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May 31, 2025
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v30.0.0 #114

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44fd980
v30
truemagic-coder May 31, 2025
5725536
fix ver
truemagic-coder May 31, 2025
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49 changes: 1 addition & 48 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -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)
Expand All @@ -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)
Expand Down Expand Up @@ -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

Expand Down Expand Up @@ -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.
Expand Down
25 changes: 0 additions & 25 deletions docs/index.rst
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Expand Up @@ -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
~~~~~~~~~~~~~~~~~~~~~~~~~~~

Expand Down
2 changes: 1 addition & 1 deletion pyproject.toml
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@@ -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"
Expand Down
24 changes: 9 additions & 15 deletions solana_agent/adapters/openai_adapter.py
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Expand Up @@ -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
Expand Down Expand Up @@ -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:
Expand All @@ -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)
Expand Down Expand Up @@ -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."""

Expand Down Expand Up @@ -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
Expand Down
21 changes: 0 additions & 21 deletions solana_agent/factories/agent_factory.py
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Expand Up @@ -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(
Expand Down
7 changes: 3 additions & 4 deletions solana_agent/interfaces/providers/llm.py
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Expand Up @@ -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
Expand All @@ -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
Expand Down Expand Up @@ -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
80 changes: 46 additions & 34 deletions solana_agent/services/agent.py
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Expand Up @@ -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)
]
Expand All @@ -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")
Expand All @@ -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
Expand Down
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