diff --git a/AGENTS.md b/AGENTS.md
new file mode 100644
index 000000000..4b41e7b7e
--- /dev/null
+++ b/AGENTS.md
@@ -0,0 +1,90 @@
+# AGENTS.md
+
+## Project overview
+
+This repository contains a course-materials RAG chatbot:
+
+- FastAPI serves the API and static frontend.
+- OpenAI's Responses API generates answers and calls the local course-search tool.
+- ChromaDB stores course metadata and embedded transcript chunks.
+- Sentence Transformers generates local embeddings.
+- The frontend is plain HTML, CSS, and JavaScript.
+
+## Important paths
+
+- `backend/app.py`: FastAPI application and HTTP endpoints.
+- `backend/rag_system.py`: orchestration for ingestion, retrieval, generation, and sessions.
+- `backend/ai_generator.py`: OpenAI Responses API integration and tool-call handling.
+- `backend/document_processor.py`: transcript parsing and chunking.
+- `backend/vector_store.py`: ChromaDB collections and semantic search.
+- `backend/search_tools.py`: model-facing course-search tool.
+- `frontend/`: browser interface.
+- `docs/`: source course transcripts.
+
+## Setup and run
+
+Use `uv` for Python dependencies. The application requires Python 3.13 or newer.
+
+```bash
+uv sync
+./run.sh
+```
+
+The application runs at `http://localhost:8000`; FastAPI documentation is at
+`http://localhost:8000/docs`.
+
+The required environment variable is:
+
+```dotenv
+OPENAI_API_KEY=your_key
+```
+
+Never print, log, hard-code, or commit API keys. Do not read or modify `.env`
+unless the user explicitly asks for environment configuration.
+
+## Architecture and behavior
+
+- Preserve the public API contracts for `POST /api/query` and `GET /api/courses`.
+- Keep retrieval source labels available to the frontend.
+- Course-specific questions should use semantic retrieval; general questions may
+  be answered without retrieval.
+- The model may perform at most one course search for each user query.
+- Conversation sessions are intentionally in-memory and retain a small history.
+- ChromaDB uses separate `course_catalog` and `course_content` collections.
+- Start the server through `run.sh`; current imports and data paths assume Uvicorn
+  runs from the `backend` directory.
+
+## Development conventions
+
+- Keep changes small and consistent with the current straightforward architecture.
+- Prefer type hints for new Python functions and Pydantic models for API schemas.
+- Keep provider-specific API handling inside `backend/ai_generator.py`.
+- Do not replace the local embedding or ChromaDB stack unless explicitly requested.
+- Do not edit the course transcripts or generated `chroma_db` data unless the task
+  concerns ingestion or the knowledge base.
+- Avoid adding frontend frameworks for small UI changes.
+- Update `README.md` when setup, configuration, endpoints, or runtime behavior changes.
+- Update both `pyproject.toml` and `uv.lock` when dependencies change.
+
+## Verification
+
+There is currently no automated test suite. At minimum, after backend changes run:
+
+```bash
+uv run python -m compileall -q backend
+git diff --check
+```
+
+For changes to retrieval or generation, add an API-free focused test or smoke check
+when practical. Do not make a live OpenAI request unless the user explicitly asks
+for live integration testing and a valid key is configured.
+
+For API or frontend behavior changes, start the app and verify the affected endpoint
+or browser flow. Report any verification that could not be run.
+
+## Security and repository hygiene
+
+- Treat model output and retrieved documents as untrusted input.
+- Do not expose stack traces, secrets, or environment values through API responses.
+- Preserve unrelated user changes in the working tree.
+- Do not commit `.env`, `.venv`, ChromaDB data, caches, or generated artifacts.
diff --git a/README.md b/README.md
index e5420d50a..6ee4a906b 100644
--- a/README.md
+++ b/README.md
@@ -4,14 +4,14 @@ A Retrieval-Augmented Generation (RAG) system designed to answer questions about
 
 ## Overview
 
-This application is a full-stack web application that enables users to query course materials and receive intelligent, context-aware responses. It uses ChromaDB for vector storage, Anthropic's Claude for AI generation, and provides a web interface for interaction.
+This application is a full-stack web application that enables users to query course materials and receive intelligent, context-aware responses. It uses ChromaDB for vector storage, OpenAI for AI generation, and provides a web interface for interaction.
 
