-
**Please, star the project on github (see top-right corner) if you appreciate our contribution to the community!**
+
Flo AI is a Python framework for building structured AI agents with support for multiple LLM providers, tool integration, and YAML-based configuration. Create production-ready AI agents with minimal code and maximum flexibility.
@@ -51,6 +49,44 @@ Flo AI is a Python framework that makes building production-ready AI agents and
- ๐ค **Team-Oriented**: Create and manage teams of AI agents working together
- ๐ **Langchain Compatible**: Works with all your favorite Langchain tools
+## ๐ Table of Contents
+
+- [๐ Quick Start](#-quick-start)
+ - [Installation](#installation)
+ - [Create Your First AI Agent in 30 seconds](#create-your-first-ai-agent-in-30-seconds)
+ - [Create a Tool-Using Agent](#create-a-tool-using-agent)
+ - [Create an Agent with Structured Output](#create-an-agent-with-structured-output)
+- [๐ YAML Configuration](#-yaml-configuration)
+- [๐ ๏ธ Tools](#๏ธ-tools)
+ - [๐ฏ @flo_tool Decorator](#-flo_tool-decorator)
+- [๐ง Reasoning Patterns](#-reasoning-patterns)
+- [๐ง LLM Providers](#-llm-providers)
+ - [OpenAI](#openai)
+ - [Anthropic Claude](#anthropic-claude)
+ - [Google Gemini](#google-gemini)
+ - [Ollama (Local)](#ollama-local)
+- [๐ Output Formatting](#-output-formatting)
+- [๐ Error Handling](#-error-handling)
+- [๐ Examples](#-examples)
+- [๐ Advanced Features](#-advanced-features)
+ - [Custom Tool Creation](#custom-tool-creation)
+ - [YAML Parser Integration](#yaml-parser-integration)
+- [๐ Agent Orchestration with Arium](#-agent-orchestration-with-arium)
+ - [๐ Key Features](#-key-features)
+ - [Quick Start: Simple Agent Chain](#quick-start-simple-agent-chain)
+ - [Advanced: Conditional Routing](#advanced-conditional-routing)
+ - [Agent + Tool Workflows](#agent--tool-workflows)
+ - [Workflow Visualization](#workflow-visualization)
+ - [Memory and Context Sharing](#memory-and-context-sharing)
+ - [๐ Use Cases for Arium](#-use-cases-for-arium)
+ - [Builder Pattern Benefits](#builder-pattern-benefits)
+- [๐ Documentation](#-documentation)
+- [๐ Why Flo AI?](#-why-flo-ai)
+- [๐ฏ Use Cases](#-use-cases)
+- [๐ค Contributing](#-contributing)
+- [๐ License](#-license)
+- [๐ Acknowledgments](#-acknowledgments)
+
## ๐ Quick Start
### Installation
@@ -65,12 +101,14 @@ poetry add flo-ai
```python
import asyncio
+from typing import Any
from flo_ai.builder.agent_builder import AgentBuilder
-from flo_ai.llm.openai_llm import OpenAI
+from flo_ai.llm import OpenAI
+from flo_ai.models.agent import Agent
-async def main():
+async def main() -> None:
# Create a simple conversational agent
- agent = (
+ agent: Agent = (
AgentBuilder()
.with_name('Math Tutor')
.with_prompt('You are a helpful math tutor.')
@@ -78,7 +116,7 @@ async def main():
.build()
)
- response = await agent.run('What is the formula for the area of a circle?')
+ response: Any = await agent.run('What is the formula for the area of a circle?')
print(f'Response: {response}')
asyncio.run(main())
@@ -88,10 +126,12 @@ asyncio.run(main())
```python
import asyncio
+from typing import Any, Dict, List, Union
from flo_ai.builder.agent_builder import AgentBuilder
from flo_ai.tool.base_tool import Tool
from flo_ai.models.base_agent import ReasoningPattern
-from flo_ai.llm.anthropic_llm import Anthropic
+from flo_ai.models.agent import Agent
+from flo_ai.llm import Anthropic
async def calculate(operation: str, x: float, y: float) -> float:
if operation == 'add':
@@ -101,7 +141,7 @@ async def calculate(operation: str, x: float, y: float) -> float:
raise ValueError(f'Unknown operation: {operation}')
# Define a calculator tool
-calculator_tool = Tool(
+calculator_tool: Tool = Tool(
name='calculate',
description='Perform basic calculations',
function=calculate,
@@ -116,7 +156,7 @@ calculator_tool = Tool(
)
# Create a tool-using agent with Claude
-agent = (
+agent: Agent = (
AgentBuilder()
.with_name('Calculator Assistant')
.with_prompt('You are a math assistant that can perform calculations.')
@@ -127,7 +167,7 @@ agent = (
.build()
)
-response = await agent.run('Calculate 5 plus 3')
+response: Any = await agent.run('Calculate 5 plus 3')
print(f'Response: {response}')
```
@@ -135,11 +175,13 @@ print(f'Response: {response}')
```python
import asyncio
+from typing import Any, Dict
from flo_ai.builder.agent_builder import AgentBuilder
-from flo_ai.llm.openai_llm import OpenAI
+from flo_ai.llm import OpenAI
+from flo_ai.models.agent import Agent
# Define output schema for structured responses
-math_schema = {
+math_schema: Dict[str, Any] = {
'type': 'object',
'properties': {
'solution': {'type': 'string', 'description': 'The step-by-step solution'},
@@ -149,7 +191,7 @@ math_schema = {
}
# Create an agent with structured output
-agent = (
+agent: Agent = (
AgentBuilder()
.with_name('Structured Math Solver')
.with_prompt('You are a math problem solver that provides structured solutions.')
@@ -158,7 +200,7 @@ agent = (
.build()
)
-response = await agent.run('Solve: 2x + 5 = 15')
+response: Any = await agent.run('Solve: 2x + 5 = 15')
print(f'Structured Response: {response}')
```
@@ -212,14 +254,19 @@ agent:
```
```python
+from typing import Any, List
from flo_ai.builder.agent_builder import AgentBuilder
+from flo_ai.models.agent import Agent
# Create agent from YAML
-builder = AgentBuilder.from_yaml(yaml_str=yaml_config)
-agent = builder.build()
+yaml_config: str = """...""" # Your YAML configuration string
+email_thread: List[str] = ["Email thread content..."]
