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One of the unique affordances of Honcho is that it allows developers to model directional relationships between Peers. What I mean by this is you can model how one Peer thinks about another Peer. There are many use cases where you don’t want every agent or human to know everything about another user such as games or multi-agent workflows. To illustrate this, the following examples shows 2 conversations. Conversation #1 (With Bob and Alice)
Conversation #2 (With Alice and Charlie)
Alice told Bob a lie in this conversation. If we stored both of these conversations in Honcho with Alice, Bob, and Charlie as Peers and let them use Honcho to get insights on each other then Bob would immediately know this deception. For example:
This is a problem. Bob shouldn’t be able to know everything about Alice in this situation. So to support these situations we support what we call Local Representations. By default insights generated for a Peer are scoped globally. This means every message sent by that Peer in any conversation updates the same representation of that Peer. However, we can enable Local Representations so Bob can form a representation Alice based only on what they observe Alice do. This feature is illustrated in the graphic below: Peer Representations We can enable local representation for a Peer by setting observe_others=True. This is shown in the Configure Reasoning page. Now if we used Bob’s local representation of Alice then Bob would only get insights on what they’ve seen Alice say to them.
Local Representations are turned off by default