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<article class="md-content__inner md-typeset">
<h1>Changing a Model</h1>
<h2 id="changing-a-model-in-pr-agent">Changing a model in PR-Agent<a class="headerlink" href="#changing-a-model-in-pr-agent" title="Permanent link">&para;</a></h2>
<p>See <a href="https://github.com/Codium-ai/pr-agent/blob/main/pr_agent/algo/__init__.py">here</a> for a list of available models.
To use a different model than the default (o4-mini), you need to edit in the <a href="https://github.com/Codium-ai/pr-agent/blob/main/pr_agent/settings/configuration.toml#L2">configuration file</a> the fields:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="k">[config]</span>
<a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a><span class="n">model</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;...&quot;</span>
<a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="n">fallback_models</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">[</span><span class="s2">&quot;...&quot;</span><span class="p">]</span>
</code></pre></div>
<p>For models and environments not from OpenAI, you might need to provide additional keys and other parameters.
You can give parameters via a configuration file, or from environment variables.</p>
<div class="admonition note">
<p class="admonition-title">Model-specific environment variables</p>
<p>See <a href="https://litellm.vercel.app/docs/proxy/quick_start#supported-llms">litellm documentation</a> for the environment variables needed per model, as they may vary and change over time. Our documentation per-model may not always be up-to-date with the latest changes.
Failing to set the needed keys of a specific model will usually result in litellm not identifying the model type, and failing to utilize it.</p>
</div>
<h3 id="openai-like-api">OpenAI like API<a class="headerlink" href="#openai-like-api" title="Permanent link">&para;</a></h3>
<p>To use an OpenAI like API, set the following in your <code>.secrets.toml</code> file:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a><span class="k">[openai]</span>
<a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a><span class="n">api_base</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;https://api.openai.com/v1&quot;</span>
<a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a><span class="n">api_key</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;sk-...&quot;</span>
</code></pre></div>
<p>or use the environment variables (make sure to use double underscores <code>__</code>):</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a><span class="nv">OPENAI__API_BASE</span><span class="o">=</span>https://api.openai.com/v1
<a id="__codelineno-2-2" name="__codelineno-2-2" href="#__codelineno-2-2"></a><span class="nv">OPENAI__KEY</span><span class="o">=</span>sk-...
</code></pre></div>
<h3 id="azure">Azure<a class="headerlink" href="#azure" title="Permanent link">&para;</a></h3>
<p>To use Azure, set in your <code>.secrets.toml</code> (working from CLI), or in the GitHub <code>Settings &gt; Secrets and variables</code> (working from GitHub App or GitHub Action):</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a><span class="k">[openai]</span>
<a id="__codelineno-3-2" name="__codelineno-3-2" href="#__codelineno-3-2"></a><span class="n">key</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;&quot;</span><span class="w"> </span><span class="c1"># your azure api key</span>
<a id="__codelineno-3-3" name="__codelineno-3-3" href="#__codelineno-3-3"></a><span class="n">api_type</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;azure&quot;</span>
<a id="__codelineno-3-4" name="__codelineno-3-4" href="#__codelineno-3-4"></a><span class="n">api_version</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s1">&#39;2023-05-15&#39;</span><span class="w"> </span><span class="c1"># Check Azure documentation for the current API version</span>
<a id="__codelineno-3-5" name="__codelineno-3-5" href="#__codelineno-3-5"></a><span class="n">api_base</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;&quot;</span><span class="w"> </span><span class="c1"># The base URL for your Azure OpenAI resource. e.g. &quot;https://&lt;your resource name&gt;.openai.azure.com&quot;</span>
<a id="__codelineno-3-6" name="__codelineno-3-6" href="#__codelineno-3-6"></a><span class="n">deployment_id</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;&quot;</span><span class="w"> </span><span class="c1"># The deployment name you chose when you deployed the engine</span>
