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Author SHA1 Message Date
João Moura
f38e7b66ad Update README.md 2024-12-30 17:08:10 -03:00
João Moura
e5366c665a Merge branch 'main' into devin/1735587338-add-tiktoken-dependency 2024-12-30 17:02:53 -03:00
Devin AI
c89a23ed41 docs: add troubleshooting section and make tiktoken optional
Co-Authored-By: Joe Moura <joao@crewai.com>
2024-12-30 19:49:41 +00:00
Devin AI
e8ebc1c551 fix: adjust tiktoken version to ~=0.7.0 for dependency compatibility
- Update tiktoken dependency to ~=0.7.0 to resolve conflict with embedchain
- Maintain compatibility with crewai-tools dependency chain
- Addresses CI build failures

Co-Authored-By: Joe Moura <joao@crewai.com>
2024-12-30 19:39:07 +00:00
Devin AI
bb5bed52e9 feat: add tiktoken as explicit dependency and document Rust requirement
- Add tiktoken>=0.8.0 as explicit dependency to ensure pre-built wheels are used
- Document Rust compiler requirement as fallback in README.md
- Addresses issue #1824 tiktoken build failure

Co-Authored-By: Joe Moura <joao@crewai.com>
2024-12-30 19:36:18 +00:00
10 changed files with 94 additions and 1081 deletions

