diff --git a/.dockerignore b/.dockerignore
index 4093c0a..3d422e8 100644
--- a/.dockerignore
+++ b/.dockerignore
@@ -147,11 +147,4 @@ data/
 reports/
 
 # Synthetic data conversations
-src/agents/utils/example_inputs/
-src/agents/utils/synthetic_conversations/
-src/agents/utils/synthetic_conversation_generation.py
-src/agents/utils/testbench_prompts.py
-src/agents/utils/langgraph_viz.py
-
-# development agents
-src/agents/student_agent/
\ No newline at end of file
+src/agents/utils/example_inputs/
\ No newline at end of file
diff --git a/.github/workflows/dev.yml b/.github/workflows/dev.yml
index 1191a86..1236ba3 100644
--- a/.github/workflows/dev.yml
+++ b/.github/workflows/dev.yml
@@ -50,6 +50,7 @@ jobs:
         if: always()
         run: |
           source .venv/bin/activate
+          export PYTHONPATH=$PYTHONPATH:.
           pytest --junit-xml=./reports/pytest.xml --tb=auto -v
 
       - name: Upload test results
diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
index 8400ca3..1da0493 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -50,6 +50,7 @@ jobs:
         if: always()
         run: |
           source .venv/bin/activate
+          export PYTHONPATH=$PYTHONPATH:.
           pytest --junit-xml=./reports/pytest.xml --tb=auto -v
 
       - name: Upload test results
diff --git a/.gitignore b/.gitignore
index 4b52234..11f861b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -50,6 +50,7 @@ coverage.xml
 *.py,cover
 .hypothesis/
 .pytest_cache/
+reports/
 
 # Translations
 *.mo
diff --git a/AGENTS.md b/AGENTS.md
new file mode 100644
index 0000000..514764f
--- /dev/null
+++ b/AGENTS.md
@@ -0,0 +1,88 @@
+# AGENTS.md
+
+This file provides guidance to AI agents when working with code in this repository.
+
+## Project Overview
+
+This is a boilerplate for creating AI educational chatbots that integrate with the **Lambda-Feedback** educational platform. It deploys as an AWS Lambda function (containerized via Docker) that receives student chat messages with educational context and returns LLM-powered chatbot responses.
+
+## Commands
+
+**Testing:**
+```bash
+pytest                              # Run all unit tests
+python tests/manual_agent_run.py   # Test agent locally with example inputs
+python tests/manual_agent_requests.py  # Test running Docker container
+```
+
+**Docker:**
+```bash
+docker build -t llm_chat .
+docker run --env-file .env -p 8080:8080 llm_chat
+```
+
+**Manual API test (while Docker is running):**
+```bash
+curl -X POST http://localhost:8080/2015-03-31/functions/function/invocations \
+  -H 'Content-Type: application/json' \
+  -d '{"body":"{\"conversationId\": \"12345Test\", \"messages\": [{\"role\": \"USER\", \"content\": \"hi\"}], \"user\": {\"type\": \"LEARNER\"}}"}'
+```
+
+**Run a single test:**
+```bash
+pytest tests/test_module.py        # Run specific test file
+pytest tests/test_index.py::test_function_name  # Run specific test
+```
+
+## Architecture
+
+### Request Flow
+
+```
+Lambda event → index.py (handler)
+  → validates via lf_toolkit ChatRequest schema
+  → src/module.py (chat_module)
+    → extracts muEd API context (messages, conversationId, question context, user type)
+    → parses educational context to prompt text via src/agent/context.py
+    → src/agent/agent.py (BaseAgent / LangGraph)
+      → routes to call_llm or summarize_conversation node
+      → calls LLM provider (OpenAI / Google / Azure / Ollama)
+  → returns ChatResponse (output, summary, conversationalStyle, processingTime)
+```
+
+### Key Files
+
+| File | Role |
+|------|------|
+| `index.py` | AWS Lambda entry point; parses event body, validates schema |
+| `src/module.py` | Transforms muEd API request → invokes agent → builds ChatResponse |
+| `src/agent/agent.py` | LangGraph stateful graph; manages message history and summarization |
+| `src/agent/prompts.py` | System prompts for tutor behavior, summarization, style detection |
+| `src/agent/llm_factory.py` | Factory classes for each LLM provider (OpenAI, Google, Azure, Ollama) |
+| `src/agent/context.py` | Converts muEd question/submission context dicts to LLM prompt text |
+| `tests/utils.py` | Shared test helpers: `assert_valid_chat_request`, `assert_valid_chat_response` |
+| `tests/example_inputs/` | Real muEd payloads used for end-to-end tests |
+
+### Agent Logic (LangGraph)
+
+`BaseAgent` maintains a state graph with two nodes:
+- **`call_llm`**: Invokes the LLM with system prompt + conversation summary + conversational style preference
+- **`summarize_conversation`**: Triggered when message count exceeds ~11; summarizes history and also extracts the student's preferred conversational style
+
+Messages are trimmed after summarization to keep context window manageable. The `summary` and `conversationalStyle` fields persist across calls via the `ChatRequest` metadata.
+
+### muEd API Format
+
+`src/module.py` handles the muEd request format (https://mued.org/). The `context` field in `ChatRequest` contains nested educational data (question parts, student submissions, task info) that gets parsed into a tutoring prompt via `src/agent/context.py`.
+
+### LLM Configuration
+
+LLM provider and model are set via environment variables (see `.env.example`). The `llm_factory.py` selects the provider at runtime. The Lambda function name/identity is set in `config.json`.
+
+The agent uses **two separate LLM instances** — `self.llm` for chat responses and `self.summarisation_llm` for conversation summarisation and style analysis. By default both use the same provider, but you can point them at different models (e.g. a cheaper model for summarisation) by changing the class in `agent.py`.
+
+## Deployment
+
+- Pushing to `dev` branch triggers the dev deployment GitHub Actions workflow
+- Pushing to `main` triggers staging deployment, with manual approval required for production
+- All environment variables (API keys, model names) are injected via GitHub Actions secrets/variables — do not hardcode them
diff --git a/CLAUDE.md b/CLAUDE.md
new file mode 100644
index 0000000..9c8ebda
--- /dev/null
+++ b/CLAUDE.md
@@ -0,0 +1,88 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+This is a boilerplate for creating AI educational chatbots that integrate with the **Lambda-Feedback** educational platform. It deploys as an AWS Lambda function (containerized via Docker) that receives student chat messages with educational context and returns LLM-powered chatbot responses.
+
+## Commands
+
+**Testing:**
+```bash
+pytest                              # Run all unit tests
+python tests/manual_agent_run.py   # Test agent locally with example inputs
+python tests/manual_agent_requests.py  # Test running Docker container
+```
+
+**Docker:**
+```bash
+docker build -t llm_chat .
+docker run --env-file .env -p 8080:8080 llm_chat
+```
+
+**Manual API test (while Docker is running):**
+```bash
+curl -X POST http://localhost:8080/2015-03-31/functions/function/invocations \
+  -H 'Content-Type: application/json' \
+  -d '{"body":"{\"conversationId\": \"12345Test\", \"messages\": [{\"role\": \"USER\", \"content\": \"hi\"}], \"user\": {\"type\": \"LEARNER\"}}"}'
+```
+
+**Run a single test:**
+```bash
+pytest tests/test_module.py        # Run specific test file
+pytest tests/test_index.py::test_function_name  # Run specific test
+```
+
+## Architecture
+
+### Request Flow
+
+```
+Lambda event → index.py (handler)
+  → validates via lf_toolkit ChatRequest schema
+  → src/module.py (chat_module)
+    → extracts muEd API context (messages, conversationId, question context, user type)
+    → parses educational context to prompt text via src/agent/context.py
+    → src/agent/agent.py (BaseAgent / LangGraph)
+      → routes to call_llm or summarize_conversation node
+      → calls LLM provider (OpenAI / Google / Azure / Ollama)
+  → returns ChatResponse (output, summary, conversationalStyle, processingTime)
+```
+
+### Key Files
+
+| File | Role |
+|------|------|
+| `index.py` | AWS Lambda entry point; parses event body, validates schema |
+| `src/module.py` | Transforms muEd API request → invokes agent → builds ChatResponse |
+| `src/agent/agent.py` | LangGraph stateful graph; manages message history and summarization |
+| `src/agent/prompts.py` | System prompts for tutor behavior, summarization, style detection |
+| `src/agent/llm_factory.py` | Factory classes for each LLM provider (OpenAI, Google, Azure, Ollama) |
+| `src/agent/context.py` | Converts muEd question/submission context dicts to LLM prompt text |
+| `tests/utils.py` | Shared test helpers: `assert_valid_chat_request`, `assert_valid_chat_response` |
+| `tests/example_inputs/` | Real muEd payloads used for end-to-end tests |
+
+### Agent Logic (LangGraph)
+
+`BaseAgent` maintains a state graph with two nodes:
+- **`call_llm`**: Invokes the LLM with system prompt + conversation summary + conversational style preference
+- **`summarize_conversation`**: Triggered when message count exceeds ~11; summarizes history and also extracts the student's preferred conversational style
+
+Messages are trimmed after summarization to keep context window manageable. The `summary` and `conversationalStyle` fields persist across calls via the `ChatRequest` metadata.
+
+### muEd API Format
+
+`src/module.py` handles the muEd request format (https://mued.org/). The `context` field in `ChatRequest` contains nested educational data (question parts, student submissions, task info) and the `user` field contains user-specific information (e.g., user type, preferences, task progress) that gets parsed into a tutoring prompt via `src/agent/context.py`.
+
+### LLM Configuration
+
+LLM provider and model are set via environment variables (see `.env.example`). The `llm_factory.py` selects the provider at runtime. The Lambda function name/identity is set in `config.json`.
+
+The agent uses **two separate LLM instances** — `self.llm` for chat responses and `self.summarisation_llm` for conversation summarisation and style analysis. By default both use the same provider, but you can point them at different models (e.g. a cheaper model for summarisation) by changing the class in `agent.py`.
+
+## Deployment
+
+- Pushing to `dev` branch triggers the dev deployment GitHub Actions workflow
+- Pushing to `main` triggers staging deployment, with manual approval required for production
+- All environment variables (API keys, model names) are injected via GitHub Actions secrets/variables — do not hardcode them
diff --git a/Dockerfile b/Dockerfile
index 9150687..38276cc 100755
--- a/Dockerfile
+++ b/Dockerfile
@@ -25,7 +25,7 @@ COPY src ./src
 
 COPY index.py .
 
-COPY index_test.py .
+COPY tests ./tests
 
 # Set the Lambda function handler
 CMD ["index.handler"]
\ No newline at end of file
diff --git a/README.md b/README.md
index 692e6f3..6c7a2f6 100755
--- a/README.md
+++ b/README.md
@@ -43,11 +43,11 @@ In GitHub, choose Use this template > Create a new repository in the repository
 
 Choose the owner, and pick a name for the new repository.
 
-> [!IMPORTANT] If you want to deploy the evaluation function to Lambda Feedback, make sure to choose the Lambda Feedback organization as the owner.
+> [!IMPORTANT] If you want to deploy the chat function to Lambda Feedback, make sure to choose the `Lambda Feedback` organization as the owner.
 
-Set the visibility to Public or Private.
+Set the visibility to `Public` or `Private`.
 
-> [!IMPORTANT] If you want to use GitHub deployment protection rules, make sure to set the visibility to Public.
+> [!IMPORTANT] If you want to use GitHub deployment protection rules, make sure to set the visibility to `Public`.
 
 Click on Create repository.
 
@@ -78,9 +78,11 @@ Also, don't forget to update or delete the Quickstart chapter from the `README.m
 
 ## Development
 
-You can create your own invocation to your own agents hosted anywhere. Copy or update the `base_agent` from `src/agents/` and edit it to match your LLM agent requirements. Import the new invocation in the `module.py` file.
+You can create your own invocation to your own agents hosted anywhere. Copy or update the `agent.py` from `src/agent/` and edit it to match your LLM agent requirements. Import the new invocation in the `module.py` file.
 
-You agent can be based on an LLM hosted anywhere, you have available currently OpenAI, AzureOpenAI, and Ollama models but you can introduce your own API call in the `src/agents/llm_factory.py`.
+Your agent can be based on an LLM hosted anywhere. OpenAI, Google AI, Azure OpenAI, and Ollama are available out of the box via `src/agent/llm_factory.py`, and you can add your own provider there too.
+
+The agent uses **two separate LLM instances** — `self.llm` for chat responses and `self.summarisation_llm` for conversation summarisation and style analysis. By default both use the same provider, but you can point them at different models (e.g. a cheaper or faster model for summarisation) by changing the class in `agent.py`.
 
 ### Prerequisites
 
@@ -90,23 +92,41 @@ You agent can be based on an LLM hosted anywhere, you have available currently O
 ### Repository Structure
 
 ```bash
-.github/workflows/
-    dev.yml                           # deploys the DEV function to Lambda Feedback
-    main.yml                          # deploys the STAGING function to Lambda Feedback
-    test-report.yml                   # gathers Pytest Report of function tests
-
-docs/               # docs for devs and users
-
-src/module.py       # chat_module function implementation
-src/module_test.py  # chat_module function tests
-src/agents/         # find all agents developed for the chat functionality
-src/agents/utils/test_prompts.py      # allows testing of any LLM agent on a couple of example inputs containing Lambda Feedback Questions and synthetic student conversations
+.
+├── .github/workflows/
+│   ├── dev.yml                           # deploys the DEV function to Lambda Feedback
+│   ├── main.yml                          # deploys the STAGING and PROD functions to Lambda Feedback
+│   └── test-report.yml                   # gathers Pytest Report of function tests
+├── docs/                                 # docs for devs and users
+├── src/
+│   ├── agent/
+│   │   ├── agent.py                      # LangGraph stateful agent logic
+│   │   ├── context.py                    # converts muEd context dicts to LLM prompt text
+│   │   ├── llm_factory.py                # factory classes for each LLM provider
+│   │   └── prompts.py                    # system prompts defining the behaviour of the chatbot
+│   └── module.py
+└── tests/                                # contains all tests for the chat function
+    ├── example_inputs/                   # muEd example payloads for end-to-end tests
+    ├── manual_agent_requests.py          # allows testing of the docker container through API requests
+    ├── manual_agent_run.py               # allows testing of any LLM agent on a couple of example inputs
+    ├── utils.py                          # shared test helpers
+    ├── test_example_inputs.py            # pytests for the example input files
+    ├── test_index.py                     # pytests
+    └── test_module.py                    # pytests
 ```
 
 
 ## Testing the Chat Function
 
-To test your function, you can either call the code directly through a python script. Or you can build the respective chat function docker container locally and call it through an API request. Below you can find details on those processes.
+To test your function, you can run the unit tests, call the code directly through a python script, or build the respective chat function docker container locally and call it through an API request. Below you can find details on those processes.
+
+### Run Unit Tests
+
+You can run the unit tests using `pytest`.
+
+```bash
+pytest
+```
 
 ### Run the Chat Script
 
@@ -116,9 +136,9 @@ You can run the Python function itself. Make sure to have a main function in eit
 python src/module.py
 ```
 
-You can also use the `testbench_agents.py` script to test the agents with example inputs from Lambda Feedback questions and synthetic conversations.
+You can also use the `manual_agent_run.py` script to test the agents with example inputs from Lambda Feedback questions and synthetic conversations.
 ```bash
-python src/agents/utils/testbench_agents.py
+python tests/manual_agent_run.py
 ```
 
 ### Calling the Docker Image Locally
@@ -150,13 +170,13 @@ This will start the chat function and expose it on port `8080` and it will be op
 ```bash
 curl --location 'http://localhost:8080/2015-03-31/functions/function/invocations' \
 --header 'Content-Type: application/json' \
---data '{"body":"{\"message\": \"hi\", \"params\": {\"conversation_id\": \"12345Test\", \"conversation_history\": [{\"type\": \"user\", 
+--data '{"body":"{\"conversationId\": \"12345Test\", \"messages\": [{\"role\": \"USER\", \"content\": \"hi\"}], \"user\": {\"type\": \"LEARNER\"}}"}'
 ```
 
 #### Call Docker Container
 ##### A. Call Docker with Python Requests
 
-In the `src/agents/utils` folder you can find the `requests_testscript.py` script that calls the POST URL of the running docker container. It reads any kind of input files with the expected schema. You can use this to test your curl calls of the chatbot.
+In the `tests/` folder you can find the `manual_agent_requests.py` script that calls the POST URL of the running docker container. It reads any kind of input files with the expected schema. You can use this to test your curl calls of the chatbot.
 
 ##### B. Call Docker Container through API request
 
@@ -169,22 +189,98 @@ http://localhost:8080/2015-03-31/functions/function/invocations
 Body (stringified within body for API request):
 
 ```JSON
-{"body":"{\"message\": \"hi\", \"params\": {\"conversation_id\": \"12345Test\", \"conversation_history\": [{\"type\": \"user\", \"content\": \"hi\"}]}}"}
+{"body":"{\"conversationId\": \"12345Test\", \"messages\": [{\"role\": \"USER\", \"content\": \"hi\"}], \"user\": {\"type\": \"LEARNER\"}}"}
 ```
 
-Body with optional Params:
-```JSON
+Body with optional fields:
+```json
 {
-    "message":"hi",
-    "params":{
-        "conversation_id":"12345Test",
-        "conversation_history":[{"type":"user","content":"hi"}],
-        "summary":" ",
-        "conversational_style":" ",
-        "question_response_details": "",
-        "include_test_data": true,
-        "agent_type": {agent_name}
+  "conversationId": "<uuid>",
+  "messages": [
+    { "role": "USER", "content": "<previous user message>" },
+    { "role": "ASSISTANT", "content": "<previous assistant reply>" },
+    { "role": "USER", "content": "<current message>" }
+  ],
+  "user": {
+    "type": "LEARNER",
+    "preference": {
+      "conversationalStyle": "<stored style string>"
+    },
+    "taskProgress": {
+      "timeSpentOnQuestion": "30 minutes",
+      "accessStatus": "a good amount of time spent on this question today.",
+      "markedDone": "This question is still being worked on.",
+      "currentPart": {
+        "position": 0,
+        "timeSpentOnPart": "10 minutes",
+        "markedDone": "This part is not marked done.",
+        "responseAreas": [
+          {
+            "responseType": "EXPRESSION",
+            "totalSubmissions": 3,
+            "wrongSubmissions": 2,
+            "latestSubmission": {
+              "submission": "<student's last answer>",
+              "feedback": "<feedback text from evaluator>",
+              "answer": "<reference answer used for evaluation>"
+            }
+          }
+        ]
+      }
+    }
+  },
+  "context": {
+    "summary": "<compressed conversation history>",
+    "set": {
+      "title": "Fundamentals",
+      "number": 2,
+      "description": "<set description>"
+    },
+    "question": {
+      "title": "Understanding Polymorphism",
+      "number": 3,
+      "guidance": "<teacher guidance>",
+      "content": "<master question content>",
+      "estimatedTime": "15-25 minutes",
+      "parts": [
+        {
+          "position": 0,
+          "content": "<part prompt>",
+          "answerContent": "<part answer>",
+          "workedSolutionSections": [
+            { "position": 0, "title": "Step 1", "content": "..." }
+          ],
+          "structuredTutorialSections": [
+            { "position": 0, "title": "Hint", "content": "..." }
+          ],
+          "responseAreas": [
+            {
+              "position": 0,
+              "responseType": "EXPRESSION",
+              "answer": "<reference answer>",
+              "preResponseText": "<label shown before input>"
+            }
+          ]
+        }
+      ]
     }
+  }
+}
+```
+
+Response:
+
+```json
+{
+  "output": {
+    "role": "ASSISTANT",
+    "content": "<assistant reply text>"
+  },
+  "metadata": {
+    "summary": "<updated conversation summary>",
+    "conversationalStyle": "<updated style string>",
+    "processingTimeMs": 1234
+  }
 }
 ```
 
diff --git a/docs/dev.md b/docs/dev.md
index 46b4e85..95aa113 100644
--- a/docs/dev.md
+++ b/docs/dev.md
@@ -10,47 +10,23 @@ Helping approach: encourages self-discovery of the answer, but will reveal the s
 </code>
 </pre>
 
-## Inputs
-Body:
-```JSON
-{
-    "message":"hi",
-    "params":{
-        "conversation_id":"12345Test",
-        "conversation_history":[{"type":"user","content":"hi"}],
-        "include_test_data": true,
-    }
-}
-```
-
-## Outputs
-```JSON
-{
-    "chatbotResponse":"hi back",
-    "metadata": {
-        "summary": "",
-        "conversational_style": "",
-        "conversation_history": [],
-    },
-    "processing_time": 0
-}
-```
-
 ## Testing the Chat Function
 
-To test your function, you can either call the code directly through a python script. Or you can build the respective chat function docker container locally and call it through an API request. Below you can find details on those processes.
+To test your function, you can run the unit tests, call the code directly through a python script, or build the respective chat function docker container locally and call it through an API request. Below you can find details on those processes.
 
