alive
VerifiedMinimal Python loop that turns any LLM into a persistent autonomous agent.
What is alive?
Alive is a compact wake-loop script that repeatedly activates an LLM agent on a timer. It reads a core identity document, loads recent memory files in priority order, collects new messages through optional adapters, then passes the assembled context to the model for decisions before sleeping.
The system emphasizes reliability through automatic context trimming, retry logic, and session logging. Memory grows only as the agent writes new files, and older entries are dropped when they would exceed the model's window.
It suits developers who want full control over an autonomous loop without adopting large agent frameworks or external services.
Capabilities
What you can build with alive
Autonomous repository maintenance
The agent can monitor code repositories, identify issues, and propose or apply fixes during each wake cycle.
Ongoing research and reporting
It explores chosen topics, gathers information, and produces updated reports stored in its memory directory.
Scheduled personal task handling
With message adapters enabled, the agent can respond to emails or chat messages and carry out recurring personal workflows.
Install alive
pip install alive-framework# Install from PyPI
pip install alive-framework
# Or clone the repo
git clone https://github.com/TheAuroraAI/alive.git
cd alive
# See it in action immediately — no API key needed
python3 alive.py --demo
# Edit the soul file to define your AI's identity
nano soul.md
# Configure your LLM provider
cp .env.example .env
nano .env
# Verify everything is configured correctly
python3 alive.py --check
# Run a single cycle to test
python3 alive.py --once
# Run the loop
python3 alive.py- 1Install the package with pip.
- 2Clone the repository if you prefer the source version.
- 3Edit soul.md to define the agent's identity and goals.
- 4Copy the example environment file and add your LLM credentials.
- 5Run the script with the once flag to test a single cycle before starting the loop.
Works with
alive: pros & cons
Pros
- +Extremely small codebase with no heavy dependencies.
- +Built-in context management prevents memory from overwhelming the model.
- +Session logs and circuit breakers improve operational reliability.
- +Agent can edit its own instructions and memory files.
Cons
- –Requires manual configuration of adapters and environment variables.
- –No built-in UI or monitoring dashboard beyond logs.
- –Context handling and reliability features are basic compared with larger frameworks.
Frequently asked questions
By default it runs every five minutes, though the interval is configurable.
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