feat: updated coordinated team structure for product launch intelligence agent
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ShubhamSaboo
parent
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2 changed files with 58 additions and 30 deletions
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@ -3,7 +3,7 @@
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A **streamlined intelligence hub** for Go-To-Market (GTM) & Product-Marketing teams.
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Built with **Streamlit + Agno (GPT-4o) + Firecrawl**, the app turns scattered public-web data into concise, actionable launch insights.
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## 3 Specialized Agents
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## 3 Specialized Agents in Coordinated Team
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| Tab | What You Get |
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|-----|--------------|
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@ -14,7 +14,7 @@ Built with **Streamlit + Agno (GPT-4o) + Firecrawl**, the app turns scattered pu
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Additional goodies:
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* 🔑 **Sidebar key input** – enter OpenAI & Firecrawl keys securely (type="password")
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* 🧠 **Specialised multi-agent core** – three expert agents collaborate for richer insight
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* 🧠 **Coordinated multi-agent team** – three expert agents work together for richer insight
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* 🔍 Product Launch Analyst (GTM strategist)
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* 💬 Market Sentiment Specialist (consumer-perception guru)
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* 📈 Launch Metrics Specialist (performance analyst)
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@ -27,7 +27,7 @@ Additional goodies:
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| Layer | Details |
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|-------|---------|
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| Data | **Firecrawl** async search + crawl API |
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| Agents | **Agno** (GPT-4o) with FirecrawlTools |
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| Agents | **Agno Team** (GPT-4o) with FirecrawlTools |
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| UI | **Streamlit** wide-layout, tabbed workflow |
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| LLM | **OpenAI GPT-4o** |
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@ -67,7 +67,17 @@ streamlit run product_launch_intelligence_agent.py
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1. Enter **API keys** in the sidebar (or ensure they are in your environment).
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2. Type a **company / product / hashtag** in the main input box.
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3. Pick a tab and hit the corresponding **Analyze** button – a spinner will appear while the agent works.
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3. Pick a tab and hit the corresponding **Analyze** button – a spinner will appear while the coordinated team works.
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4. Review the two-part analysis:
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* Bullet list of key findings
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* Expanded, richly-formatted report (tables, call-outs, recommendations)
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## 🤖 How the Coordinated Team Works
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The application uses a **coordinated team approach** where three specialized agents work together:
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- **Product Launch Analyst**: Evaluates competitive positioning, launch strategies, strengths, and weaknesses
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- **Market Sentiment Specialist**: Analyzes social media sentiment, customer feedback, and brand perception
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- **Launch Metrics Specialist**: Tracks KPIs, adoption rates, press coverage, and performance indicators
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The team coordinates based on the analysis type requested, ensuring the most appropriate agent handles each task while maintaining consistency and comprehensive coverage across all analysis types.
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@ -1,5 +1,6 @@
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import streamlit as st
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from agno.agent import Agent
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from agno.team import Team
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from agno.models.openai import OpenAIChat
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from agno.tools.firecrawl import FirecrawlTools
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from dotenv import load_dotenv
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@ -40,7 +41,7 @@ if openai_key:
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if firecrawl_key:
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os.environ["FIRECRAWL_API_KEY"] = firecrawl_key
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# Initialize agents only if both keys are provided
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# Initialize team only if both keys are provided
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if openai_key and firecrawl_key:
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# Agent 1: Competitor Launch Analyst
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launch_analyst = Agent(
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@ -107,10 +108,29 @@ if openai_key and firecrawl_key:
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exponential_backoff=True,
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delay_between_retries=2,
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)
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# Create the coordinated team
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product_intelligence_team = Team(
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name="Product Intelligence Team",
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mode="coordinate",
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model=OpenAIChat(id="gpt-4o"),
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members=[launch_analyst, sentiment_analyst, metrics_analyst],
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instructions=[
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"Coordinate the analysis based on the user's request type:",
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"1. For competitor analysis: Use the Product Launch Analyst to evaluate positioning, strengths, weaknesses, and strategic insights",
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"2. For market sentiment: Use the Market Sentiment Specialist to analyze social media sentiment, customer feedback, and brand perception",
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"3. For launch metrics: Use the Launch Metrics Specialist to track KPIs, adoption rates, press coverage, and performance indicators",
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"Always provide evidence-based insights with specific examples and data points",
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"Structure responses with clear sections and actionable recommendations",
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"Include sources section with all URLs crawled or searched"
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],
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show_tool_calls=True,
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markdown=True,
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debug_mode=True,
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show_members_responses=True,
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)
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else:
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launch_analyst = None
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sentiment_analyst = None
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metrics_analyst = None
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product_intelligence_team = None
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st.warning("⚠️ Please enter both API keys in the sidebar to use the application.")
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# ---------------- Helper to display response ----------------
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@ -127,7 +147,7 @@ def display_agent_response(resp):
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# Helper to expand bullet summary into 1200-word general report
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def expand_insight(bullet_text: str, topic: str) -> str:
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if not launch_analyst:
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if not product_intelligence_team:
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st.error("⚠️ Please enter both API keys in the sidebar first.")
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return ""
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@ -142,12 +162,12 @@ def expand_insight(bullet_text: str, topic: str) -> str:
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f"Bullet Points:\n{bullet_text}\n\n"
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f"Ensure analysis is objective, evidence-based and references the bullet insights. Keep paragraphs short (≤120 words)."
