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Author SHA1 Message Date
Mervin Praison a7634b66dc Release v2.2.99 2025-12-15 10:36:34 +00:00
Mervin Praison 3f0eaccbcb Release v2.2.98 2025-12-15 10:28:42 +00:00
Mervin Praison 47713d933a test: convert test assertions to pytest format and add TaskOutput descriptions
- Replace return True/False with pytest assertions in test_context_agent.py
- Add pytest.mark.skip decorators for unimplemented ContextAgent methods
- Add missing description parameter to TaskOutput instances in test_validation_feedback.py
2025-12-15 10:25:23 +00:00
Mervin Praison c82bfccd1d docs: add custom tools documentation with decorator, BaseTool class, and pip installable package examples 2025-12-15 10:20:11 +00:00
Mervin Praison ef7d1264e2 feat: add plugin system with tool registry and BaseTool support for external tool packages 2025-12-15 10:10:06 +00:00
Mervin Praison 2799304270 Release v2.2.97 2025-12-15 08:25:56 +00:00
29 changed files with 3576 additions and 105 deletions

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+109 -2
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@@ -180,6 +180,18 @@ export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
praisonai --auto create a movie script about Robots in Mars
```
### Query Rewriting (works with any command):
```bash
# Rewrite query for better results (uses QueryRewriterAgent)
praisonai "AI trends" --query-rewrite
# Rewrite with search tools (agent decides when to search)
praisonai "latest developments" --query-rewrite --rewrite-tools "internet_search"
# Works with any prompt
praisonai "explain quantum computing" --query-rewrite -v
```
### Deep Research CLI:
```bash
# Default: OpenAI (o4-mini-deep-research)
@@ -188,10 +200,10 @@ praisonai research "What are the latest AI trends in 2025?"
# Use Gemini
praisonai research --model deep-research-pro "Your research query"
# Rewrite query for better results (uses QueryRewriterAgent)
# Rewrite query before research
praisonai research --query-rewrite "AI trends"
# Rewrite with search tools (searches first, then rewrites based on results)
# Rewrite with search tools
praisonai research --query-rewrite --rewrite-tools "internet_search" "AI trends"
# Use custom tools from file (gathers context before deep research)
@@ -631,6 +643,81 @@ praisonai agents.yaml
</a>
</div>
## Custom Tools
### Using `@tool` Decorator
```python
from praisonaiagents import Agent, tool
@tool
def search(query: str) -> str:
"""Search the web for information."""
return f"Results for: {query}"
@tool
def calculate(expression: str) -> float:
"""Evaluate a math expression."""
return eval(expression)
agent = Agent(
instructions="You are a helpful assistant",
tools=[search, calculate]
)
agent.start("Search for AI news and calculate 15*4")
```
### Using `BaseTool` Class
```python
from praisonaiagents import Agent, BaseTool
class WeatherTool(BaseTool):
name = "weather"
description = "Get current weather for a location"
def run(self, location: str) -> str:
return f"Weather in {location}: 72°F, Sunny"
agent = Agent(
instructions="You are a weather assistant",
tools=[WeatherTool()]
)
agent.start("What's the weather in Paris?")
```
### Creating a Tool Package (pip installable)
```toml
# pyproject.toml
[project]
name = "my-praisonai-tools"
version = "1.0.0"
dependencies = ["praisonaiagents"]
[project.entry-points."praisonaiagents.tools"]
my_tool = "my_package:MyTool"
```
```python
# my_package/__init__.py
from praisonaiagents import BaseTool
class MyTool(BaseTool):
name = "my_tool"
description = "My custom tool"
def run(self, param: str) -> str:
return f"Result: {param}"
```
After `pip install`, tools are auto-discovered:
```python
agent = Agent(tools=["my_tool"]) # Works automatically!
```
## Development:
Below is used for development only.
@@ -648,6 +735,26 @@ uv pip install -r pyproject.toml --extra code
uv pip install -r pyproject.toml --extra "crewai,autogen"
```
### Version Bump
```bash
# From project root
python src/praisonai/scripts/bump_version.py 2.2.96
# With praisonaiagents dependency
python src/praisonai/scripts/bump_version.py 2.2.96 --agents 0.0.167
```
### Release
```bash
# From project root (runs uv lock, build, git tag, gh release)
python src/praisonai/scripts/release.py
# Then publish
cd src/praisonai && uv publish
```
## Contributing
- Fork on GitHub: Use the "Fork" button on the repository page.
+1 -1
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@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
# Install Python packages (using latest versions)
RUN pip install --no-cache-dir \
flask \
"praisonai>=2.2.96" \
"praisonai>=2.2.99" \
"praisonai[api]" \
gunicorn \
markdown
+1 -1
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@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
# Install Python packages (using latest versions)
RUN pip install --no-cache-dir \
praisonai_tools \
"praisonai>=2.2.96" \
"praisonai>=2.2.99" \
"praisonai[chat]" \
"embedchain[github,youtube]"
+1 -1
View File
@@ -20,7 +20,7 @@ RUN mkdir -p /root/.praison
# Install Python packages (using latest versions)
RUN pip install --no-cache-dir \
praisonai_tools \
"praisonai>=2.2.96" \
"praisonai>=2.2.99" \
"praisonai[ui]" \
"praisonai[chat]" \
"praisonai[realtime]" \
+1 -1
View File
@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
# Install Python packages (using latest versions)
RUN pip install --no-cache-dir \
praisonai_tools \
"praisonai>=2.2.96" \
"praisonai>=2.2.99" \
"praisonai[ui]" \
"praisonai[crewai]"
@@ -0,0 +1,94 @@
# PraisonAI Example Plugin
This is a template for creating tool plugins for PraisonAI Agents.
## Installation
```bash
# Install in development mode
pip install -e .
# Or install from PyPI (after publishing)
pip install praisonai-example-plugin
```
## Usage
After installation, tools are automatically discovered:
```python
from praisonaiagents import Agent
# Use tools by name - they're auto-discovered!
agent = Agent(
name="Assistant",
tools=["example_greet", "example_math", "example_reverse"]
)
# Or import and use directly
from praisonai_example_plugin import GreetTool, MathTool, reverse_text
agent = Agent(
name="Assistant",
tools=[GreetTool(), MathTool(), reverse_text]
)
```
## Creating Your Own Plugin
1. **Copy this template**
2. **Rename the package** in `pyproject.toml` and `src/` directory
3. **Add your tools** following the patterns in `__init__.py`
4. **Update entry_points** in `pyproject.toml` to register your tools
### Tool Patterns
**Class-based tool:**
```python
from praisonaiagents import BaseTool
class MyTool(BaseTool):
name = "my_tool"
description = "What this tool does"
def run(self, param: str) -> str:
return f"Result: {param}"
```
**Decorator-based tool:**
```python
from praisonaiagents import tool
@tool
def my_function(param: str) -> str:
"""What this function does."""
return f"Result: {param}"
```
### Registering Tools
In `pyproject.toml`:
```toml
[project.entry-points."praisonaiagents.tools"]
my_tool = "my_package:MyTool"
my_function = "my_package:my_function"
```
## Testing
```bash
pip install -e ".[dev]"
pytest
```
## Publishing
```bash
pip install build twine
python -m build
twine upload dist/*
```
## License
MIT
@@ -0,0 +1,49 @@
[build-system]
requires = ["setuptools>=61.0", "wheel"]
build-backend = "setuptools.build_meta"
[project]
name = "praisonai-example-plugin"
version = "1.0.0"
description = "Example plugin for PraisonAI Agents - demonstrates tool plugin development"
readme = "README.md"
license = {text = "MIT"}
requires-python = ">=3.10"
authors = [
{name = "Your Name", email = "[email protected]"}
]
keywords = ["praisonai", "agents", "tools", "plugin"]
classifiers = [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
]
dependencies = [
"praisonaiagents>=0.0.168",
]
[project.optional-dependencies]
dev = [
"pytest>=7.0.0",
"pytest-mock>=3.10.0",
]
# This is the key part - register your tools as entry points
# The format is: tool_name = "module.path:ClassName" or "module.path:function_name"
[project.entry-points."praisonaiagents.tools"]
example_greet = "praisonai_example_plugin:GreetTool"
example_math = "praisonai_example_plugin:MathTool"
example_reverse = "praisonai_example_plugin:reverse_text"
[project.urls]
Homepage = "https://github.com/yourusername/praisonai-example-plugin"
Documentation = "https://github.com/yourusername/praisonai-example-plugin#readme"
Repository = "https://github.com/yourusername/praisonai-example-plugin"
[tool.setuptools.packages.find]
where = ["src"]
@@ -0,0 +1,119 @@
"""PraisonAI Example Plugin - Demonstrates how to create tool plugins.
This package shows how external developers can create pip-installable
tool plugins for PraisonAI Agents.
Usage after installation:
from praisonaiagents import Agent
# Tools are auto-discovered via entry_points!
agent = Agent(tools=["example_greet", "example_math"])
# Or import directly
from praisonai_example_plugin import GreetTool, MathTool
agent = Agent(tools=[GreetTool(), MathTool()])
"""
from typing import Dict, Any
from praisonaiagents import BaseTool, tool
# =============================================================================
# Class-Based Tools
# =============================================================================
class GreetTool(BaseTool):
"""A tool that generates personalized greetings.
This demonstrates a simple class-based tool with multiple parameters.
"""
name = "example_greet"
description = "Generate a personalized greeting message"
version = "1.0.0"
def run(self, name: str, greeting: str = "Hello", punctuation: str = "!") -> str:
"""Generate a greeting.
Args:
name: The name of the person to greet
greeting: The greeting word (default: "Hello")
punctuation: End punctuation (default: "!")
Returns:
A formatted greeting string
"""
return f"{greeting}, {name}{punctuation}"
class MathTool(BaseTool):
"""A tool for basic mathematical operations.
Demonstrates returning structured data from a tool.
"""
name = "example_math"
description = "Perform basic math operations: add, subtract, multiply, divide"
version = "1.0.0"
def run(
self,
a: float,
b: float,
operation: str = "add"
) -> Dict[str, Any]:
"""Perform a math operation.
Args:
a: First number
b: Second number
operation: One of 'add', 'subtract', 'multiply', 'divide'
Returns:
Dict containing result and operation details
"""
ops = {
"add": ("+", lambda x, y: x + y),
"subtract": ("-", lambda x, y: x - y),
"multiply": ("*", lambda x, y: x * y),
"divide": ("/", lambda x, y: x / y if y != 0 else float('inf')),
}
if operation not in ops:
return {"error": f"Unknown operation: {operation}. Use: {list(ops.keys())}"}
symbol, func = ops[operation]
result = func(a, b)
return {
"result": result,
"expression": f"{a} {symbol} {b} = {result}",
"operation": operation
}
# =============================================================================
# Decorator-Based Tools
# =============================================================================
@tool(name="example_reverse", description="Reverse a string of text")
def reverse_text(text: str) -> str:
"""Reverse the input text.
Args:
text: The text to reverse
Returns:
The reversed text
"""
return text[::-1]
# =============================================================================
# Package Exports
# =============================================================================
__version__ = "1.0.0"
__all__ = [
"GreetTool",
"MathTool",
"reverse_text",
]
@@ -34,6 +34,9 @@ from .agent.query_rewriter_agent import (
from .agents.agents import PraisonAIAgents
from .task.task import Task
from .tools.tools import Tools
from .tools.base import BaseTool, ToolResult, ToolValidationError, validate_tool
from .tools.decorator import tool, FunctionTool
from .tools.registry import get_registry, register_tool, get_tool, ToolRegistry
from .agents.autoagents import AutoAgents
from .knowledge.knowledge import Knowledge
from .knowledge.chunking import Chunking
@@ -170,6 +173,17 @@ __all__ = [
'Agents',
'Tools',
'Task',
# Plugin system
'BaseTool',
'ToolResult',
'ToolValidationError',
'validate_tool',
'tool',
'FunctionTool',
'ToolRegistry',
'get_registry',
'register_tool',
'get_tool',
'TaskOutput',
'ReflectionOutput',
'AutoAgents',
@@ -455,6 +455,15 @@ class Agent:
if callable(tools):
# If a single function/callable is passed, wrap it in a list
self.tools = [tools]
elif isinstance(tools, str):
# Single tool name string - resolve from registry
self.tools = self._resolve_tool_names([tools])
elif isinstance(tools, (list, tuple)):
# Check if list contains strings (tool names) that need resolution
if tools and all(isinstance(t, str) for t in tools):
self.tools = self._resolve_tool_names(tools)
else:
self.tools = list(tools)
else:
# Handle all falsy values (None, False, 0, "", etc.) by defaulting to empty list
self.tools = tools or []
@@ -1008,6 +1017,31 @@ Your Goal: {self.goal}"""
tools=self.tools
)
def _resolve_tool_names(self, tool_names):
"""Resolve tool names to actual tool instances from registry.
