• CrewAI

• LlamaIndex

https://github.com/arklexai/arksim/tree/main/examples/integrations/langchain

Repo

If you want to check it out:
https://github.com/arklexai/arksim

Would love feedback from anyone building agents, especially around how people are currently testing multi-turn conversations.