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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.
The full source code is available on GitHub with working tests and Zo marketplace submission instructions.

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What It Does

The skill provides three tools that any Zo workflow can call:
ToolDescription
save_memorySave user or assistant messages to a Honcho session
query_memoryAsk natural language questions about what Honcho remembers
get_contextRetrieve conversation history formatted for LLM use (OpenAI message format)

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Setup

Install dependencies: 
pip install honcho-ai python-dotenv
Set your environment variables: 
HONCHO_API_KEY=your-api-key
HONCHO_WORKSPACE_ID=default  # optional, defaults to "default"
Get your API key at app.honcho.dev.

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Quick Start

from tools.save_memory import save_memory
from tools.query_memory import query_memory
from tools.get_context import get_context

# Save conversation turns
save_memory("alice", "I love hiking in the mountains", "user", "session-1")
save_memory("alice", "That sounds wonderful!", "assistant", "session-1")

# Query what Honcho remembers
answer = query_memory("alice", "What are my hobbies?", "session-1")
print(answer)  # "Alice enjoys hiking in the mountains."

# Get context ready for an LLM call
messages = get_context("alice", "session-1", "assistant", tokens=4000)
# Returns [{"role": "user", "content": "..."}, ...]

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Saving Messages

save_memory creates peers and sessions automatically on first use and persists the message. 
save_memory(
    user_id="alice",          # unique user identifier
    content="Hello!",         # message text
    role="user",              # "user" or "assistant"
    session_id="session-1",   # conversation identifier
    assistant_id="assistant", # optional, defaults to "assistant"
)

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Querying Memory

query_memory uses Honcho’s Dialectic API to answer natural language questions grounded in stored memory. 
answer = query_memory(
    user_id="alice",
    query="What are my interests?",
    session_id="session-1",  # optional — omit to query global memory
)

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Retrieving Context

get_context fetches recent conversation history within a token budget and returns it in OpenAI message format — ready to pass directly to an LLM. 
messages = get_context(
    user_id="alice",
    session_id="session-1",
    assistant_id="assistant",
    tokens=4000,  # max tokens to include
)
# Use directly: llm.chat.completions.create(messages=messages)

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Concept Mapping

Zo ComputerHoncho
AccountWorkspace
UserPeer
ConversationSession
MessageMessage

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Publishing to the Zo Marketplace

To submit the skill to the Zo Skills Registry:
  1. Fork the zocomputer/skills repository
  2. Copy the examples/zo directory into /Community/honcho-memory/ in your fork
  3. Run bun validate to check the skill format
  4. Submit a pull request

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Next Steps

Source Code

Full source, tests, and SKILL.md for the Zo integration

Honcho Architecture

Understand peers, sessions, and how memory works

Chat API

Learn more about querying peer memory with the Dialectic API

Get Context

Details on retrieving and formatting conversation context