That particular task didn't need parallel agents or any of the advanced features.
The prompt was:
<prompt>
Research claude pricing with caching and then review a conversation history to calculate the cost.
First, search online for pricing for anthropic api with and without caching enabled for all of the models: claude-3-haiku, claude-3-opus and claude-3.5-sonnet (sonnet 3.5).
Create a json file with ALL the pricing data.
from the llm history db, fetch the response.response_json.usage for each result under conversation_id=01j7jzcbxzrspg7qz9h8xbq1ww
llm_db=$(llm logs path)
schema=$(sqlite3 $llm_db '.schema')
example usage: {
"input_tokens": 1086,
"output_tokens": 1154,
"cache_creation_input_tokens": 2364,
"cache_read_input_tokens": 0
}
Calculate the actual costs of each prompt by using the usage object for each response based the actual token usage cached or not.
Also calculate/simulate what it would have cost if the tokens where not cached.
Create interactive graphs of different kinds to show the real cost of conversation, the cache usage, and a comparison to what it would have costed without caching.
Write to intermediary files along the way.
Ask me if anything is unclear.
</prompt>
I just gave it your task and I'll share the results tomorrow (I'm off to bed).
Comments
That particular task didn't need parallel agents or any of the advanced features.
The prompt was: <prompt> Research claude pricing with caching and then review a conversation history to calculate the cost. First, search online for pricing for anthropic api with and without caching enabled for all of the models: claude-3-haiku, claude-3-opus and claude-3.5-sonnet (sonnet 3.5). Create a json file with ALL the pricing data.
from the llm history db, fetch the response.response_json.usage for each result under conversation_id=01j7jzcbxzrspg7qz9h8xbq1ww llm_db=$(llm logs path) schema=$(sqlite3 $llm_db '.schema') example usage: { "input_tokens": 1086, "output_tokens": 1154, "cache_creation_input_tokens": 2364, "cache_read_input_tokens": 0 }
Calculate the actual costs of each prompt by using the usage object for each response based the actual token usage cached or not. Also calculate/simulate what it would have cost if the tokens where not cached. Create interactive graphs of different kinds to show the real cost of conversation, the cache usage, and a comparison to what it would have costed without caching.
Write to intermediary files along the way.
Ask me if anything is unclear. </prompt>
I just gave it your task and I'll share the results tomorrow (I'm off to bed).