How to implement prompt caching in forums?

how do u recommend we implement prompt caching in forums?

I'll explore the codebase to understand the current architecture and then provide recommendations for implementing prompt caching in forums.

Let me explore the agent and LLM infrastructure:

Now let me search for how the current system messages and context are structured:

Perfect! Now let me check the Vercel AI SDK and Anthropic documentation for prompt caching capabilities:

Let me check the Vercel AI SDK documentation more directly:

Perfect! Now I have comprehensive information. Let me provide a detailed recommendation based on the codebase and the latest documentation.

##Recommendations for Implementing Prompt Caching in Forums

Based on your codebase architecture and current usage of the Vercel AI SDK + Anthropic Claude, here's how I'd recommend implementing prompt caching:

###Current State

• Using streamText() from the ai package
• Static system prompt that's the same for every request (the "You're assisting users..." boilerplate)
• Multi-turn conversations with accumulated context (tools output, prior comments)
• Tools that execute file reads/searches that produce large context

###Recommended Approach: Message-Level Cache Control

Add Anthropic's cache_control directive to the final message in your conversation. Here's the pattern:

In agent/response-agent.ts, modify streamTextStep():

async function streamTextStep({
  owner,
  repo,
  gitRef,
  model,
  writable,
  sandboxId,
  initialMessages,
  newMessages,
}: {
  owner: string
  repo: string
  gitRef: string
  model: string
  writable: WritableStream
  sandboxId: string
  initialMessages: AgentUIMessage[]
  newMessages: UIMessage[]
}): Promise<{...}> {
  "use step"

  const workspace = await getWorkspace({
    sandboxId,
    gitContext: { owner, repo, ref: gitRef },
  })
  const allMessages = [...initialMessages, ...newMessages] as AgentUIMessage[]

  // Add cache control to the last message (the current user question)
  const messagesWithCache = allMessages.map((msg, idx) => ({
    ...msg,
    ...(idx === allMessages.length - 1 && model.includes("claude")
      ? {
          providerOptions: {
            cacheControl: { type: "ephemeral" },
          },
        }
      : {}),
  }))

  const result = streamText({
    messages: await convertToModelMessages(messagesWithCache),
    tools: getTools({ workspace }),
    system: `You're assisting users in a forum about the GitHub repository \`${owner}/${repo}\`...`,
    model,
  })
  
  // ... rest of function
}

###Key Benefits for Forums

1. Caches the conversation prefix: Each multi-turn conversation within a thread will reuse prior messages
2. System prompt cached: Your static system instructions (the repo context, tool docs) are included in the cache
3. Cost savings: Cached tokens cost 10% vs full price for input tokens (but cache writes cost 25% more)
4. Lower latency: 85% latency reduction on cache hits

###Cost Breakeven Math

• Cache write: 1.25× input cost
• Cache read: 0.1× input cost
• Breakeven: After ~2 cache reads per write (within 5-min window)

For forums this is excellent because:

• Users typically ask 2-3 follow-up questions per thread
• The 5-minute cache TTL aligns with natural conversation pace
• Initial expensive write on first message is recovered within the thread

###Where This Works Best

1. Response agent (main use case): Each user follow-up question reuses prior conversation context
2. Category agent: Lighter use case, but system prompt is stable across posts

###Implementation Details

• Only works with Anthropic Claude 3.5 Sonnet+, 3 Opus+ (not Haiku 4.5 for cost reasons - 2048 token minimum)
• Requires no extra dependencies (already using ai v6+)
• API automatically handles message→block-level translation
• Monitor usage.promptTokenDetails.cachedTokens in responses to verify it's working

###Alternative: Response-Level Caching (if you want more control)

Use language model middleware for session-based caching across different threads:

// Cache identical system prompt + identical recent comments
// Useful if many threads ask about the same code sections

This is more complex but valuable if you want cross-thread caching.

###Next Steps

1. Add cache control to the last message in streamTextStep
2. Monitor usage metrics in Anthropic dashboard (Group by token type)
3. Test with a few user conversations to validate cost reduction
4. Consider expanding to category-agent if results are positive

The message-level approach is the simplest win—minimal code changes, maximum benefit for your multi-turn conversation pattern.