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feat: add honcho-agno integration package 

Add Agno framework integration following the Toolkit pattern:
   - HonchoTools class with 5 memory tools (add_message, get_context,
     search_messages, query_user, reset_session)
   - Example scripts demonstrating usage with Agno agents
   - Comprehensive test suite
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# Honcho Agno Integration Environment Variables
# Copy this file to .env and fill in the appropriate values
# =============================================================================
# OpenAI Settings (for Agno agent)
# =============================================================================
# OpenAI API key - supports both naming conventions
OPENAI_API_KEY=your-openai-api-key-here
# LLM_OPENAI_API_KEY=your-openai-api-key-here # Alternative name
# OpenAI model to use (default: gpt-4o)
# OPENAI_MODEL=gpt-4o
# =============================================================================
# Honcho Settings
# =============================================================================
# Environment: 'local' or 'production' (default: production)
# Use 'local' for development with local Honcho server at localhost:8000
HONCHO_ENVIRONMENT=local
# API key for production Honcho (required if HONCHO_ENVIRONMENT=production)
# HONCHO_API_KEY=your-honcho-api-key-here
# Workspace ID (optional, defaults to 'default')
# HONCHO_WORKSPACE_ID=my-workspace

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Also add information on how to contact you by electronic and paper mail.
If your software can interact with users remotely through a computer
network, you should also make sure that it provides a way for users to
get its source. For example, if your program is a web application, its
interface could display a "Source" link that leads users to an archive
of the code. There are many ways you could offer source, and different
solutions will be better for different programs; see section 13 for the
specific requirements.
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU AGPL, see
<https://www.gnu.org/licenses/>.

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# Honcho Agno Integration
Give your [Agno](https://agno.com) agents persistent memory with [Honcho](https://honcho.dev).
## Installation
```bash
pip install honcho-agno
```
Or with uv:
```bash
uv add honcho-agno
```
## Quick Start
```python
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from honcho_agno import HonchoTools
# Create Honcho tools with user context
honcho_tools = HonchoTools(
app_id="my-app",
user_id="user-123",
)
# Create an agent with memory
agent = Agent(
name="Memory Agent",
model=OpenAIChat(id="gpt-4o"),
tools=[honcho_tools],
description="An assistant with persistent memory powered by Honcho.",
)
# The agent can now use memory tools
response = agent.run("Remember that I prefer Python over JavaScript")
```
## Features
The `HonchoTools` toolkit provides four memory tools:
| Tool | Description |
|------|-------------|
| `add_message` | Store messages in the session for future recall |
| `get_context` | Retrieve conversation context within token limits |
| `search_messages` | Semantic search through past messages |
| `query_user` | Query the dialectic API for user insights |
## Configuration
### Basic Configuration
```python
from honcho_agno import HonchoTools
tools = HonchoTools(
app_id="my-app", # Workspace/application identifier
user_id="user-123", # User identifier
session_id="session-456", # Optional: specific session ID
)
```
### With API Key
```python
tools = HonchoTools(
app_id="my-app",
user_id="user-123",
api_key="your-api-key",
base_url="https://api.honcho.dev", # Optional: custom endpoint
)
```
### Environment Variables
Copy `.env.template` to `.env` and configure:
**Honcho Settings:**
- `HONCHO_ENVIRONMENT`: `local` or `production` (default: production)
- `HONCHO_API_KEY`: API key for production environment
- `HONCHO_WORKSPACE_ID`: Default workspace ID
**OpenAI Settings (for examples):**
- `OPENAI_API_KEY` or `LLM_OPENAI_API_KEY`: OpenAI API key
- `OPENAI_MODEL`: Model to use (default: gpt-4o)
## Tool Details
### add_message
Store a message in the current session.
```python
# The agent can call this tool to save information
result = honcho_tools.add_message(
content="User prefers dark mode",
role="user" # or "assistant"
)
```
### get_context
Retrieve recent conversation context.
