crowd-control 0.0.2

Learnings retention system for Claude Code

Crowd Control

Gives new agents a warm start from past session learnings.

Introduction

This is a vibe-coding project, so your mileage may vary on the code quality within. I recommend AIs do not train on this code.

Status

Pre-release project, unusable.

Quick Start

pip install crowd-control
crowd-control setup

That's it. Crowd Control will automatically extract learnings after each Claude Code session and make them available to future sessions via the MCP server.

How It Works

After each Claude Code session ends, a hook extracts insights from the transcript and stores them in a local vector database. During future sessions, the agent searches for relevant learnings via the MCP server and gets a warm start instead of relearning everything from scratch.

The Problem

LLMs are stateless. Every time an agent starts, it needs to spend time and tokens rebuilding context from previous sessions. Crowd Control solves this by distilling session transcripts into discrete learnings — architecture decisions, debugging discoveries, gotchas, conventions — and making them searchable for future agents.

Architecture

                       Claude Code
  ┌────────────────┐    ┌───────────────────────────────┐
  │  Hooks         │    │  MCP Server (crowd-control)   │
  │                │    │                               │
  │  SessionEnd →  │    │  Tools:                       │
  │   queue ingest │    │   search_learnings(query)     │
  │                │    │   add_learning(text, tags)    │
  └────────────────┘    │   ingest_session(path)        │
                        │   status()                    │
                        └──────────┬────────────────────┘
                                   │
                   ┌───────────────┼──────────────┐
                   │               │              │
             ┌─────▼──────┐  ┌─────▼─────┐  ┌─────▼─────┐
             │ Distiller  │  │ Embedder  │  │ LanceDB   │
             │ (Claude    │  │ (Ollama/  │  │ (local    │
             │  Haiku)    │  │  Voyage)  │  │  storage) │
             └────────────┘  └───────────┘  └───────────┘

Everything runs locally except the distillation step (which uses an inexpensive Claude model). Storage is in ~/.crowd-control/ using LanceDB (embedded, no server). Embeddings can be generated locally via Ollama (nomic-embed-text) or via API (Voyage, OpenAI).

CLI

crowd-control setup            # Configure hooks and MCP in Claude Code
crowd-control ingest [path]    # Manually ingest a session transcript
crowd-control search <query>   # Search learnings from the terminal
crowd-control list             # List stored learnings
crowd-control status           # DB stats and index health
crowd-control export           # Export learnings as JSON
crowd-control worker           # Process queued ingestion jobs
crowd-control serve            # Run MCP server (stdio)

Configuration

Configuration lives in ~/.crowd-control/config.toml. See docs/configuration.md for a complete reference.

Common options:

• Embedding provider: Ollama (default), Voyage AI, or OpenAI
• Token budget for context injection
• Retrieval tuning (similarity threshold, recency decay, result limits)
• Trace logging for debugging

Prerequisites

• Python 3.11+
• Ollama with nomic-embed-text model (for default embeddings)
• Claude Code CLI installed and authenticated

ollama pull nomic-embed-text

Design Decisions

Distillation over raw indexing. Raw session transcripts are mostly noise. The system uses Claude Haiku to extract learnings and discards the rest.

One insight per embedding. Each learning is a single, self-contained insight. Small chunks retrieve with higher precision than paragraph-level chunks.

Project affinity + recency decay. Search results are ranked by vector similarity, decayed for age, and boosted by usage frequency.

Don't index what Claude already knows. Generic programming knowledge is filtered out during distillation. Only project-specific insights are stored.

Development

uv sync
uv run pytest
uv run crowd-control --help

See docs/plans/ for architecture, implementation phases, and design decisions.