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bugfix/mem
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2
.github/workflows/security-checker.yml
vendored
2
.github/workflows/security-checker.yml
vendored
@@ -19,5 +19,5 @@ jobs:
|
||||
run: pip install bandit
|
||||
|
||||
- name: Run Bandit
|
||||
run: bandit -c pyproject.toml -r src/ -lll
|
||||
run: bandit -c pyproject.toml -r src/ -ll
|
||||
|
||||
|
||||
4
.gitignore
vendored
4
.gitignore
vendored
@@ -17,3 +17,7 @@ rc-tests/*
|
||||
temp/*
|
||||
.vscode/*
|
||||
crew_tasks_output.json
|
||||
.codesight
|
||||
.mypy_cache
|
||||
.ruff_cache
|
||||
.venv
|
||||
|
||||
@@ -22,7 +22,8 @@ A crew in crewAI represents a collaborative group of agents working together to
|
||||
| **Max RPM** _(optional)_ | `max_rpm` | Maximum requests per minute the crew adheres to during execution. Defaults to `None`. |
|
||||
| **Language** _(optional)_ | `language` | Language used for the crew, defaults to English. |
|
||||
| **Language File** _(optional)_ | `language_file` | Path to the language file to be used for the crew. |
|
||||
| **Memory** _(optional)_ | `memory` | Utilized for storing execution memories (short-term, long-term, entity memory). Defaults to `False`. |
|
||||
| **Memory** _(optional)_ | `memory` | Utilized for storing execution memories (short-term, long-term, entity memory). |
|
||||
| **Memory Config** _(optional)_ | `memory_config` | Configuration for the memory provider to be used by the crew. |
|
||||
| **Cache** _(optional)_ | `cache` | Specifies whether to use a cache for storing the results of tools' execution. Defaults to `True`. |
|
||||
| **Embedder** _(optional)_ | `embedder` | Configuration for the embedder to be used by the crew. Mostly used by memory for now. Default is `{"provider": "openai"}`. |
|
||||
| **Full Output** _(optional)_ | `full_output` | Whether the crew should return the full output with all tasks outputs or just the final output. Defaults to `False`. |
|
||||
|
||||
@@ -25,7 +25,100 @@ By default, CrewAI uses the `gpt-4o-mini` model. It uses environment variables i
|
||||
- `OPENAI_API_BASE`
|
||||
- `OPENAI_API_KEY`
|
||||
|
||||
### 2. Custom LLM Objects
|
||||
### 2. Updating YAML files
|
||||
|
||||
You can update the `agents.yml` file to refer to the LLM you want to use:
|
||||
|
||||
```yaml Code
|
||||
researcher:
|
||||
role: Research Specialist
|
||||
goal: Conduct comprehensive research and analysis to gather relevant information,
|
||||
synthesize findings, and produce well-documented insights.
|
||||
backstory: A dedicated research professional with years of experience in academic
|
||||
investigation, literature review, and data analysis, known for thorough and
|
||||
methodical approaches to complex research questions.
|
||||
verbose: true
|
||||
llm: openai/gpt-4o
|
||||
# llm: azure/gpt-4o-mini
|
||||
# llm: gemini/gemini-pro
|
||||
# llm: anthropic/claude-3-5-sonnet-20240620
|
||||
# llm: bedrock/anthropic.claude-3-sonnet-20240229-v1:0
|
||||
# llm: mistral/mistral-large-latest
|
||||
# llm: ollama/llama3:70b
|
||||
# llm: groq/llama-3.2-90b-vision-preview
|
||||
# llm: watsonx/meta-llama/llama-3-1-70b-instruct
|
||||
# ...
|
||||
```
|
||||
|
||||
Keep in mind that you will need to set certain ENV vars depending on the model you are
|
||||
using to account for the credentials or set a custom LLM object like described below.
|
||||
Here are some of the required ENV vars for some of the LLM integrations:
|
||||
|
||||
<AccordionGroup>
|
||||
<Accordion title="OpenAI">
|
||||
```python Code
|
||||
OPENAI_API_KEY=<your-api-key>
|
||||
OPENAI_API_BASE=<optional-custom-base-url>
|
||||
OPENAI_MODEL_NAME=<openai-model-name>
|
||||
OPENAI_ORGANIZATION=<your-org-id> # OPTIONAL
|
||||
OPENAI_API_BASE=<openaiai-api-base> # OPTIONAL
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Anthropic">
|
||||
```python Code
|
||||
ANTHROPIC_API_KEY=<your-api-key>
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Google">
|
||||
```python Code
|
||||
GEMINI_API_KEY=<your-api-key>
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Azure">
|
||||
```python Code
|
||||
AZURE_API_KEY=<your-api-key> # "my-azure-api-key"
|
||||
AZURE_API_BASE=<your-resource-url> # "https://example-endpoint.openai.azure.com"
|
||||
AZURE_API_VERSION=<api-version> # "2023-05-15"
|
||||
AZURE_AD_TOKEN=<your-azure-ad-token> # Optional
|
||||
AZURE_API_TYPE=<your-azure-api-type> # Optional
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="AWS Bedrock">
|
||||
```python Code
|
||||
AWS_ACCESS_KEY_ID=<your-access-key>
|
||||
AWS_SECRET_ACCESS_KEY=<your-secret-key>
|
||||
AWS_DEFAULT_REGION=<your-region>
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Mistral">
|
||||
```python Code
|
||||
MISTRAL_API_KEY=<your-api-key>
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Groq">
|
||||
```python Code
|
||||
GROQ_API_KEY=<your-api-key>
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="IBM watsonx.ai">
|
||||
```python Code
|
||||
WATSONX_URL=<your-url> # (required) Base URL of your WatsonX instance
|
||||
WATSONX_APIKEY=<your-apikey> # (required) IBM cloud API key
|
||||
WATSONX_TOKEN=<your-token> # (required) IAM auth token (alternative to APIKEY)
|
||||
WATSONX_PROJECT_ID=<your-project-id> # (optional) Project ID of your WatsonX instance
|
||||
WATSONX_DEPLOYMENT_SPACE_ID=<your-space-id> # (optional) ID of deployment space for deployed models
|
||||
```
|
||||
</Accordion>
|
||||
</AccordionGroup>
|
||||
|
||||
### 3. Custom LLM Objects
|
||||
|
||||
Pass a custom LLM implementation or object from another library.
|
||||
|
||||
@@ -102,7 +195,7 @@ When configuring an LLM for your agent, you have access to a wide range of param
|
||||
|
||||
These are examples of how to configure LLMs for your agent.
|
||||
|
||||
<AccordionGroup>
|
||||
<AccordionGroup>
|
||||
<Accordion title="OpenAI">
|
||||
|
||||
```python Code
|
||||
@@ -133,10 +226,10 @@ These are examples of how to configure LLMs for your agent.
|
||||
model="cerebras/llama-3.1-70b",
|
||||
api_key="your-api-key-here"
|
||||
)
|
||||
agent = Agent(llm=llm, ...)
|
||||
agent = Agent(llm=llm, ...)
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
|
||||
<Accordion title="Ollama (Local LLMs)">
|
||||
|
||||
CrewAI supports using Ollama for running open-source models locally:
|
||||
@@ -150,7 +243,7 @@ These are examples of how to configure LLMs for your agent.
|
||||
|
||||
agent = Agent(
|
||||
llm=LLM(
|
||||
model="ollama/llama3.1",
|
||||
model="ollama/llama3.1",
|
||||
base_url="http://localhost:11434"
|
||||
),
|
||||
...
|
||||
@@ -164,7 +257,7 @@ These are examples of how to configure LLMs for your agent.
|
||||
from crewai import LLM
|
||||
|
||||
llm = LLM(
|
||||
model="groq/llama3-8b-8192",
|
||||
model="groq/llama3-8b-8192",
|
||||
api_key="your-api-key-here"
|
||||
)
|
||||
agent = Agent(llm=llm, ...)
|
||||
@@ -189,7 +282,7 @@ These are examples of how to configure LLMs for your agent.
|
||||
from crewai import LLM
|
||||
|
||||
llm = LLM(
|
||||
model="fireworks_ai/accounts/fireworks/models/llama-v3-70b-instruct",
|
||||
model="fireworks_ai/accounts/fireworks/models/llama-v3-70b-instruct",
|
||||
api_key="your-api-key-here"
|
||||
)
|
||||
agent = Agent(llm=llm, ...)
|
||||
@@ -224,6 +317,29 @@ These are examples of how to configure LLMs for your agent.
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="IBM watsonx.ai">
|
||||
You can use IBM Watson by seeting the following ENV vars:
|
||||
|
||||
```python Code
|
||||
WATSONX_URL=<your-url>
|
||||
WATSONX_APIKEY=<your-apikey>
|
||||
WATSONX_PROJECT_ID=<your-project-id>
|
||||
```
|
||||
|
||||
You can then define your agents llms by updating the `agents.yml`
|
||||
|
||||
```yaml Code
|
||||
researcher:
|
||||
role: Research Specialist
|
||||
goal: Conduct comprehensive research and analysis to gather relevant information,
|
||||
synthesize findings, and produce well-documented insights.
|
||||
backstory: A dedicated research professional with years of experience in academic
|
||||
investigation, literature review, and data analysis, known for thorough and
|
||||
methodical approaches to complex research questions.
|
||||
verbose: true
|
||||
llm: watsonx/meta-llama/llama-3-1-70b-instruct
|
||||
```
|
||||
|
||||
You can also set up agents more dynamically as a base level LLM instance, like bellow:
|
||||
|
||||
```python Code
|
||||
from crewai import LLM
|
||||
@@ -247,7 +363,7 @@ These are examples of how to configure LLMs for your agent.
|
||||
api_key="your-api-key-here",
|
||||
base_url="your_api_endpoint"
|
||||
)
|
||||
agent = Agent(llm=llm, ...)
|
||||
agent = Agent(llm=llm, ...)
|
||||
```
|
||||
</Accordion>
|
||||
</AccordionGroup>
|
||||
|
||||
@@ -18,6 +18,7 @@ reason, and learn from past interactions.
|
||||
| **Long-Term Memory** | Preserves valuable insights and learnings from past executions, allowing agents to build and refine their knowledge over time. |
|
||||
| **Entity Memory** | Captures and organizes information about entities (people, places, concepts) encountered during tasks, facilitating deeper understanding and relationship mapping. Uses `RAG` for storing entity information. |
|
||||
| **Contextual Memory**| Maintains the context of interactions by combining `ShortTermMemory`, `LongTermMemory`, and `EntityMemory`, aiding in the coherence and relevance of agent responses over a sequence of tasks or a conversation. |
|
||||
| **User Memory** | Stores user-specific information and preferences, enhancing personalization and user experience. |
|
||||
|
||||
## How Memory Systems Empower Agents
|
||||
|
||||
@@ -92,6 +93,47 @@ my_crew = Crew(
|
||||
)
|
||||
```
|
||||
|
||||
## Integrating Mem0 for Enhanced User Memory
|
||||
|
||||
[Mem0](https://mem0.ai/) is a self-improving memory layer for LLM applications, enabling personalized AI experiences.
|
||||
|
||||
To include user-specific memory you can get your API key [here](https://app.mem0.ai/dashboard/api-keys) and refer the [docs](https://docs.mem0.ai/platform/quickstart#4-1-create-memories) for adding user preferences.
