Update docs

Author: Shashikant86

docs/guides/ollama-integration.md

@@ -300,6 +300,20 @@ export OLLAMA_BASE_URL=http://localhost:11435
 - You need maximum speed
 - You're okay with API costs
 
+### ⚠️ Limitations
+
+While Ollama works great for evaluations, there are some limitations:
+
+#### Evolution Support
+- **Limited support for `codeoptix evolve`**: The evolution feature uses GEPA optimization, which requires processing very long prompts. Ollama may fail with 404 errors or timeouts on complex evolution tasks.
+- **Recommendation**: Use cloud providers (OpenAI, Anthropic, Google) for full evolution capabilities. For basic evolution testing, try smaller models like `llama3.1:8b` with minimal iterations.
+
+#### Performance
+- Large models (e.g., `gpt-oss:120b`) require significant RAM and may be slow on consumer hardware.
+- Evolution tasks are computationally intensive and may not complete reliably with Ollama.
+
+For advanced features like evolution, consider cloud providers or contact us for tailored enterprise solutions.
+
 ---
 
 ## 📚 Next Steps

docs/index.md

@@ -54,14 +54,15 @@ When AI coding agents dazzle with impressive code but leave you wondering about
 - ⚡ **CI/CD Integration** - Automated quality gates and GitHub Actions support
 
 !!! tip "Ollama Support - No API Key Required!"
-    **CodeOptiX now supports Ollama** - use local models without API keys:
+    **CodeOptiX supports Ollama** for evaluations - use local models without API keys:
 
     - ✅ **Ollama integration** - Run evaluations with local models
     - ✅ **No API key needed** - Perfect for open-source users
     - ✅ **Privacy-friendly** - All processing happens locally
     - ✅ **Free to use** - No cloud costs
+    - ⚠️ **Limited evolution support** - Use cloud providers for `codeoptix evolve`
 
-    **See [Ollama Integration](guides/ollama-integration/) for setup instructions.**
+    **See [Ollama Integration](guides/ollama-integration/) for setup and limitations.**
 
     **Cloud providers** (OpenAI, Anthropic, Google) still require API keys. See [Installation](getting-started/installation/#setting-up-llm-providers) for setup.
 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "codeoptix"
-version = "0.1.1"
+version = "0.1.2"
 description = "Agentic Code Optimization & Deep Evaluation for Superior Coding Agent Experience. Built by Superagentic AI."
 readme = "README.md"
 requires-python = ">=3.12"

src/codeoptix/__init__.py

@@ -5,4 +5,4 @@
 Built by Superagentic AI - Advancing AI agent optimization and autonomous systems.
 """
 
-__version__ = "0.1.1"
+__version__ = "0.1.2"

src/codeoptix/adapters/basic.py

@@ -29,7 +29,7 @@ def __init__(self, config: dict[str, Any]):
 
         provider_name = llm_config.get("provider", "ollama")
         self.llm_client: LLMClient = create_llm_client(
-            LLMProvider(provider_name), llm_config.get("api_key")
+            LLMProvider(provider_name), llm_config.get("api_key"), llm_config.get("model")
         )
 
         # Set model

src/codeoptix/cli.py

@@ -147,7 +147,11 @@ def eval(agent, behaviors, output, config, llm_provider, llm_api_key, context, f
             sys.exit(1)
 
     # Normalize provider name and decide if we need an API key
-    llm_provider = (llm_provider or os.getenv("CODEOPTIX_LLM_PROVIDER", "openai")).lower()
+    llm_provider = (
+        eval_config.get("llm_provider")
+        or llm_provider
+        or os.getenv("CODEOPTIX_LLM_PROVIDER", "openai")
+    ).lower()
     is_ollama = llm_provider == "ollama"
 
     # Create adapter
@@ -222,7 +226,10 @@ def eval(agent, behaviors, output, config, llm_provider, llm_api_key, context, f
         click.echo("🧠 Using local Ollama provider.")
 
     try:
-        llm_client = create_llm_client(llm_provider_enum, api_key=api_key)
+        llm_config = adapter_config["llm_config"]
+        llm_client = create_llm_client(
+            LLMProvider(llm_config["provider"]), llm_config.get("api_key"), llm_config.get("model")
+        )
     except Exception as e:
         click.echo(f"❌ Error: Failed to create LLM client: {e}", err=True)
         if "api_key" in str(e).lower():
@@ -470,9 +477,11 @@ def evolve(input, reflection, output, iterations, config, llm_provider, llm_api_
         adapter = create_adapter(agent_type, adapter_config)
         click.echo(f"✅ Created adapter: {adapter.get_adapter_type()}")
 
-        # Create LLM client
-        llm_provider_enum = LLMProvider[llm_provider.upper()]
-        llm_client = create_llm_client(llm_provider_enum, api_key=llm_api_key)
+        # Create LLM client (use same config as adapter for consistency)
+        llm_config = adapter_config["llm_config"]
+        llm_client = create_llm_client(
+            LLMProvider(llm_config["provider"]), llm_config.get("api_key"), llm_config.get("model")
+        )
 
         # Create evaluation engine
         eval_engine_config = evolve_config.get("evaluation", {})

src/codeoptix/utils/llm.py

@@ -214,7 +214,7 @@ def get_available_models(self) -> list[str]:
 class OllamaClient(LLMClient):
     """Ollama local model client (http://localhost:11434)."""
 
-    def __init__(self, api_key: str | None = None):
+    def __init__(self, api_key: str | None = None, model: str = "llama3.1", **kwargs: Any):
         """Initialize Ollama client.
 
         api_key is unused but kept for interface compatibility.
@@ -250,7 +250,7 @@ def chat_completion(
         """Generate a chat completion using a local Ollama model."""
         payload = {
             "model": model,
-            "prompt": messages[0]["content"],  # Use first message content as prompt
+            "prompt": messages[0]["content"],
             "stream": False,
             "options": {
                 "temperature": temperature,
@@ -315,14 +315,16 @@ def get_available_models(self) -> list[str]:
         return models
 
 
-def create_llm_client(provider: LLMProvider, api_key: str | None = None) -> LLMClient:
+def create_llm_client(
+    provider: LLMProvider, api_key: str | None = None, model: str | None = None
+) -> LLMClient:
     """Factory function to create an LLM client."""
     if provider == LLMProvider.ANTHROPIC:
-        return AnthropicClient(api_key=api_key)
+        return AnthropicClient(api_key=api_key, model=model or "claude-3-5-sonnet-20241022")
     if provider == LLMProvider.OPENAI:
-        return OpenAIClient(api_key=api_key)
+        return OpenAIClient(api_key=api_key, model=model or "gpt-4o")
     if provider == LLMProvider.GOOGLE:
-        return GoogleClient(api_key=api_key)
+        return GoogleClient(api_key=api_key, model=model or "gemini-1.5-pro")
     if provider == LLMProvider.OLLAMA:
-        return OllamaClient(api_key=api_key)
+        return OllamaClient(model=model or "llama3.1")
     raise ValueError(f"Unsupported provider: {provider}")