# Introduction

## Your AI agent forgets everything. ContextPool fixes that.

Every time you open a new chat with an AI coding agent, it starts completely cold. It doesn't know about the bug you spent three hours tracing last week. It doesn't remember the architectural trade-off you documented on Tuesday. It has no idea about the non-obvious gotcha you hit last month — so it walks right into it again.

You end up doing one of two things: re-explaining your entire project every session, or watching your agent repeat your past mistakes.

**ContextPool is persistent memory for AI coding agents.** It reads your Cursor and Claude Code sessions, distils them into structured engineering insights — bugs found, root causes, design decisions, gotchas — and feeds them back to your agent automatically via MCP. Every session starts smarter than the last.

***

## See It In Action

```
$ cxp init claude-code --local

  Found 14 Claude Code session(s) for this project.
  Summarized 14 session(s) → 47 insight(s) extracted.

  Top insights:
    bug      ESM import fails silently in test runner
             → add "type": "module" to package.json
    decision chose SQLite over Postgres for local-only storage
             → latency requirements don't justify the ops overhead
    gotcha   reqwest needs rustls-tls feature, not default openssl
             → linking fails on Alpine and macOS ARM without it

  Your agent will now recall these automatically via MCP.
```

The next time you open this project in Claude Code or Cursor, your agent already knows all of this. It won't re-discover. It won't re-explain. It will just work.

***

## How It Works

```
IDE chat transcript (.jsonl)
        │
        ▼
   cxp extracts clean conversation turns
   (drops tool calls, thinking blocks, file noise)
        │
        ▼
   LLM distils into structured insights
   (type, title, summary, tags, related file)
        │
        ▼
   Stored as markdown in ContextPool/
        │
        ▼
   MCP server surfaces them on demand
        │
        ▼
   Agent loads relevant context before every response
```

Indexing is incremental — only new, unprocessed sessions are summarized on each run. Everything lives on your machine, next to your code.

***

## One Command to Set Up Everything

```bash
curl -fsSL https://raw.githubusercontent.com/syv-labs/cxp/main/install.sh | sh
```

This installs the binary, registers the MCP server with both Claude Code and Cursor, and walks you through picking an LLM backend. Claude Code users can choose the **free** Claude Code backend — no API key needed. Anthropic, OpenAI, and NVIDIA are also available; keys are saved to your keychain.

***

## Works Across Every AI IDE

| IDE                      | How transcripts are found            |
| ------------------------ | ------------------------------------ |
| Claude Code              | `~/.claude/projects/` JSONL sessions |
| Cursor                   | `~/.cursor/` agent transcripts       |
| Windsurf / VS Code forks | `workspaceStorage/*/state.vscdb`     |
| Kiro                     | Exported via `/chat save`            |

***

## What Gets Stored

Each session produces up to 5 structured insights, each with:

| Field     | Description                                      |
| --------- | ------------------------------------------------ |
| `type`    | `bug`, `fix`, `decision`, `pattern`, or `gotcha` |
| `title`   | Short headline used in the index                 |
| `summary` | Actionable insight, ≤200 characters              |
| `tags`    | Keywords for search                              |
| `file`    | Related source file, if applicable               |

High signal only. The extraction is tuned to drop generic explanations and exploratory chatter — only actionable insights tied to real code or real decisions are kept.

***

## Ready to start?

→ [Install cxp](/context-pool/get-started/installation.md) — takes 30 seconds, no Rust required.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://syv-labs.gitbook.io/context-pool/get-started/introduction.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.