Overview of the CodeAct Agent Framework

The CodeAct Agent is a central component of OpenHands designed to consolidate all actions of an LLM agent into a unified 「code」 action space. Inspired by the original CodeAct concept (refer to the paper and related tweet for deeper background), the agent streamlines operations by allowing it to either converse naturally with a human or execute code actions. This decision-making process enables both simplicity in design and improved performance in practice.

At each turn, the agent operates in one of two primary modes:

1. Converse:
   The agent can engage in human-like dialogue to clarify, confirm, or request further details as needed. This open-ended conversation helps in understanding the task or addressing ambiguities before taking any code-related actions.
2. CodeAct:
   The agent can directly execute code to perform the task at hand. Code execution covers a range of actions, including:
   • Running any valid Linux bash command.
   • Executing Python code by simulating an interactive Python interpreter (IPython) through bash.

In essence, CodeAct empowers the agent to 「act」 on tasks by translating language instructions into concrete code operations.

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Built-In Tools and Capabilities

To implement its dual functionality, the CodeAct agent integrates several built-in tools, each with a specific role:

• execute_bash
  This tool is responsible for running Linux bash commands. It is robust enough to handle long-running or interactive processes (with STDIN input) and incorporates features like process interruption and automatic retry with background redirection for timeouts.
• execute_ipython_cell
  For tasks that require Python execution, this tool allows the agent to run Python code within an IPython environment. It supports magic commands (e.g., %pip) and maintains variable scope across the session. By running Python code through bash-enabled environments, it simulates an interactive Python shell.
• web_read and browser
  The agent can interact with web content through these tools. Specifically, web_read fetches and converts webpage content into markdown, while browser allows the agent to navigate, click, fill forms, scroll, and even handle file operations like uploads or drag-and-drop.
• str_replace_editor and edit_file (LLM-based)
  File editing is supported in two complementary ways:
  • The str_replace_editor provides a mechanism to view and edit files with precise string matching, complete with line numbering and undo support.
  • The more advanced edit_file uses a language-model-driven approach to modify file content. It supports partial edits by specifying line ranges and can handle larger files efficiently.

Beyond these tools, the agent also comes with configuration options, letting users enable or disable browsing, IPython code execution, or LLM-based file editing—ensuring flexibility depending on one』s environment and needs.

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Micro-Agents for Specialized Tasks

The CodeAct Agent further extends its capabilities through micro-agents—specialized subcomponents designed for particular recurring tasks. For example:

• npm micro-agent:
  Streamlines the installation of npm packages by providing workarounds for non-interactive shells.
• github micro-agent:
  Offers management of GitHub operations by integrating API token support and providing guidelines for pull request (PR) creation.
• flarglebargle micro-agent:
  Serves as an easter egg response handler for fun or experimental purposes.

These micro-agents encapsulate domain-specific functionality, letting the overall framework remain modular and easier to extend.

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Implementation Details – How the CodeActAgent Works

The actual implementation of the CodeAct Agent is encapsulated in its Python code (as shown in the provided code sample). Here are a few key points from the implementation:

1. Initialization and Configuration
   • The agent class, CodeActAgent, inherits from a generic Agent class.
   • During initialization, it sets up its internal data structures such as a deque for pending_actions and creates instances of a prompt manager and conversation memory.
   • It retrieves the list of enabled tools by calling a dedicated function (from the module function_calling). This function takes the current configuration settings (for example, enabling browsing or Jupyter support) and returns an array of tools the agent can use.
2. Processing Steps and Conversation Management
   • The core operation is driven by its step method, which is called repeatedly to advance the conversation and take actions.
   • This method first checks for any pending actions. If there are none, it evaluates the last user message—for example, checking for an exit command.
   • One of the agent’s roles is condensing the conversation history. It leverages a Condenser that can either return a clean view of the recent events or, if necessary, trigger a condensation action that pauses the conversation to reformat or summarize the state.
   • The conversation memory, built via the ConversationMemory object, plays a crucial role by maintaining message history with system prompts, user messages, actions, and tool responses to ensure continuity in dialogue.
3. Function Calling Interface
   • Upon constructing the message history and collating the current context, the agent packages the information as parameters and hands it over to the underlying language model (LLM) using a function calling interface.
   • The response from the LLM is then parsed into actions through the response_to_actions function provided in the tools module.
     These actions might instruct the execution of bash commands, Python code, or even further conversational responses.
4. Message Enhancement and Caching
   • The agent processes and enhances messages before sending them to the LLM. It adds context or examples to the first user message and manages message formatting (such as adding appropriate newlines between consecutive user entries).
   • For certain LLM providers, caching is employed to optimize interactions and reduce repeated prompt construction overhead.

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Main Agent Capabilities (As Documented)

According to the additional agent documentation:

• The CodeActAgent is explicitly designed to allow the consolidation of natural language communication with executable code.
• Its dual functionality is demonstrated via a data science task (for example, performing linear regression using gpt-4-turbo-2024-04-09), showcasing its practical application in real-world coding tasks.

In summary, the CodeAct Agent embodies the vision of a lean yet powerfully capable agent that balances conversation and direct code execution. Its modular design—with built-in tools, configurable micro-agents, and a robust conversation history management subsystem—makes it a versatile choice for bridging human language instructions with programmatic action execution.

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