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Merge branch 'main' of github.com:crewAIInc/crewAI into knowledge

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Lorenze Jay
2024-11-14 12:22:07 -08:00
parent 7b59c5b049 e70bc94ab6
commit 98a708ca15
41 changed files with 1242 additions and 244 deletions
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2
.github/workflows/security-checker.yml vendored
View File

@@ -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

3
docs/concepts/crews.mdx
View File

@@ -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`. |

132
docs/concepts/llms.mdx
View File

@@ -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>

42
docs/concepts/memory.mdx
View File

@@ -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
View File

@@ -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"

9
pyproject.toml
View File

@@ -1,6 +1,6 @@
[project]
name = "crewai"
version = "0.76.9"
version = "0.79.4"
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",
@@ -37,7 +37,7 @@ 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"]
fastembed = ["fastembed>=0.4.1"]
pdfplumber = [
@@ -49,6 +49,7 @@ pandas = [
openpyxl = [
"openpyxl>=3.1.5",
]
mem0 = ["mem0ai>=0.1.29"]
[tool.uv]
dev-dependencies = [
@@ -62,7 +63,7 @@ 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",

2
src/crewai/__init__.py
View File

@@ -16,7 +16,7 @@ warnings.filterwarnings(
category=UserWarning,
module="pydantic.main",
)
__version__ = "0.76.9"
__version__ = "0.79.4"
__all__ = [
"Agent",
"Crew",

74
src/crewai/agent.py
View File

@@ -132,6 +132,11 @@ 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):
@@ -155,39 +160,44 @@ 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:
@@ -267,9 +277,11 @@ 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() != "":

5
src/crewai/agents/agent_builder/utilities/base_token_process.py
View File

@@ -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,
)

44
src/crewai/agents/crew_agent_executor.py
View File

@@ -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}

6
src/crewai/cli/authentication/main.py
View File

@@ -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:

1
src/crewai/cli/run_crew.py
View File

@@ -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)

2
src/crewai/cli/templates/crew/pyproject.toml
View File

@@ -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]>=0.79.4,<1.0.0"
]
[project.scripts]

2
src/crewai/cli/templates/flow/pyproject.toml
View File

@@ -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]>=0.79.4,<1.0.0",
]
[project.scripts]

2
src/crewai/cli/templates/pipeline/pyproject.toml
View File

@@ -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 = ">=0.79.4,<1.0.0" }
asyncio = "*"
[tool.poetry.scripts]

2
src/crewai/cli/templates/pipeline_router/pyproject.toml
View File

@@ -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]>=0.79.4,<1.0.0"
]
[project.scripts]

2
src/crewai/cli/templates/tool/pyproject.toml
View File

@@ -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]>=0.79.4"
]

24
src/crewai/crew.py
View File

@@ -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
@@ -71,6 +72,7 @@ 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 +96,7 @@ 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 +117,10 @@ 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 +133,11 @@ 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.",
)
@@ -238,13 +249,22 @@ 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")

16
src/crewai/llm.py
View File

@@ -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

3
src/crewai/memory/__init__.py
View File

@@ -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"]

51
src/crewai/memory/contextual/contextual_memory.py
View File

@@ -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}"

42
src/crewai/memory/entity/entity_memory.py
View File

@@ -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=False,
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:

11
src/crewai/memory/memory.py
View File

@@ -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
)

39
src/crewai/memory/short_term/short_term_memory.py
View File

@@ -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:

6
src/crewai/memory/storage/interface.py
View File

@@ -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

2
src/crewai/memory/storage/kickoff_task_outputs_storage.py
View File

@@ -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))

2
src/crewai/memory/storage/ltm_sqlite_storage.py
View File

@@ -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
View 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

0
src/crewai/memory/user/__init__.py Normal file
View File

45
src/crewai/memory/user/user_memory.py Normal file
View 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
View 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 {}

0
src/crewai/tools/cache_tools/__init__.py Normal file
View File

5
src/crewai/types/usage_metrics.py
View 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

8
src/crewai/utilities/file_handler.py
View File

@@ -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:

13
src/crewai/utilities/token_counter_callback.py
View File

@@ -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
)

206
tests/cassettes/test_crew_kickoff_usage_metrics.yaml
View File

@@ -10,7 +10,8 @@ interactions:
criteria for your final answer: 1 bullet point about dog that''s under 15 words.\nyou
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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
View 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
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uv.lock generated
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