feat(devpulse_ai): switch to Gemini API preference

advanced_ai_agents/multi_agent_apps/devpulse_ai/README.md

@@ -69,10 +69,10 @@ cd advanced_ai_agents/multi_agent_apps/devpulse_ai
 pip install -r requirements.txt
 ```
 
-1. Set your OpenAI API key (optional for live mode)
+1. Set your Gemini API key (optional for live mode)
 
 ```bash
-export OPENAI_API_KEY=your_api_key
+export GOOGLE_API_KEY=your_api_key
 ```
 
 1. Run the verification script (no API key needed)
@@ -97,8 +97,8 @@ A modern, interactive dashboard is included to visualize the multi-agent pipelin
 streamlit run streamlit_app.py
 ```
 
-2. Configure sources and signal counts in the sidebar.
-2. Provide an OpenAI API key (optional) to use full LLM intelligence.
+1. Configure sources and signal counts in the sidebar.
+2. Provide a Gemini API key (optional) to use full LLM intelligence.
 3. View real-time progress as agents collaborate.
 
 > **Note**: The default configuration is optimized for fast demo runs.
@@ -163,5 +163,5 @@ devpulse_ai/
 ### Built With
 
 - [Agno](https://github.com/agno-agi/agno) - Multi-agent framework
-- [OpenAI GPT-4o-mini](https://openai.com/) - LLM backbone
+- [Google Gemini 1.5 Flash](https://ai.google.dev/) - LLM backbone
 - [httpx](https://www.python-httpx.org/) - Async HTTP client

advanced_ai_agents/multi_agent_apps/devpulse_ai/agents/relevance_agent.py

@@ -7,7 +7,7 @@ based on its relevance to AI/ML developers and engineers.
 
 from typing import Dict, Any, Optional
 from agno.agent import Agent
-from agno.models.openai import OpenAIChat
+from agno.models.google import Gemini
 
 
 class RelevanceAgent:
@@ -20,17 +20,17 @@ class RelevanceAgent:
     - Prioritize actionable, timely content
     """
     
-    def __init__(self, model_id: str = "gpt-4o-mini"):
+    def __init__(self, model_id: str = "gemini-1.5-flash"):
         """
         Initialize the Relevance Agent.
         
         Args:
-            model_id: OpenAI model to use for scoring.
+            model_id: Model ID to use for scoring.
         """
         self.model_id = model_id
         self.agent = Agent(
             name="Relevance Scorer",
-            model=OpenAIChat(id=model_id),
+            model=Gemini(id=model_id),
             role="Scores technical signals based on developer relevance",
             instructions=[
                 "Score each signal from 0-100 based on relevance.",

advanced_ai_agents/multi_agent_apps/devpulse_ai/agents/risk_agent.py

@@ -9,7 +9,7 @@ This agent analyzes signals for potential risks including:
 
 from typing import Dict, Any, List
 from agno.agent import Agent
-from agno.models.openai import OpenAIChat
+from agno.models.google import Gemini
 
 
 class RiskAgent:
@@ -25,17 +25,17 @@ class RiskAgent:
     
     RISK_LEVELS = ["LOW", "MEDIUM", "HIGH", "CRITICAL"]
     
-    def __init__(self, model_id: str = "gpt-4o-mini"):
+    def __init__(self, model_id: str = "gemini-1.5-flash"):
         """
         Initialize the Risk Agent.
         
         Args:
-            model_id: OpenAI model to use for risk assessment.
+            model_id: Model ID to use for risk assessment.
         """
         self.model_id = model_id
         self.agent = Agent(
             name="Risk Assessor",
-            model=OpenAIChat(id=model_id),
+            model=Gemini(id=model_id),
             role="Assesses security and breaking change risks in technical signals",
             instructions=[
                 "Analyze signals for security vulnerabilities.",

advanced_ai_agents/multi_agent_apps/devpulse_ai/agents/synthesis_agent.py

@@ -7,7 +7,7 @@ a comprehensive, actionable intelligence summary for developers.
 
 from typing import Dict, Any, List
 from agno.agent import Agent
-from agno.models.openai import OpenAIChat
+from agno.models.google import Gemini
 
 
 class SynthesisAgent:
@@ -21,17 +21,17 @@ class SynthesisAgent:
     - Produce actionable recommendations
     """
     
-    def __init__(self, model_id: str = "gpt-4o-mini"):
+    def __init__(self, model_id: str = "gemini-1.5-flash"):
         """
         Initialize the Synthesis Agent.
         
         Args:
-            model_id: OpenAI model to use for synthesis.
+            model_id: Model ID to use for synthesis.
         """
         self.model_id = model_id
         self.agent = Agent(
             name="Intelligence Synthesizer",
-            model=OpenAIChat(id=model_id),
+            model=Gemini(id=model_id),
             role="Synthesizes technical signals into actionable intelligence digests",
             instructions=[
                 "Combine relevance scores and risk assessments.",

advanced_ai_agents/multi_agent_apps/devpulse_ai/main.py

@@ -84,8 +84,8 @@ def run_pipeline():
     print("=" * 60)
     
     # Check for API key
-    if not os.environ.get("OPENAI_API_KEY"):
-        print("\n⚠️  Warning: OPENAI_API_KEY not set.")
+    if not os.environ.get("GOOGLE_API_KEY") and not os.environ.get("GEMINI_API_KEY"):
+        print("\n⚠️  Warning: GOOGLE_API_KEY not set.")
         print("   LLM-based agents will use fallback heuristics.\n")
     
     # Stage 1: Collection

advanced_ai_agents/multi_agent_apps/devpulse_ai/requirements.txt

@@ -3,3 +3,4 @@ httpx
 openai
 feedparser
 streamlit>=1.30
+google-generativeai

advanced_ai_agents/multi_agent_apps/devpulse_ai/streamlit_app.py

@@ -73,9 +73,10 @@ scores them for relevance, identifies potential risks, and synthesizes a final i
 st.sidebar.header("⚙️ Pipeline Configuration")
 
 # API Key
-api_key = st.sidebar.text_input("OpenAI API Key (optional)", type="password", help="If not provided, agents will use fallback heuristic logic.")
+api_key = st.sidebar.text_input("Gemini API Key (optional)", type="password", help="Provide a Google Gemini API key. If not provided, agents will use fallback heuristic logic.")
 if api_key:
-    os.environ["OPENAI_API_KEY"] = api_key
+    # Agno's GoogleGemini looks for GOOGLE_API_KEY
+    os.environ["GOOGLE_API_KEY"] = api_key
 
 # Source Selection
 sources = st.sidebar.multiselect(