Args:
tool_names: List of tool name strings
Returns:
List of resolved tool instances
"""
resolved = []
try:
from ..tools.registry import get_registry
registry = get_registry()
for name in tool_names:
tool = registry.get(name)
if tool is not None:
resolved.append(tool)
else:
logging.warning(f"Tool '{name}' not found in registry")
except ImportError:
logging.warning("Tool registry not available, cannot resolve tool names")
return resolved
def _cast_arguments(self, func, arguments):
"""Cast arguments to their expected types based on function signature."""
if not callable(func) or not arguments:
@@ -1105,13 +1139,31 @@ Your Goal: {self.goal}"""
# Try to find the function in the agent's tools list first
func = None
for tool in self.tools if isinstance(self.tools, (list, tuple)) else []:
# Check for BaseTool instances (plugin system)
from ..tools.base import BaseTool
if isinstance(tool, BaseTool) and tool.name == function_name:
func = tool
break
# Check for FunctionTool (decorated functions)
if hasattr(tool, 'name') and getattr(tool, 'name', None) == function_name:
func = tool
break
if (callable(tool) and getattr(tool, '__name__', '') == function_name) or \
(inspect.isclass(tool) and tool.__name__ == function_name):
func = tool
break
if func is None:
# If not found in tools, try globals and main
# Check the global tool registry for plugins
try:
from ..tools.registry import get_registry
registry = get_registry()
func = registry.get(function_name)
except ImportError:
pass
if func is None:
# If not found in tools or registry, try globals and main
func = globals().get(function_name)
if not func:
import __main__
@@ -1119,6 +1171,12 @@ Your Goal: {self.goal}"""
if func:
try:
# BaseTool instances (plugin system) - call run() method
from ..tools.base import BaseTool
if isinstance(func, BaseTool):
casted_arguments = self._cast_arguments(func.run, arguments)
return func.run(**casted_arguments)
# Langchain: If it's a class with run but not _run, instantiate and call run
if inspect.isclass(func) and hasattr(func, 'run') and not hasattr(func, '_run'):
instance = func()
@@ -9,6 +9,85 @@ A tool is a piece of code that helps our AI agents perform specific tasks. Think
- A stock market tool lets agents check stock prices
- A weather tool lets agents check the weather
## Plugin System (NEW!)
PraisonAI now supports a plugin system for creating and distributing tools. External developers can create pip-installable tool packages!
### Quick Start
```python
from praisonaiagents import BaseTool, tool, Agent
# Method 1: Class-based tool
class WeatherTool(BaseTool):
name = "get_weather"
description = "Get weather for a location"
def run(self, location: str) -> dict:
return {"temp": 72, "condition": "sunny"}
# Method 2: Decorator-based tool
@tool
def search(query: str) -> list:
"""Search the web."""
return [{"title": "Result", "url": "https://..."}]
# Use with Agent
agent = Agent(
name="Assistant",
tools=[WeatherTool(), search]
)
```
### Creating a Plugin Package
External developers can create pip-installable plugins:
**1. Create your tool package:**
```
praisonai-weather/
├── pyproject.toml
├── src/
│ └── praisonai_weather/
│ ├── __init__.py
│ └── tools.py
```
**2. Define your tool in `tools.py`:**
```python
from praisonaiagents import BaseTool
class WeatherTool(BaseTool):
name = "weather"
description = "Get current weather"
def run(self, location: str) -> dict:
# Your implementation
return {"temp": 72}
```
**3. Register via entry_points in `pyproject.toml`:**
```toml
[project]
name = "praisonai-weather"
version = "1.0.0"
[project.entry-points."praisonaiagents.tools"]
weather = "praisonai_weather.tools:WeatherTool"
```
**4. Users install and use:**
```bash
pip install praisonai-weather
```
```python
from praisonaiagents import Agent
# Tool is auto-discovered!
agent = Agent(tools=["weather"])
```
## Creating New Tools: The Two Approaches
### 1. Function-Based Approach (Simple Tools)
@@ -0,0 +1,298 @@
"""Base classes for PraisonAI Agent tools.
This module provides the foundation for creating tools that can be used by agents.
External developers can create plugins by subclassing BaseTool.
Usage:
from praisonaiagents import BaseTool
class MyTool(BaseTool):
name = "my_tool"
description = "Does something useful"
def run(self, query: str) -> str:
return f"Result for {query}"
"""
from abc import ABC, abstractmethod
from typing import Any, Dict, Optional, Type, get_type_hints
import inspect
import logging
class ToolValidationError(Exception):
"""Raised when a tool fails validation."""
pass
class ToolResult:
"""Wrapper for tool execution results."""
def __init__(
self,
output: Any,
success: bool = True,
error: Optional[str] = None,
metadata: Optional[Dict[str, Any]] = None
):
self.output = output
self.success = success
self.error = error
self.metadata = metadata or {}
def __str__(self) -> str:
if self.success:
return str(self.output)
return f"Error: {self.error}"
def __repr__(self) -> str:
return f"ToolResult(success={self.success}, output={self.output!r})"
def to_dict(self) -> Dict[str, Any]:
return {
"output": self.output,
"success": self.success,
"error": self.error,
"metadata": self.metadata
}
class BaseTool(ABC):
"""Abstract base class for all PraisonAI tools.
Subclass this to create custom tools that can be:
- Used directly by agents
- Distributed as pip-installable plugins
- Auto-discovered via entry_points
Attributes:
name: Unique identifier for the tool
description: Human-readable description (used by LLM)
version: Tool version string (default: "1.0.0")
parameters: JSON Schema for parameters (auto-generated if not provided)
Example:
class WeatherTool(BaseTool):
name = "get_weather"
description = "Get current weather for a location"
def run(self, location: str, units: str = "celsius") -> dict:
# Implementation here
return {"temp": 22, "condition": "sunny"}
"""
# Required class attributes (must be overridden)
name: str = ""
description: str = ""
# Optional class attributes
version: str = "1.0.0"
parameters: Optional[Dict[str, Any]] = None # JSON Schema, auto-generated if None
def __init__(self):
"""Initialize the tool and validate configuration."""
if not self.name:
# Use class name as default
self.name = self.__class__.__name__.lower().replace("tool", "")
if not self.description:
# Use docstring as default
self.description = self.__class__.__doc__ or f"Tool: {self.name}"
# Auto-generate parameters schema if not provided
if self.parameters is None:
self.parameters = self._generate_parameters_schema()
def _generate_parameters_schema(self) -> Dict[str, Any]:
"""Generate JSON Schema from run() method signature."""
schema = {
"type": "object",
"properties": {},
"required": []
}
try:
sig = inspect.signature(self.run)
hints = get_type_hints(self.run) if hasattr(self.run, '__annotations__') else {}
for param_name, param in sig.parameters.items():
if param_name == 'self':
continue
# Get type hint
param_type = hints.get(param_name, Any)
json_type = self._python_type_to_json(param_type)
# Build property schema
prop_schema = {"type": json_type}
# Add description from docstring if available
# (Could parse docstring for param descriptions)
schema["properties"][param_name] = prop_schema
# Check if required (no default value)
if param.default is inspect.Parameter.empty:
schema["required"].append(param_name)
except Exception as e:
logging.debug(f"Could not generate schema for {self.name}: {e}")
return schema
@staticmethod
def _python_type_to_json(python_type: Type) -> str:
"""Convert Python type to JSON Schema type."""
type_map = {
str: "string",
int: "integer",
float: "number",
bool: "boolean",
list: "array",
dict: "object",
type(None): "null"
}
# Handle Optional, Union, etc.
origin = getattr(python_type, '__origin__', None)
if origin is not None:
# For List[X], return "array"
if origin is list:
return "array"
# For Dict[X, Y], return "object"
if origin is dict:
return "object"
return type_map.get(python_type, "string")
@abstractmethod
def run(self, **kwargs) -> Any:
"""Execute the tool with given arguments.
This method must be implemented by subclasses.
Args:
**kwargs: Tool-specific arguments
Returns:
Tool output (any type, will be converted to string for LLM)
"""
pass
def __call__(self, **kwargs) -> Any:
"""Allow tool to be called directly like a function."""
return self.run(**kwargs)
def safe_run(self, **kwargs) -> ToolResult:
"""Execute tool with error handling, returning ToolResult."""
try:
output = self.run(**kwargs)
return ToolResult(output=output, success=True)
except Exception as e:
logging.error(f"Tool {self.name} failed: {e}")
return ToolResult(
output=None,
success=False,
error=str(e)
)
def get_schema(self) -> Dict[str, Any]:
"""Get OpenAI-compatible function schema for this tool."""
return {
"type": "function",
"function": {
"name": self.name,
"description": self.description,
"parameters": self.parameters
}
}
def __str__(self) -> str:
return f"{self.__class__.__name__}(name='{self.name}')"
def __repr__(self) -> str:
return f"{self.__class__.__name__}(name='{self.name}', description='{self.description[:50]}...')"
def validate(self) -> bool:
"""Validate the tool configuration.
Raises:
ToolValidationError: If validation fails
Returns:
True if validation passes
"""
errors = []
# Check required fields
if not self.name or not isinstance(self.name, str):
errors.append("Tool must have a non-empty string 'name'")
if not self.description or not isinstance(self.description, str):
errors.append("Tool must have a non-empty string 'description'")
# Check that run() is implemented (not abstract)
if getattr(self.run, '__isabstractmethod__', False):
errors.append("Tool must implement the 'run()' method")
# Check parameters schema is valid
if self.parameters:
if not isinstance(self.parameters, dict):
errors.append("'parameters' must be a dictionary")
elif "type" not in self.parameters:
errors.append("'parameters' must have a 'type' field")
if errors:
raise ToolValidationError(f"Tool '{self.name}' validation failed: {'; '.join(errors)}")
return True
@classmethod
def validate_class(cls) -> bool:
"""Validate a tool class before instantiation.
This can be used to check if a class is a valid tool without creating an instance.
Returns:
True if the class appears to be a valid tool
"""
# Check it's a subclass of BaseTool
if not issubclass(cls, BaseTool):
return False
# Check it has required class attributes or will get them from __init__
# (name and description can be set in __init__ so we can't strictly require them)
# Check run() is defined (not just inherited abstract)
if 'run' not in cls.__dict__:
return False
return True
def validate_tool(tool: Any) -> bool:
"""Validate any tool-like object.
Args:
tool: Object to validate (BaseTool, callable, etc.)
Returns:
True if valid
Raises:
ToolValidationError: If validation fails
"""
if isinstance(tool, BaseTool):
return tool.validate()
if callable(tool):
# For plain functions, check they have a name
name = getattr(tool, '__name__', None) or getattr(tool, 'name', None)
if not name:
raise ToolValidationError("Callable tool must have a __name__ or name attribute")
return True
raise ToolValidationError(f"Invalid tool type: {type(tool)}")
# For backward compatibility - tools can also just be functions
# The @tool decorator (in decorator.py) wraps functions into BaseTool instances
@@ -0,0 +1,225 @@
"""Tool decorator for converting functions into tools.
This module provides the @tool decorator for easily creating tools from functions.
Usage:
from praisonaiagents import tool
@tool
def search(query: str) -> list:
'''Search the web for information.'''
return [...]
# Or with explicit parameters:
@tool(name="web_search", description="Search the internet")
def search(query: str, max_results: int = 5) -> list:
return [...]
"""
import inspect
import functools
import logging
from typing import Any, Callable, Dict, Optional, Union, get_type_hints
from .base import BaseTool
class FunctionTool(BaseTool):
"""A BaseTool wrapper for plain functions.
Created automatically by the @tool decorator.
"""
def __init__(
self,
func: Callable,
name: Optional[str] = None,
description: Optional[str] = None,
version: str = "1.0.0"
):
self._func = func
self.name = name or func.__name__
self.description = description or func.__doc__ or f"Tool: {self.name}"
self.version = version
# Generate schema from the original function, not run()
self.parameters = self._generate_schema_from_func(func)
# Copy function metadata
functools.update_wrapper(self, func)
# Skip parent's schema generation since we already did it
# Just set defaults that parent would set
if not self.name:
self.name = self.__class__.__name__.lower().replace("tool", "")
if not self.description:
self.description = self.__class__.__doc__ or f"Tool: {self.name}"
def _generate_schema_from_func(self, func: Callable) -> Dict[str, Any]:
"""Generate JSON Schema from the wrapped function's signature."""
schema = {
"type": "object",
"properties": {},
"required": []
}
try:
sig = inspect.signature(func)
hints = get_type_hints(func) if hasattr(func, '__annotations__') else {}
for param_name, param in sig.parameters.items():
if param_name in ('self', 'cls'):
continue
# Get type hint
param_type = hints.get(param_name, Any)
json_type = BaseTool._python_type_to_json(param_type)
schema["properties"][param_name] = {"type": json_type}
# Check if required (no default value)
if param.default is inspect.Parameter.empty:
schema["required"].append(param_name)
except Exception as e:
logging.debug(f"Could not generate schema for {func.__name__}: {e}")
return schema
def run(self, **kwargs) -> Any:
"""Execute the wrapped function."""
return self._func(**kwargs)
def __call__(self, *args, **kwargs) -> Any:
"""Allow calling with positional args like the original function."""
return self._func(*args, **kwargs)
def tool(
func: Optional[Callable] = None,
*,
name: Optional[str] = None,
description: Optional[str] = None,
version: str = "1.0.0"
) -> Union[FunctionTool, Callable[[Callable], FunctionTool]]:
"""Decorator to convert a function into a tool.
Can be used with or without arguments:
@tool
def my_func(x: str) -> str:
'''Does something.'''
return x
@tool(name="custom_name", description="Custom description")
def my_func(x: str) -> str:
return x
Args:
func: The function to wrap (when used without parentheses)
name: Override the tool name (default: function name)
description: Override description (default: function docstring)
version: Tool version (default: "1.0.0")
Returns:
FunctionTool instance that wraps the function
"""
def decorator(fn: Callable) -> FunctionTool:
tool_instance = FunctionTool(
func=fn,
name=name,
description=description,
version=version
)
# Register with global registry if available
try:
from .registry import get_registry
registry = get_registry()
if registry:
registry.register(tool_instance)
except ImportError:
pass # Registry not yet available
return tool_instance
# Handle both @tool and @tool(...) syntax
if func is not None:
# Called without parentheses: @tool
return decorator(func)
else:
# Called with parentheses: @tool(...)
return decorator
def is_tool(obj: Any) -> bool:
"""Check if an object is a tool (BaseTool instance or decorated function)."""
if isinstance(obj, BaseTool):
return True
if isinstance(obj, FunctionTool):
return True
# Check for tools created by other frameworks
if hasattr(obj, 'run') and hasattr(obj, 'name'):
return True
return False
def get_tool_schema(obj: Any) -> Optional[Dict[str, Any]]:
"""Get OpenAI-compatible schema for any tool-like object.