```python
context = honcho_tools.get_context(
tokens=2000, # Max tokens to include
include_summary=True, # Include session summary
)
```
### search_messages
Search through past messages semantically.
```python
results = honcho_tools.search_messages(
query="programming preferences",
limit=10,
)
```
### query_user
Ask questions about the user via the dialectic API.
```python
insights = honcho_tools.query_user(
query="What programming languages does the user prefer?"
)
```
## Examples
See the [examples](./examples) directory for complete working examples:
- `simple_example.py`: Basic usage with HonchoTools
- `multi_tool_example.py`: Using all tools together
## Development
### Setup
```bash
cd examples/agno/python
uv sync
```
### Run Tests
```bash
uv run pytest
```
## License
AGPL-3.0-or-later

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"""
Honcho Multi-Tool Example
Demonstrates using all Honcho tools with an Agno agent:
- add_message: Store conversation history
- get_context: Retrieve session context
- search_messages: Semantic search
- query_user: Dialectic API queries
Environment Variables:
OPENAI_API_KEY or LLM_OPENAI_API_KEY: OpenAI API key
OPENAI_MODEL: Model to use (default: gpt-4o)
HONCHO_ENVIRONMENT: 'local' or 'production' (default: production)
HONCHO_API_KEY: Required for production environment
"""
import os
from dotenv import load_dotenv
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from honcho_agno import HonchoTools
load_dotenv()
# Support both OPENAI_API_KEY and LLM_OPENAI_API_KEY
if not os.getenv("OPENAI_API_KEY") and os.getenv("LLM_OPENAI_API_KEY"):
os.environ["OPENAI_API_KEY"] = os.getenv("LLM_OPENAI_API_KEY")
def main():
print("=" * 70)
print("HONCHO TOOLS + AGNO EXAMPLE")
print("=" * 70 + "\n")
# Get environment settings
honcho_env = os.getenv("HONCHO_ENVIRONMENT", "production")
# Setup Honcho tools with a specific session
honcho_tools = HonchoTools(
app_id="travel-app",
user_id="traveler-42",
session_id="trip-planning-session",
environment=honcho_env,
)
# Pre-populate with travel preferences
print("Adding travel preferences to memory...")
messages = [
"I'm planning a trip to Japan in March",
"I love trying authentic local cuisine",
"My budget is around $3000 for 10 days",
"I prefer ryokans over hotels",
"I'm interested in both traditional temples and modern Tokyo",
]
for msg in messages:
result = honcho_tools.add_message(msg, role="user")
print(f" Added: {msg[:50]}...")
print("\n" + "-" * 70 + "\n")
# Create travel planning agent with memory tools
agent = Agent(
name="Travel Planner",
model=OpenAIChat(id=os.getenv("OPENAI_MODEL", "gpt-4o")),
tools=[honcho_tools],
description=(
"A travel planning expert with access to memory tools. "
"Use get_context for recent conversation, search_messages to find "
"specific preferences, and query_user to understand the traveler."
),
instructions=[
"Always retrieve relevant context before making recommendations",
"Use search to find specific preferences mentioned",
"Query the user's knowledge to understand their travel style",
"Be specific and actionable in your recommendations",
],
)
# Run the agent with a planning request
print("Asking agent to create a personalized itinerary...\n")
response = agent.run(
"Create a 3-day Tokyo itinerary for me. First, use the memory tools to "
"understand my preferences (budget, accommodation style, interests), "
"then create a personalized plan that matches what I've told you."
)
print("=" * 70)
print("RESPONSE")
print("=" * 70)
print(response.content)
# Demonstrate search capability
print("\n" + "=" * 70)
print("DIRECT TOOL USAGE: Searching for budget info...")
print("=" * 70)
search_result = honcho_tools.search_messages("budget money cost", limit=5)
print(search_result)
if __name__ == "__main__":
main()

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"""
Simple Honcho + Agno Example
A minimal example showing how to use HonchoTools with Agno agents.
