docs: add Zo Computer integration page (#504)
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@ -107,7 +107,8 @@
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"v3/guides/integrations/mcp",
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"v3/guides/integrations/n8n",
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"v3/guides/integrations/openclaw",
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"v3/guides/integrations/hermes"
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"v3/guides/integrations/hermes",
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"v3/guides/integrations/zo-computer"
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]
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},
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{
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@ -0,0 +1,134 @@
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---
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title: "Zo Computer"
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icon: 'bolt'
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description: "Add persistent memory to Zo Computer skills using Honcho"
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sidebarTitle: 'Zo Computer'
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---
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[Zo Computer](https://zo.computer) is a cloud AI platform where users build reusable workflows called skills. The Honcho memory skill gives any Zo workflow persistent memory — saving conversations, answering questions about past interactions, and injecting context into LLM prompts.
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<Note>
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The full source code is available on [GitHub](https://github.com/plastic-labs/honcho/tree/main/examples/zo) with working tests and Zo marketplace submission instructions.
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</Note>
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## What It Does
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The skill provides three tools that any Zo workflow can call:
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| Tool | Description |
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| ---- | ----------- |
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| `save_memory` | Save user or assistant messages to a Honcho session |
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| `query_memory` | Ask natural language questions about what Honcho remembers |
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| `get_context` | Retrieve conversation history formatted for LLM use (OpenAI message format) |
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## Setup
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Install dependencies:
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```bash
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pip install honcho-ai python-dotenv
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```
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Set your environment variables:
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```bash
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HONCHO_API_KEY=your-api-key
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HONCHO_WORKSPACE_ID=default # optional, defaults to "default"
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```
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Get your API key at [app.honcho.dev](https://app.honcho.dev).
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## Quick Start
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```python
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from tools.save_memory import save_memory
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from tools.query_memory import query_memory
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from tools.get_context import get_context
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# Save conversation turns
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save_memory("alice", "I love hiking in the mountains", "user", "session-1")
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save_memory("alice", "That sounds wonderful!", "assistant", "session-1")
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# Query what Honcho remembers
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answer = query_memory("alice", "What are my hobbies?", "session-1")
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print(answer) # "Alice enjoys hiking in the mountains."
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# Get context ready for an LLM call
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messages = get_context("alice", "session-1", "assistant", tokens=4000)
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# Returns [{"role": "user", "content": "..."}, ...]
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```
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## Saving Messages
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`save_memory` creates peers and sessions automatically on first use and persists the message.
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```python
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save_memory(
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user_id="alice", # unique user identifier
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content="Hello!", # message text
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role="user", # "user" or "assistant"
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session_id="session-1", # conversation identifier
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assistant_id="assistant", # optional, defaults to "assistant"
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)
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```
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## Querying Memory
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`query_memory` uses Honcho's Dialectic API to answer natural language questions grounded in stored memory.
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```python
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answer = query_memory(
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user_id="alice",
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query="What are my interests?",
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session_id="session-1", # optional — omit to query global memory
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)
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```
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## Retrieving Context
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`get_context` fetches recent conversation history within a token budget and returns it in OpenAI message format — ready to pass directly to an LLM.
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```python
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messages = get_context(
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user_id="alice",
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session_id="session-1",
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assistant_id="assistant",
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tokens=4000, # max tokens to include
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)
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# Use directly: llm.chat.completions.create(messages=messages)
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```
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## Concept Mapping
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| Zo Computer | Honcho |
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| --- | --- |
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| Account | Workspace |
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| User | Peer |
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| Conversation | Session |
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| Message | Message |
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## Publishing to the Zo Marketplace
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To submit the skill to the [Zo Skills Registry](https://github.com/zocomputer/skills):
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1. Fork the `zocomputer/skills` repository
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2. Copy the `examples/zo` directory into `/Community/honcho-memory/` in your fork
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3. Run `bun validate` to check the skill format
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4. Submit a pull request
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## Next Steps
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<CardGroup cols={2}>
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<Card title="Source Code" icon="github" href="https://github.com/plastic-labs/honcho/tree/main/examples/zo">
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Full source, tests, and SKILL.md for the Zo integration
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</Card>
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<Card title="Honcho Architecture" icon="sitemap" href="/v3/documentation/core-concepts/architecture">
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Understand peers, sessions, and how memory works
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</Card>
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<Card title="Chat API" icon="brain" href="/v3/documentation/features/chat">
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Learn more about querying peer memory with the Dialectic API
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</Card>
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<Card title="Get Context" icon="messages" href="/v3/documentation/features/get-context">
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Details on retrieving and formatting conversation context
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</Card>
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</CardGroup>
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@ -59,6 +59,9 @@ Use Honcho as a memory layer in your agent orchestration stack:
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<Card title="CrewAI" icon="users-gear" href="/v3/guides/integrations/crewai">
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Give CrewAI agents memory that persists across sessions
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</Card>
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<Card title="Zo Computer" icon="bolt" href="/v3/guides/integrations/zo-computer">
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Persistent memory skill for Zo Computer AI workflows
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</Card>
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<Card title="n8n" icon="share-nodes" href="/v3/guides/integrations/n8n">
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Build intelligent automation workflows with persistent memory
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</Card>
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