fix: Code Rabbit grammatical catches
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Vineeth Voruganti
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@ -36,8 +36,8 @@ Honcho has a hierarchical data model centered around the entities below.
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```
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A `Workspaces` has `Peers` & `Sessions`
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A `Peer` can be in multiple `Sessions` and a can send `Messages` in a `Session`.
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A `Session` can have many `Peers` and has `Messages` sent by `Peers`.
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A `Peer` can be in multiple `Sessions` and can send `Messages` in a `Session`.
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A `Session` can have many `Peers` and stores `Messages` sent by its `Peers`.
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### <Icon icon="building" /> Workspaces
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@ -131,9 +131,9 @@ useful for an LLM to consume. There may be too many tokens that need to be
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compacted, key facts about what happened may be hard to piece together because
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they involve messages from across different sessions, etc.
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To solve this problem, Honcho has a reasoning layer that is always processing
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data that comes into Honcho to have the must informationaly dense and useful
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data that we can expose to agents. Currently, Honcho does the following tasks in
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To solve this problem, Honcho has a reasoning layer that continually processes
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incoming data to form the most informationally dense and useful representations of `Peers`
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that we can then expose to agents. Honcho does the following tasks in
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the reasoning engine.
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- **Fact Derivation**
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@ -142,16 +142,15 @@ the reasoning engine.
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- **Dreaming**
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So Honcho will reason about each `Message` it
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Honcho will reason about each `Message` it
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ingests to generate new facts and insights that are spelled out and easy to
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consume in an LLM prompt.
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We refer to this module of Honcho as the `Deriver`, because it constantly is
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We refer to this module of Honcho as the `Deriver`, because it's constantly
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deriving new insights from messages. The sum total of all these generated
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insights are what we refer to as a `Representation`, all the data related who
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insights are what we refer to as a `Representation`, all the data related to who
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and what a `Peer` is.
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Depending on the configuration of a `Peer` or `Session`, the deriver will behave
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differently and update different representations.
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@ -1,6 +1,6 @@
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---
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title: Local vs Global Representations
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description: Use Honcho that model directional relationships of Peers
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description: Model directional relationships between Peers in Honcho
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icon: location-pin
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---
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@ -10,7 +10,7 @@ how one `Peer` thinks about another `Peer`.
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There are many use cases where you don't want every agent or human to know
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everything about another user such as games or multi-agent workflows. To
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illustrate further the following examples shows 2 conversations.
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illustrate this, the following examples shows 2 conversations.
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Conversation #1 (With Bob and Alice)
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```
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@ -51,8 +51,8 @@ form a representation Alice based only on what they observe Alice do.
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This feature is illustrated in the graphic below:
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<img src="/images/local-vs-global-reps.png" alt="Peer Representations" />
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We can enable local representation for a peer by setting `observe_others=True`.
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This is show in the [Configure
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We can enable local representation for a `Peer` by setting `observe_others=True`.
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This is shown in the [Configure
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Reasoning](/v2/documentation/core-concepts/configuration) page.
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Now if we used Bob's local representation of Alice then Bob would only get
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@ -4,11 +4,11 @@ description: Learn how to check the status of the Deriver
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icon: lines-leaning
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---
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Whenever `Messages` are stored in Honcho a background process called the
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Whenever `Messages` are stored in Honcho, a background process called the
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[Deriver](/docs/v2/documentation/core-concepts/architecture#reasoning-layer) is
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triggered to reasoning about the conversation and generate insights.
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triggered to reason about the conversation and generate insights.
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The Deriver is an asynchronous process and depending on load may not immediately
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The Deriver is an asynchronous process and, depending on load may not immediately
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generated insights for the latest message you've sent. To help with this, Honcho
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provides several utilities to check the status of the Deriver.
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@ -51,8 +51,8 @@ for their entire app. These are flexible components that work in any situation.
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## Chat Bots
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A common use case for Honcho to is to build a Chatbot like ChatGPT or Claude.
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This this case you can simply
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A common use case for Honcho to is to build a chatbot like ChatGPT or Claude.
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In this case you can simply
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- Make a `Peer` for the User
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- Make a `Peer` for the AI
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@ -38,7 +38,7 @@ insight = peer.chat("How should I explain this concept?")
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Designed for developers and agents alike:
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- **Natural Language Queries**: Chat with Honcho in natural language via the [Dialectic API](../core-concepts/architecture#dialectic-api) to get insights about your users and agents
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- **Automatic Context Management**: Smart conversation summaries to have infinite chates
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- **Automatic Context Management**: Smart conversation summaries to have infinite chats
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- **Native multi-agent support**: Sessions can natively have as many participants as you need
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- **Agent-first interfaces**: MCP connections and APIs designed for agents to consume and use as tools
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- **Provider Agnostic**: Works with any LLM or Agent Framework
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@ -91,8 +91,8 @@ Plug this into your prompt to get a quick overview of the user.
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This endpoint lets you chat with Honcho about any entity in your system. Honcho
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will leverage what it has remembered and learned about the entity to provide in-context actionable insights.
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This comes in handy when you want your agent to back-channel with Honcho to
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change it's behavior at runtime.
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This is especially helpful when you want your agent to back-channel with Honcho to
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change its behavior at runtime.
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Example Queries:
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- "What's the best way to explain technical concepts to this user?"
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@ -249,11 +249,11 @@ const honcho = new Honcho({
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});
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// Get your Peers
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const alice = await client.peer("alice")
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const bob = await client.peer("bob")
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const alice = await honcho.peer("alice")
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const bob = await honcho.peer("bob")
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// Make a Session and add your peers
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const session = await client.session("session_1")
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const session = await honcho.session("session_1")
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await session.addPeers([alice, bob])
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// Add messages sent by your Peers
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@ -303,7 +303,7 @@ behavior and can be toggled off via [the configuration](/v2/documentation/core-c
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<Card title="Start Building" icon="brain" href="https://app.honcho.dev">
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Sign up for Managed Honcho and get started building agents now.
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</Card>
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<Card title="Guides" icon="book" href="/v2/documentation/guides/overview">
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<Card title="Guides" icon="book" href="/v2/guides/overview">
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Check out spellbooks to see different examples apps built with Honcho
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</Card>
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</CardGroup>
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@ -5,10 +5,10 @@ description: "Universal starter prompt for building with Honcho"
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sidebarTitle: 'Vibecoding Setup'
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---
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These docs are designed to be easily consumable for LLMs. each page has a button
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the lets you copy the page as markdown or put directly into ChatGPT or Claude.
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These docs are designed to be easily consumable for LLMs. Each page has a button
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the lets you copy the page as Markdown or paste directly into ChatGPT or Claude.
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Additionally, we follow the llms.txt standard. There is both an llms.txt and
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Additionally, we follow the llms.txt standard. There are both an llms.txt and
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llms-full.txt available.
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- [llms.txt](/llms.txt)
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