 
 ## Prerequisites
 
 - Python 3.13 or higher
 - uv (Python package manager)
-- An Anthropic API key (for Claude AI)
+- An OpenAI API key
 - **For Windows**: Use Git Bash to run the application commands - [Download Git for Windows](https://git-scm.com/downloads/win)
 
 ## Installation
@@ -30,7 +30,7 @@ This application is a full-stack web application that enables users to query cou
    
    Create a `.env` file in the root directory:
    ```bash
-   ANTHROPIC_API_KEY=your_anthropic_api_key_here
+   OPENAI_API_KEY=your_openai_api_key_here
    ```
 
 ## Running the Application
diff --git a/backend/ai_generator.py b/backend/ai_generator.py
index 0363ca90c..64102dc83 100644
--- a/backend/ai_generator.py
+++ b/backend/ai_generator.py
@@ -1,135 +1,94 @@
-import anthropic
-from typing import List, Optional, Dict, Any
-
-class AIGenerator:
-    """Handles interactions with Anthropic's Claude API for generating responses"""
-    
-    # Static system prompt to avoid rebuilding on each call
-    SYSTEM_PROMPT = """ You are an AI assistant specialized in course materials and educational content with access to a comprehensive search tool for course information.
-
-Search Tool Usage:
-- Use the search tool **only** for questions about specific course content or detailed educational materials
-- **One search per query maximum**
-- Synthesize search results into accurate, fact-based responses
-- If search yields no results, state this clearly without offering alternatives
-
-Response Protocol:
-- **General knowledge questions**: Answer using existing knowledge without searching
-- **Course-specific questions**: Search first, then answer
-- **No meta-commentary**:
- - Provide direct answers only — no reasoning process, search explanations, or question-type analysis
- - Do not mention "based on the search results"
-
-
-All responses must be:
-1. **Brief, Concise and focused** - Get to the point quickly
-2. **Educational** - Maintain instructional value
-3. **Clear** - Use accessible language
-4. **Example-supported** - Include relevant examples when they aid understanding
-Provide only the direct answer to what was asked.
-"""
-    
-    def __init__(self, api_key: str, model: str):
-        self.client = anthropic.Anthropic(api_key=api_key)
-        self.model = model
-        
-        # Pre-build base API parameters
-        self.base_params = {
-            "model": self.model,
-            "temperature": 0,
-            "max_tokens": 800
-        }
-    
-    def generate_response(self, query: str,
-                         conversation_history: Optional[str] = None,
-                         tools: Optional[List] = None,
-                         tool_manager=None) -> str:
-        """
-        Generate AI response with optional tool usage and conversation context.
-        
-        Args:
-            query: The user's question or request
-            conversation_history: Previous messages for context
-            tools: Available tools the AI can use
-            tool_manager: Manager to execute tools
-            
-        Returns:
-            Generated response as string
-        """
-        
-        # Build system content efficiently - avoid string ops when possible
-        system_content = (
-            f"{self.SYSTEM_PROMPT}\n\nPrevious conversation:\n{conversation_history}"
-            if conversation_history 
-            else self.SYSTEM_PROMPT
-        )
-        
-        # Prepare API call parameters efficiently
-        api_params = {
-            **self.base_params,
-            "messages": [{"role": "user", "content": query}],
-            "system": system_content
-        }
-        
-        # Add tools if available
-        if tools:
-            api_params["tools"] = tools
-            api_params["tool_choice"] = {"type": "auto"}
-        
-        # Get response from Claude
-        response = self.client.messages.create(**api_params)
-        
-        # Handle tool execution if needed
-        if response.stop_reason == "tool_use" and tool_manager:
-            return self._handle_tool_execution(response, api_params, tool_manager)
-        
-        # Return direct response
-        return response.content[0].text
-    
-    def _handle_tool_execution(self, initial_response, base_params: Dict[str, Any], tool_manager):
-        """
-        Handle execution of tool calls and get follow-up response.
-        
-        Args:
-            initial_response: The response containing tool use requests
-            base_params: Base API parameters
-            tool_manager: Manager to execute tools
-            
-        Returns:
-            Final response text after tool execution
-        """
-        # Start with existing messages
-        messages = base_params["messages"].copy()
-        
-        # Add AI's tool use response
-        messages.append({"role": "assistant", "content": initial_response.content})
-        
-        # Execute all tool calls and collect results