-### Run the Chat Script
+### Run Unit Tests
 
-You can run the Python function itself. Make sure to have a main function in either `src/module.py` or `index.py`.
+You can run the unit tests using `pytest`.
 
 ```bash
-python src/module.py
+pytest
 ```
 
-You can also use the `testbench_agents.py` script to test the agents with example inputs from Lambda Feedback questions and synthetic conversations.
+### Run the Chat Script
+
+You can also use the `manual_agent_run.py` script to test the agents with example inputs from Lambda Feedback questions and synthetic conversations.
 ```bash
-python src/agents/utils/testbench_agents.py
+python tests/manual_agent_run.py
 ```
 
 ### Calling the Docker Image Locally
@@ -82,13 +58,13 @@ This will start the chat function and expose it on port `8080` and it will be op
 ```bash
 curl --location 'http://localhost:8080/2015-03-31/functions/function/invocations' \
 --header 'Content-Type: application/json' \
---data '{"body":"{\"message\": \"hi\", \"params\": {\"conversation_id\": \"12345Test\", \"conversation_history\": [{\"type\": \"user\", 
+--data '{"body":"{\"conversationId\": \"12345Test\", \"messages\": [{\"role\": \"USER\", \"content\": \"hi\"}], \"user\": {\"type\": \"LEARNER\"}}"}'
 ```
 
 #### Call Docker Container
 ##### A. Call Docker with Python Requests
 
-In the `src/agents/utils` folder you can find the `requests_testscript.py` script that calls the POST URL of the running docker container. It reads any kind of input files with the expected schema. You can use this to test your curl calls of the chatbot.
+In the `tests/` folder you can find the `manual_agent_requests.py` script that calls the POST URL of the running docker container. It reads any kind of input files with the expected schema. You can use this to test your curl calls of the chatbot.
 
 ##### B. Call Docker Container through API request
 
@@ -101,22 +77,97 @@ http://localhost:8080/2015-03-31/functions/function/invocations
 Body (stringified within body for API request):
 
 ```JSON
-{"body":"{\"message\": \"hi\", \"params\": {\"conversation_id\": \"12345Test\", \"conversation_history\": [{\"type\": \"user\", \"content\": \"hi\"}]}}"}
+{"body":"{\"conversationId\": \"12345Test\", \"messages\": [{\"role\": \"USER\", \"content\": \"hi\"}], \"user\": {\"type\": \"LEARNER\"}}"}
 ```
 
