comP — The Memory System for Your AI Coding Assistant

Open-source, 100% local code analysis engine. Works with Claude Code, Cursor, Cline, and Antigravity.

🌐 Official Website

Why comP Exists

Claude Code, Cursor, and other AI coding assistants share one critical limitation:

Every time you ask a question, the AI re-reads your entire project.

1. You ask "how does this function behave?"
2. The AI reads the whole project to understand context
3. DB connections, config, type definitions, dependencies—everything—before it can answer

This "read everything, every time" pattern causes three problems:

And there's a fourth problem that's easy to overlook:

How comP Solves It

comP automatically builds a "project map + index" so the AI can grasp "this file does X" instantly, without searching around.

With comP:
  Question → comP extracts only the relevant files → AI answers via the shortest path
  Next day → session_recall restores yesterday's decisions → no re-explaining

How Much Does It Actually Help? An Honest Estimate

comP's impact depends heavily on your use case. Here's an honest breakdown:

The Biggest Effect the Numbers Don't Show: Fewer Failed Retries

Looking only at the token-reduction percentage understates the real impact. Here's the actual cost structure:

Traditional failure pattern (especially common with cheaper models):
  explore → read the wrong file → implement on a wrong assumption → test fails
  → re-explore → re-implement → ... (thousands of tokens per loop × N)

With comP:
  run_pipeline surfaces the right related code up front
  → less room for wrong assumptions → retries become rare in the first place

In other words, comP doesn't just reduce "tokens per call"—it reduces the number of calls itself. Cheaper models get lost while exploring more easily, so the cheaper the model, the bigger comP's benefit. Tasks that used to require a top-tier model now fall within reach of a cheap one—that's arguably comP's biggest real-world effect.

Installation & Setup (3 Steps)

1. Install in VS Code

1. Open VS Code
2. Go to Extensions (Ctrl+Shift+X) and search for "comP - Code Context Engine"
3. Click Install

2. Open Your Folder

Open the project you're working on in VS Code (a Git repository works best).

3. Start comP

• Click the comP icon in the Activity Bar (left sidebar)
• Click "▶ Start"
• Indexing begins in the background
• Watch the status bar (bottom of VS Code) for progress

◈ comP: 12,534 symbols | ✓ Ready

Connect Your AI Agent (One-Time Setup)

Ctrl+Shift+P → "comP: Setup Agents"

Details: docs/user/MCP_SETUP.md

How to Use It

Claude Code (Simplest)

@comP run_pipeline
Analyze every function affected if I change the authenticate() function

VS Code Native Chat (@comp)

@comp #file:src/main.rs Explain what this function does

Attached files are automatically compressed (comments stripped, skeletonized) before being sent to the LLM.

A Prompt Kit for Growing CLAUDE.md into a "comP-first" Constitution

Installing comP isn't enough—if you don't update the AI's own behavioral rules, you leave half the benefit on the table. When instructions are ambiguous, the AI defaults to "it's easier to just read the file directly." Feed the prompts below to Claude and it will rewrite your CLAUDE.md (or .github/copilot-instructions.md for Copilot) to be comP-first.

Prompt 1: Initial rollout (add comP rules)

Update CLAUDE.md. Add the following as MUST/NEVER rules:

MUST:
- For code investigation/search, call comP's run_pipeline first instead of grep/find/Bash
- Before changing existing code, check downstream impact with get_impact_graph
- At the start of a session, restore relevant past decisions with session_recall

NEVER:
- Reading the entire codebase without going through comP
- Implementing based on guesses about files not present in run_pipeline's results

Add a one-line reason for each rule, and flag any conflicts with existing rules.

Prompt 2: When direct file-reading creeps back in (learning from violations)

In the current task, you read src/ directly instead of using run_pipeline.
State in one line why that happened, and propose a one-line addition
to CLAUDE.md's NEVER section to prevent it from happening again.

💡 Think of CLAUDE.md as something that "grows one line per failure." Loop through violation → root cause → new rule, and direct-reading tends to disappear within a few cycles.

Prompt 3: comP-optimizing your entire workflow

My dev workflow has three stages: design → implementation → review.
For each stage, write a draft to add to CLAUDE.md titled "Stage-by-stage comP usage"
describing which comP tool (run_pipeline / get_context / get_impact_graph / session_recall)
should be used and how. Prioritize minimizing token usage above all else.

Prompt 4: Making session handoff a habit

Add the following to CLAUDE.md:
"Before ending a session, summarize this session's decisions and open issues
in 3 lines or fewer. Since the next session will search for this via
session_recall, always include proper nouns (function names, file names)
that are likely search keywords."

Session Memory: How It Differs from Claude's Built-in Memory

"Claude already has a memory feature—do I still need comP's session memory?" The answer: they operate at different layers, so you need both.

