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feat(examples): add Honcho memory skill for Zo Computer (#495) 

* chore: add .worktrees/ to .gitignore

* feat(examples): add Zo Computer memory skill integration

* feat(examples): add Zo Computer memory skill integration

* fix(examples): address CodeRabbit review on Zo skill integration

  - Fix version inconsistency: SKILL.md matches pyproject.toml (>=2.1.0)
  - Move client.py into tools/ package and use relative imports
  - Add assistant_id parameter to save_memory() for consistency with get_context()
  - Use UUID-based IDs in tests to prevent state leakage between runs
  - Add pytest.mark.skipif guard on integration tests (requires HONCHO_API_KEY)
  - Fix import ordering, move pytest to module level, sort __all__ alphabetically
  - Fix markdown blank lines around fenced code blocks (MD031)
  - Add rate limit delay fixture to avoid hitting Honcho free tier limits

* fix(examples): validate HONCHO_API_KEY early in client initialization

* docs(examples): note cross-peer memory behavior in shared workspaces

* docs(examples): fix save_memory and query_memory signatures in README

* docs(examples): fix markdown linting issues in README

* docs(examples): add assistant_id parameter to save_memory example in
  SKILL.md

---------

Co-authored-by: Luba Kaper <lubakaper@lubas-air.mynetworksettings.com>
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.worktrees/
api/**/*.db
api/data
api/docker-compose.yml

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# Honcho Memory Skill for Zo Computer
Give your AI persistent memory across conversations using [Honcho](https://honcho.dev).
## 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
```bash
pip install honcho-ai python-dotenv
```
Or with uv:
```bash
uv add honcho-ai python-dotenv
```
## Environment Variables
Create a `.env` file:
```env
HONCHO_API_KEY=your-api-key-here
HONCHO_WORKSPACE_ID=default
```
Get your API key at [honcho.dev](https://honcho.dev).
## Quick Start
```python
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](../../README.md) for setup instructions.
```bash
uv run pytest tests/ -v
```
## Submitting to the Zo Skill Marketplace
To publish this skill to the [Zo Skills Registry](https://github.com/zocomputer/skills):
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:
```bash
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

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---
name: honcho-memory
description: Gives AI agents persistent memory across conversations using Honcho. Automatically saves and retrieves user context so the AI remembers preferences, history, and facts between sessions. Use when you need the AI to remember past conversations, recall what a user has told it, inject relevant context into prompts, or manage separate memory spaces for different topics.
license: AGPL-3.0
compatibility: Requires Python 3.9+, honcho-ai>=2.1.0, and a Honcho API key from honcho.dev. Set HONCHO_API_KEY and optionally HONCHO_WORKSPACE_ID in your environment.
metadata:
author: plastic-labs
version: "0.1.0"
honcho-sdk: "2.1.0"
---
# Honcho Memory Skill
This skill provides three tools for storing and retrieving AI memory using [Honcho](https://honcho.dev).
## Setup
1. Get a Honcho API key at [honcho.dev](https://honcho.dev).
2. Set environment variables:
```
HONCHO_API_KEY=your-api-key
HONCHO_WORKSPACE_ID=default # optional, defaults to "default"
```
3. Install dependencies:
```
pip install honcho-ai python-dotenv
```
## Tools
### `save_memory`
Saves a conversation turn (user or assistant message) to Honcho.
**When to use:** After every message exchange to build up the user's memory.
```python
from tools.save_memory import save_memory
save_memory(
user_id="alice", # unique user identifier
content="I love hiking", # message text
role="user", # "user" or "assistant"
session_id="chat-1", # conversation session ID
assistant_id="assistant" # optional: assistant peer ID (default: "assistant")
)
```
### `query_memory`
Asks a natural language question against stored memory using Honcho's Dialectic API.
**When to use:** When the user asks "do you remember...?", or when you need to recall facts about the user before responding.
```python
from tools.query_memory import query_memory
answer = query_memory(
user_id="alice",
query="What are Alice's hobbies?",
session_id="chat-1" # optional: scope to a session
)
# Returns: "Alice enjoys hiking."
```
### `get_context`
Retrieves recent conversation history formatted for direct use in an LLM API call.
**When to use:** At the start of each LLM call to inject relevant context from past conversations.
```python
from tools.get_context import get_context
messages = get_context(
user_id="alice",
session_id="chat-1",
assistant_id="assistant",
tokens=4000 # max tokens to include
)
# Returns: [{"role": "user", "content": "..."}, ...]
```
## Concept Mapping
| Zo Computer | Honcho |
|---|---|
| Account | Workspace |
| User | Peer |
| Conversation | Session |
| Message | Message |
## Example: Full Conversation Flow
```python
from tools.save_memory import save_memory
from tools.query_memory import query_memory
from tools.get_context import get_context
user_id = "alice"
session_id = "session-1"
# 1. Save user message
save_memory(user_id, "I'm learning Rust and love rock climbing", "user", session_id)
# 2. Save assistant reply
save_memory(user_id, "That's great! Both require patience.", "assistant", session_id)
# 3. In a later session, recall what you know
print(query_memory(user_id, "What does Alice do in her free time?"))
# → "Alice is learning Rust and enjoys rock climbing."
# 4. Get context window for next LLM call
messages = get_context(user_id, session_id, "assistant", tokens=4000)
```