-        tool_results = []
-        for content_block in initial_response.content:
-            if content_block.type == "tool_use":
-                tool_result = tool_manager.execute_tool(
-                    content_block.name, 
-                    **content_block.input
-                )
-                
-                tool_results.append({
-                    "type": "tool_result",
-                    "tool_use_id": content_block.id,
-                    "content": tool_result
-                })
-        
-        # Add tool results as single message
-        if tool_results:
-            messages.append({"role": "user", "content": tool_results})
-        
-        # Prepare final API call without tools
-        final_params = {
-            **self.base_params,
-            "messages": messages,
-            "system": base_params["system"]
-        }
-        
-        # Get final response
-        final_response = self.client.messages.create(**final_params)
-        return final_response.content[0].text
\ No newline at end of file
+import json
+from typing import Any, Dict, List, Optional
+
+from openai import OpenAI
+
+
+class AIGenerator:
+    """Generate answers with OpenAI and execute local course-search tools."""
+
+    SYSTEM_PROMPT = """You are an AI assistant specialized in course materials and educational content with access to a comprehensive search tool for course information.
+
+Search Tool Usage:
+- Use the search tool only for questions about specific course content or detailed educational materials.
+- Use at most one search per query.
+- Synthesize search results into accurate, fact-based responses.
+- If search yields no results, state this clearly without offering alternatives.
+
+Response Protocol:
+- General knowledge questions: answer using existing knowledge without searching.
+- Course-specific questions: search first, then answer.
+- Provide direct answers only. Do not describe your reasoning or search process.
+- Do not say "based on the search results."
+
+Keep responses brief, educational, clear, and focused. Include an example when it materially improves understanding.
+"""
+
+    def __init__(self, api_key: str, model: str):
+        self.client = OpenAI(api_key=api_key)
+        self.model = model
+
+    def generate_response(
+        self,
+        query: str,
+        conversation_history: Optional[str] = None,
+        tools: Optional[List[Dict[str, Any]]] = None,
+        tool_manager=None,
+    ) -> str:
+        """Generate a response, executing at most one round of tool calls."""
+        instructions = self.SYSTEM_PROMPT
+        if conversation_history:
+            instructions += f"\n\nPrevious conversation:\n{conversation_history}"
+
+        openai_tools = self._convert_tools(tools or [])
+        input_items: List[Any] = [{"role": "user", "content": query}]
+        request: Dict[str, Any] = {
+            "model": self.model,
+            "instructions": instructions,
+            "input": input_items,
+            "reasoning": {"effort": "low"},
+            "max_output_tokens": 800,
+        }
+        if openai_tools:
+            request["tools"] = openai_tools
+            request["tool_choice"] = "auto"
+
+        response = self.client.responses.create(**request)
+        function_calls = [item for item in response.output if item.type == "function_call"]
+        if not function_calls or not tool_manager:
+            return response.output_text
+
+        input_items.extend(response.output)
+        for function_call in function_calls:
+            arguments = json.loads(function_call.arguments)
+            result = tool_manager.execute_tool(function_call.name, **arguments)
+            input_items.append(
+                {
+                    "type": "function_call_output",
+                    "call_id": function_call.call_id,
+                    "output": result,
+                }
+            )
+
+        final_response = self.client.responses.create(
+            model=self.model,
+            instructions=instructions,
+            input=input_items,
+            tools=openai_tools,
+            reasoning={"effort": "low"},
+            max_output_tokens=800,
+        )
+        return final_response.output_text
+
+    @staticmethod
+    def _convert_tools(tools: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+        """Convert the existing tool definitions to OpenAI function tools."""
+        return [
+            {
+                "type": "function",
+                "name": tool["name"],
+                "description": tool.get("description", ""),
+                "parameters": tool["input_schema"],
+            }
+            for tool in tools
+        ]
diff --git a/backend/app.py b/backend/app.py
index 5a69d741d..6bf38cf47 100644
--- a/backend/app.py
+++ b/backend/app.py
@@ -40,10 +40,15 @@ class QueryRequest(BaseModel):
     query: str
     session_id: Optional[str] = None
 