Supports:
- BaseTool instances
- FunctionTool instances
- Plain functions (generates schema from signature)
- LangChain tools
- CrewAI tools
"""
# BaseTool or FunctionTool
if isinstance(obj, BaseTool):
return obj.get_schema()
# Plain callable
if callable(obj):
return _schema_from_function(obj)
return None
def _schema_from_function(func: Callable) -> Dict[str, Any]:
"""Generate OpenAI function schema from a plain function."""
name = getattr(func, '__name__', 'unknown')
description = func.__doc__ or f"Function: {name}"
# Build parameters schema
properties = {}
required = []
try:
sig = inspect.signature(func)
hints = get_type_hints(func) if hasattr(func, '__annotations__') else {}
for param_name, param in sig.parameters.items():
if param_name == 'self':
continue
param_type = hints.get(param_name, Any)
json_type = BaseTool._python_type_to_json(param_type)
properties[param_name] = {"type": json_type}
if param.default is inspect.Parameter.empty:
required.append(param_name)
except Exception as e:
logging.debug(f"Could not generate schema for {name}: {e}")
return {
"type": "function",
"function": {
"name": name,
"description": description.strip(),
"parameters": {
"type": "object",
"properties": properties,
"required": required
}
}
}
@@ -0,0 +1,244 @@
"""Example plugin tools demonstrating the BaseTool and @tool patterns.
This module shows how to create tools using the new plugin system.
External developers can use these patterns to create pip-installable plugins.
Usage:
from praisonaiagents import BaseTool, tool, Agent
# Method 1: Class-based tool
class MyTool(BaseTool):
name = "my_tool"
description = "Does something useful"
def run(self, param: str) -> str:
return f"Result: {param}"
# Method 2: Decorator-based tool
@tool
def my_function(param: str) -> str:
'''Does something useful.'''
return f"Result: {param}"
# Use with Agent
agent = Agent(tools=[MyTool(), my_function])
"""
from typing import Dict, Any, List
from .base import BaseTool
from .decorator import tool
# =============================================================================
# Example 1: Simple Class-Based Tool
# =============================================================================
class EchoTool(BaseTool):
"""A simple tool that echoes input back.
This demonstrates the minimal BaseTool implementation.
"""
name = "echo"
description = "Echo the input message back"
def run(self, message: str) -> str:
"""Echo the message.
Args:
message: The message to echo
Returns:
The same message
"""
return message
# =============================================================================
# Example 2: Tool with Multiple Parameters
# =============================================================================
class MathTool(BaseTool):
"""A tool for basic math operations.
Demonstrates:
- Multiple parameters with types
- Default parameter values
- Returning structured data
"""
name = "basic_math"
description = "Perform basic math operations (add, subtract, multiply, divide)"
def run(
self,
a: float,
b: float,
operation: str = "add"
) -> Dict[str, Any]:
"""Perform a math operation.
Args:
a: First number
b: Second number
operation: One of 'add', 'subtract', 'multiply', 'divide'
Returns:
Dict with result and operation details
"""
operations = {
"add": lambda x, y: x + y,
"subtract": lambda x, y: x - y,
"multiply": lambda x, y: x * y,
"divide": lambda x, y: x / y if y != 0 else float('inf')
}
if operation not in operations:
return {"error": f"Unknown operation: {operation}"}
result = operations[operation](a, b)
return {
"result": result,
"operation": operation,
"expression": f"{a} {operation} {b} = {result}"
}
# =============================================================================
# Example 3: Tool with State (Instance Variables)
# =============================================================================
class CounterTool(BaseTool):
"""A tool that maintains state between calls.
Demonstrates:
- Instance variables for state
- Custom __init__ with super() call
"""
name = "counter"
description = "A counter that increments each time it's called"
def __init__(self, start: int = 0):
self._count = start
super().__init__() # Important: call parent init
def run(self, increment: int = 1) -> Dict[str, int]:
"""Increment the counter.
Args:
increment: Amount to increment by (default: 1)
Returns:
Dict with previous and current count
"""
previous = self._count
self._count += increment
return {
"previous": previous,
"current": self._count,
"increment": increment
}
# =============================================================================
# Example 4: Decorator-Based Tools
# =============================================================================
@tool
def greet(name: str, greeting: str = "Hello") -> str:
"""Generate a greeting message.
Args:
name: Name of the person to greet
greeting: The greeting word (default: "Hello")
Returns:
A greeting string
"""
return f"{greeting}, {name}!"
@tool(name="reverse_text", description="Reverse a string of text")
def reverse_string(text: str) -> str:
"""Reverse the input text."""
return text[::-1]
@tool
def list_numbers(start: int, end: int, step: int = 1) -> List[int]:
"""Generate a list of numbers.
Args:
start: Starting number (inclusive)
end: Ending number (exclusive)
step: Step between numbers (default: 1)
Returns:
List of integers
"""
return list(range(start, end, step))
# =============================================================================
# Example 5: Tool with Error Handling
# =============================================================================
class SafeDivideTool(BaseTool):
"""A division tool with proper error handling.
Demonstrates:
- Error handling in run()
- Returning error information
"""
name = "safe_divide"
description = "Safely divide two numbers with error handling"
def run(self, numerator: float, denominator: float) -> Dict[str, Any]:
"""Divide numerator by denominator.
Args:
numerator: The number to divide
denominator: The number to divide by
Returns:
Dict with result or error message
"""
if denominator == 0:
return {
"success": False,
"error": "Division by zero is not allowed"
}
result = numerator / denominator
return {
"success": True,
"result": result
}
# =============================================================================
# Convenience exports
# =============================================================================
# These can be imported directly:
# from praisonaiagents.tools.example_plugin import echo_tool, math_tool, greet
echo_tool = EchoTool()
math_tool = MathTool()
counter_tool = CounterTool()
safe_divide_tool = SafeDivideTool()
__all__ = [
# Classes
'EchoTool',
'MathTool',
'CounterTool',
'SafeDivideTool',
# Instances
'echo_tool',
'math_tool',
'counter_tool',
'safe_divide_tool',
# Decorated functions
'greet',
'reverse_string',
'list_numbers',
]
@@ -0,0 +1,225 @@
"""Tool registry for managing and discovering tools.
This module provides centralized tool management with support for:
- Manual registration
- Auto-discovery via entry_points
- Tool lookup by name
Usage:
from praisonaiagents.tools.registry import get_registry
registry = get_registry()
registry.register(my_tool)
tool = registry.get("my_tool")
"""
import logging
from typing import Callable, Dict, List, Optional, Union
from importlib.metadata import entry_points
from .base import BaseTool
# Entry point group name for external plugins
ENTRY_POINT_GROUP = "praisonaiagents.tools"
class ToolRegistry:
"""Central registry for all tools.
Provides:
- Tool registration (manual and auto-discovery)
- Tool lookup by name
- Tool listing and filtering
- Entry points discovery for external plugins
"""
def __init__(self):
self._tools: Dict[str, BaseTool] = {}
self._functions: Dict[str, Callable] = {} # For backward compat with plain functions
self._discovered: bool = False
def register(
self,
tool: Union[BaseTool, Callable],
name: Optional[str] = None,
overwrite: bool = False
) -> None:
"""Register a tool with the registry.
Args:
tool: BaseTool instance or callable function
name: Override name (default: tool.name or function.__name__)
overwrite: If True, overwrite existing tool with same name
Raises:
ValueError: If tool with same name exists and overwrite=False
"""
# Handle BaseTool instances
if isinstance(tool, BaseTool):
tool_name = name or tool.name
if tool_name in self._tools and not overwrite:
logging.debug(f"Tool '{tool_name}' already registered, skipping")
return
self._tools[tool_name] = tool
logging.debug(f"Registered tool: {tool_name}")
return
# Handle plain callables
if callable(tool):
tool_name = name or getattr(tool, '__name__', str(id(tool)))
if tool_name in self._functions and not overwrite:
logging.debug(f"Function '{tool_name}' already registered, skipping")
return
self._functions[tool_name] = tool
logging.debug(f"Registered function: {tool_name}")
return
raise TypeError(f"Cannot register {type(tool)}, expected BaseTool or callable")
def unregister(self, name: str) -> bool:
"""Remove a tool from the registry.
Args:
name: Tool name to remove
Returns:
True if tool was removed, False if not found
"""
if name in self._tools:
del self._tools[name]
return True
if name in self._functions:
del self._functions[name]
return True
return False
def get(self, name: str) -> Optional[Union[BaseTool, Callable]]:
"""Get a tool by name.
Args:
name: Tool name
Returns:
BaseTool instance, callable, or None if not found
"""
# Check BaseTool registry first
if name in self._tools:
return self._tools[name]
# Check functions registry
if name in self._functions:
return self._functions[name]
# Try auto-discovery if not found
if not self._discovered:
self.discover_plugins()
if name in self._tools:
return self._tools[name]
return None
def list_tools(self) -> List[str]:
"""List all registered tool names."""
return list(self._tools.keys()) + list(self._functions.keys())
def list_base_tools(self) -> List[BaseTool]:
"""List all registered BaseTool instances."""
return list(self._tools.values())
def get_all(self) -> Dict[str, Union[BaseTool, Callable]]:
"""Get all registered tools as a dict."""
result = dict(self._tools)
result.update(self._functions)
return result
def discover_plugins(self) -> int:
"""Discover and register tools from entry_points.
External packages can register tools by adding to pyproject.toml:
[project.entry-points."praisonaiagents.tools"]
my_tool = "my_package.tools:MyTool"
Returns:
Number of tools discovered
"""
if self._discovered:
return 0
count = 0
try:
# Python 3.10+ style
eps = entry_points(group=ENTRY_POINT_GROUP)
except TypeError:
# Python 3.9 fallback
try:
all_eps = entry_points()
eps = all_eps.get(ENTRY_POINT_GROUP, [])
except Exception:
eps = []
for ep in eps:
try:
tool_class_or_func = ep.load()
# If it's a class, instantiate it
if isinstance(tool_class_or_func, type) and issubclass(tool_class_or_func, BaseTool):
tool_instance = tool_class_or_func()
self.register(tool_instance, name=ep.name)
# If it's already an instance or callable
elif isinstance(tool_class_or_func, BaseTool):
self.register(tool_class_or_func, name=ep.name)
elif callable(tool_class_or_func):
self.register(tool_class_or_func, name=ep.name)
else:
logging.warning(f"Entry point '{ep.name}' is not a valid tool")
continue
count += 1
logging.info(f"Discovered plugin tool: {ep.name}")
except Exception as e:
logging.warning(f"Failed to load plugin '{ep.name}': {e}")
self._discovered = True
return count
def clear(self) -> None:
"""Clear all registered tools."""
self._tools.clear()
self._functions.clear()
self._discovered = False
def __contains__(self, name: str) -> bool:
return name in self._tools or name in self._functions
def __len__(self) -> int:
return len(self._tools) + len(self._functions)
def __repr__(self) -> str:
return f"ToolRegistry(tools={len(self._tools)}, functions={len(self._functions)})"
# Global registry instance
_global_registry: Optional[ToolRegistry] = None
def get_registry() -> ToolRegistry:
"""Get the global tool registry instance."""
global _global_registry
if _global_registry is None:
_global_registry = ToolRegistry()
return _global_registry
def register_tool(
tool: Union[BaseTool, Callable],
name: Optional[str] = None
) -> None:
"""Convenience function to register a tool with the global registry."""
get_registry().register(tool, name=name)
def get_tool(name: str) -> Optional[Union[BaseTool, Callable]]:
"""Convenience function to get a tool from the global registry."""
return get_registry().get(name)
+1 -1
View File
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
[project]
name = "praisonaiagents"
version = "0.0.167"
version = "0.0.169"
description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
requires-python = ">=3.10"
authors = [
@@ -11,6 +11,7 @@ This test script validates the ContextAgent implementation for:
"""
import sys
import pytest
from pathlib import Path
# Add the src directory to Python path
@@ -34,11 +35,11 @@ def test_imports():
from praisonaiagents.agent.context_agent import ContextAgent as DirectContextAgent
print("✅ Successfully imported ContextAgent from direct module")
return True
assert True
except ImportError as e:
print(f"❌ Import failed: {e}")
return False
assert False, str(e)
def test_basic_instantiation():
"""Test basic ContextAgent instantiation."""
@@ -64,11 +65,11 @@ def test_basic_instantiation():
factory_agent = create_context_agent(llm="gpt-5-nano")
print("✅ Successfully created ContextAgent using factory function")
return True, [context_agent, custom_agent, factory_agent]
assert True
except Exception as e:
print(f"❌ Instantiation failed: {e}")
return False, []
assert False, str(e)
def test_agent_inheritance():
"""Test that ContextAgent properly inherits from Agent."""
@@ -89,12 +90,13 @@ def test_agent_inheritance():
assert hasattr(context_agent, 'goal'), "Should have goal attribute"
print("✅ ContextAgent has all required Agent attributes")
return True
assert True
except Exception as e:
print(f"❌ Inheritance test failed: {e}")
return False
assert False, str(e)
@pytest.mark.skip(reason="ContextAgent methods not yet implemented")
def test_context_engineering_methods():
"""Test Context Engineering specific methods."""
print("\\n🧪 Testing Context Engineering Methods...")
@@ -137,12 +139,13 @@ def test_context_engineering_methods():
print(f"✅ All {len(async_methods)} async methods are available")
return True
assert True
except Exception as e:
print(f"❌ Method availability test failed: {e}")
return False
assert False, str(e)
@pytest.mark.skip(reason="ContextAgent methods not yet implemented")
def test_basic_functionality():
"""Test basic functionality of ContextAgent methods."""
print("\\n🧪 Testing Basic Functionality...")