|
||||
|
||||
|
||||
```python Code
|
||||
import os
|
||||
from crewai import Crew, Process
|
||||
from mem0 import MemoryClient
|
||||
|
||||
# Set environment variables for Mem0
|
||||
os.environ["MEM0_API_KEY"] = "m0-xx"
|
||||
|
||||
# Step 1: Record preferences based on past conversation or user input
|
||||
client = MemoryClient()
|
||||
messages = [
|
||||
{"role": "user", "content": "Hi there! I'm planning a vacation and could use some advice."},
|
||||
{"role": "assistant", "content": "Hello! I'd be happy to help with your vacation planning. What kind of destination do you prefer?"},
|
||||
{"role": "user", "content": "I am more of a beach person than a mountain person."},
|
||||
{"role": "assistant", "content": "That's interesting. Do you like hotels or Airbnb?"},
|
||||
{"role": "user", "content": "I like Airbnb more."},
|
||||
]
|
||||
client.add(messages, user_id="john")
|
||||
|
||||
# Step 2: Create a Crew with User Memory
|
||||
|
||||
crew = Crew(
|
||||
agents=[...],
|
||||
tasks=[...],
|
||||
verbose=True,
|
||||
process=Process.sequential,
|
||||
memory=True,
|
||||
memory_config={
|
||||
"provider": "mem0",
|
||||
"config": {"user_id": "john"},
|
||||
},
|
||||
)
|
||||
```
|
||||
|
||||
|
||||
## Additional Embedding Providers
|
||||
|
||||
|
||||
6
poetry.lock
generated
6
poetry.lock
generated
@@ -1597,12 +1597,12 @@ files = [
|
||||
google-auth = ">=2.14.1,<3.0.dev0"
|
||||
googleapis-common-protos = ">=1.56.2,<2.0.dev0"
|
||||
grpcio = [
|
||||
{version = ">=1.49.1,<2.0dev", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
|
||||
{version = ">=1.33.2,<2.0dev", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
|
||||
{version = ">=1.49.1,<2.0dev", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
|
||||
]
|
||||
grpcio-status = [
|
||||
{version = ">=1.49.1,<2.0.dev0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
|
||||
{version = ">=1.33.2,<2.0.dev0", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
|
||||
{version = ">=1.49.1,<2.0.dev0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
|
||||
]
|
||||
proto-plus = ">=1.22.3,<2.0.0dev"
|
||||
protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<6.0.0.dev0"
|
||||
@@ -4286,8 +4286,8 @@ files = [
|
||||
|
||||
[package.dependencies]
|
||||
numpy = [
|
||||
{version = ">=1.23.2", markers = "python_version == \"3.11\""},
|
||||
{version = ">=1.22.4", markers = "python_version < \"3.11\""},
|
||||
{version = ">=1.23.2", markers = "python_version == \"3.11\""},
|
||||
{version = ">=1.26.0", markers = "python_version >= \"3.12\""},
|
||||
]
|
||||
python-dateutil = ">=2.8.2"
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
[project]
|
||||
name = "crewai"
|
||||
version = "0.76.9"
|
||||
version = "0.80.0"
|
||||
description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.10,<=3.13"
|
||||
@@ -16,7 +16,7 @@ dependencies = [
|
||||
"opentelemetry-exporter-otlp-proto-http>=1.22.0",
|
||||
"instructor>=1.3.3",
|
||||
"regex>=2024.9.11",
|
||||
"crewai-tools>=0.13.4",
|
||||
"crewai-tools>=0.14.0",
|
||||
"click>=8.1.7",
|
||||
"python-dotenv>=1.0.0",
|
||||
"appdirs>=1.4.4",
|
||||
@@ -27,8 +27,8 @@ dependencies = [
|
||||
"pyvis>=0.3.2",
|
||||
"uv>=0.4.25",
|
||||
"tomli-w>=1.1.0",
|
||||
"chromadb>=0.4.24",
|
||||
"tomli>=2.0.2",
|
||||
"chromadb>=0.5.18",
|
||||
]
|
||||
|
||||
[project.urls]
|
||||
@@ -37,8 +37,9 @@ Documentation = "https://docs.crewai.com"
|
||||
Repository = "https://github.com/crewAIInc/crewAI"
|
||||
|
||||
[project.optional-dependencies]
|
||||
tools = ["crewai-tools>=0.13.4"]
|
||||
tools = ["crewai-tools>=0.14.0"]
|
||||
agentops = ["agentops>=0.3.0"]
|
||||
mem0 = ["mem0ai>=0.1.29"]
|
||||
|
||||
[tool.uv]
|
||||
dev-dependencies = [
|
||||
@@ -52,7 +53,7 @@ dev-dependencies = [
|
||||
"mkdocs-material-extensions>=1.3.1",
|
||||
"pillow>=10.2.0",
|
||||
"cairosvg>=2.7.1",
|
||||
"crewai-tools>=0.13.4",
|
||||
"crewai-tools>=0.14.0",
|
||||
"pytest>=8.0.0",
|
||||
"pytest-vcr>=1.0.2",
|
||||
"python-dotenv>=1.0.0",
|
||||
|
||||
@@ -14,5 +14,5 @@ warnings.filterwarnings(
|
||||
category=UserWarning,
|
||||
module="pydantic.main",
|
||||
)
|
||||
__version__ = "0.76.9"
|
||||
__version__ = "0.80.0"
|
||||
__all__ = ["Agent", "Crew", "Process", "Task", "Pipeline", "Router", "LLM", "Flow"]
|
||||
|
||||
@@ -123,6 +123,11 @@ class Agent(BaseAgent):
|
||||
@model_validator(mode="after")
|
||||
def post_init_setup(self):
|
||||
self.agent_ops_agent_name = self.role
|
||||
unnacepted_attributes = [
|
||||
"AWS_ACCESS_KEY_ID",
|
||||
"AWS_SECRET_ACCESS_KEY",
|
||||
"AWS_REGION_NAME",
|
||||
]
|
||||
|
||||
# Handle different cases for self.llm
|
||||
if isinstance(self.llm, str):
|
||||
@@ -146,39 +151,44 @@ class Agent(BaseAgent):
|
||||
if api_base:
|
||||
llm_params["base_url"] = api_base
|
||||
|
||||
set_provider = model_name.split("/")[0] if "/" in model_name else "openai"
|
||||
|
||||
# Iterate over all environment variables to find matching API keys or use defaults
|
||||
for provider, env_vars in ENV_VARS.items():
|
||||
for env_var in env_vars:
|
||||
# Check if the environment variable is set
|
||||
if "key_name" in env_var:
|
||||
env_value = os.environ.get(env_var["key_name"])
|
||||
if env_value:
|
||||
# Map key names containing "API_KEY" to "api_key"
|
||||
key_name = (
|
||||
"api_key"
|
||||
if "API_KEY" in env_var["key_name"]
|
||||
else env_var["key_name"]
|
||||
)
|
||||
# Map key names containing "API_BASE" to "api_base"
|
||||
key_name = (
|
||||
"api_base"
|
||||
if "API_BASE" in env_var["key_name"]
|
||||
else key_name
|
||||
)
|
||||
# Map key names containing "API_VERSION" to "api_version"
|
||||
key_name = (
|
||||
"api_version"
|
||||
if "API_VERSION" in env_var["key_name"]
|
||||
else key_name
|
||||
)
|
||||
llm_params[key_name] = env_value
|
||||
# Check for default values if the environment variable is not set
|
||||
elif env_var.get("default", False):
|
||||
for key, value in env_var.items():
|
||||
if key not in ["prompt", "key_name", "default"]:
|
||||
# Only add default if the key is already set in os.environ
|
||||
if key in os.environ:
|
||||
llm_params[key] = value
|
||||
if provider == set_provider:
|
||||
for env_var in env_vars:
|
||||
if env_var["key_name"] in unnacepted_attributes:
|
||||
continue
|
||||
# Check if the environment variable is set
|
||||
if "key_name" in env_var:
|
||||
env_value = os.environ.get(env_var["key_name"])
|
||||
if env_value:
|
||||
# Map key names containing "API_KEY" to "api_key"
|
||||
key_name = (
|
||||
"api_key"
|
||||
if "API_KEY" in env_var["key_name"]
|
||||
else env_var["key_name"]
|
||||
)
|
||||
# Map key names containing "API_BASE" to "api_base"
|
||||
key_name = (
|
||||
"api_base"
|
||||
if "API_BASE" in env_var["key_name"]
|
||||
else key_name
|
||||
)
|
||||
# Map key names containing "API_VERSION" to "api_version"
|
||||
key_name = (
|
||||
"api_version"
|
||||
if "API_VERSION" in env_var["key_name"]
|
||||
else key_name
|
||||
)
|
||||
llm_params[key_name] = env_value
|
||||
# Check for default values if the environment variable is not set
|
||||
elif env_var.get("default", False):
|
||||
for key, value in env_var.items():
|
||||
if key not in ["prompt", "key_name", "default"]:
|
||||
# Only add default if the key is already set in os.environ
|
||||
if key in os.environ:
|
||||
llm_params[key] = value
|
||||
|
||||
self.llm = LLM(**llm_params)
|
||||
else:
|
||||
@@ -252,9 +262,11 @@ class Agent(BaseAgent):
|
||||
|
||||
if self.crew and self.crew.memory:
|
||||
contextual_memory = ContextualMemory(
|
||||
self.crew.memory_config,
|
||||
self.crew._short_term_memory,
|
||||
self.crew._long_term_memory,
|
||||
self.crew._entity_memory,
|
||||
self.crew._user_memory,
|
||||
)
|
||||
memory = contextual_memory.build_context_for_task(task, context)
|
||||
if memory.strip() != "":
|
||||
|
||||
@@ -4,6 +4,7 @@ from crewai.types.usage_metrics import UsageMetrics
|
||||
class TokenProcess:
|
||||
total_tokens: int = 0
|
||||
prompt_tokens: int = 0
|
||||
cached_prompt_tokens: int = 0
|
||||
completion_tokens: int = 0
|
||||
successful_requests: int = 0
|
||||
|
||||
@@ -15,6 +16,9 @@ class TokenProcess:
|
||||
self.completion_tokens = self.completion_tokens + tokens
|
||||
self.total_tokens = self.total_tokens + tokens
|
||||
|
||||
def sum_cached_prompt_tokens(self, tokens: int):
|
||||
self.cached_prompt_tokens = self.cached_prompt_tokens + tokens
|
||||
|
||||
def sum_successful_requests(self, requests: int):
|
||||
self.successful_requests = self.successful_requests + requests
|
||||
|
||||
@@ -22,6 +26,7 @@ class TokenProcess:
|
||||
return UsageMetrics(
|
||||
total_tokens=self.total_tokens,
|
||||
prompt_tokens=self.prompt_tokens,
|
||||
cached_prompt_tokens=self.cached_prompt_tokens,
|
||||
completion_tokens=self.completion_tokens,
|
||||
successful_requests=self.successful_requests,
|
||||
)
|
||||
|
||||
@@ -145,25 +145,26 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
|
||||
formatted_answer.result = action_result
|
||||
self._show_logs(formatted_answer)
|
||||
|
||||
if self.step_callback:
|
||||
self.step_callback(formatted_answer)
|
||||
if self.step_callback:
|
||||
self.step_callback(formatted_answer)
|
||||
|
||||
if self._should_force_answer():
|
||||
if self.have_forced_answer:
|
||||
return AgentFinish(
|
||||
output=self._i18n.errors(
|
||||
"force_final_answer_error"
|
||||
).format(formatted_answer.text),
|
||||
text=formatted_answer.text,
|
||||
)
|
||||
else:
|
||||
formatted_answer.text += (
|
||||
f'\n{self._i18n.errors("force_final_answer")}'
|
||||
)
|
||||
self.have_forced_answer = True
|
||||
self.messages.append(
|
||||
