feat: supervsior crew

apps/ai_api/.env.example

@@ -1,7 +1,5 @@
 IS_DEBUG=True
 API_KEY=sample_api_key
-IS_DEBUG=True
-API_KEY=sample_api_key
 OPENAI_API_KEY=sk-proj-xxxx
 CEREBRAS_API_KEY=csk-xxxx
 SERPER_API_KEY=xxxx

apps/ai_api/eda_ai_api/api/routes/router.py

@@ -1,7 +1,8 @@
 from fastapi import APIRouter
 
-from eda_ai_api.api.routes import grant, heartbeat
+from eda_ai_api.api.routes import grant, heartbeat, supervisor
 
 api_router = APIRouter()
 api_router.include_router(heartbeat.router, tags=["health"], prefix="/health")
 api_router.include_router(grant.router, tags=["discovery"], prefix="/grant")
+api_router.include_router(supervisor.router, tags=["supervisor"], prefix="/supervisor")

apps/ai_api/eda_ai_api/api/routes/supervisor.py

@@ -0,0 +1,69 @@
+from fastapi import APIRouter
+from eda_ai_api.models.supervisor import SupervisorRequest, SupervisorResponse
+from crewai import Agent, Crew, Task, Process
+from crewai.project import CrewBase, agent, crew, task
+from opportunity_finder.crew import OpportunityFinderCrew
+from crewai import LLM
+import os
+
+router = APIRouter()
+
+
+# Use the same Cerebras LLM configuration as in OpportunityFinderCrew
+cerebras_llm = LLM(
+    model="sambanova/Meta-Llama-3.1-70B-Instruct",
+    api_key=os.environ.get("SAMBANOVA_API_KEY"),
+    # base_url="https://api.cerebras.ai/v1",
+    temperature=0.5,
+)
+
+
+@CrewBase
+class SupervisorCrew:
+    @agent
+    def decision_maker(self) -> Agent:
+        return Agent(
+            role="Decision Maker",
+            goal="Analyze user requests and route them to appropriate endpoints",
+            backstory="I am an AI supervisor that determines which endpoint to call based on user messages",
+            allow_delegation=False,
+            llm=cerebras_llm,  # Add explicit LLM configuration
+        )
+
+    @task
+    def route_decision_task(self, message: str) -> Task:
+        return Task(
+            description=f"""
+            Analyze this message: "{message}"
+            Choose between these endpoints:
+            1. discovery - For finding grant opportunities
+            2. heartbeat - For checking system health
+            
+            Return only: "discovery" or "heartbeat"
+            """,
+            agent=self.decision_maker,
+        )
+
+    @crew
+    def crew(self) -> Crew:
+        return Crew(
+            agents=[self.decision_maker],
+            tasks=[self.route_decision_task],
+            process=Process.sequential,
+            verbose=True,
+        )
+
+
+@router.post("/supervisor", response_model=SupervisorResponse)
+async def supervisor_route(request: SupervisorRequest) -> SupervisorResponse:
+    supervisor = SupervisorCrew()
+    decision = supervisor.crew().kickoff(inputs={"message": request.message})
+
+    if decision.lower() == "discovery":
+        topics = ["AI", "Technology"]  # Extract topics from message
+        crew = OpportunityFinderCrew()
+        result = crew.crew().kickoff(inputs={"topics": ", ".join(topics)})
+    else:
+        result = {"is_alive": True}
+
+    return SupervisorResponse(result=str(result))

apps/ai_api/eda_ai_api/models/supervisor.py

@@ -0,0 +1,9 @@
+from pydantic import BaseModel
+
+
+class SupervisorResponse(BaseModel):
+    result: str
+
+
+class SupervisorRequest(BaseModel):
+    message: str

plugins/supervisor/.env.example

@@ -0,0 +1,6 @@
+OPENAI_API_KEY=sk-xxxx
+# GROQ_API_KEY=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
+CEREBRAS_API_KEY=csk-xxxx
+SERPER_API_KEY=xxxx
+LANGTRACE_API_KEY=xxxx
+LANGTRACE_API_HOST=http://localhost:3000/api/trace