View File

@@ -30,47 +30,7 @@ from crewai.telemetry import Telemetry
T = TypeVar("T", bound=Union[BaseModel, Dict[str, Any]])
def start(condition: Optional[Union[str, dict, Callable]] = None) -> Callable:
"""
Marks a method as a flow's starting point.
This decorator designates a method as an entry point for the flow execution.
It can optionally specify conditions that trigger the start based on other
method executions.
Parameters
----------
condition : Optional[Union[str, dict, Callable]], optional
Defines when the start method should execute. Can be:
- str: Name of a method that triggers this start
- dict: Contains "type" ("AND"/"OR") and "methods" (list of triggers)
- Callable: A method reference that triggers this start
Default is None, meaning unconditional start.
Returns
-------
Callable
A decorator function that marks the method as a flow start point.
Raises
------
ValueError
If the condition format is invalid.
Examples
--------
>>> @start() # Unconditional start
>>> def begin_flow(self):
... pass
>>> @start("method_name") # Start after specific method
>>> def conditional_start(self):
... pass
>>> @start(and_("method1", "method2")) # Start after multiple methods
>>> def complex_start(self):
... pass
"""
def start(condition=None):
def decorator(func):
func.__is_start_method__ = True
if condition is not None:
@@ -95,42 +55,8 @@ def start(condition: Optional[Union[str, dict, Callable]] = None) -> Callable:
return decorator
def listen(condition: Union[str, dict, Callable]) -> Callable:
"""
Creates a listener that executes when specified conditions are met.
This decorator sets up a method to execute in response to other method
executions in the flow. It supports both simple and complex triggering
conditions.
Parameters
----------
condition : Union[str, dict, Callable]
Specifies when the listener should execute. Can be:
- str: Name of a method that triggers this listener
- dict: Contains "type" ("AND"/"OR") and "methods" (list of triggers)
- Callable: A method reference that triggers this listener
Returns
-------
Callable
A decorator function that sets up the method as a listener.
Raises
------
ValueError
If the condition format is invalid.
Examples
--------
>>> @listen("process_data") # Listen to single method
>>> def handle_processed_data(self):
... pass
>>> @listen(or_("success", "failure")) # Listen to multiple methods
>>> def handle_completion(self):
... pass
"""
def listen(condition):
def decorator(func):
if isinstance(condition, str):
func.__trigger_methods__ = [condition]
@@ -154,49 +80,10 @@ def listen(condition: Union[str, dict, Callable]) -> Callable:
return decorator
def router(condition: Union[str, dict, Callable]) -> Callable:
"""
Creates a routing method that directs flow execution based on conditions.
This decorator marks a method as a router, which can dynamically determine
the next steps in the flow based on its return value. Routers are triggered
by specified conditions and can return constants that determine which path
the flow should take.
Parameters
----------
condition : Union[str, dict, Callable]
Specifies when the router should execute. Can be:
- str: Name of a method that triggers this router
- dict: Contains "type" ("AND"/"OR") and "methods" (list of triggers)
- Callable: A method reference that triggers this router
Returns
-------
Callable
A decorator function that sets up the method as a router.
Raises
------
ValueError
If the condition format is invalid.
Examples
--------
>>> @router("check_status")
>>> def route_based_on_status(self):
... if self.state.status == "success":
... return SUCCESS
... return FAILURE
>>> @router(and_("validate", "process"))
>>> def complex_routing(self):
... if all([self.state.valid, self.state.processed]):
... return CONTINUE
... return STOP
"""
def router(condition):
def decorator(func):
func.__is_router__ = True
# Handle conditions like listen/start
if isinstance(condition, str):
func.__trigger_methods__ = [condition]
func.__condition_type__ = "OR"
@@ -218,39 +105,8 @@ def router(condition: Union[str, dict, Callable]) -> Callable:
return decorator
def or_(*conditions: Union[str, dict, Callable]) -> dict:
"""
Combines multiple conditions with OR logic for flow control.
Creates a condition that is satisfied when any of the specified conditions
are met. This is used with @start, @listen, or @router decorators to create
complex triggering conditions.
Parameters
----------
*conditions : Union[str, dict, Callable]
Variable number of conditions that can be:
- str: Method names
- dict: Existing condition dictionaries
- Callable: Method references
Returns
-------
dict
A condition dictionary with format:
{"type": "OR", "methods": list_of_method_names}
Raises
------
ValueError
If any condition is invalid.
Examples
--------
>>> @listen(or_("success", "timeout"))
>>> def handle_completion(self):
... pass
"""
def or_(*conditions):
methods = []
for condition in conditions:
if isinstance(condition, dict) and "methods" in condition:
@@ -264,39 +120,7 @@ def or_(*conditions: Union[str, dict, Callable]) -> dict:
return {"type": "OR", "methods": methods}
def and_(*conditions: Union[str, dict, Callable]) -> dict:
"""
Combines multiple conditions with AND logic for flow control.
Creates a condition that is satisfied only when all specified conditions
are met. This is used with @start, @listen, or @router decorators to create
complex triggering conditions.
Parameters
----------
*conditions : Union[str, dict, Callable]
Variable number of conditions that can be:
- str: Method names
- dict: Existing condition dictionaries
- Callable: Method references
Returns
-------
dict
A condition dictionary with format:
{"type": "AND", "methods": list_of_method_names}
Raises
------
ValueError
If any condition is invalid.
Examples
--------
>>> @listen(and_("validated", "processed"))
>>> def handle_complete_data(self):
... pass
"""
def and_(*conditions):
methods = []
for condition in conditions:
if isinstance(condition, dict) and "methods" in condition:
@@ -462,23 +286,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
return final_output
async def _execute_start_method(self, start_method_name: str) -> None:
"""
Executes a flow's start method and its triggered listeners.
This internal method handles the execution of methods marked with @start
decorator and manages the subsequent chain of listener executions.
Parameters
----------
start_method_name : str
The name of the start method to execute.
Notes
-----
- Executes the start method and captures its result
- Triggers execution of any listeners waiting on this start method
- Part of the flow's initialization sequence
"""
result = await self._execute_method(
start_method_name, self._methods[start_method_name]
)
@@ -499,28 +306,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
return result
async def _execute_listeners(self, trigger_method: str, result: Any) -> None:
"""
Executes all listeners and routers triggered by a method completion.
This internal method manages the execution flow by:
1. First executing all triggered routers sequentially
2. Then executing all triggered listeners in parallel
Parameters
----------
trigger_method : str
The name of the method that triggered these listeners.
result : Any
The result from the triggering method, passed to listeners
that accept parameters.
Notes
-----
- Routers are executed sequentially to maintain flow control
- Each router's result becomes the new trigger_method
- Normal listeners are executed in parallel for efficiency
- Listeners can receive the trigger method's result as a parameter
"""
# First, handle routers repeatedly until no router triggers anymore
while True:
routers_triggered = self._find_triggered_methods(
@@ -550,33 +335,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
def _find_triggered_methods(
self, trigger_method: str, router_only: bool
) -> List[str]:
"""
Finds all methods that should be triggered based on conditions.
This internal method evaluates both OR and AND conditions to determine
which methods should be executed next in the flow.
Parameters
----------
trigger_method : str
The name of the method that just completed execution.
router_only : bool
If True, only consider router methods.
If False, only consider non-router methods.
Returns
-------
List[str]
Names of methods that should be triggered.
Notes
-----
- Handles both OR and AND conditions:
* OR: Triggers if any condition is met
* AND: Triggers only when all conditions are met
- Maintains state for AND conditions using _pending_and_listeners
- Separates router and normal listener evaluation
"""
triggered = []
for listener_name, (condition_type, methods) in self._listeners.items():
is_router = listener_name in self._routers
@@ -605,33 +363,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
return triggered
async def _execute_single_listener(self, listener_name: str, result: Any) -> None:
"""
Executes a single listener method with proper event handling.
This internal method manages the execution of an individual listener,
including parameter inspection, event emission, and error handling.
Parameters
----------
listener_name : str
The name of the listener method to execute.
result : Any
The result from the triggering method, which may be passed
to the listener if it accepts parameters.
Notes
-----
- Inspects method signature to determine if it accepts the trigger result
- Emits events for method execution start and finish
- Handles errors gracefully with detailed logging
- Recursively triggers listeners of this listener
- Supports both parameterized and parameter-less listeners
Error Handling
-------------
Catches and logs any exceptions during execution, preventing
individual listener failures from breaking the entire flow.
"""
try:
method = self._methods[listener_name]

