When implementing LLM/AI APIs (including streaming), treat three things as contract-critical: (1) compute token usage with a real tokenizer, (2) ensure the intended system/user context is actually passed through every planning/tool stage, and (3) emit only the events your client expects. **1) Token usage must use tokenizer-based counting** Avoid `len(text)` for `prompt_tokens/total_tokens`; it’s character-based and will be wrong across languages/models. Use a tokenizer compatible with your target model. ```python import tiktoken tokenizer = tiktoken.get_encoding("cl100k_base") context_token_used = sum(len(tokenizer.encode(m["content"])) for m in messages) # use similar encoding for prompt_tokens/completion_tokens ``` **2) Prompt plumbing must be correct** If an agent/planning function takes `prompt`/`sys_prompt`/history, pass the correct variables. Add a small assertion or unit test to ensure downstream components receive the system prompt intended for the run (not a different variable like history or an uninitialized prompt). **3) Streaming/compatibility endpoints must filter noise** If the upstream emits multiple event types (e.g., content vs reference chunks), only forward the subset your client UI consumes. Example logic: ```python # keep only the LLM response and its related finalization/reference if event not in ["message", "message_end"]: continue ``` **How to apply** - For every LLM endpoint response, confirm token fields are derived from tokenizer counts. - For every agent/tool/planning path, verify the system prompt and user history are the same objects used to build the actual prompt that reaches planning. - For streaming, ensure you accumulate deltas consistently and only emit client-meaningful events.