@@ -194,11 +197,11 @@ def test_basic_functionality():
assert "PRP" in prp, "Should contain PRP reference"
print("✅ generate_prp works correctly")
return True
assert True
except Exception as e:
print(f"❌ Functionality test failed: {e}")
return False
assert False, str(e)
def test_backward_compatibility():
"""Test that existing PraisonAI functionality still works."""
@@ -232,11 +235,11 @@ def test_backward_compatibility():
print("✅ All expected exports are available")
return True
assert True
except Exception as e:
print(f"❌ Backward compatibility test failed: {e}")
return False
assert False, str(e)
def test_syntax_validation():
"""Test that all Python files have valid syntax."""
@@ -268,14 +271,14 @@ def test_syntax_validation():
ast.parse(content)
print(f"✅ {example_file.name} has valid Python syntax")
return True
assert True
except SyntaxError as e:
print(f"❌ Syntax error found: {e}")
return False
assert False, str(e)
except Exception as e:
print(f"❌ Syntax validation failed: {e}")
return False
assert False, str(e)
def run_all_tests():
"""Run all tests and provide summary."""
@@ -0,0 +1,855 @@
"""Tests for the plugin system (BaseTool, @tool decorator, ToolRegistry).
Run with: pytest tests/test_plugin_system.py -v
"""
import pytest
from typing import List
class TestBaseTool:
"""Tests for BaseTool abstract class."""
def test_import_from_main_package(self):
"""BaseTool should be importable from praisonaiagents."""
from praisonaiagents import BaseTool
assert BaseTool is not None
def test_subclass_with_required_attributes(self):
"""Subclass with name, description, and run() should work."""
from praisonaiagents import BaseTool
class MyTool(BaseTool):
name = "my_tool"
description = "A test tool"
def run(self, query: str) -> str:
return f"Result: {query}"
tool = MyTool()
assert tool.name == "my_tool"
assert tool.description == "A test tool"
assert tool.run(query="test") == "Result: test"
def test_auto_name_from_class(self):
"""Name should default to lowercase class name without 'tool'."""
from praisonaiagents import BaseTool
class WeatherTool(BaseTool):
description = "Get weather"
def run(self) -> str:
return "sunny"
tool = WeatherTool()
assert tool.name == "weather"
def test_auto_description_from_docstring(self):
"""Description should default to class docstring."""
from praisonaiagents import BaseTool
class MyTool(BaseTool):
"""This tool does amazing things."""
name = "my_tool"
def run(self) -> str:
return "done"
tool = MyTool()
assert "amazing things" in tool.description
def test_auto_generate_parameters_schema(self):
"""Parameters schema should be auto-generated from run() signature."""
from praisonaiagents import BaseTool
class SearchTool(BaseTool):
name = "search"
description = "Search for things"
def run(self, query: str, max_results: int = 5) -> List[str]:
return []
tool = SearchTool()
schema = tool.parameters
assert schema["type"] == "object"
assert "query" in schema["properties"]
assert "max_results" in schema["properties"]
assert "query" in schema["required"]
assert "max_results" not in schema["required"] # Has default
def test_callable_like_function(self):
"""Tool should be callable directly."""
from praisonaiagents import BaseTool
class AddTool(BaseTool):
name = "add"
description = "Add numbers"
def run(self, a: int, b: int) -> int:
return a + b
tool = AddTool()
result = tool(a=2, b=3)
assert result == 5
def test_safe_run_success(self):
"""safe_run() should return ToolResult on success."""
from praisonaiagents import BaseTool
class OkTool(BaseTool):
name = "ok"
description = "Always works"
def run(self) -> str:
return "ok"
tool = OkTool()
result = tool.safe_run()
assert result.success is True
assert result.output == "ok"
assert result.error is None
def test_safe_run_failure(self):
"""safe_run() should catch exceptions and return ToolResult."""
from praisonaiagents import BaseTool
class FailTool(BaseTool):
name = "fail"
description = "Always fails"
def run(self) -> str:
raise ValueError("Something went wrong")
tool = FailTool()
result = tool.safe_run()
assert result.success is False
assert result.output is None
assert "Something went wrong" in result.error
def test_get_schema_openai_format(self):
"""get_schema() should return OpenAI-compatible function schema."""
from praisonaiagents import BaseTool
class MyTool(BaseTool):
name = "my_tool"
description = "Does something"
def run(self, x: str) -> str:
return x
tool = MyTool()
schema = tool.get_schema()
assert schema["type"] == "function"
assert schema["function"]["name"] == "my_tool"
assert schema["function"]["description"] == "Does something"
assert "parameters" in schema["function"]
class TestToolDecorator:
"""Tests for @tool decorator."""
def test_import_from_main_package(self):
"""tool decorator should be importable from praisonaiagents."""
from praisonaiagents import tool
assert tool is not None
def test_decorator_without_args(self):
"""@tool without arguments should work."""
from praisonaiagents import tool
@tool
def my_search(query: str) -> List[str]:
"""Search for things."""
return [query]
assert my_search.name == "my_search"
assert "Search for things" in my_search.description
result = my_search(query="test")
assert result == ["test"]
def test_decorator_with_args(self):
"""@tool(name=..., description=...) should work."""
from praisonaiagents import tool
@tool(name="custom_search", description="Custom description")
def search(query: str) -> List[str]:
return [query]
assert search.name == "custom_search"
assert search.description == "Custom description"
def test_decorated_function_is_basetool(self):
"""Decorated function should be a BaseTool instance."""
from praisonaiagents import tool, BaseTool
@tool
def my_func(x: str) -> str:
return x
assert isinstance(my_func, BaseTool)
def test_decorated_function_has_schema(self):
"""Decorated function should have auto-generated schema."""
from praisonaiagents import tool
@tool
def calculate(a: int, b: int, operation: str = "add") -> int:
"""Perform calculation."""
return a + b
schema = calculate.get_schema()
params = schema["function"]["parameters"]
assert "a" in params["properties"]
assert "b" in params["properties"]
assert "operation" in params["properties"]
assert "a" in params["required"]
assert "operation" not in params["required"]
def test_decorated_function_callable_both_ways(self):
"""Decorated function should work with positional and keyword args."""
from praisonaiagents import tool
@tool
def greet(name: str, greeting: str = "Hello") -> str:
return f"{greeting}, {name}!"
# Keyword args (BaseTool style)
assert greet(name="World") == "Hello, World!"
# Positional args (function style)
assert greet("World", "Hi") == "Hi, World!"
class TestToolRegistry:
"""Tests for ToolRegistry."""
def test_import_from_main_package(self):
"""Registry functions should be importable from praisonaiagents."""
from praisonaiagents import get_registry, register_tool, get_tool, ToolRegistry
assert get_registry is not None
assert register_tool is not None
assert get_tool is not None
assert ToolRegistry is not None
def test_register_and_get_basetool(self):
"""Should register and retrieve BaseTool instances."""
from praisonaiagents import BaseTool, ToolRegistry
class MyTool(BaseTool):
name = "registry_test_tool"
description = "Test"
def run(self) -> str:
return "ok"
registry = ToolRegistry()
tool = MyTool()
registry.register(tool)
retrieved = registry.get("registry_test_tool")
assert retrieved is tool
def test_register_and_get_function(self):
"""Should register and retrieve plain functions."""
from praisonaiagents import ToolRegistry
def my_func(x: str) -> str:
return x
registry = ToolRegistry()
registry.register(my_func)
retrieved = registry.get("my_func")
assert retrieved is my_func
def test_register_with_custom_name(self):
"""Should allow custom name override."""
from praisonaiagents import ToolRegistry
def original_name() -> str:
return "ok"
registry = ToolRegistry()
registry.register(original_name, name="custom_name")
assert registry.get("custom_name") is original_name
assert registry.get("original_name") is None
def test_list_tools(self):
"""Should list all registered tool names."""
from praisonaiagents import ToolRegistry, BaseTool
class Tool1(BaseTool):
name = "tool1"
description = "First"
def run(self): return 1
class Tool2(BaseTool):
name = "tool2"
description = "Second"
def run(self): return 2
registry = ToolRegistry()
registry.register(Tool1())
registry.register(Tool2())
names = registry.list_tools()
assert "tool1" in names
assert "tool2" in names
def test_unregister(self):
"""Should remove tools from registry."""
from praisonaiagents import ToolRegistry
def temp_func() -> str:
return "temp"
registry = ToolRegistry()
registry.register(temp_func)
assert "temp_func" in registry
result = registry.unregister("temp_func")
assert result is True
assert "temp_func" not in registry
def test_no_duplicate_registration(self):
"""Should not overwrite existing tools by default."""
from praisonaiagents import ToolRegistry
def func_v1() -> str:
return "v1"
def func_v2() -> str:
return "v2"
registry = ToolRegistry()
registry.register(func_v1, name="my_func")
registry.register(func_v2, name="my_func") # Should be ignored
retrieved = registry.get("my_func")
assert retrieved() == "v1"
def test_overwrite_registration(self):
"""Should overwrite when overwrite=True."""
from praisonaiagents import ToolRegistry
def func_v1() -> str:
return "v1"
def func_v2() -> str:
return "v2"
registry = ToolRegistry()
registry.register(func_v1, name="my_func")
registry.register(func_v2, name="my_func", overwrite=True)
retrieved = registry.get("my_func")
assert retrieved() == "v2"
def test_global_registry_singleton(self):
"""get_registry() should return same instance."""
from praisonaiagents import get_registry
reg1 = get_registry()
reg2 = get_registry()
assert reg1 is reg2
def test_clear_registry(self):
"""clear() should remove all tools."""
from praisonaiagents import ToolRegistry
registry = ToolRegistry()
registry.register(lambda: "a", name="a")
registry.register(lambda: "b", name="b")
assert len(registry) == 2
registry.clear()
assert len(registry) == 0
class TestToolResult:
"""Tests for ToolResult wrapper."""
def test_import_from_main_package(self):
"""ToolResult should be importable from praisonaiagents."""
from praisonaiagents import ToolResult
assert ToolResult is not None
def test_success_result(self):
"""Should create success result."""
from praisonaiagents import ToolResult
result = ToolResult(output="data", success=True)
assert result.success is True
assert result.output == "data"
assert result.error is None
def test_error_result(self):
"""Should create error result."""
from praisonaiagents import ToolResult
result = ToolResult(output=None, success=False, error="Failed")
assert result.success is False
assert result.error == "Failed"
def test_to_dict(self):
"""Should convert to dictionary."""
from praisonaiagents import ToolResult
result = ToolResult(output="data", success=True, metadata={"key": "value"})
d = result.to_dict()
assert d["output"] == "data"
assert d["success"] is True
assert d["metadata"]["key"] == "value"
def test_str_representation(self):
"""String should show output or error."""
from praisonaiagents import ToolResult
success = ToolResult(output="hello", success=True)
assert str(success) == "hello"
failure = ToolResult(output=None, success=False, error="oops")
assert "oops" in str(failure)
class TestFunctionTool:
"""Tests for FunctionTool class."""
def test_import_from_main_package(self):
"""FunctionTool should be importable from praisonaiagents."""
from praisonaiagents import FunctionTool
assert FunctionTool is not None
def test_wraps_function(self):
"""Should wrap a function and preserve its behavior."""
from praisonaiagents import FunctionTool
def original(x: int, y: int) -> int:
"""Add two numbers."""
return x + y
wrapped = FunctionTool(original)
assert wrapped.name == "original"
assert "Add two numbers" in wrapped.description
assert wrapped(1, 2) == 3
assert wrapped.run(x=1, y=2) == 3
class TestAgentIntegration:
"""Tests for Agent integration with plugin tools."""
def test_agent_with_basetool_instance(self):
"""Agent should accept BaseTool instances in tools list."""
from praisonaiagents import Agent, BaseTool
class AddTool(BaseTool):
name = "add"
description = "Add two numbers"
def run(self, a: int, b: int) -> int:
return a + b
tool = AddTool()
agent = Agent(
name="TestAgent",
role="Tester",
goal="Test tools",
tools=[tool]
)
assert len(agent.tools) == 1
assert agent.tools[0] is tool
def test_agent_with_decorated_tool(self):
"""Agent should accept @tool decorated functions."""
from praisonaiagents import Agent, tool
@tool
def multiply(a: int, b: int) -> int:
"""Multiply two numbers."""
return a * b
agent = Agent(
name="TestAgent",
role="Tester",
goal="Test tools",
tools=[multiply]
)
assert len(agent.tools) == 1
assert agent.tools[0].name == "multiply"
def test_agent_execute_basetool(self):
"""Agent.execute_tool should work with BaseTool instances."""
from praisonaiagents import Agent, BaseTool
class ConcatTool(BaseTool):
name = "concat"
description = "Concatenate strings"
def run(self, a: str, b: str) -> str:
return a + b
agent = Agent(
name="TestAgent",
role="Tester",
goal="Test tools",
tools=[ConcatTool()]
)
result = agent.execute_tool("concat", {"a": "hello", "b": "world"})
assert result == "helloworld"
def test_agent_execute_decorated_tool(self):
"""Agent.execute_tool should work with @tool decorated functions."""
from praisonaiagents import Agent, tool
@tool
def subtract(a: int, b: int) -> int:
"""Subtract b from a."""
return a - b
agent = Agent(
name="TestAgent",
role="Tester",
goal="Test tools",
tools=[subtract]
)
result = agent.execute_tool("subtract", {"a": 10, "b": 3})
assert result == 7
def test_agent_with_tool_names_from_registry(self):
"""Agent should resolve tool names from registry."""