self._format_msg(formatted_answer.text, role="assistant")
|
||||
)
|
||||
if self._should_force_answer():
|
||||
if self.have_forced_answer:
|
||||
return AgentFinish(
|
||||
thought="",
|
||||
output=self._i18n.errors(
|
||||
"force_final_answer_error"
|
||||
).format(formatted_answer.text),
|
||||
text=formatted_answer.text,
|
||||
)
|
||||
else:
|
||||
formatted_answer.text += (
|
||||
f'\n{self._i18n.errors("force_final_answer")}'
|
||||
)
|
||||
self.have_forced_answer = True
|
||||
self.messages.append(
|
||||
self._format_msg(formatted_answer.text, role="assistant")
|
||||
)
|
||||
|
||||
except OutputParserException as e:
|
||||
self.messages.append({"role": "user", "content": e.error})
|
||||
@@ -332,9 +333,9 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
|
||||
if self.crew is not None and hasattr(self.crew, "_train_iteration"):
|
||||
train_iteration = self.crew._train_iteration
|
||||
if agent_id in training_data and isinstance(train_iteration, int):
|
||||
training_data[agent_id][train_iteration][
|
||||
"improved_output"
|
||||
] = result.output
|
||||
training_data[agent_id][train_iteration]["improved_output"] = (
|
||||
result.output
|
||||
)
|
||||
training_handler.save(training_data)
|
||||
else:
|
||||
self._logger.log(
|
||||
@@ -385,4 +386,5 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
|
||||
return CrewAgentParser(agent=self.agent).parse(answer)
|
||||
|
||||
def _format_msg(self, prompt: str, role: str = "user") -> Dict[str, str]:
|
||||
prompt = prompt.rstrip()
|
||||
return {"role": role, "content": prompt}
|
||||
|
||||
@@ -34,7 +34,9 @@ class AuthenticationCommand:
|
||||
"scope": "openid",
|
||||
"audience": AUTH0_AUDIENCE,
|
||||
}
|
||||
response = requests.post(url=self.DEVICE_CODE_URL, data=device_code_payload)
|
||||
response = requests.post(
|
||||
url=self.DEVICE_CODE_URL, data=device_code_payload, timeout=20
|
||||
)
|
||||
response.raise_for_status()
|
||||
return response.json()
|
||||
|
||||
@@ -54,7 +56,7 @@ class AuthenticationCommand:
|
||||
|
||||
attempts = 0
|
||||
while True and attempts < 5:
|
||||
response = requests.post(self.TOKEN_URL, data=token_payload)
|
||||
response = requests.post(self.TOKEN_URL, data=token_payload, timeout=30)
|
||||
token_data = response.json()
|
||||
|
||||
if response.status_code == 200:
|
||||
|
||||
@@ -24,7 +24,6 @@ def run_crew() -> None:
|
||||
f"Please run `crewai update` to update your pyproject.toml to use uv.",
|
||||
fg="red",
|
||||
)
|
||||
print()
|
||||
|
||||
try:
|
||||
subprocess.run(command, capture_output=False, text=True, check=True)
|
||||
|
||||
@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
|
||||
authors = [{ name = "Your Name", email = "you@example.com" }]
|
||||
requires-python = ">=3.10,<=3.13"
|
||||
dependencies = [
|
||||
"crewai[tools]>=0.76.9,<1.0.0"
|
||||
"crewai[tools]>=0.80.0,<1.0.0"
|
||||
]
|
||||
|
||||
[project.scripts]
|
||||
|
||||
@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
|
||||
authors = [{ name = "Your Name", email = "you@example.com" }]
|
||||
requires-python = ">=3.10,<=3.13"
|
||||
dependencies = [
|
||||
"crewai[tools]>=0.76.9,<1.0.0",
|
||||
"crewai[tools]>=0.80.0,<1.0.0",
|
||||
]
|
||||
|
||||
[project.scripts]
|
||||
|
||||
@@ -6,7 +6,7 @@ authors = ["Your Name <you@example.com>"]
|
||||
|
||||
[tool.poetry.dependencies]
|
||||
python = ">=3.10,<=3.13"
|
||||
crewai = { extras = ["tools"], version = ">=0.76.9,<1.0.0" }
|
||||
crewai = { extras = ["tools"], version = ">=0.80.0,<1.0.0" }
|
||||
asyncio = "*"
|
||||
|
||||
[tool.poetry.scripts]
|
||||
|
||||
@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
|
||||
authors = ["Your Name <you@example.com>"]
|
||||
requires-python = ">=3.10,<=3.13"
|
||||
dependencies = [
|
||||
"crewai[tools]>=0.76.9,<1.0.0"
|
||||
"crewai[tools]>=0.80.0,<1.0.0"
|
||||
]
|
||||
|
||||
[project.scripts]
|
||||
|
||||
@@ -5,6 +5,6 @@ description = "Power up your crews with {{folder_name}}"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.10,<=3.13"
|
||||
dependencies = [
|
||||
"crewai[tools]>=0.76.9"
|
||||
"crewai[tools]>=0.80.0"
|
||||
]
|
||||
|
||||
|
||||
@@ -5,7 +5,7 @@ import uuid
|
||||
import warnings
|
||||
from concurrent.futures import Future
|
||||
from hashlib import md5
|
||||
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
|
||||
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
|
||||
|
||||
from pydantic import (
|
||||
UUID4,
|
||||
@@ -27,6 +27,7 @@ from crewai.llm import LLM
|
||||
from crewai.memory.entity.entity_memory import EntityMemory
|
||||
from crewai.memory.long_term.long_term_memory import LongTermMemory
|
||||
from crewai.memory.short_term.short_term_memory import ShortTermMemory
|
||||
from crewai.memory.user.user_memory import UserMemory
|
||||
from crewai.process import Process
|
||||
from crewai.task import Task
|
||||
from crewai.tasks.conditional_task import ConditionalTask
|
||||
@@ -35,9 +36,7 @@ from crewai.telemetry import Telemetry
|
||||
from crewai.tools.agent_tools.agent_tools import AgentTools
|
||||
from crewai.types.usage_metrics import UsageMetrics
|
||||
from crewai.utilities import I18N, FileHandler, Logger, RPMController
|
||||
from crewai.utilities.constants import (
|
||||
TRAINING_DATA_FILE,
|
||||
)
|
||||
from crewai.utilities.constants import TRAINING_DATA_FILE
|
||||
from crewai.utilities.evaluators.crew_evaluator_handler import CrewEvaluator
|
||||
from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
|
||||
from crewai.utilities.formatter import (
|
||||
@@ -71,6 +70,7 @@ class Crew(BaseModel):
|
||||
manager_llm: The language model that will run manager agent.
|
||||
manager_agent: Custom agent that will be used as manager.
|
||||
memory: Whether the crew should use memory to store memories of it's execution.
|
||||
memory_config: Configuration for the memory to be used for the crew.
|
||||
cache: Whether the crew should use a cache to store the results of the tools execution.
|
||||
function_calling_llm: The language model that will run the tool calling for all the agents.
|
||||
process: The process flow that the crew will follow (e.g., sequential, hierarchical).
|
||||
@@ -94,6 +94,7 @@ class Crew(BaseModel):
|
||||
_short_term_memory: Optional[InstanceOf[ShortTermMemory]] = PrivateAttr()
|
||||
_long_term_memory: Optional[InstanceOf[LongTermMemory]] = PrivateAttr()
|
||||
_entity_memory: Optional[InstanceOf[EntityMemory]] = PrivateAttr()
|
||||
_user_memory: Optional[InstanceOf[UserMemory]] = PrivateAttr()
|
||||
_train: Optional[bool] = PrivateAttr(default=False)
|
||||
_train_iteration: Optional[int] = PrivateAttr()
|
||||
_inputs: Optional[Dict[str, Any]] = PrivateAttr(default=None)
|
||||
@@ -114,6 +115,10 @@ class Crew(BaseModel):
|
||||
default=False,
|
||||
description="Whether the crew should use memory to store memories of it's execution",
|
||||
)
|
||||
memory_config: Optional[Dict[str, Any]] = Field(
|
||||
default=None,
|
||||
description="Configuration for the memory to be used for the crew.",
|
||||
)
|
||||
short_term_memory: Optional[InstanceOf[ShortTermMemory]] = Field(
|
||||
default=None,
|
||||
description="An Instance of the ShortTermMemory to be used by the Crew",
|
||||
@@ -126,7 +131,11 @@ class Crew(BaseModel):
|
||||
default=None,
|
||||
description="An Instance of the EntityMemory to be used by the Crew",
|
||||
)
|
||||
embedder: Optional[Any] = Field(
|
||||
user_memory: Optional[InstanceOf[UserMemory]] = Field(
|
||||
default=None,
|
||||
description="An instance of the UserMemory to be used by the Crew to store/fetch memories of a specific user.",
|
||||
)
|
||||
embedder: Optional[dict] = Field(
|
||||
default=None,
|
||||
description="Configuration for the embedder to be used for the crew.",
|
||||
)
|
||||
@@ -154,6 +163,16 @@ class Crew(BaseModel):
|
||||
default=None,
|
||||
description="Callback to be executed after each task for all agents execution.",
|
||||
)
|
||||
before_kickoff_callbacks: List[
|
||||
Callable[[Optional[Dict[str, Any]]], Optional[Dict[str, Any]]]
|
||||
] = Field(
|
||||
default_factory=list,
|
||||
description="List of callbacks to be executed before crew kickoff. It may be used to adjust inputs before the crew is executed.",
|
||||
)
|
||||
after_kickoff_callbacks: List[Callable[[CrewOutput], CrewOutput]] = Field(
|
||||
default_factory=list,
|
||||
description="List of callbacks to be executed after crew kickoff. It may be used to adjust the output of the crew.",
|
||||
)
|
||||
max_rpm: Optional[int] = Field(
|
||||
default=None,
|
||||
description="Maximum number of requests per minute for the crew execution to be respected.",
|
||||
@@ -238,13 +257,22 @@ class Crew(BaseModel):
|
||||
self._short_term_memory = (
|
||||
self.short_term_memory
|
||||
if self.short_term_memory
|
||||
else ShortTermMemory(crew=self, embedder_config=self.embedder)
|
||||
else ShortTermMemory(
|
||||
crew=self,
|
||||
embedder_config=self.embedder,
|
||||
)
|
||||
)
|
||||
self._entity_memory = (
|
||||
self.entity_memory
|
||||
if self.entity_memory
|
||||
else EntityMemory(crew=self, embedder_config=self.embedder)
|
||||
)
|
||||
if hasattr(self, "memory_config") and self.memory_config is not None:
|
||||
self._user_memory = (
|
||||
self.user_memory if self.user_memory else UserMemory(crew=self)
|
||||
)
|
||||
else:
|
||||
self._user_memory = None
|
||||
return self
|
||||
|
||||
@model_validator(mode="after")
|
||||
@@ -458,6 +486,9 @@ class Crew(BaseModel):
|
||||
self,
|
||||
inputs: Optional[Dict[str, Any]] = None,
|
||||
) -> CrewOutput:
|
||||
for callback in self.before_kickoff_callbacks:
|
||||
inputs = callback(inputs)
|
||||
|
||||
"""Starts the crew to work on its assigned tasks."""
