pat-agent 0.1.5

PAT — an AI coding agent built from scratch.

PAT Agent

PAT Agent is a smart command-line assistant that can help you work with files, run commands, search code, and automate repetitive tasks.

It is designed to be easy to use, even if you are not a programmer.

Why People Use PAT

• It can read and edit files for you.
• It can run terminal commands safely.
• It can connect to external tools through MCP servers (including OAuth2 providers like Google Workspace).
• It can run specialized mini-agents called subagents.
• It plans, reasons, and verifies steps for higher accuracy in complex tasks.
• It uses embedding-based memory so recall is fast and relevant.

5-Minute Quick Demo

This section is for first-time users.

1) Install

pip install pat-agent 

After installation, the CLI command is:

pat-agent

2) Configure API Key and Base URL

pat-agent configure apikey YOUR_API_KEY
pat-agent configure baseurl YOUR_BASE_URL

Pat supports 3 Base urls :

1. Open router : https://openrouter.ai/api/v1
2. Openapi : https://api.openai.com/v1
3. Gemini : https://generativelanguage.googleapis.com/v1beta/openai/

Check what is configured:

pat-agent configure show

3) Start the Agent

pat-agent init 
pat-agent

You can now type plain English requests, for example:

• List all Python files in this project.
• Create a new README section for troubleshooting.
• Search for where session data is saved.

4) Run One Prompt and Exit (Optional)

pat-agent --prompt "Summarize this project in simple words"

Screenshot / Demo Image

Commands You Will Use Most

Main Command

pat-agent [--prompt "..."] [--cwd PATH]

• --prompt or -p: run once and exit.
• --cwd or -c: run PAT in a specific folder.

Configure Commands

pat-agent configure apikey VALUE
pat-agent configure baseurl VALUE
pat-agent configure show
pat-agent configure delete apikey
pat-agent configure delete baseurl

What each one does:

• configure apikey: saves your API key securely in OS keyring.
• configure baseurl: saves API endpoint URL securely in OS keyring.
• configure show: shows current configured values (API key is masked).
• configure delete: removes a saved value.

Interactive Commands (Inside PAT)

When PAT is running, type these commands:

• /help : show help.
• /clear : clear current conversation.
• /config : show current active settings.
• /model <name> : switch model for this session.
• /approval <policy> : change safety approval mode.
• /tools : list available tools.
• /mcp : show MCP server status.
• /stats : show session stats.
• /save : save current session.
• /sessions : list saved sessions.
• /resume <session_id> : resume a saved session.
• /checkpoint : create a checkpoint.
• /listcheckpoints : list checkpoints.
• /restore <checkpoint_id> : restore a checkpoint.
• /exit or /quit : close PAT.

Configuration System (Simple Explanation)

PAT loads settings from two places:

1. Global config on your machine.
2. Project config in your project folder.

Project config has higher priority.

Config File Name

• config.toml

Project Config Location

• .ai-agent/config.toml inside your project

Example Config (Safe Starting Point)

max_turns = 100
approval = "on-request"

[model]
name = "claude-opus-4.7"
temperature = 1.0
context_window = 256000

[shell_environment]
ignore_default_excludes = false
exclude_patterns = ["*KEY*", "*TOKEN*", "*SECRET*"]

How PAT Handles API Key and Base URL

PAT checks credentials in this order:

1. OS keyring (recommended)
2. Environment variables

Environment variable fallback names:

• API_KEY
• BASE_URL

Tools, MCP, and Subagents

Built-in Tools

PAT includes built-in tools for:

• File reading and writing
• File editing
• Directory listing
• Pattern search and glob search
• Shell commands
• Web search and web fetch
• Memory and todo tracking
• Multi-file patch application

MCP Support

PAT can connect to MCP servers from config and auto-load their tools. OAuth2-based MCP connections are supported for providers like Google Workspace.

MCP tool names are registered in this format:

• server_name__tool_name

Configure MCP Servers (Quick Guide)

Add MCP servers to your project config at .ai-agent/config.toml.

HTTP/SSE MCP server with OAuth2:

[mcp_servers.google_workspace]
url = "https://your-mcp.example.com"

[mcp_servers.google_workspace.oauth]
client_id = "YOUR_CLIENT_ID"
client_secret = "YOUR_CLIENT_SECRET"
scopes = ["https://www.googleapis.com/auth/drive.readonly"]

Notes:

• If you omit client_id and client_secret, PAT uses the MCP server's OAuth discovery flow (browser consent).
• You can use auth_token instead of oauth for non-OAuth servers.
• For local stdio MCP servers, use command and args instead of url.

Example stdio MCP server:

[mcp_servers.local_files]
command = "node"
args = ["server.js"]

Subagents

Subagents are focused mini-agents that PAT can call for specific tasks.

Default subagents:

• subagent_codebase_investigator
• subagent_code_reviewer
• subagent_dependency_tracer
• subagent_root_cause_investigator
• subagent_regression_hunter
• subagent_architecture_mapper

You can create your own subagents.

Create Your Own Subagent

Add this to .ai-agent/config.toml:

[[user_subagents]]
name = "doc_writer"
description = "Writes and improves docs"
goal_prompt = "You are a documentation specialist. Write clear and beginner-friendly docs."
allowed_tools = ["read_file", "glob", "grep", "write_file", "edit"]
max_turns = 20
timeout_seconds = 600

Once loaded, the subagent appears as a tool named:

• subagent_doc_writer

Approval Policies (Safety)

Available policies:

• on-request
• on-failure
• auto
• auto-edit
• never
• yolo

If you are new, start with:

• on-request

Plain-Language Architecture

PAT follows a simple flow:

1. You ask something.
2. PAT sends your request + context to the model.
3. The model decides if tools are needed.
4. PAT runs tools (with approval checks if needed).
5. PAT returns the final response.

Reasoning and Investigation

PAT uses a deliberate problem-solving loop for better accuracy:

• Plans and reasons before acting on non-trivial tasks.
• Verifies results after each major step.
• During debugging or investigation, it builds a system-level understanding first, traces dependencies, estimates confidence, and then answers.

Embedding Memory

PAT stores memory as embeddings so it can search and recall relevant context quickly. This improves long-running tasks without requiring you to restate details.

This loop continues until the task is done.

Troubleshooting

• If PAT says API key is missing, run:
  • pat-agent configure apikey YOUR_API_KEY
• If PAT cannot reach your provider, check base URL:
  • pat-agent configure baseurl YOUR_BASE_URL
• If tools are missing, run /tools and verify your config is not restricting them.
• If MCP tools are missing, run /mcp and check MCP server config.

Project Structure (High Level)

Pat-Code/
  main.py
  agent/
  client/
  config/
  context/
  tools/
  safety/
  ui/

License

MIT