-Body with optional Params:
-```JSON
+Input Body with optional fields:
+```json
 {
-    "message":"hi",
-    "params":{
-        "conversation_id":"12345Test",
-        "conversation_history":[{"type":"user","content":"hi"}],
-        "summary":" ",
-        "conversational_style":" ",
-        "question_response_details": "",
-        "include_test_data": true,
-        "agent_type": {agent_name}
+  "conversationId": "<uuid>",
+  "messages": [
+    { "role": "USER", "content": "<previous user message>" },
+    { "role": "ASSISTANT", "content": "<previous assistant reply>" },
+    { "role": "USER", "content": "<current message>" }
+  ],
+  "user": {
+    "type": "LEARNER",
+    "preference": {
+      "conversationalStyle": "<stored style string>"
+    },
+    "taskProgress": {
+      "timeSpentOnQuestion": "30 minutes",
+      "accessStatus": "a good amount of time spent on this question today.",
+      "markedDone": "This question is still being worked on.",
+      "currentPart": {
+        "position": 0,
+        "timeSpentOnPart": "10 minutes",
+        "markedDone": "This part is not marked done.",
+        "responseAreas": [
+          {
+            "responseType": "EXPRESSION",
+            "totalSubmissions": 3,
+            "wrongSubmissions": 2,
+            "latestSubmission": {
+              "submission": "<student's last answer>",
+              "feedback": "<feedback text from evaluator>",
+              "answer": "<reference answer used for evaluation>"
+            }
+          }
+        ]
+      }
     }
+  },
+  "context": {
+    "summary": "<compressed conversation history>",
+    "set": {
+      "title": "Fundamentals",
+      "number": 2,
+      "description": "<set description>"
+    },
+    "question": {
+      "title": "Understanding Polymorphism",
+      "number": 3,
+      "guidance": "<teacher guidance>",
+      "content": "<master question content>",
+      "estimatedTime": "15-25 minutes",
+      "parts": [
+        {
+          "position": 0,
+          "content": "<part prompt>",
+          "answerContent": "<part answer>",
+          "workedSolutionSections": [
+            { "position": 0, "title": "Step 1", "content": "..." }
+          ],
+          "structuredTutorialSections": [
+            { "position": 0, "title": "Hint", "content": "..." }
+          ],
+          "responseAreas": [
+            {
+              "position": 0,
+              "responseType": "EXPRESSION",
+              "answer": "<reference answer>",
+              "preResponseText": "<label shown before input>"
+            }
+          ]
+        }
+      ]
+    }
+  }
 }
 ```
 
+Output Response:
+
+```json
+{
+  "output": {
+    "role": "ASSISTANT",
+    "content": "<assistant reply text>"
+  },
+  "metadata": {
+    "summary": "<updated conversation summary>",
+    "conversationalStyle": "<updated style string>",
+    "processingTimeMs": 1234
+  }
+}
+```
diff --git a/index.py b/index.py
index 62f4c47..d0610fe 100644
--- a/index.py
+++ b/index.py
@@ -1,56 +1,36 @@
 import json
-try:
-    from .src.module import chat_module
-    from .src.agents.utils.types import JsonType
-except ImportError:
-    from src.module import chat_module
-    from src.agents.utils.types import JsonType
+from pydantic import ValidationError
 
-def handler(event: JsonType, context):
+from lf_toolkit.chat import ChatRequest
+from src.module import chat_module
+
+
+def handler(event, context):
     """
     Lambda handler function
     """
-    # Log the input event for debugging purposes
-    # print("Received event:", " ".join(json.dumps(event, indent=2).splitlines()))
-
     if "body" in event:
         try:
             event = json.loads(event["body"])
         except json.JSONDecodeError:
             return {
                 "statusCode": 400,
-                "body": "Invalid JSON format in the body or body not found. Please check the input."
+                "body": "Invalid JSON format in the body. Please check the input.",
             }
 
-    if "message" not in event:
-        return {
-            "statusCode": 400,
-            "body": "Missing 'message' key in event. Please confirm the key in the json body."
-        }
-    if "params" not in event:
-        return {
-            "statusCode": 400,
-            "body": "Missing 'params' key in event. Please confirm the key in the json body. Make sure it contains the necessary conversation_id."
-        }
-    
-    message = event.get("message")
-    params = event.get("params")
+    try:
+        request = ChatRequest.model_validate(event)
+    except ValidationError as e:
+        return {"statusCode": 400, "body": e.json()}
 
     try:
-        chatbot_response = chat_module(message, params)
+        result = chat_module(request)
     except Exception as e:
         return {
             "statusCode": 500,
-            "body": f"An error occurred within the chat_module(): {str(e)}"
+            "body": f"An error occurred within the chat_module(): {str(e)}",
         }
 
-    # Create a response
-    response = {
-        "statusCode": 200,
-        "body": json.dumps(chatbot_response)
-    }
-
-    # Log the response for debugging purposes
+    response = {"statusCode": 200, "body": result.model_dump_json()}
     print("Returning response:", " ".join(json.dumps(response, indent=2).splitlines()))
-
-    return response
\ No newline at end of file
+    return response
diff --git a/index_test.py b/index_test.py
deleted file mode 100644
index fb8e7bf..0000000
--- a/index_test.py
+++ /dev/null
@@ -1,66 +0,0 @@
-import unittest
-import json
-
-try:
-    from .index import handler
-except ImportError:
-    from index import handler
-
-class TestChatIndexFunction(unittest.TestCase):
-    """
-    TestCase Class used to test the algorithm.
-    ---
-    Tests are used here to check that the algorithm written
-    is working as it should.
-
-    It's best practise to write these tests first to get a
-    kind of 'specification' for how your algorithm should
-    work, and you should run these tests before committing
-    your code to AWS.
-
-    Read the docs on how to use unittest here:
-    https://docs.python.org/3/library/unittest.html
-
-    Use module() to check your algorithm works
-    as it should.
-
-    The expected input of the hander is a JsonType.
-    """
-
-    def test_missing_argument(self):
-        arguments = ["message", "params"]
-
-        for arg in arguments:
-            event = {
-                "message": "Hello, World",
-                "params": {"conversation_id": "1234Test", "conversation_history": [{"type": "user", "content": "Hello, World"}]}
-            }
-            event.pop(arg)
-            event = {"body":json.dumps(event)}
-
-            result = handler(event, None)
-
-            self.assertEqual(result.get("statusCode"), 400)
-    
-    def test_correct_arguments(self):
-        event = {
-            "message": "Hello, World",
-            "params": {"conversation_id": "1234Test", "conversation_history": [{"type": "user", "content": "Hello, World"}]}
-        }
-        event = {"body":json.dumps(event)}
-
-        result = handler(event, None)
-
-        self.assertEqual(result.get("statusCode"), 200)
-
-    def test_correct_response(self):
-        event = {
-            "message": "Hello, World",
-            "params": {"conversation_id": "1234Test", "conversation_history": [{"type": "user", "content": "Hello, World"}]}
-        }
-        event = {"body":json.dumps(event)}
-
-        result = handler(event, None)
-
-        self.assertEqual(result.get("statusCode"), 200)
-        
\ No newline at end of file
diff --git a/src/__init__.py b/src/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/agent/agent.py b/src/agent/agent.py
new file mode 100644
index 0000000..2873186
--- /dev/null
+++ b/src/agent/agent.py
@@ -0,0 +1,118 @@
+from src.agent.llm_factory import OpenAILLMs, GoogleAILLMs
+from src.agent.prompts import \
+    role_prompt, conv_pref_prompt, update_conv_pref_prompt, summary_prompt, update_summary_prompt, summary_system_prompt
+
+from langgraph.graph import StateGraph, START, END
+from langchain_core.messages import SystemMessage, RemoveMessage, HumanMessage, AIMessage
+from langchain_core.runnables.config import RunnableConfig
+from langgraph.graph.message import add_messages
+from typing import Annotated, TypeAlias
+from typing_extensions import TypedDict
+
+"""
+Base agent: LLM chat with automatic conversation summarisation and style analysis.
+
+Nodes:
+- call_llm:               responds to the student using role_prompt + optional question context + conversation summary
+- summarize_conversation: triggered after max_messages_to_summarize messages; compresses history and
+                          analyses the student's conversational style (returned in output metadata only)
+"""
+
+ValidMessageTypes: TypeAlias = SystemMessage | HumanMessage | AIMessage
+AllMessageTypes: TypeAlias = ValidMessageTypes | RemoveMessage
+
+class State(TypedDict):
+    messages: Annotated[list[AllMessageTypes], add_messages]
+    summary: str
+    conversationalStyle: str
+
+class BaseAgent:
+    def __init__(self):
+        # Main chat LLM — change to GoogleAILLMs(), AzureLLMs(), or OllamaLLMs() if preferred
+        self.llm = GoogleAILLMs().get_llm()
+        # Summarisation LLM — can be set to a different/cheaper model than the chat LLM
+        self.summarisation_llm = GoogleAILLMs().get_llm()
+
+        self.max_messages_to_summarize = 11
+        self.role_prompt = role_prompt
+        self.summary_prompt = summary_prompt
+        self.update_summary_prompt = update_summary_prompt
+        self.conversation_preference_prompt = conv_pref_prompt
+        self.update_conversation_preference_prompt = update_conv_pref_prompt
+
+        workflow = StateGraph(State)
+        workflow.add_node("call_llm", self.call_model)
+        workflow.add_node("summarize_conversation", self.summarize_conversation)
+        workflow.add_conditional_edges(source=START, path=self.should_summarize)
+        workflow.add_edge("summarize_conversation", "call_llm")
+        workflow.add_edge("call_llm", END)
+        self.app = workflow.compile()
+
+    def call_model(self, state: State, config: RunnableConfig) -> dict:
+        """Invoke the chat LLM with role prompt, optional question context, and conversation summary."""
+        system_message = self.role_prompt
+
+        context_prompt = config.get("configurable", {}).get("context_prompt", "")
+        if context_prompt:
+            system_message += f"## Known Question Materials: {context_prompt} \n\n"
+
+        summary = state.get("summary", "")
+        conversationalStyle = state.get("conversationalStyle", "")
+        if summary:
+            system_message += summary_system_prompt.format(summary=summary)
+        if conversationalStyle:
+            system_message += f"## Known conversational style and preferences of the student for this conversation: {conversationalStyle}. \n\nYour answer must be in line with this conversational style."
+
+        messages = [SystemMessage(content=system_message)] + state["messages"]
+        response = self.llm.invoke(self._valid(messages))
+        return {"messages": [response]}
+
+    def summarize_conversation(self, state: State) -> dict:
+        """Summarise history and analyse conversational style when message count exceeds the threshold."""
+        summary = state.get("summary", "")
+        conversationalStyle = state.get("conversationalStyle", "")
+
+        summary_message = (
+            f"This is summary of the conversation to date: {summary}\n\n" + self.update_summary_prompt
+        ) if summary else self.summary_prompt
+
+        style_message = (
+            f"This is the previous conversational style of the student for this conversation: {conversationalStyle}\n\n" + self.update_conversation_preference_prompt
+        ) if conversationalStyle else self.conversation_preference_prompt
+
+        summary_response = self.summarisation_llm.invoke(self._valid(state["messages"][:-1] + [HumanMessage(content=summary_message)]))
+        conversational_style_response = self.summarisation_llm.invoke(self._valid(state["messages"][:-1] + [HumanMessage(content=style_message)]))
+
+        delete_messages: list[AllMessageTypes] = [RemoveMessage(id=m.id) for m in state["messages"][:-3]]
+        return {"summary": summary_response.content, "conversationalStyle": conversational_style_response.content, "messages": delete_messages}
+
+    def should_summarize(self, state: State) -> str:
+        valid = self._valid(state["messages"])
+        if not valid:
+            raise Exception("Internal Error: No valid messages found in the conversation history.")
+        nr_messages = len(valid) - (1 if "system" in valid[-1].type else 0)
+        return "summarize_conversation" if nr_messages > self.max_messages_to_summarize else "call_llm"
+
+    def _valid(self, messages: list[AllMessageTypes]) -> list[ValidMessageTypes]:
+        return [m for m in messages if m.type != "remove"]
+
+
+agent = BaseAgent()
+
+def invoke_base_agent(messages: list, summary: str, conversationalStyle: str, context_prompt: str, session_id: str | None = None) -> dict:
+    """
+    Invoke the agent with the full conversation history and return the chatbot reply plus updated metadata.
+    Summarisation and style analysis trigger automatically once message count exceeds max_messages_to_summarize.
+    """
+    print(f"in invoke_base_agent(), thread_id = {session_id}")
+    config: RunnableConfig = {"configurable": {"context_prompt": context_prompt}}
+    state = agent.app.invoke(
+        State(messages=messages, summary=summary, conversationalStyle=conversationalStyle),
+        config=config,
+    )
+    print("in invoke_base_agent(), response generated by chatbot")
+    return {
+        "output": state["messages"][-1].content,
+        "summary": state.get("summary", ""),
+        "conversationalStyle": state.get("conversationalStyle", ""),
+    }
diff --git a/src/agent/context.py b/src/agent/context.py
new file mode 100644
index 0000000..a038747
--- /dev/null
+++ b/src/agent/context.py
@@ -0,0 +1,155 @@
+from typing import Optional, Dict, Any, List
+
+from src.agent.prompts import response_format_prompt
+
+
+def parse_json_to_prompt(context: dict, task_progress: dict) -> str:
+    """Convert muEd context and task progress directly into an LLM-friendly prompt string."""
+
+    question = context.get("question")
+    if not question:
+        return "# ERROR: Question details unavailable\n\nPlease describe the question you're working on so I can assist you effectively."
+
+    set_data = context.get("set", {})
+    current_part = task_progress.get("currentPart", {}) if task_progress else {}
+    current_part_position = current_part.get("position")
+    submissions = current_part.get("responseAreas", [])
+
+    sections = []
+
+    # 1. Question header
+    title_parts = []
+    set_number = set_data.get("number")
+    if set_number is not None and set_data.get("title"):
+        title_parts.append(f"## Set {set_number + 1}: {set_data['title']}")
+
+    question_num = ""
+    q_number = question.get("number")
+    if set_number is not None and q_number is not None:
+        question_num = f"{set_number + 1}.{q_number + 1}"
+    title_parts.append(f"### Question {question_num}: {question.get('title', '')}")
+
+    current_part_letter = _part_letter(current_part_position) if current_part_position is not None else ""
+    progress_indicator = f"Currently working on: Part ({current_part_letter})" if current_part_letter else ""
+
+    estimated_time = question.get("estimatedTime", "Not specified")
+    sections.append(f"""
+# Question Context
+
+{chr(10).join(title_parts)}
+
+{progress_indicator}
+
+### Question Details
+- Guidance: {question.get('guidance') or 'None provided'}
+- Description: {question.get('content') or 'No description available'}
+- Expected Duration: {estimated_time}
+
+---
+""")
+
+    # 2. Progress summary
+    if task_progress:
+        progress_items = []
+        if task_progress.get("timeSpentOnQuestion"):
+            progress_items.append(f"- Time spent today: {task_progress['timeSpentOnQuestion']}")
+        if task_progress.get("accessStatus"):
+            progress_items.append(f"- Status: {task_progress['accessStatus']}")
+        if task_progress.get("markedDone"):
+            progress_items.append(f"- Completion: {task_progress['markedDone']}")
+        if progress_items:
+            sections.append(f"\n## Progress Summary\n\n{chr(10).join(progress_items)}\n\n---\n")
+
+    # 3. Parts
+    for i, part in enumerate(question.get("parts", [])):
+        part_position = part.get("position", i)
+        is_current = current_part_position == part_position
+        time_on_part = current_part.get("timeSpentOnPart") if is_current else None
+        sections.append(_format_part(part, part_position, is_current, time_on_part, submissions))
+
+    # Combine
+    intro = (
+        "\n# Personalized Learning Assistant\n\n"
+        "I have detailed information about your current question, including your progress, responses, "
+        "and any feedback you've received. This context helps me provide targeted assistance based on "
+        "your specific situation.\n\n"
+    )
+    valid_sections = [s.strip() for s in sections if s and s.strip()]
+    response_format = (
+        "# Response Formatting\n" + response_format_prompt
+        if response_format_prompt
+        else ""
+    )
+    content = intro + "\n".join(valid_sections) + "\n" + response_format
+    content = content.replace("&#x20;&#x20;", " ").replace("&#x20", " ")
+    return "\n".join(line for line in content.split("\n") if line.strip() or not line).strip()
+
+
+def _part_letter(position: int) -> str:
+    return chr(96 + (position + 1))
+
+def _format_part(part: dict, part_position: int, is_current: bool, time_on_part: Optional[str], submissions: list) -> str:
+    letter = _part_letter(part_position)
+    status_text = " [CURRENTLY WORKING ON]" if is_current else ""
+    header = f"## Part ({letter}){status_text}"
+    if is_current and time_on_part and time_on_part != "No recorded duration":
+        header += f"\n\n*Time spent on this part: {time_on_part}*"
+
+    content_text = part.get("content", "").strip()
+    content = f"### Part Content\n\n{content_text}" if content_text else "### Part Content\n\nNo content provided"
+
+    response_areas = []
+    for j, ra in enumerate(part.get("responseAreas", [])):
+        ra_position = ra.get("position", j)
+        student_work = _get_student_work(ra_position, submissions)
+        response_areas.append(_format_response_area(ra_position, ra.get("preResponseText"), ra.get("answer"), student_work))
+    ra_block = f"\n### Response Areas\n\n{''.join(response_areas)}" if response_areas else ""
+
+    answer = part.get("answerContent")
+    answer_block = f"### Final Answer\n\n{answer}" if answer else "### Final Answer\n\nNo direct answer specified for this part"
+
+    solutions = [
+        f"{ws.get('title', f'#### Solution {i+1}')}\n\n{ws.get('content', '').strip() or 'No content available'}"
+        for i, ws in enumerate(part.get("workedSolutionSections", []))
+    ]
+    solutions_block = "### Worked Solutions\n\n" + "\n".join(solutions) if solutions else "### Worked Solutions\n\nNone available"
+
+    tutorials = [
+        f"{ts.get('title', f'#### Tutorial {i+1}')}\n\n{ts.get('content', '').strip() or 'No content available'}"
+        for i, ts in enumerate(part.get("structuredTutorialSections", []))
+    ]
+    tutorials_block = "### Structured Tutorials\n\n" + "\n".join(tutorials) if tutorials else "### Structured Tutorials\n\nNone available"
+
+    return "\n".join([header, content, ra_block, answer_block, solutions_block, tutorials_block]) + "\n---\n"
+
+def _get_student_work(ra_position: int, submissions: list) -> Dict[str, Any]:
+    if ra_position < len(submissions):
+        s = submissions[ra_position]
+        latest = s.get("latestSubmission") or {}
+        if latest:
+            return {
+                "has_submissions": True,
+                "latest_response": latest.get("submission"),
+                "latest_feedback": latest.get("feedback"),
+                "total_submissions": s.get("totalSubmissions"),
+                "total_wrong": s.get("wrongSubmissions"),
+            }
+    return {"has_submissions": False}
+
+def _format_response_area(position: int, task_description: Optional[str], expected_answer: Any, student_work: Dict[str, Any]) -> str:
+    task_text = f"- Task: {task_description}" if task_description else "- Task: Not specified"
+    if not student_work.get("has_submissions"):
+        submission_text = "- Your Work on this response area: No response submitted yet"
+    else:
+        submission_text = (
+            f"- Your Work on this response area:\n"
+            f"  - Latest response: {student_work.get('latest_response', 'None')}\n"
+            f"  - Latest feedback: {student_work.get('latest_feedback', 'None')}\n"
+            f"  - Total attempts: {student_work.get('total_submissions', 0)} out of which {student_work.get('total_wrong', 0)} were incorrect"
+        )
+    return (
+        f"\n#### Response Area {position + 1}\n\n"
+        f"{task_text}\n"
+        f"- Expected Answer (confidential): {expected_answer}\n"
+        f"{submission_text}\n"
+    )
diff --git a/src/agents/llm_factory.py b/src/agent/llm_factory.py
similarity index 91%
rename from src/agents/llm_factory.py
rename to src/agent/llm_factory.py
index 5b6b1d5..07e887b 100644
--- a/src/agents/llm_factory.py
+++ b/src/agent/llm_factory.py
@@ -18,11 +18,11 @@ def __init__(self, temperature: int = 0):
                         temperature=temperature,
                         max_tokens=None,
                     )
-        self._azure_embedding = AzureOpenAIEmbeddings(azure_deployment=os.environ['AZURE_OPENAI_EMBEDDING_1536_DEPLOYMENT'], 
+        self._azure_embedding = AzureOpenAIEmbeddings(azure_deployment=os.environ['AZURE_OPENAI_EMBEDDING_1536_DEPLOYMENT'],
