Fix llm_config fallback (#4415)

Co-authored-by: openhands <openhands@all-hands.dev>

docs/modules/usage/configuration-options.md

@@ -140,7 +140,11 @@ The LLM (Large Language Model) configuration options are defined in the `[llm]`
 
 To use these with the docker command, pass in `-e LLM_<option>`. Example: `-e LLM_NUM_RETRIES`.
 
-### AWS Credentials
+:::note
+For development setups, you can also define custom named LLM configurations. See [Custom LLM Configurations](https://docs.all-hands.dev/modules/usage/llms/custom-llm-configs) for details.
+:::
+
+**AWS Credentials**
 - `aws_access_key_id`
   - Type: `str`
   - Default: `""`

docs/modules/usage/llms/custom-llm-configs.md

@@ -0,0 +1,106 @@
+# Custom LLM Configurations
+
+OpenHands supports defining multiple named LLM configurations in your `config.toml` file. This feature allows you to use different LLM configurations for different purposes, such as using a cheaper model for tasks that don't require high-quality responses, or using different models with different parameters for specific agents.
+
+## How It Works
+
+Named LLM configurations are defined in the `config.toml` file using sections that start with `llm.`. For example:
+
+```toml
+# Default LLM configuration
+[llm]
+model = "gpt-4"
+api_key = "your-api-key"
+temperature = 0.0
+
+# Custom LLM configuration for a cheaper model
+[llm.gpt3]
+model = "gpt-3.5-turbo"
+api_key = "your-api-key"
+temperature = 0.2
+
+# Another custom configuration with different parameters
+[llm.high-creativity]
+model = "gpt-4"
+api_key = "your-api-key"
+temperature = 0.8
+top_p = 0.9
+```
+
+Each named configuration inherits all settings from the default `[llm]` section and can override any of those settings. You can define as many custom configurations as needed.
+
+## Using Custom Configurations
+
+### With Agents
+
+You can specify which LLM configuration an agent should use by setting the `llm_config` parameter in the agent's configuration section:
+
+```toml
+[agent.RepoExplorerAgent]
+# Use the cheaper GPT-3 configuration for this agent
+llm_config = 'gpt3'
+
+[agent.CodeWriterAgent]
+# Use the high creativity configuration for this agent
+llm_config = 'high-creativity'
+```
+
+### Configuration Options
+
+Each named LLM configuration supports all the same options as the default LLM configuration. These include:
+
+- Model selection (`model`)
+- API configuration (`api_key`, `base_url`, etc.)
+- Model parameters (`temperature`, `top_p`, etc.)
+- Retry settings (`num_retries`, `retry_multiplier`, etc.)
+- Token limits (`max_input_tokens`, `max_output_tokens`)
+- And all other LLM configuration options
+
+For a complete list of available options, see the LLM Configuration section in the [Configuration Options](../configuration-options.md) documentation.
+
+## Use Cases
+
+Custom LLM configurations are particularly useful in several scenarios:
+
+- **Cost Optimization**: Use cheaper models for tasks that don't require high-quality responses, like repository exploration or simple file operations.
+- **Task-Specific Tuning**: Configure different temperature and top_p values for tasks that require different levels of creativity or determinism.
+- **Different Providers**: Use different LLM providers or API endpoints for different tasks.
+- **Testing and Development**: Easily switch between different model configurations during development and testing.
+
+## Example: Cost Optimization
+
+A practical example of using custom LLM configurations to optimize costs:
+
+```toml
+# Default configuration using GPT-4 for high-quality responses
+[llm]
+model = "gpt-4"
+api_key = "your-api-key"
+temperature = 0.0
+
+# Cheaper configuration for repository exploration
+[llm.repo-explorer]
+model = "gpt-3.5-turbo"
+temperature = 0.2
+
+# Configuration for code generation
+[llm.code-gen]
+model = "gpt-4"
+temperature = 0.0
+max_output_tokens = 2000
+
+[agent.RepoExplorerAgent]
+llm_config = 'repo-explorer'
+
+[agent.CodeWriterAgent]
+llm_config = 'code-gen'
+```
+
+In this example:
+- Repository exploration uses a cheaper model since it mainly involves understanding and navigating code
+- Code generation uses GPT-4 with a higher token limit for generating larger code blocks
+- The default configuration remains available for other tasks
+
+:::note
+Custom LLM configurations are only available when using OpenHands in development mode, via `main.py` or `cli.py`. When running via `docker run`, please use the standard configuration options.
+:::

