changed ai consultant agent from exa to tavily

memory_agents/ai_consultant_agent/README.md

@@ -2,22 +2,22 @@
 
 ## AI Consultant Agent with Memori
 
-An AI-powered consulting agent that uses **Memori v3** as a long-term memory fabric and **ExaAI** for research. Built with Streamlit for the UI.
+An AI-powered consulting agent that uses **Memori v3** as a long-term memory fabric and **Tavily** for research. Built with Streamlit for the UI.
 
 ## Features
 
 - 🧠 **AI Readiness Assessment**: Analyze a company’s AI maturity, goals, and constraints.
 - 🎯 **Use-Case Recommendations**: Suggest where to integrate AI (workflows, CX, analytics, product, ecosystem).
 - 💵 **Cost Bands**: Provide rough cost bands and complexity for proposed AI initiatives.
-- ⚙️ **Web / Case-Study Research**: Use **ExaAI** to pull in relevant case studies and industry examples.
+- ⚙️ **Web / Case-Study Research**: Use **Tavily** to pull in relevant case studies and industry examples.
 - 🧾 **Persistent Memory (Memori v3)**: Store and reuse context across assessments and follow-up questions.
 
 ## Prerequisites
 
 - Python 3.11 or higher
 - [uv](https://github.com/astral-sh/uv) package manager (fast Python package installer)
 - OpenAI API key (`OPENAI_API_KEY`)
-- ExaAI API key (`EXA_API_KEY`)
+- Tavily API key (`TAVILY_API_KEY`)
 - Memori API key (`MEMORI_API_KEY`)
 - (Optional) `SQLITE_DB_PATH` if you want to override the default `./memori.sqlite` path
 