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[project]
name = "honcho-zo-skill"
version = "0.1.0"
description = "Honcho persistent memory skill for Zo Computer"
readme = "README.md"
requires-python = ">=3.9"
dependencies = [
"honcho-ai>=2.1.0",
"python-dotenv>=1.0.0",
]
[project.optional-dependencies]
dev = [
"pytest>=8.0.0",
]
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[tool.hatch.build.targets.wheel]
packages = ["tools"]
[tool.pytest.ini_options]
pythonpath = ["."]

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"""Basic import and structure tests for honcho-zo-skill.
These tests validate package structure and imports without requiring
a running Honcho server.
"""
import os
import sys
import pytest
# Add parent directory to path so tools/ can be imported
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
def test_save_memory_import():
"""Test that save_memory can be imported."""
from tools.save_memory import save_memory
assert callable(save_memory)
def test_query_memory_import():
"""Test that query_memory can be imported."""
from tools.query_memory import query_memory
assert callable(query_memory)
def test_get_context_import():
"""Test that get_context can be imported."""
from tools.get_context import get_context
assert callable(get_context)
def test_tools_package_import():
"""Test that the tools package exports all three functions."""
import tools
assert hasattr(tools, "save_memory")
assert hasattr(tools, "query_memory")
assert hasattr(tools, "get_context")
def test_tools_all_exports():
"""Test that __all__ contains expected exports."""
import tools
assert hasattr(tools, "__all__")
expected = ["get_context", "query_memory", "save_memory"]
for name in expected:
assert name in tools.__all__, f"{name} not in __all__"
def test_save_memory_raises_on_empty_content():
"""Test that save_memory raises ValueError for empty content."""
from tools.save_memory import save_memory
with pytest.raises(ValueError, match="content must not be empty"):
save_memory("user1", "", "user", "session1")
def test_query_memory_raises_on_empty_query():
"""Test that query_memory raises ValueError for empty query."""
from tools.query_memory import query_memory
with pytest.raises(ValueError, match="query must not be empty"):
query_memory("user1", "")

