strands-labs/ai-functions

What it solves

Strands AI Functions provides a way to treat AI agents as standard Python functions. It solves the problem of "prompt-and-pray" development by introducing post-conditions that force agents to self-correct their output until it meets specific requirements. It also bridges the gap between LLM text outputs and real-world data by allowing agents to generate and execute Python code to return native objects like Pandas DataFrames.

How it works

The library uses a @ai_function decorator to turn Python functions into agentic tasks. The docstring serves as the prompt template, and return type annotations define the expected output format.

Key architectural components include:

• Post-Conditions: Validation functions that trigger automatic re-prompting if the agent's output fails a check.
• AI Threads: Stateful handles that maintain conversation history across multiple calls.
• Coordinator and Workers: A system that allows multiple threads to discover and message each other, enabling multi-agent collaboration.
• Distributed Execution: A WebSocket-based protocol that allows coordinators and workers to run across different processes or machines.
• Memory Optimization: A "backpropagation" system for natural language, where feedback on a result is used to update the prompts, facts, or code stored in a memory backend to improve future runs.

Who it’s for

Python developers building complex AI workflows who need guaranteed output shapes, stateful interactions, and the ability to orchestrate teams of agents across distributed systems.

Highlights

• Self-Correcting Loops: Automatic retries based on custom Python or AI-driven post-conditions.
• Native Python Integration: Ability to return complex objects (e.g., DataFrames) via local code execution.
• Multi-Agent Orchestration: Built-in list_threads and send_message tools for autonomous agent-to-agent communication.
• Runtime Interoperability: Wrappers for external runtimes like Claude Code, Kiro, and Codex that treat them as standard threads.
• Distributed Runtime: A CLI and WebSocket server that turns the coordinator into a machine-wide agent runtime.
• Natural Language Optimization: A feedback loop that updates prompts and logic based on performance feedback.