openhands/core/config/llm_config.py

@@ -138,8 +138,19 @@ class LLMConfig:
         This function is used to create an LLMConfig object from a dictionary,
         with the exception of the 'draft_editor' key, which is a nested LLMConfig object.
         """
-        args = {k: v for k, v in llm_config_dict.items() if not isinstance(v, dict)}
-        if 'draft_editor' in llm_config_dict:
-            draft_editor_config = LLMConfig(**llm_config_dict['draft_editor'])
-            args['draft_editor'] = draft_editor_config
+        # Keep None values to preserve defaults, filter out other dicts
+        args = {
+            k: v
+            for k, v in llm_config_dict.items()
+            if not isinstance(v, dict) or v is None
+        }
+        if (
+            'draft_editor' in llm_config_dict
+            and llm_config_dict['draft_editor'] is not None
+        ):
+            if isinstance(llm_config_dict['draft_editor'], LLMConfig):
+                args['draft_editor'] = llm_config_dict['draft_editor']
+            else:
+                draft_editor_config = LLMConfig(**llm_config_dict['draft_editor'])
+                args['draft_editor'] = draft_editor_config
         return cls(**args)

openhands/core/config/utils.py

@@ -144,15 +144,48 @@ def load_from_toml(cfg: AppConfig, toml_file: str = 'config.toml'):
                     logger.openhands_logger.debug(
                         'Attempt to load default LLM config from config toml'
                     )
-                    llm_config = LLMConfig.from_dict(value)
-                    cfg.set_llm_config(llm_config, 'llm')
+                    # TODO clean up draft_editor
+                    # Extract generic LLM fields, keeping draft_editor
+                    generic_llm_fields = {}
+                    for k, v in value.items():
+                        if not isinstance(v, dict) or k == 'draft_editor':
+                            generic_llm_fields[k] = v
+                    generic_llm_config = LLMConfig.from_dict(generic_llm_fields)
+                    cfg.set_llm_config(generic_llm_config, 'llm')
+
+                    # Process custom named LLM configs
                     for nested_key, nested_value in value.items():
                         if isinstance(nested_value, dict):
                             logger.openhands_logger.debug(
-                                f'Attempt to load group {nested_key} from config toml as llm config'
+                                f'Processing custom LLM config "{nested_key}":'
                             )
-                            llm_config = LLMConfig.from_dict(nested_value)
-                            cfg.set_llm_config(llm_config, nested_key)
+                            # Apply generic LLM config with custom LLM overrides, e.g.
+                            # [llm]
+                            # model="..."
+                            # num_retries = 5
+                            # [llm.claude]
+                            # model="claude-3-5-sonnet"
+                            # results in num_retries APPLIED to claude-3-5-sonnet
+                            custom_fields = {}
+                            for k, v in nested_value.items():
+                                if not isinstance(v, dict) or k == 'draft_editor':
+                                    custom_fields[k] = v
+                            merged_llm_dict = generic_llm_config.__dict__.copy()
+                            merged_llm_dict.update(custom_fields)
+                            # TODO clean up draft_editor
+                            # Handle draft_editor with fallback values:
+                            # - If draft_editor is "null", use None
+                            # - If draft_editor is in custom fields, use that value
+                            # - If draft_editor is not specified, fall back to generic config value
+                            if 'draft_editor' in custom_fields:
+                                if custom_fields['draft_editor'] == 'null':
+                                    merged_llm_dict['draft_editor'] = None
+                            else:
+                                merged_llm_dict['draft_editor'] = (
+                                    generic_llm_config.draft_editor
+                                )
+                            custom_llm_config = LLMConfig.from_dict(merged_llm_dict)
+                            cfg.set_llm_config(custom_llm_config, nested_key)
                 elif key is not None and key.lower() == 'security':
                     logger.openhands_logger.debug(
                         'Attempt to load security config from config toml'
@@ -458,7 +491,11 @@ def setup_config_from_args(args: argparse.Namespace) -> AppConfig:
 
