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fix: zo computer skill

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examples/zo/DISPLAY.json Normal file
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{
"icon": "brain",
"tags": ["ai", "memory", "data", "cli"],
"secrets": [
{
"secret_name": "HONCHO_API_KEY",
"description": "Required. Create one at app.honcho.dev › API keys."
},
{
"secret_name": "HONCHO_WORKSPACE_ID",
"description": "Optional. Honcho workspace ID. Defaults to default."
},
{
"secret_name": "HONCHO_USER_ID",
"description": "Optional. User peer ID. Defaults to the Zo Computer username when available, then user."
},
{
"secret_name": "HONCHO_ASSISTANT_ID",
"description": "Optional. Assistant peer ID. Defaults to assistant."
},
{
"secret_name": "HONCHO_SESSION_ID",
"description": "Optional. Default session ID. Defaults to default."
},
{
"secret_name": "HONCHO_BASE_URL",
"description": "Optional. Custom Honcho API URL. Defaults to Honcho production."
}
]
}

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examples/zo/README.md
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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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examples/zo/SKILL.md
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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.
description: Long-term memory for AI agents via the official Honcho CLI. Save conversation turns, ask natural-language memory questions, and pull context for prompts.
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.
compatibility: Requires uv, honcho-cli, and HONCHO_API_KEY. The wrapper auto-installs honcho-cli with uv if missing.
metadata:
author: plastic-labs
version: "0.1.0"
honcho-sdk: "2.1.0"
version: "0.4.0"
honcho-cli: "0.1.0+"
---
# Honcho Memory
# Honcho Memory Skill
Use this when an agent needs persistent memory across conversations.
This skill provides three tools for storing and retrieving AI memory using [Honcho](https://honcho.dev).
This skill is intentionally thin: `scripts/memory.py` is a small Zo-friendly wrapper around the official `honcho` CLI.
## Setup
1. Get a Honcho API key at [honcho.dev](https://honcho.dev).
2. Set environment variables:
1. Get a Honcho API key from [app.honcho.dev](https://app.honcho.dev/api-keys).
2. Add `HONCHO_API_KEY` in [Settings > Advanced](/?t=settings&s=advanced).
3. Optional: add `HONCHO_WORKSPACE_ID`, `HONCHO_USER_ID`, `HONCHO_ASSISTANT_ID`, or `HONCHO_SESSION_ID`.
4. Verify:
```
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")
)
```bash
python3 Skills/honcho-memory/scripts/memory.py test
```
### `query_memory`
## Agent workflow
Asks a natural language question against stored memory using Honcho's Dialectic API.
Before responding, get context when memory would help:
**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."
```bash
python3 Skills/honcho-memory/scripts/memory.py context --session chat-1
```
### `get_context`
After meaningful exchanges, save the turn:
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": "..."}, ...]
```bash
python3 Skills/honcho-memory/scripts/memory.py save "User prefers concise tools" --session chat-1
```
## Concept Mapping
Ask Honcho directly:
| 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)
```bash
python3 Skills/honcho-memory/scripts/memory.py ask "What should I know about the user's preferences?"
```
## Commands
```bash
python3 Skills/honcho-memory/scripts/memory.py save "memory" --session chat-1
python3 Skills/honcho-memory/scripts/memory.py ask "question"
python3 Skills/honcho-memory/scripts/memory.py context --session chat-1 --tokens 2000
python3 Skills/honcho-memory/scripts/memory.py search "topic"
python3 Skills/honcho-memory/scripts/memory.py messages --session chat-1
python3 Skills/honcho-memory/scripts/memory.py doctor
python3 Skills/honcho-memory/scripts/memory.py test
```
For a full exchange, pipe JSON:
```bash
cat messages.json | python3 Skills/honcho-memory/scripts/memory.py save --session chat-1
```
```json
[
{"role": "user", "content": "I'm learning Rust"},
{"role": "assistant", "content": "Nice — Rust rewards careful thinking."}
]
```
## Direct CLI
Use the official CLI directly when you need lower-level control:
```bash
honcho doctor --json
honcho message create "memory" -p "$ZO_USER" -s chat-1 --json
honcho session context chat-1 --json
honcho peer chat "what does the user prefer?" -p "$ZO_USER" --json
```

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examples/zo/pyproject.toml
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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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examples/zo/scripts/memory.py Executable file
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#!/usr/bin/env python3
"""Small Zo Skill wrapper around the official Honcho CLI."""
