Hooks

Hooks#

Note

This is a new feature added in response to issue #156.

The hook system allows tools and plugins to register callbacks that execute at various points in gptme’s lifecycle. This enables powerful extensions like automatic linting, memory management, pre-commit checks, and more.

Hook Types#

The following hook types are available:

Message Lifecycle Hooks#

  • MESSAGE_PRE_PROCESS: Before processing a user message

  • MESSAGE_POST_PROCESS: After message processing completes

  • MESSAGE_TRANSFORM: Transform message content before processing

Tool Lifecycle Hooks#

  • TOOL_PRE_EXECUTE: Before executing any tool

  • TOOL_POST_EXECUTE: After executing any tool

  • TOOL_TRANSFORM: Transform tool execution

File Operation Hooks#

  • FILE_PRE_SAVE: Before saving a file

  • FILE_POST_SAVE: After saving a file

  • FILE_PRE_PATCH: Before patching a file

  • FILE_POST_PATCH: After patching a file

Session Lifecycle Hooks#

  • SESSION_START: At session start

  • SESSION_END: At session end

Generation Hooks#

  • GENERATION_PRE: Before generating response

  • GENERATION_POST: After generating response

  • GENERATION_INTERRUPT: Interrupt generation

Usage#

Registering Hooks from Tools#

Tools can register hooks in their ToolSpec definition:

from gptme.tools.base import ToolSpec
from gptme.hooks import HookType
from gptme.message import Message

def on_file_save(path, content, created):
    """Hook function called after a file is saved."""
    if path.suffix == ".py":
        # Run linting on Python files
        return Message("system", f"Linted {path}")
    return None

tool = ToolSpec(
    name="linter",
    desc="Automatic linting tool",
    hooks={
        "file_save": (
            HookType.FILE_POST_SAVE.value,  # Hook type
            on_file_save,                    # Hook function
            10                               # Priority (higher = runs first)
        )
    }
)

Registering Hooks Programmatically#

You can also register hooks directly:

from gptme.hooks import register_hook, HookType

def my_hook_function(log, workspace):
    """Custom hook function."""
    # Do something
    return Message("system", "Hook executed!")

register_hook(
    name="my_custom_hook",
    hook_type=HookType.MESSAGE_PRE_PROCESS,
    func=my_hook_function,
    priority=0,
    enabled=True
)

Hook Function Signatures#

Hook functions receive different arguments depending on the hook type:

# Message hooks
def message_hook(log, workspace):
    pass

# Tool hooks
def tool_hook(tool_name, tool_use):
    pass

# File hooks
def file_hook(path, content, created=False):
    pass

# Session hooks
def session_hook(logdir, workspace, manager=None, initial_msgs=None):
    pass

Hook functions can:

  • Return None (no action)

  • Return a single Message object

  • Return a generator that yields Message objects

  • Raise exceptions (which are caught and logged)

Managing Hooks#

Query Hooks#

from gptme.hooks import get_hooks, HookType

# Get all hooks
all_hooks = get_hooks()

# Get hooks of a specific type
tool_hooks = get_hooks(HookType.TOOL_POST_EXECUTE)

Enable/Disable Hooks#

from gptme.hooks import enable_hook, disable_hook

# Disable a hook
disable_hook("linter.file_save")

# Re-enable it
enable_hook("linter.file_save")

Unregister Hooks#

from gptme.hooks import unregister_hook, HookType

# Unregister from specific type
unregister_hook("my_hook", HookType.FILE_POST_SAVE)

# Unregister from all types
unregister_hook("my_hook")

Examples#

Pre-commit Hook#

Automatically run pre-commit checks after files are saved:

from pathlib import Path
from gptme.tools.base import ToolSpec
from gptme.hooks import HookType
from gptme.message import Message
import subprocess

def run_precommit(path: Path, content: str, created: bool):
    """Run pre-commit on saved file."""
    try:
        result = subprocess.run(
            ["pre-commit", "run", "--files", str(path)],
            capture_output=True,
            text=True,
            timeout=30
        )
        if result.returncode != 0:
            yield Message("system", f"Pre-commit checks failed:\n{result.stdout}")
        else:
            yield Message("system", "Pre-commit checks passed", hide=True)
    except subprocess.TimeoutExpired:
        yield Message("system", "Pre-commit checks timed out", hide=True)

tool = ToolSpec(
    name="precommit",
    desc="Automatic pre-commit checks",
    hooks={
        "precommit_check": (
            HookType.FILE_POST_SAVE.value,
            run_precommit,
            5  # Run after other hooks
        )
    }
)

Memory/Context Hook#

Automatically add context at session start:

def add_context(logdir, workspace, initial_msgs):
    """Add relevant context at session start."""
    context = load_relevant_context(workspace)
    if context:
        yield Message("system", f"Loaded context:\n{context}", pinned=True)

tool = ToolSpec(
    name="memory",
    desc="Automatic context loading",
    hooks={
        "load_context": (
            HookType.SESSION_START.value,
            add_context,
            10
        )
    }
)