View File

@@ -1,14 +1,12 @@
# flow_visualizer.py
import os
from pathlib import Path
from pyvis.network import Network
from crewai.flow.config import COLORS, NODE_STYLES
from crewai.flow.html_template_handler import HTMLTemplateHandler
from crewai.flow.legend_generator import generate_legend_items_html, get_legend_items
from crewai.flow.path_utils import safe_path_join, validate_path_exists
from crewai.flow.utils import calculate_node_levels
from crewai.flow.visualization_utils import (
add_edges,
@@ -18,209 +16,89 @@ from crewai.flow.visualization_utils import (
class FlowPlot:
"""Handles the creation and rendering of flow visualization diagrams."""
def __init__(self, flow):
"""
Initialize FlowPlot with a flow object.
Parameters
----------
flow : Flow
A Flow instance to visualize.
Raises
------
ValueError
If flow object is invalid or missing required attributes.
"""
if not hasattr(flow, '_methods'):
raise ValueError("Invalid flow object: missing '_methods' attribute")
if not hasattr(flow, '_listeners'):
raise ValueError("Invalid flow object: missing '_listeners' attribute")
if not hasattr(flow, '_start_methods'):
raise ValueError("Invalid flow object: missing '_start_methods' attribute")
self.flow = flow
self.colors = COLORS
self.node_styles = NODE_STYLES
def plot(self, filename):
"""
Generate and save an HTML visualization of the flow.
net = Network(
directed=True,
height="750px",
width="100%",
bgcolor=self.colors["bg"],
layout=None,
)
Parameters
----------
filename : str
Name of the output file (without extension).
Raises
------
ValueError
If filename is invalid or network generation fails.
IOError
If file operations fail or visualization cannot be generated.
RuntimeError
If network visualization generation fails.
"""
if not filename or not isinstance(filename, str):
raise ValueError("Filename must be a non-empty string")
try:
# Initialize network
net = Network(
directed=True,
height="750px",
width="100%",
bgcolor=self.colors["bg"],
layout=None,
)
# Set options to disable physics
net.set_options(
"""
var options = {
"nodes": {
"font": {
"multi": "html"
}
},
"physics": {
"enabled": false
}
}
# Set options to disable physics
net.set_options(
"""
)
var options = {
"nodes": {
"font": {
"multi": "html"
}
},
"physics": {
"enabled": false
}
}
"""
)
# Calculate levels for nodes
try:
node_levels = calculate_node_levels(self.flow)
except Exception as e:
raise ValueError(f"Failed to calculate node levels: {str(e)}")
# Calculate levels for nodes
node_levels = calculate_node_levels(self.flow)
# Compute positions
try:
node_positions = compute_positions(self.flow, node_levels)
except Exception as e:
raise ValueError(f"Failed to compute node positions: {str(e)}")
# Compute positions
node_positions = compute_positions(self.flow, node_levels)
# Add nodes to the network
try:
add_nodes_to_network(net, self.flow, node_positions, self.node_styles)
except Exception as e:
raise RuntimeError(f"Failed to add nodes to network: {str(e)}")
# Add nodes to the network
add_nodes_to_network(net, self.flow, node_positions, self.node_styles)
# Add edges to the network
try:
add_edges(net, self.flow, node_positions, self.colors)
except Exception as e:
raise RuntimeError(f"Failed to add edges to network: {str(e)}")
# Add edges to the network
add_edges(net, self.flow, node_positions, self.colors)
# Generate HTML
try:
network_html = net.generate_html()
final_html_content = self._generate_final_html(network_html)
except Exception as e:
raise RuntimeError(f"Failed to generate network visualization: {str(e)}")
network_html = net.generate_html()
final_html_content = self._generate_final_html(network_html)
# Save the final HTML content to the file
try:
with open(f"{filename}.html", "w", encoding="utf-8") as f:
f.write(final_html_content)
print(f"Plot saved as {filename}.html")
except IOError as e:
raise IOError(f"Failed to save flow visualization to {filename}.html: {str(e)}")
# Save the final HTML content to the file
with open(f"{filename}.html", "w", encoding="utf-8") as f:
f.write(final_html_content)
print(f"Plot saved as {filename}.html")
except (ValueError, RuntimeError, IOError) as e:
raise e
except Exception as e:
raise RuntimeError(f"Unexpected error during flow visualization: {str(e)}")
finally:
self._cleanup_pyvis_lib()
self._cleanup_pyvis_lib()
def _generate_final_html(self, network_html):
"""
Generate the final HTML content with network visualization and legend.
# Extract just the body content from the generated HTML
current_dir = os.path.dirname(__file__)
template_path = os.path.join(
current_dir, "assets", "crewai_flow_visual_template.html"
)
logo_path = os.path.join(current_dir, "assets", "crewai_logo.svg")
Parameters
----------
network_html : str
HTML content generated by pyvis Network.
html_handler = HTMLTemplateHandler(template_path, logo_path)
network_body = html_handler.extract_body_content(network_html)
Returns
-------
str
Complete HTML content with styling and legend.
Raises
------
IOError
If template or logo files cannot be accessed.
ValueError
If network_html is invalid.
"""
if not network_html:
raise ValueError("Invalid network HTML content")
try:
# Extract just the body content from the generated HTML
current_dir = os.path.dirname(__file__)
template_path = safe_path_join("assets", "crewai_flow_visual_template.html", root=current_dir)
logo_path = safe_path_join("assets", "crewai_logo.svg", root=current_dir)
if not os.path.exists(template_path):
raise IOError(f"Template file not found: {template_path}")
if not os.path.exists(logo_path):
raise IOError(f"Logo file not found: {logo_path}")
html_handler = HTMLTemplateHandler(template_path, logo_path)
network_body = html_handler.extract_body_content(network_html)
# Generate the legend items HTML
legend_items = get_legend_items(self.colors)
legend_items_html = generate_legend_items_html(legend_items)
final_html_content = html_handler.generate_final_html(
network_body, legend_items_html
)
return final_html_content
except Exception as e:
raise IOError(f"Failed to generate visualization HTML: {str(e)}")
# Generate the legend items HTML
legend_items = get_legend_items(self.colors)
legend_items_html = generate_legend_items_html(legend_items)
final_html_content = html_handler.generate_final_html(
network_body, legend_items_html
)
return final_html_content
def _cleanup_pyvis_lib(self):
"""
Clean up the generated lib folder from pyvis.
This method safely removes the temporary lib directory created by pyvis
during network visualization generation.
"""
# Clean up the generated lib folder
lib_folder = os.path.join(os.getcwd(), "lib")
try:
lib_folder = safe_path_join("lib", root=os.getcwd())
if os.path.exists(lib_folder) and os.path.isdir(lib_folder):
import shutil
shutil.rmtree(lib_folder)
except ValueError as e:
print(f"Error validating lib folder path: {e}")
except Exception as e:
print(f"Error cleaning up lib folder: {e}")
print(f"Error cleaning up {lib_folder}: {e}")
def plot_flow(flow, filename="flow_plot"):
"""
Convenience function to create and save a flow visualization.
Parameters
----------
flow : Flow
Flow instance to visualize.
filename : str, optional
Output filename without extension, by default "flow_plot".
Raises
------
ValueError
If flow object or filename is invalid.
IOError
If file operations fail.
"""
visualizer = FlowPlot(flow)
visualizer.plot(filename)