|
||||
self._execution_span = self._telemetry.crew_execution_span(self, inputs)
|
||||
self._task_output_handler.reset()
|
||||
@@ -500,6 +531,9 @@ class Crew(BaseModel):
|
||||
f"The process '{self.process}' is not implemented yet."
|
||||
)
|
||||
|
||||
for callback in self.after_kickoff_callbacks:
|
||||
result = callback(result)
|
||||
|
||||
metrics += [agent._token_process.get_summary() for agent in self.agents]
|
||||
|
||||
self.usage_metrics = UsageMetrics()
|
||||
|
||||
@@ -118,12 +118,12 @@ class LLM:
|
||||
|
||||
litellm.drop_params = True
|
||||
litellm.set_verbose = False
|
||||
litellm.callbacks = callbacks
|
||||
self.set_callbacks(callbacks)
|
||||
|
||||
def call(self, messages: List[Dict[str, str]], callbacks: List[Any] = []) -> str:
|
||||
with suppress_warnings():
|
||||
if callbacks and len(callbacks) > 0:
|
||||
litellm.callbacks = callbacks
|
||||
self.set_callbacks(callbacks)
|
||||
|
||||
try:
|
||||
params = {
|
||||
@@ -181,3 +181,15 @@ class LLM:
|
||||
def get_context_window_size(self) -> int:
|
||||
# Only using 75% of the context window size to avoid cutting the message in the middle
|
||||
return int(LLM_CONTEXT_WINDOW_SIZES.get(self.model, 8192) * 0.75)
|
||||
|
||||
def set_callbacks(self, callbacks: List[Any]):
|
||||
callback_types = [type(callback) for callback in callbacks]
|
||||
for callback in litellm.success_callback[:]:
|
||||
if type(callback) in callback_types:
|
||||
litellm.success_callback.remove(callback)
|
||||
|
||||
for callback in litellm._async_success_callback[:]:
|
||||
if type(callback) in callback_types:
|
||||
litellm._async_success_callback.remove(callback)
|
||||
|
||||
litellm.callbacks = callbacks
|
||||
|
||||
@@ -1,5 +1,6 @@
|
||||
from .entity.entity_memory import EntityMemory
|
||||
from .long_term.long_term_memory import LongTermMemory
|
||||
from .short_term.short_term_memory import ShortTermMemory
|
||||
from .user.user_memory import UserMemory
|
||||
|
||||
__all__ = ["EntityMemory", "LongTermMemory", "ShortTermMemory"]
|
||||
__all__ = ["UserMemory", "EntityMemory", "LongTermMemory", "ShortTermMemory"]
|
||||
|
||||
@@ -1,13 +1,25 @@
|
||||
from typing import Optional
|
||||
from typing import Optional, Dict, Any
|
||||
|
||||
from crewai.memory import EntityMemory, LongTermMemory, ShortTermMemory
|
||||
from crewai.memory import EntityMemory, LongTermMemory, ShortTermMemory, UserMemory
|
||||
|
||||
|
||||
class ContextualMemory:
|
||||
def __init__(self, stm: ShortTermMemory, ltm: LongTermMemory, em: EntityMemory):
|
||||
def __init__(
|
||||
self,
|
||||
memory_config: Optional[Dict[str, Any]],
|
||||
stm: ShortTermMemory,
|
||||
ltm: LongTermMemory,
|
||||
em: EntityMemory,
|
||||
um: UserMemory,
|
||||
):
|
||||
if memory_config is not None:
|
||||
self.memory_provider = memory_config.get("provider")
|
||||
else:
|
||||
self.memory_provider = None
|
||||
self.stm = stm
|
||||
self.ltm = ltm
|
||||
self.em = em
|
||||
self.um = um
|
||||
|
||||
def build_context_for_task(self, task, context) -> str:
|
||||
"""
|
||||
@@ -23,6 +35,8 @@ class ContextualMemory:
|
||||
context.append(self._fetch_ltm_context(task.description))
|
||||
context.append(self._fetch_stm_context(query))
|
||||
context.append(self._fetch_entity_context(query))
|
||||
if self.memory_provider == "mem0":
|
||||
context.append(self._fetch_user_context(query))
|
||||
return "\n".join(filter(None, context))
|
||||
|
||||
def _fetch_stm_context(self, query) -> str:
|
||||
@@ -32,9 +46,11 @@ class ContextualMemory:
|
||||
"""
|
||||
stm_results = self.stm.search(query)
|
||||
formatted_results = "\n".join(
|
||||
[f"- {result['context']}" for result in stm_results]
|
||||
[
|
||||
f"- {result['memory'] if self.memory_provider == 'mem0' else result['context']}"
|
||||
for result in stm_results
|
||||
]
|
||||
)
|
||||
print("formatted_results stm", formatted_results)
|
||||
return f"Recent Insights:\n{formatted_results}" if stm_results else ""
|
||||
|
||||
def _fetch_ltm_context(self, task) -> Optional[str]:
|
||||
@@ -54,8 +70,6 @@ class ContextualMemory:
|
||||
formatted_results = list(dict.fromkeys(formatted_results))
|
||||
formatted_results = "\n".join([f"- {result}" for result in formatted_results]) # type: ignore # Incompatible types in assignment (expression has type "str", variable has type "list[str]")
|
||||
|
||||
print("formatted_results ltm", formatted_results)
|
||||
|
||||
return f"Historical Data:\n{formatted_results}" if ltm_results else ""
|
||||
|
||||
def _fetch_entity_context(self, query) -> str:
|
||||
@@ -65,7 +79,26 @@ class ContextualMemory:
|
||||
"""
|
||||
em_results = self.em.search(query)
|
||||
formatted_results = "\n".join(
|
||||
[f"- {result['context']}" for result in em_results] # type: ignore # Invalid index type "str" for "str"; expected type "SupportsIndex | slice"
|
||||
[
|
||||
f"- {result['memory'] if self.memory_provider == 'mem0' else result['context']}"
|
||||
for result in em_results
|
||||
] # type: ignore # Invalid index type "str" for "str"; expected type "SupportsIndex | slice"
|
||||
)
|
||||
print("formatted_results em", formatted_results)
|
||||
return f"Entities:\n{formatted_results}" if em_results else ""
|
||||
|
||||
def _fetch_user_context(self, query: str) -> str:
|
||||
"""
|
||||
Fetches and formats relevant user information from User Memory.
|
||||
Args:
|
||||
query (str): The search query to find relevant user memories.
|
||||
Returns:
|
||||
str: Formatted user memories as bullet points, or an empty string if none found.
|
||||
"""
|
||||
user_memories = self.um.search(query)
|
||||
if not user_memories:
|
||||
return ""
|
||||
|
||||
formatted_memories = "\n".join(
|
||||
f"- {result['memory']}" for result in user_memories
|
||||
)
|
||||
return f"User memories/preferences:\n{formatted_memories}"
|
||||
|
||||
@@ -11,21 +11,43 @@ class EntityMemory(Memory):
|
||||
"""
|
||||
|
||||
def __init__(self, crew=None, embedder_config=None, storage=None):
|
||||
storage = (
|
||||
storage
|
||||
if storage
|
||||
else RAGStorage(
|
||||
type="entities",
|
||||
allow_reset=True,
|
||||
embedder_config=embedder_config,
|
||||
crew=crew,
|
||||
if hasattr(crew, "memory_config") and crew.memory_config is not None:
|
||||
self.memory_provider = crew.memory_config.get("provider")
|
||||
else:
|
||||
self.memory_provider = None
|
||||
|
||||
if self.memory_provider == "mem0":
|
||||
try:
|
||||
from crewai.memory.storage.mem0_storage import Mem0Storage
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"Mem0 is not installed. Please install it with `pip install mem0ai`."
|
||||
)
|
||||
storage = Mem0Storage(type="entities", crew=crew)
|
||||
else:
|
||||
storage = (
|
||||
storage
|
||||
if storage
|
||||
else RAGStorage(
|
||||
type="entities",
|
||||
allow_reset=True,
|
||||
embedder_config=embedder_config,
|
||||
crew=crew,
|
||||
)
|
||||
)
|
||||
)
|
||||
super().__init__(storage)
|
||||
|
||||
def save(self, item: EntityMemoryItem) -> None: # type: ignore # BUG?: Signature of "save" incompatible with supertype "Memory"
|
||||
"""Saves an entity item into the SQLite storage."""
|
||||
data = f"{item.name}({item.type}): {item.description}"
|
||||
if self.memory_provider == "mem0":
|
||||
data = f"""
|
||||
Remember details about the following entity:
|
||||
Name: {item.name}
|
||||
Type: {item.type}
|
||||
Entity Description: {item.description}
|
||||
"""
|
||||
else:
|
||||
data = f"{item.name}({item.type}): {item.description}"
|
||||
super().save(data, item.metadata)
|
||||
|
||||
def reset(self) -> None:
|
||||
|
||||
@@ -23,5 +23,12 @@ class Memory:
|
||||
|
||||
self.storage.save(value, metadata)
|
||||
|
||||
def search(self, query: str) -> List[Dict[str, Any]]:
|
||||
return self.storage.search(query)
|
||||
def search(
|
||||
self,
|
||||
query: str,
|
||||
limit: int = 3,
|
||||
score_threshold: float = 0.35,
|
||||
) -> List[Any]:
|
||||
return self.storage.search(
|
||||
query=query, limit=limit, score_threshold=score_threshold
|
||||
)
|
||||
|
||||
@@ -14,13 +14,27 @@ class ShortTermMemory(Memory):
|
||||
"""
|
||||
|
||||
def __init__(self, crew=None, embedder_config=None, storage=None):
|
||||
storage = (
|
||||
storage
|
||||
if storage
|
||||
else RAGStorage(
|
||||
type="short_term", embedder_config=embedder_config, crew=crew
|
||||
if hasattr(crew, "memory_config") and crew.memory_config is not None:
|
||||
self.memory_provider = crew.memory_config.get("provider")
|
||||
else:
|
||||
self.memory_provider = None
|
||||
|
||||
if self.memory_provider == "mem0":
|
||||
try:
|
||||
from crewai.memory.storage.mem0_storage import Mem0Storage
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"Mem0 is not installed. Please install it with `pip install mem0ai`."