Linting Hook#

Automatically lint files after saving:

def lint_file(path: Path, content: str, created: bool):
    """Lint Python files."""
    if path.suffix != ".py":
        return

    import subprocess
    result = subprocess.run(
        ["ruff", "check", str(path)],
        capture_output=True,
        text=True
    )

    if result.returncode != 0:
        yield Message("system", f"Linting issues:\n{result.stdout}")

tool = ToolSpec(
    name="linter",
    desc="Automatic Python linting",
    hooks={
        "lint": (HookType.FILE_POST_SAVE.value, lint_file, 5)
    }
)

Built-in Hooks#

gptme ships with several built-in hooks that provide core functionality:

Session & Context

  • active_context: Selects relevant files to include before generation

  • agents_md_inject: Loads AGENTS.md/CLAUDE.md when the working directory changes

  • cwd_tracking: Tracks the current working directory across tool calls

  • time_awareness: Injects current time into context

  • token_awareness: Monitors token budget and warns when approaching limits

  • cost_awareness: Tracks and reports LLM API costs

  • cache_awareness: Surfaces cache hit rates for prompt caching

Tool Confirmation

  • cli_confirm: Terminal-based tool confirmation with preview

  • auto_confirm: Auto-approves tools in autonomous/non-interactive mode

  • server_confirm: Confirmation via WebUI/API for server mode

User Input

  • elicitation: Structured user input (forms, choices) in CLI

  • server_elicit: Elicitation via WebUI/API for server mode

  • form_autodetect: Detects when assistant output contains form-like choices

Code Quality

  • markdown_validation: Detects codeblock cut-offs in generated content

Agent Awareness

  • workspace_agents: Detects parallel agents (gptme, Claude Code, Codex, Goose, OpenCode, Amp) running in the same workspace

Best Practices#

  1. Keep hooks fast: Hooks run synchronously and can slow down operations

  2. Handle errors gracefully: Use try-except to prevent hook failures from breaking the system

  3. Use priorities wisely: Higher priority hooks run first (use for dependencies)

  4. Return Messages appropriately: Use hide=True for verbose/debug messages

  5. Test hooks thoroughly: Hooks run in the main execution path

  6. Document hook behavior: Explain what your hooks do and when they run

  7. Consider disabling hooks: Make hooks easy to disable via configuration

Thread Safety#

The hook registry is thread-safe. Each thread maintains its own tool state, and hooks are registered per-thread.

When running in server mode with multiple workers, hooks must be registered in each worker process.

Configuration#

Hooks can be configured via environment variables:

# Example: disable specific hooks
export GPTME_HOOKS_DISABLED="linter.lint,precommit.precommit_check"

# Example: set hook priorities
export GPTME_HOOK_PRIORITY_LINTER=20

Migration Guide#

Converting Existing Features to Hooks#

If you have features that should be hooks:

  1. Identify the appropriate hook type: Choose from the available hook types

  2. Extract the logic: Move the feature logic into a hook function

  3. Register the hook: Add it to a ToolSpec or register programmatically

  4. Test thoroughly: Ensure the hook works in all scenarios

  5. Update documentation: Document the new hook

Example: Converting pre-commit checks to a hook#

Before (hard-coded in chat.py):

# In chat.py
if check_for_modifications(log):
    run_precommit_checks()

After (as a hook):

# In a tool
def precommit_hook(log, workspace):
    if check_for_modifications(log):
        run_precommit_checks()

tool = ToolSpec(
    name="precommit",
    hooks={
        "check": (HookType.MESSAGE_POST_PROCESS.value, precommit_hook, 5)
    }
)

API Reference#

Hook system for extending gptme functionality at various lifecycle points.

This package provides a hook registry for registering and triggering hooks at various points in the gptme lifecycle. The system is split into:

  • types: Type definitions (Protocol classes, HookType enum, Hook dataclass)

  • registry: Hook registry, registration, and execution infrastructure

  • confirm: Tool confirmation hooks

  • elicitation: Structured user input hooks

Individual hook implementations live in their own modules (e.g., cwd_changed, time_awareness, workspace_agents).

gptme.hooks.init_hooks(allowlist: list[str] | None = None, interactive: bool = False, no_confirm: bool = False, server: bool = False) → None#

Initialize and register hooks in a thread-safe manner.

Mode detection for confirmation hooks: - Interactive CLI mode with confirmation: Registers cli_confirm hook - Server mode with confirmation: Registers server_confirm hook - Non-interactive mode: No confirmation hook (autonomous/auto-confirm)

Parameters:

  • allowlist – Explicit list of hooks to register (replaces defaults). If not provided, defaults will be loaded from env/config.

  • interactive – Whether running in interactive mode (CLI).

  • no_confirm – Whether to skip tool confirmations.

  • server – Whether running in server mode (API/WebUI).

See Also#