+
+builder: AgentBuilder = AgentBuilder.from_yaml(yaml_str=yaml_config)
+agent: Agent = builder.build()
# Use the agent
-result = await agent.run(email_thread)
+result: Any = await agent.run(email_thread)
```
## ๐ ๏ธ Tools
@@ -227,13 +274,17 @@ result = await agent.run(email_thread)
Create custom tools easily with async support:
```python
+from typing import List
from flo_ai.tool.base_tool import Tool
+from flo_ai.builder.agent_builder import AgentBuilder
+from flo_ai.llm import OpenAI
+from flo_ai.models.agent import Agent
async def weather_lookup(city: str) -> str:
# Your weather API call here
return f"Weather in {city}: Sunny, 25ยฐC"
-weather_tool = Tool(
+weather_tool: Tool = Tool(
name='weather_lookup',
description='Get current weather for a city',
function=weather_lookup,
@@ -246,7 +297,7 @@ weather_tool = Tool(
)
# Add to your agent
-agent = (
+agent: Agent = (
AgentBuilder()
.with_name('Weather Assistant')
.with_llm(OpenAI(model='gpt-4o-mini'))
@@ -260,7 +311,11 @@ agent = (
The `@flo_tool` decorator automatically converts any Python function into a `Tool` object with minimal boilerplate:
```python
+from typing import Any, Dict, Union
from flo_ai.tool import flo_tool
+from flo_ai.builder.agent_builder import AgentBuilder
+from flo_ai.llm import OpenAI
+from flo_ai.models.agent import Agent
@flo_tool(
description="Perform mathematical calculations",
@@ -270,9 +325,9 @@ from flo_ai.tool import flo_tool
"y": "Second number"
}
)
-async def calculate(operation: str, x: float, y: float) -> float:
+async def calculate(operation: str, x: float, y: float) -> Union[float, str]:
"""Calculate mathematical operations between two numbers."""
- operations = {
+ operations: Dict[str, callable] = {
'add': lambda: x + y,
'subtract': lambda: x - y,
'multiply': lambda: x * y,
@@ -283,10 +338,10 @@ async def calculate(operation: str, x: float, y: float) -> float:
return operations[operation]()
# Function can be called normally
-result = await calculate("add", 5, 3) # Returns 8
+result: Union[float, str] = await calculate("add", 5, 3) # Returns 8
# Tool object is automatically available
-agent = (
+agent: Agent = (
AgentBuilder()
.with_name('Calculator Agent')
.with_llm(OpenAI(model='gpt-4o-mini'))
@@ -304,10 +359,13 @@ agent = (
**Simple Usage:**
```python
+from flo_ai.tool import flo_tool
+
@flo_tool()
async def convert_units(value: float, from_unit: str, to_unit: str) -> str:
"""Convert between different units (km/miles, kg/lbs, celsius/fahrenheit)."""
# Implementation here
+ result: float = 0.0 # Your conversion logic here
return f"{value} {from_unit} = {result} {to_unit}"
# Tool is automatically available as convert_units.tool
@@ -315,6 +373,9 @@ async def convert_units(value: float, from_unit: str, to_unit: str) -> str:
**With Custom Metadata:**
```python
+from typing import Optional
+from flo_ai.tool import flo_tool
+
@flo_tool(
name="weather_checker",
description="Get current weather information for a city",
@@ -323,7 +384,7 @@ async def convert_units(value: float, from_unit: str, to_unit: str) -> str:
"country": "The country (optional)",
}
)
-async def get_weather(city: str, country: str = None) -> str:
+async def get_weather(city: str, country: Optional[str] = None) -> str:
"""Get weather information for a specific city."""
return f"Weather in {city}: sunny"
```
@@ -340,8 +401,11 @@ Flo AI supports multiple reasoning patterns:
```python
from flo_ai.models.base_agent import ReasoningPattern
+from flo_ai.builder.agent_builder import AgentBuilder
+from flo_ai.llm import OpenAI
+from flo_ai.models.agent import Agent
-agent = (
+agent: Agent = (
AgentBuilder()
.with_name('Reasoning Agent')
.with_llm(OpenAI(model='gpt-4o'))
@@ -354,9 +418,9 @@ agent = (
### OpenAI
```python
-from flo_ai.llm.openai_llm import OpenAI
+from flo_ai.llm import OpenAI
-llm = OpenAI(
+llm: OpenAI = OpenAI(
model='gpt-4o',
temperature=0.7,
api_key='your-api-key' # or set OPENAI_API_KEY env var
@@ -365,20 +429,31 @@ llm = OpenAI(
### Anthropic Claude
```python
-from flo_ai.llm.anthropic_llm import Anthropic
+from flo_ai.llm import Anthropic
-llm = Anthropic(
+llm: Anthropic = Anthropic(
model='claude-3-5-sonnet-20240620',
temperature=0.7,
api_key='your-api-key' # or set ANTHROPIC_API_KEY env var
)
```
+### Google Gemini
+```python
+from flo_ai.llm import Gemini
+
+llm: Gemini = Gemini(
+ model='gemini-2.5-flash', # or gemini-2.5-pro
+ temperature=0.7,
+ api_key='your-api-key' # or set GOOGLE_API_KEY env var
+)
+```
+
### Ollama (Local)
```python
-from flo_ai.llm.ollama_llm import Ollama
+from flo_ai.llm import Ollama
-llm = Ollama(
+llm: Ollama = Ollama(
model='llama2',
base_url='http://localhost:11434'
)
@@ -390,13 +465,16 @@ Use Pydantic models or JSON schemas for structured outputs:
```python
from pydantic import BaseModel, Field
+from flo_ai.builder.agent_builder import AgentBuilder
+from flo_ai.llm import OpenAI
+from flo_ai.models.agent import Agent
class MathSolution(BaseModel):
solution: str = Field(description="Step-by-step solution")
answer: str = Field(description="Final answer")
confidence: float = Field(description="Confidence level (0-1)")