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)
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long_resp = launch_analyst.run(prompt)
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long_resp = product_intelligence_team.run(prompt)
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return long_resp.content if hasattr(long_resp, "content") else str(long_resp)
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# Helper to craft competitor-focused launch report for product managers
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def expand_competitor_report(bullet_text: str, competitor: str) -> str:
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if not launch_analyst:
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if not product_intelligence_team:
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st.error("⚠️ Please enter both API keys in the sidebar first.")
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return ""
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@ -169,12 +189,12 @@ def expand_competitor_report(bullet_text: str, competitor: str) -> str:
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f"• Populate the tables with specific points derived from the bullets.\n"
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f"• Only include rows that contain meaningful data; omit any blank entries."
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)
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resp = launch_analyst.run(prompt)
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resp = product_intelligence_team.run(prompt)
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return resp.content if hasattr(resp, "content") else str(resp)
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# Helper to craft market sentiment report
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def expand_sentiment_report(bullet_text: str, product: str) -> str:
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if not sentiment_analyst:
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if not product_intelligence_team:
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st.error("⚠️ Please enter both API keys in the sidebar first.")
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return ""
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@ -188,12 +208,12 @@ def expand_sentiment_report(bullet_text: str, product: str) -> str:
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f"Provide a short paragraph (≤120 words) summarising the overall sentiment balance and key drivers.\n\n"
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f"Tagged Bullets:\n{bullet_text}"
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)
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resp = sentiment_analyst.run(prompt)
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resp = product_intelligence_team.run(prompt)
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return resp.content if hasattr(resp, "content") else str(resp)
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# Helper to craft launch metrics report
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def expand_metrics_report(bullet_text: str, launch: str) -> str:
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if not metrics_analyst:
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if not product_intelligence_team:
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st.error("⚠️ Please enter both API keys in the sidebar first.")
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return ""
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@ -209,7 +229,7 @@ def expand_metrics_report(bullet_text: str, launch: str) -> str:
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f"Brief paragraph (≤120 words) highlighting what the metrics imply about launch success and next steps.\n\n"
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f"KPI Bullets:\n{bullet_text}"
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)
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resp = metrics_analyst.run(prompt)
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resp = product_intelligence_team.run(prompt)
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return resp.content if hasattr(resp, "content") else str(resp)
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# ---------------- UI ----------------
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@ -226,7 +246,7 @@ with st.container():
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company_name = st.text_input(
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label="Company Name",
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placeholder="Enter company name (e.g., OpenAI, Tesla, Spotify)",
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help="This company will be analyzed by all three specialized agents",
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help="This company will be analyzed by the coordinated team of specialized agents",
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label_visibility="collapsed"
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)
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with col2:
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@ -289,12 +309,12 @@ with analysis_tabs[0]:
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st.info("⏳ Ready to analyze")
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if analyze_btn:
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if not launch_analyst:
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if not product_intelligence_team:
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st.error("⚠️ Please enter both API keys in the sidebar first.")
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else:
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with st.spinner("🔍 Launch Analyst gathering competitive intelligence..."):
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with st.spinner("🔍 Product Intelligence Team analyzing competitive strategy..."):
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try:
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bullets = launch_analyst.run(
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bullets = product_intelligence_team.run(
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f"Generate up to 16 evidence-based insight bullets about {company_name}'s most recent product launches.\n"
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f"Format requirements:\n"
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f"• Start every bullet with exactly one tag: Positioning | Strength | Weakness | Learning\n"
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@ -353,12 +373,12 @@ with analysis_tabs[1]:
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st.info("⏳ Ready to analyze")
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if sentiment_btn:
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if not sentiment_analyst:
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if not product_intelligence_team:
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st.error("⚠️ Please enter both API keys in the sidebar first.")
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else:
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with st.spinner("💬 Sentiment Specialist analyzing market perception..."):
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with st.spinner("💬 Product Intelligence Team analyzing market sentiment..."):
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try:
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bullets = sentiment_analyst.run(
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bullets = product_intelligence_team.run(
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f"Summarize market sentiment for {company_name} in <=10 bullets. "
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f"Cover top positive & negative themes with source mentions (G2, Reddit, Twitter, customer reviews)."
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)
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@ -415,12 +435,12 @@ with analysis_tabs[2]:
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st.info("⏳ Ready to analyze")
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if metrics_btn:
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if not metrics_analyst:
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if not product_intelligence_team:
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st.error("⚠️ Please enter both API keys in the sidebar first.")
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else:
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with st.spinner("📈 Metrics Specialist analyzing launch performance..."):
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with st.spinner("📈 Product Intelligence Team analyzing launch metrics..."):
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try:
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bullets = metrics_analyst.run(
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bullets = product_intelligence_team.run(
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f"List (max 10 bullets) the most important publicly available KPIs & qualitative signals for {company_name}'s recent product launches. "
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f"Include engagement stats, press coverage, adoption metrics, and market traction data if available."
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)
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@ -448,7 +468,7 @@ with analysis_tabs[2]:
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with st.sidebar.container():
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st.markdown("### 🤖 System Status")
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if openai_key and firecrawl_key:
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st.success("✅ All agents ready")
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st.success("✅ Product Intelligence Team ready")
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else:
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st.error("❌ API keys required")
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@ -456,7 +476,7 @@ st.sidebar.divider()
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# Multi-agent system info
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with st.sidebar.container():
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st.markdown("### 🎯 Specialized Agents")
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st.markdown("### 🎯 Coordinated Team")
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agents_info = [
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("🔍", "Product Launch Analyst", "Strategic GTM expert"),
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@ -500,5 +520,3 @@ with st.sidebar.container():
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**K** - Market sentiment
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**L** - Launch metrics
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""")
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else:
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st.info("Enter a company name to enable quick actions")
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