# Cursor Chat: ai-agent-book

## Metadata
- **Project**: ai-agent-book
- **Path**: `/Users/boj`
- **Date**: 2025-09-13 21:54:36
- **Session ID**: `b8ece4cd-a865-4611-959f-aba63dea6812`

## Conversation

### 👤 You

In week2/context-compression, write a comparison experiment project for different context compression strategies.

You should write an agent researching the current affiliations of all OpenAI co-founders. The system prompt should instruct the agent to first find the list of all cofounders, and then search them one by one.

You should write:
1. a web search tool to query a public search API service, and crawl all the webpage text content for each search result returned (convert html to text).
2. a fetch web page tool to fetch the contents of a web page.

The context compression strategies you should implement:
1. no compression: put all original webpage text content to agent context, expected to fail after a few tool calls because the web pages are too large (128K context)
2. non-context-aware summarization: in the web search tool, summarize each web page content using LLM before putting into main agent context. Expected to lose important context related to current affiliation.
3. context-aware summarization: in the web search tool, put all the web page contents together into context, with the LLM-generated query, and generate a concise context-aware summarization to put into main agent context
4. context-aware summarization with citations: in the web search tool, similar to (3), but include citations and links to crawled pages. This is to demonstrate answering additional questions: when the user asks clarification questions after the agent answer, it would use the fetch web page tool to get the original page for answering the follow-up question.
5. full context for the last tool, but context-aware summarization for earlier tool call history (windowed context): the last tool call use the original full search history content, but while the main LLM is creating the next tool call or final response, the full content get summarized into a context-aware summarization with citations. In the message history (other than the last message), it only preserves the summarization with citations.

Use kimi k2 model similar to week1/context repo. You should implement stream response parsing and displaying on console.

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### 🤖 Cursor Assistant

I'll create a comprehensive context compression comparison experiment project for you. Let me start by examining the week1/context project structure to understand the patterns used there.

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### 🤖 Cursor Assistant

I'll create a comprehensive context compression comparison experiment project. Let me set up the project structure and implement all the required components.

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### 🤖 Cursor Assistant

Now I'll create the configuration and requirements files first:

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### 🤖 Cursor Assistant

Now I'll implement the web tools with HTML to text conversion:

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### 🤖 Cursor Assistant

Now I'll implement the 5 context compression strategies:

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### 🤖 Cursor Assistant

Now I'll create the main agent with streaming support for Kimi K2:

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### 🤖 Cursor Assistant

Now I'll create the experiment runner to compare all strategies:

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### 🤖 Cursor Assistant

Now I'll create a demo script and comprehensive documentation:

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### 🤖 Cursor Assistant

Let me create a quick start script to make it easy to test the project:

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### 🤖 Cursor Assistant

Perfect! I've successfully created a comprehensive context compression comparison experiment project in `week2/context-compression`. Here's what was implemented:

## Project Summary

### Core Features Implemented:

1. **Web Tools** (`web_tools.py`):
   - Web search tool using Serper API (with mock fallback)
   - Webpage fetching with HTML to text conversion
   - Caching to avoid redundant fetches

2. **5 Compression Strategies** (`compression_strategies.py`):
   - **No Compression**: Raw content (demonstrates failure)
   - **Non-Context-Aware**: Independent page summaries
   - **Context-Aware**: Query-focused combined summaries
   - **With Citations**: Includes source tracking for follow-ups
   - **Windowed Context**: Full recent, compressed history

3. **Research Agent** (`agent.py`):
   - Kimi K2 model integration (similar to week1/context)
   - **Streaming response support** with real-time display
   - System prompt for OpenAI co-founders research
   - Dynamic context management based on strategy

4. **Experiment Runner** (`experiment.py`):
   - Automated testing of all 5 strategies
   - Metrics collection and comparison
   - Visual results table with color coding
   - JSON output for further analysis

5. **Interactive Tools**:
   - `demo.py`: Interactive strategy selection with streaming
   - `quickstart.py`: Environment checker and quick test
   - Comprehensive README with usage instructions

### Key Implementation Highlights:

- **Streaming Parsing**: The agent properly handles Kimi K2's streaming responses, displaying them in real-time on the console
- **Research Task**: Automatically searches for OpenAI co-founders then researches each one individually
- **Mock Data**: Includes fallback mock data for testing without API keys
- **Follow-up Support**: Citation strategy enables answering clarification questions
- **Metrics Tracking**: Compression ratios, execution time, context overflows

### Usage:

1. **Quick Test**:
```bash
cd projects/week2/context-compression
python quickstart.py
```

2. **Run Full Experiment**:
```bash
python experiment.py
```

3. **Interactive Demo**:
```bash
python demo.py
```

The project demonstrates how different context compression strategies affect:
- **Success rate** (no compression fails)
- **Information quality** (context-aware preserves relevance)
- **Efficiency** (windowed balances detail and size)
- **Traceability** (citations enable follow-ups)

The experiment clearly shows the trade-offs between compression ratio, information preservation, and computational cost, providing valuable insights for designing efficient LLM agents with large context windows.

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*Exported from [Cursor View](https://github.com/saharmor/cursor-view)*