|
||||
)
|
||||
storage = Mem0Storage(type="short_term", crew=crew)
|
||||
else:
|
||||
storage = (
|
||||
storage
|
||||
if storage
|
||||
else RAGStorage(
|
||||
type="short_term", embedder_config=embedder_config, crew=crew
|
||||
)
|
||||
)
|
||||
)
|
||||
super().__init__(storage)
|
||||
|
||||
def save(
|
||||
@@ -30,11 +44,20 @@ class ShortTermMemory(Memory):
|
||||
agent: Optional[str] = None,
|
||||
) -> None:
|
||||
item = ShortTermMemoryItem(data=value, metadata=metadata, agent=agent)
|
||||
if self.memory_provider == "mem0":
|
||||
item.data = f"Remember the following insights from Agent run: {item.data}"
|
||||
|
||||
super().save(value=item.data, metadata=item.metadata, agent=item.agent)
|
||||
|
||||
def search(self, query: str, score_threshold: float = 0.35):
|
||||
return self.storage.search(query=query, score_threshold=score_threshold) # type: ignore # BUG? The reference is to the parent class, but the parent class does not have this parameters
|
||||
def search(
|
||||
self,
|
||||
query: str,
|
||||
limit: int = 3,
|
||||
score_threshold: float = 0.35,
|
||||
):
|
||||
return self.storage.search(
|
||||
query=query, limit=limit, score_threshold=score_threshold
|
||||
) # type: ignore # BUG? The reference is to the parent class, but the parent class does not have this parameters
|
||||
|
||||
def reset(self) -> None:
|
||||
try:
|
||||
|
||||
@@ -7,8 +7,10 @@ class Storage:
|
||||
def save(self, value: Any, metadata: Dict[str, Any]) -> None:
|
||||
pass
|
||||
|
||||
def search(self, key: str) -> List[Dict[str, Any]]: # type: ignore
|
||||
pass
|
||||
def search(
|
||||
self, query: str, limit: int, score_threshold: float
|
||||
) -> Dict[str, Any] | List[Any]:
|
||||
return {}
|
||||
|
||||
def reset(self) -> None:
|
||||
pass
|
||||
|
||||
@@ -103,7 +103,7 @@ class KickoffTaskOutputsSQLiteStorage:
|
||||
else value
|
||||
)
|
||||
|
||||
query = f"UPDATE latest_kickoff_task_outputs SET {', '.join(fields)} WHERE task_index = ?"
|
||||
query = f"UPDATE latest_kickoff_task_outputs SET {', '.join(fields)} WHERE task_index = ?" # nosec
|
||||
values.append(task_index)
|
||||
|
||||
cursor.execute(query, tuple(values))
|
||||
|
||||
@@ -83,7 +83,7 @@ class LTMSQLiteStorage:
|
||||
WHERE task_description = ?
|
||||
ORDER BY datetime DESC, score ASC
|
||||
LIMIT {latest_n}
|
||||
""",
|
||||
""", # nosec
|
||||
(task_description,),
|
||||
)
|
||||
rows = cursor.fetchall()
|
||||
|
||||
104
src/crewai/memory/storage/mem0_storage.py
Normal file
104
src/crewai/memory/storage/mem0_storage.py
Normal file
@@ -0,0 +1,104 @@
|
||||
import os
|
||||
from typing import Any, Dict, List
|
||||
|
||||
from mem0 import MemoryClient
|
||||
from crewai.memory.storage.interface import Storage
|
||||
|
||||
|
||||
class Mem0Storage(Storage):
|
||||
"""
|
||||
Extends Storage to handle embedding and searching across entities using Mem0.
|
||||
"""
|
||||
|
||||
def __init__(self, type, crew=None):
|
||||
super().__init__()
|
||||
|
||||
if type not in ["user", "short_term", "long_term", "entities"]:
|
||||
raise ValueError("Invalid type for Mem0Storage. Must be 'user' or 'agent'.")
|
||||
|
||||
self.memory_type = type
|
||||
self.crew = crew
|
||||
self.memory_config = crew.memory_config
|
||||
|
||||
# User ID is required for user memory type "user" since it's used as a unique identifier for the user.
|
||||
user_id = self._get_user_id()
|
||||
if type == "user" and not user_id:
|
||||
raise ValueError("User ID is required for user memory type")
|
||||
|
||||
# API key in memory config overrides the environment variable
|
||||
mem0_api_key = self.memory_config.get("config", {}).get("api_key") or os.getenv(
|
||||
"MEM0_API_KEY"
|
||||
)
|
||||
self.memory = MemoryClient(api_key=mem0_api_key)
|
||||
|
||||
def _sanitize_role(self, role: str) -> str:
|
||||
"""
|
||||
Sanitizes agent roles to ensure valid directory names.
|
||||
"""
|
||||
return role.replace("\n", "").replace(" ", "_").replace("/", "_")
|
||||
|
||||
def save(self, value: Any, metadata: Dict[str, Any]) -> None:
|
||||
user_id = self._get_user_id()
|
||||
agent_name = self._get_agent_name()
|
||||
if self.memory_type == "user":
|
||||
self.memory.add(value, user_id=user_id, metadata={**metadata})
|
||||
elif self.memory_type == "short_term":
|
||||
agent_name = self._get_agent_name()
|
||||
self.memory.add(
|
||||
value, agent_id=agent_name, metadata={"type": "short_term", **metadata}
|
||||
)
|
||||
elif self.memory_type == "long_term":
|
||||
agent_name = self._get_agent_name()
|
||||
self.memory.add(
|
||||
value,
|
||||
agent_id=agent_name,
|
||||
infer=False,
|
||||
metadata={"type": "long_term", **metadata},
|
||||
)
|
||||
elif self.memory_type == "entities":
|
||||
entity_name = None
|
||||
self.memory.add(
|
||||
value, user_id=entity_name, metadata={"type": "entity", **metadata}
|
||||
)
|
||||
|
||||
def search(
|
||||
self,
|
||||
query: str,
|
||||
limit: int = 3,
|
||||
score_threshold: float = 0.35,
|
||||
) -> List[Any]:
|
||||
params = {"query": query, "limit": limit}
|
||||
if self.memory_type == "user":
|
||||
user_id = self._get_user_id()
|
||||
params["user_id"] = user_id
|
||||
elif self.memory_type == "short_term":
|
||||
agent_name = self._get_agent_name()
|
||||
params["agent_id"] = agent_name
|
||||
params["metadata"] = {"type": "short_term"}
|
||||
elif self.memory_type == "long_term":
|
||||
agent_name = self._get_agent_name()
|
||||
params["agent_id"] = agent_name
|
||||
params["metadata"] = {"type": "long_term"}
|
||||
elif self.memory_type == "entities":
|
||||
agent_name = self._get_agent_name()
|
||||
params["agent_id"] = agent_name
|
||||
params["metadata"] = {"type": "entity"}
|
||||
|
||||
# Discard the filters for now since we create the filters
|
||||
# automatically when the crew is created.
|
||||
results = self.memory.search(**params)
|
||||
return [r for r in results if r["score"] >= score_threshold]
|
||||
|
||||
def _get_user_id(self):
|
||||
if self.memory_type == "user":
|
||||
if hasattr(self, "memory_config") and self.memory_config is not None:
|
||||
return self.memory_config.get("config", {}).get("user_id")
|
||||
else:
|
||||
return None
|
||||
return None
|
||||
|
||||
def _get_agent_name(self):
|
||||
agents = self.crew.agents if self.crew else []
|
||||
agents = [self._sanitize_role(agent.role) for agent in agents]
|
||||
agents = "_".join(agents)
|
||||
return agents
|
||||
@@ -51,8 +51,6 @@ class RAGStorage(BaseRAGStorage):
|
||||
self._initialize_app()
|
||||
|
||||
def _set_embedder_config(self):
|
||||
import chromadb.utils.embedding_functions as embedding_functions
|
||||
|
||||
if self.embedder_config is None:
|
||||
self.embedder_config = self._create_default_embedding_function()
|
||||
|
||||
@@ -61,12 +59,20 @@ class RAGStorage(BaseRAGStorage):
|
||||
config = self.embedder_config.get("config", {})
|
||||
model_name = config.get("model")
|
||||
if provider == "openai":
|
||||
self.embedder_config = embedding_functions.OpenAIEmbeddingFunction(
|
||||
from chromadb.utils.embedding_functions.openai_embedding_function import (
|
||||
OpenAIEmbeddingFunction,
|
||||
)
|
||||
|
||||
self.embedder_config = OpenAIEmbeddingFunction(
|
||||
api_key=config.get("api_key") or os.getenv("OPENAI_API_KEY"),
|
||||
model_name=model_name,
|
||||
)
|
||||
elif provider == "azure":
|
||||
self.embedder_config = embedding_functions.OpenAIEmbeddingFunction(
|
||||
from chromadb.utils.embedding_functions.openai_embedding_function import (
|
||||
OpenAIEmbeddingFunction,
|
||||
)
|
||||
|
||||
self.embedder_config = OpenAIEmbeddingFunction(
|
||||
api_key=config.get("api_key"),
|
||||
api_base=config.get("api_base"),
|
||||
api_type=config.get("api_type", "azure"),
|
||||
@@ -74,45 +80,55 @@ class RAGStorage(BaseRAGStorage):
|
||||
model_name=model_name,
|
||||
)
|
||||
elif provider == "ollama":
|
||||
from openai import OpenAI
|
||||
from chromadb.utils.embedding_functions.ollama_embedding_function import (
|
||||
OllamaEmbeddingFunction,
|
||||
)
|
||||
|
||||
class OllamaEmbeddingFunction(EmbeddingFunction):
|
||||
def __call__(self, input: Documents) -> Embeddings:
|
||||
client = OpenAI(
|
||||
base_url="http://localhost:11434/v1",
|
||||
api_key=config.get("api_key", "ollama"),
|
||||
)
|
||||
try:
|
||||
response = client.embeddings.create(
|
||||
input=input, model=model_name
|
||||
)
|
||||
embeddings = [item.embedding for item in response.data]
|
||||
return cast(Embeddings, embeddings)
|
||||
except Exception as e:
|
||||
raise e
|
||||
|
||||
self.embedder_config = OllamaEmbeddingFunction()
|
||||
self.embedder_config = OllamaEmbeddingFunction(
|
||||
url=config.get("url", "http://localhost:11434/api/embeddings"),
|
||||
model_name=model_name,
|
||||
)
|
||||
elif provider == "vertexai":
|
||||
self.embedder_config = (
|
||||
embedding_functions.GoogleVertexEmbeddingFunction(
|
||||
model_name=model_name,
|
||||
api_key=config.get("api_key"),
|
||||
)
|
||||
from chromadb.utils.embedding_functions.google_embedding_function import (
|
||||
GoogleVertexEmbeddingFunction,
|
||||
)
|
||||
elif provider == "google":
|
||||
self.embedder_config = (
|
||||
embedding_functions.GoogleGenerativeAiEmbeddingFunction(
|
||||
model_name=model_name,
|
||||
api_key=config.get("api_key"),
|
||||
)
|
||||
)
|
||||
elif provider == "cohere":
|
||||
self.embedder_config = embedding_functions.CohereEmbeddingFunction(
|
||||
|
||||
self.embedder_config = GoogleVertexEmbeddingFunction(
|
||||
model_name=model_name,
|
||||
api_key=config.get("api_key"),
|
||||
)
|
||||
elif provider == "google":
|
||||
from chromadb.utils.embedding_functions.google_embedding_function import (
|
||||
GoogleGenerativeAiEmbeddingFunction,
|
||||
)
|
||||
|
||||
self.embedder_config = GoogleGenerativeAiEmbeddingFunction(
|
||||
model_name=model_name,
|
||||
api_key=config.get("api_key"),
|
||||
)
|
||||
elif provider == "cohere":
|
||||
from chromadb.utils.embedding_functions.cohere_embedding_function import (
|
||||
CohereEmbeddingFunction,
|
||||
)
|
||||
|
||||
self.embedder_config = CohereEmbeddingFunction(
|
||||
model_name=model_name,
|
||||
api_key=config.get("api_key"),
|
||||
)
|
||||
elif provider == "bedrock":
|
||||
from chromadb.utils.embedding_functions.amazon_bedrock_embedding_function import (
|
||||
AmazonBedrockEmbeddingFunction,
|
||||
)
|
||||
|
||||
self.embedder_config = AmazonBedrockEmbeddingFunction(
|
||||
session=config.get("session"),
|
||||
)
|
||||
elif provider == "huggingface":
|
||||
self.embedder_config = embedding_functions.HuggingFaceEmbeddingServer(
|
||||
from chromadb.utils.embedding_functions.huggingface_embedding_function import (
|
||||
HuggingFaceEmbeddingServer,
|
||||
)
|
||||
|
||||
self.embedder_config = HuggingFaceEmbeddingServer(
|
||||
url=config.get("api_url"),
|
||||
)
|
||||
elif provider == "watson":
|
||||
@@ -253,8 +269,10 @@ class RAGStorage(BaseRAGStorage):
|
||||
)
|
||||
|
||||
def _create_default_embedding_function(self):
|
||||
import chromadb.utils.embedding_functions as embedding_functions
|
||||
from chromadb.utils.embedding_functions.openai_embedding_function import (
|
||||
OpenAIEmbeddingFunction,
|
||||
)
|
||||
|
||||
return embedding_functions.OpenAIEmbeddingFunction(
|
||||
return OpenAIEmbeddingFunction(
|
||||
api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
|
||||
)
|
||||
|
||||
0
src/crewai/memory/user/__init__.py
Normal file
0
src/crewai/memory/user/__init__.py
Normal file
45
src/crewai/memory/user/user_memory.py
Normal file
45
src/crewai/memory/user/user_memory.py
Normal file
@@ -0,0 +1,45 @@
|
||||
from typing import Any, Dict, Optional
|
||||
|
||||
from crewai.memory.memory import Memory
|
||||
|
||||
|
||||
class UserMemory(Memory):
|
||||
"""
|
||||
UserMemory class for handling user memory storage and retrieval.
