AI Intern — AI-Powered Coding Assistant

A terminal & web-based AI coding assistant built on the deepagents library. It can explore, plan, and modify code in any local repository through a modern chat interface.

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Features

• Multi-Provider LLM Support — Azure OpenAI, OpenAI, Google Gemini, and local Ollama models via a unified factory.
• Deep Agent — Powered by deepagents with built-in planning (write_todos), filesystem tools (ls, read_file, write_file, edit_file, grep, glob), shell execution, and sub-agent spawning.
• Model Context Protocol (MCP) — Native integration via langchain_mcp_adapters to connect standard MCP servers (e.g., Microsoft Docs) for extended context.
• Custom Tooling Extensibility — Includes robust custom tools (such as think for deep reasoning) loaded directly into the agent mapping.
• Chainlit Web UI — Real-time token streaming, custom aesthetic tool step indicators ("Editing...", "Thinking..."), inline visual Diff and Terminal blocks, and a dynamic Tasks sidebar.
• In-Memory Persistence — Conversation memory is maintained across turns within a session via LangGraph's MemorySaver checkpointer with unique thread_ids.
• CLI Mode — Lightweight terminal interface for quick interactions.

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Project Structure

ai-intern/
├── llm_factory.py           # Multi-provider LLM initialization
├── coding_assistant.py      # DeepAgent configuration & system prompt
├── mcp_client.py            # Model Context Protocol server configuration
├── tools.py                 # Custom Langchain tools (e.g., think)
├── assistant_ui.py          # Chainlit web UI (main entry point)
├── assistant_cli.py         # CLI interface (alternative)
├── .env.example             # Template for API keys
├── requirements-simple.txt  # Core dependencies
├── requirements.txt         # Full pip freeze
├── architecture.md          # Architectural overview, diagram, and usecases 
└── readme.md

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Quick Start

1. Clone & Install

git clone <repo-url>
cd ai-intern
python -m venv env
env\Scripts\activate        # Windows
pip install -r requirements-simple.txt

2. Configure API Keys

cp .env.example .env
# Edit .env with your actual API keys

3. Run the Web UI

# First time only — create the Chainlit SQLite schema
python init_db.py

chainlit run assistant_ui.py

Open http://localhost:8000 and enter the name of a sibling project folder (e.g., my-project). The assistant will have full access to that repository.

4. (Alternative) Run the CLI

python assistant_cli.py

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How It Works

1. You provide a project folder name — the assistant maps it to a sibling directory alongside ai-intern/.
2. The agent plans first — it uses write_todos to create a task list before touching any code.
3. It explores efficiently — grep and glob find files & line numbers directly instead of navigating with ls.
4. It streams results in real-time — tokens appear word-by-word, tool calls show as collapsible steps, and the task sidebar updates live.
5. Memory persists across turns — within a session, the agent remembers everything you've discussed.

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Configuration

Switching LLM Providers

Edit the provider in coding_assistant.py:

llm = get_llm(provider="azure")      # Azure OpenAI (default)
llm = get_llm(provider="openai")     # OpenAI
llm = get_llm(provider="google")     # Google Gemini
llm = get_llm(provider="ollama")     # Local Ollama

Persistent Memory (Production)

Conversation history and agent state are persisted to SQLite automatically:

• agent_data/chainlit_ui.db — Chainlit UI threads, messages, history sidebar
• agent_data/checkpoints_lg.db — LangGraph agent state (tool calls, messages per thread)
• /memories/ — Cross-session long-term memory via deepagents StoreBackend (agent writes here to remember things across conversations)

To use PostgreSQL in production, swap SQLAlchemyDataLayer conninfo and AsyncSqliteSaver for their Postgres equivalents.

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Environment Variables

Variable	Required	Description
AZURE_OPENAI_API_KEY	For Azure	Azure OpenAI API key
AZURE_OPENAI_ENDPOINT	For Azure	Azure resource endpoint
AZURE_OPENAI_API_VERSION	For Azure	API version
AZURE_OPENAI_DEPLOYMENT_NAME	For Azure	Model deployment name
OPENAI_API_KEY	For OpenAI	OpenAI API key
GOOGLE_API_KEY	For Google	Google Gemini API key
TAVILY_API_KEY	For MCP	Tavily web search API key
CHAINLIT_AUTH_SECRET	Yes	Secret for Chainlit cookie auth
CHAINLIT_USER	Yes	Login username
CHAINLIT_PASSWORD	Yes	Login password

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ToDO

• Docker-based sandboxed execution
• Project-level AGENTS.md for persistent context
• Agent skills which can be added dynamically
• Set iteration limit dynamically for each query
• git tools so the coding agent can push code
• Swap SQLite for PostgreSQL for multi-user production deployments
• checkpointer in conversation
• Git tooling
• See content from python files of packages within the code base
• Trust command (user based auth)
• Unable to replace text trying different approach
• Optimized for building requirements and technical documentation

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License

MIT