                                         openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
                                         model=os.environ["AZURE_OPENAI_EMBEDDING_1536_MODEL"])
 
-    def get_llm(self):    
+    def get_llm(self):
         return self._azure_llm
 
     def get_embedding(self):
@@ -31,7 +31,7 @@ def get_embedding(self):
 class OllamaLLMs:
     def __init__(self):
         self._ollama_llm = Ollama(
-            model=os.environ['OLLAMA_MODEL'], # Any of the available models listed in the API docs
+            model=os.environ['OLLAMA_MODEL'],
             base_url=os.environ['OLLAMA_BASE_URL'],
             headers={
                 'X-API-Key': os.environ['OLLAMA_API_KEY'],
@@ -52,7 +52,7 @@ def get_llm(self):
 
     def get_embedding(self):
         return self._ollama_embedding
-    
+
 class OpenAILLMs:
     def __init__(self, temperature: int = 0):
         self._openai_llm = ChatOpenAI(
@@ -65,21 +65,20 @@ def __init__(self, temperature: int = 0):
             model='text-embedding-ada-002',
             api_key=os.environ['OPENAI_API_KEY'],
         )
-    
+
     def get_llm(self):
         return self._openai_llm
-    
+
     def get_embedding(self):
         return self._openai_embedding
 
 class GoogleAILLMs:
     def __init__(self, temperature: int = 0):
-
         self._google_llm = ChatGoogleGenerativeAI(
             model=os.environ['GOOGLE_AI_MODEL'],
             temperature=temperature,
             google_api_key=os.environ['GOOGLE_AI_API_KEY'],
         )
-    
+
     def get_llm(self):
-        return self._google_llm
\ No newline at end of file
+        return self._google_llm
diff --git a/src/agents/base_agent/base_prompts.py b/src/agent/prompts.py
similarity index 88%
rename from src/agents/base_agent/base_prompts.py
rename to src/agent/prompts.py
index ecffce2..49e3fbf 100644
--- a/src/agents/base_agent/base_prompts.py
+++ b/src/agent/prompts.py
@@ -1,3 +1,17 @@
+# Description of the prompts:
+# 
+# 1. role_prompt: Sets the overall role and behaviour of the chatbot.
+# 1b. response_format_prompt: Provides specific instructions on how the chatbot should format its responses, for example regarding mathematical equations formatting and language.
+# 
+# 2. summary_prompt: Used to generate a summary of the conversation.
+# 2. update_summary_prompt: Used to update the conversation summary with new messages.
+# 2. summary_system_prompt: Provides context for the chatbot based on the existing summary.
+# 
+# 3. conv_pref_prompt: Used to analyze and extract the student's conversational style and learning preferences.
+# 3. update_conv_pref_prompt: Used to update the conversational style based on new interactions.
+# 
+
+# 1. Role Prompt
 role_prompt = """You are a highly skilled and patient AI tutor dedicated to helping me, the student, discover answers and master concepts. Your teaching approach focuses on student-centered learning, fostering critical thinking, active engagement, and confidence building.
 
 ## Teaching Methods:
@@ -23,6 +37,33 @@
 ## Governance:
 You are a chatbot deployed in Lambda Feedback, an online self-study platform. You are collaboratively working through exercises with students from Imperial College London."""
 
+# 1b. Response Format Prompt
+response_format_prompt = """Mathematical equations are in KaTeX format, preserve them the same. Ensure mathematical equations are surrounded by one '$' for in-line equations and '$$' for block equations.
+Example: '$E=mc^2$' or '$$E=mc^2$$'.
+Use British English spellings."""
+
+# 2. Summary Prompts
+summary_guidelines = """Ensure the summary is:
+
+Concise: Keep the summary brief while including all essential information.
+Structured: Organize the summary into sections such as 'Topics Discussed' and 'Top 3 Key Detailed Ideas'.
+Neutral and Accurate: Avoid adding interpretations or opinions; focus only on the content shared.
+When summarizing: If the conversation is technical, highlight significant concepts, solutions, and terminology. If context involves problem-solving, detail the problem and the steps or solutions provided. If the user asks for creative input, briefly describe the ideas presented.
+Last messages: Include the most recent 5 messages to provide context for the summary.
+
+Provide the summary in a bulleted format for clarity. Avoid redundant details while preserving the core intent of the discussion."""
+
+summary_prompt = f"""Summarize the conversation between a student and a tutor. Your summary should highlight the major topics discussed during the session, followed by a detailed recollection of the last five significant points or ideas. Ensure the summary flows smoothly to maintain the continuity of the discussion.
+
+{summary_guidelines}"""
+
+update_summary_prompt = f"""Update the summary by taking into account the new messages above.
+
+{summary_guidelines}"""
+
+summary_system_prompt = "You are continuing a tutoring session with the student. Background context: {summary}. Use this context to inform your understanding but do not explicitly restate, refer to, or incorporate the details directly in your responses unless the user brings them up. Respond naturally to the user's current input, assuming prior knowledge from the summary."
+
+# 3. Conversational Preference Prompt
 pref_guidelines = """**Guidelines:**
 - Use concise, objective language.
 - Note the student's educational goals, such as understanding foundational concepts, passing an exam, getting top marks, code implementation, hands-on practice, etc.
@@ -75,23 +116,3 @@
 
 {pref_guidelines}
 """
-
-summary_guidelines = """Ensure the summary is:
-
-Concise: Keep the summary brief while including all essential information.
-Structured: Organize the summary into sections such as 'Topics Discussed' and 'Top 3 Key Detailed Ideas'.
-Neutral and Accurate: Avoid adding interpretations or opinions; focus only on the content shared.
-When summarizing: If the conversation is technical, highlight significant concepts, solutions, and terminology. If context involves problem-solving, detail the problem and the steps or solutions provided. If the user asks for creative input, briefly describe the ideas presented.
-Last messages: Include the most recent 5 messages to provide context for the summary.
-
-Provide the summary in a bulleted format for clarity. Avoid redundant details while preserving the core intent of the discussion."""
-
-summary_prompt = f"""Summarize the conversation between a student and a tutor. Your summary should highlight the major topics discussed during the session, followed by a detailed recollection of the last five significant points or ideas. Ensure the summary flows smoothly to maintain the continuity of the discussion.
-
-{summary_guidelines}"""
-
-update_summary_prompt = f"""Update the summary by taking into account the new messages above.
-
-{summary_guidelines}"""
-
-summary_system_prompt = "You are continuing a tutoring session with the student. Background context: {summary}. Use this context to inform your understanding but do not explicitly restate, refer to, or incorporate the details directly in your responses unless the user brings them up. Respond naturally to the user's current input, assuming prior knowledge from the summary."
\ No newline at end of file
diff --git a/src/agents/__init__.py b/src/agents/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/agents/base_agent/base_agent.py b/src/agents/base_agent/base_agent.py
deleted file mode 100644
index 37b6214..0000000
--- a/src/agents/base_agent/base_agent.py
+++ /dev/null
@@ -1,203 +0,0 @@
-try:
-    from ..llm_factory import OpenAILLMs, GoogleAILLMs
-    from .base_prompts import \
-        role_prompt, conv_pref_prompt, update_conv_pref_prompt, summary_prompt, update_summary_prompt, summary_system_prompt
-    from ..utils.types import InvokeAgentResponseType
-except ImportError:
-    from src.agents.llm_factory import OpenAILLMs, GoogleAILLMs
-    from src.agents.base_agent.base_prompts import \
-        role_prompt, conv_pref_prompt, update_conv_pref_prompt, summary_prompt, update_summary_prompt, summary_system_prompt
-    from src.agents.utils.types import InvokeAgentResponseType
-
-from langgraph.graph import StateGraph, START, END
-from langchain_core.messages import SystemMessage, RemoveMessage, HumanMessage, AIMessage
-from langchain_core.runnables.config import RunnableConfig
-from langgraph.graph.message import add_messages
-from typing import Annotated, TypeAlias
-from typing_extensions import TypedDict
-
-"""
-Base agent for development [LLM workflow with a summarisation, profiling, and chat agent that receives an external conversation history].
-
-This agent is designed to:
-- [summarise_prompt]        summarise the conversation after 'max_messages_to_summarize' number of messages is reached in the conversation
-- [conv_pref_prompt]        analyse the conversation style of the student 
-- [role_prompt]             role of a tutor to answer student's questions on the topic  
-"""
-
-ValidMessageTypes: TypeAlias = SystemMessage | HumanMessage | AIMessage
-AllMessageTypes: TypeAlias = ValidMessageTypes | RemoveMessage
-
-class State(TypedDict):
-    messages: Annotated[list[AllMessageTypes], add_messages]
-    summary: str
-    conversationalStyle: str
-
-class BaseAgent:
-    def __init__(self):
-        llm = GoogleAILLMs()                  # OpenAILLMs() or GoogleAILLMs()
-        self.llm = llm.get_llm()
-        summarisation_llm = GoogleAILLMs()    # OpenAILLMs() or GoogleAILLMs()
-        self.summarisation_llm = summarisation_llm.get_llm()
-        self.summary = ""
-        self.conversationalStyle = ""
-
-        # Define Agent's specific Parameters
-        self.max_messages_to_summarize = 11
-        self.role_prompt = role_prompt
-        self.summary_prompt = summary_prompt
-        self.update_summary_prompt = update_summary_prompt
-        self.conversation_preference_prompt = conv_pref_prompt
-        self.update_conversation_preference_prompt = update_conv_pref_prompt
-
-        # Define a new graph for the conversation & compile it
-        self.workflow = StateGraph(State)
-        self.workflow_definition()
-        self.app = self.workflow.compile()
-
-    def call_model(self, state: State, config: RunnableConfig) -> str:
-        """Call the LLM model knowing the role system prompt, the summary and the conversational style."""
-        
-        # Default AI tutor role prompt
-        system_message = self.role_prompt
-
-        # Adding external student progress and question context details from data queries
-        question_response_details = config["configurable"].get("question_response_details", "")
-        if question_response_details:
-            system_message += f"## Known Question Materials: {question_response_details} \n\n"
-
-        # Adding summary and conversational style to the system message
-        summary = state.get("summary", "")
-        conversationalStyle = state.get("conversationalStyle", "")
-        if summary:
-            system_message += summary_system_prompt.format(summary=summary)
-        if conversationalStyle:
-            system_message += f"## Known conversational style and preferences of the student for this conversation: {conversationalStyle}. \n\nYour answer must be in line with this conversational style."
-
-        messages = [SystemMessage(content=system_message)] + state['messages']
-
-        valid_messages = self.check_for_valid_messages(messages)
-        response = self.llm.invoke(valid_messages)
-
-        # Save summary for fetching outside the class
-        self.summary = summary
-        self.conversationalStyle = conversationalStyle
-
-        return {"summary": summary, "messages": [response]}
-    
-    def check_for_valid_messages(self, messages: list[AllMessageTypes]) -> list[ValidMessageTypes]:
-        """ Removing the RemoveMessage() from the list of messages """
-
-        valid_messages: list[ValidMessageTypes] = []
-        for message in messages:
-            if message.type != 'remove':
-                valid_messages.append(message)
-        return valid_messages
-    
-    def summarize_conversation(self, state: State, config: RunnableConfig) -> dict:
-        """Summarize the conversation."""
-
-        summary = state.get("summary", "")
-        previous_summary = config["configurable"].get("summary", "")
-        previous_conversationalStyle = config["configurable"].get("conversational_style", "")
-        if previous_summary:
-            summary = previous_summary
-        
-        if summary:
-            summary_message = (
-                f"This is summary of the conversation to date: {summary}\n\n" +
-                self.update_summary_prompt
-            )
-        else:
-            summary_message = self.summary_prompt
-        
-        if previous_conversationalStyle:
-            conversationalStyle_message = (
-                f"This is the previous conversational style of the student for this conversation: {previous_conversationalStyle}\n\n" +
-                self.update_conversation_preference_prompt
-            )
-        else:
-            conversationalStyle_message = self.conversation_preference_prompt
-
-        # STEP 1: Summarize the conversation
-        messages = state["messages"][:-1] + [HumanMessage(content=summary_message)] 
-        valid_messages = self.check_for_valid_messages(messages)
-        summary_response = self.summarisation_llm.invoke(valid_messages)
-
-        # STEP 2: Analyze the conversational style
-        messages = state["messages"][:-1] + [HumanMessage(content=conversationalStyle_message)]
-        valid_messages = self.check_for_valid_messages(messages)
-        conversationalStyle_response = self.summarisation_llm.invoke(valid_messages)
-
-        # Delete messages that are no longer wanted, except the last ones
-        delete_messages: list[AllMessageTypes] = [RemoveMessage(id=m.id) for m in state["messages"][:-3]]
-
-        return {"summary": summary_response.content, "conversationalStyle": conversationalStyle_response.content, "messages": delete_messages}
-    
-    def should_summarize(self, state: State) -> str:
-        """
-        Return the next node to execute. 
-        If there are more than X messages, then we summarize the conversation.
-        Otherwise, we call the LLM.
-        """
-
-        messages = state["messages"]
-        valid_messages = self.check_for_valid_messages(messages)
-        nr_messages = len(valid_messages)
-        if len(valid_messages) == 0:
-            raise Exception("Internal Error: No valid messages found in the conversation history. Conversation history might be empty.")
-        if "system" in valid_messages[-1].type:
-            nr_messages -= 1
-
-        # always pairs of (sent, response) + 1 latest message
-        if nr_messages > self.max_messages_to_summarize:
-            return "summarize_conversation"
-        return "call_llm"    
-
-    def workflow_definition(self) -> None:
-        self.workflow.add_node("call_llm", self.call_model)
-        self.workflow.add_node("summarize_conversation", self.summarize_conversation)
-
-        self.workflow.add_conditional_edges(source=START, path=self.should_summarize)
-        self.workflow.add_edge("summarize_conversation", "call_llm")
-        self.workflow.add_edge("call_llm", END)
-
-    def get_summary(self) -> str:
-        return self.summary
-    
-    def get_conversational_style(self) -> str:
-        return self.conversationalStyle
-
-    def print_update(self, update: dict) -> None:
-        for k, v in update.items():
-            for m in v["messages"]:
-                m.pretty_print()
-            if "summary" in v:
-                print(v["summary"])
-
-    def pretty_response_value(self, event: dict) -> str:
-        return event["messages"][-1].content
-    
-agent = BaseAgent()
-def invoke_base_agent(query: str, conversation_history: list, summary: str, conversationalStyle: str, question_response_details: str, session_id: str) -> InvokeAgentResponseType:
-    """
-    Call an agent that has no conversation memory and expects to receive all past messages in the params and the latest human request in the query.
-    If conversation history longer than X, the agent will summarize the conversation and will provide a conversational style analysis.
-    """
-    print(f'in invoke_base_agent(), thread_id = {session_id}')
-
-    config = {"configurable": {"thread_id": session_id, "summary": summary, "conversational_style": conversationalStyle, "question_response_details": question_response_details}}
-    response_events = agent.app.invoke({"messages": conversation_history, "summary": summary, "conversational_style": conversationalStyle}, config=config, stream_mode="values") #updates
-    pretty_printed_response = agent.pretty_response_value(response_events) # get last event/ai answer in the response
-
-    # Gather Metadata from the agent
-    summary = agent.get_summary()
-    conversationalStyle = agent.get_conversational_style()
-
-    print(f'in invoke_base_agent(), response generated by chatbot')
-
-    return {
-        "input": query,
-        "output": pretty_printed_response,
-        "intermediate_steps": [str(summary), conversationalStyle, conversation_history]
-    }
\ No newline at end of file
diff --git a/src/agents/student_agent/student_agent.py b/src/agents/student_agent/student_agent.py
deleted file mode 100644
index 4c5f54e..0000000
--- a/src/agents/student_agent/student_agent.py
+++ /dev/null
@@ -1,145 +0,0 @@
-try:
-    from ..llm_factory import OpenAILLMs
-    from .student_prompts import \
-        base_student_persona, curious_student_persona, contradicting_student_persona, reliant_student_persona, confused_student_persona, unrelated_student_persona, \
-        process_prompt
-    from ..utils.types import InvokeAgentResponseType
-except ImportError:
-    from src.agents.llm_factory import OpenAILLMs
-    from src.agents.student_agent.student_prompts import \
-        base_student_persona, curious_student_persona, contradicting_student_persona, reliant_student_persona, confused_student_persona, unrelated_student_persona, \
-        process_prompt
-    from src.agents.utils.types import InvokeAgentResponseType
-
-from langgraph.graph import StateGraph, START, END
-from langchain_core.messages import SystemMessage, RemoveMessage, HumanMessage, AIMessage
-from langchain_core.runnables.config import RunnableConfig
-from langgraph.graph.message import add_messages
-from typing import Annotated, TypeAlias
-from typing_extensions import TypedDict
-
-"""
-Student agent for synthetic evaluation of the other LLM tutors. This agent is designed to be a student that requires help in the conversation. 
-[LLM workflow with a summarisation, and chat agent that receives an external conversation history].
-
-This agent is designed to:
-- [role_prompt]             role of a student to ask questions on the topic  
-- [student_type]            student's learning profile and comprehension level [many profiles can be chosen from the student_prompts.py]
-"""
-
-ValidMessageTypes: TypeAlias = SystemMessage | HumanMessage | AIMessage
-AllMessageTypes: TypeAlias = ValidMessageTypes | RemoveMessage
-
-class State(TypedDict):
-    messages: Annotated[list[AllMessageTypes], add_messages]
-    summary: str
-
-class StudentAgent:
-    def __init__(self, student_type: str):
-        llm = OpenAILLMs(temperature=0.75)
-        self.llm = llm.get_llm()
-        self.summary = ""
-        self.conversationalStyle = ""
-        self.type = student_type
-
-        # Define Agent's specific Personas
-        self.role_prompt = process_prompt
-        if self.type == "base":
-            self.role_prompt += base_student_persona
-        elif self.type == "curious":
-            self.role_prompt += curious_student_persona
-        elif self.type == "contradicting":
-            self.role_prompt += contradicting_student_persona
-        elif self.type == "reliant":
-            self.role_prompt += reliant_student_persona
-        elif self.type == "confused":
-            self.role_prompt += confused_student_persona
-        elif self.type == "unrelated":
-            self.role_prompt += unrelated_student_persona
-        else:
-            raise Exception("Unknown Student Agent Type")
-        # Define a new graph for the conversation & compile it
-        self.workflow = StateGraph(State)
-        self.workflow_definition()
-        self.app = self.workflow.compile()
-
-    def call_model(self, state: State, config: RunnableConfig) -> str:
-        """Call the LLM model knowing the role system prompt, the summary and the conversational style."""
-        
-        # Default AI tutor role prompt
-        system_message = self.role_prompt
-
-        # Adding external student progress and question context details from data queries
-        question_response_details = config["configurable"].get("question_response_details", "")
-        if question_response_details:
-            # convert "my" to "your" in the question_response_details to preserve the student agent as the user
-            question_response_details = question_response_details.replace("My", "Your")
-            question_response_details = question_response_details.replace("my", "your")
-            question_response_details = question_response_details.replace("I am", "you are")
-            system_message += f"\n\n## Known Learning Materials: {question_response_details} \n\n"
-
-        # Adding summary and conversational style to the system message
-        summary = state.get("summary", "")
-        previous_summary = config["configurable"].get("summary", "")
-        if previous_summary:
-            summary = previous_summary
-        if summary:
-            system_message += f"## Summary of conversation earlier: {summary} \n\n"
-
-        messages = [SystemMessage(content=system_message)] + state['messages']
-
-        valid_messages = self.check_for_valid_messages(messages)
-        response = self.llm.invoke(valid_messages)
-
