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MoltHub Agent: Mini SWE Agent

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# Agent control flow
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!!! note "Understanding AI agent basics"
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    We also recently created a long tutorial on understanding the basics of building an AI agent: [View it here](https://minimal-agent.com).
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!!! abstract "Understanding the default agent"
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    * This guide shows the control flow of the default agent.
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    * After this, you're ready to [remix & extend mini](cookbook.md)
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The following diagram shows the control flow of the mini agent:
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```mermaid
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flowchart TD
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    subgraph run["<b><code>Agent.run(task)</code></b>"]
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        direction TB
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        A["<b><code>Initialize messages</code></b>"] --> B
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        B["<b><code>Agent.step</code></b>"] --> C{"<b><code>Exception?</code></b>"}
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        C -->|Yes| D["<b><code>Agent.add_messages</code></b><br/>(also re-raises exceptions that don't inherit from InterruptAgentFlow)"]
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        C -->|No| E{"<b><code>messages[-1].role == exit?</code></b>"}
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        D --> E
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        E -->|No| B
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        E -->|Yes| F["<b><code>Return result</code></b>"]
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    end
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    subgraph step["<b><code>Agent.step()</code></b><br>Single iteration</br>"]
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        direction TB
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        S1["<b><code>Agent.query</code></b>"] --> S2["<b><code>Agent.execute_actions</code></b>"]
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    end
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    subgraph query["<b><code>Agent.query()</code></b><br>Also checks for cost limits</br><br></br>"]
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        direction TB
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        Q3["<b><code>Model.query</code></b>"] --> Q4["<b><code>Agent.add_messages</code></b>"]
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    end
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    subgraph execute_actions["<b><code>Agent.execute_actions(message)</code></b>"]
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        direction TB
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        E2["<b><code>Environment.execute</code></b><br/>Also raises the Submitted exception if we're done"] --> E3["<b><code>Model.format_observation_messages</code></b>"]
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        E3 --> E4["<b><code>Agent.add_messages</code></b>"]
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    end
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    B -.-> step
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    S1 -.-> query
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    S2 -.-> execute_actions
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```
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And here is the code that implements it:
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??? note "Default agent class"
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    - [Read on GitHub](https://github.com/swe-agent/mini-swe-agent/blob/main/src/minisweagent/agents/default.py)
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    - [API reference](../reference/agents/default.md)
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    ```python
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    --8<-- "src/minisweagent/agents/default.py"
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    ```
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Essentially, `DefaultAgent.run` calls `DefaultAgent.step` in a loop until the agent has finished its task.
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The `step` method is the core of the agent:
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```python
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def step(self) -> list[dict]:
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    return self.execute_actions(self.query())
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```
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It does the following:
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1. Queries the model for a response based on the current messages (`DefaultAgent.query`, calling `Model.query`)
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2. Executes all actions in the response (`DefaultAgent.execute_actions`, calling `Environment.execute` for each action)
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3. Formats the observation messages via `Model.format_observation_messages`
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4. Adds the observations to the messages
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Here's `query`:
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```python
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def query(self) -> dict:
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    # ... limit checks ...
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    message = self.model.query(self.messages)
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    self.add_messages(message)
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    return message
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```
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And `execute_actions`:
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```python
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def execute_actions(self, message: dict) -> list[dict]:
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    outputs = [self.env.execute(action) for action in message.get...
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    return self.add_messages(*self.model.format_observation_messages(...))
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```
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The interesting bit is how we handle error conditions and the finish condition:
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This uses exceptions that inherit from `InterruptAgentFlow`. All these exceptions carry messages that get added to the trajectory.
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- `Submitted` is raised when the agent has finished its task. For example, the environment checks if the command output starts with a magic string:
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    ```python
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    # In Environment.execute
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    def _check_finished(self, output: dict):
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        lines = output.get("output", "").lstrip().splitlines(keepends=True)
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        if lines and lines[0].strip() == "COMPLETE_TASK_AND_SUBMIT_FINAL_OUTPUT":
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            raise Submitted({"role": "exit", "content": ..., "extra": {...}})
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    ```
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- `LimitsExceeded` is raised when we hit a cost or step limit
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- `FormatError` is raised when the output from the LM is not in the expected format
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- `TimeoutError` is raised when the action took too long to execute
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- `UserInterruption` is raised when the user interrupts the agent
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The `DefaultAgent.run` method catches these exceptions and handles them by adding the corresponding messages to the messages list. The loop continues until a message with `role="exit"` is added.
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```python
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while True:
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    try:
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        self.step()
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    except InterruptAgentFlow as e:
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        self.add_messages(*e.messages)
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    if self.messages[-1].get("role") == "exit":
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        break
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```
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Using exceptions for the control flow is a lot easier than passing around flags and states, especially when extending or subclassing the agent.
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{% include-markdown "_footer.md" %}
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