     # Override with command line arguments if provided
     if args.llm_config:
-        llm_config = get_llm_config_arg(args.llm_config)
+        # if we didn't already load it, get it from the toml file
+        if args.llm_config not in config.llms:
+            llm_config = get_llm_config_arg(args.llm_config)
+        else:
+            llm_config = config.llms[args.llm_config]
         if llm_config is None:
             raise ValueError(f'Invalid toml file, cannot read {args.llm_config}')
         config.set_llm_config(llm_config)

tests/unit/test_llm_config.py

@@ -0,0 +1,228 @@
+import pathlib
+
+import pytest
+
+from openhands.core.config import AppConfig
+from openhands.core.config.utils import load_from_toml
+
+
+@pytest.fixture
+def default_config(monkeypatch):
+    # Fixture to provide a default AppConfig instance
+    yield AppConfig()
+
+
+@pytest.fixture
+def generic_llm_toml(tmp_path: pathlib.Path) -> str:
+    """Fixture to create a generic LLM TOML configuration with all custom LLMs
+    providing mandatory 'model' and 'api_key', and testing fallback to the generic section values
+    for other attributes like 'num_retries'.
+    """
+    toml_content = """
+[core]
+workspace_base = "./workspace"
+
+[llm]
+model = "base-model"
+api_key = "base-api-key"
+embedding_model = "base-embedding"
+num_retries = 3
+
+[llm.custom1]
+model = "custom-model-1"
+api_key = "custom-api-key-1"
+# 'num_retries' is not overridden and should fallback to the value from [llm]
+
+[llm.custom2]
+model = "custom-model-2"
+api_key = "custom-api-key-2"
+num_retries = 5  # Overridden value
+
+[llm.custom3]
+model = "custom-model-3"
+api_key = "custom-api-key-3"
+# No overrides for additional attributes
+    """
+    toml_file = tmp_path / 'llm_config.toml'
+    toml_file.write_text(toml_content)
+    return str(toml_file)
+
+
+def test_load_from_toml_llm_with_fallback(
+    default_config: AppConfig, generic_llm_toml: str
+) -> None:
+    """Test that custom LLM configurations fallback non-overridden attributes
+    like 'num_retries' from the generic [llm] section.
+    """
+    load_from_toml(default_config, generic_llm_toml)
+
+    # Verify generic LLM configuration
+    generic_llm = default_config.get_llm_config('llm')
+    assert generic_llm.model == 'base-model'
+    assert generic_llm.api_key == 'base-api-key'
+    assert generic_llm.embedding_model == 'base-embedding'
+    assert generic_llm.num_retries == 3
+
+    # Verify custom1 LLM falls back 'num_retries' from base
+    custom1 = default_config.get_llm_config('custom1')
+    assert custom1.model == 'custom-model-1'
+    assert custom1.api_key == 'custom-api-key-1'
+    assert custom1.embedding_model == 'base-embedding'
+    assert custom1.num_retries == 3  # from [llm]
+
+    # Verify custom2 LLM overrides 'num_retries'
+    custom2 = default_config.get_llm_config('custom2')
+    assert custom2.model == 'custom-model-2'
+    assert custom2.api_key == 'custom-api-key-2'
+    assert custom2.embedding_model == 'base-embedding'
+    assert custom2.num_retries == 5  # overridden value
+
+    # Verify custom3 LLM inherits all attributes except 'model' and 'api_key'
+    custom3 = default_config.get_llm_config('custom3')
+    assert custom3.model == 'custom-model-3'
+    assert custom3.api_key == 'custom-api-key-3'
+    assert custom3.embedding_model == 'base-embedding'
+    assert custom3.num_retries == 3  # from [llm]
+
+
+def test_load_from_toml_llm_custom_overrides_all(
+    default_config: AppConfig, tmp_path: pathlib.Path
+) -> None:
+    """Test that a custom LLM can fully override all attributes from the generic [llm] section."""
+    toml_content = """
+[core]
+workspace_base = "./workspace"
+
+[llm]
+model = "base-model"
+api_key = "base-api-key"
+embedding_model = "base-embedding"
+num_retries = 3
+
+[llm.custom_full]
+model = "full-custom-model"
+api_key = "full-custom-api-key"
+embedding_model = "full-custom-embedding"
+num_retries = 10
+    """
+    toml_file = tmp_path / 'full_override_llm.toml'
+    toml_file.write_text(toml_content)
+
+    load_from_toml(default_config, str(toml_file))
+
+    # Verify generic LLM configuration remains unchanged
+    generic_llm = default_config.get_llm_config('llm')
+    assert generic_llm.model == 'base-model'
+    assert generic_llm.api_key == 'base-api-key'