View File

@@ -1,53 +1,26 @@
import base64
import re
from pathlib import Path
from crewai.flow.path_utils import safe_path_join, validate_path_exists
class HTMLTemplateHandler:
"""Handles HTML template processing and generation for flow visualization diagrams."""
def __init__(self, template_path, logo_path):
"""
Initialize HTMLTemplateHandler with validated template and logo paths.
Parameters
----------
template_path : str
Path to the HTML template file.
logo_path : str
Path to the logo image file.
Raises
------
ValueError
If template or logo paths are invalid or files don't exist.
"""
try:
self.template_path = validate_path_exists(template_path, "file")
self.logo_path = validate_path_exists(logo_path, "file")
except ValueError as e:
raise ValueError(f"Invalid template or logo path: {e}")
self.template_path = template_path
self.logo_path = logo_path
def read_template(self):
"""Read and return the HTML template file contents."""
with open(self.template_path, "r", encoding="utf-8") as f:
return f.read()
def encode_logo(self):
"""Convert the logo SVG file to base64 encoded string."""
with open(self.logo_path, "rb") as logo_file:
logo_svg_data = logo_file.read()
return base64.b64encode(logo_svg_data).decode("utf-8")
def extract_body_content(self, html):
"""Extract and return content between body tags from HTML string."""
match = re.search("<body.*?>(.*?)</body>", html, re.DOTALL)
return match.group(1) if match else ""
def generate_legend_items_html(self, legend_items):
"""Generate HTML markup for the legend items."""
legend_items_html = ""
for item in legend_items:
if "border" in item:
@@ -75,7 +48,6 @@ class HTMLTemplateHandler:
return legend_items_html
def generate_final_html(self, network_body, legend_items_html, title="Flow Plot"):
"""Combine all components into final HTML document with network visualization."""
html_template = self.read_template()
logo_svg_base64 = self.encode_logo()

View File

@@ -1,4 +1,3 @@
def get_legend_items(colors):
return [
{"label": "Start Method", "color": colors["start"]},