-        # Save summary for fetching outside the class
-        self.summary = summary
-
-        return {"summary": summary, "messages": [response]}
-    
-    def check_for_valid_messages(self, messages: list[AllMessageTypes]) -> list[ValidMessageTypes]:
-        """ Removing the RemoveMessage() from the list of messages """
-
-        valid_messages: list[ValidMessageTypes] = []
-        for message in messages:
-            if message.type != 'remove':
-                valid_messages.append(message)
-        return valid_messages  
-
-    def workflow_definition(self) -> None:
-        self.workflow.add_node("call_llm", self.call_model)
-
-        self.workflow.add_edge(START, "call_llm")
-        self.workflow.add_edge("call_llm", END)
-
-    def get_summary(self) -> str:
-        return self.summary
-
-    def print_update(self, update: dict) -> None:
-        for k, v in update.items():
-            for m in v["messages"]:
-                m.pretty_print()
-            if "summary" in v:
-                print(v["summary"])
-
-    def pretty_response_value(self, event: dict) -> str:
-        return event["messages"][-1].content
-    
-def invoke_student_agent(query: str, conversation_history: list, summary: str, student_type:str, question_response_details: str, session_id: str) -> InvokeAgentResponseType:
-    """
-    Call a base student agents that forms a basic conversation with the tutor agent.
-    """
-    print(f'in invoke_student_agent(), student_type: {student_type}')
-    agent = StudentAgent(student_type=student_type)
-
-    config = {"configurable": {"thread_id": session_id, "summary": summary, "question_response_details": question_response_details}}
-    response_events = agent.app.invoke({"messages": conversation_history + [AIMessage(content=query)]}, config=config, stream_mode="values") #updates
-    pretty_printed_response = agent.pretty_response_value(response_events) # get last event/ai answer in the response
-
-    # Gather Metadata from the agent
-    summary = agent.get_summary()
-
-    return {
-        "input": query,
-        "output": pretty_printed_response,
-        "intermediate_steps": [str(summary), conversation_history]
-    }
\ No newline at end of file
diff --git a/src/agents/student_agent/student_prompts.py b/src/agents/student_agent/student_prompts.py
deleted file mode 100644
index e7a8e8e..0000000
--- a/src/agents/student_agent/student_prompts.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# PROMPTS generated with the help of ChatGPT GPT-4o Nov 2024
-
-process_prompt = "Maintain the flow of the conversation by responding directly to the latest message in one sentence. Stay in character as "
-
-base_student_persona = "a student who seeks assistance. Ask questions from a first-person perspective, requesting clarification on how to solve the promblem from the known materials."
-curious_student_persona = "a curious and inquisitive student. Ask thoughtful, detailed questions from a first-person perspective to clarify concepts, explore real-life applications, and uncover complexities. Don’t hesitate to challenge assumptions and ask for clarification when needed."
-contradicting_student_persona = "a skeptical student. Ask questions from a first-person perspective, questioning my reasoning, identifying potential flaws, and challenging explanations. Request clarification whenever something seems unclear or incorrect."
-reliant_student_persona = "a student who relies heavily on your help. Ask questions from a first-person perspective, seeking help for even small problems, and requesting clarification or further assistance to ensure understanding."
-confused_student_persona = "a student who feels confused and uncertain about the topic. Ask questions from a first-person perspective, expressing uncertainty about the material and requesting clarification on both the topic and the tutor’s reasoning."
-unrelated_student_persona = "a student who engages in casual conversation. Ask lighthearted or unrelated questions from a first-person perspective, discussing personal interests or unrelated topics rather than focusing on the material."
\ No newline at end of file
diff --git a/src/agents/utils/example_inputs/example_input_1.json b/src/agents/utils/example_inputs/example_input_1.json
deleted file mode 100644
index 5bd8071..0000000
--- a/src/agents/utils/example_inputs/example_input_1.json
+++ /dev/null
@@ -1,168 +0,0 @@
-{
-  "message": "i dont remember anything",
-  "params": {
-    "include_test_data": true,
-    "conversation_history": [
-      { "type": "user", "content": "what should I do?" },
-      {
-        "type": "assistant",
-        "content": "It seems like you're currently working on Part (a) of the dot product question. Since you haven't submitted an answer yet, let's take a moment to break it down together. \n\nWhat do you remember about how to calculate the dot product of two vectors? Can you describe the steps you would take?"
-      },
-      { "type": "user", "content": "i dont remember anything" }
-    ],
-    "summary": "",
-    "conversational_style": "",
-    "question_response_details": {
-      "questionSubmissionSummary": [
-        {
-          "publishedPartId": "0e0432e4-90a2-47a1-b597-55f76596b7d5",
-          "publishedPartPosition": 0,
-          "publishedResponseAreaId": "ce28b12b-f583-4d83-a0e4-36b17127746a",
-          "publishedResponseAreaPosition": 0,
-          "responseAreaUniversalId": "f8857876-482d-411c-8ffc-734fb07712cf",
-          "publishedResponseAreaPreResponseText": "$\\vec{a} \\ \\cdot \\ \\vec{b} \\ =$",
-          "publishedResponseType": "NUMBER",
-          "publishedResponseConfig": null,
-          "totalSubmissions": 0,
-          "totalWrongSubmissions": 0
-        },
-        {
-          "publishedPartId": "a74a6fef-8c94-474c-b381-8d97a4b54725",
-          "publishedPartPosition": 1,
-          "publishedResponseAreaId": "f3be55ed-af9b-4483-ba45-636ccbad7a24",
-          "publishedResponseAreaPosition": 0,
-          "responseAreaUniversalId": "054bbcce-facb-4aaa-a1d2-7bec7627a6ef",
-          "publishedResponseAreaPreResponseText": "$\\left(\\vec{a}  - \\vec{b}\\right)\\cdot \\vec{c}\\  =$",
-          "publishedResponseType": "NUMBER",
-          "publishedResponseConfig": null,
-          "totalSubmissions": 0,
-          "totalWrongSubmissions": 0
-        },
-        {
-          "publishedPartId": "66160c45-0554-470a-88ae-82f36e755ef6",
-          "publishedPartPosition": 2,
-          "publishedResponseAreaId": "79260525-1edb-4f19-bcdd-d827b5b692f3",
-          "publishedResponseAreaPosition": 0,
-          "responseAreaUniversalId": "bba9308c-55c7-4733-9315-37bc26fc8ec1",
-          "publishedResponseAreaPreResponseText": "$\\left( \\ \\vec{a}  \\cdot \\vec{c} \\ \\right) \\vec{b}\\  =$",
-          "publishedResponseType": "MATRIX",
-          "publishedResponseConfig": { "cols": 1, "rows": 3 },
-          "totalSubmissions": 0,
-          "totalWrongSubmissions": 0
-        }
-      ],
-      "questionInformation": {
-        "questionTitle": "Dot Product",
-        "questionGuidance": "",
-        "questionContent": "$$\n\\vec{a}=\\begin{bmatrix}1 \\\\ 3\\\\ -2\\end{bmatrix} \\quad \\vec{b}=\\begin{bmatrix}0 \\\\ 3\\\\ 1\\end{bmatrix} \\quad \\vec{c}=\\begin{bmatrix} 1 \\\\\\ -1\\\\ -3\\end{bmatrix}\n$$",
-        "durationLowerBound": 1,
-        "durationUpperBound": 4,
-        "parts": [
-          {
-            "publishedPartId": "0e0432e4-90a2-47a1-b597-55f76596b7d5",
-            "publishedPartPosition": 0,
-            "publishedPartContent": "",
-            "publishedPartAnswerContent": "",
-            "publishedWorkedSolutionSections": [
-              {
-                "id": "a7b1150a-05b7-4337-b902-5c6b1835cc74",
-                "position": 0,
-                "title": "",
-                "content": "Assuming the given basis is orthonormal, the dot product between two vectors, $\\vec{a}$ and $\\vec{b}$, can be calculated simply by multiplying their corresponding components and summing them up:\n\n&#x20;      &#x20;\n\n$$\n\\begin{array}{rl}\\vec{a} \\cdot \\vec{b} &=\\begin{bmatrix}1 \\\\ 3\\\\ -2\\end{bmatrix}•\\begin{bmatrix}0 \\\\ 3\\\\ 1\\end{bmatrix} \\\\\\\\ &= (1 \\cdot 0) + (3 \\cdot 3) + (-2 \\cdot 1) \\\\\\\\ &= 0 + 9 + (-2) \\\\\\\\ &= 7\\end{array}\n$$\n\n&#x20;        &#x20;\n\nTherefore, the dot product between vectors $\\vec{a}$ and $\\vec{b}$ is \\$7\\$"
-              }
-            ],
-            "publishedResponseAreas": [
-              {
-                "id": "ce28b12b-f583-4d83-a0e4-36b17127746a",
-                "position": 0,
-                "universalResponseAreaId": "f8857876-482d-411c-8ffc-734fb07712cf",
-                "preResponseText": "$\\vec{a} \\ \\cdot \\ \\vec{b} \\ =$",
-                "Response": {
-                  "id": "4d0dfa85-fc34-4649-bb6e-0f194e8a5a04",
-                  "responseType": "NUMBER",
-                  "config": null,
-                  "answer": 7
-                },
-                "responseType": "NUMBER",
-                "answer": 7
-              }
-            ]
-          },
-          {
-            "publishedPartId": "a74a6fef-8c94-474c-b381-8d97a4b54725",
-            "publishedPartPosition": 1,
-            "publishedPartContent": "",
-            "publishedPartAnswerContent": "",
-            "publishedWorkedSolutionSections": [
-              {
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-                "content": "To calculate $\\left(\\vec{a} - \\vec{b}\\right) \\cdot \\vec{c}$ , we first need to find the vector resulting from the subtraction of $\\vec{b}$ from $\\vec{a}$:\n\n&#x20;    &#x20;\n\n$$\n\\begin{array}{rl}\\vec{a} - \\vec{b} &= \\begin{bmatrix}1 \\\\ 3 \\\\ -2\\end{bmatrix} - \\begin{bmatrix}0 \\\\ 3 \\\\ 1\\end{bmatrix} \\\\\\\\&= \\begin{bmatrix}1 - 0 \\\\ 3 - 3 \\\\ -2 - 1\\end{bmatrix} \\\\\\\\&= \\begin{bmatrix}1 \\\\ 0 \\\\ -3\\end{bmatrix}\\end{array}\n$$\n\n\n\nNext, we can compute the dot product of $\\left(\\vec{a} - \\vec{b}\\right)$ and $\\vec{c}$\n\n&#x20;     &#x20;\n\n$$\n\\begin{array}{rl}\\left(\\vec{a} - \\vec{b}\\right) \\cdot \\vec{c} &= \\begin{bmatrix}1 \\\\ 0 \\\\ -3\\end{bmatrix} \\cdot \\begin{bmatrix}1 \\\\ -1 \\\\ -3\\end{bmatrix} \\\\\\\\ &= (1 \\cdot 1) + (0 \\cdot -1) + (-3 \\cdot -3) \\\\\\\\&= 1 + 0 + 9 \\\\\\\\&= 10\\end{array}\n$$\n\n\n\nTherefore, $\\left(\\vec{a} - \\vec{b}\\right) \\cdot \\vec{c}$ is equal to \\$10"
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diff --git a/src/agents/utils/example_inputs/example_input_2.json b/src/agents/utils/example_inputs/example_input_2.json
deleted file mode 100644
index 503ece0..0000000
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-{
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-        "questionTitle": "Piecewise function Fourier series",
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-        "questionContent": "Find $a_0$, $a_n$ and $b_n$ for the Fourier series of $f(x)$, which is assumed to have period $4$.\n\n&#x20;&#x20;\n\n$$\nf(x)= \\begin{cases}0, & -2 \\leq x<-1 \\\\\\ \\frac{2 k}{3}, & -1 \\leq x<1 \\\\\\ -\\frac{k}{2}, & 1 \\leq x<2.\\end{cases}\n$$\n",
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-            "publishedPartAnswerContent": "$$\na_0=\\frac{5k}{12}\n$$\n\n&#x20;&#x20;\n\n&#x20;&#x20;\n\n$$\n\\begin{align*}\na_n &= \\dfrac{11k}{6n \\pi} \\sin \\left( \\dfrac{n \\pi}{2} \\right) \\\\[1em]\n &= \\dfrac{11k}{6n \\pi} \\frac{1-(-1)^n}{2}  (-1)^{^{\\frac{n+3}{2}}} \\\\[1em]\n&= (-1)^{n+1}\\frac{11k}{6(2n-1)\\pi}\n\\end{align*}\n$$\n\n&#x20;&#x20;\n\n&#x20;&#x20;\n\n$$\n\\begin{align*}\nb_n &= \\frac{k}{2n \\pi} \\left( \\cos \\left( \\frac{n \\pi}{2} \\right) - \\cos(n \\pi) \\right) \\\\[1em]\nb_n &= \\frac{k}{2n \\pi} \\left( \\frac{1+(-1)^n}{2}(-1)^{\\frac{n}{2}} - (-1)^n \\right)\n\\end{align*}\n\n$$\n",
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-                "id": "d41e73da-331d-4897-b6f6-15031469502c",
-                "position": 0,
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-                "content": "Recall the Fourier series equations for period $2L$:\n\n&#x20;&#x20;\n\n$$\na_0 = \\frac{1}{L} \\int_{-L}^L {f(x)} \\, \\text{d}x\n\n$$\n\n&#x20;&#x20;\n\n$$\na_n = \\frac{1}{L} \\int_{-L}^L {f(x)} \\cos\\left(\\frac{n \\pi x}{L}\\right) \\, \\text{d}x\n$$\n\n&#x20;&#x20;\n\n$$\nb_n = \\frac{1}{L} \\int_{-L}^L {f(x)} \\sin\\left(\\frac{n \\pi x}{L}\\right) \\, \\text{d}x\n$$\n\n***\n\n$$\n2L=4\n$$\n\n$$\nL=2\n$$\n\n***\n\n### **Finding $a_0$:**\n\n$$\na_0 = \\frac{1}{2} \\int_{-2}^2 {f(x)} \\, \\text{d}x\n$$\n\n&#x20;&#x20;\n\n$$\na_0 = \\frac{1}{2} \\left( \\int_{-2}^{-1} 0 \\, \\text{d}x + \\int_{-1}^1 \\frac{2k}{3} \\, \\text{d}x + \\int_1^2 -\\frac{k}{2} \\, \\text{d}x \\right)\n\n$$\n\n&#x20;&#x20;\n\n$$\na_0=\\frac{5k}{12}\n$$\n\n***\n\n### **Finding $a_n$:**\n\n$$\na_n = \\frac{1}{2} \\int_{-2}^2 {f(x)} \\cos \\left( \\frac{n \\pi x}{L} \\right) \\, \\text{d}x\n\n$$\n\n&#x20;&#x20;\n\n$$\na_n = \\frac{1}{2} \\left( \\int_{-2}^{-1} 0 \\, \\text{d}x + \\int_{-1}^1 \\frac{2k}{3} \\cos \\left( \\frac{n \\pi x}{2} \\right) \\, \\text{d}x + \\int_1^2 -\\frac{k}{2} \\cos \\left( \\frac{n \\pi x}{2} \\right) \\, \\text{d}x \\right)\n\n$$\n\n&#x20;&#x20;\n\nAfter evaluating the integrals, the following is obtained:\n\n&#x20;&#x20;\n\n$$\na_n=\\frac{k}{3}\\left(\\frac{2}{n \\pi} \\sin \\left(\\frac{n \\pi}{2}\\right)-\\frac{2}{n \\pi} \\sin \\left(-\\frac{n \\pi}{2}\\right)\\right)-\\frac{k}{4}\\left(\\frac{2}{n \\pi} \\sin (n \\pi)-\\frac{2}{n \\pi} \\sin \\left(\\frac{n \\pi}{2}\\right)\\right)\n$$\n\n&#x20;&#x20;\n\n*   The second $\\sin$ term can be written as: $-\\frac{2}{n \\pi} \\sin \\left(-\\frac{n \\pi}{2}\\right)=\\frac{2}{n \\pi} \\sin \\left(\\frac{n \\pi}{2}\\right)$\n*   The third $\\sin$ term is always zero.\n\n&#x20;&#x20;\n\n$$\na_n=\\frac{k}{3}\\left(\\frac{4}{n \\pi} \\sin \\left(\\frac{n \\pi}{2}\\right)\\right)-\\frac{k}{4}\\left(-\\frac{2}{n \\pi} \\sin \\left(\\frac{n \\pi}{2}\\right)\\right)\n$$\n\n&#x20;&#x20;\n\nSome manipulation results in:\n\n&#x20;&#x20;\n\n$$\na_n = \\dfrac{11k}{6n \\pi} \\sin \\left( \\dfrac{n \\pi}{2} \\right)\n$$\n\n**Further simplification of $a_n$:**\n\nThe $\\sin \\left( \\dfrac{n \\pi}{2} \\right)$ term can be simplified by considering the pattern with increasing $n$:\n\n&#x20;&#x20;\n\n$$\n\\sin\\left(\\frac{n\\pi}{2}\\right) = \\begin{cases}\n0 & \\text{if $n=0$} \\\\\n1 & \\text{if $n=1$} \\\\\n0 & \\text{if $n=2$} \\\\\n-1 & \\text{if $n=3$} \\\\\n0 & \\text{if $n=4$} \\\\\n\\vdots & \\vdots\n\\end{cases}\n$$\n\nThis can be achieved as follows:\n\n$$\n\\sin \\left( \\dfrac{n \\pi}{2} \\right)=\\frac{1-(-1)^n}{2} (-1)^{^{\\frac{n+3}{2}}}\n$$\n\n&#x20;&#x20;\n\n$$\na_n = \\dfrac{11k}{6n \\pi} \\frac{1-(-1)^n}{2}  (-1)^{^{\\frac{n+3}{2}}}\n$$\n\n$$\n\n\n$$\n\n&#x20;&#x20;\n\nHowever, since $\\sin \\left( \\dfrac{n \\pi}{2} \\right)$ is zero for all even $n$, the expression can alternatively be written as:\n\n&#x20;&#x20;\n\n$$\na_n=(-1)^{n+1}\\frac{11k}{6(2n-1)\\pi} \n$$\n\n***\n\n### **Finding $b_n$:**\n\n$$\nb_n = \\frac{1}{2} \\int_{-2}^2 {f(x)} \\sin\\left(\\frac{n \\pi x}{L}\\right) \\, \\text{d}x\n$$\n\n&#x20;&#x20;\n\n$$\nb_n = \\frac{1}{2} \\left( \\int_{-2}^{-1} 0 \\, \\text{d}x + \\int_{-1}^1 \\frac{2k}{3} \\sin \\left( \\frac{n \\pi x}{2} \\right) \\, \\text{d}x + \\int_1^2 -\\frac{k}{2} \\sin \\left( \\frac{n \\pi x}{2} \\right) \\, \\text{d}x \\right)\n$$\n\n&#x20;&#x20;\n\nAfter evaluating the integrals, the following is obtained:\n\n&#x20;  &#x20;\n\n$$\nb_n=\\frac{k}{3}\\left(-\\frac{2}{n \\pi} \\cos \\left(\\frac{n \\pi}{2}\\right)+\\frac{2}{n \\pi} \\cos \\left(\\frac{n \\pi}{2}\\right)\\right)-\\frac{k}{4}\\left(-\\frac{2}{n \\pi} \\cos (n \\pi)+\\frac{2}{n \\pi} \\cos \\left(\\frac{n \\pi}{2}\\right)\\right)\n$$\n\n(note that the second $\\cos$ term has positive argument, because $\\cos$ is an even function.)\n\n&#x20;&#x20;\n\nSimplifying this expression yields the answer. Note that $\\cos(n\\pi)$ has been replaced with $(-1)^n$.\n\n&#x20;&#x20;\n\n$$\nb_n = \\frac{k}{2n \\pi} \\left( \\cos \\left( \\frac{n \\pi}{2} \\right) - \\cos(n \\pi) \\right)\n$$\n\n&#x20;&#x20;\n\n**Further simplification of** $b_n$**:**\n\n*   The $\\cos(n\\pi)$ term can be replaced by $(-1)^n$.\n*   The $\\cos\\left(\\frac{n\\pi}{2}\\right)$ term can be simplified by considering the pattern with increasing $n$:\n\n    &#x20;&#x20;\n\n$$\n\\cos\\left(\\frac{n\\pi}{2}\\right) = \\begin{cases}\n1 & \\text{if $n=0$} \\\\\n0 & \\text{if $n=1$} \\\\\n-1 & \\text{if $n=2$} \\\\\n0 & \\text{if $n=3$} \\\\\n1 & \\text{if $n=4$} \\\\\n\\vdots & \\vdots\n\\end{cases}\n$$\n\nThis can be achieved as follows:\n\n$$\n\\cos\\left(\\frac{n\\pi}{2}\\right) =\\frac{1+(-1)^n}{2}(-1)^{\\frac{n}{2}}\n$$\n\nFinally, this yields:\n\n&#x20;&#x20;\n\n$$\nb_n = \\frac{k}{2n \\pi} \\left( \\frac{1+(-1)^n}{2}(-1)^{\\frac{n}{2}} - (-1)^n \\right)\n$$\n\n&#x20;&#x20;\n\n(Note that this expression may seem less concise that simply including the cos(n\\*pi/2) but is much more desirable and efficient in a numerical algorithm).\n"
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diff --git a/src/agents/utils/example_inputs/example_input_3.json b/src/agents/utils/example_inputs/example_input_3.json
deleted file mode 100644
index d93ac92..0000000
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-        "timeTaken": "13 minutes",
-        "accessStatus": "too much time spent on this question.",
-        "markedDone": "Part (a) was marked done",
-        "currentPart": {
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diff --git a/src/agents/utils/langgraph_viz.py b/src/agents/utils/langgraph_viz.py
deleted file mode 100644
index 79e15db..0000000
--- a/src/agents/utils/langgraph_viz.py
+++ /dev/null
@@ -1,13 +0,0 @@
-"""
-Helper script to visualise the agent graph using pygraphviz.
-Setup on mac [see more here https://github.com/pygraphviz/pygraphviz/blob/main/INSTALL.txt]:
-# $ brew install graphviz
-# $ pip install pygraphviz
-"""
-
-agent = ...
-
-
-graph = agent.app.get_graph()
-print(graph)
-graph.draw_png("./graph.png")
\ No newline at end of file
diff --git a/src/agents/utils/parse_json_context_to_prompt.py b/src/agents/utils/parse_json_context_to_prompt.py
deleted file mode 100644
index e9f9dcd..0000000
--- a/src/agents/utils/parse_json_context_to_prompt.py
+++ /dev/null
@@ -1,334 +0,0 @@
-"""
-Refactored JSON to prompt parser using improved, clearer structure.
-"""
-
-from typing import List, Optional, Dict, Any, Union
-from .prompt_context_templates import PromptFormatter
-
-# Definitions questionSubmissionSummary type
-class StudentLatestSubmission:
-    def __init__(
-        self,
-        universalResponseAreaId: Optional[str] = None,
-        answer: Optional[str] = None,
-        submission: Optional[str] = None,
-        feedback: Optional[str] = None,
-        rawResponse: Optional[dict] = None,
-    ):
-        self.universalResponseAreaId = universalResponseAreaId
-        self.answer = answer
-        self.submission = submission
-        self.feedback = feedback
-        self.rawResponse = rawResponse
-
-class StudentWorkResponseArea:
-    def __init__(
-        self,
-        publishedPartId: Optional[str] = None,
-        publishedPartPosition: Optional[int] = None,
-        publishedResponseAreaId: Optional[str] = None,
-        publishedResponseAreaPosition: Optional[int] = None,
-        responseAreaUniversalId: Optional[str] = None,
-        publishedResponseAreaPreResponseText: Optional[str] = None,
-        publishedResponseType: Optional[str] = None,
-        publishedResponseConfig: Optional[dict] = None,
-        totalSubmissions: Optional[int] = None,
-        totalWrongSubmissions: Optional[int] = None,
-        latestSubmission: Optional[StudentLatestSubmission] = None,
-    ):
-        self.publishedPartId = publishedPartId
-        self.publishedPartPosition = publishedPartPosition
-        self.publishedResponseAreaId = publishedResponseAreaId
-        self.publishedResponseAreaPosition = publishedResponseAreaPosition
-        self.responseAreaUniversalId = responseAreaUniversalId
-        self.publishedResponseAreaPreResponseText = publishedResponseAreaPreResponseText
-        self.publishedResponseType = publishedResponseType
-        self.publishedResponseConfig = publishedResponseConfig
-        self.latestSubmission = StudentLatestSubmission(**latestSubmission) if latestSubmission else None
-        self.totalSubmissions = totalSubmissions
-        self.totalWrongSubmissions = totalWrongSubmissions
-
-# questionInformation type
-class ResponseAreaDetails:
-    def __init__(
-        self,
-        id: Optional[str] = None,
-        position: Optional[int] = None,
-        universalResponseAreaId: Optional[str] = None,
-        preResponseText: Optional[str] = None,
-        responseType: Optional[str] = None,
-        answer: Optional[dict] = None,
-        Response: Optional[dict] = None,
-    ):
-        self.id = id
-        self.position = position
-        self.universalResponseAreaId = universalResponseAreaId
-        self.preResponseText = preResponseText
-        self.responseType = responseType
-        self.answer = answer
-        self.Response = Response
-
-class PartDetails:
-    def __init__(
-        self,
-        publishedPartId: Optional[str] = None,
-        publishedPartPosition: Optional[int] = None,
-        publishedPartContent: Optional[str] = None,
-        publishedPartAnswerContent: Optional[str] = None,
-        publishedWorkedSolutionSections: Optional[List[dict]] = [],
-        publishedStructuredTutorialSections: Optional[List[dict]] = [],
-        publishedResponseAreas: Optional[List[Optional[ResponseAreaDetails]]] = [],
-    ):
-        self.publishedPartId = publishedPartId
-        self.publishedPartPosition = publishedPartPosition
-        self.publishedPartContent = publishedPartContent
-        self.publishedPartAnswerContent = publishedPartAnswerContent
-        self.publishedWorkedSolutionSections = publishedWorkedSolutionSections