+class SourceCitation(BaseModel):
+    """A display label and optional destination for a retrieved source."""
+    text: str
+    link: Optional[str] = None
+
 class QueryResponse(BaseModel):
     """Response model for course queries"""
     answer: str
-    sources: List[str]
+    sources: List[SourceCitation]
     session_id: str
 
 class CourseStats(BaseModel):
diff --git a/backend/config.py b/backend/config.py
index d9f6392ef..836271997 100644
--- a/backend/config.py
+++ b/backend/config.py
@@ -8,9 +8,9 @@
 @dataclass
 class Config:
     """Configuration settings for the RAG system"""
-    # Anthropic API settings
-    ANTHROPIC_API_KEY: str = os.getenv("ANTHROPIC_API_KEY", "")
-    ANTHROPIC_MODEL: str = "claude-sonnet-4-20250514"
+    # OpenAI API settings
+    OPENAI_API_KEY: str = os.getenv("OPENAI_API_KEY", "")
+    OPENAI_MODEL: str = "gpt-5.6"
     
     # Embedding model settings
     EMBEDDING_MODEL: str = "all-MiniLM-L6-v2"
@@ -26,4 +26,3 @@ class Config:
 
 config = Config()
 
-
diff --git a/backend/rag_system.py b/backend/rag_system.py
index 50d848c8e..5fba93533 100644
--- a/backend/rag_system.py
+++ b/backend/rag_system.py
@@ -4,7 +4,7 @@
 from vector_store import VectorStore
 from ai_generator import AIGenerator
 from session_manager import SessionManager
-from search_tools import ToolManager, CourseSearchTool
+from search_tools import ToolManager, CourseSearchTool, SourceCitation
 from models import Course, Lesson, CourseChunk
 
 class RAGSystem:
@@ -16,7 +16,7 @@ def __init__(self, config):
         # Initialize core components
         self.document_processor = DocumentProcessor(config.CHUNK_SIZE, config.CHUNK_OVERLAP)
         self.vector_store = VectorStore(config.CHROMA_PATH, config.EMBEDDING_MODEL, config.MAX_RESULTS)
-        self.ai_generator = AIGenerator(config.ANTHROPIC_API_KEY, config.ANTHROPIC_MODEL)
+        self.ai_generator = AIGenerator(config.OPENAI_API_KEY, config.OPENAI_MODEL)
         self.session_manager = SessionManager(config.MAX_HISTORY)
         
         # Initialize search tools
@@ -99,7 +99,7 @@ def add_course_folder(self, folder_path: str, clear_existing: bool = False) -> T
         
         return total_courses, total_chunks
     
-    def query(self, query: str, session_id: Optional[str] = None) -> Tuple[str, List[str]]:
+    def query(self, query: str, session_id: Optional[str] = None) -> Tuple[str, List[SourceCitation]]:
         """
         Process a user query using the RAG system with tool-based search.
         
