dbos-crewai 0.0.2
CrewAI DBOS durable agent
Requires Python: >=3.10

# CrewAI DBOS durable agent

> [!NOTE]
> This repo is work-in-progress. Don't use it in production yet :)

This repo demonstrates how to add durable execution support into CrewAI agents.
It integrates DBOS with `CrewAgentExecutor.invoke` and related methods to provide out-of-the-box durable execution and checkpointing.

This is based on PR: https://github.com/crewAIInc/crewAI/pull/3526


### Overview

* **File Structure:**
  * The `dbos_crewai/` folder contains all the relevant files.
    * `dbos_agent.py`: the main entrypoint for using DBOS agents.
    * `dbos_agent_executor.py`: executor for managing the agent main loop.
    * `dbos_llm.py`: wrapping llm calls as DBOS steps.
    * `dbos_util.py`: define `StepConfig` for configurable step retries.
  * Tests are under `tests/test_dbos_agent.py`.

* **Workflows:**

  * `DBOSAgentExecutor.invoke` is automatically decorated a DBOS workflow.

* **Steps:**

  * LLM requests are automatically decorated as DBOS steps.
  * Outside DBOS, these remain ordinary functions.
  * Inside DBOS workflows, step outputs are automatically checkpointed in Postgres.

* **Tooling:**

  * Users may pass in DBOS-decorated functions (workflows, steps, transactions) as tools or event handlers.
  * The integration does *not* automatically wrap tools in DBOS decorators, so users retain full control. For example, they can pass in a workflow which will be invoked as a child workflow; or they can pass in a step.
  * Tools behave as normal functions when not used with DBOS.

### Example

To use the integration, users only need to add a few lines of DBOS code on top of their existing agent code. Here is an example of using CrewAI with DBOS (this example is complete, it can be run "as is").

```python
from crewai import Agent, Task
from crewai.tools import tool
from dbos_crewai import DBOSAgent
from dbos import DBOS, SetWorkflowID, DBOSConfig
import os

config: DBOSConfig = {
    "name": "dbos-crewai-starter",
    "system_database_url": os.environ.get("DBOS_SYSTEM_DATABASE_URL"),
    "conductor_key": os.environ.get("DBOS_CONDUCTOR_KEY"),
}
DBOS(config=config)

@tool
@DBOS.step()  # Decorate this function as a DBOS step
def multiplier(first_number: int, second_number: int) -> float:
    """Useful for when you need to multiply two numbers together."""
    return first_number * second_number

# Agent declaration remains the same
orig_agent = Agent(
    role="test role",
    goal="test goal",
    backstory="test backstory",
    tools=[multiplier],
    allow_delegation=False,
)

# Wrap the original agent in a DBOS agent
dbos_agent = DBOSAgent(
    agent_name="test_agent_execution_with_tools",
    orig_agent=orig_agent,
)

task = Task(
    description="What is 3 times 4?",
    agent=dbos_agent,
    expected_output="The result of the multiplication.",
)

def main():
    # Launch DBOS before running workflows
    DBOS.launch()
    # Optionally set a workflow ID for tracking progress. If unspecified, a UUID will be generated as the ID.
    with SetWorkflowID("test_execution"):
        # The main agent execution loop is automatically a DBOS workflow, and the LLM calls are DBOS steps. Tools that are annotated with DBOS.step() are also DBOS steps.
        output = dbos_agent.execute_task(task)
    print(output)

if __name__ == "__main__":
    main()
```