|
||||
Inherits from the Memory class and utilizes an instance of a class that
|
||||
adheres to the Storage for data storage, specifically working with
|
||||
MemoryItem instances.
|
||||
"""
|
||||
|
||||
def __init__(self, crew=None):
|
||||
try:
|
||||
from crewai.memory.storage.mem0_storage import Mem0Storage
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"Mem0 is not installed. Please install it with `pip install mem0ai`."
|
||||
)
|
||||
storage = Mem0Storage(type="user", crew=crew)
|
||||
super().__init__(storage)
|
||||
|
||||
def save(
|
||||
self,
|
||||
value,
|
||||
metadata: Optional[Dict[str, Any]] = None,
|
||||
agent: Optional[str] = None,
|
||||
) -> None:
|
||||
# TODO: Change this function since we want to take care of the case where we save memories for the usr
|
||||
data = f"Remember the details about the user: {value}"
|
||||
super().save(data, metadata)
|
||||
|
||||
def search(
|
||||
self,
|
||||
query: str,
|
||||
limit: int = 3,
|
||||
score_threshold: float = 0.35,
|
||||
):
|
||||
results = super().search(
|
||||
query=query,
|
||||
limit=limit,
|
||||
score_threshold=score_threshold,
|
||||
)
|
||||
return results
|
||||
8
src/crewai/memory/user/user_memory_item.py
Normal file
8
src/crewai/memory/user/user_memory_item.py
Normal file
@@ -0,0 +1,8 @@
|
||||
from typing import Any, Dict, Optional
|
||||
|
||||
|
||||
class UserMemoryItem:
|
||||
def __init__(self, data: Any, user: str, metadata: Optional[Dict[str, Any]] = None):
|
||||
self.data = data
|
||||
self.user = user
|
||||
self.metadata = metadata if metadata is not None else {}
|
||||
@@ -1,5 +1,7 @@
|
||||
from .annotations import (
|
||||
after_kickoff,
|
||||
agent,
|
||||
before_kickoff,
|
||||
cache_handler,
|
||||
callback,
|
||||
crew,
|
||||
@@ -26,4 +28,6 @@ __all__ = [
|
||||
"llm",
|
||||
"cache_handler",
|
||||
"pipeline",
|
||||
"before_kickoff",
|
||||
"after_kickoff",
|
||||
]
|
||||
|
||||
@@ -5,6 +5,16 @@ from crewai import Crew
|
||||
from crewai.project.utils import memoize
|
||||
|
||||
|
||||
def before_kickoff(func):
|
||||
func.is_before_kickoff = True
|
||||
return func
|
||||
|
||||
|
||||
def after_kickoff(func):
|
||||
func.is_after_kickoff = True
|
||||
return func
|
||||
|
||||
|
||||
def task(func):
|
||||
func.is_task = True
|
||||
|
||||
@@ -99,6 +109,19 @@ def crew(func) -> Callable[..., Crew]:
|
||||
self.agents = instantiated_agents
|
||||
self.tasks = instantiated_tasks
|
||||
|
||||
return func(self, *args, **kwargs)
|
||||
crew = func(self, *args, **kwargs)
|
||||
|
||||
return wrapper
|
||||
def callback_wrapper(callback, instance):
|
||||
def wrapper(*args, **kwargs):
|
||||
return callback(instance, *args, **kwargs)
|
||||
|
||||
return wrapper
|
||||
|
||||
for _, callback in self._before_kickoff.items():
|
||||
crew.before_kickoff_callbacks.append(callback_wrapper(callback, self))
|
||||
for _, callback in self._after_kickoff.items():
|
||||
crew.after_kickoff_callbacks.append(callback_wrapper(callback, self))
|
||||
|
||||
return crew
|
||||
|
||||
return memoize(wrapper)
|
||||
|
||||
@@ -34,18 +34,39 @@ def CrewBase(cls: T) -> T:
|
||||
self.map_all_agent_variables()
|
||||
self.map_all_task_variables()
|
||||
|
||||
# Preserve task and agent information
|
||||
self._original_tasks = {
|
||||
# Preserve all decorated functions
|
||||
self._original_functions = {
|
||||
name: method
|
||||
for name, method in cls.__dict__.items()
|
||||
if hasattr(method, "is_task") and method.is_task
|
||||
}
|
||||
self._original_agents = {
|
||||
name: method
|
||||
for name, method in cls.__dict__.items()
|
||||
if hasattr(method, "is_agent") and method.is_agent
|
||||
if any(
|
||||
hasattr(method, attr)
|
||||
for attr in [
|
||||
"is_task",
|
||||
"is_agent",
|
||||
"is_before_kickoff",
|
||||
"is_after_kickoff",
|
||||
"is_kickoff",
|
||||
]
|
||||
)
|
||||
}
|
||||
|
||||
# Store specific function types
|
||||
self._original_tasks = self._filter_functions(
|
||||
self._original_functions, "is_task"
|
||||
)
|
||||
self._original_agents = self._filter_functions(
|
||||
self._original_functions, "is_agent"
|
||||
)
|
||||
self._before_kickoff = self._filter_functions(
|
||||
self._original_functions, "is_before_kickoff"
|
||||
)
|
||||
self._after_kickoff = self._filter_functions(
|
||||
self._original_functions, "is_after_kickoff"
|
||||
)
|
||||
self._kickoff = self._filter_functions(
|
||||
self._original_functions, "is_kickoff"
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def load_yaml(config_path: Path):
|
||||
try:
|
||||
|
||||
0
src/crewai/tools/cache_tools/__init__.py
Normal file
0
src/crewai/tools/cache_tools/__init__.py
Normal file
@@ -8,6 +8,7 @@ class UsageMetrics(BaseModel):
|
||||
Attributes:
|
||||
total_tokens: Total number of tokens used.
|
||||
prompt_tokens: Number of tokens used in prompts.
|
||||
cached_prompt_tokens: Number of cached prompt tokens used.
|
||||
completion_tokens: Number of tokens used in completions.
|
||||
successful_requests: Number of successful requests made.
|
||||
"""
|
||||
@@ -16,6 +17,9 @@ class UsageMetrics(BaseModel):
|
||||
prompt_tokens: int = Field(
|
||||
default=0, description="Number of tokens used in prompts."
|
||||
)
|
||||
cached_prompt_tokens: int = Field(
|
||||
default=0, description="Number of cached prompt tokens used."
|
||||
)
|
||||
completion_tokens: int = Field(
|
||||
default=0, description="Number of tokens used in completions."
|
||||
)
|
||||
@@ -32,5 +36,6 @@ class UsageMetrics(BaseModel):
|
||||
"""
|
||||
self.total_tokens += usage_metrics.total_tokens
|
||||
self.prompt_tokens += usage_metrics.prompt_tokens
|
||||
self.cached_prompt_tokens += usage_metrics.cached_prompt_tokens
|
||||
self.completion_tokens += usage_metrics.completion_tokens
|
||||
self.successful_requests += usage_metrics.successful_requests
|
||||
|
||||
@@ -16,7 +16,11 @@ class FileHandler:
|
||||
|
||||
def log(self, **kwargs):
|
||||
now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||||
message = f"{now}: " + ", ".join([f"{key}=\"{value}\"" for key, value in kwargs.items()]) + "\n"
|
||||
message = (
|
||||
f"{now}: "
|
||||
+ ", ".join([f'{key}="{value}"' for key, value in kwargs.items()])
|
||||
+ "\n"
|
||||
)
|
||||
with open(self._path, "a", encoding="utf-8") as file:
|
||||
file.write(message + "\n")
|
||||
|
||||
@@ -63,7 +67,7 @@ class PickleHandler:
|
||||
|
||||
with open(self.file_path, "rb") as file:
|
||||
try:
|
||||
return pickle.load(file)
|
||||
return pickle.load(file) # nosec
|
||||
except EOFError:
|
||||
return {} # Return an empty dictionary if the file is empty or corrupted
|
||||
except Exception:
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
from litellm.integrations.custom_logger import CustomLogger
|
||||
|
||||
from litellm.types.utils import Usage
|
||||
from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
|
||||
|
||||
|
||||
@@ -11,8 +11,11 @@ class TokenCalcHandler(CustomLogger):
|
||||
if self.token_cost_process is None:
|
||||
return
|
||||
|
||||
usage : Usage = response_obj["usage"]
|
||||
self.token_cost_process.sum_successful_requests(1)
|
||||
self.token_cost_process.sum_prompt_tokens(response_obj["usage"].prompt_tokens)
|
||||
self.token_cost_process.sum_completion_tokens(
|
||||
response_obj["usage"].completion_tokens
|
||||
)
|
||||
self.token_cost_process.sum_prompt_tokens(usage.prompt_tokens)
|
||||
self.token_cost_process.sum_completion_tokens(usage.completion_tokens)
|
||||
if usage.prompt_tokens_details:
|
||||
self.token_cost_process.sum_cached_prompt_tokens(
|
||||
usage.prompt_tokens_details.cached_tokens
|
||||
)
|
||||
|
||||
449
tests/cassettes/test_after_crew_modification.yaml
Normal file
449
tests/cassettes/test_after_crew_modification.yaml
Normal file
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tests/config/agents.yaml
Normal file
21
tests/config/agents.yaml
Normal file
@@ -0,0 +1,21 @@
|
||||
researcher:
|
||||
role: >
|
||||
{topic} Senior Data Researcher
|
||||
goal: >
|
||||
Uncover cutting-edge developments in {topic}
|
||||
backstory: >
|
||||
You're a seasoned researcher with a knack for uncovering the latest
|
||||
developments in {topic}. Known for your ability to find the most relevant
|
||||
information and present it in a clear and concise manner.
