Quick Diagnostic Workflows

Workflow: "Runtime Not Connecting"

The client shows a connection error, banner error, or the chat never loads.

Step 1: Verify the runtime is running

curl -v http://localhost:3001/api/copilotkit/info

• No response / connection refused -> The server is not running. Start it.
• 404 -> The basePath is wrong. Check createCopilotEndpoint({ basePath }) vs the URL you are hitting.
• 500 -> The agent loading failed. Check server logs for the error.
• 200 with JSON -> Runtime is up. Proceed to step 2.

Step 2: Check the client configuration

<CopilotKitProvider runtimeUrl="/api/copilotkit">

• Does runtimeUrl match the runtime's basePath exactly?
• If cross-origin (e.g., runtime on port 3001, app on port 3000), is CORS configured?
• If using a proxy (Next.js rewrites, nginx), does the proxy preserve the full path?

Step 3: Check browser network tab

1. Look for the GET request to /info
2. If it is blocked by CORS, you will see a preflight OPTIONS failure
3. If it returns an error, the error body contains the CopilotKitErrorCode

Step 4: Check package versions

npm ls @copilotkit/runtime @copilotkit/react @copilotkit/core @ag-ui/client

All @copilotkit/* packages should be the same version. Mismatches cause VERSION_MISMATCH errors.

Step 5: Check CORS (if cross-origin)

Default CORS allows all origins without credentials. If you need credentials:

createCopilotEndpoint({
  runtime,
  basePath: "/api/copilotkit",
  cors: {
    origin: "https://your-frontend.com",
    credentials: true,
  },
});

And on the client:

<CopilotKitProvider
  runtimeUrl="https://your-api.com/api/copilotkit"
  credentials="include"
/>

Workflow: "Agent Not Responding"

The chat connects but messages are never answered, or the agent returns an error.

Step 1: Verify agent is registered

curl http://localhost:3001/api/copilotkit/info | jq '.agents'

Check that the agent name matches the agentId prop in CopilotChat or useAgent.

Step 2: Check the SSE stream

1. Open browser DevTools > Network tab
2. Send a message in the chat
3. Find the POST to /agent/:agentId/run
4. Check the response:
   • 404 -> Agent not found in runtime
   • 500 -> Server error during agent execution
   • 200 with empty body -> Agent started but produced no events
   • 200 with events -> Check the events (step 3)

Step 3: Inspect the event stream

Look at the SSE events in the response:

• Only RunStartedEvent then nothing -> Agent is stalled. Check server logs. Common causes:

  • Missing LLM API key (agent cannot call the model)
  • Agent waiting for a tool result that never comes
  • Reasoning event stall (Anthropic models, issue #3323)

• RunErrorEvent present -> Read the error message. Common causes:

  • LLM API returned an error (rate limit, invalid key, model not found)
  • Agent code threw an exception

• RunFinishedEvent without text messages -> Agent completed but produced no output. Check the agent's prompt and logic.

Step 4: Check LLM API key

For BuiltInAgent, verify the environment variable:

Step 5: Check the agent's model string

new BuiltInAgent({
  model: "openai/gpt-4o", // Must be "provider/model-name"
});

Invalid model strings throw Error: Invalid model string "..." or Error: Unknown provider "...".

Step 6: Check server-side logs

The SSE response handler logs errors with full stack traces:

Error running agent: <error>
Error stack: <stack trace>
Error details: { name, message, cause }

Workflow: "Streaming Failures"

The agent starts responding but the stream cuts off, duplicates events, or corrupts messages.

Step 1: Check for premature stream termination

1. Look at the SSE response in the Network tab
2. Does it end with RunFinishedEvent? If not:
   • Connection closed mid-stream -> Hosting platform timeout (Vercel: 30s default, Railway: 5min). Consider using Intelligence mode for long-running agents.
   • Error in the stream -> Check for RunErrorEvent before the cutoff
   • Client navigated away -> Expected behavior, the abort signal cleaned up the stream

Step 2: Check for event ordering issues

Events must follow a logical sequence:

• TextMessageStart before TextMessageChunk before TextMessageEnd
• ToolCallStart before ToolCallArgs before ToolCallEnd
• RunStarted at the beginning, RunFinished at the end

If events are out of order, the issue is in the agent's Observable implementation.