import argparse
import json
import os
import shutil
import subprocess
import sys
import time
from pathlib import Path
def default_user_peer() -> str:
for name in ("HONCHO_USER_ID", "HONCHO_PEER_ID", "ZO_USER", "ZO_USERNAME", "USER"):
value = os.environ.get(name)
if value and value not in {"root", "zo"}:
return value
return "user"
DEFAULT_USER = default_user_peer()
DEFAULT_ASSISTANT = os.environ.get("HONCHO_ASSISTANT_ID", "assistant")
DEFAULT_SESSION = os.environ.get("HONCHO_SESSION_ID", "default")
DEFAULT_WORKSPACE = os.environ.get("HONCHO_WORKSPACE_ID", "default")
def fail(message: str, code: int = 1):
print(f"ERROR: {message}", file=sys.stderr)
raise SystemExit(code)
def ensure_cli() -> str:
binary = shutil.which("honcho") or str(Path.home() / ".local/bin/honcho")
if Path(binary).exists():
return binary
if not shutil.which("uv"):
fail("Honcho CLI is missing and `uv` is not installed. Install uv, then run: uv tool install honcho-cli")
subprocess.run(["uv", "tool", "install", "honcho-cli"], check=True)
binary = shutil.which("honcho") or str(Path.home() / ".local/bin/honcho")
if not Path(binary).exists():
fail("Honcho CLI installed but `honcho` was not found. Try opening a new shell or add ~/.local/bin to PATH.")
return binary
def require_key():
if not os.environ.get("HONCHO_API_KEY"):
fail("HONCHO_API_KEY is missing. Add it in Zo Settings > Advanced > Secrets.", 3)
def env() -> dict[str, str]:
out = os.environ.copy()
out["PATH"] = f"{Path.home() / '.local/bin'}:{out.get('PATH', '')}"
out.setdefault("HONCHO_JSON", "1")
return out
def scope(args, include_session: bool = True) -> list[str]:
flags = ["--workspace", args.workspace or DEFAULT_WORKSPACE]
if getattr(args, "user", None):
flags += ["--peer", args.user]
if include_session and getattr(args, "session", None):
flags += ["--session", args.session]
return flags
def run_honcho(args_list: list[str], *, capture: bool = False, input_text: str | None = None, allow_doctor_partial: bool = False):
require_key()
binary = ensure_cli()
cmd = [binary, *args_list]
if "--json" not in cmd:
cmd.append("--json")
result = subprocess.run(
cmd,
input=input_text,
text=True,
capture_output=capture,
env=env(),
)
if result.returncode != 0:
if allow_doctor_partial and args_list and args_list[0] == "doctor":
text = result.stdout or result.stderr or ""
try:
data = json.loads(text)
checks = data.get("checks", [])
critical_failures = [c for c in checks if not c.get("ok") and c.get("check") != "Config file"]
if not critical_failures:
return text
except Exception:
pass
message = (result.stderr or result.stdout or "Honcho command failed").strip()
fail(message, result.returncode)
return result.stdout if capture else ""
def parse_messages(args) -> list[dict[str, str]]:
raw = args.text
if not raw and not sys.stdin.isatty():
raw = sys.stdin.read().strip()
if not raw:
fail("Provide message text, JSON, or pipe JSON into stdin.")
try:
data = json.loads(raw)
if isinstance(data, dict):
data = [data]
if isinstance(data, list):
messages = []
for item in data:
if not isinstance(item, dict) or "content" not in item:
fail("JSON messages must be objects with at least a content field.")
messages.append({"role": str(item.get("role", args.role)), "content": str(item["content"])})
return messages
except json.JSONDecodeError:
pass
return [{"role": args.role, "content": raw}]
def peer_for_role(role: str, args) -> str:
normalized = role.lower()
if normalized in {"assistant", "ai", "bot"}:
return args.assistant or DEFAULT_ASSISTANT
return args.user or DEFAULT_USER
def cmd_save(args):
session = args.session or DEFAULT_SESSION
workspace = args.workspace or DEFAULT_WORKSPACE
messages = parse_messages(args)
run_honcho(["session", "create", session, "--peers", f"{args.user or DEFAULT_USER},{args.assistant or DEFAULT_ASSISTANT}", "--workspace", workspace], capture=True)
for item in messages:
peer = peer_for_role(item["role"], args)
metadata = json.dumps({"role": item["role"], "source": "zo-skill:honcho-memory"})
run_honcho(["message", "create", item["content"], "--peer", peer, "--session", session, "--workspace", workspace, "--metadata", metadata], capture=True)
print(f"Saved {len(messages)} message(s) to Honcho session '{session}'.")
