Fix wording in the intro to agents
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@ -97,14 +97,16 @@ But once you start going for more complicated behaviours like letting an LLM cal
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- for a multi-step agent where the LLM output determines the loop, you need to give a different prompt to the LLM based on what happened in the last loop iteration: so you need some kind of memory.
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See? With these two examples, we already found the need for a few items to help us:
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- of course an LLM that acts as the engine powering the system
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- a list of tools that the agent can access
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- a parser that extracts tool calls from the LLM output
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- system prompt synced with the parser
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- memory
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But wait, since we give room to LLMs in decisions, surely they will make mistakes, so for better performance we need error logging and retry mechanism?
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These will not be that straightforward to implement correctly, especially not together. That's why we decided that we needed to build a few abstractions to help people use these.
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- Of course, an LLM that acts as the engine powering the system
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- A list of tools that the agent can access
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- A parser that extracts tool calls from the LLM output
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- A system prompt synced with the parser
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- A memory
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But wait, since we give room to LLMs in decisions, surely they will make mistakes: so we need error logging and retry mechanisms.
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All these elements need tight coupling to make a well-functioning system. That's why we decided we needed to make basic building blocks to make all this stuff work together.
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### Code agents
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@ -115,7 +115,7 @@ def get_weather_api(location: str, date_time: str) -> str:
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In general, to ease the load on your LLM, the good question to ask yourself is: "How easy would it be for me, if I was dumb and using this tool for the first time ever, to program with this tool and correct my own errors?".
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### Give more stuff to the agent
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### Give more arguments to the agent
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To pass some additional objects to your agent than thes smple string that tells it the task to run, you can use argument `additional_args` to pass any type of object:
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