Step 3: Check for duplicate events

If the same message appears multiple times:

• Message ID collision -> Check issue #3410 (OpenAI-compatible providers reusing IDs)
• Agent re-running -> The runId changed mid-conversation. Check for HITL issues (issue #3456).

Step 4: Check for message corruption

If message content is garbled or mixed:

• Model-specific issue -> DeepSeek and some models produce malformed streaming chunks (issue #3351)
• Encoding issue -> Verify the SSE response has Content-Type: text/event-stream and is UTF-8

Step 5: Check hosting platform limits

For long agent runs, consider:

• Intelligence mode (persisted threads, WebSocket updates)
• Increasing the platform timeout if possible
• Breaking the agent work into smaller runs

Workflow: "Frontend Tool Not Working"

A frontend tool registered with useFrontendTool is not being called or not returning results.

Step 1: Verify tool registration

Check that the tool is registered before the agent runs:

useFrontendTool({
  name: "get_weather", // Must match exactly what the agent calls
  description: "Get weather",
  parameters: z.object({ city: z.string() }),
  execute: async ({ city }) => {
    /* ... */
  },
});

Step 2: Check the SSE stream for tool events

Look for ToolCallStartEvent in the SSE stream:

• Not present -> The agent decided not to call the tool. Check the tool description.
• Present but no ToolCallResultEvent -> The frontend did not respond. Check:
  • Is the component with useFrontendTool mounted?
  • Did the execute handler throw? (Check tool_handler_failed error)
  • Is the tool name an exact match (case-sensitive)?

Step 3: Check tool argument parsing

If tool_argument_parse_failed error appears:

• The LLM generated arguments that do not match the Zod/JSON schema
• Check ToolCallArgsEvent for the raw arguments
• Consider relaxing the schema or improving parameter descriptions

Step 4: Check HITL tool flow

For renderAndWaitForResponse tools:

• The tool renders UI and waits for user input
• If the tool does not execute after user confirmation, check issue #3442
• The runId may change after HITL resolve (issue #3456)

Workflow: "Transcription Not Working"

Voice input fails or produces errors.

Step 1: Check transcription service configuration

const runtime = new CopilotRuntime({
  agents: {
    /* ... */
  },
  transcriptionService: myTranscriptionService, // Must be provided
});

If not configured, the error code is service_not_configured (HTTP 503).

Step 2: Check the /info response

curl http://localhost:3001/api/copilotkit/info | jq '.audioFileTranscriptionEnabled'

Should be true. If false, the transcription service is not configured.

Step 3: Check browser microphone permissions

• The browser must grant microphone access
• AudioRecorderError: "Microphone permission denied" -> User denied permission
• AudioRecorderError: "No microphone found" -> No microphone hardware detected

Step 4: Check transcription provider credentials

• auth_failed -> API key is invalid or expired
• rate_limited -> Too many requests, wait and retry
• provider_error -> Provider-side issue, check provider status page

Step 5: Check audio format

• invalid_audio_format -> Browser sends unsupported format
• audio_too_long / audio_too_short -> Recording duration out of bounds

Escalation Path

If the issue is unresolved after following these workflows:

1. Check the CopilotKit GitHub Issues: Search https://github.com/CopilotKit/CopilotKit/issues for your error message or symptom.

2. Enable the Web Inspector: Add <CopilotKitWebInspector /> to capture detailed event traces.

3. Collect a diagnostic bundle:

   • Package versions (npm ls @copilotkit/*)
   • Runtime /info response
   • SSE stream capture (copy from Network tab)
   • Server-side error logs
   • Browser console errors

4. File a GitHub issue: https://github.com/CopilotKit/CopilotKit/issues/new with the diagnostic bundle.

5. Reach out to the CopilotKit team: Book time with the CopilotKit team via their Discord (https://discord.gg/copilotkit) or contact support for urgent production issues.