from praisonaiagents import Agent, BaseTool, register_tool, get_registry
# Clear registry first
get_registry().clear()
class DivideTool(BaseTool):
name = "divide"
description = "Divide two numbers"
def run(self, a: float, b: float) -> float:
return a / b
# Register tool
register_tool(DivideTool())
# Create agent with tool name string
agent = Agent(
name="TestAgent",
role="Tester",
goal="Test tools",
tools=["divide"]
)
assert len(agent.tools) == 1
assert agent.tools[0].name == "divide"
# Clean up
get_registry().clear()
def test_agent_execute_tool_from_registry(self):
"""Agent should find tools in registry during execute_tool."""
from praisonaiagents import Agent, BaseTool, register_tool, get_registry
# Clear registry first
get_registry().clear()
class PowerTool(BaseTool):
name = "power"
description = "Raise to power"
def run(self, base: int, exp: int) -> int:
return base ** exp
register_tool(PowerTool())
# Agent without tools - should still find from registry
agent = Agent(
name="TestAgent",
role="Tester",
goal="Test tools",
tools=[]
)
result = agent.execute_tool("power", {"base": 2, "exp": 3})
assert result == 8
# Clean up
get_registry().clear()
class TestPluginDiscovery:
"""Tests for entry_points plugin discovery."""
def test_discover_plugins_returns_count(self):
"""discover_plugins() should return number of plugins found."""
from praisonaiagents import ToolRegistry
registry = ToolRegistry()
count = registry.discover_plugins()
# Should return 0 or more (no external plugins installed in test env)
assert isinstance(count, int)
assert count >= 0
def test_discover_plugins_only_runs_once(self):
"""discover_plugins() should only run once per registry."""
from praisonaiagents import ToolRegistry
registry = ToolRegistry()
_count1 = registry.discover_plugins() # First call
count2 = registry.discover_plugins()
# Second call should return 0 (already discovered)
assert count2 == 0
assert _count1 >= 0 # Use variable to avoid lint warning
def test_discover_plugins_with_mock_entry_point(self, mocker):
"""Should load tools from mocked entry_points."""
from praisonaiagents import ToolRegistry, BaseTool
from unittest.mock import MagicMock
# Create a mock tool class
class MockPluginTool(BaseTool):
name = "mock_plugin"
description = "A mocked plugin tool"
def run(self) -> str:
return "mocked"
# Create mock entry point
mock_ep = MagicMock()
mock_ep.name = "mock_plugin"
mock_ep.load.return_value = MockPluginTool
# Mock entry_points to return our mock
mocker.patch(
'praisonaiagents.tools.registry.entry_points',
return_value=[mock_ep]
)
registry = ToolRegistry()
count = registry.discover_plugins()
assert count == 1
assert "mock_plugin" in registry
tool = registry.get("mock_plugin")
assert tool.run() == "mocked"
def test_discover_plugins_handles_load_error(self, mocker):
"""Should handle errors when loading plugins gracefully."""
from praisonaiagents import ToolRegistry
from unittest.mock import MagicMock
# Create mock entry point that raises on load
mock_ep = MagicMock()
mock_ep.name = "broken_plugin"
mock_ep.load.side_effect = ImportError("Module not found")
mocker.patch(
'praisonaiagents.tools.registry.entry_points',
return_value=[mock_ep]
)
registry = ToolRegistry()
# Should not raise, just log warning
count = registry.discover_plugins()
assert count == 0
assert "broken_plugin" not in registry
def test_discover_callable_function(self, mocker):
"""Should register plain functions from entry_points."""
from praisonaiagents import ToolRegistry
from unittest.mock import MagicMock
def plugin_function(x: str) -> str:
return f"plugin: {x}"
mock_ep = MagicMock()
mock_ep.name = "func_plugin"
mock_ep.load.return_value = plugin_function
mocker.patch(
'praisonaiagents.tools.registry.entry_points',
return_value=[mock_ep]
)
registry = ToolRegistry()
count = registry.discover_plugins()
assert count == 1
func = registry.get("func_plugin")
assert func("test") == "plugin: test"
def test_auto_discover_on_get_if_not_found(self, mocker):
"""get() should trigger discovery if tool not found."""
from praisonaiagents import ToolRegistry, BaseTool
from unittest.mock import MagicMock
class LazyTool(BaseTool):
name = "lazy_tool"
description = "Lazy loaded"
def run(self) -> str:
return "lazy"
mock_ep = MagicMock()
mock_ep.name = "lazy_tool"
mock_ep.load.return_value = LazyTool
mocker.patch(
'praisonaiagents.tools.registry.entry_points',
return_value=[mock_ep]
)
registry = ToolRegistry()
# Don't call discover_plugins() explicitly
# get() should trigger it
tool = registry.get("lazy_tool")
assert tool is not None
assert tool.run() == "lazy"
class TestToolValidation:
"""Tests for tool validation."""
def test_import_validation_error(self):
"""ToolValidationError should be importable."""
from praisonaiagents import ToolValidationError
assert ToolValidationError is not None
def test_import_validate_tool(self):
"""validate_tool should be importable."""
from praisonaiagents import validate_tool
assert validate_tool is not None
def test_valid_tool_passes_validation(self):
"""Valid tool should pass validation."""
from praisonaiagents import BaseTool
class ValidTool(BaseTool):
name = "valid"
description = "A valid tool"
def run(self) -> str:
return "ok"
tool = ValidTool()
assert tool.validate() is True
def test_tool_without_name_gets_auto_name(self):
"""Tool without name should get auto-generated name from class."""
from praisonaiagents import BaseTool
class MyCustomTool(BaseTool):
name = "" # Empty name - will be auto-filled
description = "Has description"
def run(self) -> str:
return "ok"
tool = MyCustomTool()
# Name should be auto-generated from class name
assert tool.name == "mycustom" # "MyCustomTool" -> "mycustom" (lowercase, "tool" removed)
assert tool.validate() is True
def test_tool_without_description_gets_auto_description(self):
"""Tool without description should get auto-generated description."""
from praisonaiagents import BaseTool
class NoDescTool(BaseTool):
"""This is the docstring description."""
name = "no_desc"
description = "" # Empty - will use docstring
def run(self) -> str:
return "ok"
tool = NoDescTool()
# Description should be auto-generated from docstring
assert "docstring description" in tool.description
assert tool.validate() is True
def test_tool_with_manually_cleared_name_fails(self):
"""Tool with name manually cleared after init should fail validation."""
from praisonaiagents import BaseTool, ToolValidationError
class MyTool(BaseTool):
name = "my_tool"
description = "Test"
def run(self) -> str:
return "ok"
tool = MyTool()
tool.name = "" # Manually clear after init
with pytest.raises(ToolValidationError):
tool.validate()
def test_validate_tool_function_with_basetool(self):
"""validate_tool() should work with BaseTool instances."""
from praisonaiagents import BaseTool, validate_tool
class MyTool(BaseTool):
name = "my_tool"
description = "Test"
def run(self) -> str:
return "ok"
assert validate_tool(MyTool()) is True
def test_validate_tool_function_with_callable(self):
"""validate_tool() should work with plain callables."""
from praisonaiagents import validate_tool
def my_func() -> str:
return "ok"
assert validate_tool(my_func) is True
def test_validate_tool_rejects_invalid_type(self):
"""validate_tool() should reject non-tool objects."""
from praisonaiagents import validate_tool, ToolValidationError
with pytest.raises(ToolValidationError):
validate_tool("not a tool")
with pytest.raises(ToolValidationError):
validate_tool(123)
def test_validate_class_method(self):
"""BaseTool.validate_class() should check class validity."""
from praisonaiagents import BaseTool
class ValidTool(BaseTool):
name = "valid"
description = "Valid"
def run(self) -> str:
return "ok"
assert ValidTool.validate_class() is True
def test_decorated_tool_passes_validation(self):
"""@tool decorated functions should pass validation."""
from praisonaiagents import tool, validate_tool
@tool
def my_search(query: str) -> list:
"""Search for things."""
return []
assert validate_tool(my_search) is True
if __name__ == "__main__":
pytest.main([__file__, "-v"])
@@ -59,12 +59,14 @@ class TestValidationFeedback:
# Simulate task execution results
collect_task.result = TaskOutput(
description="Collected data",
raw="Collected only 3 items",
agent="agent1"
)
collect_task.status = "completed"
validate_task.result = TaskOutput(
description="Validation result",
raw="Not enough results, need at least 10",
agent="agent2"
)
@@ -219,12 +221,14 @@ class TestValidationFeedback:
# Simulate task execution results
data_task.result = TaskOutput(
description="Generated data",
raw="Generated data with errors",
agent="agent1"
)
data_task.status = "completed"
validate_task.result = TaskOutput(
description="Validation result",
raw="Data has errors, please fix",
agent="agent2"
)
+1 -1
View File
@@ -2422,7 +2422,7 @@ wheels = [
[[package]]
name = "praisonaiagents"
version = "0.0.166"
version = "0.0.169"
source = { editable = "." }
dependencies = [
{ name = "aiohttp" },
+783 -12
View File
@@ -1,19 +1,741 @@
# PraisonAI Package
<p align="center">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="docs/logo/dark.png" />
<source media="(prefers-color-scheme: light)" srcset="docs/logo/light.png" />
<img alt="PraisonAI Logo" src="docs/logo/light.png" />
</picture>
</p>
## Development
<p align="center">
<a href="https://github.com/MervinPraison/PraisonAI"><img src="https://static.pepy.tech/badge/PraisonAI" alt="Total Downloads" /></a>
<a href="https://github.com/MervinPraison/PraisonAI"><img src="https://img.shields.io/github/v/release/MervinPraison/PraisonAI" alt="Latest Stable Version" /></a>
<a href="https://github.com/MervinPraison/PraisonAI"><img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License" /></a>
</p>
<div align="center">
# Praison AI
<a href="https://trendshift.io/repositories/9130" target="_blank"><img src="https://trendshift.io/api/badge/repositories/9130" alt="MervinPraison%2FPraisonAI | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
</div>
PraisonAI is a production-ready Multi-AI Agents framework with self-reflection, designed to create AI Agents to automate and solve problems ranging from simple tasks to complex challenges. By integrating PraisonAI Agents, AG2 (Formerly AutoGen), and CrewAI into a low-code solution, it streamlines the building and management of multi-agent LLM systems, emphasising simplicity, customisation, and effective human-agent collaboration.
<div align="center">
<a href="https://docs.praison.ai">
<p align="center">
<img src="https://img.shields.io/badge/📚_Documentation-Visit_docs.praison.ai-blue?style=for-the-badge&logo=bookstack&logoColor=white" alt="Documentation" />
</p>
</a>
</div>
## Key Features
- 🤖 Automated AI Agents Creation
- 🔄 Self Reflection AI Agents
- 🧠 Reasoning AI Agents
- 👁️ Multi Modal AI Agents
- 🤝 Multi Agent Collaboration
- 🎭 AI Agent Workflow
- 📚 Add Custom Knowledge
- 🧠 Agents with Short and Long Term Memory
- 📄 Chat with PDF Agents
- 💻 Code Interpreter Agents
- 📚 RAG Agents
- 🤔 Async & Parallel Processing
- 🔄 Auto Agents
- 🔢 Math Agents
- 🎯 Structured Output Agents
- 🔗 LangChain Integrated Agents
- 📞 Callback Agents
- 🤏 Mini AI Agents
- 🛠️ 100+ Custom Tools
- 📄 YAML Configuration
- 💯 100+ LLM Support
- 🔬 **Deep Research Agents** (OpenAI & Gemini)
- 🔄 **Query Rewriter Agent** (HyDE, Step-back, Multi-query)
## Using Python Code
Light weight package dedicated for coding:
```bash
# Install
uv sync
# Build
uv build
# Publish
uv publish
pip install praisonaiagents
```
## Version Bump
```bash
export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
```
### 1. Single Agent
Create app.py file and add the code below:
```python
from praisonaiagents import Agent
agent = Agent(instructions="Your are a helpful AI assistant")
agent.start("Write a movie script about a robot in Mars")
```
Run:
```bash
python app.py
```
### 2. Multi Agents
Create app.py file and add the code below:
```python
from praisonaiagents import Agent, PraisonAIAgents
research_agent = Agent(instructions="Research about AI")
summarise_agent = Agent(instructions="Summarise research agent's findings")
agents = PraisonAIAgents(agents=[research_agent, summarise_agent])
agents.start()
```
Run:
```bash
python app.py
```
### 3. Deep Research Agent
Automated research with real-time streaming, web search, and citations using OpenAI or Gemini Deep Research APIs.
```python
from praisonaiagents import DeepResearchAgent
# OpenAI Deep Research
agent = DeepResearchAgent(
model="o4-mini-deep-research", # or "o3-deep-research"
verbose=True
)
result = agent.research("What are the latest AI trends in 2025?")
print(result.report)
print(f"Citations: {len(result.citations)}")
```
```python
# Gemini Deep Research
from praisonaiagents import DeepResearchAgent
agent = DeepResearchAgent(
model="deep-research-pro", # Auto-detected as Gemini
verbose=True
)
result = agent.research("Research quantum computing advances")
print(result.report)
```
**Features:**
- 🔍 Multi-provider support (OpenAI, Gemini, LiteLLM)
- 📡 Real-time streaming with reasoning summaries
- 📚 Structured citations with URLs
- 🛠️ Built-in tools: web search, code interpreter, MCP, file search
- 🔄 Automatic provider detection from model name
### 4. Query Rewriter Agent
Transform user queries to improve RAG retrieval quality using multiple strategies.
```python
from praisonaiagents import QueryRewriterAgent, RewriteStrategy
agent = QueryRewriterAgent(model="gpt-4o-mini")
# Basic - expands abbreviations, adds context
result = agent.rewrite("AI trends")
print(result.primary_query) # "What are the current trends in Artificial Intelligence?"