+    assert generic_llm.embedding_model == 'base-embedding'
+    assert generic_llm.num_retries == 3
+
+    # Verify custom_full LLM overrides all attributes
+    custom_full = default_config.get_llm_config('custom_full')
+    assert custom_full.model == 'full-custom-model'
+    assert custom_full.api_key == 'full-custom-api-key'
+    assert custom_full.embedding_model == 'full-custom-embedding'
+    assert custom_full.num_retries == 10  # overridden value
+
+
+def test_load_from_toml_llm_custom_partial_override(
+    default_config: AppConfig, generic_llm_toml: str
+) -> None:
+    """Test that custom LLM configurations can partially override attributes
+    from the generic [llm] section while inheriting others.
+    """
+    load_from_toml(default_config, generic_llm_toml)
+
+    # Verify custom1 LLM overrides 'model' and 'api_key' but inherits 'num_retries'
+    custom1 = default_config.get_llm_config('custom1')
+    assert custom1.model == 'custom-model-1'
+    assert custom1.api_key == 'custom-api-key-1'
+    assert custom1.embedding_model == 'base-embedding'
+    assert custom1.num_retries == 3  # from [llm]
+
+    # Verify custom2 LLM overrides 'model', 'api_key', and 'num_retries'
+    custom2 = default_config.get_llm_config('custom2')
+    assert custom2.model == 'custom-model-2'
+    assert custom2.api_key == 'custom-api-key-2'
+    assert custom2.embedding_model == 'base-embedding'
+    assert custom2.num_retries == 5  # Overridden value
+
+
+def test_load_from_toml_llm_custom_no_override(
+    default_config: AppConfig, generic_llm_toml: str
+) -> None:
+    """Test that custom LLM configurations with no additional overrides
+    inherit all non-specified attributes from the generic [llm] section.
+    """
+    load_from_toml(default_config, generic_llm_toml)
+
+    # Verify custom3 LLM inherits 'embedding_model' and 'num_retries' from generic
+    custom3 = default_config.get_llm_config('custom3')
+    assert custom3.model == 'custom-model-3'
+    assert custom3.api_key == 'custom-api-key-3'
+    assert custom3.embedding_model == 'base-embedding'
+    assert custom3.num_retries == 3  # from [llm]
+
+
+def test_load_from_toml_llm_missing_generic(
+    default_config: AppConfig, tmp_path: pathlib.Path
+) -> None:
+    """Test that custom LLM configurations without a generic [llm] section
+    use only their own attributes and fallback to defaults for others.
+    """
+    toml_content = """
+[core]
+workspace_base = "./workspace"
+
+[llm.custom_only]
+model = "custom-only-model"
+api_key = "custom-only-api-key"
+    """
+    toml_file = tmp_path / 'custom_only_llm.toml'
+    toml_file.write_text(toml_content)
+
+    load_from_toml(default_config, str(toml_file))
+
+    # Verify custom_only LLM uses its own attributes and defaults for others
+    custom_only = default_config.get_llm_config('custom_only')
+    assert custom_only.model == 'custom-only-model'
+    assert custom_only.api_key == 'custom-only-api-key'
+    assert custom_only.embedding_model == 'local'  # default value
+    assert custom_only.num_retries == 8  # default value
+
+
+def test_load_from_toml_llm_invalid_config(
+    default_config: AppConfig, tmp_path: pathlib.Path
+) -> None:
+    """Test that invalid custom LLM configurations do not override the generic
+    and raise appropriate warnings.
+    """
+    toml_content = """
+[core]
+workspace_base = "./workspace"
+
+[llm]
+model = "base-model"
+api_key = "base-api-key"
+num_retries = 3
+
+[llm.invalid_custom]
+unknown_attr = "should_not_exist"
+    """
+    toml_file = tmp_path / 'invalid_custom_llm.toml'
+    toml_file.write_text(toml_content)
+
+    load_from_toml(default_config, str(toml_file))
+
+    # Verify generic LLM is loaded correctly
+    generic_llm = default_config.get_llm_config('llm')
+    assert generic_llm.model == 'base-model'
+    assert generic_llm.api_key == 'base-api-key'
+    assert generic_llm.num_retries == 3
+
+    # Verify invalid_custom LLM does not override generic attributes
+    custom_invalid = default_config.get_llm_config('invalid_custom')
+    assert custom_invalid.model == 'base-model'
+    assert custom_invalid.api_key == 'base-api-key'
+    assert custom_invalid.num_retries == 3  # default value
+    assert custom_invalid.embedding_model == 'local'  # default value