Environment Variables:
OPENAI_API_KEY or LLM_OPENAI_API_KEY: OpenAI API key
HONCHO_ENVIRONMENT: 'local' or 'production' (default: production)
HONCHO_API_KEY: Required for production environment
"""
import os
from dotenv import load_dotenv
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from honcho_agno import HonchoTools
load_dotenv()
# Support both OPENAI_API_KEY and LLM_OPENAI_API_KEY
if not os.getenv("OPENAI_API_KEY") and os.getenv("LLM_OPENAI_API_KEY"):
os.environ["OPENAI_API_KEY"] = os.getenv("LLM_OPENAI_API_KEY")
def main():
# Get environment settings
honcho_env = os.getenv("HONCHO_ENVIRONMENT", "production")
# Initialize Honcho tools with user context
honcho_tools = HonchoTools(
app_id="agno-demo",
user_id="demo-user",
environment=honcho_env,
)
# Add some conversation history manually
print("Adding conversation history...")
honcho_tools.add_message("I'm learning Python programming", role="user")
honcho_tools.add_message(
"I'm also interested in web development with FastAPI", role="user"
)
# Create an agent with memory tools
agent = Agent(
name="Programming Mentor",
model=OpenAIChat(id=os.getenv("OPENAI_MODEL", "gpt-4o")),
tools=[honcho_tools],
description="A programming mentor that remembers user interests and progress.",
instructions=[
"Use the memory tools to understand the user's background",
"Provide personalized recommendations based on their interests",
],
)
# The agent can now query memories and provide personalized responses
print("\nAsking the agent for recommendations...")
response = agent.run(
"Based on what you know about me, what should I learn next? "
"Use your memory tools to check my interests first."
)
print("\n" + "=" * 60)
print("RESPONSE")
print("=" * 60)
print(response.content)
if __name__ == "__main__":
main()

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[project]
name = "honcho-agno"
version = "0.1.0"
description = "Agno integration with Honcho for persistent agent memory"
readme = "README.md"
requires-python = ">=3.10"
license = {text = "AGPL-3.0-or-later"}
authors = [
{name = "Plastic Labs", email = "hello@plasticlabs.ai"}
]
maintainers = [
{name = "Plastic Labs", email = "hello@plasticlabs.ai"}
]
keywords = [
"honcho",
"agno",
"ai-agents",
"memory",
"agent-memory",
"persistent-memory"
]
classifiers = [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
"Topic :: Software Development :: Libraries :: Python Modules",
"Framework :: Pydantic",
]
dependencies = [
"agno>=1.4.0",
"honcho-ai>=1.6.0",
"openai>=1.0.0",
"python-dotenv>=1.0.0",
]
[project.urls]
Homepage = "https://honcho.dev"
Documentation = "https://docs.honcho.dev/v2/integrations/agno"
Repository = "https://github.com/plastic-labs/honcho"
"Bug Tracker" = "https://github.com/plastic-labs/honcho/issues"
Changelog = "https://github.com/plastic-labs/honcho/blob/main/CHANGELOG.md"
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[tool.hatch.build.targets.wheel]
packages = ["src/honcho_agno"]
[tool.pytest.ini_options]
testpaths = ["tests"]
python_files = ["test_*.py"]

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"""
Honcho Agno Integration
This package provides seamless integration between Honcho and Agno,
enabling AI agents to maintain persistent memory across conversations.
Example:
```python
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from honcho_agno import HonchoTools
# Create Honcho tools
honcho_tools = HonchoTools(
app_id="my-app",
user_id="user-123",
)
# Create agent with memory
agent = Agent(
name="Memory Agent",
model=OpenAIChat(id="gpt-4o"),
tools=[honcho_tools],
description="An assistant with persistent memory powered by Honcho.",
)
# Run the agent
response = agent.run("Remember that I prefer Python over JavaScript")
```
"""
from honcho_agno.exceptions import (
HonchoDependencyError,
HonchoSessionError,
HonchoToolError,
)
from honcho_agno.tools import HonchoTools
__version__ = "0.1.0"
__all__ = [
"HonchoTools",
"HonchoDependencyError",
"HonchoSessionError",
"HonchoToolError",
]

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"""
Exception classes for Honcho Agno integration.
