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clean up setup.md

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Steve Androulakis
2025-05-26 14:28:45 -07:00
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@@ -58,6 +58,8 @@ See the section Goal-Specific Tool Configuration below for tool configuration fo
### LLM Configuration
Note: We recommend using OpenAI's GPT-4o or Claude 3.5 Sonnet for the best results, but you can use any model supported by LiteLLM.
The agent uses LiteLLM to interact with various LLM providers. Configure the following environment variables in your `.env` file:
- `LLM_MODEL`: The model to use (e.g., "openai/gpt-4o", "anthropic/claude-3-sonnet", "google/gemini-pro", etc.)
@@ -90,45 +92,6 @@ LLM_BASE_URL=http://localhost:11434
For a complete list of supported models and providers, visit the [LiteLLM documentation](https://docs.litellm.ai/docs/providers).
### Option 1: OpenAI
If using OpenAI, ensure you have an OpenAI key for the GPT-4o model. Set this in the `OPENAI_API_KEY` environment variable in `.env`.
### Option 2: Google Gemini
To use Google Gemini:
1. Obtain a Google API key and set it in the `GOOGLE_API_KEY` environment variable in `.env`.
2. Set `LLM_PROVIDER=google` in your `.env` file.
### Option 3: Anthropic Claude (recommended)
I find that Claude Sonnet 3.5 performs better than the other hosted LLMs for this use case.
To use Anthropic:
1. Obtain an Anthropic API key and set it in the `ANTHROPIC_API_KEY` environment variable in `.env`.
2. Set `LLM_PROVIDER=anthropic` in your `.env` file.
### Option 4: Deepseek-V3
To use Deepseek-V3:
1. Obtain a Deepseek API key and set it in the `DEEPSEEK_API_KEY` environment variable in `.env`.
2. Set `LLM_PROVIDER=deepseek` in your `.env` file.
### Option 5: Local LLM via Ollama (not recommended)
To use a local LLM with Ollama:
1. Install [Ollama](https://ollama.com) and the [Qwen2.5 14B](https://ollama.com/library/qwen2.5) model.
- Run `ollama run <OLLAMA_MODEL_NAME>` to start the model. Note that this model is about 9GB to download.
- Example: `ollama run qwen2.5:14b`
2. Set `LLM_PROVIDER=ollama` in your `.env` file and `OLLAMA_MODEL_NAME` to the name of the model you installed.
Note: I found the other (hosted) LLMs to be MUCH more reliable for this use case. However, you can switch to Ollama if desired, and choose a suitably large model if your computer has the resources.
## Configuring Temporal Connection
By default, this application will connect to a local Temporal server (`localhost:7233`) in the default namespace, using the `agent-task-queue` task queue. You can override these settings in your `.env` file.