tests/unit/test_llm_draft_config.py

@@ -0,0 +1,92 @@
+import pathlib
+
+import pytest
+
+from openhands.core.config import AppConfig
+from openhands.core.config.utils import load_from_toml
+
+
+@pytest.fixture
+def draft_llm_toml(tmp_path: pathlib.Path) -> str:
+    toml_content = """
+[core]
+workspace_base = "./workspace"
+
+[llm]
+model = "base-model"
+api_key = "base-api-key"
+draft_editor = { model = "draft-model", api_key = "draft-api-key" }
+
+[llm.custom1]
+model = "custom-model-1"
+api_key = "custom-api-key-1"
+# Should use draft_editor from [llm] as fallback
+
+[llm.custom2]
+model = "custom-model-2"
+api_key = "custom-api-key-2"
+draft_editor = { model = "custom-draft", api_key = "custom-draft-key" }
+
+[llm.custom3]
+model = "custom-model-3"
+api_key = "custom-api-key-3"
+draft_editor = "null"  # Explicitly set to null in TOML
+    """
+    toml_file = tmp_path / 'llm_config.toml'
+    toml_file.write_text(toml_content)
+    return str(toml_file)
+
+
+def test_draft_editor_fallback(draft_llm_toml):
+    """Test that draft_editor is correctly handled in different scenarios:
+    - Falls back to generic [llm] section value
+    - Uses custom value when specified
+    - Can be explicitly set to null
+    """
+    config = AppConfig()
+
+    # Verify default draft_editor is None
+    default_llm = config.get_llm_config('llm')
+    assert default_llm.draft_editor is None
+
+    # Load config from TOML
+    load_from_toml(config, draft_llm_toml)
+
+    # Verify generic LLM draft_editor
+    generic_llm = config.get_llm_config('llm')
+    assert generic_llm.draft_editor is not None
+    assert generic_llm.draft_editor.model == 'draft-model'
+    assert generic_llm.draft_editor.api_key == 'draft-api-key'
+
+    # Verify custom1 uses draft_editor from generic as fallback
+    custom1 = config.get_llm_config('custom1')
+    assert custom1.model == 'custom-model-1'
+    assert custom1.draft_editor is not None
+    assert custom1.draft_editor.model == 'draft-model'
+    assert custom1.draft_editor.api_key == 'draft-api-key'
+
+    # Verify custom2 overrides draft_editor
+    custom2 = config.get_llm_config('custom2')
+    assert custom2.model == 'custom-model-2'
+    assert custom2.draft_editor is not None
+    assert custom2.draft_editor.model == 'custom-draft'
+    assert custom2.draft_editor.api_key == 'custom-draft-key'
+
+    # Verify custom3 has draft_editor explicitly set to None
+    custom3 = config.get_llm_config('custom3')
+    assert custom3.model == 'custom-model-3'
+    assert custom3.draft_editor is None
+
+
+def test_draft_editor_defaults(draft_llm_toml):
+    """Test that draft_editor uses default values from LLMConfig when not specified"""
+    config = AppConfig()
+    load_from_toml(config, draft_llm_toml)
+
+    generic_llm = config.get_llm_config('llm')
+    assert generic_llm.draft_editor.num_retries == 8  # Default from LLMConfig
+    assert generic_llm.draft_editor.embedding_model == 'local'  # Default from LLMConfig
+
+    custom2 = config.get_llm_config('custom2')
+    assert custom2.draft_editor.num_retries == 8  # Default from LLMConfig
+    assert custom2.draft_editor.embedding_model == 'local'  # Default from LLMConfig