|
||||
verbose: true
|
||||
|
||||
reporting_analyst:
|
||||
role: >
|
||||
{topic} Reporting Analyst
|
||||
goal: >
|
||||
Create detailed reports based on {topic} data analysis and research findings
|
||||
backstory: >
|
||||
You're a meticulous analyst with a keen eye for detail. You're known for
|
||||
your ability to turn complex data into clear and concise reports, making
|
||||
it easy for others to understand and act on the information you provide.
|
||||
verbose: true
|
||||
17
tests/config/tasks.yaml
Normal file
17
tests/config/tasks.yaml
Normal file
@@ -0,0 +1,17 @@
|
||||
research_task:
|
||||
description: >
|
||||
Conduct a thorough research about {topic}
|
||||
Make sure you find any interesting and relevant information given
|
||||
the current year is 2024.
|
||||
expected_output: >
|
||||
A list with 10 bullet points of the most relevant information about {topic}
|
||||
agent: researcher
|
||||
|
||||
reporting_task:
|
||||
description: >
|
||||
Review the context you got and expand each topic into a full section for a report.
|
||||
Make sure the report is detailed and contains any and all relevant information.
|
||||
expected_output: >
|
||||
A fully fledge reports with the mains topics, each with a full section of information.
|
||||
Formatted as markdown without '```'
|
||||
agent: reporting_analyst
|
||||
@@ -564,6 +564,7 @@ def test_crew_kickoff_usage_metrics():
|
||||
assert result.token_usage.prompt_tokens > 0
|
||||
assert result.token_usage.completion_tokens > 0
|
||||
assert result.token_usage.successful_requests > 0
|
||||
assert result.token_usage.cached_prompt_tokens == 0
|
||||
|
||||
|
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def test_agents_rpm_is_never_set_if_crew_max_RPM_is_not_set():
|
||||
@@ -1280,10 +1281,11 @@ def test_agent_usage_metrics_are_captured_for_hierarchical_process():
|
||||
assert result.raw == "Howdy!"
|
||||
|
||||
assert result.token_usage == UsageMetrics(
|
||||
total_tokens=2626,
|
||||
prompt_tokens=2482,
|
||||
completion_tokens=144,
|
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successful_requests=5,
|
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total_tokens=1673,
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prompt_tokens=1562,
|
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completion_tokens=111,
|
||||
successful_requests=3,
|
||||
cached_prompt_tokens=0
|
||||
)
|
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|
||||
|
||||
@@ -1777,26 +1779,22 @@ def test_crew_train_success(
|
||||
]
|
||||
)
|
||||
|
||||
crew_training_handler.assert_has_calls(
|
||||
[
|
||||
mock.call("training_data.pkl"),
|
||||
mock.call().load(),
|
||||
mock.call("trained_agents_data.pkl"),
|
||||
mock.call().save_trained_data(
|
||||
agent_id="Researcher",
|
||||
trained_data=task_evaluator().evaluate_training_data().model_dump(),
|
||||
),
|
||||
mock.call("trained_agents_data.pkl"),
|
||||
mock.call().save_trained_data(
|
||||
agent_id="Senior Writer",
|
||||
trained_data=task_evaluator().evaluate_training_data().model_dump(),
|
||||
),
|
||||
mock.call(),
|
||||
mock.call().load(),
|
||||
mock.call(),
|
||||
mock.call().load(),
|
||||
]
|
||||
)
|
||||
crew_training_handler.assert_any_call("training_data.pkl")
|
||||
crew_training_handler().load.assert_called()
|
||||
|
||||
crew_training_handler.assert_any_call("trained_agents_data.pkl")
|
||||
crew_training_handler().load.assert_called()
|
||||
|
||||
crew_training_handler().save_trained_data.assert_has_calls([
|
||||
mock.call(
|
||||
agent_id="Researcher",
|
||||
trained_data=task_evaluator().evaluate_training_data().model_dump(),
|
||||
),
|
||||
mock.call(
|
||||
agent_id="Senior Writer",
|
||||
trained_data=task_evaluator().evaluate_training_data().model_dump(),
|
||||
)
|
||||
])
|
||||
|
||||
|
||||
def test_crew_train_error():
|
||||
|
||||
30
tests/llm_test.py
Normal file
30
tests/llm_test.py
Normal file
@@ -0,0 +1,30 @@
|
||||
import pytest
|
||||
|
||||
from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
|
||||
from crewai.llm import LLM
|
||||
from crewai.utilities.token_counter_callback import TokenCalcHandler
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
def test_llm_callback_replacement():
|
||||
llm = LLM(model="gpt-4o-mini")
|
||||
|
||||
calc_handler_1 = TokenCalcHandler(token_cost_process=TokenProcess())
|
||||
calc_handler_2 = TokenCalcHandler(token_cost_process=TokenProcess())
|
||||
|
||||
llm.call(
|
||||
messages=[{"role": "user", "content": "Hello, world!"}],
|
||||
callbacks=[calc_handler_1],
|
||||
)
|
||||
usage_metrics_1 = calc_handler_1.token_cost_process.get_summary()
|
||||
|
||||
llm.call(
|
||||
messages=[{"role": "user", "content": "Hello, world from another agent!"}],
|
||||
callbacks=[calc_handler_2],
|
||||
)
|
||||
usage_metrics_2 = calc_handler_2.token_cost_process.get_summary()
|
||||
|
||||
# The first handler should not have been updated
|
||||
assert usage_metrics_1.successful_requests == 1
|
||||
assert usage_metrics_2.successful_requests == 1
|
||||
assert usage_metrics_1 == calc_handler_1.token_cost_process.get_summary()
|
||||
270
tests/memory/cassettes/test_save_and_search_with_provider.yaml
Normal file
270
tests/memory/cassettes/test_save_and_search_with_provider.yaml
Normal file
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import pytest
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|
||||
from crewai.agent import Agent
|
||||
from crewai.project import agent, task
|
||||
from crewai.crew import Crew
|
||||
from crewai.project import CrewBase, after_kickoff, agent, before_kickoff, crew, task
|
||||
from crewai.task import Task
|
||||
|
||||
|
||||
@@ -23,6 +26,43 @@ class SimpleCrew:
|
||||
)
|
||||
|
||||
|
||||
@CrewBase
|
||||
class TestCrew:
|
||||
agents_config = "config/agents.yaml"
|
||||
tasks_config = "config/tasks.yaml"
|
||||
|
||||
@agent
|
||||
def researcher(self):
|
||||
return Agent(config=self.agents_config["researcher"])
|
||||
|
||||
@agent
|
||||
def reporting_analyst(self):
|
||||
return Agent(config=self.agents_config["reporting_analyst"])
|
||||
|
||||
@task
|
||||
def research_task(self):
|
||||
return Task(config=self.tasks_config["research_task"])
|
||||
|
||||
@task
|
||||
def reporting_task(self):
|
||||
return Task(config=self.tasks_config["reporting_task"])
|
||||
|
||||
@before_kickoff
|
||||
def modify_inputs(self, inputs):
|
||||
if inputs:
|
||||
inputs["topic"] = "Bicycles"
|
||||
return inputs
|
||||
|
||||
@after_kickoff
|
||||
def modify_outputs(self, outputs):
|
||||
outputs.raw = outputs.raw + " post processed"
|
||||
return outputs
|
||||
|
||||
@crew
|
||||
def crew(self):
|
||||
return Crew(agents=self.agents, tasks=self.tasks, verbose=True)
|
||||
|
||||
|
||||
def test_agent_memoization():
|
||||
crew = SimpleCrew()
|
||||
first_call_result = crew.simple_agent()
|
||||
@@ -43,6 +83,16 @@ def test_task_memoization():
|
||||
), "Task memoization is not working as expected"
|
||||
|
||||
|
||||
def test_crew_memoization():
|
||||
crew = TestCrew()
|
||||
first_call_result = crew.crew()
|
||||
second_call_result = crew.crew()
|
||||
|
||||
assert (
|
||||
first_call_result is second_call_result
|
||||
), "Crew references should point to the same object"
|
||||
|
||||
|
||||
def test_task_name():
|
||||
simple_task = SimpleCrew().simple_task()
|
||||
assert (
|
||||
@@ -53,3 +103,84 @@ def test_task_name():
|
||||
assert (
|
||||
custom_named_task.name == "Custom"
|
||||
), "Custom task name is not being set as expected"
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
def test_before_kickoff_modification():
|
||||
crew = TestCrew()
|
||||
inputs = {"topic": "LLMs"}
|
||||
result = crew.crew().kickoff(inputs=inputs)
|
||||
assert "bicycles" in result.raw, "Before kickoff function did not modify inputs"
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
def test_after_kickoff_modification():
|
||||
crew = TestCrew()
|
||||
# Assuming the crew execution returns a dict
|
||||
result = crew.crew().kickoff({"topic": "LLMs"})
|
||||
|
||||
assert (
|
||||
"post processed" in result.raw
|
||||
), "After kickoff function did not modify outputs"
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
def test_before_kickoff_with_none_input():
|
||||
crew = TestCrew()
|
||||
crew.crew().kickoff(None)
|
||||
# Test should pass without raising exceptions
|
||||
|
||||
|
||||
@pytest.mark.vcr(filter_headers=["authorization"])
|
||||
def test_multiple_before_after_kickoff():
|
||||
@CrewBase
|
||||
class MultipleHooksCrew:
|
||||
agents_config = "config/agents.yaml"
|
||||
tasks_config = "config/tasks.yaml"
|
||||
|
||||
@agent
|
||||
def researcher(self):
|
||||
return Agent(config=self.agents_config["researcher"])
|
||||
|
||||
@agent
|
||||
def reporting_analyst(self):
|
||||
return Agent(config=self.agents_config["reporting_analyst"])
|
||||
|
||||
@task
|
||||
def research_task(self):
|
||||
return Task(config=self.tasks_config["research_task"])
|
||||
|
||||
@task
|
||||
def reporting_task(self):
|
||||
return Task(config=self.tasks_config["reporting_task"])
|
||||
|
||||
@before_kickoff
|
||||
def first_before(self, inputs):
|
||||
inputs["topic"] = "Bicycles"
|
||||
return inputs
|
||||
|
||||
@before_kickoff
|
||||
def second_before(self, inputs):
|
||||
inputs["topic"] = "plants"
|
||||
return inputs
|
||||
|
||||
@after_kickoff
|
||||
def first_after(self, outputs):
|
||||
outputs.raw = outputs.raw + " processed first"
|
||||
return outputs
|
||||
|
||||
@after_kickoff
|
||||
def second_after(self, outputs):
|
||||
outputs.raw = outputs.raw + " processed second"
|
||||
return outputs
|
||||
|
||||
@crew
|
||||
def crew(self):
|
||||
return Crew(agents=self.agents, tasks=self.tasks, verbose=True)
|
||||
|
||||
crew = MultipleHooksCrew()
|
||||
result = crew.crew().kickoff({"topic": "LLMs"})
|
||||
|
||||
assert "plants" in result.raw, "First before_kickoff not executed"