plugins/supervisor/.gitignore

@@ -0,0 +1,2 @@
+.env
+__pycache__/

plugins/supervisor/README.md

@@ -0,0 +1,54 @@
+# OpportunityFinder Crew
+
+Welcome to the OpportunityFinder Crew project, powered by [crewAI](https://crewai.com). This template is designed to help you set up a multi-agent AI system with ease, leveraging the powerful and flexible framework provided by crewAI. Our goal is to enable your agents to collaborate effectively on complex tasks, maximizing their collective intelligence and capabilities.
+
+## Installation
+
+Ensure you have Python >=3.10 <=3.13 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
+
+First, if you haven't already, install uv:
+
+```bash
+pip install uv
+```
+
+Next, navigate to your project directory and install the dependencies:
+
+(Optional) Lock the dependencies and install them by using the CLI command:
+```bash
+crewai install
+```
+### Customizing
+
+**Add your `OPENAI_API_KEY` into the `.env` file**
+
+- Modify `src/opportunity_finder/config/agents.yaml` to define your agents
+- Modify `src/opportunity_finder/config/tasks.yaml` to define your tasks
+- Modify `src/opportunity_finder/crew.py` to add your own logic, tools and specific args
+- Modify `src/opportunity_finder/main.py` to add custom inputs for your agents and tasks
+
+## Running the Project
+
+To kickstart your crew of AI agents and begin task execution, run this from the root folder of your project:
+
+```bash
+$ crewai run
+```
+
+This command initializes the opportunity-finder Crew, assembling the agents and assigning them tasks as defined in your configuration.
+
+This example, unmodified, will run the create a `report.md` file with the output of a research on LLMs in the root folder.
+
+## Understanding Your Crew
+
+The opportunity-finder Crew is composed of multiple AI agents, each with unique roles, goals, and tools. These agents collaborate on a series of tasks, defined in `config/tasks.yaml`, leveraging their collective skills to achieve complex objectives. The `config/agents.yaml` file outlines the capabilities and configurations of each agent in your crew.
+
+## Support
+
+For support, questions, or feedback regarding the OpportunityFinder Crew or crewAI.
+- Visit our [documentation](https://docs.crewai.com)
+- Reach out to us through our [GitHub repository](https://github.com/joaomdmoura/crewai)
+- [Join our Discord](https://discord.com/invite/X4JWnZnxPb)
+- [Chat with our docs](https://chatg.pt/DWjSBZn)
+
+Let's create wonders together with the power and simplicity of crewAI.

plugins/supervisor/pyproject.toml

@@ -0,0 +1,17 @@
+[project]
+name = "supervisor"
+version = "0.1.0"
+description = "Supervisor crew using crewAI"
+authors = [{ name = "Your Name", email = "[email protected]" }]
+requires-python = ">=3.10,<=3.13"
+dependencies = [
+    "crewai[agentops,tools]>=0.76.2,<1.0.0",
+]
+
+[project.scripts]
+supervisor = "supervisor.main:run"
+run_crew = "supervisor.main:run"
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"

plugins/supervisor/src/opportunity_finder/config/agents.yaml

@@ -0,0 +1,4 @@
+decision_maker:
+  role: "Decision Maker"
+  goal: "Analyze user requests and route them to appropriate endpoints"
+  backstory: "You're an expert supervisor that determines which endpoint to call based on user messages. You understand the context and requirements for different types of requests and can accurately route them to the correct service."

plugins/supervisor/src/opportunity_finder/config/tasks.yaml

@@ -0,0 +1,9 @@
+route_decision_task:
+  description: >
+    Analyze the user message and determine the appropriate service to handle the request.
+    Consider the context and requirements to route between:
+    1. discovery - For finding grant opportunities
+    2. heartbeat - For checking system health
+  expected_output: >
+    A single word response: either "discovery" or "heartbeat"
+  agent: decision_maker

plugins/supervisor/src/opportunity_finder/crew.py

@@ -0,0 +1,43 @@
+import os
+import datetime
+from crewai import Agent, Crew, Process, Task, LLM
+from crewai.project import CrewBase, agent, crew, task
+
+cerebras_llm = LLM(
+    model="cerebras/llama3.1-70b",
+    api_key=os.environ.get("CEREBRAS_API_KEY"),
+    base_url="https://api.cerebras.ai/v1",
+    temperature=0.5,
+)
+
+
+@CrewBase
+class SupervisorCrew:
+    """Supervisor crew"""
+
+    @agent
+    def decision_maker(self) -> Agent:
+        return Agent(
+            config=self.agents_config["decision_maker"],
+            verbose=True,
+            allow_delegation=False,
+            llm=cerebras_llm,
+        )
+
+    @task
+    def route_decision_task(self, message: str) -> Task:
+        return Task(
+            config=self.tasks_config["route_decision_task"],
+            context={"message": message},
+        )
+
+    @crew
+    def crew(self) -> Crew:
+        """Creates the Supervisor crew"""
+        return Crew(
+            agents=[self.decision_maker],
+            tasks=[self.route_decision_task],
+            process=Process.sequential,
+            verbose=True,
+            output_log_file=f'SupervisorCrew_{datetime.datetime.now().strftime("%Y%m%d_%H%M%S")}.log',
+        )

plugins/supervisor/src/opportunity_finder/main.py

@@ -0,0 +1,14 @@
+from supervisor.crew import SupervisorCrew
+
+
+def run():
+    """Run the supervisor crew"""
+    crew = SupervisorCrew()
+    result = crew.crew().kickoff(
+        inputs={"message": "Find grant opportunities for AI projects"}
+    )
+    print(f"Decision: {result}")
+
+
+if __name__ == "__main__":
+    run()

plugins/supervisor/src/opportunity_finder/tools/custom_tool.py

@@ -0,0 +1,12 @@
+from crewai_tools import BaseTool
+
+
+class MyCustomTool(BaseTool):
+    name: str = "Name of my tool"
+    description: str = (
+        "Clear description for what this tool is useful for, you agent will need this information to use it."
+    )
+
+    def _run(self, argument: str) -> str:
+        # Implementation goes here
+        return "this is an example of a tool output, ignore it and move along."