-agent = (
+agent: Agent = (
AgentBuilder()
.with_name('Math Solver')
.with_llm(OpenAI(model='gpt-4o'))
@@ -410,7 +488,11 @@ agent = (
Built-in retry mechanisms and error recovery:
```python
-agent = (
+from flo_ai.builder.agent_builder import AgentBuilder
+from flo_ai.llm import OpenAI
+from flo_ai.models.agent import Agent
+
+agent: Agent = (
AgentBuilder()
.with_name('Robust Agent')
.with_llm(OpenAI(model='gpt-4o'))
@@ -434,13 +516,14 @@ Check out the `examples/` directory for comprehensive examples:
### Custom Tool Creation
```python
+from typing import Dict, Any
from flo_ai.tool.base_tool import Tool
-async def custom_function(param1: str, param2: int) -> dict:
+async def custom_function(param1: str, param2: int) -> Dict[str, str]:
# Your async logic here
return {"result": f"Processed {param1} with {param2}"}
-custom_tool = Tool(
+custom_tool: Tool = Tool(
name='custom_function',
description='A custom async tool',
function=custom_function,
@@ -453,13 +536,18 @@ custom_tool = Tool(
### YAML Parser Integration
```python
+from typing import Dict, Any
from flo_ai.formatter.yaml_format_parser import FloYamlParser
+from flo_ai.builder.agent_builder import AgentBuilder
+from flo_ai.llm import OpenAI
+from flo_ai.models.agent import Agent
# Create parser from YAML definition
-parser = FloYamlParser.create(yaml_dict=yaml_config)
-output_schema = parser.get_format()
+yaml_config: Dict[str, Any] = {} # Your YAML configuration dict
+parser: FloYamlParser = FloYamlParser.create(yaml_dict=yaml_config)
+output_schema: Any = parser.get_format()
-agent = (
+agent: Agent = (
AgentBuilder()
.with_name('YAML Configured Agent')
.with_llm(OpenAI(model='gpt-4o'))
@@ -468,6 +556,312 @@ agent = (
)
```
+## ๐ Agent Orchestration with Arium
+
+Arium is Flo AI's powerful workflow orchestration engine that allows you to create complex multi-agent workflows with ease. Think of it as a conductor for your AI agents, coordinating their interactions and data flow.
+
+### ๐ Key Features
+
+- **๐ Multi-Agent Workflows**: Orchestrate multiple agents working together
+- **๐ฏ Flexible Routing**: Route between agents based on context and conditions
+- **๐ง Shared Memory**: Agents share conversation history and context
+- **๐ Visual Workflows**: Generate flow diagrams of your agent interactions
+- **โก Builder Pattern**: Fluent API for easy workflow construction
+- **๐ Reusable Workflows**: Build once, run multiple times with different inputs
+
+### Quick Start: Simple Agent Chain
+
+```python
+import asyncio
+from typing import Any, List
+from flo_ai.arium import AriumBuilder
+from flo_ai.models.agent import Agent
+from flo_ai.llm.openai_llm import OpenAI
+
+async def simple_chain() -> List[Any]:
+ llm: OpenAI = OpenAI(model='gpt-4o-mini')
+
+ # Create agents
+ analyst: Agent = Agent(
+ name='content_analyst',
+ system_prompt='Analyze the input and extract key insights.',
+ llm=llm
+ )
+
+ summarizer: Agent = Agent(
+ name='summarizer',
+ system_prompt='Create a concise summary based on the analysis.',
+ llm=llm
+ )
+
+ # Build and run workflow
+ result: List[Any] = await (
+ AriumBuilder()
+ .add_agents([analyst, summarizer])
+ .start_with(analyst)
+ .connect(analyst, summarizer) # analyst โ summarizer
+ .end_with(summarizer)
+ .build_and_run(["Analyze this complex business report..."])
+ )
+
+ return result
+
+asyncio.run(simple_chain())
+```
+
+### Advanced: Conditional Routing
+
+```python
+import asyncio
+from typing import Any, List
+from flo_ai.arium import AriumBuilder
+from flo_ai.models.agent import Agent
+from flo_ai.llm.openai_llm import OpenAI
+from flo_ai.arium.memory import BaseMemory
+
+async def conditional_workflow() -> List[Any]:
+ llm: OpenAI = OpenAI(model='gpt-4o-mini')
+
+ # Create specialized agents
+ classifier: Agent = Agent(
+ name='classifier',
+ system_prompt='Classify the input as either "technical" or "business".',
+ llm=llm
+ )
+
+ tech_specialist: Agent = Agent(
+ name='tech_specialist',
+ system_prompt='Provide technical analysis and solutions.',
+ llm=llm
+ )
+
+ business_specialist: Agent = Agent(
+ name='business_specialist',
+ system_prompt='Provide business analysis and recommendations.',
+ llm=llm
+ )
+
+ final_agent: Agent = Agent(
+ name='final_reviewer',
+ system_prompt='Provide final review and conclusions.',
+ llm=llm
+ )
+
+ # Define routing logic
+ def route_by_type(memory: BaseMemory) -> str:
+ """Route based on classification result"""
+ messages: List[Any] = memory.get()
+ last_message: str = str(messages[-1]) if messages else ""
+
+ if "technical" in last_message.lower():
+ return "tech_specialist"
+ else:
+ return "business_specialist"
+
+ # Build workflow with conditional routing
+ result: List[Any] = await (
+ AriumBuilder()
+ .add_agents([classifier, tech_specialist, business_specialist, final_agent])
+ .start_with(classifier)
+ .add_edge(classifier, [tech_specialist, business_specialist], route_by_type)
+ .connect(tech_specialist, final_agent)
+ .connect(business_specialist, final_agent)
+ .end_with(final_agent)
+ .build_and_run(["How can we optimize our database performance?"])