@@ -144,4 +144,4 @@ def get_course_analytics(self) -> Dict:
         return {
             "total_courses": self.vector_store.get_course_count(),
             "course_titles": self.vector_store.get_existing_course_titles()
-        }
\ No newline at end of file
+        }
diff --git a/backend/search_tools.py b/backend/search_tools.py
index adfe82352..89e61c370 100644
--- a/backend/search_tools.py
+++ b/backend/search_tools.py
@@ -1,8 +1,15 @@
-from typing import Dict, Any, Optional, Protocol
+from typing import Dict, Any, Optional, TypedDict
 from abc import ABC, abstractmethod
 from vector_store import VectorStore, SearchResults
 
 
+class SourceCitation(TypedDict):
+    """Structured source metadata returned to API consumers."""
+
+    text: str
+    link: Optional[str]
+
+
 class Tool(ABC):
     """Abstract base class for all tools"""
     
@@ -22,7 +29,7 @@ class CourseSearchTool(Tool):
     
     def __init__(self, vector_store: VectorStore):
         self.store = vector_store
-        self.last_sources = []  # Track sources from last search
+        self.last_sources: list[SourceCitation] = []  # Track sources from last search
     
     def get_tool_definition(self) -> Dict[str, Any]:
         """Return Anthropic tool definition for this tool"""
@@ -88,7 +95,8 @@ def execute(self, query: str, course_name: Optional[str] = None, lesson_number:
     def _format_results(self, results: SearchResults) -> str:
         """Format search results with course and lesson context"""
         formatted = []
-        sources = []  # Track sources for the UI
+        sources: list[SourceCitation] = []  # Track sources for the UI
+        seen_sources: set[str] = set()
         
         for doc, meta in zip(results.documents, results.metadata):
             course_title = meta.get('course_title', 'unknown')
@@ -100,11 +108,18 @@ def _format_results(self, results: SearchResults) -> str:
                 header += f" - Lesson {lesson_num}"
             header += "]"
             
-            # Track source for the UI
-            source = course_title
+            # Track each lesson once for the UI, even when several chunks match.
+            source_text = course_title
             if lesson_num is not None:
-                source += f" - Lesson {lesson_num}"
-            sources.append(source)
+                source_text += f" - Lesson {lesson_num}"
+            if source_text not in seen_sources:
+                lesson_link = (
+                    self.store.get_lesson_link(course_title, lesson_num)
+                    if lesson_num is not None
+                    else None
+                )
+                sources.append({"text": source_text, "link": lesson_link})
+                seen_sources.add(source_text)
             
             formatted.append(f"{header}\n{doc}")
         
@@ -139,7 +154,7 @@ def execute_tool(self, tool_name: str, **kwargs) -> str:
         
         return self.tools[tool_name].execute(**kwargs)
     
-    def get_last_sources(self) -> list:
+    def get_last_sources(self) -> list[SourceCitation]:
         """Get sources from the last search operation"""
         # Check all tools for last_sources attribute
         for tool in self.tools.values():
@@ -151,4 +166,4 @@ def reset_sources(self):
         """Reset sources from all tools that track sources"""
         for tool in self.tools.values():
             if hasattr(tool, 'last_sources'):
-                tool.last_sources = []
\ No newline at end of file
+                tool.last_sources = []
diff --git a/frontend/script.js b/frontend/script.js
index 562a8a363..bc59c0aef 100644
--- a/frontend/script.js
+++ b/frontend/script.js
@@ -119,24 +119,67 @@ function addMessage(content, type, sources = null, isWelcome = false) {
     // Convert markdown to HTML for assistant messages
     const displayContent = type === 'assistant' ? marked.parse(content) : escapeHtml(content);
     
