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Local vs Global Representation

Peers in Honcho are abstract entities that can represent humans, agents, or NPCs. Honcho has a two-layer approach to forming representations of Peers.

  • Global Representation: Representation owned by a Peer that is constructed from everything the Peer has sent within Honcho.
  • Local Representation: The representation that a Peer forms of other Peers, based on the messages those other Peers have sent (as observed by the Peer forming the representation).
  • At the Session level, you can configure which Peers are able to observe messages from other Peers in that Session. This determines which Peers form representations of others within the Session.

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Ingest Arbitrary Data

To facilitate the construction of a Peer’s global representation, Honcho is able to ingest arbitrary data to a Peer by adding messages to the Peer directly, outside of the context of a Session.

  • Currently, Honcho supports the ingestion of arbitrary text data.

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Queue Status

To help developers understand when a Peer’s representation is fully up to date, Honcho exposes the ability to poll the status of Peer-centric queues that construct representations.

  • If no Session is specified, the queue status reflects pending work for the Peer’s global representation.
  • If a Session is specified, the queue status reflects pending work for the Peer’s working representation in that Session.

Honcho supports full-text search across message content across different scopes.

  • You can search within a specific Session, Peer, or across all messages in a Workspace.
  • Search results are ordered by relevance, making it easy to quickly retrieve important past messages.

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Scoped API Keys

Builders can create scoped API keys to control access to different resources within Honcho.

  • Workspace-Level Keys: Access to everything scoped to a Workspace.
  • Peer-Level Keys: Access to everything scoped to a Peer.
  • Session-Level Keys: Access to everything scoped to a Session.

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Get Context

Honcho provides a powerful context retrieval feature that delivers formatted conversation context from sessions, making it easy to integrate with LLMs like OpenAI, Anthropic, and others.

  • By default, the context includes a blend of summary and messages which covers the entire history of the session.
  • Summaries are generated automatically at intervals, and recent messages are included based on your specified token budget for the context.
  • You can set any token limit, and if you prefer, you can disable summaries so that the context consists entirely of the most recent messages up to your chosen limit.