View File

@@ -1,135 +0,0 @@
"""
Path utilities for secure file operations in CrewAI flow module.
This module provides utilities for secure path handling to prevent directory
traversal attacks and ensure paths remain within allowed boundaries.
"""
import os
from pathlib import Path
from typing import List, Union
def safe_path_join(*parts: str, root: Union[str, Path, None] = None) -> str:
"""
Safely join path components and ensure the result is within allowed boundaries.
Parameters
----------
*parts : str
Variable number of path components to join.
root : Union[str, Path, None], optional
Root directory to use as base. If None, uses current working directory.
Returns
-------
str
String representation of the resolved path.
Raises
------
ValueError
If the resulting path would be outside the root directory
or if any path component is invalid.
"""
if not parts:
raise ValueError("No path components provided")
try:
# Convert all parts to strings and clean them
clean_parts = [str(part).strip() for part in parts if part]
if not clean_parts:
raise ValueError("No valid path components provided")
# Establish root directory
root_path = Path(root).resolve() if root else Path.cwd()
# Join and resolve the full path
full_path = Path(root_path, *clean_parts).resolve()
# Check if the resolved path is within root
if not str(full_path).startswith(str(root_path)):
raise ValueError(
f"Invalid path: Potential directory traversal. Path must be within {root_path}"
)
return str(full_path)
except Exception as e:
if isinstance(e, ValueError):
raise
raise ValueError(f"Invalid path components: {str(e)}")
def validate_path_exists(path: Union[str, Path], file_type: str = "file") -> str:
"""
Validate that a path exists and is of the expected type.
Parameters
----------
path : Union[str, Path]
Path to validate.
file_type : str, optional
Expected type ('file' or 'directory'), by default 'file'.
Returns
-------
str
Validated path as string.
Raises
------
ValueError
If path doesn't exist or is not of expected type.
"""
try:
path_obj = Path(path).resolve()
if not path_obj.exists():
raise ValueError(f"Path does not exist: {path}")
if file_type == "file" and not path_obj.is_file():
raise ValueError(f"Path is not a file: {path}")
elif file_type == "directory" and not path_obj.is_dir():
raise ValueError(f"Path is not a directory: {path}")
return str(path_obj)
except Exception as e:
if isinstance(e, ValueError):
raise
raise ValueError(f"Invalid path: {str(e)}")
def list_files(directory: Union[str, Path], pattern: str = "*") -> List[str]:
"""
Safely list files in a directory matching a pattern.
Parameters
----------
directory : Union[str, Path]
Directory to search in.
pattern : str, optional
Glob pattern to match files against, by default "*".
Returns
-------
List[str]
List of matching file paths.
Raises
------
ValueError
If directory is invalid or inaccessible.
"""
try:
dir_path = Path(directory).resolve()
if not dir_path.is_dir():
raise ValueError(f"Not a directory: {directory}")
return [str(p) for p in dir_path.glob(pattern) if p.is_file()]
except Exception as e:
if isinstance(e, ValueError):
raise
raise ValueError(f"Error listing files: {str(e)}")