-    let html = `<div class="message-content">${displayContent}</div>`;
-    
+    messageDiv.innerHTML = `<div class="message-content">${displayContent}</div>`;
+
     if (sources && sources.length > 0) {
-        html += `
-            <details class="sources-collapsible">
-                <summary class="sources-header">Sources</summary>
-                <div class="sources-content">${sources.join(', ')}</div>
-            </details>
-        `;
+        messageDiv.appendChild(createSourcesSection(sources));
     }
-    
-    messageDiv.innerHTML = html;
+
     chatMessages.appendChild(messageDiv);
     chatMessages.scrollTop = chatMessages.scrollHeight;
     
     return messageId;
 }
 
+function createSourcesSection(sources) {
+    const details = document.createElement('details');
+    details.className = 'sources-collapsible';
+
+    const summary = document.createElement('summary');
+    summary.className = 'sources-header';
+    summary.textContent = 'Sources';
+    details.appendChild(summary);
+
+    const content = document.createElement('div');
+    content.className = 'sources-content';
+
+    sources.forEach((source, index) => {
+        if (index > 0) {
+            content.appendChild(document.createTextNode(', '));
+        }
+
+        const label = typeof source === 'string' ? source : source.text;
+        const safeUrl = typeof source === 'object' ? getSafeHttpUrl(source.link) : null;
+
+        if (safeUrl) {
+            const link = document.createElement('a');
+            link.href = safeUrl;
+            link.target = '_blank';
+            link.rel = 'noopener noreferrer';
+            link.textContent = label;
+            content.appendChild(link);
+        } else {
+            const text = document.createElement('span');
+            text.textContent = label;
+            content.appendChild(text);
+        }
+    });
+
+    details.appendChild(content);
+    return details;
+}
+
+function getSafeHttpUrl(value) {
+    if (!value) return null;
+
+    try {
+        const url = new URL(value);
+        return ['http:', 'https:'].includes(url.protocol) ? url.href : null;
+    } catch {
+        return null;
+    }
+}
+
 // Helper function to escape HTML for user messages
 function escapeHtml(text) {
     const div = document.createElement('div');
@@ -188,4 +231,4 @@ async function loadCourseStats() {
             courseTitles.innerHTML = '<span class="error">Failed to load courses</span>';
         }
     }
-}
\ No newline at end of file
+}
diff --git a/frontend/style.css b/frontend/style.css
index 825d03675..86d746bcc 100644
--- a/frontend/style.css
+++ b/frontend/style.css
@@ -245,6 +245,22 @@ header h1 {
     color: var(--text-secondary);
 }
 
+.sources-content a {
+    color: #60a5fa;
+    text-decoration: underline;
+    text-underline-offset: 2px;
+}
+
+.sources-content a:hover {
+    color: #93c5fd;
+}
+
+.sources-content a:focus-visible {
+    outline: 2px solid var(--primary-color);
+    outline-offset: 2px;
+    border-radius: 2px;
+}
+
 /* Markdown formatting styles */
 .message-content h1,
 .message-content h2,
diff --git a/pyproject.toml b/pyproject.toml
index 3f05e2de0..a84e7a0dc 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -6,10 +6,10 @@ readme = "README.md"
 requires-python = ">=3.13"
 dependencies = [
     "chromadb==1.0.15",
-    "anthropic==0.58.2",
     "sentence-transformers==5.0.0",
     "fastapi==0.116.1",
     "uvicorn==0.35.0",
     "python-multipart==0.0.20",
     "python-dotenv==1.1.1",
+    "openai>=2.53.0",
 ]
diff --git a/run.sh b/run.sh
index 80e3853d8..b05485af5 100755
--- a/run.sh
+++ b/run.sh
@@ -10,7 +10,7 @@ if [ ! -d "backend" ]; then
 fi
 
 echo "Starting Course Materials RAG System..."
-echo "Make sure you have set your ANTHROPIC_API_KEY in .env"
+echo "Make sure you have set your OPENAI_API_KEY in .env"
 