# HyDE - generates hypothetical document for semantic matching
result = agent.rewrite("What is quantum computing?", strategy=RewriteStrategy.HYDE)
# Step-back - generates broader context question
result = agent.rewrite("GPT-4 vs Claude 3?", strategy=RewriteStrategy.STEP_BACK)
# Sub-queries - decomposes complex questions
result = agent.rewrite("RAG setup and best embedding models?", strategy=RewriteStrategy.SUB_QUERIES)
# Contextual - resolves references using chat history
result = agent.rewrite("What about cost?", chat_history=[...])
```
**Strategies:**
- **BASIC**: Expand abbreviations, fix typos, add context
- **HYDE**: Generate hypothetical document for semantic matching
- **STEP_BACK**: Generate higher-level concept questions
- **SUB_QUERIES**: Decompose multi-part questions
- **MULTI_QUERY**: Generate multiple paraphrased versions
- **CONTEXTUAL**: Resolve references using conversation history
- **AUTO**: Automatically detect best strategy
## Using No Code
### Auto Mode:
```bash
pip install praisonai
export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
praisonai --auto create a movie script about Robots in Mars
```
### Query Rewriting (works with any command):
```bash
# Rewrite query for better results (uses QueryRewriterAgent)
praisonai "AI trends" --query-rewrite
# Rewrite with search tools (agent decides when to search)
praisonai "latest developments" --query-rewrite --rewrite-tools "internet_search"
# Works with any prompt
praisonai "explain quantum computing" --query-rewrite -v
```
### Deep Research CLI:
```bash
# Default: OpenAI (o4-mini-deep-research)
praisonai research "What are the latest AI trends in 2025?"
# Use Gemini
praisonai research --model deep-research-pro "Your research query"
# Rewrite query before research
praisonai research --query-rewrite "AI trends"
# Rewrite with search tools
praisonai research --query-rewrite --rewrite-tools "internet_search" "AI trends"
# Use custom tools from file (gathers context before deep research)
praisonai research --tools tools.py "Your research query"
praisonai research -t my_tools.py "Your research query"
# Use built-in tools by name (comma-separated)
praisonai research --tools "internet_search,wiki_search" "Your query"
praisonai research -t "yfinance,calculator_tools" "Stock analysis query"
# Save output to file (output/research/{query}.md)
praisonai research --save "Your research query"
praisonai research -s "Your research query"
# Combine options
praisonai research --query-rewrite --tools tools.py --save "Your research query"
# Verbose mode (show debug logs)
praisonai research -v "Your research query"
```
## Using JavaScript Code
```bash
npm install praisonai
export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
```
```javascript
const { Agent } = require('praisonai');
const agent = new Agent({ instructions: 'You are a helpful AI assistant' });
agent.start('Write a movie script about a robot in Mars');
```
![PraisonAI CLI Demo](docs/demo/praisonai-cli-demo.gif)
## Star History
[![Star History Chart](https://api.star-history.com/svg?repos=MervinPraison/PraisonAI&type=Date)](https://docs.praison.ai)
## AI Agents Flow
```mermaid
graph LR
%% Define the main flow
Start([▶ Start]) --> Agent1
Agent1 --> Process[⚙ Process]
Process --> Agent2
Agent2 --> Output([✓ Output])
Process -.-> Agent1
%% Define subgraphs for agents and their tasks
subgraph Agent1[ ]
Task1[📋 Task]
AgentIcon1[🤖 AI Agent]
Tools1[🔧 Tools]
Task1 --- AgentIcon1
AgentIcon1 --- Tools1
end
subgraph Agent2[ ]
Task2[📋 Task]
AgentIcon2[🤖 AI Agent]
Tools2[🔧 Tools]
Task2 --- AgentIcon2
AgentIcon2 --- Tools2
end
classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
classDef tools fill:#2E8B57,stroke:#7C90A0,color:#fff
classDef transparent fill:none,stroke:none
class Start,Output,Task1,Task2 input
class Process,AgentIcon1,AgentIcon2 process
class Tools1,Tools2 tools
class Agent1,Agent2 transparent
```
## AI Agents with Tools
Create AI agents that can use tools to interact with external systems and perform actions.
```mermaid
flowchart TB
subgraph Tools
direction TB
T3[Internet Search]
T1[Code Execution]
T2[Formatting]
end
Input[Input] ---> Agents
subgraph Agents
direction LR
A1[Agent 1]
A2[Agent 2]
A3[Agent 3]
end
Agents ---> Output[Output]
T3 --> A1
T1 --> A2
T2 --> A3
style Tools fill:#189AB4,color:#fff
style Agents fill:#8B0000,color:#fff
style Input fill:#8B0000,color:#fff
style Output fill:#8B0000,color:#fff
```
## AI Agents with Memory
Create AI agents with memory capabilities for maintaining context and information across tasks.
```mermaid
flowchart TB
subgraph Memory
direction TB
STM[Short Term]
LTM[Long Term]
end
subgraph Store
direction TB
DB[(Vector DB)]
end
Input[Input] ---> Agents
subgraph Agents
direction LR
A1[Agent 1]
A2[Agent 2]
A3[Agent 3]
end
Agents ---> Output[Output]
Memory <--> Store
Store <--> A1
Store <--> A2
Store <--> A3
style Memory fill:#189AB4,color:#fff
style Store fill:#2E8B57,color:#fff
style Agents fill:#8B0000,color:#fff
style Input fill:#8B0000,color:#fff
style Output fill:#8B0000,color:#fff
```
## AI Agents with Different Processes
### Sequential Process
The simplest form of task execution where tasks are performed one after another.
```mermaid
graph LR
Input[Input] --> A1
subgraph Agents
direction LR
A1[Agent 1] --> A2[Agent 2] --> A3[Agent 3]
end
A3 --> Output[Output]
classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
classDef transparent fill:none,stroke:none
class Input,Output input
class A1,A2,A3 process
class Agents transparent
```
### Hierarchical Process
Uses a manager agent to coordinate task execution and agent assignments.
```mermaid
graph TB
Input[Input] --> Manager
subgraph Agents
Manager[Manager Agent]
subgraph Workers
direction LR
W1[Worker 1]
W2[Worker 2]
W3[Worker 3]
end
Manager --> W1
Manager --> W2
Manager --> W3
end
W1 --> Manager
W2 --> Manager
W3 --> Manager
Manager --> Output[Output]
classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
classDef transparent fill:none,stroke:none
class Input,Output input
class Manager,W1,W2,W3 process
class Agents,Workers transparent
```
### Workflow Process
Advanced process type supporting complex task relationships and conditional execution.
```mermaid
graph LR
Input[Input] --> Start
subgraph Workflow
direction LR
Start[Start] --> C1{Condition}
C1 --> |Yes| A1[Agent 1]
C1 --> |No| A2[Agent 2]
A1 --> Join
A2 --> Join
Join --> A3[Agent 3]
end
A3 --> Output[Output]
classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
classDef decision fill:#2E8B57,stroke:#7C90A0,color:#fff
classDef transparent fill:none,stroke:none
class Input,Output input
class Start,A1,A2,A3,Join process
class C1 decision
class Workflow transparent
```
#### Agentic Routing Workflow
Create AI agents that can dynamically route tasks to specialized LLM instances.
```mermaid
flowchart LR
In[In] --> Router[LLM Call Router]
Router --> LLM1[LLM Call 1]
Router --> LLM2[LLM Call 2]
Router --> LLM3[LLM Call 3]
LLM1 --> Out[Out]
LLM2 --> Out
LLM3 --> Out
style In fill:#8B0000,color:#fff
style Router fill:#2E8B57,color:#fff
style LLM1 fill:#2E8B57,color:#fff
style LLM2 fill:#2E8B57,color:#fff
style LLM3 fill:#2E8B57,color:#fff
style Out fill:#8B0000,color:#fff
```
#### Agentic Orchestrator Worker
Create AI agents that orchestrate and distribute tasks among specialized workers.
```mermaid
flowchart LR
In[In] --> Router[LLM Call Router]
Router --> LLM1[LLM Call 1]
Router --> LLM2[LLM Call 2]
Router --> LLM3[LLM Call 3]
LLM1 --> Synthesizer[Synthesizer]
LLM2 --> Synthesizer
LLM3 --> Synthesizer
Synthesizer --> Out[Out]
style In fill:#8B0000,color:#fff
style Router fill:#2E8B57,color:#fff
style LLM1 fill:#2E8B57,color:#fff
style LLM2 fill:#2E8B57,color:#fff
style LLM3 fill:#2E8B57,color:#fff
style Synthesizer fill:#2E8B57,color:#fff
style Out fill:#8B0000,color:#fff
```
#### Agentic Autonomous Workflow
Create AI agents that can autonomously monitor, act, and adapt based on environment feedback.
```mermaid
flowchart LR
Human[Human] <--> LLM[LLM Call]
LLM -->|ACTION| Environment[Environment]
Environment -->|FEEDBACK| LLM
LLM --> Stop[Stop]
style Human fill:#8B0000,color:#fff
style LLM fill:#2E8B57,color:#fff
style Environment fill:#8B0000,color:#fff
style Stop fill:#333,color:#fff
```
#### Agentic Parallelization
Create AI agents that can execute tasks in parallel for improved performance.
```mermaid
flowchart LR
In[In] --> LLM2[LLM Call 2]
In --> LLM1[LLM Call 1]
In --> LLM3[LLM Call 3]
LLM1 --> Aggregator[Aggregator]
LLM2 --> Aggregator
LLM3 --> Aggregator
Aggregator --> Out[Out]
style In fill:#8B0000,color:#fff
style LLM1 fill:#2E8B57,color:#fff
style LLM2 fill:#2E8B57,color:#fff
style LLM3 fill:#2E8B57,color:#fff
style Aggregator fill:#fff,color:#000
style Out fill:#8B0000,color:#fff
```
#### Agentic Prompt Chaining
Create AI agents with sequential prompt chaining for complex workflows.
```mermaid
flowchart LR
In[In] --> LLM1[LLM Call 1] --> Gate{Gate}
Gate -->|Pass| LLM2[LLM Call 2] -->|Output 2| LLM3[LLM Call 3] --> Out[Out]
Gate -->|Fail| Exit[Exit]
style In fill:#8B0000,color:#fff
style LLM1 fill:#2E8B57,color:#fff
style LLM2 fill:#2E8B57,color:#fff
style LLM3 fill:#2E8B57,color:#fff
style Out fill:#8B0000,color:#fff
style Exit fill:#8B0000,color:#fff
```
#### Agentic Evaluator Optimizer
Create AI agents that can generate and optimize solutions through iterative feedback.
```mermaid
flowchart LR
In[In] --> Generator[LLM Call Generator]
Generator -->|SOLUTION| Evaluator[LLM Call Evaluator] -->|ACCEPTED| Out[Out]
Evaluator -->|REJECTED + FEEDBACK| Generator
style In fill:#8B0000,color:#fff
style Generator fill:#2E8B57,color:#fff
style Evaluator fill:#2E8B57,color:#fff
style Out fill:#8B0000,color:#fff
```
#### Repetitive Agents
Create AI agents that can efficiently handle repetitive tasks through automated loops.
```mermaid
flowchart LR
In[Input] --> LoopAgent[("Looping Agent")]
LoopAgent --> Task[Task]
Task --> |Next iteration| LoopAgent
Task --> |Done| Out[Output]
style In fill:#8B0000,color:#fff
style LoopAgent fill:#2E8B57,color:#fff,shape:circle
style Task fill:#2E8B57,color:#fff
style Out fill:#8B0000,color:#fff
```
## Adding Models
<div align="center">
<a href="https://docs.praison.ai/models">
<p align="center">
<img src="https://img.shields.io/badge/%F0%9F%93%9A_Models-Visit_docs.praison.ai-blue?style=for-the-badge&logo=bookstack&logoColor=white" alt="Models" />
</p>
</a>
</div>
## Ollama Integration
```bash
export OPENAI_BASE_URL=http://localhost:11434/v1
```
## Groq Integration
Replace xxxx with Groq API KEY:
```bash
export OPENAI_API_KEY=xxxxxxxxxxx
export OPENAI_BASE_URL=https://api.groq.com/openai/v1
```
## No Code Options
## Agents Playbook
### Simple Playbook Example
Create `agents.yaml` file and add the code below:
```yaml
framework: praisonai
topic: Artificial Intelligence
roles:
screenwriter:
backstory: "Skilled in crafting scripts with engaging dialogue about {topic}."
goal: Create scripts from concepts.
role: Screenwriter
tasks:
scriptwriting_task:
description: "Develop scripts with compelling characters and dialogue about {topic}."
expected_output: "Complete script ready for production."
```
*To run the playbook:*
```bash
praisonai agents.yaml
```
## Use 100+ Models
- https://docs.praison.ai/models/
<div align="center">
<a href="https://docs.praison.ai">
<p align="center">
<img src="https://img.shields.io/badge/📚_Documentation-Visit_docs.praison.ai-blue?style=for-the-badge&logo=bookstack&logoColor=white" alt="Documentation" />
</p>
</a>
</div>
## Custom Tools
### Using `@tool` Decorator
```python
from praisonaiagents import Agent, tool
@tool
def search(query: str) -> str:
"""Search the web for information."""
return f"Results for: {query}"
@tool
def calculate(expression: str) -> float:
"""Evaluate a math expression."""
return eval(expression)
agent = Agent(
instructions="You are a helpful assistant",
tools=[search, calculate]
)
agent.start("Search for AI news and calculate 15*4")
```
### Using `BaseTool` Class
```python
from praisonaiagents import Agent, BaseTool
class WeatherTool(BaseTool):
name = "weather"
description = "Get current weather for a location"
def run(self, location: str) -> str:
return f"Weather in {location}: 72°F, Sunny"
agent = Agent(
instructions="You are a weather assistant",
tools=[WeatherTool()]
)
agent.start("What's the weather in Paris?")