@@ -65,7 +65,7 @@ Create a `.env` file in this directory:
 
 ```bash
 OPENAI_API_KEY=your_openai_api_key_here
-EXA_API_KEY=your_exa_api_key_here
+TAVILY_API_KEY=your_tavily_api_key_here
 # Optional:
 # SQLITE_DB_PATH=./memori.sqlite
 ```
@@ -113,7 +113,7 @@ In the UI you can:
 ```text
 ai_consultant_agent/
 ├── app.py              # Streamlit interface (assessment + memory tabs)
-├── workflow.py         # ExaAI research + OpenAI-based consulting workflow
+├── workflow.py         # Tavily research + OpenAI-based consulting workflow
 ├── pyproject.toml      # Project dependencies (uv format)
 ├── README.md           # This file
 ├── requirements.txt    # PIP-style dependency list

memory_agents/ai_consultant_agent/app.py

@@ -40,19 +40,15 @@ def _load_inline_image(path: str, height_px: int) -> str:
 
 
 # Reuse existing logos from other agents
-memori_img_inline = _load_inline_image(
-    "../job_search_agent/assets/Memori_Logo.png", height_px=90
-)
-exa_img_inline = _load_inline_image(
-    "../job_search_agent/assets/exa_logo.png", height_px=70
-)
+memori_img_inline = _load_inline_image("assets/Memori_Logo.png", height_px=90)
+tavily_img_inline = _load_inline_image("assets/tavily_logo.png", height_px=70)
 
 title_html = f"""
 <div style='display:flex; align-items:center; width:120%; padding:8px 0;'>
   <h1 style='margin:0; padding:0; font-size:2.5rem; font-weight:800; display:flex; align-items:center; gap:5px;'>
     <span>AI Consultant Agent with</span>
     {memori_img_inline}and
-    {exa_img_inline}
+    {tavily_img_inline}
   </h1>
 </div>
 """
@@ -76,26 +72,26 @@ def _load_inline_image(path: str, height_px: int) -> str:
         help="Used for Memori Advanced Augmentation and higher quotas.",
     )
 
-    exa_api_key_input = st.text_input(
-        "ExaAI API Key",
-        value=os.getenv("EXA_API_KEY", ""),
+    tavily_api_key_input = st.text_input(
+        "Tavily API Key",
+        value=os.getenv("TAVILY_API_KEY", ""),
         type="password",
-        help="Your ExaAI API key for web/case-study search",
+        help="Your Tavily API key for web/case-study search",
     )
 
     if st.button("Save API Keys"):
         if openai_api_key_input:
             os.environ["OPENAI_API_KEY"] = openai_api_key_input
         if memori_api_key_input:
             os.environ["MEMORI_API_KEY"] = memori_api_key_input
-        if exa_api_key_input:
-            os.environ["EXA_API_KEY"] = exa_api_key_input
-        if openai_api_key_input or exa_api_key_input or memori_api_key_input:
+        if tavily_api_key_input:
+            os.environ["TAVILY_API_KEY"] = tavily_api_key_input
+        if openai_api_key_input or tavily_api_key_input or memori_api_key_input:
             st.success("✅ API keys saved for this session")
         else:
             st.warning("Please enter at least one API key")
 
-    both_keys_present = bool(os.getenv("EXA_API_KEY")) and bool(
+    both_keys_present = bool(os.getenv("TAVILY_API_KEY")) and bool(
         os.getenv("OPENAI_API_KEY")
     )
     if both_keys_present:
@@ -107,13 +103,13 @@ def _load_inline_image(path: str, height_px: int) -> str:
     st.markdown("### 💡 About")
     st.markdown(
         """
-        This application acts as an **AI consultant** for companies:
-        - Assesses **AI readiness** and where to integrate AI.
-        - Suggests **use cases** across workforce, tools, and ecosystem.
-        - Provides rough **cost bands** and risks.
-        - Uses **Memori** + to remember past assessments and Q&A.
+        This application acts as an *AI consultant* for companies:
+        - Assesses *AI readiness* and where to integrate AI.
+        - Suggests *use cases* across workforce, tools, and ecosystem.
+        - Provides rough *cost bands* and risks.
+        - Uses *Memori* + to remember past assessments and Q&A.
 
-        Web research is powered by **ExaAI**, and reasoning is powered by **OpenAI** via Memori.
+        Web research is powered by *Tavily, and reasoning is powered by **OpenAI* via Memori.
 
         ---
 
@@ -122,7 +118,7 @@ def _load_inline_image(path: str, height_px: int) -> str:
     )
 
 # Get API keys from environment
-exa_key = os.getenv("EXA_API_KEY", "")
+tavily_key = os.getenv("TAVILY_API_KEY", "")
 
 # Initialize session state
 if "assessment_markdown" not in st.session_state:
@@ -166,8 +162,8 @@ def _load_inline_image(path: str, height_px: int) -> str:
             st.warning(f"Memori v3 initialization note: {str(e)}")
 
 # Check if keys are set for required services
-if not exa_key:
-    st.warning("⚠️ Please enter your ExaAI API key in the sidebar to run assessments!")
+if not tavily_key:
+    st.warning("⚠️ Please enter your Tavily API key in the sidebar to run assessments!")
     st.stop()
 if "openai_client" not in st.session_state:
     st.warning(
@@ -285,7 +281,7 @@ def _load_inline_image(path: str, height_px: int) -> str:
                         st.session_state.company_profile = profile
 
                         st.markdown(
-                            f"## 🧾 AI Readiness & Cost Assessment for **{profile.company_name}**"