"""
class HonchoDependencyError(ImportError):
"""Raised when required Agno dependencies are not installed."""
def __init__(self, framework: str, install_command: str) -> None:
self.framework = framework
self.install_command = install_command
super().__init__(
f"{framework} dependencies not found. Install with: {install_command}"
)
class HonchoSessionError(Exception):
"""Raised when there is an error with Honcho session operations."""
pass
class HonchoToolError(Exception):
"""Raised when a Honcho tool operation fails."""
pass

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"""
Honcho Tools for Agno
This module provides a Toolkit that allows Agno agents to interact with Honcho's
memory system, including session context, semantic search, and dialectic API.
"""
import logging
import uuid
from typing import Literal, Optional
from agno.tools import Toolkit
from honcho import Honcho
logger = logging.getLogger(__name__)
class HonchoTools(Toolkit):
"""
Honcho toolkit for Agno agents.
Provides memory tools for session context retrieval, semantic search,
message storage, and querying user knowledge via the Dialectic API.
Example:
```python
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from honcho_agno import HonchoTools
honcho_tools = HonchoTools(
app_id="my-app",
user_id="user-123",
)
agent = Agent(
model=OpenAIChat(id="gpt-4o"),
tools=[honcho_tools],
)
```
"""
def __init__(
self,
app_id: str = "default",
user_id: str = "default",
session_id: Optional[str] = None,
api_key: Optional[str] = None,
base_url: Optional[str] = None,
environment: Optional[Literal["local", "production"]] = "production",
honcho_client: Optional[Honcho] = None,
) -> None:
"""
Initialize the Honcho toolkit.
Args:
app_id: Application/workspace ID for scoping operations.
Maps to Honcho's workspace_id.
user_id: User identifier. Creates a peer with this ID.
session_id: Optional session ID. If not provided, a new UUID
will be generated.
api_key: Optional API key for Honcho. If not provided, will
attempt to read from HONCHO_API_KEY environment variable.
base_url: Optional base URL for the Honcho API.
environment: Environment to use. Options: "local", "production".
Defaults to "production".
honcho_client: Optional pre-configured Honcho client instance.
If provided, other connection parameters are ignored.
"""
super().__init__(name="honcho")
# Initialize Honcho client
if honcho_client is not None:
self.honcho = honcho_client
else:
client_kwargs: dict = {"workspace_id": app_id}
if api_key is not None:
client_kwargs["api_key"] = api_key
if base_url is not None:
client_kwargs["base_url"] = base_url
if environment is not None:
client_kwargs["environment"] = environment
self.honcho = Honcho(**client_kwargs)
# Store identifiers
self.app_id = app_id
self.user_id = user_id
self.session_id = session_id or str(uuid.uuid4())
# Create peers for user and assistant
self.user = self.honcho.peer(user_id)
self.assistant = self.honcho.peer("assistant")
# Create or get session
self.session = self.honcho.session(self.session_id)
# Register tools
self.register(self.add_message)
self.register(self.get_context)
self.register(self.search_messages)
self.register(self.query_user)
def add_message(self, content: str, role: str = "user") -> str:
"""
Store a message in the current session.
Use this tool to save important information from the conversation
that should be remembered for future interactions.
Args:
content: The message content to store.
role: The role of the message sender. Use 'user' for user messages
or 'assistant' for AI responses.
Returns:
Confirmation message indicating the memory was saved.