-        self.publishedStructuredTutorialSections = publishedStructuredTutorialSections
-        self.publishedResponseAreas = [ResponseAreaDetails(**publishedResponseArea) for publishedResponseArea in publishedResponseAreas]
-
-class QuestionDetails:
-    def __init__(
-        self,
-        setNumber: Optional[int] = None,
-        setName: Optional[str] = None,
-        setDescription: Optional[str] = None,
-        questionNumber: Optional[int] = None,
-        questionTitle: Optional[str] = None,
-        questionGuidance: Optional[str] = None,
-        questionContent: Optional[str] = None,
-        durationLowerBound: Optional[int] = None,
-        durationUpperBound: Optional[int] = None,
-        parts: Optional[List[PartDetails]] = [],
-    ):
-        self.setNumber = setNumber
-        self.setName = setName
-        self.setDescription = setDescription
-        self.questionNumber = questionNumber
-        self.questionTitle = questionTitle
-        self.questionGuidance = questionGuidance
-        self.questionContent = questionContent
-        self.durationLowerBound = durationLowerBound
-        self.durationUpperBound = durationUpperBound
-        self.parts = [PartDetails(**part) for part in parts] 
-
-# questionAccessInformation type
-class CurrentPart:
-    def __init__(
-        self, 
-        id: str = None, 
-        position: int = None, 
-        universalPartId: Optional[str] = None,
-        timeTakenPart: Optional[str] = None, 
-        markedDonePart: Optional[str] = None
-    ):
-        self.id = id
-        self.position = position
-        self.universalPartId = universalPartId
-        self.timeTakenPart = timeTakenPart
-        self.markedDonePart = markedDonePart
-
-class QuestionAccessInformation:
-    def __init__(
-        self,
-        estimatedMinimumTime: Optional[str] = None,
-        estimaredMaximumTime: Optional[str] = None,
-        timeTaken: Optional[str] = None,
-        accessStatus: Optional[str] = None,
-        markedDone: Optional[str] = None,
-        currentPart: Optional[Dict[str, Union[str, int]]] = {},
-    ):
-        self.estimatedMinimumTime = estimatedMinimumTime
-        self.estimaredMaximumTime = estimaredMaximumTime
-        self.timeTaken = timeTaken
-        self.accessStatus = accessStatus
-        self.markedDone = markedDone
-        self.currentPart = CurrentPart(**currentPart)
-
-
-def parse_json_to_structured_prompt(
-    question_submission_summary: Optional[List[StudentWorkResponseArea]],
-    question_information: Optional[QuestionDetails],
-    question_access_information: Optional[QuestionAccessInformation]
-) -> Optional[str]:
-    """
-    Parse JSON data into a well-structured, LLM-friendly prompt.
-    
-    Args:
-        question_submission_summary: Student's work and submissions
-        question_information: Question details and structure
-        question_access_information: Current progress and timing info
-        
-    Returns:
-        Formatted prompt string or error message
-    """
-    
-    if not question_information:
-        return PromptFormatter.format_error_message()
-    
-    # Convert to proper objects
-    submission_summary = [StudentWorkResponseArea(**summary) for summary in question_submission_summary]
-    question_info = QuestionDetails(**question_information)
-    access_info = QuestionAccessInformation(**question_access_information) if question_access_information else None
-    
-    # TODO: EXPERIMENTAL - Remove later
-    # if question_info.setNumber is not None:
-    #     if (question_info.setNumber + 1) % 2 != 0:
-    #         return PromptFormatter.format_no_context_message()
-    
-    # Build prompt sections
-    sections = []
-    
-    # 1. Question Header
-    current_part_letter = None
-    if access_info and access_info.currentPart:
-        current_part_letter = PromptFormatter.get_part_letter(access_info.currentPart.position)
-    
-    set_info = {
-        'number': question_info.setNumber,
-        'name': question_info.setName
-    }
-    
-    question_data = {
-        'number': question_info.questionNumber,
-        'title': question_info.questionTitle,
-        'guidance': question_info.questionGuidance,
-        'content': question_info.questionContent,
-        'duration_lower': question_info.durationLowerBound,
-        'duration_upper': question_info.durationUpperBound
-    }
-    
-    sections.append(PromptFormatter.format_question_header(
-        set_info, 
-        question_data, 
-        current_part_letter
-    ))
-    
-    # 2. Progress Summary (if available)
-    if access_info:
-        progress_section = PromptFormatter.format_progress_summary(
-            access_info.timeTaken,
-            access_info.accessStatus,
-            access_info.markedDone
-        )
-        if progress_section:
-            sections.append(progress_section)
-    
-    # 3. Parts Details
-    for part in question_info.parts:
-        sections.append(_format_single_part(
-            part, 
-            access_info.currentPart if access_info else None,
-            submission_summary
-        ))
-    
-    # 4. Combine into final prompt
-    return PromptFormatter.format_complete_prompt(sections)
-
-
-def _format_single_part(
-    part: PartDetails, 
-    current_part: Optional[CurrentPart],
-    submissions: List[StudentWorkResponseArea]
-) -> str:
-    """Format a single part with all its components."""
-    
-    if not part:
-        return ""
-    
-    part_sections = []
-    part_letter = PromptFormatter.get_part_letter(part.publishedPartPosition)
-    
-    # Determine if this is the current part
-    is_current = current_part and current_part.id == part.publishedPartId
-    time_on_part = current_part.timeTakenPart if is_current and current_part else None
-    
-    # 1. Part Header
-    part_sections.append(PromptFormatter.format_part_header(
-        part_letter, 
-        is_current, 
-        time_on_part
-    ))
-    
-    # 2. Part Content
-    part_sections.append(PromptFormatter.format_part_content(part.publishedPartContent))
-    
-    # 3. Response Areas
-    response_areas = []
-    for response_area in part.publishedResponseAreas:
-        student_work = _extract_student_work_for_area(response_area, submissions)
-        response_areas.append(PromptFormatter.format_single_response_area(
-            response_area.position,
-            response_area.preResponseText,
-            response_area.answer,
-            student_work
-        ))
-    
-    if response_areas:
-        part_sections.append(PromptFormatter.format_response_areas(response_areas))
-    
-    # 4. Final Part Answer
-    part_sections.append(PromptFormatter.format_part_answer(part.publishedPartAnswerContent))
-    
-    # 5. Worked Solutions
-    solutions_data = []
-    if part.publishedWorkedSolutionSections:
-        for ws in part.publishedWorkedSolutionSections:
-            solutions_data.append({
-                'title': ws.get('title', ''),
-                'content': ws.get('content', ''),
-                'position': ws.get('position', 0)
-            })
-
-    #  6. Structured Tutorial Sections
-    tutorial_data = []
-    if part.publishedStructuredTutorialSections:
-        for ts in part.publishedStructuredTutorialSections:
-            tutorial_data.append({
-                'title': ts.get('title', ''),
-                'content': ts.get('content', ''),
-                'position': ts.get('position', 0)
-            })
-    
-    part_sections.append(PromptFormatter.format_worked_solutions(solutions_data))
-    part_sections.append(PromptFormatter.format_structured_tutorials(tutorial_data))
-
-    return "\n".join(part_sections) + "\n---\n"
-
-
-def _extract_student_work_for_area(
-    response_area: ResponseAreaDetails, 
-    submissions: List[StudentWorkResponseArea]
-) -> Dict[str, Any]:
-    """Extract student work data for a specific response area."""
-    
-    for submission in submissions:
-        if (submission.publishedResponseAreaId == response_area.id and 
-            submission.latestSubmission):
-            
-            return {
-                'has_submissions': True,
-                'latest_response': submission.latestSubmission.submission,
-                'latest_feedback': submission.latestSubmission.feedback,
-                'total_submissions': submission.totalSubmissions,
-                'total_wrong': submission.totalWrongSubmissions
-            }
-    
-    return {'has_submissions': False}
-
-
-# Convenience function that maintains the original interface
-def parse_json_to_prompt(
-    questionSubmissionSummary: Optional[List[StudentWorkResponseArea]],
-    questionInformation: Optional[QuestionDetails],
-    questionAccessInformation: Optional[QuestionAccessInformation]
-) -> Optional[str]:
-    """
-    Legacy wrapper for backward compatibility.
-    Recommended to use parse_json_to_structured_prompt for new code.
-    """
-    return parse_json_to_structured_prompt(
-        questionSubmissionSummary,
-        questionInformation,
-        questionAccessInformation
-    )
diff --git a/src/agents/utils/prompt_context_templates.py b/src/agents/utils/prompt_context_templates.py
deleted file mode 100644
index e77a208..0000000
--- a/src/agents/utils/prompt_context_templates.py
+++ /dev/null
@@ -1,263 +0,0 @@
-"""
-Improved prompt templates with clearer structure for LLM consumption.
-Uses hierarchical organization and consistent formatting.
-"""
-
-from typing import Optional, List, Dict, Any
-
-class PromptFormatter:
-    """Centralized prompt formatting with clear structure."""
-    
-    @staticmethod
-    def format_error_message() -> str:
-        """Return structured error message."""
-        return """
-# ERROR: Question details unavailable
-
-Please describe the question you're working on so I can assist you effectively.
-"""
-
-    @staticmethod
-    def format_no_context_message() -> str:
-        """Return structured no-context message."""
-        return """
-# NOTICE: Question details not provided
-
-Please tell me about the question you're working on. I'll use British English spellings.
-"""
-
-    @staticmethod
-    def format_question_header(
-        set_info: Dict[str, Any],
-        question_info: Dict[str, Any],
-        current_part: Optional[str] = None
-    ) -> str:
-        """Format the main question header with metadata."""
-        
-        # Build title components
-        title_parts = []
-        if set_info.get('number') is not None and set_info.get('name'):
-            title_parts.append(f"## Set {set_info['number'] + 1}: {set_info['name']}")
-        
-        question_num = ""
-        if set_info.get('number') is not None and question_info.get('number') is not None:
-            question_num = f"{set_info['number'] + 1}.{question_info['number'] + 1}"
-        
-        title_parts.append(f"## Question {question_num}: {question_info['title']}")
-        
-        # Current progress indicator
-        progress_indicator = ""
-        if current_part:
-            progress_indicator = f"### Currently working on: Part ({current_part})"
-        
-        # Question metadata
-        guidance = question_info.get('guidance', 'None provided')
-        content = question_info.get('content', 'No description available')
-        
-        # Duration formatting
-        duration_text = "- Expected Duration: "
-        if question_info.get('duration_lower') and question_info.get('duration_upper'):
-            duration_text += f"{question_info['duration_lower']}-{question_info['duration_upper']} minutes"
-        else:
-            duration_text += "Not specified"
-        
-        return f"""
-# Question Context
-
-{"\n".join(title_parts)}
-
-{progress_indicator}
-
-### Question Details
-- Guidance: {guidance}
-- Description: {content}
-{duration_text}
-
-> Note: Mathematical equations are in KaTeX format, preserve them the same. Ensure mathematical equations are surrounded by one '$' for in-line equations and '$$' for block equations. 
-Example: '$E=mc^2$' or '$$E=mc^2$$'.
-Use British English spellings.
-
----
-"""
-
-    @staticmethod
-    def format_progress_summary(
-        time_taken: Optional[str] = None,
-        access_status: Optional[str] = None,
-        marked_done: Optional[str] = None
-    ) -> str:
-        """Format progress and timing information."""
-        if not any([time_taken, access_status, marked_done]):
-            return ""
-        
-        progress_items = []
-        if time_taken:
-            progress_items.append(f"- Time spent today: {time_taken}")
-        if access_status:
-            progress_items.append(f"- Status: {access_status}")
-        if marked_done:
-            progress_items.append(f"- Completion: {marked_done}")
-        
-        return f"""
-# Progress Summary
-
-{"\n".join(progress_items)}
-
----
-"""
-
-    @staticmethod
-    def format_part_header(
-        part_letter: str,
-        is_current: bool = False,
-        time_on_part: Optional[str] = None
-    ) -> str:
-        """Format part header with clear indicators."""
-        
-        status_text = " [CURRENTLY WORKING ON]" if is_current else ""
-        
-        header = f"## Part ({part_letter}){status_text}"
-        
-        if is_current and time_on_part:
-            time_display = time_on_part if time_on_part != 'No recorded duration' else 'No time recorded'
-            header += f"\n\n*Time spent on this part: {time_display}*"
-        
-        return header
-
-    @staticmethod
-    def format_part_content(content: Optional[str]) -> str:
-        """Format part content with clear labeling."""
-        if not content or not content.strip():
-            return "### Part Content\n\nNo content provided"
-        
-        return f"### Part Content\n\n{content.strip()}"
-
-    @staticmethod
-    def format_response_areas(response_areas: List[str]) -> str:
-        """Format multiple response areas with clear separation."""
-        if not response_areas:
-            return "### Response Areas\n\nNone defined"
-        
-        return f"""
-### Response Areas
-
-{"\n".join(response_areas)}
-"""
-
-    @staticmethod
-    def format_single_response_area(
-        position: int,
-        task_description: Optional[str],
-        expected_answer: Any,
-        student_work: Dict[str, Any]
-    ) -> str:
-        """Format a single response area with student work."""
-        
-        # Format task description
-        task_text = f"- Task: {task_description}" if task_description else "- Task: Not specified"
-        
-        # Format expected answer (keep secret)
-        answer_text = f"- Expected Answer (confidential): {expected_answer}"
-        
-        # Format student submissions
-        submission_text = PromptFormatter._format_student_submissions(student_work)
-        
-        return f"""
-#### Response Area {position + 1}
-
-{task_text}
-{answer_text}
-{submission_text}
-"""
-
-    @staticmethod
-    def _format_student_submissions(student_work: Dict[str, Any]) -> str:
-        """Format student submission history."""
-        if not student_work.get('has_submissions'):
-            return "- Your Work: No responses submitted yet"
-        
-        latest = student_work.get('latest_response', 'None')
-        feedback = student_work.get('latest_feedback', 'None')
-        total = student_work.get('total_submissions', 0)
-        wrong = student_work.get('total_wrong', 0)
-        
-        return f"""- Your Work:
-  - Latest response: {latest}
-  - Latest feedback: {feedback}
-  - Total attempts: {total}
-  - Incorrect attempts: {wrong}"""
-
-    @staticmethod
-    def format_part_answer(answer_content: Optional[str]) -> str:
-        """Format the final part answer."""
-        if not answer_content:
-            return "### Final Answer\n\nNo direct answer specified for this part"
-        
-        return f"### Final Answer\n\n{answer_content}"
-
-    @staticmethod
-    def format_worked_solutions(solutions: List[Dict[str, Any]]) -> str:
-        """Format worked solutions section."""
-        if not solutions:
-            return "### Worked Solutions\n\nNone available"
-        
-        solution_texts = []
-        for i, solution in enumerate(solutions):
-            title = solution.get('title', f'Solution {i + 1}')
-            content = solution.get('content', '').strip() or 'No content available'
-            solution_texts.append(f"#### {title}\n\n{content}")
-        
-        return f"""### Worked Solutions
-
-{"\n".join(solution_texts)}"""
-
-    @staticmethod
-    def format_structured_tutorials(tutorials: List[Dict[str, Any]]) -> str:
-        """Format structured tutorials section."""
-        if not tutorials:
-            return "### Structured Tutorials\n\nNone available"
-        
-        tutorial_texts = []
-        for i, tutorial in enumerate(tutorials):
-            title = tutorial.get('title', f'Tutorial {i + 1}')
-            content = tutorial.get('content', '').strip() or 'No content available'
-            tutorial_texts.append(f"#### {title}\n\n{content}")
-        
-        return f"""### Structured Tutorials
-
-{"\n".join(tutorial_texts)}"""
-
-    @staticmethod
-    def format_complete_prompt(sections: List[str]) -> str:
-        """Combine all sections into a complete, well-structured prompt."""
-        
-        intro = """
-# Personalized Learning Assistant
-
-I have detailed information about your current question, including your progress, responses, and any feedback you've received. This context helps me provide targeted assistance based on your specific situation.
-
-"""
-        
-        # Filter out empty sections and join
-        valid_sections = [section.strip() for section in sections if section and section.strip()]
-        
-        content = intro + "\n".join(valid_sections)
-        
-        # Clean up formatting
-        content = content.replace("&#x20;&#x20;", " ").replace("&#x20", " ")
-        content = "\n".join(line for line in content.split("\n") if line.strip() or not line)
-        
-        return content.strip()
-
-    @staticmethod
-    def get_part_letter(position: int) -> str:
-        """Convert position to lowercase letter (1-indexed)."""
-        return chr(96 + (position + 1))
-
-
-# Legacy function wrappers for backward compatibility
-def get_error_prompt() -> str:
-    return PromptFormatter.format_error_message()
-
-def get_no_context_prompt() -> str:
-    return PromptFormatter.format_no_context_message()
diff --git a/src/agents/utils/synthetic_conversation_generation.py b/src/agents/utils/synthetic_conversation_generation.py
deleted file mode 100644
index 9235756..0000000
--- a/src/agents/utils/synthetic_conversation_generation.py
+++ /dev/null
@@ -1,132 +0,0 @@
-"""
-## Synthetic Dataset Generator ##
--> GOAL: Generate a synthetic dataset of conversations between a tutor and a student [both LLMs].
-
-For each question/scenario example in the example_inputs folder, a pipeline of two agents will be invoked. 
-The agents will play the role of a tutor and a student conversing about the question/scenario.
-
-The conversations will be 20 turns long, with the tutor and student taking turns to send a message.
-
-The tutor can be one of the following types:
-- Informational Agent (base)
-The tutor agent can be selected by changing the "agent_type" field in this script.
-
-The student can have multiple skill levels and conversational styles. Those are defined by the prompts used by the LLM.
-
-Any of the models accessible through the API calls defined in the 'llm_factory.py' can be used for either the tutor and the agent LLM.
-"""
-
-import csv
-import json
-try:
-  from ..student_agent.student_agent import invoke_student_agent
-  from .parse_json_context_to_prompt import parse_json_to_prompt
-  from ..base_agent.base_agent import invoke_base_agent
-except ImportError:
-  from src.agents.student_agent.student_agent import invoke_student_agent
-  from src.agents.utils.parse_json_context_to_prompt import parse_json_to_prompt
-  from src.agents.base_agent.base_agent import invoke_base_agent
-import os
-
-
-def generate_synthetic_conversations(raw_text: str, num_turns: int, student_agent_type: str, tutor_agent_type: str):
-  """
-  Generate a synthetic dataset of conversations between a tutor and a student [both LLMs].
-  """
-  if tutor_agent_type == "base": 
-    invoke_tutor_agent = invoke_base_agent
-  else:
-    raise ValueError("Invalid tutor agent type")
-    
-  parsed_json = json.loads(raw_text)
-  params = parsed_json["params"]
-  conversation_id = params["conversation_id"]
-  include_test_data = params["include_test_data"]
-  summary = ""
-  conversational_style = ""
-  question_response_details = params["question_response_details"]
-  question_submission_summary = question_response_details["questionSubmissionSummary"] if "questionSubmissionSummary" in question_response_details else []
-  question_information = question_response_details["questionInformation"] if "questionInformation" in question_response_details else {}
-  question_access_information = question_response_details["questionAccessInformation"] if "questionAccessInformation" in question_response_details else {}
-  question_response_details_prompt = parse_json_to_prompt(
-    question_submission_summary,
-    question_information,
-    question_access_information
-  )
-  
-  #  Generate Conversation
-  conversation_history = []
-  message = "Ask a question."
-  for i in range(0,num_turns):
-    print(f"Turn {i+1} of {num_turns}")
-    if len(conversation_history) == 0:
-      message = "Ask me a question regarding your thoughts on the learning materials that you are currently woking on."
-    else:
-      message = conversation_history[-1]["content"]
-
-    if i % 2 == 0:
-      # Student starts
-      student_response = invoke_student_agent(message, conversation_history[:-1], summary, student_agent_type, question_response_details_prompt, conversation_id)
-      conversation_history.append({
-        "role": "user",
-        "content": student_response["output"]
-      })
-    else:
-      tutor_response = invoke_tutor_agent(message, conversation_history, summary, conversational_style, question_response_details_prompt, conversation_id)
-      conversation_history.append({
-        "role": "assistant",
-        "content": tutor_response["output"]
-      })
-
-      if "summary" in tutor_response:
-        summary = tutor_response["summary"]
-      if "conversationalStyle" in tutor_response:
-        conversational_style = tutor_response["conversationalStyle"]
-  
-  #  Save Conversation
-  conversation_output = {
-    "conversation_id": conversation_id+"_"+student_agent_type+"_"+tutor_agent_type+"_synthetic",
-    "student_agent_type": student_agent_type,
-    "tutor_agent_type": tutor_agent_type,
-    "conversation": conversation_history
-  }
-  return conversation_output
-
-
-if __name__ == "__main__":
-  num_turns = 6
-  tutor_agent_types   = ["base"]                           
-  # Students can be "base", "curious", "contradicting", "reliant", "confused", "unrelated"
-  student_agent_types = ["base", "curious", "contradicting", "reliant", "confused", "unrelated"]  
-
-  #  Read all question files
-  questions = []
-  example_inputs_folder = "src/agents/utils/example_inputs/"
-  output_folder = "src/agents/utils/synthetic_conversations/"
-  for filename in os.listdir(example_inputs_folder):
-    if filename.endswith("1.json"):
-      questions.append(os.path.join(example_inputs_folder, filename))
-
-    for tutor_agent_type in tutor_agent_types:
-      # Open CSV file for writing
-      csv_filename = os.path.join(output_folder, "all_conversations_"+tutor_agent_type+".csv")
-      with open(csv_filename, "w", newline='') as csvfile:
-        csv_writer = csv.writer(csvfile)
-        # Write the header
-        csv_writer.writerow(["tutor", "student", "conversation", "conversation_id"])
-      
-        for student_agent_type in student_agent_types:
-          for question in questions:
-            print(f"Generating synthetic conversation for {question} with tutor: {tutor_agent_type} and student: {student_agent_type}")
-            with open(question, "r") as file:
-              raw_text = file.read()
-
-              conversation = generate_synthetic_conversations(raw_text, num_turns, student_agent_type, tutor_agent_type)
-
-              conversation_output_filename = output_folder + question.split('/')[-1].replace(".json", "_"+student_agent_type+"_"+tutor_agent_type+"_conversation.json")
-              with open(conversation_output_filename, "w") as file:
-                json.dump(conversation, file, indent=2)
-
-              # Write to CSV
-              conversation_id = conversation["conversation_id"]
-              csv_writer.writerow([tutor_agent_type, student_agent_type, conversation["conversation"], conversation_id])
diff --git a/src/agents/utils/synthetic_conversations/NOTE.md b/src/agents/utils/synthetic_conversations/NOTE.md
deleted file mode 100644
index 4bb113d..0000000
--- a/src/agents/utils/synthetic_conversations/NOTE.md
+++ /dev/null
@@ -1,4 +0,0 @@
-For evaluation purposes of the developed agent, you can use `synthetic_conversation_generation.py` to review the performance of your LLM tutor by running a multi-agent communication with a student agent (available in `src/agents/`).
-
-This folder contains all the synthetic conversations generated by an LLM student discussing with an LLM tutor.
-The files are generated by running the `synthetic_conversation_generation.py`.
\ No newline at end of file
diff --git a/src/agents/utils/testbench_agents.py b/src/agents/utils/testbench_agents.py
deleted file mode 100644
index 46d0d36..0000000
--- a/src/agents/utils/testbench_agents.py
+++ /dev/null
@@ -1,82 +0,0 @@
-"""
- Conversation turn-based Testbench of the agent's performance.
- Select an example input file and write your query. Then run the agent to get the response.
-"""
-
-import json
-try:
-    from .parse_json_context_to_prompt import parse_json_to_prompt
-    from ..base_agent.base_agent import invoke_base_agent
-except ImportError:
-    from src.agents.utils.parse_json_context_to_prompt import parse_json_to_prompt
-    from src.agents.base_agent.base_agent import invoke_base_agent
-
-# File path for the input text
-path = "src/agents/utils/example_inputs/"
-input_file = path + "example_input_1.json"
-
-# Step 1: Read the input file
-with open(input_file, "r") as file:
-    raw_text = file.read()
-    
-# Step 5: Parse into JSON
-try:
-    parsed_json = json.loads(raw_text)
-
-    """
-      STEP 2: Extract the parameters from the JSON
-    """
-    # NOTE: #### This is the testing message!! #####
-    message = "Hi, how do I solve this problem?" 
-    # NOTE: ########################################
-
-    # replace "mock" in the message and conversation history with the actual message
-    parsed_json["message"] = message
-    parsed_json["params"]["conversation_history"][-1]["content"] = message
-
-    params = parsed_json["params"]
-
-    if "include_test_data" in params:
-        include_test_data = params["include_test_data"]
-    if "conversation_history" in params:
-        conversation_history = params["conversation_history"]
-    if "summary" in params:
-        summary = params["summary"]
-    if "conversational_style" in params:
-        conversationalStyle = params["conversational_style"]
-    if "question_response_details" in params:
-        question_response_details = params["question_response_details"]
-        question_submission_summary = question_response_details["questionSubmissionSummary"] if "questionSubmissionSummary" in question_response_details else []
-        question_information = question_response_details["questionInformation"] if "questionInformation" in question_response_details else {}
-        question_access_information = question_response_details["questionAccessInformation"] if "questionAccessInformation" in question_response_details else {}
-        question_response_details_prompt = parse_json_to_prompt(
-            question_submission_summary,
-            question_information,
-            question_access_information
-        )
-        print("Question Response Details Prompt:", question_response_details_prompt, "\n\n")
-
-    if "conversation_id" in params:
-        conversation_id = params["conversation_id"]
-    else:
-        raise Exception("Internal Error: The conversation id is required in the parameters of the chat module.")
-
-    """
-      STEP 3: Call the LLM agent to get a response to the user's message
-    """
-    response = invoke_base_agent(query=message, \
-                            conversation_history=conversation_history, \
-                            summary=summary, \
-                            conversationalStyle=conversationalStyle, \
-                            question_response_details=question_response_details_prompt, \
-                            session_id=conversation_id)
-    
-    print(response)
-    print("AI Response:", response['output'])
-    
-
-except json.JSONDecodeError as e:
-    print("Error decoding JSON:", e)
-
-
-
diff --git a/src/agents/utils/types.py b/src/agents/utils/types.py
deleted file mode 100644
index d038dbc..0000000
--- a/src/agents/utils/types.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from typing import Any, Dict, TypeAlias
-
-JsonType = Dict[str, Any]
-InvokeAgentResponseType: TypeAlias = Dict[str, Any]
diff --git a/src/module.py b/src/module.py
index 7cbbdd8..133fdc2 100755
--- a/src/module.py
+++ b/src/module.py
@@ -1,91 +1,74 @@
 import time
-from typing import Any
-from lf_toolkit.chat.result import ChatResult as Result
-from lf_toolkit.chat.params import ChatParams as Params
-
-try:
-    from .agents.utils.parse_json_context_to_prompt import parse_json_to_prompt
-    from .agents.base_agent.base_agent import invoke_base_agent
-    from .agents.utils.types import JsonType
-except ImportError:
-    from src.agents.utils.parse_json_context_to_prompt import parse_json_to_prompt
-    from src.agents.base_agent.base_agent import invoke_base_agent
-    from src.agents.utils.types import JsonType
-
-def chat_module(message: Any, params: Params) -> JsonType:
-    """
-    Function used by student to converse with a chatbot.
-    ---
-    The handler function passes three arguments to module():
+from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
+
+from lf_toolkit.chat import ChatRequest, ChatResponse, Message
+from lf_toolkit.shared.mued_api_v0_1_0 import Role
+
+from src.agent.context import parse_json_to_prompt
+from src.agent.agent import invoke_base_agent
 
-    - `message` which is the message sent by the student.
-    - `params` which are any extra parameters that may be useful,
-        e.g., conversation history and summary, conversational style of user, conversation id.
 
-    The output of this function is what is returned as the API response
-    and therefore must be JSON-encodable. It must also conform to the
-    response schema.
+def chat_module(request: ChatRequest) -> ChatResponse:
+    """
+    Main entry point — receives a ChatRequest and returns a ChatResponse.
 
-    Any standard python library may be used, as well as any package
-    available on pip (provided it is added to requirements.txt).
+    The ChatRequest contains:
+    - `messages`: conversation history; the last message is the current student input.
+    - `conversationId`: unique session identifier.
+    - `user`: optional user info (preference, task progress).
+    - `context`: optional educational context (question details, prior conversation summary).
 
-    The way you wish to structure you code (all in this function, or
-    split into many) is entirely up to you. All that matters are the
-    return types and that module() is the main function used
-    to output the Chatbot response.
+    Edit src/agent/prompts.py to change the chatbot's behaviour.
+    Edit src/agent/agent.py to change the agent logic (summarisation threshold, LLM provider, etc.).
     """
 
-    result = Result()
-    include_test_data = False
-    conversation_history = []
-    summary = ""
+    conversation_id = request.conversationId
+
+    context = request.context or {}
+    task_progress = (request.user.taskProgress or {}) if request.user else {}
+
+    summary = context.get("summary", "") or ""
+
     conversationalStyle = ""
-    question_response_details_prompt = ""
-
-    if "include_test_data" in params:
-        include_test_data = params["include_test_data"]
-    if "conversation_history" in params:
-        conversation_history = params["conversation_history"]
-    if "summary" in params:
-        summary = params["summary"]
-    if "conversational_style" in params:
-        conversationalStyle = params["conversational_style"]
-    if "question_response_details" in params:
-        question_response_details = params["question_response_details"]
-        question_submission_summary = question_response_details["questionSubmissionSummary"] if "questionSubmissionSummary" in question_response_details else []
-        question_information = question_response_details["questionInformation"] if "questionInformation" in question_response_details else {}
-        question_access_information = question_response_details["questionAccessInformation"] if "questionAccessInformation" in question_response_details else {}
-        try:
-            question_response_details_prompt = parse_json_to_prompt(
-                question_submission_summary,
-                question_information,
-                question_access_information
-            )
-            print("INFO:: ", question_response_details_prompt)
-        except Exception as e:
-            print("ERROR:: ", e)
-            raise Exception("Internal Error: The question response details could not be parsed.")
-    if "conversation_id" in params:
-        conversation_id = params["conversation_id"]
-    else:
-        raise Exception("Internal Error: The conversation id is required in the parameters of the chat module.")
-    
-    start_time = time.time()
+    if request.user and request.user.preference:
+        conversationalStyle = request.user.preference.model_dump().get("conversationalStyle", "") or ""
+
+    messages = _to_langchain_messages(request.messages)
 
-    chatbot_response = invoke_base_agent(query=message, \
-                            conversation_history=conversation_history, \
-                            summary=summary, \
-                            conversationalStyle=conversationalStyle, \
-                            question_response_details=question_response_details_prompt, \
-                            session_id=conversation_id)
+    try:
+        context_prompt = parse_json_to_prompt(context, task_progress)
+    except Exception as e:
+        print("ERROR:: ", e)
+        raise Exception("Internal Error: The context prompt could not be parsed.")
 
+    start_time = time.time()
+    chatbot_response = invoke_base_agent(
+        messages=messages,
+        summary=summary,
+        conversationalStyle=conversationalStyle,
+        context_prompt=context_prompt,
+        session_id=conversation_id,
+    )
     end_time = time.time()
 
-    result._processing_time = end_time - start_time
-    result.add_response("chatbot_response", chatbot_response["output"])
-    result.add_metadata("summary", chatbot_response["intermediate_steps"][0])
-    result.add_metadata("conversational_style", chatbot_response["intermediate_steps"][1])
-    result.add_metadata("conversation_history", chatbot_response["intermediate_steps"][2])
-    result.add_processing_time(end_time - start_time)
+    return ChatResponse(
+        output=Message(role=Role.ASSISTANT, content=chatbot_response["output"]),
+        metadata={
+            "summary": chatbot_response["summary"],
+            "conversationalStyle": chatbot_response["conversationalStyle"],
+            "processingTimeMs": round((end_time - start_time) * 1000),
+        },
+    )
+
 
-    return result.to_dict(include_test_data=include_test_data)
\ No newline at end of file
+def _to_langchain_messages(messages):
+    result = []
+    for m in messages:
+        role = str(m.role).upper()
+        if role == "USER":
+            result.append(HumanMessage(content=m.content))
+        elif role == "ASSISTANT":
+            result.append(AIMessage(content=m.content))
+        elif role == "SYSTEM":
+            result.append(SystemMessage(content=m.content))
+    return result
diff --git a/src/module_test.py b/src/module_test.py
deleted file mode 100755
index 222eef2..0000000
--- a/src/module_test.py
+++ /dev/null
@@ -1,86 +0,0 @@
-import unittest
-
-try:
-    from .module import Params, chat_module
-except ImportError:
-    from module import Params, chat_module
-
-class TestChatModuleFunction(unittest.TestCase):
-    """
-    TestCase Class used to test the algorithm.
-    ---
-    Tests are used here to check that the algorithm written
-    is working as it should.
-
-    It's best practise to write these tests first to get a
-    kind of 'specification' for how your algorithm should
-    work, and you should run these tests before committing
-    your code to AWS.
-
-    Read the docs on how to use unittest here:
-    https://docs.python.org/3/library/unittest.html
-
-    Use module() to check your algorithm works
-    as it should.
-    """
-
-    def test_missing_parameters(self):
-        # Checking state for missing parameters on default agent
-        response = "Hello, World"
-        expected_params = Params(include_test_data=True, conversation_history=[{ "type": "user", "content": response }], \
-                                    summary="", conversational_style="", \
-                                    question_response_details={}, conversation_id="1234Test")
-
-        for p in expected_params:
-            params = expected_params.copy()
-            # except for the special parameters
-            if p not in ["include_test_data", "conversation_id", "conversation_history"]:
-                params.pop(p)
-
-                result = chat_module(response, params)
-
-                self.assertIsNotNone(result)
-                self.assertEqual("error" in result, False)
-            elif p == "include_test_data":
-                params.pop(p)
-
-                result = chat_module(response, params)
-
-                # check if result has nothing except for the chatbot_response
-                self.assertIsNotNone(result.get("chatbot_response"))
-                self.assertEqual(len(result), 1)
-            elif p == "conversation_id":
-                params.pop(p)
-                
-                with self.assertRaises(Exception) as cm:
-                    chat_module(response, params)
-
-                self.assertTrue("Internal Error" in str(cm.exception))
-                self.assertTrue("conversation id" in str(cm.exception))
-            elif p == "conversation_history":
-                params.pop(p)
-
-                with self.assertRaises(Exception) as cm:
-                    chat_module(response, params)
-
-                self.assertTrue("Internal Error" in str(cm.exception))
-                self.assertTrue("conversation history" in str(cm.exception))
-
-    def test_agent_output(self):
-        # Checking the output of the agent
-        response = "Hello, World"
-        params = Params(conversation_id="1234Test", conversation_history=[{ "type": "user", "content": response }])
-
-        result = chat_module(response, params)
-
-        self.assertIsNotNone(result.get("chatbot_response"))
-
-    def test_processing_time_calc(self):
-        # Checking the processing time calculation
-        response = "Hello, World"
-        params = Params(include_test_data=True, conversation_id="1234Test", conversation_history=[{ "type": "user", "content": response }])
-
-        result = chat_module(response, params)
-
-        self.assertIsNotNone(result.get("processing_time"))
-        self.assertGreaterEqual(result.get("processing_time"), 0)
\ No newline at end of file
diff --git a/tests/example_inputs/example_input_0.json b/tests/example_inputs/example_input_0.json
new file mode 100644
index 0000000..3154cfc
--- /dev/null
+++ b/tests/example_inputs/example_input_0.json
@@ -0,0 +1,7 @@
+{
+  "conversationId": "example-conversation-id-0",
+  "messages": [
+    { "role": "USER", "content": "Hi, how do I solve this problem?" }
+  ],
+  "user": { "type": "LEARNER" }
+}
diff --git a/tests/example_inputs/example_input_1.json b/tests/example_inputs/example_input_1.json
new file mode 100644
index 0000000..ada3502
--- /dev/null
+++ b/tests/example_inputs/example_input_1.json
@@ -0,0 +1,123 @@
+{
+  "conversationId": "example-conversation-id-1",
+  "messages": [
+    { "role": "USER", "content": "How can I start solving this question?" }
+  ],
+  "user": {
+    "type": "LEARNER",
+    "preference": { "conversationalStyle": "" },
+    "taskProgress": {
+      "timeSpentOnQuestion": "58 minutes",
+      "accessStatus": "too much time spent on this question today.",
+      "markedDone": "This question is still being worked on.",
+      "currentPart": {
+        "position": 0,
+        "timeSpentOnPart": "37 minutes",
+        "markedDone": "This part is not marked done.",
+        "responseAreas": [
+          {
+            "responseType": "NUMBER",
+            "totalSubmissions": 1,
+            "wrongSubmissions": 1,
+            "latestSubmission": {
+              "submission": 1,
+              "feedback": "Incorrect",
+              "answer": "26"
+            }
+          },
+          {
+            "responseType": "NUMBER",
+            "totalSubmissions": 1,
+            "wrongSubmissions": 1,
+            "latestSubmission": {
+              "submission": 20,
+              "feedback": "Incorrect",
+              "answer": "53"
+            }
+          }
+        ]
+      }
+    }
+  },
+  "context": {
+    "summary": "",
+    "set": {
+      "title": "Computing",
+      "number": 8,
+      "description": "Exercises on number systems and binary arithmetic."
+    },
+    "question": {
+      "title": "Binary Numbers",
+      "number": 0,
+      "guidance": "",
+      "content": "",
+      "estimatedTime": "5-10 minutes",
+      "parts": [
+        {
+          "position": 0,
+          "content": "Convert the following numbers to decimal: $11010_2$, $110101_2$.",
+          "answerContent": "$11010_2 = 26_{10}$\\n\\n$110101_2 = 53_{10}$&#x20;\\n\\n&#x20;&#x20;",
+          "workedSolutionSections": [
+            {
+              "position": 0,
+              "title": "",
+              "content": "$11010_2 = (0\\\\times1) + (1\\\\times2) + (0\\\\times4) + (1\\\\times8) + (1\\\\times16) = \\\\boxed{26_{10}}$&#x20;\\n\\n***\\n\\n$110101_2 = (1\\\\times1) + (0\\\\times2) + (1\\\\times4) + (0\\\\times8) + (1\\\\times16) + (1\\\\times32) = \\\\boxed{53_{10}}$&#x20;\\n\\n&#x20;"
+            }
+          ],
+          "structuredTutorialSections": [],
+          "responseAreas": [
+            {
+              "position": 0,
+              "responseType": "NUMBER",
+              "answer": 26,
+              "preResponseText": "$11010_2 =$"
+            },
+            {
+              "position": 1,
+              "responseType": "NUMBER",
+              "answer": 53,
+              "preResponseText": "$110101_2$"
+            }
+          ]
+        },
+        {
+          "position": 1,
+          "content": "Convert the following numbers to binary: $101_{10}$, $16_{10}$.",
+          "answerContent": "$101_{10} = 1100101_2$&#x20;\\n\\n$16_{10} = 10000_2$&#x20;\\n\\n&#x20;&#x20;",
+          "workedSolutionSections": [
+            {
+              "position": 0,
+              "title": "",
+              "content": ""
+            },
+            {
+              "position": 0,
+              "title": "By repeated division",