 # Change to backend directory and start the server
-cd backend && uv run uvicorn app:app --reload --port 8000
\ No newline at end of file
+cd backend && uv run uvicorn app:app --reload --port 8000
diff --git a/tests/test_search_tools.py b/tests/test_search_tools.py
new file mode 100644
index 000000000..fdbe4fa15
--- /dev/null
+++ b/tests/test_search_tools.py
@@ -0,0 +1,82 @@
+import sys
+import unittest
+from pathlib import Path
+
+
+BACKEND_DIR = Path(__file__).resolve().parents[1] / "backend"
+sys.path.insert(0, str(BACKEND_DIR))
+
+from search_tools import CourseSearchTool
+from vector_store import SearchResults
+
+
+class FakeVectorStore:
+    def __init__(self, lesson_links=None):
+        self.lesson_links = lesson_links or {}
+        self.lesson_link_calls = []
+
+    def get_lesson_link(self, course_title, lesson_number):
+        self.lesson_link_calls.append((course_title, lesson_number))
+        return self.lesson_links.get((course_title, lesson_number))
+
+
+class CourseSearchToolFormattingTests(unittest.TestCase):
+    def test_returns_structured_deduplicated_sources_without_changing_model_text(self):
+        store = FakeVectorStore({("RAG Course", 2): "https://example.com/lesson-2"})
+        tool = CourseSearchTool(store)
+        results = SearchResults(
+            documents=["First chunk", "Second chunk"],
+            metadata=[
+                {"course_title": "RAG Course", "lesson_number": 2},
+                {"course_title": "RAG Course", "lesson_number": 2},
+            ],
+            distances=[0.1, 0.2],
+        )
+
+        formatted = tool._format_results(results)
+
+        self.assertEqual(
+            formatted,
+            "[RAG Course - Lesson 2]\nFirst chunk\n\n"
+            "[RAG Course - Lesson 2]\nSecond chunk",
+        )
+        self.assertEqual(
+            tool.last_sources,
+            [{"text": "RAG Course - Lesson 2", "link": "https://example.com/lesson-2"}],
+        )
+        self.assertEqual(store.lesson_link_calls, [("RAG Course", 2)])
+
+    def test_missing_lesson_link_is_returned_as_null(self):
+        tool = CourseSearchTool(FakeVectorStore())
+        results = SearchResults(
+            documents=["Chunk"],
+            metadata=[{"course_title": "RAG Course", "lesson_number": 3}],
+            distances=[0.1],
+        )
+
+        tool._format_results(results)
+
+        self.assertEqual(
+            tool.last_sources,
+            [{"text": "RAG Course - Lesson 3", "link": None}],
+        )
+
+    def test_lesson_zero_uses_its_lesson_link(self):
+        store = FakeVectorStore({("RAG Course", 0): "https://example.com/lesson-0"})
+        tool = CourseSearchTool(store)
+        results = SearchResults(
+            documents=["Introduction"],
+            metadata=[{"course_title": "RAG Course", "lesson_number": 0}],
+            distances=[0.1],
+        )
+
+        tool._format_results(results)
+
+        self.assertEqual(
+            tool.last_sources,
+            [{"text": "RAG Course - Lesson 0", "link": "https://example.com/lesson-0"}],
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/uv.lock b/uv.lock
index 9ae65c557..c8fe9de88 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 2
+revision = 3
 requires-python = ">=3.13"
 
 [[package]]
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-wheels = [
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 [[package]]
 name = "anyio"
 version = "4.9.0"
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 name = "opentelemetry-api"
 version = "1.35.0"
@@ -1552,9 +1567,9 @@ name = "starting-codebase"
 version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
-    { name = "anthropic" },
     { name = "chromadb" },
     { name = "fastapi" },
+    { name = "openai" },
     { name = "python-dotenv" },
     { name = "python-multipart" },
     { name = "sentence-transformers" },
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