View File

@@ -1,25 +1,9 @@
"""
Utility functions for flow visualization and dependency analysis.
This module provides core functionality for analyzing and manipulating flow structures,
including node level calculation, ancestor tracking, and return value analysis.
Functions in this module are primarily used by the visualization system to create
accurate and informative flow diagrams.
Example
-------
>>> flow = Flow()
>>> node_levels = calculate_node_levels(flow)
>>> ancestors = build_ancestor_dict(flow)
"""
import ast
import inspect
import textwrap
from typing import Any, Dict, List, Optional, Set, Union
def get_possible_return_constants(function: Any) -> Optional[List[str]]:
def get_possible_return_constants(function):
try:
source = inspect.getsource(function)
except OSError:
@@ -93,34 +77,11 @@ def get_possible_return_constants(function: Any) -> Optional[List[str]]:
return list(return_values) if return_values else None
def calculate_node_levels(flow: Any) -> Dict[str, int]:
"""
Calculate the hierarchical level of each node in the flow.
Performs a breadth-first traversal of the flow graph to assign levels
to nodes, starting with start methods at level 0.
Parameters
----------
flow : Any
The flow instance containing methods, listeners, and router configurations.
Returns
-------
Dict[str, int]
Dictionary mapping method names to their hierarchical levels.
Notes
-----
- Start methods are assigned level 0
- Each subsequent connected node is assigned level = parent_level + 1
- Handles both OR and AND conditions for listeners
- Processes router paths separately
"""
levels: Dict[str, int] = {}
queue: List[str] = []
visited: Set[str] = set()
pending_and_listeners: Dict[str, Set[str]] = {}
def calculate_node_levels(flow):
levels = {}
queue = []
visited = set()
pending_and_listeners = {}
# Make all start methods at level 0
for method_name, method in flow._methods.items():
@@ -179,20 +140,7 @@ def calculate_node_levels(flow: Any) -> Dict[str, int]:
return levels
def count_outgoing_edges(flow: Any) -> Dict[str, int]:
"""
Count the number of outgoing edges for each method in the flow.
Parameters
----------
flow : Any
The flow instance to analyze.
Returns
-------
Dict[str, int]
Dictionary mapping method names to their outgoing edge count.
"""
def count_outgoing_edges(flow):
counts = {}
for method_name in flow._methods:
counts[method_name] = 0
@@ -204,53 +152,16 @@ def count_outgoing_edges(flow: Any) -> Dict[str, int]:
return counts
def build_ancestor_dict(flow: Any) -> Dict[str, Set[str]]:
"""
Build a dictionary mapping each node to its ancestor nodes.
Parameters
----------
flow : Any
The flow instance to analyze.
Returns
-------
Dict[str, Set[str]]
Dictionary mapping each node to a set of its ancestor nodes.
"""
ancestors: Dict[str, Set[str]] = {node: set() for node in flow._methods}
visited: Set[str] = set()
def build_ancestor_dict(flow):
ancestors = {node: set() for node in flow._methods}
visited = set()
for node in flow._methods:
if node not in visited:
dfs_ancestors(node, ancestors, visited, flow)
return ancestors
def dfs_ancestors(
node: str,
ancestors: Dict[str, Set[str]],
visited: Set[str],
flow: Any
) -> None:
"""
Perform depth-first search to build ancestor relationships.
Parameters
----------
node : str
Current node being processed.
ancestors : Dict[str, Set[str]]
Dictionary tracking ancestor relationships.
visited : Set[str]
Set of already visited nodes.
flow : Any
The flow instance being analyzed.
Notes
-----
This function modifies the ancestors dictionary in-place to build
the complete ancestor graph.
"""
def dfs_ancestors(node, ancestors, visited, flow):
if node in visited:
return
visited.add(node)
@@ -274,48 +185,12 @@ def dfs_ancestors(
dfs_ancestors(listener_name, ancestors, visited, flow)
def is_ancestor(node: str, ancestor_candidate: str, ancestors: Dict[str, Set[str]]) -> bool:
"""
Check if one node is an ancestor of another.
Parameters
----------
node : str
The node to check ancestors for.
ancestor_candidate : str
The potential ancestor node.
ancestors : Dict[str, Set[str]]
Dictionary containing ancestor relationships.
Returns
-------
bool
True if ancestor_candidate is an ancestor of node, False otherwise.
"""
def is_ancestor(node, ancestor_candidate, ancestors):
return ancestor_candidate in ancestors.get(node, set())
def build_parent_children_dict(flow: Any) -> Dict[str, List[str]]:
"""
Build a dictionary mapping parent nodes to their children.
Parameters
----------
flow : Any
The flow instance to analyze.
Returns
-------
Dict[str, List[str]]
Dictionary mapping parent method names to lists of their child method names.
Notes
-----
- Maps listeners to their trigger methods
- Maps router methods to their paths and listeners
- Children lists are sorted for consistent ordering
"""
parent_children: Dict[str, List[str]] = {}
def build_parent_children_dict(flow):
parent_children = {}
# Map listeners to their trigger methods
for listener_name, (_, trigger_methods) in flow._listeners.items():
@@ -339,24 +214,7 @@ def build_parent_children_dict(flow: Any) -> Dict[str, List[str]]:
return parent_children
def get_child_index(parent: str, child: str, parent_children: Dict[str, List[str]]) -> int:
"""
Get the index of a child node in its parent's sorted children list.
Parameters
----------
parent : str
The parent node name.
child : str
The child node name to find the index for.
parent_children : Dict[str, List[str]]
Dictionary mapping parents to their children lists.
Returns
-------
int
Zero-based index of the child in its parent's sorted children list.
"""
def get_child_index(parent, child, parent_children):
children = parent_children.get(parent, [])
children.sort()
return children.index(child)