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"""Functional tests for Honcho Zo skill tools.
These tests require a Honcho API key set in the HONCHO_API_KEY environment
variable. They run against the Honcho cloud API (honcho.dev) by default.
Set HONCHO_WORKSPACE_ID to scope tests to a specific workspace.
"""
import os
import sys
import time
import uuid
import pytest
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from tools.get_context import get_context
from tools.query_memory import query_memory
from tools.save_memory import save_memory
pytestmark = pytest.mark.skipif(
not os.getenv("HONCHO_API_KEY"),
reason="HONCHO_API_KEY not set — skipping integration tests",
)
@pytest.fixture(autouse=True)
def rate_limit_delay():
"""Pause between tests to stay under the Honcho API rate limit (5 req/sec)."""
yield
time.sleep(0.5)
def unique_id(prefix: str) -> str:
"""Generate a unique ID with a prefix to avoid test state leakage."""
return f"{prefix}_{uuid.uuid4().hex[:8]}"
class TestSaveMemory:
"""Tests for save_memory tool."""
def test_returns_confirmation_string(self):
"""Test that save_memory returns a non-empty confirmation string."""
result = save_memory(unique_id("user"), "Hello, I love hiking!", "user", unique_id("session"))
assert isinstance(result, str)
assert len(result) > 0
def test_saves_user_message(self):
"""Test saving a user-role message."""
user_id = unique_id("user")
result = save_memory(user_id, "I enjoy Python programming", "user", unique_id("session"))
assert isinstance(result, str)
assert "user" in result.lower() or user_id in result
def test_saves_assistant_message(self):
"""Test saving an assistant-role message."""
result = save_memory(unique_id("user"), "That sounds great!", "assistant", unique_id("session"))
assert isinstance(result, str)
assert len(result) > 0
def test_saves_multiple_turns(self):
"""Test saving multiple turns in the same session."""
user_id = unique_id("user")
session_id = unique_id("session")
result1 = save_memory(user_id, "I love mountains", "user", session_id)
result2 = save_memory(user_id, "That's wonderful!", "assistant", session_id)
assert isinstance(result1, str) and len(result1) > 0
assert isinstance(result2, str) and len(result2) > 0
def test_non_assistant_role_treated_as_user(self):
"""Test that any role other than 'assistant' is treated as user."""
result = save_memory(unique_id("user"), "Testing role fallback", "human", unique_id("session"))
assert isinstance(result, str)
assert len(result) > 0
def test_custom_assistant_id(self):
"""Test that a custom assistant_id is accepted."""
result = save_memory(
unique_id("user"), "Hello!", "assistant", unique_id("session"), assistant_id="my-bot"
)
assert isinstance(result, str)
assert len(result) > 0
class TestQueryMemory:
"""Tests for query_memory tool."""
def test_returns_string(self):
"""Test that query_memory returns a string response."""
user_id = unique_id("user")
session_id = unique_id("session")
save_memory(user_id, "I love pizza and Italian food", "user", session_id)
result = query_memory(user_id, "What does the user enjoy?")
assert isinstance(result, str)
assert len(result) > 0
def test_returns_string_with_session_scope(self):
"""Test query_memory scoped to a specific session."""
user_id = unique_id("user")
session_id = unique_id("session")
save_memory(user_id, "My favorite color is blue", "user", session_id)
result = query_memory(user_id, "What is the user's favorite color?", session_id)
assert isinstance(result, str)
assert len(result) > 0
def test_returns_fallback_for_unknown_user(self):
"""Test that query_memory returns a non-empty string even for new users."""
result = query_memory(unique_id("user"), "What do I like?")
assert isinstance(result, str)
assert len(result) > 0
class TestGetContext:
"""Tests for get_context tool."""
def test_returns_list(self):
"""Test that get_context returns a list."""
user_id = unique_id("user")
session_id = unique_id("session")
save_memory(user_id, "Hello there!", "user", session_id)
result = get_context(user_id, session_id, "assistant")
assert isinstance(result, list)
def test_returns_openai_format(self):
"""Test that returned messages are in OpenAI format."""
user_id = unique_id("user")
session_id = unique_id("session")
save_memory(user_id, "My name is Alex", "user", session_id)
save_memory(user_id, "Nice to meet you, Alex!", "assistant", session_id)
result = get_context(user_id, session_id, "assistant")
assert isinstance(result, list)
for msg in result:
assert "role" in msg
assert "content" in msg
assert msg["role"] in ("user", "assistant", "system")
assert isinstance(msg["content"], str)
def test_respects_token_limit(self):
"""Test that context respects the token limit parameter."""
user_id = unique_id("user")
session_id = unique_id("session")
for i in range(5):
save_memory(user_id, f"Message number {i} with some content", "user", session_id)
result_small = get_context(user_id, session_id, "assistant", tokens=100)
result_large = get_context(user_id, session_id, "assistant", tokens=8000)
assert isinstance(result_small, list)
assert isinstance(result_large, list)
assert len(result_large) >= len(result_small)
def test_empty_session_returns_list(self):
"""Test that get_context returns an empty list for a session with no messages."""
result = get_context(unique_id("user"), unique_id("session"), "assistant")
assert isinstance(result, list)
class TestToolsWorkTogether:
"""Integration tests using all three tools in sequence."""
def test_save_query_roundtrip(self):
"""Test saving a message and then querying it."""
user_id = unique_id("user")
session_id = unique_id("session")
save_memory(user_id, "I am a software engineer who loves Rust", "user", session_id)
result = query_memory(user_id, "What is the user's profession?", session_id)
assert isinstance(result, str)
assert len(result) > 0
def test_save_then_get_context(self):
"""Test that saved messages appear in context."""
user_id = unique_id("user")
session_id = unique_id("session")
save_memory(user_id, "Hello!", "user", session_id)
save_memory(user_id, "Hi there!", "assistant", session_id)
messages = get_context(user_id, session_id, "assistant")
assert isinstance(messages, list)
assert len(messages) >= 1

