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Helpline

https://github.com/coleam00/helpline
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Category
AI Agents
Rank
No. 1451Tools index

Previous survey · No. 1457 ·

Pricing
Open Source
Type
AGENT
Builder
coleam00
GitHub
111 stars
Date

About

Demonstration codebase showing how to build an AI Layer in large codebases: CLAUDE.md hierarchy, hooks, skills, LSP, MCP, and plugins.

What it does

Helpline is a compact, synthetic customer-support system designed for practicing agent-assisted work under realistic constraints. Its services share models and storage, while layered guidance, structured code search, symbol navigation, scoped workflows, and session hooks help an AI coding assistant understand changes before acting.

Why it's ranked here

Helpline is a strong teaching artifact because the application creates credible coordination risks, then pairs each risk with an inspectable agent aid. The included validator and language-server checks make the setup more convincing than documentation alone. Its value is educational, not as a deployable helpdesk.

What's good

The example connects abstract agent practices to concrete failure boundaries: shared domain types, billing rules, routing conventions, and distributed tests. Portable pieces are separated from project-specific guidance. The explorer is deliberately read-only, structured search uses Python syntax trees, and navigation checks verify actual symbol resolution.

Tradeoffs

The product layer is intentionally small and synthetic. Storage is a process-wide in-memory dictionary, the HTTP gateway only imitates a web framework, and authentication contains a fixed demo secret. Several domain-specific workflows must be rebuilt for another repository. The portable tooling also targets Claude Code rather than coding agents generally.

How to use it well

Use Helpline as a short workshop or reference when adding repository-aware guidance to a medium or large Python project. Run its tests and validators, inspect how shared-code risks become scoped instructions, then adapt the patterns to local conventions. It does not supply a production helpdesk, persistent database, or production authentication.

Technical notes+

pyproject.toml requires Python 3.11 or newer, declares no runtime dependencies, and provides pytest, pyright, and mcp through the dev extra; [tool.uv] package = false makes this a non-package workspace. packages/core/models.py defines dataclass domain objects and enums, while packages/db/connection.py implements the shared in-memory store. services/api/app.py contains a minimal dictionary-backed dispatcher that translates HelplineError status codes into responses. docs/lsp-setup.md documents stdio communication with pyright-langserver, including initialization and definition-resolution checks.

Observed

Primary language
Python, requiring version 3.11 or newer.
Install surface
Uses uv with a dev extra containing pytest, pyright, and mcp.
Packaging
Configured as a non-package uv project with no runtime dependencies.
Interfaces
Includes a Claude Code plugin and an MCP server for structured code search.
Application structure
Five services share two internal packages in a monorepo.
Testing structure
Pytest searches both the top-level tests area and service directories.

Read from README.md, pyproject.toml, packages/db/__init__.py, packages/core/errors.py, packages/core/models.py, packages/core/__init__.py, packages/db/connection.py, packages/db/repositories.py, docs/lsp-setup.md, scripts/seed_data.py, services/api/app.py, services/api/routes.py, services/api/tickets.py, services/auth/tokens.py, services/api/__init__.py.

What it can do

  • Demonstrate AI layer integration in large codebases

    Existing codebase structure → Implementation example with AI layer

  • Implement CLAUDE.md hierarchy system

    Project documentation requirements → Structured documentation hierarchy

  • Create AI-powered hooks for code integration

    Application events and triggers → Automated AI response hooks

  • Build skills-based AI functionality

    Skill definitions and parameters → Executable AI skills framework

  • Integrate Language Server Protocol (LSP)

    Code editor and language files → Enhanced code intelligence features

  • Implement Model Context Protocol (MCP)

    AI model context requirements → Standardized model communication layer

  • Support plugin architecture

    Plugin specifications and code → Extensible application functionality

Tags

claude-codemcpskillsdemo

Tech Stack

Python

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