# OpenAI Rate Limit Error - Fix Summary
## Problem
You encountered an OpenAI API quota error (429 - insufficient quota) even though you configured Groq as your LLM:
```
openai.RateLimitError: Error code: 429 - {'error': {'message': 'You exceeded your current quota, please check your plan and billing details...'}}
```
## Root Cause
CrewAI was defaulting to OpenAI's API in several places:
1. **Agents without explicit LLM**: When agents don't have an LLM specified, CrewAI defaults to OpenAI
2. **Crew manager operations**: Some internal operations may use OpenAI by default
3. **AgentOps instrumentation**: LLM call instrumentation might trigger OpenAI calls
## Solutions Applied
### 1. ✅ Assigned Groq LLM to All Agents
Added `llm=llm` parameter to both agents:
```python
def agent_one(self) -> Agent:
return Agent(
role="Data Analyst",
goal="Analyze data trends in the market",
backstory="An experienced data analyst with a background in economics",
verbose=True,
llm=llm, # Use Groq LLM
)
```
### 2. ✅ Set Manager LLM in Crew
Added `manager_llm=llm` to the Crew configuration:
```python
def crew(self) -> Crew:
return Crew(
agents=[self.agent_one(), self.agent_two()],
tasks=[self.task_one(), self.task_two()],
process=Process.sequential,
verbose=True,
manager_llm=llm, # Use Groq for any manager operations
)
```
### 3. ✅ Disabled AgentOps LLM Instrumentation
Changed `instrument_llm_calls` to `False`:
```python
agentops.init(os.getenv("AGENTOPS_API_KEY"), instrument_llm_calls=False)
```
## Result
✅ **Script now runs successfully using only Groq API**
✅ **No more OpenAI API calls**
✅ **Exit code: 0 (Success)**
## Verification
The logs show:
- `'split_model': 'llama-3.1-8b-instant'` - Groq model being used
- `'combined_model_name': 'groq/llama-3'` - Groq provider confirmed
- AgentOps session: https://app.agentops.ai/sessions?trace_id=7ef08cd01d82304364d8562665283721
## Alternative Solutions (If Issues Persist)
### Option A: Use Free Ollama Locally
```python
llm = LLM(
model="ollama/llama3.1",
base_url="http://localhost:11434"
)
```
### Option B: Use Free HuggingFace Models
```python
from langchain_huggingface import HuggingFaceEndpoint
llm = HuggingFaceEndpoint(
repo_id="meta-llama/Llama-3.1-8B-Instruct",
huggingfacehub_api_token=os.getenv("HUGGINGFACE_API_KEY")
)
```
### Option C: Add Dummy OpenAI Key (Not Recommended)
Add to `.env`:
```
OPENAI_API_KEY=sk-dummy-key-not-used
```
## Next Steps
Your code is now working! You can:
1. ✅ Run the script without OpenAI quota errors
2. Monitor your AgentOps dashboard for insights
3. Extend your crew with more agents and tasks
4. Consider using Ollama for completely free local inference