def cmd_ask(args):
output = run_honcho(["peer", "chat", args.question, *scope(args), "--reasoning", args.reasoning], capture=True)
print(output.strip())
def cmd_context(args):
command = ["session", "context", args.session or DEFAULT_SESSION, "--workspace", args.workspace or DEFAULT_WORKSPACE]
if args.tokens:
command += ["--tokens", str(args.tokens)]
if not args.summary:
command.append("--no-summary")
output = run_honcho(command, capture=True)
print(output.strip())
def cmd_messages(args):
command = ["message", "list", args.session or DEFAULT_SESSION, "--workspace", args.workspace or DEFAULT_WORKSPACE, "--last", str(args.limit)]
if args.user:
command += ["--peer", args.user]
output = run_honcho(command, capture=True)
print(output.strip())
def cmd_search(args):
output = run_honcho(["conclusion", "search", args.query, "--observer", args.user or DEFAULT_USER, "--workspace", args.workspace or DEFAULT_WORKSPACE, "--top-k", str(args.limit)], capture=True)
print(output.strip())
def cmd_doctor(args):
output = run_honcho(["doctor"], capture=True, allow_doctor_partial=True)
print(output.strip())
def cmd_test(args):
test_session = args.session
if not test_session or test_session == DEFAULT_SESSION:
test_session = f"honcho-memory-test-{int(time.time())}"
content = f"Honcho CLI skill test at {int(time.time())}"
save_args = argparse.Namespace(
text=content,
role="user",
user=args.user or DEFAULT_USER,
assistant=args.assistant or DEFAULT_ASSISTANT,
session=test_session,
workspace=args.workspace or DEFAULT_WORKSPACE,
)
cmd_save(save_args)
output = run_honcho(["message", "list", test_session, "--workspace", args.workspace or DEFAULT_WORKSPACE, "--last", "5"], capture=True)
if content not in output:
fail("Test message was saved but not found in message list.")
print(f"OK: Honcho CLI saved and listed a test message in session '{test_session}'.")
def add_common(parser):
parser.add_argument("--user", default=DEFAULT_USER, help="User peer ID")
parser.add_argument("--assistant", default=DEFAULT_ASSISTANT, help="Assistant peer ID")
parser.add_argument("--session", default=DEFAULT_SESSION, help="Session ID")
parser.add_argument("--workspace", default=DEFAULT_WORKSPACE, help="Workspace ID")
def build_parser():
parser = argparse.ArgumentParser(description="Simple Honcho memory CLI wrapper")
sub = parser.add_subparsers(dest="command", required=True)
save = sub.add_parser("save", help="Save one message or JSON messages to Honcho")
save.add_argument("text", nargs="?", help="Message text or JSON messages")
save.add_argument("--role", default="user", choices=["user", "assistant", "system"], help="Role for plain text input")
add_common(save)
save.set_defaults(func=cmd_save)
ask = sub.add_parser("ask", help="Ask Honcho about remembered context")
ask.add_argument("question")
ask.add_argument("--reasoning", default="medium", choices=["minimal", "low", "medium", "high", "max"])
add_common(ask)
ask.set_defaults(func=cmd_ask)
context = sub.add_parser("context", help="Print an LLM-ready context window as JSON")
context.add_argument("--tokens", type=int, default=0)
context.add_argument("--summary", action=argparse.BooleanOptionalAction, default=True)
add_common(context)
context.set_defaults(func=cmd_context)
messages = sub.add_parser("messages", help="List recent session messages")
messages.add_argument("--limit", type=int, default=20)
add_common(messages)
messages.set_defaults(func=cmd_messages)
search = sub.add_parser("search", help="Search Honcho conclusions for the user peer")
search.add_argument("query")
search.add_argument("--limit", type=int, default=10)
add_common(search)
search.set_defaults(func=cmd_search)
doctor = sub.add_parser("doctor", help="Check Honcho CLI connectivity")
doctor.add_argument("--workspace", default=DEFAULT_WORKSPACE)
doctor.set_defaults(func=cmd_doctor)
test = sub.add_parser("test", help="Save and list a tiny memory to verify setup")
add_common(test)
test.set_defaults(func=cmd_test)
return parser
def main():
args = build_parser().parse_args()
args.func(args)
if __name__ == "__main__":
main()

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examples/zo/tests/test_basic.py
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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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examples/zo/tests/test_tools.py
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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

7
examples/zo/tools/__init__.py
View File

@ -1,7 +0,0 @@
"""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"]

32
examples/zo/tools/client.py
View File

@ -1,32 +0,0 @@
"""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)

42
examples/zo/tools/get_context.py
View File

@ -1,42 +0,0 @@
"""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)

37
examples/zo/tools/query_memory.py
View File

@ -1,37 +0,0 @@
"""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."

45
examples/zo/tools/save_memory.py
View File

@ -1,45 +0,0 @@
"""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}'."

503
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