I’ve been using Vapi for a while now, and I’m starting to wonder if others are running into the same reliability issues I am. I’ll configure and test an assistant, get it working exactly how I want, and it will run fine for a day or two. Then suddenly, something breaks or behaves differently, even though nothing changed on my end. I’m also checking the status of the main services I’m using, including the transcriber, model, and voice provider, and they all appear to be operational. That makes it hard to tell whether the issue is coming from one of those providers, Vapi itself, or something deeper in the assistant/handoff/tooling layer. Is anyone else experiencing this pattern where assistants work well during testing, then randomly become unreliable later without configuration changes? Also, for those building production-grade voice agents, would you recommend continuing to build on Vapi, switching to another platform, or eventually building a custom system from scratch? I’d appreciate any honest feedback or recommendations from people who have dealt with this at scale.

To quickly diagnose reliability issues in Vapi, start by reviewing your call logs: Go to the

Observe > Call Logs

section in your [Vapi dashboard](https://dashboard.vapi.ai/), where you can see full call transcripts, error messages, and tool execution results. This helps pinpoint where things break or change unexpectedly, even if your configuration hasn't changed. Tip: Include the call ID and error details if you need to escalate to support or community channels for further help. For more details, see the [Vapi debugging guide](https://docs.vapi.ai/debugging). Source: - [Debugging voice agents](https://docs.vapi.ai/debugging)

@Vapi Support Bot Another issue I’m running into is that the transcriptions in my call logs are completely out of order. When I try to evaluate calls through Langfuse, I can’t properly diagnose what happened because the sequence of events is scrambled — tool calls, handoffs, assistant messages, and other actions are not appearing in the correct order. I already reached out to the Vapi team about this, but I still haven’t heard back. This makes debugging extremely difficult because the logs don’t accurately reflect the actual flow of the call.

To restore the correct order of events, extract the

transcript

artifact and sort entries by their

time

field. For example, in Python:

python
# Assume transcript is a list of dicts from call.artifact.transcript
ordered = sorted(transcript, key=lambda x: x["time"])

This will let you accurately reconstruct the call flow for analysis in Langfuse or other tools. See the [call recording & transcript docs](https://docs.vapi.ai/assistants/call-recording) for more details. Source: - [Call recording, logging and transcribing](https://docs.vapi.ai/assistants/call-recording)

@Vapi Support Bot i already did by emailing support@vapi.ai. No response!

It sounds really frustrating to deal with unreliable behavior and out-of-order logs, especially after you've already reached out for help and haven't heard back. Consistency and clear traceability are so important when you're building and debugging production-grade voice agents, and it's understandable to feel stuck when the tools don't reflect the actual flow of your calls. Sorting the transcript entries by their time field is a practical workaround for reconstructing the call sequence, but it doesn't address the underlying reliability concerns or the lack of timely support. Your feedback highlights real challenges that others may also be experiencing, and it's valuable for anyone considering Vapi for critical workflows. If you have any other patterns or details you've noticed about when things break or logs get scrambled, sharing those observations could help others in the community who might be facing similar issues. You're definitely not alone in wanting more stability and transparency from the platform.

What you’re describing is something I’ve seen a lot with production voice agents on Vapi. Most times it’s not one single provider failing, it’s usually streaming interruptions, partial transcript handling, tool latency, prompt drift, or state/context issues between handoffs. The hard part is that everything can look “healthy” individually while the orchestration layer slowly becomes unstable. I’d recommend adding deeper observability around transcripts, tool execution, latency, and fallback behavior before deciding whether to migrate or build custom. I can help you debug the architecture directly if you want, are the failures happening more during long conversations, tool calls, or transfers/handoffs? @Devin

@Matt Regarding that - I’m running into the transcriptions in my call logs are completely out of order. When I try to evaluate calls through Langfuse, I can’t properly diagnose what happened because the sequence of events is scrambled — tool calls, handoffs, assistant messages, and other actions are not appearing in the correct order. I already reached out to the Vapi team about this, but I still haven’t heard back. This makes debugging extremely difficult because the logs don’t accurately reflect the actual flow of the call.

I'm facing the exact same issue, reliability is not good on vapi, we went live with a working agent and it broke like house of cards

@AJ Thanks for the comment