+ )
+
+ return result
+```
+
+### Agent + Tool Workflows
+
+```python
+import asyncio
+from typing import Any, List
+from flo_ai.tool import flo_tool
+from flo_ai.arium import AriumBuilder
+from flo_ai.models.agent import Agent
+from flo_ai.llm.openai_llm import OpenAI
+
+@flo_tool(description="Search for relevant information")
+async def search_tool(query: str) -> str:
+ # Your search implementation
+ return f"Search results for: {query}"
+
+@flo_tool(description="Perform calculations")
+async def calculator(expression: str) -> float:
+ # Your calculation implementation
+ return eval(expression) # Note: Use safely in production
+
+async def agent_tool_workflow() -> List[Any]:
+ llm: OpenAI = OpenAI(model='gpt-4o-mini')
+
+ research_agent: Agent = Agent(
+ name='researcher',
+ system_prompt='Research topics and gather information.',
+ llm=llm
+ )
+
+ analyst_agent: Agent = Agent(
+ name='analyst',
+ system_prompt='Analyze data and provide insights.',
+ llm=llm
+ )
+
+ # Mix agents and tools in workflow
+ result: List[Any] = await (
+ AriumBuilder()
+ .add_agent(research_agent)
+ .add_tools([search_tool.tool, calculator.tool])
+ .add_agent(analyst_agent)
+ .start_with(research_agent)
+ .connect(research_agent, search_tool.tool)
+ .connect(search_tool.tool, calculator.tool)
+ .connect(calculator.tool, analyst_agent)
+ .end_with(analyst_agent)
+ .build_and_run(["Research market trends for Q4 2024"])
+ )
+
+ return result
+```
+
+### Workflow Visualization
+
+```python
+from typing import Any, List, Callable, Optional
+from flo_ai.arium import AriumBuilder
+from flo_ai.arium.arium import Arium
+from flo_ai.models.agent import Agent
+from flo_ai.tool.base_tool import Tool
+
+# Assume these are defined elsewhere
+agent1: Agent = ... # Your agent definitions
+agent2: Agent = ...
+agent3: Agent = ...
+tool1: Tool = ... # Your tool definitions
+tool2: Tool = ...
+router_function: Callable = ... # Your router function
+
+# Build workflow and generate visual diagram
+arium: Arium = (
+ AriumBuilder()
+ .add_agents([agent1, agent2, agent3])
+ .add_tools([tool1, tool2])
+ .start_with(agent1)
+ .connect(agent1, tool1)
+ .add_edge(tool1, [agent2, agent3], router_function)
+ .end_with(agent2)
+ .end_with(agent3)
+ .visualize("my_workflow.png", "Customer Service Workflow") # Generates PNG
+ .build()
+)
+
+# Run the workflow
+result: List[Any] = await arium.run(["Customer complaint about billing"])
+```
+
+### Memory and Context Sharing
+
+All agents in an Arium workflow share the same memory, enabling them to build on each other's work:
+
+```python
+from typing import Any, List
+from flo_ai.arium import AriumBuilder
+from flo_ai.arium.memory import MessageMemory
+from flo_ai.arium.arium import Arium
+from flo_ai.models.agent import Agent
+
+# Assume these agents are defined elsewhere
+agent1: Agent = ...
+agent2: Agent = ...
+agent3: Agent = ...
+
+# Custom memory for persistent context
+custom_memory: MessageMemory = MessageMemory()
+
+result: List[Any] = await (
+ AriumBuilder()
+ .with_memory(custom_memory) # Shared across all agents
+ .add_agents([agent1, agent2, agent3])
+ .start_with(agent1)
+ .connect(agent1, agent2)
+ .connect(agent2, agent3)
+ .end_with(agent3)
+ .build_and_run(["Initial context and instructions"])
+)
+
+# Build the arium for reuse
+arium: Arium = (
+ AriumBuilder()
+ .with_memory(custom_memory)
+ .add_agents([agent1, agent2, agent3])
+ .start_with(agent1)
+ .connect(agent1, agent2)
+ .connect(agent2, agent3)
+ .end_with(agent3)
+ .build()
+)
+
+# Memory persists and can be reused
+result2: List[Any] = await arium.run(["Follow-up question based on previous context"])
+```
+
+### ๐ Use Cases for Arium
+
+- **๐ Content Pipeline**: Research โ Writing โ Editing โ Publishing
+- **๐ Analysis Workflows**: Data Collection โ Processing โ Analysis โ Reporting
+- **๐ฏ Decision Trees**: Classification โ Specialized Processing โ Final Decision
+- **๐ค Customer Service**: Intent Detection โ Specialist Routing โ Resolution
+- **๐งช Research Workflows**: Question Generation โ Investigation โ Synthesis โ Validation
+- **๐ Document Processing**: Extraction โ Validation โ Transformation โ Storage
+
+### Builder Pattern Benefits
+
+The AriumBuilder provides a fluent, intuitive API:
+
+```python
+from typing import Any, List
+from flo_ai.arium import AriumBuilder
+from flo_ai.arium.arium import Arium
+from flo_ai.models.agent import Agent
+from flo_ai.tool.base_tool import Tool
+
+# Assume these are defined elsewhere
+agent1: Agent = ...
+agent2: Agent = ...
+tool1: Tool = ...
