The {{open_autonomy}} framework comes with *developer mode* (`dev` mode) tooling to enable faster AI agent developing and debugging. The `dev` mode supports running AI agents with a number of functionalities enabled:

* **Hot reload**, which enables hot code swapping and reflects changes on the agent blueprint code as well as on the local `open-aea` repository without rebuilding or restarting the containers manually.
* **Hardhat Instance**, which enables the inclusion of a pre-configured Hardhat Instance image in the deployment, which can be used as a test blockchain.
* **ACN Instance**, which enables inclusion of a pre-configured ACN Instance image, which can be used to test various agent communication functionalities, for example establishing the Tendermint network at the startup.
* **Execution replay** of a previous agent instance in the AI agent.
* **Benchmark** the performance of an AI agent.

Before starting this guide, ensure that your machine satisfies the framework requirements and that you have followed the [set up guide](../../guides/set_up.md). As a result you should have a Pipenv workspace folder.

## Build and run an AI agent in `dev` mode

1. **Fetch the AI agent.** Fetch the Hello World AI agent from the remote registry.

    ```bash
    autonomy fetch valory/hello_world:0.1.0:<hash> --service
    ```

2. **Build the deployment.** Within the AI agent folder, execute the command below to build the AI agent deployment in `dev` mode, including a pre-configured Hardhat instance.

    ```bash
    autonomy deploy build --dev --packages-dir ~/service/packages --open-aea-dir ~/git/open-aea/ --use-hardhat -ltm
    ```

    This will create a deployment environment in `dev` mode within the `./abci_build_*` folder.
  
    You must modify the paths in the command above appropriately, pointing to:

    * the path to the local registry (/packages directory),
    * the path to the local {{open_aea_repository}}.
  
    If you don't want to specify the `open-aea` repository manually, you can install the `open-aea` in editable mode using

    ```bash
    pip3 install -e PATH_TO_OPEN_AEA_REPOSITORY
    ```

    Same thing goes for packages, if you're already in the `packages/` directory (eg. running the command from `packages/author/services/ai_agent`) you don't have to specify the value for `--packages-dir` flag explicitly.

    > Note: When building development deployments you don't have to define keys manually, `autonomy` generates the keys automatically in development mode. You can still specify the keys file if you already have one and you're planning on using it.

3. **Run the AI agent.** Navigate to the deployment environment folder (`./abci_build_*`) and run the deployment locally.

    ```bash
    cd abci_build_*
    autonomy deploy run
    ```

	You can cancel the local execution at any time by pressing ++ctrl+c++.

## Hot reload

Once the agent instances are running, you can make changes to the agent blueprint's code as well as the local {{open_aea_repository}}, and it will trigger the AI agent restart.

The trigger is caused by any Python file closing in either the `service/packages` or the `open-aea/` directory. So even if you haven't made any change and still want to restart the AI agent, just open any Python file press `Ctrl+S` or save it from the file menu and it will trigger the restart.


## Hardhat instance

By default the command that builds the AI agent deployment (`autonomy deploy build`) only includes the agent instance nodes and the Tendermint nodes. If you want to include a local Hardhat node as a test blockchain for the ledger connection, you can do so by using the `--use-hardhat` flag in that command.

The deployment setup will include a Hardhat node (image `valory/open-autonomy-hardhat`) using `hardhat` as container name. Therefore, in order to use this node, you must set a [ai-agent-level override](../../configure_service/service_configuration_file.md#ai-agent-level-overrides) so that the `valory/ledger` connection address is set to `http://hardhat:8545`.
You can achieve this by editing the AI agent configuration file `service.yaml` as follows:

```yaml title="service.yaml"
# (...)
---
public_id: valory/ledger:0.1.0
type: connection
config:
  ledger_apis:
    ethereum:
      address: ${LEDGER_RPC:str:http://hardhat:8545}
```

Alternatively, you can leave the default `service.yaml` and export an environment variable:

```yaml title="service.yaml"
# (...)
---
public_id: valory/ledger:0.1.0
type: connection
config:
  ledger_apis:
    ethereum:
      address: ${LEDGER_RPC:str:http://localhost:8545}
```

```bash
export LEDGER_RPC = http://hardhat:8545
```

If you require specific custom contracts to test your AI agent, read the [guide to include custom contracts](https://github.com/valory-xyz/autonolas-registries/blob/main/docs/running_with_custom_contracts.md).

## ACN instance

You can also include an ACN node for agent communication using the `--use-acn` flag in `autonomy deploy build`.

The deployment setup will include an ACN node (image `valory/open-acn-node`) using `acn` as container name. Similarly as above, in order to use this node you must set a [ai-agent-level override](../../configure_service/service_configuration_file.md#ai-agent-level-overrides) so that the `valory/p2p_libp2p_client` connection parameters are set to the appropriate values.
You can achieve this by editing the AI agent configuration file `service.yaml` as follows:

```yaml title="service.yaml"
# (...)
---
public_id: valory/p2p_libp2p_client:0.1.0
type: connection
config:
  nodes:
  - uri: acn:11000
    public_key: 03c74dbfbe7bbc1b42429f78778017a3cd7eaf9d59d1634c9505a3f7c1a9350e71
cert_requests:
- identifier: acn
  ledger_id: ethereum
  message_format: '{public_key}'
  not_after: '2025-01-01'
  not_before: '2024-01-01'
  public_key: 03c74dbfbe7bbc1b42429f78778017a3cd7eaf9d59d1634c9505a3f7c1a9350e71
  save_path: .certs/acn_cosmos_11000.txt
is_abstract: false
```
