honcho/examples/zo

Honcho Memory Skill for Zo Computer

Give your AI persistent memory across conversations using Honcho.

Features

• Auto-Memory: Save user and assistant messages to Honcho with one call
• Query Memory: Ask natural language questions about what Honcho remembers ("What are my hobbies?")
• Context Injection: Retrieve conversation context formatted for direct LLM use
• Multi-Workspace Support: Manage separate memory spaces via HONCHO_WORKSPACE_ID

Installation

pip install honcho-ai python-dotenv

Or with uv:

uv add honcho-ai python-dotenv

Environment Variables

Create a .env file:

HONCHO_API_KEY=your-api-key-here
HONCHO_WORKSPACE_ID=default

Get your API key at honcho.dev.

Quick Start

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

# Save a conversation turn
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)
# messages is a list of {"role": ..., "content": ...} dicts

Tool Reference

save_memory(user_id, content, role, session_id, assistant_id="assistant")

Saves a message to Honcho memory.

Param	Type	Description
user_id	str	Unique user identifier
content	str	Message text
role	str	"user" or "assistant"
session_id	str	Session/conversation identifier
assistant_id	str	Peer ID for the assistant. Defaults to "assistant"

Returns a confirmation string.

---

query_memory(user_id, query, session_id=None)

Queries stored memory using Honcho's Dialectic API.

Param	Type	Description
user_id	str	Unique user identifier
query	str	Natural language question
session_id	str | None	Optional: scope to a specific session. Defaults to None (global memory)

Returns a natural language answer.

Note: In shared workspaces, query_memory may return data from other peers if the queried user has no stored memory yet. The Dialectic API draws from workspace-level context as a fallback. Use unique HONCHO_WORKSPACE_ID values per user group in production to prevent cross-peer data leakage.

---

get_context(user_id, session_id, assistant_id, tokens=4000)

Retrieves conversation context in OpenAI message format.

Param	Type	Description
user_id	str	Unique user identifier
session_id	str	Session/conversation identifier
assistant_id	str	Peer ID for the assistant
tokens	int	Max tokens to include (default: 4000)

Returns a list of {"role": ..., "content": ...} dicts.

Concept Mapping

Zo Computer	Honcho
Account	Workspace
User	Peer
Conversation	Session
Message	Message

Running Tests

Requires a running Honcho server. See the main repo for setup instructions.

uv run pytest tests/ -v

Submitting to the Zo Skill Marketplace

To publish this skill to the Zo Skills Registry:

1. Fork the zocomputer/skills repository.

2. Copy this directory into the /Community folder of your fork, naming it honcho-memory:

   Community/
   └── honcho-memory/
       ├── SKILL.md
       ├── README.md
       ├── client.py
       ├── pyproject.toml
       └── tools/
   

3. Validate your skill:

   bun validate
   

4. Submit a pull request to the upstream registry repository.

Once merged, the skill will be automatically added to the Zo marketplace manifest.json.

License

AGPL-3.0-or-later