+inputs: List[str] = ["Your input messages"]
+
+# All builder methods return self for chaining
+workflow: Arium = (
+ AriumBuilder()
+ .add_agent(agent1) # Add components
+ .add_tool(tool1)
+ .start_with(agent1) # Define flow
+ .connect(agent1, tool1)
+ .end_with(tool1)
+ .build() # Create Arium instance
+)
+
+# Or build and run in one step
+result: List[Any] = await (
+ AriumBuilder()
+ .add_agents([agent1, agent2])
+ .start_with(agent1)
+ .connect(agent1, agent2)
+ .end_with(agent2)
+ .build_and_run(inputs) # Build + run together
+)
+```
+
+**Validation Built-in**: The builder automatically validates your workflow:
+- โ
Ensures at least one agent/tool
+- โ
Requires start and end nodes
+- โ
Validates routing functions
+- โ
Checks for unreachable nodes
+
+> ๐ **For detailed Arium documentation and advanced patterns, see [flo_ai/flo_ai/arium/README.md](flo_ai/flo_ai/arium/README.md)**
+
## ๐ Documentation
Visit our [comprehensive documentation](https://flo-ai.rootflo.ai) for:
diff --git a/flo_ai/examples/arium_linear_usage.py b/flo_ai/examples/arium_linear_usage.py
new file mode 100644
index 00000000..b1fefabb
--- /dev/null
+++ b/flo_ai/examples/arium_linear_usage.py
@@ -0,0 +1,113 @@
+import asyncio
+from flo_ai.arium.builder import AriumBuilder
+from flo_ai.models.agent import Agent
+from flo_ai.llm.openai_llm import OpenAI
+
+
+async def simple_example():
+ """
+ Simple example: 2 agents connected directly together
+ Agent 1 (Greeter) -> Agent 2 (Responder)
+ """
+ llm = OpenAI(model='gpt-4o-mini', temperature=0.7)
+
+ # Agent 1: Greeter
+ greeter = Agent(
+ name='greeter',
+ system_prompt='You are a friendly greeter. Say hello and introduce the topic to the next agent.',
+ llm=llm,
+ )
+
+ # Agent 2: Responder
+ responder = Agent(
+ name='responder',
+ system_prompt='You are a helpful responder. Provide a thoughtful response to what the greeter shared.',
+ llm=llm,
+ )
+
+ # Connect agents directly: greeter -> responder
+ result = await (
+ AriumBuilder()
+ .add_agent(greeter)
+ .add_agent(responder)
+ .start_with(greeter)
+ .connect(greeter, responder) # Direct connection
+ .end_with(responder)
+ .build_and_run(["Hello, I'd like to learn about Python programming!"])
+ )
+
+ print('Simple Example Result:')
+ print(result)
+ return result
+
+
+async def main():
+ """
+ Example showing how to create 2 simple agents connected directly together
+ using the AriumBuilder.
+ """
+
+ # Create LLM instance
+ llm = OpenAI(model='gpt-4o-mini', temperature=0.7)
+
+ # Create first agent - Content Analyst
+ content_analyst = Agent(
+ name='content_analyst',
+ system_prompt="""You are a content analyst. When you receive input, analyze it and provide:
+ 1. A brief summary of the content
+ 2. The main topics covered
+ 3. Any insights or observations
+
+ Pass your analysis to the next agent for final processing.""",
+ llm=llm,
+ role='Content Analyst',
+ )
+
+ # Create second agent - Summary Generator
+ summary_generator = Agent(
+ name='summary_generator',
+ system_prompt="""You are a summary generator. You receive analysis from the content analyst.
+ Your job is to create a concise, well-structured final summary that includes:
+ 1. Key takeaways
+ 2. Actionable insights
+ 3. A clear conclusion
+
+ Make your response clear and professional.""",
+ llm=llm,
+ role='Summary Generator',
+ )
+
+ # Create Arium workflow using AriumBuilder
+ print('Building Arium workflow...')
+
+ result = await (
+ AriumBuilder()
+ .add_agents([content_analyst, summary_generator])
+ .start_with(content_analyst)
+ .connect(content_analyst, summary_generator) # Direct connection
+ .end_with(summary_generator)
+ .build_and_run(
+ [
+ 'Machine learning is revolutionizing various industries. '
+ 'From healthcare to finance, AI systems are being deployed '
+ 'to automate processes, improve decision-making, and enhance '
+ 'customer experiences. However, challenges remain around '
+ 'data privacy, algorithmic bias, and the need for skilled '
+ 'professionals to manage these systems effectively.'
+ ]
+ )
+ )
+
+ print('\n' + '=' * 50)
+ print('ARIUM WORKFLOW RESULT:')
+ print('=' * 50)
+ print(result)
+
+
+if __name__ == '__main__':
+ print('Running simple example first...')
+ asyncio.run(simple_example())
+
+ print('\n' + '=' * 80)
+ print('Now running detailed example...')
+ asyncio.run(main())
diff --git a/flo_ai/examples/example_graph_visualization.py b/flo_ai/examples/example_graph_visualization.py
new file mode 100644
index 00000000..8c42bb93
--- /dev/null
+++ b/flo_ai/examples/example_graph_visualization.py
@@ -0,0 +1,159 @@
+#!/usr/bin/env python3
+"""
+Example script demonstrating the graph visualization feature in BaseArium.
+This script creates a simple workflow with agents and tools, then generates a PNG visualization.
+"""
+
+from flo_ai.arium.base import BaseArium
+from flo_ai.models.agent import Agent
+from flo_ai.tool.flo_tool import flo_tool
+from flo_ai.llm.openai_llm import OpenAI
+from typing import Literal
+
+
+# Create a simple validation tool using the @flo_tool decorator
+@flo_tool(
+ name='validation_tool',
+ description='Validates input data and returns validation status',
+ parameter_descriptions={
+ 'data': 'The data to validate',
+ 'strict': 'Whether to use strict validation rules',
+ },
+)
+async def validate_data(data: str, strict: bool = False) -> str:
+ """Validate the input data according to specified rules."""
