Language model (LLM) settings

Overview

The LLM settings allows you to bring your own LLM and API key to use with Faheem Code. This can be any model that is supported by litellm, but it requires a powerful model to work properly. See our recommended models here. You can also configure some additional LLM settings on this page.

Basic LLM settings

The most popular providers and models are available in the basic settings. Some of the providers have been verified to work with Faheem Code such as the Faheem Code provider, Anthropic, OpenAI and Mistral AI.

1. Choose your preferred provider using the LLM Provider dropdown.
2. Choose your favorite model using the LLM Model dropdown.
3. Set the API Key for your chosen provider and model and click Save Changes.

This will set the LLM for all new conversations. If you want to use this new LLM for older conversations, you must first restart older conversations.

Advanced LLM settings

Toggling the Advanced settings, allows you to set custom models as well as some additional LLM settings. You can use this when your preferred provider or model does not exist in the basic settings dropdowns.

1. Custom Model: Set your custom model with the provider as the prefix. For information on how to specify the custom model, follow the specific provider docs on litellm. We also have some guides for popular providers.
2. Base URL: If your provider has a specific base URL, specify it here.
3. API Key: Set the API key for your custom model.
4. Click Save Changes

Memory condensation

The memory condenser manages the language model's context by ensuring only the most important and relevant information is presented. Keeping the context focused improves latency and reduces token consumption, especially in long-running conversations.

• Enable memory condensation - Turn on this setting to activate this feature.
• Memory condenser max history size - The condenser will summarize the history after this many events.

LLM profiles

LLM profiles allow you to save multiple LLM configurations and switch between them, even during an active conversation. This is useful when you want to use different models for different tasks—for example, a faster model for simple tasks and a more powerful model for complex reasoning.

Creating an LLM profile

Profiles are automatically created when you save a configuration on the LLM settings page. To create a new profile:

1. Navigate to Settings > LLM.
2. Configure your desired LLM provider, model, and API key.
3. Click Save Changes.

A new profile will be created with your configuration. The most recently saved profile becomes the active profile for new conversations.

Alternatively, you can click the Add LLM Profile button in the Available Profiles section to create a new profile directly.

Note

When saving a local LLM profile, the configuration is validated against the backend before it is persisted. If validation fails (for example, an invalid API key or unavailable model), the save is blocked and the error is shown. Older backends that do not support validation skip this check and save normally.

Managing LLM profiles

You can manage your saved profiles in the Available Profiles section of the LLM settings page. Each profile shows:

• Profile name: A unique identifier for the configuration
• Model: The LLM model associated with the profile
• Active badge: Indicates which profile is currently active

Click the menu icon (three dots) on any profile to access these actions:

• Edit: Modify the profile's LLM configuration
• Rename: Change the profile name
• Set as Active: Make this profile the default for new conversations
• Delete: Remove the profile

Note

You can save up to 10 LLM profiles per account. Delete unused profiles if you need to create new ones.

Switching profiles during a conversation

One of the most powerful features of LLM profiles is the ability to switch models mid-conversation without losing context. This allows you to:

• Start with a fast, cost-effective model for initial exploration
• Switch to a more powerful model when the task requires deeper reasoning
• Use specialized models for specific types of tasks

For example, you might create profiles like these:

Example Profile	Example Use	Example Cost Pattern
claude-opus-4-7	Frontend design and visual polish	Higher cost
gpt-5.5	Planning, instruction following, or review	Balanced for complex reasoning
minimax-m2.7	Day-to-day implementation	Lower cost

The profile names above are examples. Use names that match the saved profiles in your Faheem Code environment.

To switch profiles during an active conversation:

1. Look for the profile selector button in the chat input area. It displays the name of the currently active profile.
2. Click the button to open the profile menu.
3. Select the profile you want to switch to.

The conversation will continue with the new model, maintaining all previous context and history. The switch takes effect immediately for subsequent messages.

Tip

The profile selector shows a checkmark next to the currently active profile. If no profile matches the running model, the button will show "Select a model" as a placeholder.

Switching profiles with the /model slash command

You can also list and switch profiles directly from the chat input using the /model slash command:

• /model — Lists your saved LLM profiles.
• /model <profile-name> — Switches the running conversation to that profile.

This is equivalent to using the profile selector button and works without leaving the chat. Profile names must match the saved profile exactly. The switch applies to future agent steps; it does not rerun earlier messages.

A common workflow is to use a stronger model for planning and then switch to a lower-cost model for implementation:

1. Start the conversation with gpt-5.5 selected.

2. Ask Faheem Code to plan the work before editing files:

   Plan the Faheem Code features page. Do not edit files yet.

3. Send /model to list available profiles.

4. Send /model minimax-m2.7 to switch profiles.

5. Ask Faheem Code to implement the plan:

   Now implement the plan.

Screenshot: Faheem Code showing example /model command output that lists saved profiles and switches to another profile.

Note

Model switching requires saved LLM profiles. If /model is not suggested in the chat input, create profiles in Settings > LLM and confirm that your backend supports profile switching.

Letting the agent select models dynamically

When the model selection tool is available, the agent can choose a saved profile for the next phase of work. For example, it can implement frontend changes with a design-focused model and then switch to an instruction-following model for review.

In the Agent SDK, this capability is exposed as the built-in SwitchLLMTool, which produces switch_llm tool calls. Faheem Code displays those tool calls as Switch LLM profile events in the conversation timeline so you can see when and why the model changed.

Create the profiles you want the agent to choose from, then ask Faheem Code to use specific profiles for different phases of the task. For example:

Implement a simple web page on the features of Faheem Code with Claude Opus 4.7, and then switch to GPT-5.5 and review the code.

Screenshot: Faheem Code showing example switch_llm tool calls that move a task between saved profiles.

The model selection tool behaves as follows:

• The current model decides to call the tool and provides a short reason.
• The switch takes effect on the next LLM call after the tool succeeds.
• Conversation history, files, and task state are preserved.
• If a profile name is missing or misspelled, the tool returns an error and the agent should choose a valid profile or ask for help.

For custom SDK agents, include SwitchLLMTool when constructing the agent. See the SDK example: examples/01_standalone_sdk/49_switch_llm_tool.py.

How profile switching works

When you switch profiles during a conversation:

1. The new LLM configuration is loaded from your saved profile
2. The conversation context (all previous messages and actions) is preserved
3. Future messages are processed using the new model
4. The conversation metadata is updated to reflect the new model

This seamless switching allows you to leverage different models' strengths without starting a new conversation or losing your progress.

Best practices for using LLM profiles

• Name profiles descriptively: Use names like "Claude Sonnet - Fast" or "GPT-4 - Complex Tasks" to easily identify which profile to use.
• Create task-specific profiles: Set up profiles optimized for different workflows, such as code review, documentation, or debugging.
• Keep API keys updated: Ensure each profile has a valid API key.
• Test before critical work: When switching profiles mid-conversation, send a simple test message to confirm the new model is responding correctly.