"""
try:
peer = self.user if role == "user" else self.assistant
self.session.add_messages([peer.message(content)])
return f"Message saved successfully to session {self.session_id}"
except Exception as e:
logger.exception("Error saving message")
return f"Error saving message: {e!s}"
def get_context(
self,
tokens: Optional[int] = None,
include_summary: bool = True,
) -> str:
"""
Retrieve recent conversation context within token limits.
Use this tool to get optimized context from the current session,
including messages and optional summary, that fits within token budgets.
Args:
tokens: Maximum number of tokens to include. If not specified,
returns all available context.
include_summary: Whether to include session summary in the context.
Returns:
Formatted string containing conversation context.
"""
try:
context = self.session.get_context(
summary=include_summary,
tokens=tokens,
)
result = []
# Add summary if present
if context.summary:
result.append("=== Session Summary ===")
result.append(context.summary.content)
result.append("")
# Add peer representation if present
if context.peer_representation:
result.append("=== Peer Representation ===")
result.append(context.peer_representation)
result.append("")
# Add peer card if present
if context.peer_card:
result.append("=== Peer Card ===")
result.extend(context.peer_card)
result.append("")
# Add messages
if context.messages:
result.append(f"=== Messages ({len(context.messages)}) ===")
for msg in context.messages:
result.append(f"{msg.peer_id}: {msg.content}")
return "\n".join(result) if result else "No context available"
except Exception as e:
logger.exception("Error retrieving context")
return f"Error retrieving context: {e!s}"
def search_messages(
self,
query: str,
limit: int = 10,
) -> str:
"""
Search through session messages using semantic similarity.
Use this tool to find relevant past messages from the conversation
history based on semantic meaning rather than exact keyword matching.
Args:
query: Search query for semantic matching.
limit: Number of results to return (1-100).
Returns:
Formatted string with search results.
"""
try:
messages = self.session.search(query=query, limit=limit)
if not messages:
return f"No messages found matching '{query}'"
result = [f"=== Search Results for '{query}' ({len(messages)} found) ==="]
for i, msg in enumerate(messages, 1):
result.append(f"\n{i}. [{msg.peer_id}] {msg.content}")
if hasattr(msg, "created_at") and msg.created_at:
result.append(f" Created: {msg.created_at}")
return "\n".join(result)
except Exception as e:
logger.exception("Error searching messages")
return f"Error searching messages: {e!s}"
def query_user(self, query: str) -> str:
"""
Query the system's knowledge about the user.
Use this tool to ask questions about user preferences, interests,
or past interactions. The system uses dialectic reasoning to
provide insights based on the user's long-term representation.
Args:
query: Natural language question about the user.
Examples: "What does the user like?", "What are their preferences?"
Returns:
Response from the dialectic API with insights about the user.
"""
try:
response = self.user.chat(
query=query,
stream=False,
session=self.session_id,
)
return str(response) if response else "No relevant information found."
except Exception as e:
logger.exception("Error querying user knowledge")
return f"Error querying user knowledge: {e!s}"
def reset_session(self) -> str:
"""
Create a new session, clearing the conversation history.
Use this tool to start a fresh conversation while maintaining
the user's long-term memory and representation.
Returns:
Confirmation with the new session ID.
"""
try:
self.session_id = str(uuid.uuid4())
self.session = self.honcho.session(self.session_id)
return f"Session reset. New session ID: {self.session_id}"
except Exception as e:
logger.exception("Error resetting session")
return f"Error resetting session: {e!s}"

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"""
Tests for Honcho Agno Tools
Tests the Agno-Honcho tool integration layer using real Honcho SDK.
Focuses on tool interface compliance and result formatting.