+ if not data:
+ return 'invalid: empty data'
+
+ if strict and len(data) < 10:
+ return 'invalid: data too short for strict validation'
+
+ return 'valid: data passed validation'
+
+
+# Create mock agents for demonstration
+def create_sample_agents():
+ """Create sample agents for the demonstration"""
+ # Note: This would normally require a valid OpenAI API key
+ # For demonstration purposes, we'll use a mock LLM
+ try:
+ llm = OpenAI(model='gpt-4')
+ except Exception:
+ # If OpenAI is not available, create a mock LLM
+ class MockLLM:
+ def __init__(self, model):
+ self.model = model
+
+ llm = MockLLM('gpt-4')
+
+ input_processor = Agent(
+ name='input_processor',
+ system_prompt='Process incoming requests',
+ llm=llm,
+ role='Input Processor',
+ )
+
+ analyzer = Agent(
+ name='analyzer',
+ system_prompt='Analyze processed data',
+ llm=llm,
+ role='Data Analyzer',
+ )
+
+ decision_maker = Agent(
+ name='decision_maker',
+ system_prompt='Make decisions based on analysis',
+ llm=llm,
+ role='Decision Maker',
+ )
+
+ output_formatter = Agent(
+ name='output_formatter',
+ system_prompt='Format final output',
+ llm=llm,
+ role='Output Formatter',
+ )
+
+ return input_processor, analyzer, decision_maker, output_formatter
+
+
+# Router function for demonstration
+def analysis_router(
+ analysis_result: str,
+) -> Literal['decision_maker', 'output_formatter']:
+ """Route based on analysis result"""
+ if 'complex' in analysis_result.lower():
+ return 'decision_maker'
+ else:
+ return 'output_formatter'
+
+
+def main():
+ """Create a sample workflow and generate visualization"""
+
+ # Create the BaseArium instance
+ arium = BaseArium()
+
+ # Create sample agents and tools
+ input_processor, analyzer, decision_maker, output_formatter = create_sample_agents()
+
+ # Get the tool from the decorated function
+ validation_tool = validate_data.tool
+
+ # Add nodes to the arium
+ arium.add_nodes(
+ [input_processor, validation_tool, analyzer, decision_maker, output_formatter]
+ )
+
+ # Set up the workflow
+ # Start with input processor
+ arium.start_at(input_processor)
+
+ # Input processor -> Validation tool
+ arium.add_edge('input_processor', ['validation_tool'])
+
+ # Validation tool -> Analyzer
+ arium.add_edge('validation_tool', ['analyzer'])
+
+ # Analyzer -> Decision maker OR Output formatter (with router)
+ arium.add_edge(
+ 'analyzer', ['decision_maker', 'output_formatter'], router=analysis_router
+ )
+
+ # Decision maker -> Output formatter
+ arium.add_edge('decision_maker', ['output_formatter'])
+
+ # Output formatter -> End
+ arium.add_end_to(output_formatter)
+
+ # Validate the graph
+ try:
+ arium.validate_graph()
+ print('โ
Graph validation successful!')
+ except ValueError as e:
+ print(f'โ Graph validation failed: {e}')
+ return
+
+ # Generate visualization
+ print('๐จ Generating graph visualization...')
+
+ # Generate with default settings
+ arium.visualize_graph('workflow_graph.png')
+
+ # Generate with custom settings
+ arium.visualize_graph(
+ output_path='custom_workflow_graph.png',
+ figsize=(14, 10),
+ node_size=4000,
+ font_size=12,
+ dpi=400,
+ )
+
+ print('โ
Graph visualization completed!')
+ print('๐ Check the following files:')
+ print(' - workflow_graph.png (default settings)')
+ print(' - custom_workflow_graph.png (custom settings)')
+
+
+if __name__ == '__main__':
+ main()
diff --git a/flo_ai/examples/multi_tool_example.py b/flo_ai/examples/multi_tool_example.py
index 41583f96..7be1464e 100644
--- a/flo_ai/examples/multi_tool_example.py
+++ b/flo_ai/examples/multi_tool_example.py
@@ -2,10 +2,8 @@
from flo_ai.builder.agent_builder import AgentBuilder
from flo_ai.tool.base_tool import Tool
from flo_ai.models.base_agent import ReasoningPattern
-from flo_ai.llm.openai_llm import OpenAI
-from flo_ai.llm.anthropic_llm import Anthropic
-from flo_ai.llm.base_llm import BaseLLM
+from flo_ai.llm import Anthropic, Gemini, OpenAI, BaseLLM
async def create_tools():
@@ -137,10 +135,14 @@ async def main():
openai_llm = OpenAI(model='gpt-4-turbo-preview', temperature=0.7)
await test_multi_tool_agent(openai_llm, 'OpenAI Multi-Tool Agent')
- # # Test with Claude
+ # Test with Claude
claude_llm = Anthropic(model='claude-3-5-sonnet-20240620', temperature=0.7)
await test_multi_tool_agent(claude_llm, 'Claude Multi-Tool Agent')
+ # Test with Gemini
+ gemini_llm = Gemini(model='gemini-2.5-flash', temperature=0.7)
+ await test_multi_tool_agent(gemini_llm, 'Gemini Multi-Tool Agent')
+
if __name__ == '__main__':
asyncio.run(main())
diff --git a/flo_ai/examples/yaml_agent_example.py b/flo_ai/examples/yaml_agent_example.py
index e725c21b..d50e4c45 100644
--- a/flo_ai/examples/yaml_agent_example.py
+++ b/flo_ai/examples/yaml_agent_example.py
@@ -3,7 +3,6 @@
# Example YAML configuration
yaml_config = """
apiVersion: flo/alpha-v1
-kind: FloAgent
metadata:
name: email-summary-flo
version: 1.0.0
diff --git a/flo_ai/flo_ai/arium/README.md b/flo_ai/flo_ai/arium/README.md
new file mode 100644
index 00000000..e0d4b06e
--- /dev/null
+++ b/flo_ai/flo_ai/arium/README.md
@@ -0,0 +1,171 @@
+# Arium Builder Pattern
+
+The `AriumBuilder` provides a fluent, easy-to-use interface for building and running Arium workflows. It eliminates the need for manual graph construction and makes it simple to create complex multi-agent workflows.
+
+## Quick Start
+
+```python
+from flo_ai.arium import AriumBuilder, create_arium
+
+# Simple workflow
+result = await (AriumBuilder()
+ .add_agent(my_agent)
+ .add_tool(my_tool)
+ .start_with(my_agent)
+ .connect(my_agent, my_tool)
+ .end_with(my_tool)
+ .build_and_run(["Hello, world!"]))