|
||||
assert "processed first" in result.raw, "First after_kickoff not executed"
|
||||
assert "processed second" in result.raw, "Second after_kickoff not executed"
|
||||
|
||||
98
uv.lock
generated
98
uv.lock
generated
@@ -490,28 +490,32 @@ wheels = [
|
||||
|
||||
[[package]]
|
||||
name = "chroma-hnswlib"
|
||||
version = "0.7.3"
|
||||
version = "0.7.6"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "numpy" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/c0/59/1224cbae62c7b84c84088cdf6c106b9b2b893783c000d22c442a1672bc75/chroma-hnswlib-0.7.3.tar.gz", hash = "sha256:b6137bedde49fffda6af93b0297fe00429fc61e5a072b1ed9377f909ed95a932", size = 31876 }
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sdist = { url = "https://files.pythonhosted.org/packages/73/09/10d57569e399ce9cbc5eee2134996581c957f63a9addfa6ca657daf006b8/chroma_hnswlib-0.7.6.tar.gz", hash = "sha256:4dce282543039681160259d29fcde6151cc9106c6461e0485f57cdccd83059b7", size = 32256 }
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wheels = [
|
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{ url = "https://files.pythonhosted.org/packages/1a/36/d1069ffa520efcf93f6d81b527e3c7311e12363742fdc786cbdaea3ab02e/chroma_hnswlib-0.7.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:59d6a7c6f863c67aeb23e79a64001d537060b6995c3eca9a06e349ff7b0998ca", size = 219588 },
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{ url = "https://files.pythonhosted.org/packages/c3/e8/263d331f5ce29367f6f8854cd7fa1f54fce72ab4f92ab957525ef9165a9c/chroma_hnswlib-0.7.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:d71a3f4f232f537b6152947006bd32bc1629a8686df22fd97777b70f416c127a", size = 197094 },
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{ url = "https://files.pythonhosted.org/packages/a9/72/a9b61ae00d490c26359a8e10f3974c0d38065b894e6a2573ec6a7597f8e3/chroma_hnswlib-0.7.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1c92dc1ebe062188e53970ba13f6b07e0ae32e64c9770eb7f7ffa83f149d4210", size = 2315620 },
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{ url = "https://files.pythonhosted.org/packages/2f/48/f7609a3cb15a24c5d8ec18911ce10ac94144e9a89584f0a86bf9871b024c/chroma_hnswlib-0.7.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:49da700a6656fed8753f68d44b8cc8ae46efc99fc8a22a6d970dc1697f49b403", size = 2350956 },
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{ url = "https://files.pythonhosted.org/packages/cc/3d/ca311b8f79744db3f4faad8fd9140af80d34c94829d3ed1726c98cf4a611/chroma_hnswlib-0.7.3-cp310-cp310-win_amd64.whl", hash = "sha256:108bc4c293d819b56476d8f7865803cb03afd6ca128a2a04d678fffc139af029", size = 150598 },
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{ url = "https://files.pythonhosted.org/packages/94/3f/844393b0d2ea1072b7704d6eff5c595e05ae8b831b96340cdb76b2fe995c/chroma_hnswlib-0.7.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:11e7ca93fb8192214ac2b9c0943641ac0daf8f9d4591bb7b73be808a83835667", size = 221219 },
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{ url = "https://files.pythonhosted.org/packages/ba/f4/c81a40da5473d5d80fc9d0c5bd5b1cb64e530a6ea941c69f195fe81c488c/chroma_hnswlib-0.7.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f96f4d5699e486eb1fb95849fe35ab79ab0901265805be7e60f4eaa83ce263ec", size = 2332260 },
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{ url = "https://files.pythonhosted.org/packages/48/0e/068b658a547d6090b969014146321e28dae1411da54b76d081e51a2af22b/chroma_hnswlib-0.7.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:368e57fe9ebae05ee5844840fa588028a023d1182b0cfdb1d13f607c9ea05756", size = 2367211 },
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{ url = "https://files.pythonhosted.org/packages/d2/32/a91850c7aa8a34f61838913155103808fe90da6f1ea4302731b59e9ba6f2/chroma_hnswlib-0.7.3-cp311-cp311-win_amd64.whl", hash = "sha256:b7dca27b8896b494456db0fd705b689ac6b73af78e186eb6a42fea2de4f71c6f", size = 151574 },
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{ url = "https://files.pythonhosted.org/packages/fd/58/101bfa6bc41bc6cc55fbb5103c75462a7bf882e1704256eb4934df85b6a8/chroma_hnswlib-0.7.6-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:6f007b608c96362b8f0c8b6b2ac94f67f83fcbabd857c378ae82007ec92f4d82", size = 183854 },
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{ url = "https://files.pythonhosted.org/packages/17/ff/95d49bb5ce134f10d6aa08d5f3bec624eaff945f0b17d8c3fce888b9a54a/chroma_hnswlib-0.7.6-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:456fd88fa0d14e6b385358515aef69fc89b3c2191706fd9aee62087b62aad09c", size = 2358774 },
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{ url = "https://files.pythonhosted.org/packages/3a/6d/27826180a54df80dbba8a4f338b022ba21c0c8af96fd08ff8510626dee8f/chroma_hnswlib-0.7.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5dfaae825499c2beaa3b75a12d7ec713b64226df72a5c4097203e3ed532680da", size = 2392739 },
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{ url = "https://files.pythonhosted.org/packages/d6/63/ee3e8b7a8f931918755faacf783093b61f32f59042769d9db615999c3de0/chroma_hnswlib-0.7.6-cp310-cp310-win_amd64.whl", hash = "sha256:2487201982241fb1581be26524145092c95902cb09fc2646ccfbc407de3328ec", size = 150955 },
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{ url = "https://files.pythonhosted.org/packages/0d/19/aa6f2139f1ff7ad23a690ebf2a511b2594ab359915d7979f76f3213e46c4/chroma_hnswlib-0.7.6-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:4b4ab4e11f1083dd0a11ee4f0e0b183ca9f0f2ed63ededba1935b13ce2b3606f", size = 185000 },
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||||
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||||
{ url = "https://files.pythonhosted.org/packages/93/ac/782b8d72de1c57b64fdf5cb94711540db99a92768d93d973174c62d45eb8/chroma_hnswlib-0.7.6-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:e87e9b616c281bfbe748d01705817c71211613c3b063021f7ed5e47173556cb7", size = 197804 },
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||||
{ url = "https://files.pythonhosted.org/packages/32/4e/fd9ce0764228e9a98f6ff46af05e92804090b5557035968c5b4198bc7af9/chroma_hnswlib-0.7.6-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:ec5ca25bc7b66d2ecbf14502b5729cde25f70945d22f2aaf523c2d747ea68912", size = 185421 },
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||||
{ url = "https://files.pythonhosted.org/packages/d9/3d/b59a8dedebd82545d873235ef2d06f95be244dfece7ee4a1a6044f080b18/chroma_hnswlib-0.7.6-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:305ae491de9d5f3c51e8bd52d84fdf2545a4a2bc7af49765cda286b7bb30b1d4", size = 2389672 },
|
||||
{ url = "https://files.pythonhosted.org/packages/74/1e/80a033ea4466338824974a34f418e7b034a7748bf906f56466f5caa434b0/chroma_hnswlib-0.7.6-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:822ede968d25a2c88823ca078a58f92c9b5c4142e38c7c8b4c48178894a0a3c5", size = 2436986 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "chromadb"
|
||||
version = "0.4.24"
|
||||
version = "0.5.18"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "bcrypt" },
|
||||
@@ -519,6 +523,7 @@ dependencies = [
|
||||
{ name = "chroma-hnswlib" },
|
||||
{ name = "fastapi" },
|
||||
{ name = "grpcio" },
|
||||
{ name = "httpx" },
|
||||
{ name = "importlib-resources" },
|
||||
{ name = "kubernetes" },
|
||||
{ name = "mmh3" },
|
||||
@@ -531,11 +536,10 @@ dependencies = [
|
||||
{ name = "orjson" },
|
||||
{ name = "overrides" },
|
||||
{ name = "posthog" },
|
||||
{ name = "pulsar-client" },
|
||||
{ name = "pydantic" },
|
||||
{ name = "pypika" },
|
||||
{ name = "pyyaml" },
|
||||
{ name = "requests" },
|
||||
{ name = "rich" },
|
||||
{ name = "tenacity" },
|
||||
{ name = "tokenizers" },
|
||||
{ name = "tqdm" },
|
||||
@@ -543,9 +547,9 @@ dependencies = [
|
||||
{ name = "typing-extensions" },
|
||||
{ name = "uvicorn", extra = ["standard"] },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/47/6b/a5465827d8017b658d18ad1e63d2dc31109dec717c6bd068e82485186f4b/chromadb-0.4.24.tar.gz", hash = "sha256:a5c80b4e4ad9b236ed2d4899a5b9e8002b489293f2881cb2cadab5b199ee1c72", size = 13667084 }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/15/95/d1a3f14c864e37d009606b82bd837090902b5e5a8e892fcab07eeaec0438/chromadb-0.5.18.tar.gz", hash = "sha256:cfbb3e5aeeb1dd532b47d80ed9185e8a9886c09af41c8e6123edf94395d76aec", size = 33620708 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/cc/63/b7d76109331318423f9cfb89bd89c99e19f5d0b47a5105439a629224d297/chromadb-0.4.24-py3-none-any.whl", hash = "sha256:3a08e237a4ad28b5d176685bd22429a03717fe09d35022fb230d516108da01da", size = 525452 },
|
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{ url = "https://files.pythonhosted.org/packages/82/85/4d2f8b9202153105ad4514ae09e9fe6f3b353a45e44e0ef7eca03dd8b9dc/chromadb-0.5.18-py3-none-any.whl", hash = "sha256:9dd3827b5e04b4ff0a5ea0df28a78bac88a09f45be37fcd7fe20f879b57c43cf", size = 615499 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@@ -604,7 +608,7 @@ wheels = [
|
||||
|
||||
[[package]]
|
||||
name = "crewai"
|
||||
version = "0.76.9"
|
||||
version = "0.80.0"
|
||||
source = { editable = "." }
|
||||
dependencies = [
|
||||
{ name = "appdirs" },
|
||||
@@ -634,6 +638,9 @@ dependencies = [
|
||||
agentops = [
|
||||
{ name = "agentops" },
|
||||
]
|
||||
mem0 = [
|
||||
{ name = "mem0ai" },
|
||||
]
|
||||
tools = [
|
||||
{ name = "crewai-tools" },
|
||||
]
|
||||
@@ -663,15 +670,16 @@ requires-dist = [
|
||||
{ name = "agentops", marker = "extra == 'agentops'", specifier = ">=0.3.0" },
|
||||
{ name = "appdirs", specifier = ">=1.4.4" },
|
||||
{ name = "auth0-python", specifier = ">=4.7.1" },
|
||||
{ name = "chromadb", specifier = ">=0.4.24" },
|
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{ name = "chromadb", specifier = ">=0.5.18" },
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Reference in New Issue
Block a user