View File

@@ -1,23 +1,5 @@
"""
Utilities for creating visual representations of flow structures.
This module provides functions for generating network visualizations of flows,
including node placement, edge creation, and visual styling. It handles the
conversion of flow structures into visual network graphs with appropriate
styling and layout.
Example
-------
>>> flow = Flow()
>>> net = Network(directed=True)
>>> node_positions = compute_positions(flow, node_levels)
>>> add_nodes_to_network(net, flow, node_positions, node_styles)
>>> add_edges(net, flow, node_positions, colors)
"""
import ast
import inspect
from typing import Any, Dict, List, Optional, Tuple, Union
from .utils import (
build_ancestor_dict,
@@ -27,25 +9,8 @@ from .utils import (
)
def method_calls_crew(method: Any) -> bool:
"""
Check if the method contains a call to `.crew()`.
Parameters
----------
method : Any
The method to analyze for crew() calls.
Returns
-------
bool
True if the method calls .crew(), False otherwise.
Notes
-----
Uses AST analysis to detect method calls, specifically looking for
attribute access of 'crew'.
"""
def method_calls_crew(method):
"""Check if the method calls `.crew()`."""
try:
source = inspect.getsource(method)
source = inspect.cleandoc(source)
@@ -55,7 +20,6 @@ def method_calls_crew(method: Any) -> bool:
return False
class CrewCallVisitor(ast.NodeVisitor):
"""AST visitor to detect .crew() method calls."""
def __init__(self):
self.found = False
@@ -70,34 +34,7 @@ def method_calls_crew(method: Any) -> bool:
return visitor.found
def add_nodes_to_network(
net: Any,
flow: Any,
node_positions: Dict[str, Tuple[float, float]],
node_styles: Dict[str, Dict[str, Any]]
) -> None:
"""
Add nodes to the network visualization with appropriate styling.
Parameters
----------
net : Any
The pyvis Network instance to add nodes to.
flow : Any
The flow instance containing method information.
node_positions : Dict[str, Tuple[float, float]]
Dictionary mapping node names to their (x, y) positions.
node_styles : Dict[str, Dict[str, Any]]
Dictionary containing style configurations for different node types.
Notes
-----
Node types include:
- Start methods
- Router methods
- Crew methods
- Regular methods
"""
def add_nodes_to_network(net, flow, node_positions, node_styles):
def human_friendly_label(method_name):
return method_name.replace("_", " ").title()
@@ -136,33 +73,9 @@ def add_nodes_to_network(
)
def compute_positions(
flow: Any,
node_levels: Dict[str, int],
y_spacing: float = 150,
x_spacing: float = 150
) -> Dict[str, Tuple[float, float]]:
"""
Compute the (x, y) positions for each node in the flow graph.
Parameters
----------
flow : Any
The flow instance to compute positions for.
node_levels : Dict[str, int]
Dictionary mapping node names to their hierarchical levels.
y_spacing : float, optional
Vertical spacing between levels, by default 150.
x_spacing : float, optional
Horizontal spacing between nodes, by default 150.
Returns
-------
Dict[str, Tuple[float, float]]
Dictionary mapping node names to their (x, y) coordinates.
"""
level_nodes: Dict[int, List[str]] = {}
node_positions: Dict[str, Tuple[float, float]] = {}
def compute_positions(flow, node_levels, y_spacing=150, x_spacing=150):
level_nodes = {}
node_positions = {}
for method_name, level in node_levels.items():
level_nodes.setdefault(level, []).append(method_name)
@@ -177,33 +90,7 @@ def compute_positions(
return node_positions
def add_edges(
net: Any,
flow: Any,
node_positions: Dict[str, Tuple[float, float]],
colors: Dict[str, str]
) -> None:
edge_smooth: Dict[str, Union[str, float]] = {"type": "continuous"} # Default value
"""
Add edges to the network visualization with appropriate styling.
Parameters
----------
net : Any
The pyvis Network instance to add edges to.
flow : Any
The flow instance containing edge information.
node_positions : Dict[str, Tuple[float, float]]
Dictionary mapping node names to their positions.
colors : Dict[str, str]
Dictionary mapping edge types to their colors.
Notes
-----
- Handles both normal listener edges and router edges
- Applies appropriate styling (color, dashes) based on edge type
- Adds curvature to edges when needed (cycles or multiple children)
"""
def add_edges(net, flow, node_positions, colors):
ancestors = build_ancestor_dict(flow)
parent_children = build_parent_children_dict(flow)
@@ -239,7 +126,7 @@ def add_edges(
else:
edge_smooth = {"type": "cubicBezier"}
else:
edge_smooth.update({"type": "continuous"})
edge_smooth = False
edge_style = {
"color": edge_color,
@@ -302,7 +189,7 @@ def add_edges(
else:
edge_smooth = {"type": "cubicBezier"}
else:
edge_smooth.update({"type": "continuous"})
edge_smooth = False
edge_style = {
"color": colors["router_edge"],

View File

@@ -1,5 +1,3 @@
import json
import logging
from typing import Any, List, Optional
from pydantic import BaseModel, Field
@@ -7,8 +5,6 @@ from pydantic import BaseModel, Field
from crewai.agent import Agent
from crewai.task import Task
logger = logging.getLogger(__name__)
class PlanPerTask(BaseModel):
task: str = Field(..., description="The task for which the plan is created")
@@ -72,39 +68,19 @@ class CrewPlanner:
output_pydantic=PlannerTaskPydanticOutput,
)
def _get_agent_knowledge(self, task: Task) -> List[str]:
"""
Safely retrieve knowledge source content from the task's agent.
Args:
task: The task containing an agent with potential knowledge sources
Returns:
List[str]: A list of knowledge source strings
"""
try:
if task.agent and task.agent.knowledge_sources:
return [source.content for source in task.agent.knowledge_sources]
except AttributeError:
logger.warning("Error accessing agent knowledge sources")
return []
def _create_tasks_summary(self) -> str:
"""Creates a summary of all tasks."""
tasks_summary = []
for idx, task in enumerate(self.tasks):
knowledge_list = self._get_agent_knowledge(task)
task_summary = f"""
tasks_summary.append(
f"""
Task Number {idx + 1} - {task.description}
"task_description": {task.description}
"task_expected_output": {task.expected_output}
"agent": {task.agent.role if task.agent else "None"}
"agent_goal": {task.agent.goal if task.agent else "None"}
"task_tools": {task.tools}
"agent_tools": %s%s""" % (
f"[{', '.join(str(tool) for tool in task.agent.tools)}]" if task.agent and task.agent.tools else '"agent has no tools"',
f',\n "agent_knowledge": "[\\"{knowledge_list[0]}\\"]"' if knowledge_list and str(knowledge_list) != "None" else ""
)
tasks_summary.append(task_summary)
"agent_tools": {task.agent.tools if task.agent else "None"}
"""
)
return " ".join(tasks_summary)