+```
+
+## Key Features
+
+- **Fluent Interface**: Chain method calls for readable workflow construction
+- **Automatic Compilation**: No need to manually call `compile()`
+- **Default Memory**: Uses `MessageMemory` by default if none provided
+- **Easy Connections**: Simple `connect()` method for linear workflows
+- **Flexible Routing**: Full support for custom router functions
+- **Visualization**: Built-in graph visualization support
+- **Reusable Workflows**: Build once, run multiple times
+
+## API Reference
+
+### AriumBuilder Methods
+
+| Method | Description |
+|--------|-------------|
+| `with_memory(memory)` | Set custom memory for the workflow |
+| `add_agent(agent)` | Add a single agent |
+| `add_agents(agents)` | Add multiple agents |
+| `add_tool(tool)` | Add a single tool |
+| `add_tools(tools)` | Add multiple tools |
+| `start_with(node)` | Set the starting node |
+| `end_with(node)` | Add an ending node |
+| `connect(from_node, to_node)` | Simple connection between nodes |
+| `add_edge(from_node, to_nodes, router)` | Add edge with optional router |
+| `build()` | Build the Arium instance |
+| `build_and_run(inputs)` | Build and run in one step |
+| `visualize(output_path, title)` | Generate workflow visualization |
+| `reset()` | Reset builder to start fresh |
+
+### Convenience Functions
+
+- `create_arium()` - Returns a new `AriumBuilder` instance
+
+## Usage Patterns
+
+### 1. Linear Workflow
+
+```python
+result = await (AriumBuilder()
+ .add_agent(agent1)
+ .add_tool(tool1)
+ .add_agent(agent2)
+ .start_with(agent1)
+ .connect(agent1, tool1)
+ .connect(tool1, agent2)
+ .end_with(agent2)
+ .build_and_run(inputs))
+```
+
+### 2. Branching Workflow
+
+```python
+def my_router(memory) -> Literal["path_a", "path_b"]:
+ # Your routing logic here
+ return "path_a"
+
+result = await (AriumBuilder()
+ .add_agent(classifier)
+ .add_tool(tool_a)
+ .add_tool(tool_b)
+ .add_agent(final_agent)
+ .start_with(classifier)
+ .add_edge(classifier, [tool_a, tool_b], my_router)
+ .connect(tool_a, final_agent)
+ .connect(tool_b, final_agent)
+ .end_with(final_agent)
+ .build_and_run(inputs))
+```
+
+### 3. Build and Reuse
+
+```python
+# Build once
+arium = (AriumBuilder()
+ .add_agent(my_agent)
+ .start_with(my_agent)
+ .end_with(my_agent)
+ .build())
+
+# Run multiple times
+result1 = await arium.run(["Input 1"])
+result2 = await arium.run(["Input 2"])
+```
+
+### 4. Complex Multi-Agent Workflow
+
+```python
+arium = (AriumBuilder()
+ .add_agents([agent1, agent2, agent3])
+ .add_tools([tool1, tool2])
+ .with_memory(custom_memory)
+ .start_with(agent1)
+ .connect(agent1, tool1)
+ .connect(tool1, agent2)
+ .add_edge(agent2, [agent3, tool2], router_fn)
+ .connect(tool2, agent3)
+ .end_with(agent3)
+ .visualize("my_workflow.png", "My Workflow")
+ .build())
+
+result = await arium.run(inputs)
+```
+
+## Migration from Manual Construction
+
+### Before (Manual)
+```python
+from flo_ai.arium import Arium
+from flo_ai.arium.memory import MessageMemory
+
+# Manual construction
+arium = Arium(MessageMemory())
+arium.add_nodes([agent1, tool1, agent2])
+arium.start_at(agent1)
+arium.add_edge(agent1.name, [tool1.name])
+arium.add_edge(tool1.name, [agent2.name])
+arium.add_end_to(agent2)
+arium.compile()
+
+result = await arium.run(inputs)
+```
+
+### After (Builder)
+```python
+from flo_ai.arium import AriumBuilder
+
+# Builder pattern
+result = await (AriumBuilder()
+ .add_agent(agent1)
+ .add_tool(tool1)
+ .add_agent(agent2)
+ .start_with(agent1)
+ .connect(agent1, tool1)
+ .connect(tool1, agent2)
+ .end_with(agent2)
+ .build_and_run(inputs))
+```
+
+## Error Handling
+
+The builder includes comprehensive validation:
+
+- Ensures at least one agent or tool is added
+- Requires a start node to be specified
+- Requires at least one end node
+- Validates router function signatures
+- Checks for orphaned nodes
+
+## Examples
+
+See `examples.py` for complete working examples of different workflow patterns.
\ No newline at end of file
diff --git a/flo_ai/flo_ai/arium/__init__.py b/flo_ai/flo_ai/arium/__init__.py
new file mode 100644
index 00000000..4417d222
--- /dev/null
+++ b/flo_ai/flo_ai/arium/__init__.py
@@ -0,0 +1,17 @@
+from .arium import Arium
+from .base import BaseArium
+from .builder import AriumBuilder, create_arium
+from .memory import MessageMemory, BaseMemory
+from .models import StartNode, EndNode, Edge
+
+__all__ = [
+ 'Arium',
+ 'BaseArium',
+ 'AriumBuilder',
+ 'create_arium',
+ 'MessageMemory',
+ 'BaseMemory',
+ 'StartNode',
+ 'EndNode',
+ 'Edge',
+]
diff --git a/flo_ai/flo_ai/arium/arium.py b/flo_ai/flo_ai/arium/arium.py
new file mode 100644
index 00000000..561d604f
--- /dev/null
+++ b/flo_ai/flo_ai/arium/arium.py
@@ -0,0 +1,75 @@
+from flo_ai.arium.base import BaseArium
+from flo_ai.arium.memory import MessageMemory, BaseMemory
+from flo_ai.llm.base_llm import ImageMessage
+from typing import List
+from flo_ai.models.agent import Agent
+from flo_ai.tool.base_tool import Tool
+from flo_ai.arium.models import StartNode, EndNode
+from flo_ai.utils.logger import logger
+
+
+class Arium(BaseArium):
+ def __init__(self, memory: BaseMemory):
+ super().__init__()
+ self.is_compiled = False
+ self.memory = memory if memory else MessageMemory()
+
+ def compile(self):
+ self.validate_graph()
+ self.is_compiled = True
+
+ async def run(self, inputs: List[str | ImageMessage]):
+ if not self.is_compiled:
+ raise ValueError('Arium is not compiled')
+
+ if not self.memory:
+ raise ValueError('Arium has no memory')
+
+ if not self.nodes:
+ raise ValueError('Arium has no nodes')
+
+ return await self._execute_graph(inputs)
+