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"""Honcho memory tools for Zo Computer."""
from tools.get_context import get_context
from tools.query_memory import query_memory
from tools.save_memory import save_memory
__all__ = ["get_context", "query_memory", "save_memory"]

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"""Honcho client initialization for Zo Computer skill."""
import os
from dotenv import load_dotenv
from honcho import Honcho
load_dotenv()
def get_client(workspace_id: str | None = None) -> Honcho:
"""Initialize and return a Honcho client.
Reads HONCHO_API_KEY and HONCHO_WORKSPACE_ID from environment variables.
The workspace_id parameter overrides the environment variable if provided.
Args:
workspace_id: Optional workspace ID override. Falls back to the
HONCHO_WORKSPACE_ID env var, then to "default".
Returns:
Configured Honcho client instance.
"""
api_key = os.getenv("HONCHO_API_KEY")
if not api_key:
raise ValueError(
"HONCHO_API_KEY is required. Set it in your environment or .env file."
)
env_workspace = os.getenv("HONCHO_WORKSPACE_ID")
resolved_workspace = workspace_id or env_workspace or "default"
return Honcho(api_key=api_key, workspace_id=resolved_workspace)

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"""Retrieve conversation context from Honcho formatted for LLM use."""
from __future__ import annotations
from .client import get_client
def get_context(
user_id: str,
session_id: str,
assistant_id: str,
tokens: int = 4000,
) -> list[dict[str, str]]:
"""Retrieve conversation context ready for injection into an LLM prompt.
Fetches recent messages from a Honcho session within the given token
budget and converts them to OpenAI-compatible message format. Use the
returned list directly as the ``messages`` parameter in an LLM API call.
Args:
user_id: Unique identifier for the user peer. Used to ensure the
peer is registered in the session before fetching context.
session_id: Identifier for the conversation session.
assistant_id: Peer ID representing the assistant. This determines
which role is mapped to ``"assistant"`` in the output.
tokens: Maximum number of tokens to include in the context window.
Defaults to 4000.
Returns:
A list of message dicts in OpenAI format:
``[{"role": "user" | "assistant", "content": "..."}]``.
Returns an empty list if the session has no messages.
"""
honcho = get_client()
user_peer = honcho.peer(user_id)
assistant_peer = honcho.peer(assistant_id)
session = honcho.session(session_id)
session.add_peers([user_peer, assistant_peer])
context = session.context(tokens=tokens)
return context.to_openai(assistant=assistant_id)

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"""Query a user's Honcho memory using the Dialectic API."""
from __future__ import annotations
from .client import get_client
def query_memory(user_id: str, query: str, session_id: str | None = None) -> str:
"""Query stored memory for a user using Honcho's Dialectic API.
Sends a natural language question to Honcho and returns an answer
grounded in the peer's long-term representation and stored observations.
Args:
user_id: Unique identifier for the user peer.
query: Natural language question, e.g. "What are my hobbies?".
session_id: Optional session ID to scope the query to a specific
conversation. If omitted, the query draws from global memory.
Returns:
A natural language answer from Honcho's Dialectic API, or a
default message if no relevant information was found.
Raises:
ValueError: If query is empty.
"""
if not query:
raise ValueError("query must not be empty")
honcho = get_client()
peer = honcho.peer(user_id)
response = peer.chat(query=query, session=session_id)
if response:
return str(response)
return "No relevant information found in memory."

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"""Save a conversation message to Honcho memory."""
from .client import get_client
def save_memory(
user_id: str,
content: str,
role: str,
session_id: str,
assistant_id: str = "assistant",
) -> str:
"""Save a single conversation turn to Honcho memory.
Creates the peer and session if they do not already exist. Registers
the peer in the session on first use, then persists the message.
Args:
user_id: Unique identifier for the user peer.
content: Text content of the message to save.
role: Either "user" or "assistant". Determines which peer sends
the message. Any value other than "assistant" is treated as "user".
session_id: Identifier for the conversation session.
assistant_id: Peer ID for the assistant. Defaults to "assistant".
Returns:
A confirmation string describing what was saved.
Raises:
ValueError: If content is empty.
"""
if not content:
raise ValueError("content must not be empty")
honcho = get_client()
user_peer = honcho.peer(user_id)
assistant_peer = honcho.peer(assistant_id)
session = honcho.session(session_id)
session.add_peers([user_peer, assistant_peer])
sender = assistant_peer if role == "assistant" else user_peer
session.add_messages([sender.message(content)])
return f"Saved {role} message to session '{session_id}' for user '{user_id}'."

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