+              "content": "$101_{10}$ can be divided by 2 to give a quotient of 50 and a remainder of 1. The remainder is the first binary digit: $1_2$.\\n\\n***\\n\\n$50_{10}$ divided by 2 gives a quotient of 25 and a remainder of zero, so the second binary digit is 0: $01_2$.\\n\\n***\\n\\n$25_{10}$ divided by 2 gives a quotient of 12 and a remainder of 1, so the third binary digit is 1: $101_2$\\n\\n***\\n\\n$12_{10}$ divided by 2 gives a quotient of 6 and a remainder of 0, so the fourth binary digit is 0: $0101_2$\\n\\n***\\n\\n$6_{10}$ divided by 2 gives a quotient of 3 and a remainder of 0, so the fifth binary digit is 0: $00101_2$\\n\\n***\\n\\n$3_{10}$ divided by 2 gives a quotient of 1 and a remainder of 1, so the sixth binary digit is 1: $100101_2$\\n\\n***\\n\\n$1_{10}$ divided by 2 gives a quotient of zero and a remainder of 1, so the seventh binary digit is 1: $\\\\boxed{1100101_2}$.&#x20;\\n\\n&#x20;&#x20;"
+            },
+            {
+              "position": 1,
+              "title": "By inspection",
+              "content": "$101_{10}$ can be written in terms of powers of 2 as $101 = 64 + 32 + 4 + 1 = 2^6 + 2^5 + 2^2 + 2^0$, so its binary representation is $\\\\boxed{1100101_2}$.\\n\\n***\\n\\n$16_{10}$ is already a power of 2 ($2^4$), so its binary representation will have 1 bit set: $\\\\boxed{10000_2}$.&#x20;\\n\\n&#x20; &#x20;"
+            }
+          ],
+          "structuredTutorialSections": [],
+          "responseAreas": [
+            {
+              "position": 0,
+              "responseType": "NUMBER",
+              "answer": 1100101,
+              "preResponseText": "$101_{10} = $"
+            },
+            {
+              "position": 1,
+              "responseType": "NUMBER",
+              "answer": 10000,
+              "preResponseText": "$16_{10} = $"
+            }
+          ]
+        }
+      ]
+    }
+  }
+}
diff --git a/tests/example_inputs/example_input_2.json b/tests/example_inputs/example_input_2.json
new file mode 100644
index 0000000..a0dd5db
--- /dev/null
+++ b/tests/example_inputs/example_input_2.json
@@ -0,0 +1,97 @@
+{
+  "conversationId": "example-conversation-id-2",
+  "messages": [{ "role": "USER", "content": "help" }],
+  "user": {
+    "type": "LEARNER",
+    "preference": {
+      "conversationalStyle": "student prefers direct and short responses"
+    },
+    "taskProgress": {
+      "timeSpentOnQuestion": "less than one minute",
+      "accessStatus": "",
+      "markedDone": "This question is still being worked on.",
+      "currentPart": {
+        "position": 0,
+        "timeSpentOnPart": "less than one minute",
+        "markedDone": "This part is marked done.",
+        "responseAreas": [
+          {
+            "responseType": "NUMERIC_UNITS",
+            "totalSubmissions": 3,
+            "wrongSubmissions": 2,
+            "latestSubmission": {
+              "submission": "939.7 J",
+              "feedback": "Correct",
+              "answer": "\"939.7 J\""
+            }
+          }
+        ]
+      }
+    }
+  },
+  "context": {
+    "summary": "noting was discussed yet",
+    "set": { "title": "Physics", "number": 7, "description": "" },
+    "question": {
+      "title": "Work done",
+      "number": 0,
+      "guidance": "See section 1.11.",
+      "content": "**(L3)** A father pulls a child on a sled with a rope that has a constant tension of $F =100\\\\text{N}$ and makes an angle of $20^\\\\circ$ to the horizontal. Calculate the work he performs after pulling the child for $10\\\\text{m}$.&#x20;\\n\\n![](image)\\n",
+      "estimatedTime": "10 minutes",
+      "parts": [
+        {
+          "position": 0,
+          "content": "Calculate the work he performs after pulling the child for $10\\\\text{m}$.&#x20;\\n",
+          "answerContent": "$W = 939.7\\\\text{J}$\\n",
+          "workedSolutionSections": [
+            {
+              "position": 0,
+              "title": "",
+              "content": "We require the force component parallel to the direction of travel, and so $W = \\\\vec{F}\\\\cdot\\\\vec{d}$ (the projection of $\\\\vec{F}$ onto $\\\\vec{d}$) (**section 1.11**). Hence applying the dot product definition to evaluate this (**section 1.8**):\\n\\n***\\n\\n$$\\n\\\\begin{aligned}\\nW & = \\\\vec{F}\\\\cdot \\\\vec{d} \\\\\\\\\\n& = 100\\\\cdot10\\\\cos\\\\theta \\\\\\\\\\n& = 939.7\\\\text{J} \\n\\\\end{aligned}\\n$$\\n"
+            }
+          ],
+          "structuredTutorialSections": [
+            {
+              "position": 0,
+              "title": null,
+              "content": "Can you express the work done as a dot product (**section 1.11**)?\\n\\n\\n***\\n\\nEvaluate the dot product using the dot product definition (**section 1.8**).\\n"
+            }
+          ],
+          "responseAreas": [
+            {
+              "position": 0,
+              "responseType": "NUMERIC_UNITS",
+              "answer": "939.7 J",
+              "preResponseText": "$W=$"
+            }
+          ]
+        },
+        {
+          "position": 1,
+          "content": "Why does he not pull at a shallower angle (closer to the horizontal)?\\n",
+          "answerContent": "",
+          "workedSolutionSections": [
+            {
+              "position": 0,
+              "title": "",
+              "content": "Pulling at a shallower angle (closer to horizontal) would require less work or allow him to pull less strongly, but it reduces the reaction force that he feels too, which reduces his maximum pulling force, and also allow him to gain more leverage as he pulls (he can lean forward and use his weight to pull whilst pivoted at his feet).&#x20;\\n"
+            }
+          ],
+          "structuredTutorialSections": [],
+          "responseAreas": [
+            {
+              "position": 0,
+              "responseType": "MULTIPLE_CHOICE",
+              "answer": [
+                "[true] ... reduces the reaction force acting on him, hence reducing the maximum pulling force.",
+                "[false] ... is a more awkward angle for the man to maintain, causing him to strain his muscles.",
+                "[false] ... increases the friction between the sledge and the ground, making it more difficult to move."
+              ],
+              "preResponseText": "Pulling at a shallower angle..."
+            }
+          ]
+        }
+      ]
+    }
+  }
+}
diff --git a/tests/example_inputs/example_input_3.json b/tests/example_inputs/example_input_3.json
new file mode 100644
index 0000000..d62d794
--- /dev/null
+++ b/tests/example_inputs/example_input_3.json
@@ -0,0 +1,244 @@
+{
+  "conversationId": "example-conversation-id-3",
+  "messages": [
+    { "role": "USER", "content": "hi, I need help with this question" },
+    {
+      "role": "ASSISTANT",
+      "content": "Of course! I can see you're working on vector arithmetic. What would you like help with?"
+    },
+    { "role": "USER", "content": "I'm confused about how to add vectors" },
+    {
+      "role": "ASSISTANT",
+      "content": "Vector addition is straightforward — you add the corresponding components. For vectors a and b, you add the first elements together, then the second, then the third."
+    },
+    { "role": "USER", "content": "ok so for a+b I get [-1, 5, -1]?" },
+    {
+      "role": "ASSISTANT",
+      "content": "Exactly right! a+b = [-1, 5, -1]. Well done."
+    },
+    {
+      "role": "USER",
+      "content": "and b+a should be the same because of commutativity?"
+    },
+    {
+      "role": "ASSISTANT",
+      "content": "Correct — vector addition is commutative, so b+a = a+b = [-1, 5, -1]."
+    },
+    {
+      "role": "USER",
+      "content": "I submitted both and got the first one right but the second one wrong, I put [-1, 5, 1] by mistake"
+    },
+    {
+      "role": "ASSISTANT",
+      "content": "That's just a sign error on the last component — easy to do. The last element of a is 3 and of b is -4, so their sum is -1, not 1."
+    },
+    {
+      "role": "USER",
+      "content": "got it. now I'm stuck on part b, what does 3c mean?"
+    },
+    {
+      "role": "ASSISTANT",
+      "content": "Scalar multiplication — you multiply every component of c by 3. So 3c = [3×0, 3×4, 3×(-1)] = [0, 12, -3]."
+    },
+    { "role": "USER", "content": "I am still confused" }
+  ],
+  "user": {
+    "type": "LEARNER",
+    "preference": {
+      "conversationalStyle": "The student prefers concise answers with worked examples."
+    },
+    "taskProgress": {
+      "timeSpentOnQuestion": "13 minutes",
+      "accessStatus": "too much time spent on this question.",
+      "markedDone": "Part (a) was marked done.",
+      "currentPart": {
+        "position": 0,
+        "timeSpentOnPart": "10 minutes",
+        "markedDone": "This part is not marked done.",
+        "responseAreas": [
+          {
+            "responseType": "MATRIX",
+            "totalSubmissions": 1,
+            "wrongSubmissions": 0,
+            "latestSubmission": {
+              "submission": [["-1"], ["5"], ["-1"]],
+              "feedback": "Correct",
+              "answer": [["-1"], ["5"], ["-1"]]
+            }
+          },
+          {
+            "responseType": "MATRIX",
+            "totalSubmissions": 1,
+            "wrongSubmissions": 1,
+            "latestSubmission": {
+              "submission": [["-1"], ["5"], ["1"]],
+              "feedback": "Missing negative sign in the last element.",
+              "answer": [["-1"], ["5"], ["-1"]]
+            }
+          }
+        ]
+      }
+    }
+  },
+  "context": {
+    "summary": "",
+    "set": {
+      "title": "Linear Algebra",
+      "number": 1,
+      "description": "Vectors, matrices, and linear transformations."
+    },
+    "question": {
+      "title": "Vector Arithmetics",
+      "number": 1,
+      "guidance": null,
+      "content": "$$\n\\vec{a}=\\begin{bmatrix}1 \\\\ 2\\\\ 3\\end{bmatrix} \\quad \\vec{b}=\\begin{bmatrix}-2 \\\\ 3\\\\ -4\\end{bmatrix} \\quad \\vec{c}=\\begin{bmatrix} 0 \\\\\\ 4\\\\ -1\\end{bmatrix}\n$$",
+      "estimatedTime": "1-3 minutes",
+      "parts": [
+        {
+          "position": 0,
+          "content": "",
+          "answerContent": "",
+          "workedSolutionSections": [
+            {
+              "position": 1,
+              "title": "",
+              "content": "$$\n\\vec{a}+\\vec{b} = \\begin{bmatrix}-1 \\\\ 5\\\\ -1\\end{bmatrix}\n$$\n\nSince vector addition is commutative, $\\vec{b}+\\vec{a}=\\vec{a}+\\vec{b}$.\n"
+            }
+          ],
+          "structuredTutorialSections": [],
+          "responseAreas": [
+            {
+              "position": 0,
+              "responseType": "MATRIX",
+              "answer": [["-1"], ["5"], ["-1"]],
+              "preResponseText": "$\\vec{a}+\\vec{b}=$"
+            },
+            {
+              "position": 1,
+              "responseType": "MATRIX",
+              "answer": [["-1"], ["5"], ["-1"]],
+              "preResponseText": "$\\vec{b}+\\vec{a}=$"
+            }
+          ]
+        },
+        {
+          "position": 1,
+          "content": "",
+          "answerContent": "",
+          "workedSolutionSections": [
+            {
+              "position": 1,
+              "title": "$3\\vec{c}$",
+              "content": "$$\n3\\vec{c} = \\begin{bmatrix}0 \\\\ 12\\\\ -3\\end{bmatrix}\n$$"
+            },
+            {
+              "position": 2,
+              "title": "$-\\vec{a}$",
+              "content": "$$\n-\\vec{a} = \\begin{bmatrix}-1 \\\\ -2\\\\ -3\\end{bmatrix}\n$$"
+            },
+            {
+              "position": 3,
+              "title": "$\\frac{\\vec{b}}{2}$",
+              "content": "$$\n\\frac{\\vec{b}}{2} = \\begin{bmatrix}-1 \\\\ 1.5\\\\ -2\\end{bmatrix}\n$$"
+            }
+          ],
+          "structuredTutorialSections": [],
+          "responseAreas": [
+            {
+              "position": 0,
+              "responseType": "MATRIX",
+              "answer": [["0"], ["12"], ["-3"]],
+              "preResponseText": "$3\\vec{c}=$"
+            },
+            {
+              "position": 1,
+              "responseType": "MATRIX",
+              "answer": [["-1"], ["-2"], ["-3"]],
+              "preResponseText": "$-\\vec{a}=$"
+            },
+            {
+              "position": 2,
+              "responseType": "MATRIX",
+              "answer": [["-1"], ["1.5"], ["-2"]],
+              "preResponseText": "$\\frac{\\vec{b}}{2}=$"
+            }
+          ]
+        },
+        {
+          "position": 2,
+          "content": "",
+          "answerContent": "",
+          "workedSolutionSections": [
+            {
+              "position": 1,
+              "title": "$3\\vec{a}-3\\vec{c}$",
+              "content": "$$\n3\\vec{a}-3\\vec{c} = \\begin{bmatrix}3 \\\\ -6\\\\ 12\\end{bmatrix}\n$$"
+            },
+            {
+              "position": 2,
+              "title": "$3(\\vec{a}-\\vec{c})$",
+              "content": "$$\n3(\\vec{a}-\\vec{c}) = \\begin{bmatrix}3 \\\\ -6\\\\ 12\\end{bmatrix}\n$$"
+            }
+          ],
+          "structuredTutorialSections": [],
+          "responseAreas": [
+            {
+              "position": 0,
+              "responseType": "MATRIX",
+              "answer": [["3"], ["-6"], ["12"]],
+              "preResponseText": "$3\\vec{a}-3\\vec{c}=$"
+            },
+            {
+              "position": 1,
+              "responseType": "MATRIX",
+              "answer": [["3"], ["-6"], ["12"]],
+              "preResponseText": "$3(\\vec{a}-\\vec{c})=$"
+            }
+          ]
+        },
+        {
+          "position": 3,
+          "content": "",
+          "answerContent": "",
+          "workedSolutionSections": [
+            {
+              "position": 1,
+              "title": "",
+              "content": "$$\n-5(\\vec{a}+\\vec{c})+\\vec{b} = \\begin{bmatrix}-7 \\\\ -27\\\\ -14\\end{bmatrix}\n$$"
+            }
+          ],
+          "structuredTutorialSections": [],
+          "responseAreas": [
+            {
+              "position": 0,
+              "responseType": "MATRIX",
+              "answer": [["-7"], ["-27"], ["-14"]],
+              "preResponseText": "$-5(\\vec{a}+\\vec{c})+\\vec{b}=$"
+            }
+          ]
+        },
+        {
+          "position": 4,
+          "content": "",
+          "answerContent": "",
+          "workedSolutionSections": [
+            {
+              "position": 1,
+              "title": "",
+              "content": "$$\n3\\vec{a}-2\\vec{c}+3\\vec{b} = \\begin{bmatrix}-3 \\\\ 7\\\\ -1\\end{bmatrix}\n$$"
+            }
+          ],
+          "structuredTutorialSections": [],
+          "responseAreas": [
+            {
+              "position": 0,
+              "responseType": "MATRIX",
+              "answer": [["-3"], ["7"], ["-1"]],
+              "preResponseText": "$3\\vec{a}-2\\vec{c}+3\\vec{b}=$"
+            }
+          ]
+        }
+      ]
+    }
+  }
+}
diff --git a/src/agents/utils/requests_testscript.py b/tests/manual_agent_requests.py
similarity index 93%
rename from src/agents/utils/requests_testscript.py
rename to tests/manual_agent_requests.py
index 6b8e433..57023b5 100644
--- a/src/agents/utils/requests_testscript.py
+++ b/tests/manual_agent_requests.py
@@ -9,7 +9,7 @@
 url = "http://localhost:8080/2015-03-31/functions/function/invocations"
 
 # File path for the input text
-path = "src/agents/utils/example_inputs/"
+path = "tests/example_inputs/"
 input_file = path + "example_input_1.json"
 
 # Step 1: Read the input file
diff --git a/tests/manual_agent_run.py b/tests/manual_agent_run.py
new file mode 100644
index 0000000..43a500b
--- /dev/null
+++ b/tests/manual_agent_run.py
@@ -0,0 +1,38 @@
+"""
+Conversation turn-based Testbench of the agent's performance.
+Select an example input file and write your query. Then run the agent to get the response.
+"""
+
+import json
+from lf_toolkit.chat import ChatRequest
+from src.module import chat_module
+
+# File path for the input text
+path = "tests/example_inputs/"
+input_file = path + "example_input_1.json"
+
+# Step 1: Read the input file
+with open(input_file, "r") as file:
+    raw_text = file.read()
+
+# Step 2: Parse into JSON
+try:
+    parsed_json = json.loads(raw_text)
+
+    # NOTE: #### This is the testing message #####
+    message = "Hi, how do I solve this problem?"
+    # NOTE: ########################################
+
+    # Replace the last message content with the testing message
+    parsed_json["messages"][-1]["content"] = message
+
+    # Step 3: Validate into a ChatRequest
+    request = ChatRequest.model_validate(parsed_json)
+
+    # Step 4: Call the chat module to get a response
+    response = chat_module(request)
+
+    print(json.dumps(json.loads(response.model_dump_json()), indent=4))
+
+except json.JSONDecodeError as e:
+    print("Error decoding JSON:", e)
diff --git a/tests/test_example_inputs.py b/tests/test_example_inputs.py
new file mode 100644
index 0000000..3373fd1
--- /dev/null
+++ b/tests/test_example_inputs.py
@@ -0,0 +1,41 @@
+import unittest
+import json
+import os
+from index import handler
+from tests.utils import assert_valid_chat_request, assert_valid_chat_response
+
+EXAMPLE_INPUTS_DIR = "tests/example_inputs"
+
+
+class TestExampleInputs(unittest.TestCase):
+    """
+    End-to-end tests for each example input in tests/example_inputs/.
+    Each file must be a valid muEd ChatRequest and produce a valid muEd ChatResponse.
+    """
+
+    def _test(self, filename: str):
+        with open(os.path.join(EXAMPLE_INPUTS_DIR, filename)) as f:
+            payload = json.load(f)
+        assert_valid_chat_request(self, payload)
+        result = handler({"body": json.dumps(payload)}, None)
+        assert_valid_chat_response(self, result)
+        return payload, json.loads(result["body"])
+
+    def test_example_input_0_simple(self):
+        self._test("example_input_0.json")
+
+    def test_example_input_1(self):
+        self._test("example_input_1.json")
+
+    def test_example_input_2_metadata_persists(self):
+        """Example input 2 has an existing summary and conversationalStyle — verifies they persist in the response."""
+        payload, result_body = self._test("example_input_2.json")
+        self.assertEqual(result_body["metadata"]["summary"], payload["context"]["summary"])
+        self.assertEqual(result_body["metadata"]["conversationalStyle"], payload["user"]["preference"]["conversationalStyle"])
+
+    def test_example_input_3_metadata_updates(self):
+        """Example input 3 has 13 messages — enough to trigger summarisation.
+        Verifies that summary and conversationalStyle are populated in the response."""
+        _, result_body = self._test("example_input_3.json")
+        self.assertGreater(len(result_body["metadata"]["summary"]), 0, "summary should be populated after summarisation")
+        self.assertGreater(len(result_body["metadata"]["conversationalStyle"]), 0, "conversationalStyle should be populated after summarisation")
diff --git a/tests/test_index.py b/tests/test_index.py
new file mode 100644
index 0000000..b6047ec
--- /dev/null
+++ b/tests/test_index.py
@@ -0,0 +1,30 @@
+import unittest
+import json
+from index import handler
+from tests.utils import assert_valid_chat_request, assert_valid_chat_response
+
+
+def make_event(body: dict) -> dict:
+    return {"body": json.dumps(body)}
+
+
+BASE_BODY = {
+    "messages": [{"role": "USER", "content": "Hello, World"}],
+    "conversationId": "1234Test",
+}
+
+
+class TestChatIndexFunction(unittest.TestCase):
+
+    def test_missing_messages(self):
+        body = {k: v for k, v in BASE_BODY.items() if k != "messages"}
+        result = handler(make_event(body), None)
+        self.assertEqual(result.get("statusCode"), 400)
+
+    def test_invalid_json_body(self):
+        result = handler({"body": "not valid json"}, None)
+        self.assertEqual(result.get("statusCode"), 400)
+
+    def test_response_format(self):
+        assert_valid_chat_request(self, BASE_BODY)
+        assert_valid_chat_response(self, handler(make_event(BASE_BODY), None))
diff --git a/tests/test_module.py b/tests/test_module.py
new file mode 100755
index 0000000..43be647
--- /dev/null
+++ b/tests/test_module.py
@@ -0,0 +1,19 @@
+import unittest
+from lf_toolkit.chat import ChatRequest
+from src.module import chat_module
+from tests.utils import assert_valid_chat_response
+
+
+def make_request(**kwargs):
+    defaults = {
+        "messages": [{"role": "USER", "content": "Hello, World"}],
+        "conversationId": "1234Test",
+    }
+    defaults.update(kwargs)
+    return ChatRequest.model_validate(defaults)
+
+
+class TestChatModuleFunction(unittest.TestCase):
+
+    def test_response_format(self):
+        assert_valid_chat_response(self, chat_module(make_request()))
diff --git a/tests/utils.py b/tests/utils.py
new file mode 100644
index 0000000..113c36e
--- /dev/null
+++ b/tests/utils.py
@@ -0,0 +1,35 @@
+import json
+import unittest
+from lf_toolkit.chat import ChatRequest, ChatResponse
+
+
+def assert_valid_chat_request(test: unittest.TestCase, payload: dict):
+    """Assert a dict is a valid muEd ChatRequest (at least one message)."""
+    try:
+        request = ChatRequest.model_validate(payload)
+    except Exception as e:
+        test.fail(f"Payload is not a valid ChatRequest: {e}")
+    test.assertGreater(len(request.messages), 0, "messages must not be empty")
+
+
+def assert_valid_chat_response(test: unittest.TestCase, result):
+    """
+    Assert a result matches the expected muEd ChatResponse format.
+    Accepts either a ChatResponse object or a Lambda handler result dict.
+    """
+    if isinstance(result, ChatResponse):
+        body = json.loads(result.model_dump_json())
+    else:
+        test.assertEqual(result.get("statusCode"), 200)
+        body = json.loads(result["body"])
+
+    output = body.get("output", {})
+    test.assertEqual(output.get("role"), "ASSISTANT")
+    test.assertIsInstance(output.get("content"), str)
+    test.assertGreater(len(output["content"]), 0)
+
+    metadata = body.get("metadata", {})
+    test.assertIsInstance(metadata.get("summary"), str)
+    test.assertIsInstance(metadata.get("conversationalStyle"), str)
+    test.assertIsInstance(metadata.get("processingTimeMs"), int)
+    test.assertGreaterEqual(metadata["processingTimeMs"], 0)