## Get Agent Config

`agent_configs.retrieve(stragent_config_id)  -> AgentConfigRetrieveResponse`

**get** `/v5/agent_configs/{agent_config_id}`

Fetch a single stored agent configuration by id, including its `persistent_workspace` flag and any `repos` override.

This returns the saved record as-is and does not compute task params; use
`{agent_config_id}/resolve` when you need the config projected into the params
a task would run with. A user caller can only read a config they created unless
fine-grained access control grants access, while a service account can read any
config under the account; a missing or out-of-scope id returns a 404.

### Parameters

- `agent_config_id: str`

### Returns

- `class AgentConfigRetrieveResponse: …`

  - `id: str`

  - `allowed_tools: List[str]`

  - `created_at: datetime`

  - `harness: str`

  - `model: str`

  - `name: str`

  - `system_prompt: str`

  - `updated_at: datetime`

  - `description: Optional[str]`

  - `object: Optional[Literal["agent_config"]]`

    - `"agent_config"`

  - `persistent_workspace: Optional[bool]`

  - `repos: Optional[List[RepoSpec]]`

    - `url: str`

    - `depth: Optional[int]`

    - `path: Optional[str]`

### Example

```python
import os
from scale_gp_beta import SGPClient

client = SGPClient(
    api_key=os.environ.get("SGP_API_KEY"),  # This is the default and can be omitted
)
agent_config = client.agent_configs.retrieve(
    "agent_config_id",
)
print(agent_config.id)
```

#### Response

```json
{
  "id": "id",
  "allowed_tools": [
    "string"
  ],
  "created_at": "2019-12-27T18:11:19.117Z",
  "harness": "harness",
  "model": "model",
  "name": "name",
  "system_prompt": "system_prompt",
  "updated_at": "2019-12-27T18:11:19.117Z",
  "description": "description",
  "object": "agent_config",
  "persistent_workspace": true,
  "repos": [
    {
      "url": "x",
      "depth": 1,
      "path": "path"
    }
  ]
}
```