LLMs Are Not All You Need | Pinecone

An agent can be thought of as a logical wrapper around an LLM, allowing us to add several features to our AI systems, primarily:

• Tool usage, such as calling APIs for info, executing code,


• Internal thoughts over multiple generation steps


• Ability to use various tools and reasoning steps to answer more complex queries.


• Parallel agents can go and complete

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For any conversational use case, such as chatbots, we need conversational memory. The idea behind conversational memory is simple: rather than sending just the most recent interaction to our LLM, we send a history of interactions + the most recent interaction to our LLM — typically in a chat log style format.

With and without conversational memory.

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