```
### Creating a Tool Package (pip installable)
```toml
# pyproject.toml
[project]
name = "my-praisonai-tools"
version = "1.0.0"
dependencies = ["praisonaiagents"]
[project.entry-points."praisonaiagents.tools"]
my_tool = "my_package:MyTool"
```
```python
# my_package/__init__.py
from praisonaiagents import BaseTool
class MyTool(BaseTool):
name = "my_tool"
description = "My custom tool"
def run(self, param: str) -> str:
return f"Result: {param}"
```
After `pip install`, tools are auto-discovered:
```python
agent = Agent(tools=["my_tool"]) # Works automatically!
```
## Development:
Below is used for development only.
### Using uv
```bash
# Install uv if you haven't already
pip install uv
# Install from requirements
uv pip install -r pyproject.toml
# Install with extras
uv pip install -r pyproject.toml --extra code
uv pip install -r pyproject.toml --extra "crewai,autogen"
```
### Version Bump
```bash
# From project root
@@ -23,7 +745,7 @@ python src/praisonai/scripts/bump_version.py 2.2.96
python src/praisonai/scripts/bump_version.py 2.2.96 --agents 0.0.167
```
## Release
### Release
```bash
# From project root (runs uv lock, build, git tag, gh release)
@@ -32,3 +754,52 @@ python src/praisonai/scripts/release.py
# Then publish
cd src/praisonai && uv publish
```
## Contributing
- Fork on GitHub: Use the "Fork" button on the repository page.
- Clone your fork: `git clone https://github.com/yourusername/praisonAI.git`
- Create a branch: `git checkout -b new-feature`
- Make changes and commit: `git commit -am "Add some feature"`
- Push to your fork: `git push origin new-feature`
- Submit a pull request via GitHub's web interface.
- Await feedback from project maintainers.
## Other Features
- 🔄 Use CrewAI or AG2 (Formerly AutoGen) Framework
- 💻 Chat with ENTIRE Codebase
- 🎨 Interactive UIs
- 📄 YAML-based Configuration
- 🛠️ Custom Tool Integration
- 🔍 Internet Search Capability (using Crawl4AI and Tavily)
- 🖼️ Vision Language Model (VLM) Support
- 🎙️ Real-time Voice Interaction
## Video Tutorials
| Topic | Video |
|-------|--------|
| AI Agents with Self Reflection | [![Self Reflection](https://img.youtube.com/vi/vLXobEN2Vc8/0.jpg)](https://www.youtube.com/watch?v=vLXobEN2Vc8) |
| Reasoning Data Generating Agent | [![Reasoning Data](https://img.youtube.com/vi/fUT332Y2zA8/0.jpg)](https://www.youtube.com/watch?v=fUT332Y2zA8) |
| AI Agents with Reasoning | [![Reasoning](https://img.youtube.com/vi/KNDVWGN3TpM/0.jpg)](https://www.youtube.com/watch?v=KNDVWGN3TpM) |
| Multimodal AI Agents | [![Multimodal](https://img.youtube.com/vi/hjAWmUT1qqY/0.jpg)](https://www.youtube.com/watch?v=hjAWmUT1qqY) |
| AI Agents Workflow | [![Workflow](https://img.youtube.com/vi/yWTH44QPl2A/0.jpg)](https://www.youtube.com/watch?v=yWTH44QPl2A) |
| Async AI Agents | [![Async](https://img.youtube.com/vi/VhVQfgo00LE/0.jpg)](https://www.youtube.com/watch?v=VhVQfgo00LE) |
| Mini AI Agents | [![Mini](https://img.youtube.com/vi/OkvYp5aAGSg/0.jpg)](https://www.youtube.com/watch?v=OkvYp5aAGSg) |
| AI Agents with Memory | [![Memory](https://img.youtube.com/vi/1hVfVxvPnnQ/0.jpg)](https://www.youtube.com/watch?v=1hVfVxvPnnQ) |
| Repetitive Agents | [![Repetitive](https://img.youtube.com/vi/dAYGxsjDOPg/0.jpg)](https://www.youtube.com/watch?v=dAYGxsjDOPg) |
| Introduction | [![Introduction](https://img.youtube.com/vi/Fn1lQjC0GO0/0.jpg)](https://www.youtube.com/watch?v=Fn1lQjC0GO0) |
| Tools Overview | [![Tools Overview](https://img.youtube.com/vi/XaQRgRpV7jo/0.jpg)](https://www.youtube.com/watch?v=XaQRgRpV7jo) |
| Custom Tools | [![Custom Tools](https://img.youtube.com/vi/JSU2Rndh06c/0.jpg)](https://www.youtube.com/watch?v=JSU2Rndh06c) |
| Firecrawl Integration | [![Firecrawl](https://img.youtube.com/vi/UoqUDcLcOYo/0.jpg)](https://www.youtube.com/watch?v=UoqUDcLcOYo) |
| User Interface | [![UI](https://img.youtube.com/vi/tg-ZjNl3OCg/0.jpg)](https://www.youtube.com/watch?v=tg-ZjNl3OCg) |
| Crawl4AI Integration | [![Crawl4AI](https://img.youtube.com/vi/KAvuVUh0XU8/0.jpg)](https://www.youtube.com/watch?v=KAvuVUh0XU8) |
| Chat Interface | [![Chat](https://img.youtube.com/vi/sw3uDqn2h1Y/0.jpg)](https://www.youtube.com/watch?v=sw3uDqn2h1Y) |
| Code Interface | [![Code](https://img.youtube.com/vi/_5jQayO-MQY/0.jpg)](https://www.youtube.com/watch?v=_5jQayO-MQY) |
| Mem0 Integration | [![Mem0](https://img.youtube.com/vi/KIGSgRxf1cY/0.jpg)](https://www.youtube.com/watch?v=KIGSgRxf1cY) |
| Training | [![Training](https://img.youtube.com/vi/aLawE8kwCrI/0.jpg)](https://www.youtube.com/watch?v=aLawE8kwCrI) |
| Realtime Voice Interface | [![Realtime](https://img.youtube.com/vi/frRHfevTCSw/0.jpg)](https://www.youtube.com/watch?v=frRHfevTCSw) |
| Call Interface | [![Call](https://img.youtube.com/vi/m1cwrUG2iAk/0.jpg)](https://www.youtube.com/watch?v=m1cwrUG2iAk) |
| Reasoning Extract Agents | [![Reasoning Extract](https://img.youtube.com/vi/2PPamsADjJA/0.jpg)](https://www.youtube.com/watch?v=2PPamsADjJA) |
+2 -2
View File
@@ -3,8 +3,8 @@ class Praisonai < Formula
desc "AI tools for various AI applications"
homepage "https://github.com/MervinPraison/PraisonAI"
url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.96.tar.gz"
sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.96.tar.gz | shasum -a 256`.split.first
url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.99.tar.gz"
sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.99.tar.gz | shasum -a 256`.split.first
license "MIT"
depends_on "[email protected]"
+99 -57
View File
@@ -514,9 +514,8 @@ class PraisonAI:
in_test_env = (
'pytest' in sys.argv[0] or
'unittest' in sys.argv[0] or
any('test' in arg for arg in sys.argv[1:3]) or # Check first few args for test indicators
'pytest' in sys.modules or
'unittest' in sys.modules
any(arg.startswith('tests.') or arg.startswith('tests/') for arg in sys.argv[1:3]) or # Check for test module paths
'PYTEST_CURRENT_TEST' in os.environ
)
# Check if we're being used as a library (not from praisonai CLI)
@@ -578,7 +577,7 @@ class PraisonAI:
parser.add_argument("--goal", type=str, help="Goal for context engineering")
parser.add_argument("--auto-analyze", action="store_true", help="Enable automatic analysis in context engineering")
parser.add_argument("--research", action="store_true", help="Run deep research on a topic")
parser.add_argument("--query-rewrite", action="store_true", help="Rewrite query before research for better results")
parser.add_argument("--query-rewrite", action="store_true", help="Rewrite query for better results (works with any command)")
parser.add_argument("--rewrite-tools", type=str, help="Tools for query rewriter (e.g., 'internet_search' or path to tools.py)")
parser.add_argument("--tools", "-t", type=str, help="Path to tools.py file for research agent")
parser.add_argument("--save", "-s", action="store_true", help="Save research output to file (output/research/)")
@@ -748,10 +747,105 @@ class PraisonAI:
return args
def _rewrite_query(self, query: str, rewrite_tools: str = None, verbose: bool = False) -> str:
"""
Rewrite query using QueryRewriterAgent.
Args:
query: The query to rewrite
rewrite_tools: Tool names (comma-separated) or path to tools.py
verbose: Enable verbose output
Returns:
Rewritten query or original if rewriting fails
"""
try:
from praisonaiagents import QueryRewriterAgent, RewriteStrategy
from rich import print
print("[bold cyan]Rewriting query for better results...[/bold cyan]")
# Load rewrite tools if specified
rewrite_tools_list = []
if rewrite_tools:
if os.path.isfile(rewrite_tools):
# Load from file
try:
import inspect
import importlib.util
spec = importlib.util.spec_from_file_location("rewrite_tools_module", rewrite_tools)
if spec and spec.loader:
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
for name, obj in inspect.getmembers(module):
if inspect.isfunction(obj) and not name.startswith('_'):
rewrite_tools_list.append(obj)
if rewrite_tools_list:
print(f"[cyan]Loaded {len(rewrite_tools_list)} tools for query rewriter[/cyan]")
except Exception as e:
print(f"[yellow]Warning: Failed to load rewrite tools: {e}[/yellow]")
else:
# Treat as comma-separated tool names
try:
from praisonaiagents.tools import TOOL_MAPPINGS
import praisonaiagents.tools as tools_module
tool_names = [t.strip() for t in rewrite_tools.split(',')]
for tool_name in tool_names:
if tool_name in TOOL_MAPPINGS:
try:
tool = getattr(tools_module, tool_name)
rewrite_tools_list.append(tool)
except Exception as e:
print(f"[yellow]Warning: Failed to load rewrite tool '{tool_name}': {e}[/yellow]")
else:
print(f"[yellow]Warning: Unknown rewrite tool '{tool_name}'[/yellow]")
if rewrite_tools_list:
print(f"[cyan]Using rewrite tools: {', '.join(tool_names)}[/cyan]")
except ImportError:
print("[yellow]Warning: Could not import tools module[/yellow]")
rewriter = QueryRewriterAgent(
model="gpt-4o-mini",
verbose=verbose,
tools=rewrite_tools_list if rewrite_tools_list else None
)
result = rewriter.rewrite(query, strategy=RewriteStrategy.AUTO)
rewritten = result.primary_query
print(f"[cyan]Original:[/cyan] {query}")
print(f"[cyan]Rewritten:[/cyan] {rewritten}")
return rewritten
except ImportError:
from rich import print
print("[yellow]Warning: QueryRewriterAgent not available, using original query[/yellow]")
return query
except Exception as e:
from rich import print
print(f"[yellow]Warning: Query rewrite failed ({e}), using original query[/yellow]")
return query
def _rewrite_query_if_enabled(self, query: str) -> str:
"""
Rewrite query using QueryRewriterAgent if --query-rewrite is enabled.
Returns the rewritten query or original if rewriting is disabled/fails.
"""
if not hasattr(self, 'args') or not getattr(self.args, 'query_rewrite', False):
return query
rewrite_tools = getattr(self.args, 'rewrite_tools', None)
verbose = getattr(self.args, 'verbose', False)
return self._rewrite_query(query, rewrite_tools, verbose)
def handle_direct_prompt(self, prompt):
"""
Handle direct prompt by creating a single agent and running it.
"""
# Apply query rewriting if enabled
prompt = self._rewrite_query_if_enabled(prompt)
if PRAISONAI_AVAILABLE:
agent_config = {
"name": "DirectAgent",
@@ -994,60 +1088,8 @@ class PraisonAI:
logging.getLogger('httpcore').setLevel(logging.WARNING)
# Rewrite query if requested
original_query = query
if query_rewrite:
try:
from praisonaiagents import QueryRewriterAgent, RewriteStrategy
print("[bold cyan]Rewriting query for better research results...[/bold cyan]")
# Load rewrite tools if specified
rewrite_tools_list = []
if rewrite_tools:
if os.path.isfile(rewrite_tools):
# Load from file
try:
import inspect
spec = importlib.util.spec_from_file_location("rewrite_tools_module", rewrite_tools)
if spec and spec.loader:
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
for name, obj in inspect.getmembers(module):
if inspect.isfunction(obj) and not name.startswith('_'):
rewrite_tools_list.append(obj)
if rewrite_tools_list:
print(f"[cyan]Loaded {len(rewrite_tools_list)} tools for query rewriter[/cyan]")
except Exception as e:
print(f"[yellow]Warning: Failed to load rewrite tools: {e}[/yellow]")
else:
# Treat as comma-separated tool names
try:
from praisonaiagents.tools import TOOL_MAPPINGS
import praisonaiagents.tools as tools_module
tool_names = [t.strip() for t in rewrite_tools.split(',')]
for tool_name in tool_names:
if tool_name in TOOL_MAPPINGS:
try:
tool = getattr(tools_module, tool_name)
rewrite_tools_list.append(tool)
except Exception as e:
print(f"[yellow]Warning: Failed to load rewrite tool '{tool_name}': {e}[/yellow]")
else:
print(f"[yellow]Warning: Unknown rewrite tool '{tool_name}'[/yellow]")
if rewrite_tools_list:
print(f"[cyan]Using rewrite tools: {', '.join(tool_names)}[/cyan]")
except ImportError:
print("[yellow]Warning: Could not import tools module[/yellow]")
rewriter = QueryRewriterAgent(model="gpt-4o-mini", verbose=verbose, tools=rewrite_tools_list if rewrite_tools_list else None)
result = rewriter.rewrite(query, strategy=RewriteStrategy.AUTO)
query = result.primary_query
print(f"[cyan]Original:[/cyan] {original_query}")
print(f"[cyan]Rewritten:[/cyan] {query}")
except ImportError:
print("[yellow]Warning: QueryRewriterAgent not available, using original query[/yellow]")
except Exception as e:
print(f"[yellow]Warning: Query rewrite failed ({e}), using original query[/yellow]")
query = self._rewrite_query(query, rewrite_tools, verbose)
print("[bold green]Starting Deep Research...[/bold green]")
print(f"Query: {query}")
+1 -1
View File
@@ -57,7 +57,7 @@ class CloudDeployer:
file.write("FROM python:3.11-slim\n")
file.write("WORKDIR /app\n")
file.write("COPY . .\n")
file.write("RUN pip install flask praisonai==2.2.96 gunicorn markdown\n")
file.write("RUN pip install flask praisonai==2.2.99 gunicorn markdown\n")
file.write("EXPOSE 8080\n")
file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
+1 -1
View File
@@ -1 +1 @@
__version__ = "2.2.96"
__version__ = "2.2.99"
+1 -1
View File
@@ -12,7 +12,7 @@ dependencies = [
"rich>=13.7",
"markdown>=3.5",
"pyparsing>=3.0.0",
"praisonaiagents>=0.0.167",
"praisonaiagents>=0.0.169",
"python-dotenv>=0.19.0",
"instructor>=1.3.3",
"PyYAML>=6.0",
+12 -3
View File
@@ -62,11 +62,20 @@ def main():
print(f"\n🚀 Releasing PraisonAI {tag}\n")
# 1. uv lock
print("📦 Running uv lock...")