View File

@@ -1,84 +0,0 @@
"""
Tests for verifying the integration of knowledge sources in the planning process.
This module ensures that agent knowledge is properly included during task planning.
"""
from unittest.mock import patch
import pytest
from crewai.agent import Agent
from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
from crewai.task import Task
from crewai.utilities.planning_handler import CrewPlanner
@pytest.fixture
def mock_knowledge_source():
"""
Create a mock knowledge source with test content.
Returns:
StringKnowledgeSource:
A knowledge source containing AI-related test content
"""
content = """
Important context about AI:
1. AI systems use machine learning algorithms
2. Neural networks are a key component
3. Training data is essential for good performance
"""
return StringKnowledgeSource(content=content)
@patch('crewai.knowledge.storage.knowledge_storage.chromadb')
def test_knowledge_included_in_planning(mock_chroma):
"""Test that verifies knowledge sources are properly included in planning."""
# Mock ChromaDB collection
mock_collection = mock_chroma.return_value.get_or_create_collection.return_value
mock_collection.add.return_value = None
# Create an agent with knowledge
agent = Agent(
role="AI Researcher",
goal="Research and explain AI concepts",
backstory="Expert in artificial intelligence",
knowledge_sources=[
StringKnowledgeSource(
content="AI systems require careful training and validation."
)
]
)
# Create a task for the agent
task = Task(
description="Explain the basics of AI systems",
expected_output="A clear explanation of AI fundamentals",
agent=agent
)
# Create a crew planner
planner = CrewPlanner([task], None)
# Get the task summary
task_summary = planner._create_tasks_summary()
# Verify that knowledge is included in planning when present
assert "AI systems require careful training" in task_summary, \
"Knowledge content should be present in task summary when knowledge exists"
assert '"agent_knowledge"' in task_summary, \
"agent_knowledge field should be present in task summary when knowledge exists"
# Verify that knowledge is properly formatted
assert isinstance(task.agent.knowledge_sources, list), \
"Knowledge sources should be stored in a list"
assert len(task.agent.knowledge_sources) > 0, \
"At least one knowledge source should be present"
assert task.agent.knowledge_sources[0].content in task_summary, \
"Knowledge source content should be included in task summary"
# Verify that other expected components are still present
assert task.description in task_summary, \
"Task description should be present in task summary"
assert task.expected_output in task_summary, \
"Expected output should be present in task summary"
assert agent.role in task_summary, \
"Agent role should be present in task summary"

View File

@@ -1,14 +1,10 @@
from typing import Optional
from unittest.mock import MagicMock, patch
from unittest.mock import patch
import pytest
from pydantic import BaseModel
from crewai.agent import Agent
from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
from crewai.task import Task
from crewai.tasks.task_output import TaskOutput
from crewai.tools.base_tool import BaseTool
from crewai.utilities.planning_handler import (
CrewPlanner,
PlannerTaskPydanticOutput,
@@ -96,72 +92,7 @@ class TestCrewPlanner:
tasks_summary = crew_planner._create_tasks_summary()
assert isinstance(tasks_summary, str)
assert tasks_summary.startswith("\n Task Number 1 - Task 1")
assert '"agent_tools": "agent has no tools"' in tasks_summary
# Knowledge field should not be present when empty
assert '"agent_knowledge"' not in tasks_summary
@patch('crewai.knowledge.storage.knowledge_storage.chromadb')
def test_create_tasks_summary_with_knowledge_and_tools(self, mock_chroma):
"""Test task summary generation with both knowledge and tools present."""
# Mock ChromaDB collection
mock_collection = mock_chroma.return_value.get_or_create_collection.return_value
mock_collection.add.return_value = None
# Create mock tools with proper string descriptions and structured tool support
class MockTool(BaseTool):
name: str
description: str
def __init__(self, name: str, description: str):
tool_data = {"name": name, "description": description}
super().__init__(**tool_data)
def __str__(self):
return self.name
def __repr__(self):
return self.name
def to_structured_tool(self):
return self
def _run(self, *args, **kwargs):
pass
def _generate_description(self) -> str:
"""Override _generate_description to avoid args_schema handling."""
return self.description
tool1 = MockTool("tool1", "Tool 1 description")
tool2 = MockTool("tool2", "Tool 2 description")
# Create a task with knowledge and tools
task = Task(
description="Task with knowledge and tools",
expected_output="Expected output",
agent=Agent(
role="Test Agent",
goal="Test Goal",
backstory="Test Backstory",
tools=[tool1, tool2],
knowledge_sources=[
StringKnowledgeSource(content="Test knowledge content")
]
)
)
# Create planner with the new task
planner = CrewPlanner([task], None)
tasks_summary = planner._create_tasks_summary()
# Verify task summary content
assert isinstance(tasks_summary, str)
assert task.description in tasks_summary
assert task.expected_output in tasks_summary
assert '"agent_tools": [tool1, tool2]' in tasks_summary
assert '"agent_knowledge": "[\\"Test knowledge content\\"]"' in tasks_summary
assert task.agent.role in tasks_summary
assert task.agent.goal in tasks_summary
assert tasks_summary.endswith('"agent_tools": []\n ')
def test_handle_crew_planning_different_llm(self, crew_planner_different_llm):
with patch.object(Task, "execute_sync") as execute: