v30.0.0 (#114)

truemagic-coder · web-flow · commit c560a8992305 · 2025-05-31T10:29:52.000-07:00
* v30

* fix ver
diff --git a/README.md b/README.md
@@ -18,7 +18,6 @@ Build your AI agents in three lines of code!
 * Three lines of code setup
 * Simple Agent Definition
 * Fast Responses
-* Multi-Vendor Support
 * Solana Integration
 * Multi-Agent Swarm
 * Multi-Modal (Images & Audio & Text)
@@ -44,7 +43,6 @@ Build your AI agents in three lines of code!
 * Easy three lines of code setup
 * Simple agent definition using JSON
 * Fast AI responses
-* Multi-vendor support including OpenAI, Grok, and Gemini AI services
 * Solana Integration
 * MCP tool usage with first-class support for [Zapier](https://zapier.com/mcp)
 * Integrated observability and tracing via [Pydantic Logfire](https://pydantic.dev/logfire)
@@ -79,25 +77,10 @@ Build your AI agents in three lines of code!
 ### AI Models Used
 
 **OpenAI**
-* [gpt-4.1](https://platform.openai.com/docs/models/gpt-4.1) (agent - can be overridden)
-* [gpt-4.1-nano](https://platform.openai.com/docs/models/gpt-4.1-nano) (router - can be overridden)
+* [gpt-4.1-nano](https://platform.openai.com/docs/models/gpt-4.1-nano) (agent & router)
 * [text-embedding-3-large](https://platform.openai.com/docs/models/text-embedding-3-large) (embedding)
 * [tts-1](https://platform.openai.com/docs/models/tts-1) (audio TTS)
 * [gpt-4o-mini-transcribe](https://platform.openai.com/docs/models/gpt-4o-mini-transcribe) (audio transcription)
-* [gpt-image-1](https://platform.openai.com/docs/models/gpt-image-1) (image generation - can be overridden)
-* [gpt-4o-mini-search-preview](https://platform.openai.com/docs/models/gpt-4o-mini-search-preview) (Internet search)
-
-**Grok**
-* [grok-3-fast](https://x.ai/api#pricing) (agent - optional)
-* [grok-3-mini-fast](https://x.ai/api#pricing) (router - optional)
-* [grok-2-image](https://x.ai/api#pricing) (image generation - optional)
-
-**Gemini**
-* [gemini-2.5-flash-preview-04-17](https://ai.google.dev/gemini-api/docs/models#gemini-2.5-flash-preview) (agent & router - optional)
-* [imagen-3.0-generate-002](https://ai.google.dev/gemini-api/docs/models#imagen-3) (image generation - optional)
-
-**Ollama**
-* [gemma:4b-it-qat](https://ollama.com/library/gemma3) - (agent & router - optional)
 
 ## Installation
 
@@ -432,36 +415,6 @@ config = {
 }
 ```
 
-### Grok
-
-```python
-config = {
-    "grok": {
-        "api_key": "your-grok-api-key",
-    },
-}
-```
-
-### Gemini
-
-```python
-config = {
-    "gemini": {
-        "api_key": "your-gemini-api-key",
-    },
-}
-```
-
-### Ollama
-
-```python
-config = {
-    "ollama": {
-        "api_key": "use-this-key-1010"
-    },
-}
-```
-
 ### Knowledge Base
 
 The Knowledge Base (KB) is meant to store text values and/or PDFs (extracts text) - can handle very large PDFs.
diff --git a/docs/index.rst b/docs/index.rst
@@ -366,31 +366,6 @@ Observability and Tracing - Optional
       },
    }
 
-
-Grok - Optional
-~~~~~~~~~~~~~~~~
-
-.. code-block:: python
-
-   config = {
-      "grok": {
-         "api_key": "your-grok-api-key",
-      },
-   }
-
-
-Gemini - Optional
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. code-block:: python
-
-   config = {
-      "gemini": {
-         "api_key": "your-gemini-api-key",
-      },
-   }
-
-
 Knowledge Base - Optional
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
diff --git a/pyproject.toml b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "solana-agent"
-version = "29.3.0"
+version = "30.0.0"
 description = "AI Agents for Solana"
 authors = ["Bevan Hunt <bevan@bevanhunt.com>"]
 license = "MIT"
diff --git a/solana_agent/adapters/openai_adapter.py b/solana_agent/adapters/openai_adapter.py
@@ -33,8 +33,8 @@
 
 T = TypeVar("T", bound=BaseModel)
 
-DEFAULT_CHAT_MODEL = "gpt-4.1"
-DEFAULT_VISION_MODEL = "gpt-4.1"
+DEFAULT_CHAT_MODEL = "gpt-4.1-nano"
+DEFAULT_VISION_MODEL = "gpt-4.1-nano"
 DEFAULT_PARSE_MODEL = "gpt-4.1-nano"
 DEFAULT_EMBEDDING_MODEL = "text-embedding-3-large"
 DEFAULT_EMBEDDING_DIMENSIONS = 3072
@@ -163,9 +163,8 @@ async def generate_text(
         api_key: Optional[str] = None,
         base_url: Optional[str] = None,
         model: Optional[str] = None,
-        functions: Optional[List[Dict[str, Any]]] = None,
-        function_call: Optional[Union[str, Dict[str, Any]]] = None,
-    ) -> Any:  # pragma: no cover
+        tools: Optional[List[Dict[str, Any]]] = None,
+    ) -> str:  # pragma: no cover
         """Generate text or function call from OpenAI models."""
         messages = []
         if system_prompt:
@@ -176,10 +175,8 @@ async def generate_text(
             "messages": messages,
             "model": model or self.text_model,
         }
-        if functions:
-            request_params["functions"] = functions
-        if function_call:
-            request_params["function_call"] = function_call
+        if tools:
+            request_params["tools"] = tools
 
         if api_key and base_url:
             client = AsyncOpenAI(api_key=api_key, base_url=base_url)
@@ -410,8 +407,7 @@ async def parse_structured_output(
         api_key: Optional[str] = None,
         base_url: Optional[str] = None,
         model: Optional[str] = None,
-        functions: Optional[List[Dict[str, Any]]] = None,
-        function_call: Optional[Union[str, Dict[str, Any]]] = None,
+        tools: Optional[List[Dict[str, Any]]] = None,
     ) -> T:  # pragma: no cover
         """Generate structured output using Pydantic model parsing with Instructor."""
 
@@ -439,10 +435,8 @@ async def parse_structured_output(
                 "response_model": model_class,
                 "max_retries": 2,  # Automatically retry on validation errors
             }
-            if functions:
-                create_args["tools"] = functions
-            if function_call:
-                create_args["function_call"] = function_call
+            if tools:
+                create_args["tools"] = tools
 
             response = await patched_client.chat.completions.create(**create_args)
             return response
diff --git a/solana_agent/factories/agent_factory.py b/solana_agent/factories/agent_factory.py
@@ -195,27 +195,6 @@ def create_from_config(config: Dict[str, Any]) -> QueryService:
                 model="gemini-2.5-flash-preview-05-20",
             )  # pragma: no cover
 
-        elif "grok" in config and "api_key" in config["grok"]:
-            # Create primary services
-            agent_service = AgentService(
-                llm_provider=llm_adapter,
-                business_mission=business_mission,
-                config=config,
-                api_key=config["grok"]["api_key"],
-                base_url="https://api.x.ai/v1",
-                model="grok-3-fast",
-                output_guardrails=output_guardrails,
-            )  # pragma: no cover
-
-            # Create routing service
-            routing_service = RoutingService(
-                llm_provider=llm_adapter,
-                agent_service=agent_service,
-                api_key=config["gemini"]["api_key"],
-                base_url="https://api.x.ai/v1",
-                model="grok-3-mini-fast",
-            )  # pragma: no cover
-
         elif "ollama" in config and "api_key" in config["ollama"]:
             # Create primary services
             agent_service = AgentService(
diff --git a/solana_agent/interfaces/providers/llm.py b/solana_agent/interfaces/providers/llm.py
@@ -28,8 +28,7 @@ async def generate_text(
         api_key: Optional[str] = None,
         base_url: Optional[str] = None,
         model: Optional[str] = None,
-        functions: Optional[List[Dict[str, Any]]] = None,
-        function_call: Optional[Union[str, Dict[str, Any]]] = None,
+        tools: Optional[List[Dict[str, Any]]] = None,
     ) -> Any:
         """Generate text from the language model."""
         pass
@@ -43,8 +42,7 @@ async def parse_structured_output(
         api_key: Optional[str] = None,
         base_url: Optional[str] = None,
         model: Optional[str] = None,
-        functions: Optional[List[Dict[str, Any]]] = None,
-        function_call: Optional[Union[str, Dict[str, Any]]] = None,
+        tools: Optional[List[Dict[str, Any]]] = None,
     ) -> T:
         """Generate structured output using a specific model class."""
         pass
@@ -106,6 +104,7 @@ async def generate_text_with_images(
         images: List[Union[str, bytes]],
         system_prompt: str = "",
         detail: Literal["low", "high", "auto"] = "auto",
+        tools: Optional[List[Dict[str, Any]]] = None,
     ) -> str:
         """Generate text from the language model using images."""
         pass
diff --git a/solana_agent/services/agent.py b/solana_agent/services/agent.py
@@ -267,11 +267,15 @@ async def generate_response(
         full_prompt += f"USER IDENTIFIER: {user_id}"
 
         # Get OpenAI function schemas for this agent's tools
-        functions = [
+        tools = [
             {
-                "name": tool["name"],
-                "description": tool.get("description", ""),
-                "parameters": tool.get("parameters", {}),
+                "type": "function",
+                "function": {
+                    "name": tool["name"],
+                    "description": tool.get("description", ""),
+                    "parameters": tool.get("parameters", {}),
+                    "strict": True,
+                },
             }
             for tool in self.get_agent_tools(agent_name)
         ]
@@ -286,24 +290,33 @@ async def generate_response(
                     api_key=self.api_key,
                     base_url=self.base_url,
                     model=self.model,
-                    functions=functions if functions else None,
-                    function_call="auto" if functions else None,
+                    tools=tools if tools else None,
                 )
                 yield model_instance
                 return
 
             # --- Streaming text/audio with tool support (as before) ---
             response_text = ""
             while True:
-                response = await self.llm_provider.generate_text(
-                    prompt=full_prompt,
-                    system_prompt=system_prompt,
-                    functions=functions if functions else None,
-                    function_call="auto" if functions else None,
-                    api_key=self.api_key,
-                    base_url=self.base_url,
-                    model=self.model,
-                )
+                if not images:
+                    response = await self.llm_provider.generate_text(
+                        prompt=full_prompt,
+                        system_prompt=system_prompt,
+                        api_key=self.api_key,
+                        base_url=self.base_url,
+                        model=self.model,
+                        tools=tools if tools else None,
+                    )
+                else:
+                    response = await self.llm_provider.generate_text_with_images(
+                        prompt=full_prompt,
+                        system_prompt=system_prompt,
+                        api_key=self.api_key,
+                        base_url=self.base_url,
+                        model=self.model,
+                        tools=tools if tools else None,
+                        images=images,
+                    )
                 if (
                     not response
                     or not hasattr(response, "choices")
@@ -316,25 +329,24 @@ async def generate_response(
                 choice = response.choices[0]
                 message = getattr(choice, "message", choice)
 
-                # If the model wants to call a function/tool
-                if hasattr(message, "function_call") and message.function_call:
-                    function_name = message.function_call.name
-                    arguments = json.loads(message.function_call.arguments)
-                    logger.info(
-                        f"Model requested tool '{function_name}' with args: {arguments}"
-                    )
-
-                    # Execute the tool (async)
-                    tool_result = await self.execute_tool(
-                        agent_name, function_name, arguments
-                    )
-
-                    # Add the tool result to the prompt for the next round
-                    full_prompt += (
-                        f"\n\nTool '{function_name}' was called with arguments {arguments}.\n"
-                        f"Result: {tool_result}\n"
-                    )
-                    continue  # Loop again, LLM will see tool result and may call another tool or finish
+                if hasattr(message, "tool_calls") and message.tool_calls:
+                    for tool_call in message.tool_calls:
+                        if tool_call.type == "function":
+                            function_name = tool_call.function.name
+                            arguments = json.loads(tool_call.function.arguments)
+                            logger.info(
+                                f"Model requested tool '{function_name}' with args: {arguments}"
+                            )
+                            # Execute the tool (async)
+                            tool_result = await self.execute_tool(
+                                agent_name, function_name, arguments
+                            )
+                            # Add the tool result to the prompt for the next round
+                            full_prompt += (
+                                f"\n\nTool '{function_name}' was called with arguments {arguments}.\n"
+                                f"Result: {tool_result}\n"
+                            )
+                    continue
 
                 # Otherwise, it's a normal message (final answer)
                 response_text = message.content