"""
import uuid
import pytest
from honcho_agno import HonchoTools
class TestHonchoToolsInitialization:
"""Tests for HonchoTools initialization."""
def test_default_initialization(self):
"""Test that toolkit initializes with default parameters."""
tools = HonchoTools()
assert tools is not None
assert tools.name == "honcho"
assert tools.honcho is not None
assert tools.session_id is not None
assert tools.user_id == "default"
assert tools.app_id == "default"
def test_custom_initialization(self):
"""Test initialization with custom parameters."""
custom_session = str(uuid.uuid4())
tools = HonchoTools(
app_id="test-app",
user_id="test-user",
session_id=custom_session,
)
assert tools.user_id == "test-user"
assert tools.app_id == "test-app"
assert tools.session_id == custom_session
def test_session_auto_generation(self):
"""Test that session_id is auto-generated if not provided."""
tools = HonchoTools(
app_id="test-app",
user_id="test-user",
)
assert tools.session_id is not None
# Should be a valid UUID format
uuid.UUID(tools.session_id)
class TestAddMessage:
"""Tests for add_message tool."""
def test_add_user_message(self):
"""Test adding a user message."""
tools = HonchoTools(
app_id="test-app",
user_id=f"add-msg-user-{uuid.uuid4()}",
session_id=f"add-msg-session-{uuid.uuid4()}",
)
result = tools.add_message("Test message content", role="user")
assert isinstance(result, str)
assert "saved" in result.lower() or "success" in result.lower()
def test_add_assistant_message(self):
"""Test adding an assistant message."""
tools = HonchoTools(
app_id="test-app",
user_id=f"add-msg-user-{uuid.uuid4()}",
session_id=f"add-msg-session-{uuid.uuid4()}",
)
result = tools.add_message("Assistant response", role="assistant")
assert isinstance(result, str)
assert "saved" in result.lower() or "success" in result.lower()
def test_add_multiple_messages(self):
"""Test adding multiple messages in sequence."""
tools = HonchoTools(
app_id="test-app",
user_id=f"multi-msg-user-{uuid.uuid4()}",
session_id=f"multi-msg-session-{uuid.uuid4()}",
)
messages = [
("Hello, I need help", "user"),
("Of course! How can I assist?", "assistant"),
("I want to learn Python", "user"),
]
for content, role in messages:
result = tools.add_message(content, role=role)
assert isinstance(result, str)
assert "error" not in result.lower()
class TestGetContext:
"""Tests for get_context tool."""
def test_get_empty_context(self):
"""Test getting context from empty session."""
tools = HonchoTools(
app_id="test-app",
user_id=f"context-user-{uuid.uuid4()}",
session_id=f"empty-context-{uuid.uuid4()}",
)
result = tools.get_context()
assert isinstance(result, str)
def test_get_context_with_messages(self):
"""Test getting context after adding messages."""
tools = HonchoTools(
app_id="test-app",
user_id=f"context-user-{uuid.uuid4()}",
session_id=f"context-session-{uuid.uuid4()}",
)
# Add messages first
tools.add_message("I like pizza", role="user")
tools.add_message("Great choice!", role="assistant")
result = tools.get_context()
assert isinstance(result, str)
assert len(result) > 0
def test_get_context_with_token_limit(self):
"""Test getting context with token limit."""
tools = HonchoTools(
app_id="test-app",
user_id=f"token-user-{uuid.uuid4()}",
session_id=f"token-session-{uuid.uuid4()}",
)
tools.add_message("This is a test message", role="user")
result = tools.get_context(tokens=1000)
assert isinstance(result, str)
def test_get_context_without_summary(self):
"""Test getting context without summary."""
tools = HonchoTools(
app_id="test-app",
user_id=f"nosummary-user-{uuid.uuid4()}",
session_id=f"nosummary-session-{uuid.uuid4()}",
)
tools.add_message("Test message", role="user")
result = tools.get_context(include_summary=False)
assert isinstance(result, str)
class TestSearchMessages:
"""Tests for search_messages tool."""
def test_search_returns_formatted_string(self):
"""Test that search returns formatted results."""
