> ## Documentation Index
> Fetch the complete documentation index at: https://icrl.dev/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Testing With Mock LLM

> Develop and test ICRL flows without external model calls

## Overview

The mock LLM provider lets you run the full ICRL training and evaluation pipeline without API keys or network access. It uses pattern matching on prompts to generate deterministic plans, reasoning, and actions for file system tasks.

## Source Files

| File                      | Purpose                          |
| ------------------------- | -------------------------------- |
| `examples/mock_llm.py`    | `MockLLMProvider` implementation |
| `tests/test_with_mock.py` | End-to-end demo using the mock   |

## Run

```bash theme={null}
uv run python tests/test_with_mock.py
```

## Why Use It

| Benefit           | Description                      |
| ----------------- | -------------------------------- |
| **Deterministic** | Same inputs produce same outputs |
| **Fast**          | No network latency               |
| **Zero cost**     | No API usage                     |
| **CI-friendly**   | Tests run without secrets        |

## Provider Contract

The mock implements the same interface as any Python LLM provider:

```python theme={null}
async def complete(messages: list[Message]) -> str:
    ...
```

You can swap `MockLLMProvider` with `LiteLLMProvider` without changing `Agent` wiring:

```python theme={null}
from examples.mock_llm import MockLLMProvider
from icrl import Agent

agent = Agent(
    llm=MockLLMProvider(),  # or LiteLLMProvider(model="gpt-4o-mini", ...)
    db_path="./trajectories",
    plan_prompt=PLAN_PROMPT,
    reason_prompt=REASON_PROMPT,
    act_prompt=ACT_PROMPT,
    k=2,
    max_steps=10,
)
```

## What the Demo Covers

`tests/test_with_mock.py` runs four phases:

1. **Training** — Trains on file system tasks; successful trajectories are stored
2. **Persistence** — Creates a new agent that loads trajectories from disk
3. **Retrieval** — Shows semantic search over stored trajectories
4. **Evaluation** — Runs held-out tasks with retrieval enabled