</code></pre></div>
<p>and set in your configuration file:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-4-1" name="__codelineno-4-1" href="#__codelineno-4-1"></a><span class="k">[config]</span>
<a id="__codelineno-4-2" name="__codelineno-4-2" href="#__codelineno-4-2"></a><span class="n">model</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="w"> </span><span class="c1"># the OpenAI model you&#39;ve deployed on Azure (e.g. gpt-4o)</span>
<a id="__codelineno-4-3" name="__codelineno-4-3" href="#__codelineno-4-3"></a><span class="n">fallback_models</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;...&quot;</span><span class="p">]</span>
</code></pre></div>
<p>To use Azure AD (Entra id) based authentication set in your <code>.secrets.toml</code> (working from CLI), or in the GitHub <code>Settings &gt; Secrets and variables</code> (working from GitHub App or GitHub Action):</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-5-1" name="__codelineno-5-1" href="#__codelineno-5-1"></a><span class="k">[azure_ad]</span>
<a id="__codelineno-5-2" name="__codelineno-5-2" href="#__codelineno-5-2"></a><span class="n">client_id</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;&quot;</span><span class="w"> </span><span class="c1"># Your Azure AD application client ID</span>
<a id="__codelineno-5-3" name="__codelineno-5-3" href="#__codelineno-5-3"></a><span class="n">client_secret</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;&quot;</span><span class="w"> </span><span class="c1"># Your Azure AD application client secret</span>
<a id="__codelineno-5-4" name="__codelineno-5-4" href="#__codelineno-5-4"></a><span class="n">tenant_id</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;&quot;</span><span class="w"> </span><span class="c1"># Your Azure AD tenant ID</span>
<a id="__codelineno-5-5" name="__codelineno-5-5" href="#__codelineno-5-5"></a><span class="n">api_base</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;&quot;</span><span class="w"> </span><span class="c1"># Your Azure OpenAI service base URL (e.g., https://openai.xyz.com/)</span>
</code></pre></div>
<p>Passing custom headers to the underlying LLM Model API can be done by setting extra_headers parameter to litellm.</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-6-1" name="__codelineno-6-1" href="#__codelineno-6-1"></a><span class="k">[litellm]</span>
<a id="__codelineno-6-2" name="__codelineno-6-2" href="#__codelineno-6-2"></a><span class="n">extra_headers</span><span class="o">=</span><span class="s1">&#39;{&quot;projectId&quot;: &quot;&lt;authorized projectId &gt;&quot;, ...}&#39;</span><span class="err">)</span><span class="w"> </span><span class="c1">#The value of this setting should be a JSON string representing the desired headers, a ValueError is thrown otherwise.</span>
</code></pre></div>
<p>This enables users to pass authorization tokens or API keys, when routing requests through an API management gateway.</p>
<h3 id="ollama">Ollama<a class="headerlink" href="#ollama" title="Permanent link">&para;</a></h3>
<p>You can run models locally through either <a href="https://docs.litellm.ai/docs/providers/vllm">VLLM</a> or <a href="https://docs.litellm.ai/docs/providers/ollama">Ollama</a></p>
<p>E.g. to use a new model locally via Ollama, set in <code>.secrets.toml</code> or in a configuration file:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-7-1" name="__codelineno-7-1" href="#__codelineno-7-1"></a><span class="k">[config]</span>
<a id="__codelineno-7-2" name="__codelineno-7-2" href="#__codelineno-7-2"></a><span class="n">model</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;ollama/qwen2.5-coder:32b&quot;</span>
<a id="__codelineno-7-3" name="__codelineno-7-3" href="#__codelineno-7-3"></a><span class="n">fallback_models</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;ollama/qwen2.5-coder:32b&quot;</span><span class="p">]</span>
<a id="__codelineno-7-4" name="__codelineno-7-4" href="#__codelineno-7-4"></a><span class="n">custom_model_max_tokens</span><span class="o">=</span><span class="mi">128000</span><span class="w"> </span><span class="c1"># set the maximal input tokens for the model</span>
<a id="__codelineno-7-5" name="__codelineno-7-5" href="#__codelineno-7-5"></a><span class="n">duplicate_examples</span><span class="o">=</span><span class="kc">true</span><span class="w"> </span><span class="c1"># will duplicate the examples in the prompt, to help the model to generate structured output</span>
<a id="__codelineno-7-6" name="__codelineno-7-6" href="#__codelineno-7-6"></a>
<a id="__codelineno-7-7" name="__codelineno-7-7" href="#__codelineno-7-7"></a><span class="k">[ollama]</span>
<a id="__codelineno-7-8" name="__codelineno-7-8" href="#__codelineno-7-8"></a><span class="n">api_base</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;http://localhost:11434&quot;</span><span class="w"> </span><span class="c1"># or whatever port you&#39;re running Ollama on</span>
</code></pre></div>
<p>By default, Ollama uses a context window size of 2048 tokens. In most cases this is not enough to cover pr-agent prompt and pull-request diff. Context window size can be overridden with the <code>OLLAMA_CONTEXT_LENGTH</code> environment variable. For example, to set the default context length to 8K, use: <code>OLLAMA_CONTEXT_LENGTH=8192 ollama serve</code>. More information you can find on the <a href="https://github.com/ollama/ollama/blob/main/docs/faq.md#how-can-i-specify-the-context-window-size">official ollama faq</a>.</p>
<p>Please note that the <code>custom_model_max_tokens</code> setting should be configured in accordance with the <code>OLLAMA_CONTEXT_LENGTH</code>. Failure to do so may result in unexpected model output.</p>
<div class="admonition note">
<p class="admonition-title">Local models vs commercial models</p>
<p>Qodo Merge is compatible with almost any AI model, but analyzing complex code repositories and pull requests requires a model specifically optimized for code analysis.</p>
<p>Commercial models such as GPT-4, Claude Sonnet, and Gemini have demonstrated robust capabilities in generating structured output for code analysis tasks with large input. In contrast, most open-source models currently available (as of January 2025) face challenges with these complex tasks.</p>
<p>Based on our testing, local open-source models are suitable for experimentation and learning purposes (mainly for the <code>ask</code> command), but they are not suitable for production-level code analysis tasks.</p>
<p>Hence, for production workflows and real-world usage, we recommend using commercial models.</p>
</div>
<h3 id="hugging-face">Hugging Face<a class="headerlink" href="#hugging-face" title="Permanent link">&para;</a></h3>
<p>To use a new model with Hugging Face Inference Endpoints, for example, set:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-8-1" name="__codelineno-8-1" href="#__codelineno-8-1"></a><span class="k">[config]</span><span class="w"> </span><span class="c1"># in configuration.toml</span>
<a id="__codelineno-8-2" name="__codelineno-8-2" href="#__codelineno-8-2"></a><span class="n">model</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;huggingface/meta-llama/Llama-2-7b-chat-hf&quot;</span>
<a id="__codelineno-8-3" name="__codelineno-8-3" href="#__codelineno-8-3"></a><span class="n">fallback_models</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;huggingface/meta-llama/Llama-2-7b-chat-hf&quot;</span><span class="p">]</span>
<a id="__codelineno-8-4" name="__codelineno-8-4" href="#__codelineno-8-4"></a><span class="n">custom_model_max_tokens</span><span class="o">=</span><span class="err">... # set the maximal input tokens for the model</span>
<a id="__codelineno-8-5" name="__codelineno-8-5" href="#__codelineno-8-5"></a>
<a id="__codelineno-8-6" name="__codelineno-8-6" href="#__codelineno-8-6"></a><span class="k">[huggingface]</span><span class="w"> </span><span class="c1"># in .secrets.toml</span>
<a id="__codelineno-8-7" name="__codelineno-8-7" href="#__codelineno-8-7"></a><span class="n">key</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="err">... # your Hugging Face api key</span>
<a id="__codelineno-8-8" name="__codelineno-8-8" href="#__codelineno-8-8"></a><span class="n">api_base</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="err">... # the base url for your Hugging Face </span><span class="mf">inf</span><span class="n">erence</span><span class="w"> </span><span class="n">endpoint</span>
</code></pre></div>
<p>(you can obtain a Llama2 key from <a href="https://replicate.com/replicate/llama-2-70b-chat/api">here</a>)</p>
<h3 id="replicate">Replicate<a class="headerlink" href="#replicate" title="Permanent link">&para;</a></h3>
<p>To use Llama2 model with Replicate, for example, set:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-9-1" name="__codelineno-9-1" href="#__codelineno-9-1"></a><span class="k">[config]</span><span class="w"> </span><span class="c1"># in configuration.toml</span>
<a id="__codelineno-9-2" name="__codelineno-9-2" href="#__codelineno-9-2"></a><span class="n">model</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;replicate/llama-2-70b-chat:2c1608e18606fad2812020dc541930f2d0495ce32eee50074220b87300bc16e1&quot;</span>
<a id="__codelineno-9-3" name="__codelineno-9-3" href="#__codelineno-9-3"></a><span class="n">fallback_models</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;replicate/llama-2-70b-chat:2c1608e18606fad2812020dc541930f2d0495ce32eee50074220b87300bc16e1&quot;</span><span class="p">]</span>
<a id="__codelineno-9-4" name="__codelineno-9-4" href="#__codelineno-9-4"></a><span class="k">[replicate]</span><span class="w"> </span><span class="c1"># in .secrets.toml</span>
<a id="__codelineno-9-5" name="__codelineno-9-5" href="#__codelineno-9-5"></a><span class="n">key</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="err">...</span>
</code></pre></div>
<p>(you can obtain a Llama2 key from <a href="https://replicate.com/replicate/llama-2-70b-chat/api">here</a>)</p>
<p>Also, review the <a href="https://github.com/Codium-ai/pr-agent/blob/main/pr_agent/algo/ai_handler.py">AiHandler</a> file for instructions on how to set keys for other models.</p>
<h3 id="groq">Groq<a class="headerlink" href="#groq" title="Permanent link">&para;</a></h3>
<p>To use Llama3 model with Groq, for example, set:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-10-1" name="__codelineno-10-1" href="#__codelineno-10-1"></a><span class="k">[config]</span><span class="w"> </span><span class="c1"># in configuration.toml</span>
<a id="__codelineno-10-2" name="__codelineno-10-2" href="#__codelineno-10-2"></a><span class="n">model</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;llama3-70b-8192&quot;</span>
<a id="__codelineno-10-3" name="__codelineno-10-3" href="#__codelineno-10-3"></a><span class="n">fallback_models</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">[</span><span class="s2">&quot;groq/llama3-70b-8192&quot;</span><span class="p">]</span>
<a id="__codelineno-10-4" name="__codelineno-10-4" href="#__codelineno-10-4"></a><span class="k">[groq]</span><span class="w"> </span><span class="c1"># in .secrets.toml</span>
<a id="__codelineno-10-5" name="__codelineno-10-5" href="#__codelineno-10-5"></a><span class="n">key</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="err">... # your Groq api key</span>
</code></pre></div>
<p>(you can obtain a Groq key from <a href="https://console.groq.com/keys">here</a>)</p>
<h3 id="xai">xAI<a class="headerlink" href="#xai" title="Permanent link">&para;</a></h3>
<p>To use xAI's models with PR-Agent, set:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-11-1" name="__codelineno-11-1" href="#__codelineno-11-1"></a><span class="k">[config]</span><span class="w"> </span><span class="c1"># in configuration.toml</span>
<a id="__codelineno-11-2" name="__codelineno-11-2" href="#__codelineno-11-2"></a><span class="n">model</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;xai/grok-2-latest&quot;</span>
<a id="__codelineno-11-3" name="__codelineno-11-3" href="#__codelineno-11-3"></a><span class="n">fallback_models</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">[</span><span class="s2">&quot;xai/grok-2-latest&quot;</span><span class="p">]</span><span class="w"> </span><span class="c1"># or any other model as fallback</span>
<a id="__codelineno-11-4" name="__codelineno-11-4" href="#__codelineno-11-4"></a>
<a id="__codelineno-11-5" name="__codelineno-11-5" href="#__codelineno-11-5"></a><span class="k">[xai]</span><span class="w"> </span><span class="c1"># in .secrets.toml</span>
<a id="__codelineno-11-6" name="__codelineno-11-6" href="#__codelineno-11-6"></a><span class="n">key</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;...&quot;</span><span class="w"> </span><span class="c1"># your xAI API key</span>
</code></pre></div>
<p>You can obtain an xAI API key from <a href="https://console.x.ai/">xAI's console</a> by creating an account and navigating to the developer settings page.</p>
<h3 id="vertex-ai">Vertex AI<a class="headerlink" href="#vertex-ai" title="Permanent link">&para;</a></h3>
<p>To use Google's Vertex AI platform and its associated models (chat-bison/codechat-bison) set:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-12-1" name="__codelineno-12-1" href="#__codelineno-12-1"></a><span class="k">[config]</span><span class="w"> </span><span class="c1"># in configuration.toml</span>
<a id="__codelineno-12-2" name="__codelineno-12-2" href="#__codelineno-12-2"></a><span class="n">model</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;vertex_ai/codechat-bison&quot;</span>
<a id="__codelineno-12-3" name="__codelineno-12-3" href="#__codelineno-12-3"></a><span class="n">fallback_models</span><span class="o">=</span><span class="s2">&quot;vertex_ai/codechat-bison&quot;</span>
<a id="__codelineno-12-4" name="__codelineno-12-4" href="#__codelineno-12-4"></a>
<a id="__codelineno-12-5" name="__codelineno-12-5" href="#__codelineno-12-5"></a><span class="k">[vertexai]</span><span class="w"> </span><span class="c1"># in .secrets.toml</span>
<a id="__codelineno-12-6" name="__codelineno-12-6" href="#__codelineno-12-6"></a><span class="n">vertex_project</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;my-google-cloud-project&quot;</span>
<a id="__codelineno-12-7" name="__codelineno-12-7" href="#__codelineno-12-7"></a><span class="n">vertex_location</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;&quot;</span>
</code></pre></div>
<p>Your <a href="https://cloud.google.com/docs/authentication/application-default-credentials">application default credentials</a> will be used for authentication so there is no need to set explicit credentials in most environments.</p>
<p>If you do want to set explicit credentials, then you can use the <code>GOOGLE_APPLICATION_CREDENTIALS</code> environment variable set to a path to a json credentials file.</p>
<h3 id="google-ai-studio">Google AI Studio<a class="headerlink" href="#google-ai-studio" title="Permanent link">&para;</a></h3>
<p>To use <a href="https://aistudio.google.com/">Google AI Studio</a> models, set the relevant models in the configuration section of the configuration file:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-13-1" name="__codelineno-13-1" href="#__codelineno-13-1"></a><span class="k">[config]</span><span class="w"> </span><span class="c1"># in configuration.toml</span>
<a id="__codelineno-13-2" name="__codelineno-13-2" href="#__codelineno-13-2"></a><span class="n">model</span><span class="o">=</span><span class="s2">&quot;gemini/gemini-1.5-flash&quot;</span>
<a id="__codelineno-13-3" name="__codelineno-13-3" href="#__codelineno-13-3"></a><span class="n">fallback_models</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;gemini/gemini-1.5-flash&quot;</span><span class="p">]</span>
<a id="__codelineno-13-4" name="__codelineno-13-4" href="#__codelineno-13-4"></a>
<a id="__codelineno-13-5" name="__codelineno-13-5" href="#__codelineno-13-5"></a><span class="k">[google_ai_studio]</span><span class="w"> </span><span class="c1"># in .secrets.toml</span>
<a id="__codelineno-13-6" name="__codelineno-13-6" href="#__codelineno-13-6"></a><span class="n">gemini_api_key</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;...&quot;</span>
</code></pre></div>
<p>If you don't want to set the API key in the .secrets.toml file, you can set the <code>GOOGLE_AI_STUDIO.GEMINI_API_KEY</code> environment variable.</p>
<h3 id="anthropic">Anthropic<a class="headerlink" href="#anthropic" title="Permanent link">&para;</a></h3>
<p>To use Anthropic models, set the relevant models in the configuration section of the configuration file:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-14-1" name="__codelineno-14-1" href="#__codelineno-14-1"></a><span class="k">[config]</span>
<a id="__codelineno-14-2" name="__codelineno-14-2" href="#__codelineno-14-2"></a><span class="n">model</span><span class="o">=</span><span class="s2">&quot;anthropic/claude-3-opus-20240229&quot;</span>
<a id="__codelineno-14-3" name="__codelineno-14-3" href="#__codelineno-14-3"></a><span class="n">fallback_models</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;anthropic/claude-3-opus-20240229&quot;</span><span class="p">]</span>
</code></pre></div>
<p>And also set the api key in the .secrets.toml file:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-15-1" name="__codelineno-15-1" href="#__codelineno-15-1"></a><span class="k">[anthropic]</span>
<a id="__codelineno-15-2" name="__codelineno-15-2" href="#__codelineno-15-2"></a><span class="n">KEY</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;...&quot;</span>
</code></pre></div>
<p>See <a href="https://docs.litellm.ai/docs/providers/anthropic#usage">litellm</a> documentation for more information about the environment variables required for Anthropic.</p>
<h3 id="amazon-bedrock">Amazon Bedrock<a class="headerlink" href="#amazon-bedrock" title="Permanent link">&para;</a></h3>
<p>To use Amazon Bedrock and its foundational models, add the below configuration:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-16-1" name="__codelineno-16-1" href="#__codelineno-16-1"></a><span class="k">[config]</span><span class="w"> </span><span class="c1"># in configuration.toml</span>
<a id="__codelineno-16-2" name="__codelineno-16-2" href="#__codelineno-16-2"></a><span class="n">model</span><span class="o">=</span><span class="s2">&quot;bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0&quot;</span>
<a id="__codelineno-16-3" name="__codelineno-16-3" href="#__codelineno-16-3"></a><span class="n">fallback_models</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0&quot;</span><span class="p">]</span>
<a id="__codelineno-16-4" name="__codelineno-16-4" href="#__codelineno-16-4"></a>
<a id="__codelineno-16-5" name="__codelineno-16-5" href="#__codelineno-16-5"></a><span class="k">[aws]</span>
<a id="__codelineno-16-6" name="__codelineno-16-6" href="#__codelineno-16-6"></a><span class="n">AWS_ACCESS_KEY_ID</span><span class="o">=</span><span class="s2">&quot;...&quot;</span>
<a id="__codelineno-16-7" name="__codelineno-16-7" href="#__codelineno-16-7"></a><span class="n">AWS_SECRET_ACCESS_KEY</span><span class="o">=</span><span class="s2">&quot;...&quot;</span>
<a id="__codelineno-16-8" name="__codelineno-16-8" href="#__codelineno-16-8"></a><span class="n">AWS_REGION_NAME</span><span class="o">=</span><span class="s2">&quot;...&quot;</span>
</code></pre></div>
<p>You can also use the new Meta Llama 4 models available on Amazon Bedrock:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-17-1" name="__codelineno-17-1" href="#__codelineno-17-1"></a><span class="k">[config]</span><span class="w"> </span><span class="c1"># in configuration.toml</span>
<a id="__codelineno-17-2" name="__codelineno-17-2" href="#__codelineno-17-2"></a><span class="n">model</span><span class="o">=</span><span class="s2">&quot;bedrock/us.meta.llama4-scout-17b-instruct-v1:0&quot;</span>
<a id="__codelineno-17-3" name="__codelineno-17-3" href="#__codelineno-17-3"></a><span class="n">fallback_models</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;bedrock/us.meta.llama4-maverick-17b-instruct-v1:0&quot;</span><span class="p">]</span>
</code></pre></div>
<p>See <a href="https://docs.litellm.ai/docs/providers/bedrock#usage">litellm</a> documentation for more information about the environment variables required for Amazon Bedrock.</p>
<h3 id="deepseek">DeepSeek<a class="headerlink" href="#deepseek" title="Permanent link">&para;</a></h3>
<p>To use deepseek-chat model with DeepSeek, for example, set:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-18-1" name="__codelineno-18-1" href="#__codelineno-18-1"></a><span class="k">[config]</span><span class="w"> </span><span class="c1"># in configuration.toml</span>
<a id="__codelineno-18-2" name="__codelineno-18-2" href="#__codelineno-18-2"></a><span class="n">model</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;deepseek/deepseek-chat&quot;</span>
<a id="__codelineno-18-3" name="__codelineno-18-3" href="#__codelineno-18-3"></a><span class="n">fallback_models</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;deepseek/deepseek-chat&quot;</span><span class="p">]</span>
</code></pre></div>
<p>and fill up your key</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-19-1" name="__codelineno-19-1" href="#__codelineno-19-1"></a><span class="k">[deepseek]</span><span class="w"> </span><span class="c1"># in .secrets.toml</span>
<a id="__codelineno-19-2" name="__codelineno-19-2" href="#__codelineno-19-2"></a><span class="n">key</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="err">...</span>
</code></pre></div>
<p>(you can obtain a deepseek-chat key from <a href="https://platform.deepseek.com">here</a>)</p>
<h3 id="deepinfra">DeepInfra<a class="headerlink" href="#deepinfra" title="Permanent link">&para;</a></h3>
<p>To use DeepSeek model with DeepInfra, for example, set:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-20-1" name="__codelineno-20-1" href="#__codelineno-20-1"></a><span class="k">[config]</span><span class="w"> </span><span class="c1"># in configuration.toml</span>
<a id="__codelineno-20-2" name="__codelineno-20-2" href="#__codelineno-20-2"></a><span class="n">model</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;deepinfra/deepseek-ai/DeepSeek-R1-Distill-Llama-70B&quot;</span>
<a id="__codelineno-20-3" name="__codelineno-20-3" href="#__codelineno-20-3"></a><span class="n">fallback_models</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">[</span><span class="s2">&quot;deepinfra/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B&quot;</span><span class="p">]</span>
<a id="__codelineno-20-4" name="__codelineno-20-4" href="#__codelineno-20-4"></a><span class="k">[deepinfra]</span><span class="w"> </span><span class="c1"># in .secrets.toml</span>
<a id="__codelineno-20-5" name="__codelineno-20-5" href="#__codelineno-20-5"></a><span class="n">key</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="err">... # your DeepInfra api key</span>
</code></pre></div>
<p>(you can obtain a DeepInfra key from <a href="https://deepinfra.com/dash/api_keys">here</a>)</p>
<h3 id="mistral">Mistral<a class="headerlink" href="#mistral" title="Permanent link">&para;</a></h3>
<p>To use models like Mistral or Codestral with Mistral, for example, set:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-21-1" name="__codelineno-21-1" href="#__codelineno-21-1"></a><span class="k">[config]</span><span class="w"> </span><span class="c1"># in configuration.toml</span>
<a id="__codelineno-21-2" name="__codelineno-21-2" href="#__codelineno-21-2"></a><span class="n">model</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;mistral/mistral-small-latest&quot;</span>
<a id="__codelineno-21-3" name="__codelineno-21-3" href="#__codelineno-21-3"></a><span class="n">fallback_models</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">[</span><span class="s2">&quot;mistral/mistral-medium-latest&quot;</span><span class="p">]</span>
<a id="__codelineno-21-4" name="__codelineno-21-4" href="#__codelineno-21-4"></a><span class="k">[mistral]</span><span class="w"> </span><span class="c1"># in .secrets.toml</span>
<a id="__codelineno-21-5" name="__codelineno-21-5" href="#__codelineno-21-5"></a><span class="n">key</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;...&quot;</span><span class="w"> </span><span class="c1"># your Mistral api key</span>
</code></pre></div>
<p>(you can obtain a Mistral key from <a href="https://console.mistral.ai/api-keys">here</a>)</p>
<h3 id="codestral">Codestral<a class="headerlink" href="#codestral" title="Permanent link">&para;</a></h3>
<p>To use Codestral model with Codestral, for example, set:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-22-1" name="__codelineno-22-1" href="#__codelineno-22-1"></a><span class="k">[config]</span><span class="w"> </span><span class="c1"># in configuration.toml</span>
<a id="__codelineno-22-2" name="__codelineno-22-2" href="#__codelineno-22-2"></a><span class="n">model</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;codestral/codestral-latest&quot;</span>
<a id="__codelineno-22-3" name="__codelineno-22-3" href="#__codelineno-22-3"></a><span class="n">fallback_models</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">[</span><span class="s2">&quot;codestral/codestral-2405&quot;</span><span class="p">]</span>
<a id="__codelineno-22-4" name="__codelineno-22-4" href="#__codelineno-22-4"></a><span class="k">[codestral]</span><span class="w"> </span><span class="c1"># in .secrets.toml</span>
<a id="__codelineno-22-5" name="__codelineno-22-5" href="#__codelineno-22-5"></a><span class="n">key</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;...&quot;</span><span class="w"> </span><span class="c1"># your Codestral api key</span>
</code></pre></div>
<p>(you can obtain a Codestral key from <a href="https://console.mistral.ai/codestral">here</a>)</p>
<h3 id="openrouter">Openrouter<a class="headerlink" href="#openrouter" title="Permanent link">&para;</a></h3>
<p>To use model from Openrouter, for example, set:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-23-1" name="__codelineno-23-1" href="#__codelineno-23-1"></a><span class="k">[config]</span><span class="w"> </span><span class="c1"># in configuration.toml </span>
<a id="__codelineno-23-2" name="__codelineno-23-2" href="#__codelineno-23-2"></a><span class="n">model</span><span class="o">=</span><span class="s2">&quot;openrouter/anthropic/claude-3.7-sonnet&quot;</span>
<a id="__codelineno-23-3" name="__codelineno-23-3" href="#__codelineno-23-3"></a><span class="n">fallback_models</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;openrouter/deepseek/deepseek-chat&quot;</span><span class="p">]</span>
<a id="__codelineno-23-4" name="__codelineno-23-4" href="#__codelineno-23-4"></a><span class="n">custom_model_max_tokens</span><span class="o">=</span><span class="mi">20000</span>
<a id="__codelineno-23-5" name="__codelineno-23-5" href="#__codelineno-23-5"></a>
<a id="__codelineno-23-6" name="__codelineno-23-6" href="#__codelineno-23-6"></a><span class="k">[openrouter]</span><span class="w"> </span><span class="c1"># in .secrets.toml or passed an environment variable openrouter__key</span>
<a id="__codelineno-23-7" name="__codelineno-23-7" href="#__codelineno-23-7"></a><span class="n">key</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;...&quot;</span><span class="w"> </span><span class="c1"># your openrouter api key</span>
</code></pre></div>
<p>(you can obtain an Openrouter API key from <a href="https://openrouter.ai/settings/keys">here</a>)</p>
<h3 id="custom-models">Custom models<a class="headerlink" href="#custom-models" title="Permanent link">&para;</a></h3>
<p>If the relevant model doesn't appear <a href="https://github.com/Codium-ai/pr-agent/blob/main/pr_agent/algo/__init__.py">here</a>, you can still use it as a custom model:</p>
<ol>
<li>Set the model name in the configuration file:</li>
</ol>
<div class="highlight"><pre><span></span><code><a id="__codelineno-24-1" name="__codelineno-24-1" href="#__codelineno-24-1"></a><span class="k">[config]</span>
<a id="__codelineno-24-2" name="__codelineno-24-2" href="#__codelineno-24-2"></a><span class="n">model</span><span class="o">=</span><span class="s2">&quot;custom_model_name&quot;</span>
<a id="__codelineno-24-3" name="__codelineno-24-3" href="#__codelineno-24-3"></a><span class="n">fallback_models</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;custom_model_name&quot;</span><span class="p">]</span>
</code></pre></div>
<ol>
<li>Set the maximal tokens for the model:</li>
</ol>
<div class="highlight"><pre><span></span><code><a id="__codelineno-25-1" name="__codelineno-25-1" href="#__codelineno-25-1"></a><span class="k">[config]</span>
<a id="__codelineno-25-2" name="__codelineno-25-2" href="#__codelineno-25-2"></a><span class="n">custom_model_max_tokens</span><span class="o">=</span><span class="w"> </span><span class="err">...</span>
</code></pre></div>
<ol>
<li>
<p>Go to <a href="https://litellm.vercel.app/docs/proxy/quick_start#supported-llms">litellm documentation</a>, find the model you want to use, and set the relevant environment variables.</p>
</li>
<li>
<p>Most reasoning models do not support chat-style inputs (<code>system</code> and <code>user</code> messages) or temperature settings.
To bypass chat templates and temperature controls, set <code>config.custom_reasoning_model = true</code> in your configuration file.</p>
</li>
</ol>
<h2 id="dedicated-parameters">Dedicated parameters<a class="headerlink" href="#dedicated-parameters" title="Permanent link">&para;</a></h2>
<h3 id="openai-models">OpenAI models<a class="headerlink" href="#openai-models" title="Permanent link">&para;</a></h3>
<div class="highlight"><pre><span></span><code><a id="__codelineno-26-1" name="__codelineno-26-1" href="#__codelineno-26-1"></a><span class="k">[config]</span>
<a id="__codelineno-26-2" name="__codelineno-26-2" href="#__codelineno-26-2"></a><span class="n">reasoning_efffort</span><span class="o">=</span><span class="w"> </span><span class="err">= </span><span class="s2">&quot;medium&quot;</span><span class="w"> </span><span class="c1"># &quot;low&quot;, &quot;medium&quot;, &quot;high&quot;</span>
</code></pre></div>
<p>With the OpenAI models that support reasoning effort (eg: o4-mini), you can specify its reasoning effort via <code>config</code> section. The default value is <code>medium</code>. You can change it to <code>high</code> or <code>low</code> based on your usage.</p>
<h3 id="anthropic-models">Anthropic models<a class="headerlink" href="#anthropic-models" title="Permanent link">&para;</a></h3>
<div class="highlight"><pre><span></span><code><a id="__codelineno-27-1" name="__codelineno-27-1" href="#__codelineno-27-1"></a><span class="k">[config]</span>
<a id="__codelineno-27-2" name="__codelineno-27-2" href="#__codelineno-27-2"></a><span class="n">enable_claude_extended_thinking</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="kc">false</span><span class="w"> </span><span class="c1"># Set to true to enable extended thinking feature</span>
<a id="__codelineno-27-3" name="__codelineno-27-3" href="#__codelineno-27-3"></a><span class="n">extended_thinking_budget_tokens</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">2048</span>
<a id="__codelineno-27-4" name="__codelineno-27-4" href="#__codelineno-27-4"></a><span class="n">extended_thinking_max_output_tokens</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">4096</span>
</code></pre></div>
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