Where It Really Shines (Real Examples)

Case 1: "Wait, why did we do it this way?" a week later

@comP session_recall
Check why we limited retries to 3 last week

Claude's built-in memory keeps a summary, so "we discussed retry limits" might survive, but the technical rationale ("why 3") tends to get lost. comP's BM25 index pulls up that exact conversation.

Case 2: Handoff to a cheaper model

When design work happens on a top-tier model and implementation on a cheap one, the cheap model tends to get lost in long context explanations. session_recall injects only the decisions that matter, drastically cutting handoff cost.

Case 3: Environments where cloud memory is disabled for confidentiality

Even when corporate policy disables cloud-side memory, comP is 100% local, so you can keep session memory while staying compliant.

The Rule of Thumb (worth one line in your CLAUDE.md)

comP (in-repo) is the source of truth for technical decisions;
Claude's memory is the source of truth for personal/preference context.

Recording the same decision in both risks drift when only one gets updated—fixing the division of responsibility is the key.

How It Works

1. Auto-logging: Every conversation is BM25-indexed when the chat ends
2. Persistence: Saved to ~/.claude/projects/comP/memory/session/
3. Retrieval: Keyword search instantly restores relevant past conversations

Excluding Files & Folders

Create .comp/ignore in your project root (same syntax as .gitignore):

node_modules/
vendor/
dist/
build/
target/
__pycache__/
*.min.js

Auto-excluded: hidden directories starting with ., anything matching .gitignore, node_modules, venv, __pycache__, coverage, vendor, out

You can also exclude paths from VS Code settings:

{ "comp.exclude": ["env", "data", "logs"] }

Controlling Token Budget & Compression Level

Customize via .comp/config.json:

{
  "max_nodes": 100000,
  "on_limit_exceeded": "warn",
  "default_budget_tokens": 8000,
  "compression_rules": { "*.md": 0, "*.rs": 2, "*.ts": 1 }
}

DB size guide: small repo (~1k files) 1–5 MB, medium (~10k) 20–80 MB, large (100k+) 200 MB–1 GB. Metadata only.

How It Works (Technical Details)

1. Indexer (Rust daemon): Parses 30+ languages with tree-sitter, stores results in SQLite
2. Search engine: BM25 full-text search + graph traversal + semantic scoring
3. MCP server: Exposes run_pipeline, get_context, get_impact_graph, session_recall
4. VS Code extension: Manages the daemon, UI, and commands

Code files (30+ languages)
   ↓ [tree-sitter parsing]
SQLite graph DB (.comp/index.db)
   ↓ [BM25 + graph traversal]
MCP server
   ↓
AI agent (Claude Code, Cursor, Cline, etc.)
   ↓ [context compression]
LLM API (fewer tokens = lower cost)

Supported languages (30+): C, C++, C#, Go, Java, JavaScript, TypeScript, Python, Rust, Ruby, Bash, Kotlin, Swift, PHP, Dart, Elixir, Haskell, Lua, R, Zig, SQL, HTML, CSS, YAML, Scala, and more.

Security & Privacy

• 🔐 100% local execution: code and session history are never sent to the cloud
• 🛡️ Auto-excluded: .comp/ is automatically added to .gitignore
• 📋 Auditable: no external APIs, no telemetry
• 🏢 Enterprise-ready: fully isolated on-prem operation, safe for confidential code

Troubleshooting

"comP isn't indexing"

1. Check progress in the status bar
2. If stuck: Ctrl+Shift+P → "comP: Force Re-index"
3. Verify .comp/ exists and is listed in .gitignore

"MCP stopped working after upgrading comP"

VS Code installs extensions into a directory that carries the version number, and deletes the old one on upgrade. Config files record the absolute path of comp-daemon, so before v0.9.4 an upgrade left them pointing at an executable that no longer existed — the sidebar kept working, but MCP tools disappeared.

Since v0.9.4 comP repairs this automatically on every activation. Restart your agent when you see the repair notification. On older versions, re-run "comP: Setup Agents" once.

"MCP connection failed"

1. Re-run "comP: Setup Agents"
2. Verify the config file was generated
3. Check the Output panel (View → Output → "comP") for logs

"The AI reads files directly instead of using comP"

→ See the "A Prompt Kit for Growing CLAUDE.md into a comP-first Constitution" section above. Prompt 1 resolves this in most cases.

"Indexing is slow"

• Large repos (>100k files) take time on first run only. Subsequent runs are incremental (fast)
• The comP daemon typically uses <500MB RAM

Agent Compatibility

Any MCP 2024-11-05-compliant client should work in principle. Open an issue if you hit a problem.

Roadmap

License, Contributing & Support

• MIT License — LICENSE
• Contributions welcome — CONTRIBUTING.md
• ☕ GitHub Sponsors / 💖 Star this repo

Questions & Bug Reports

• 📖 docs/ / 🐛 Issue / 💬 Discussions