tools = HonchoTools(
app_id="test-app",
user_id=f"search-user-{uuid.uuid4()}",
session_id=f"search-session-{uuid.uuid4()}",
)
# Add searchable content
tools.add_message("I enjoy Python programming and data science", role="user")
result = tools.search_messages("programming", limit=5)
assert isinstance(result, str)
assert "Search Results" in result or "No messages found" in result
def test_search_with_limit(self):
"""Test search with custom limit."""
tools = HonchoTools(
app_id="test-app",
user_id=f"search-limit-user-{uuid.uuid4()}",
session_id=f"search-limit-session-{uuid.uuid4()}",
)
# Add multiple messages
for i in range(5):
tools.add_message(f"Test message number {i} about coding", role="user")
result = tools.search_messages("coding", limit=3)
assert isinstance(result, str)
def test_search_no_results(self):
"""Test search with no matching results."""
tools = HonchoTools(
app_id="test-app",
user_id=f"search-empty-user-{uuid.uuid4()}",
session_id=f"search-empty-session-{uuid.uuid4()}",
)
result = tools.search_messages("xyznonexistent123abcdef", limit=5)
assert isinstance(result, str)
# Should indicate no results found
assert "No messages found" in result or "0 found" in result.lower()
class TestQueryUser:
"""Tests for query_user tool."""
def test_query_returns_response(self):
"""Test that query returns a response string."""
tools = HonchoTools(
app_id="test-app",
user_id=f"query-user-{uuid.uuid4()}",
session_id=f"query-session-{uuid.uuid4()}",
)
# Add context first
tools.add_message("I love hiking and outdoor activities", role="user")
tools.add_message("I also enjoy photography", role="user")
result = tools.query_user("What does the user enjoy?")
assert isinstance(result, str)
assert len(result) > 0
def test_query_without_context(self):
"""Test query on user with minimal context."""
tools = HonchoTools(
app_id="test-app",
user_id=f"query-empty-user-{uuid.uuid4()}",
session_id=f"query-empty-session-{uuid.uuid4()}",
)
result = tools.query_user("What are the user's preferences?")
assert isinstance(result, str)
class TestResetSession:
"""Tests for reset_session tool."""
def test_reset_creates_new_session(self):
"""Test that reset creates a new session."""
tools = HonchoTools(
app_id="test-app",
user_id=f"reset-user-{uuid.uuid4()}",
session_id=f"original-session-{uuid.uuid4()}",
)
original_session = tools.session_id
result = tools.reset_session()
assert isinstance(result, str)
assert tools.session_id != original_session
assert "reset" in result.lower() or "new" in result.lower()
class TestToolsIntegration:
"""Integration tests for all tools working together."""
def test_all_tools_in_sequence(self):
"""Test using all tools in a realistic sequence."""
tools = HonchoTools(
app_id="test-app",
user_id=f"integration-user-{uuid.uuid4()}",
session_id=f"integration-session-{uuid.uuid4()}",
)
# Add messages
add_result = tools.add_message(
"I'm interested in AI and machine learning", role="user"
)
assert isinstance(add_result, str)
assert "error" not in add_result.lower()
# Get context
context_result = tools.get_context()
assert isinstance(context_result, str)
# Search messages
search_result = tools.search_messages("AI", limit=10)
assert isinstance(search_result, str)
# Query user
query_result = tools.query_user("What topics interest the user?")
assert isinstance(query_result, str)
def test_multiple_sessions_same_user(self):
"""Test using multiple sessions for the same user."""
user_id = f"multi-session-user-{uuid.uuid4()}"
# First session
tools1 = HonchoTools(
app_id="test-app",
user_id=user_id,
session_id=f"session-1-{uuid.uuid4()}",
)
tools1.add_message("I like Python", role="user")
# Second session
tools2 = HonchoTools(
app_id="test-app",
user_id=user_id,
session_id=f"session-2-{uuid.uuid4()}",
)
tools2.add_message("I also like JavaScript", role="user")
# Both sessions should work independently
context1 = tools1.get_context()
context2 = tools2.get_context()
assert isinstance(context1, str)
assert isinstance(context2, str)

738
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