Agent CLI

A command-line interface for the personal housekeeper assistant agent.

Setup

Create a .env file in the project root (not in this directory) with the following variables:

# Main chat model
MODEL=gpt-4o
BASE_URL=https://api.openai.com/v1
API_KEY=your-api-key-here
MODEL_CLIENT_TYPE=openai

# Embedding model for memory search (using mem0ai)
EMBEDDING_MODEL=text-embedding-3-small
EMBEDDING_BASE_URL=https://api.openai.com/v1
EMBEDDING_API_KEY=your-api-key-here
EMBEDDING_CLIENT_TYPE=openai
EMBEDDING_DIMENSIONS=1536

# Summary model for memory generation (optional, defaults to main model)
SUMMARY_MODEL=gpt-4o-mini
SUMMARY_BASE_URL=https://api.openai.com/v1
SUMMARY_API_KEY=your-api-key-here
SUMMARY_CLIENT_TYPE=openai

# Database
DATABASE_URL=postgresql://user:password@localhost:5432/byte

Make sure the database is set up and running (required for memory storage).

Usage

Run the CLI from the agent package:

pnpm start

Or with Bun directly:

bun run index.ts

Features

Interactive Chat: Type your messages and get responses from the AI agent
Long-term Memory: Conversations are automatically saved with LLM-generated summaries
Context Loading: Automatically loads recent conversations (last 60 minutes)
Memory Search: The agent can search through past conversations using natural language and embeddings
Tool Calling: Supports automatic tool execution with multi-step reasoning
Multi-Provider Support: Works with OpenAI, Anthropic, and Google AI (via Vercel AI SDK)

Commands

Type your message and press Enter to chat
Type exit or quit to close the application

Environment Variables

Required

MODEL: The main LLM model ID (e.g., gpt-4o, claude-3-5-sonnet-20241022)
BASE_URL: The API base URL for the main model
API_KEY: Your API key for the main model
EMBEDDING_MODEL: The embedding model for memory search (e.g., text-embedding-3-small)
DATABASE_URL: PostgreSQL connection string with pgvector extension enabled

Optional

MODEL_CLIENT_TYPE: The model provider type (default: openai, options: openai, anthropic, google)
EMBEDDING_BASE_URL: Base URL for embedding API (default: same as BASE_URL)
EMBEDDING_API_KEY: API key for embedding (default: same as API_KEY)
EMBEDDING_CLIENT_TYPE: Provider type for embedding (default: openai)
EMBEDDING_DIMENSIONS: The dimensions of the embedding model (default: 1536)
SUMMARY_MODEL: The model used to summarize conversations for memory (default: same as MODEL)
SUMMARY_BASE_URL: Base URL for summary model (default: same as BASE_URL)
SUMMARY_API_KEY: API key for summary model (default: same as API_KEY)
SUMMARY_CLIENT_TYPE: Provider type for summary model (default: same as MODEL_CLIENT_TYPE)

Memory System

The agent uses mem0ai for sophisticated memory management:

Conversation Storage: After each conversation, mem0ai uses an LLM to extract and store key information
Embedding Generation: The extracted information is converted to embedding vectors using your configured embedding model
Vector Storage: Embeddings are stored in PostgreSQL with pgvector extension
Semantic Search: The agent can search past memories using natural language queries via vector similarity
Context Loading: Recent conversations are automatically loaded from history into context

Model Recommendations

Main Model: Use a powerful model like gpt-4o or claude-3-5-sonnet-20241022 for best conversation quality
Summary Model: Use a cheaper/faster model like gpt-4o-mini for memory extraction to save costs
Embedding Model: Use text-embedding-3-small (1536 dims) or text-embedding-3-large (3072 dims) for OpenAI

Database Requirements

The memory system requires PostgreSQL with the pgvector extension installed. Make sure:

PostgreSQL is installed and running
pgvector extension is enabled: CREATE EXTENSION IF NOT EXISTS vector;
mem0ai will automatically create the required tables on first run

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