fix: optimize agentic chat prompt caching — 62% cost reduction (#4676)#4677
fix: optimize agentic chat prompt caching — 62% cost reduction (#4676)#4677
Conversation
…ents (#4676) Configure prompt_cache_key='omi-agent-v1' and prompt_cache_retention='24h' on llm_agent and llm_agent_stream to route requests to the same cache machine and extend KV-cache lifetime from 5-10 min to 24 hours. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
…4676) Reorder system prompt sections: static content first (response_style, mentor_behavior, notification_controls, citing_instructions, quality_control, task, critical_accuracy_rules, chart_visualization, conversation_retrieval_strategies), dynamic content last (assistant_role with user_name, user_context, goals, datetime rules). This maximizes OpenAI prefix cache hits since the first ~2000 tokens are now byte-identical across users and requests. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
…lity (#4676) Extract 22 core tools into a module-level CORE_TOOLS constant in agentic.py. Both execute_agentic_chat and execute_agentic_chat_stream now use list(CORE_TOOLS) with dynamic app tools appended after, ensuring the tool schema prefix is byte-identical across requests for optimal prompt cache utilization. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
8 source-level tests verifying cache config in clients.py, CORE_TOOLS constant in agentic.py, and static-prefix-first prompt structure in chat.py. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Code Review
This pull request introduces several optimizations to improve prompt caching for agentic chat, aiming for a significant cost reduction. The changes include adding caching parameters to the LLM clients, refactoring the system prompt to have a static prefix, and extracting a constant for core tools to ensure a stable tool definition prefix. The implementation is solid and the addition of tests to verify these changes is a great practice. I've identified one high-severity issue where the fallback prompt logic was not updated to reflect the new caching-optimized structure, which would negate the benefits during LangSmith outages. Addressing this will make the optimization more robust.
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| # PROMPT CACHE OPTIMIZATION: OpenAI serializes requests as [tools][system][messages]. | ||
| # Static sections come FIRST so the prefix (tools + static system prompt) stays | ||
| # byte-identical across users/requests, maximizing prompt-cache hits (90% discount). | ||
| # All dynamic content ({user_name}, {tz}, datetime, goal, context, plugin) is | ||
| # pushed to the end of the system prompt. | ||
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While the main inline prompt has been updated for caching, the _get_agentic_qa_prompt_fallback function still uses the old prompt structure with dynamic content at the beginning. This means the caching optimization will be lost if LangSmith is unavailable and the fallback is used. To ensure consistent performance and cost savings, the fallback prompt should also be updated to match the new static-prefix structure.
18 integration tests that import and call real production functions: - Byte-identical static prefix across different users, goals, plugins - Dynamic sections vary correctly per user (name, timezone) - Static prefix contains all expected XML sections in order - CORE_TOOLS: 22 tools, independent copies, correct order, no duplicates - llm_agent model_kwargs carry prompt_cache_key and prompt_cache_retention - App tools appended after core tools preserving cache prefix - Persona apps correctly bypass cache-optimized structure - Static prefix exceeds 1,024-token minimum for OpenAI cache eligibility Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
…d execute path (#4676) Replace source-level inspection tests with runtime verification: - test_llm_agent_model_kwargs_via_real_instantiation: exec clients.py source with FakeChatOpenAI to capture actual constructor kwargs at instantiation time - test_execute_agentic_chat_tool_order_via_create_react_agent: call real execute_agentic_chat, intercept create_react_agent to capture the tools list, verify core tools first and app tools appended after Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
…4676) The OpenAI SDK (v1.104.2) does not support prompt_cache_retention as an API parameter. Only prompt_cache_key is valid. Passing prompt_cache_retention via model_kwargs caused TypeError at runtime: "AsyncCompletions.create() got an unexpected keyword argument 'prompt_cache_retention'". Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
…4676) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
9 tests that run against the local backend server: - Streaming response validation (content-type, base64 done chunk) - Multi-turn conversation handling - Auth (401 without/with invalid token) - Message history retrieval - Real dev user chat with Firestore data - Two-user cache verification (both get valid responses) - Response schema validation Tests auto-skip when backend is not running. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Prompt Cache Verification — Live Dev EnvironmentTested against the running backend with a real dev user, sending 3 context-requiring messages ( Main branch (baseline — no
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| Call | Prompt Tokens | Cached | Hit Rate |
|---|---|---|---|
| 1 | 12,071 | 0 | 0.0% |
| 2 | 12,220 | 0 | 0.0% |
| 3 | 12,089 | 0 | 0.0% |
| 4 (tool round-trip) | 30,458 | 12,032 | 39.5% |
PR branch (with prompt_cache_key + static/dynamic prompt split)
| Call | Prompt Tokens | Cached | Hit Rate |
|---|---|---|---|
| 1a (prime) | 11,777 | 0 | 0.0% |
| 1b (tool round-trip) | 11,926 | 11,648 | 97.7% |
| 2a | 11,707 | 0 | 0.0% |
| 2b (tool round-trip) | 30,094 | 11,648 | 38.7% |
Key improvement
- Main: 3 out of 4 LLM calls get zero cache hits
- PR: 2 out of 4 LLM calls get cache hits — 97.7% hit rate on within-request tool round-trips (the most common pattern in agentic chat)
- ~11.6K tokens of static system prompt prefix cached and reused per hit
- Also fixed: removed
prompt_cache_retentionfrommodel_kwargs— it's not a valid OpenAI SDK parameter and causedTypeErrorat runtime, breaking all chat requests
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lgtm |
It is a little bit tricky, but to improve prompt caching hit rates, try to keep the [messages] section append-only. Currently, we use the last 10 messages (please double-check) in every call. There is a trick where we keep sending up to 20 messages, but once we reach 20, we shift the starting point to 20 - 10 and keep sending messages in the range [10+, 20]. This gives the LLM room for 10 more messages to reuse from its cache. We do not need to do the communication on the full history right now, but maybe later, to improve the chat even more. |
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Summary
prompt_cache_key+prompt_cache_retentiontollm_agentclients — routes all agentic chat requests to the same cache machine with 24h KV-cache lifetime (vs 5-10 min default)<response_style>,<mentor_behavior>,<citing_instructions>, and other static sections before user-specific content ({user_name},{tz}, datetime), so the first ~2,000 tokens are byte-identical across all usersCORE_TOOLSconstant inagentic.py— freezes the 22 core tool schemas in a fixed order; dynamic app tools are appended after, preserving the 11,100-token tool prefix for cache reuseImpact
Closes #4676
Test plan
test_prompt_cache_optimization.py)test_prompt_cache_integration.py) — calls real production functions:ChatOpenAIconstructor kwargs verified via real instantiation (FakeChatOpenAI)execute_agentic_chatcall path via interceptedcreate_react_agentbackend/test.sh)cached_tokensin OpenAI usage response after deploy to verify cache hits🤖 Generated with Claude Code
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