# 1. Copy root README.md to package dir for PyPI
print("📄 Copying README.md...")
root_readme = root / "README.md"
pkg_readme = praisonai_dir / "README.md"
if root_readme.exists():
import shutil
shutil.copy(root_readme, pkg_readme)
print(f" ✅ Copied {root_readme} -> {pkg_readme}")
# 2. uv lock
print("\n📦 Running uv lock...")
run(["uv", "lock"], cwd=praisonai_dir)
# 2. uv build
# 3. uv build
print("\n🔨 Running uv build...")
run(["uv", "build"], cwd=praisonai_dir)
@@ -0,0 +1,275 @@
"""
Tests for QueryRewriterAgent CLI integration
Run with: python -m pytest tests/unit/test_query_rewriter_cli.py -v
"""
import pytest
from unittest.mock import patch, MagicMock
import argparse
class TestQueryRewriterCLIArgs:
"""Test CLI argument parsing for query rewriter."""
def test_query_rewrite_arg_exists(self):
"""Test --query-rewrite argument is available."""
from praisonai.cli import PraisonAI
# Create instance and check args can be parsed
with patch('sys.argv', ['praisonai', '--help']):
try:
PraisonAI()
except SystemExit:
pass # --help causes exit, that's fine
def test_rewrite_tools_arg_exists(self):
"""Test --rewrite-tools argument is available."""
from praisonai.cli import PraisonAI
with patch('sys.argv', ['praisonai', '--help']):
try:
PraisonAI()
except SystemExit:
pass
class TestRewriteQueryMethod:
"""Test the _rewrite_query helper method."""
def test_rewrite_query_returns_rewritten(self):
"""Test _rewrite_query returns rewritten query."""
from praisonai.cli import PraisonAI
# Setup mock
mock_result = MagicMock()
mock_result.primary_query = "What are the current trends in artificial intelligence?"
mock_agent = MagicMock()
mock_agent.rewrite.return_value = mock_result
# Patch at the import location inside the method
with patch.dict('sys.modules', {'praisonaiagents': MagicMock()}):
import sys
sys.modules['praisonaiagents'].QueryRewriterAgent = MagicMock(return_value=mock_agent)
sys.modules['praisonaiagents'].RewriteStrategy = MagicMock()
praison = PraisonAI()
result = praison._rewrite_query("AI trends", None, False)
assert result == "What are the current trends in artificial intelligence?"
def test_rewrite_query_fallback_on_import_error(self):
"""Test _rewrite_query returns original on ImportError."""
from praisonai.cli import PraisonAI
# Mock the import to raise ImportError
praison = PraisonAI()
with patch.object(praison, '_rewrite_query') as mock_method:
# Simulate the actual behavior - returns original on error
mock_method.return_value = "AI trends"
result = mock_method("AI trends", None, False)
assert result == "AI trends"
def test_rewrite_query_fallback_on_exception(self):
"""Test _rewrite_query returns original on exception."""
from praisonai.cli import PraisonAI
# Patch to raise exception
with patch.dict('sys.modules', {'praisonaiagents': MagicMock()}):
import sys
sys.modules['praisonaiagents'].QueryRewriterAgent = MagicMock(side_effect=Exception("Test error"))
praison = PraisonAI()
result = praison._rewrite_query("AI trends", None, False)
# Should return original query on error
assert result == "AI trends"
class TestRewriteQueryIfEnabled:
"""Test the _rewrite_query_if_enabled wrapper method."""
def test_returns_original_when_disabled(self):
"""Test returns original query when --query-rewrite not set."""
from praisonai.cli import PraisonAI
praison = PraisonAI()
praison.args = argparse.Namespace(query_rewrite=False)
result = praison._rewrite_query_if_enabled("test query")
assert result == "test query"
def test_returns_original_when_no_args(self):
"""Test returns original query when args not set."""
from praisonai.cli import PraisonAI
praison = PraisonAI()
# Don't set args at all
if hasattr(praison, 'args'):
delattr(praison, 'args')
result = praison._rewrite_query_if_enabled("test query")
assert result == "test query"
def test_calls_rewrite_when_enabled(self):
"""Test calls _rewrite_query when --query-rewrite is set."""
from praisonai.cli import PraisonAI
praison = PraisonAI()
praison.args = argparse.Namespace(
query_rewrite=True,
rewrite_tools=None,
verbose=False
)
with patch.object(praison, '_rewrite_query', return_value="rewritten query") as mock_rewrite:
result = praison._rewrite_query_if_enabled("original query")
mock_rewrite.assert_called_once_with("original query", None, False)
assert result == "rewritten query"
def test_passes_tools_and_verbose(self):
"""Test passes rewrite_tools and verbose to _rewrite_query."""
from praisonai.cli import PraisonAI
praison = PraisonAI()
praison.args = argparse.Namespace(
query_rewrite=True,
rewrite_tools="internet_search",
verbose=True
)
with patch.object(praison, '_rewrite_query', return_value="rewritten") as mock_rewrite:
praison._rewrite_query_if_enabled("query")
mock_rewrite.assert_called_once_with("query", "internet_search", True)
class TestHandleDirectPromptWithRewrite:
"""Test handle_direct_prompt integrates query rewriting."""
def test_applies_rewrite_before_agent(self):
"""Test query is rewritten before passing to agent."""
from praisonai.cli import PraisonAI
praison = PraisonAI()
praison.args = argparse.Namespace(
query_rewrite=True,
rewrite_tools=None,
verbose=False,
llm=None
)
with patch.object(praison, '_rewrite_query_if_enabled', return_value="rewritten prompt") as mock_rewrite:
with patch('praisonai.cli.PRAISONAI_AVAILABLE', True):
with patch('praisonai.cli.PraisonAgent') as mock_agent_class:
mock_agent = MagicMock()
mock_agent.start.return_value = "result"
mock_agent_class.return_value = mock_agent
praison.handle_direct_prompt("original prompt")
# Verify rewrite was called
mock_rewrite.assert_called_once_with("original prompt")
# Verify agent received rewritten prompt
mock_agent.start.assert_called_once_with("rewritten prompt")
class TestToolLoading:
"""Test tool loading for query rewriter."""
def test_load_builtin_tool_by_name(self):
"""Test loading built-in tool by name."""
from praisonai.cli import PraisonAI
# Setup mocks
mock_result = MagicMock()
mock_result.primary_query = "rewritten"
mock_agent = MagicMock()
mock_agent.rewrite.return_value = mock_result
mock_tool = MagicMock()
mock_tools_module = MagicMock()
mock_tools_module.TOOL_MAPPINGS = {'internet_search': 'praisonaiagents.tools'}
mock_tools_module.internet_search = mock_tool
mock_praisonaiagents = MagicMock()
mock_praisonaiagents.QueryRewriterAgent = MagicMock(return_value=mock_agent)
mock_praisonaiagents.RewriteStrategy = MagicMock()
mock_praisonaiagents.tools = mock_tools_module
with patch.dict('sys.modules', {
'praisonaiagents': mock_praisonaiagents,
'praisonaiagents.tools': mock_tools_module
}):
praison = PraisonAI()
result = praison._rewrite_query("query", "internet_search", False)
# Verify result
assert result == "rewritten"
def test_handles_unknown_tool_gracefully(self):
"""Test handles unknown tool name gracefully."""
from praisonai.cli import PraisonAI
# Setup mocks
mock_result = MagicMock()
mock_result.primary_query = "rewritten"
mock_agent = MagicMock()
mock_agent.rewrite.return_value = mock_result
mock_tools_module = MagicMock()
mock_tools_module.TOOL_MAPPINGS = {} # Empty - no tools
mock_praisonaiagents = MagicMock()
mock_praisonaiagents.QueryRewriterAgent = MagicMock(return_value=mock_agent)
mock_praisonaiagents.RewriteStrategy = MagicMock()
mock_praisonaiagents.tools = mock_tools_module
with patch.dict('sys.modules', {
'praisonaiagents': mock_praisonaiagents,
'praisonaiagents.tools': mock_tools_module
}):
praison = PraisonAI()
# Should not raise, just warn
result = praison._rewrite_query("query", "unknown_tool", False)
assert result == "rewritten"
class TestQueryRewriterIntegration:
"""Integration tests for QueryRewriterAgent with CLI."""
def test_full_rewrite_flow_mocked(self):
"""Test full rewrite flow with mocked components."""
from praisonai.cli import PraisonAI
# Setup mocks
mock_result = MagicMock()
mock_result.primary_query = "What are the latest developments in AI technology?"
mock_agent = MagicMock()
mock_agent.rewrite.return_value = mock_result
mock_praisonaiagents = MagicMock()
mock_praisonaiagents.QueryRewriterAgent = MagicMock(return_value=mock_agent)
mock_praisonaiagents.RewriteStrategy = MagicMock()
with patch.dict('sys.modules', {'praisonaiagents': mock_praisonaiagents}):
praison = PraisonAI()
praison.args = argparse.Namespace(
query_rewrite=True,
rewrite_tools=None,
verbose=False,
llm=None
)
result = praison._rewrite_query_if_enabled("AI trends")
assert "AI" in result or "developments" in result
mock_agent.rewrite.assert_called_once()
if __name__ == "__main__":
pytest.main([__file__, "-v"])
+4 -4
View File
@@ -4197,7 +4197,7 @@ requires-dist = [
{ name = "praisonai-tools", marker = "extra == 'autogen'", specifier = ">=0.0.22" },
{ name = "praisonai-tools", marker = "extra == 'autogen-v4'", specifier = ">=0.0.22" },
{ name = "praisonai-tools", marker = "extra == 'crewai'", specifier = ">=0.0.22" },
{ name = "praisonaiagents", specifier = ">=0.0.167" },
{ name = "praisonaiagents", specifier = ">=0.0.169" },
{ name = "pyautogen", marker = "extra == 'autogen'", specifier = "==0.2.29" },
{ name = "pydantic", marker = "extra == 'chat'", specifier = "<=2.10.1" },
{ name = "pydantic", marker = "extra == 'code'", specifier = "<=2.10.1" },
@@ -4252,7 +4252,7 @@ wheels = [
[[package]]
name = "praisonaiagents"
version = "0.0.167"
version = "0.0.169"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "aiohttp" },
@@ -4261,9 +4261,9 @@ dependencies = [
{ name = "pydantic" },
{ name = "rich" },
]
sdist = { url = "https://files.pythonhosted.org/packages/8e/26/2b77721ee85f5943ea5f1515453a32bfb7f6442128f5eb2ce964d2016d46/praisonaiagents-0.0.167.tar.gz", hash = "sha256:fd052746bcb40f14886f58786ec06cc6efa7f564e1ff0dea7802bface72b560b", size = 304811, upload-time = "2025-12-15T07:56:09.8Z" }
sdist = { url = "https://files.pythonhosted.org/packages/4e/0c/94820a01306d53ce99f2b8d0f424566e74fe883aaca0e1a5c81f8364dddf/praisonaiagents-0.0.169.tar.gz", hash = "sha256:0774dd1fd8a5066e4ab2d612153d07bf1ac275105c10396730a9b542d4060ff3", size = 319791, upload-time = "2025-12-15T10:09:24.429Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/1b/3c/5297203ed5385b2fa2c9e7b3576a7ff685a72fd4c431bad6d2768ea5f03c/praisonaiagents-0.0.167-py3-none-any.whl", hash = "sha256:fee9645ed990b060a20aa95caf64d4524b0e31c852c46b2eb8344351886210aa", size = 322038, upload-time = "2025-12-15T07:56:08.184Z" },
{ url = "https://files.pythonhosted.org/packages/d6/f4/9342ee5340a2fdf1e3b9b8c1d10d9f2962c4fc577925c41b81db7b9beea1/praisonaiagents-0.0.169-py3-none-any.whl", hash = "sha256:ea491a2097224544f7ebb19b19daaabfc814c22bf92fb2adeb8e7d3f33d1233c", size = 333017, upload-time = "2025-12-15T10:09:23.016Z" },
]
[[package]]