+                            f"## 🧾 AI Readiness & Cost Assessment for *{profile.company_name}*"
                         )
                         st.markdown(assessment_markdown)
 
@@ -299,7 +295,7 @@ def _load_inline_image(path: str, height_px: int) -> str:
         st.markdown(
             "### Last Assessment Result "
             + (
-                f"for **{st.session_state.company_profile.company_name}**"
+                f"for *{st.session_state.company_profile.company_name}*"
                 if st.session_state.company_profile
                 else ""
             )
@@ -311,12 +307,12 @@ def _load_inline_image(path: str, height_px: int) -> str:
 
     if st.session_state.company_profile:
         st.info(
-            f"Most recent company: **{st.session_state.company_profile.company_name}** "
+            f"Most recent company: *{st.session_state.company_profile.company_name}* "
             f"({st.session_state.company_profile.industry})"
         )
     else:
         st.info(
-            "Run at least one assessment in the **AI Assessment** tab to ground the memory context."
+            "Run at least one assessment in the *AI Assessment* tab to ground the memory context."
         )
 
     for message in st.session_state.memory_messages:

memory_agents/ai_consultant_agent/workflow.py

@@ -1,13 +1,13 @@
 """
 AI Consultant Workflow
-Uses LangChain for reasoning and ExaAI for web/case-study research.
+Uses LangChain for reasoning and Tavily for web/case-study research.
 """
 
 import os
 from typing import List, Optional, Tuple, Any
 
 from dotenv import load_dotenv
-from exa_py import Exa
+from tavily import TavilyClient
 from pydantic import BaseModel, Field
 
 # Load environment variables from .env if present
@@ -54,7 +54,7 @@ class CompanyProfile(BaseModel):
 
 
 class ResearchSnippet(BaseModel):
-    """Single ExaAI search result distilled for prompting."""
+    """Single Tavily search result distilled for prompting."""
 
     title: str
     url: str
@@ -81,40 +81,43 @@ def _build_research_query(profile: CompanyProfile) -> str:
     return " ".join(parts)
 
 
-def search_ai_case_studies_with_exa(
+def search_ai_case_studies_with_tavily(
     profile: CompanyProfile, max_results: int = 5
 ) -> List[ResearchSnippet]:
     """
-    Use ExaAI to retrieve a handful of relevant AI case studies / examples.
+    Use Tavily to retrieve a handful of relevant AI case studies / examples.
     """
-    exa_key = os.getenv("EXA_API_KEY")
-    if not exa_key:
-        raise RuntimeError("EXA_API_KEY not set in environment variables")
+    tavily_key = os.getenv("TAVILY_API_KEY")
+    if not tavily_key:
+        raise RuntimeError("TAVILY_API_KEY not set in environment variables")
 
-    client = Exa(api_key=exa_key)
+    client = TavilyClient(api_key=tavily_key)
     query = _build_research_query(profile)
 
     try:
-        results = client.search_and_contents(
+        # Use advanced depth to get richer snippets; return up to max_results
+        results = client.search(
             query=query,
-            num_results=max_results,
-            type="auto",
-            text=True,
+            search_depth="advanced",
+            max_results=max_results,
+            include_answer=False,
+            include_raw_content=False,
         )
     except Exception as e:
-        raise RuntimeError(f"Error calling ExaAI: {e}") from e
+        raise RuntimeError(f"Error calling Tavily: {e}") from e
 
     snippets: List[ResearchSnippet] = []
-    for r in results.results:
-        if not (r.title or r.url or r.text):
-            continue
-        text = (r.text or "").strip()
-        # Keep a concise but useful excerpt
+    for r in results.get("results", []):
+        title = (r.get("title") or "AI case study").strip()
+        url = r.get("url") or ""
+        text = (r.get("content") or r.get("snippet") or "").strip()
         snippet_text = text[:800] if text else ""
+        if not (title or url or snippet_text):
+            continue
         snippets.append(
             ResearchSnippet(
-                title=r.title or "AI case study",
-                url=r.url or "",
+                title=title,
+                url=url,
                 snippet=snippet_text,
             )
         )
@@ -127,15 +130,15 @@ def run_ai_assessment(
 ) -> Tuple[str, List[ResearchSnippet]]:
     """
     Main workflow:
-    - Pull a few relevant case studies via ExaAI.
+    - Pull a few relevant case studies via Tavily.
     - Ask the LLM (LangChain-compatible) to produce a structured consulting report.
 
     Returns:
         assessment_markdown: str  -> Markdown report for display.
         research_snippets: List[ResearchSnippet] -> For optional debugging / display.
     """
     # Step 1: web research
-    research_snippets = search_ai_case_studies_with_exa(profile, max_results=5)
+    research_snippets = search_ai_case_studies_with_tavily(profile, max_results=5)
 
     # Step 2: build prompt for the consultant LLM
     research_section = ""