+ async def _execute_graph(self, inputs: List[str | ImageMessage]):
+ [self.memory.add(msg) for msg in inputs]
+
+ current_node = self.nodes[self.start_node_name]
+ current_edge = self.edges[self.start_node_name]
+
+ logger.info(f'Executing graph from {current_node.name}')
+ while current_node.name != self.end_node_name:
+ # execute current node
+ result = await self._execute_node(current_node)
+
+ # update results to memory
+ self._add_to_memory(result)
+
+ # find next node post current node
+ next_node_name = current_edge.router_fn(memory=self.memory)
+
+ # find next edge
+ # TODO: next_node_name might not be in self.edges if it's the end node. Handle this case
+ next_edge = (
+ self.edges[next_node_name] if next_node_name in self.edges else None
+ )
+
+ # update current node
+ current_node = self.nodes[next_node_name]
+ current_edge = next_edge
+
+ return self.memory.get()
+
+ async def _execute_node(self, node: Agent | Tool | StartNode | EndNode):
+ if isinstance(node, Agent):
+ return await node.run(self.memory.get())
+ elif isinstance(node, Tool):
+ return await node.execute(self.memory.get())
+ elif isinstance(node, StartNode):
+ return None
+ elif isinstance(node, EndNode):
+ return None
+
+ def _add_to_memory(self, result: str):
+ # TODO result will be None for start and end nodes
+ if result:
+ self.memory.add(result)
diff --git a/flo_ai/flo_ai/arium/base.py b/flo_ai/flo_ai/arium/base.py
new file mode 100644
index 00000000..5ce2c8a4
--- /dev/null
+++ b/flo_ai/flo_ai/arium/base.py
@@ -0,0 +1,352 @@
+import inspect
+from functools import partial
+from flo_ai.models.agent import Agent
+from flo_ai.tool.base_tool import Tool
+from flo_ai.utils.logger import logger
+from typing import List, Optional, Callable, Literal, get_origin, get_args, Dict
+from flo_ai.arium.models import StartNode, EndNode, Edge, default_router
+from pathlib import Path
+
+
+class BaseArium:
+ def __init__(self):
+ self.start_node_name = '__start__'
+ self.end_node_name = '__end__'
+ self.nodes: Dict[str, Agent | Tool | StartNode | EndNode] = dict()
+ self.edges: Dict[str, Edge] = dict()
+
+ def add_nodes(self, agents: List[Agent | Tool | StartNode | EndNode]):
+ self.nodes.update({agent.name: agent for agent in agents})
+
+ def start_at(self, node: Agent | Tool | StartNode | EndNode):
+ start_node = StartNode()
+ if start_node.name in self.nodes:
+ raise ValueError(f'Start node {start_node.name} already exists')
+ self.nodes[start_node.name] = start_node
+ self.edges[start_node.name] = Edge(
+ router_fn=partial(default_router, to_node=node.name), to_nodes=[node.name]
+ )
+
+ def add_end_to(self, node: Agent | Tool | StartNode | EndNode):
+ end_node = EndNode()
+ if end_node.name in self.nodes:
+ raise ValueError(f'End node {end_node.name} already exists')
+ self.nodes[end_node.name] = end_node
+ self.edges[node.name] = Edge(
+ router_fn=partial(default_router, to_node=end_node.name),
+ to_nodes=[end_node.name],
+ )
+
+ def _check_router_return_type(self, router: Callable) -> Optional[List]:
+ try:
+ # Get the function signature
+ sig = inspect.signature(router)
+ return_annotation = sig.return_annotation
+
+ # Check if there's no return annotation
+ if return_annotation == inspect.Signature.empty:
+ return None
+
+ # Check if the return type is a Literal
+ origin = get_origin(return_annotation)
+
+ # In Python 3.8+, Literal types have get_origin() return typing.Literal
+ if origin is Literal:
+ # Extract the literal values
+ literal_values = list(get_args(return_annotation))
+ return literal_values
+
+ return None
+
+ except Exception as e:
+ logger.error(f'Error checking router return type: {e}')
+ return None
+
+ def add_edge(
+ self,
+ from_node: str,
+ to_nodes: List[str] = None,
+ router: Optional[Callable] = None,
+ ):
+ if router and not callable(router):
+ raise ValueError('Router must be a callable')
+
+ if not to_nodes:
+ raise ValueError('To nodes must be provided')
+
+ if not router and len(to_nodes) != 1:
+ raise ValueError(
+ 'Exactly one to node must be provided if router is not provided'
+ )
+
+ if from_node not in self.nodes:
+ raise ValueError(f'Node {from_node} not found')
+
+ wrong_nodes = [
+ wrong_to_node
+ for wrong_to_node in to_nodes
+ if wrong_to_node not in self.nodes
+ ]
+ if wrong_nodes:
+ raise ValueError(f'Nodes {wrong_nodes} not found')
+
+ if router:
+ literal_values = self._check_router_return_type(router)
+ if literal_values is None:
+ raise ValueError('Router return type is not a Literal')
+
+ invalid_literals = [val for val in literal_values if val not in to_nodes]
+ if invalid_literals:
+ raise ValueError(
+ f'Router return type includes literal values {invalid_literals} that are not in to_nodes {to_nodes}'
+ )
+
+ if set(literal_values) != set(to_nodes):
+ raise ValueError(
+ f'Router return type values {literal_values} do not match to_nodes {to_nodes}'
+ )
+
+ self.edges[from_node] = Edge(
+ router_fn=router
+ if router
+ else partial(default_router, to_node=to_nodes[0]),
+ to_nodes=to_nodes,
+ )
+
+ def check_orphan_nodes(self) -> List[str]:
+ if not self.nodes:
+ return []
+
+ # Get all nodes with outgoing edges
+ nodes_with_outgoing = set(self.edges.keys())
+
+ # Get all nodes with incoming edges by examining router return types and to_nodes
+ nodes_with_incoming = set()
+
+ for _, target in self.edges.items():
+ nodes_with_incoming.update(target.to_nodes)
+
+ # Find orphan nodes: nodes that have neither incoming nor outgoing edges
+ all_nodes = set(self.nodes.keys())
+ connected_nodes = nodes_with_outgoing.union(nodes_with_incoming)
+ orphan_nodes = all_nodes - connected_nodes
+
+ return list(orphan_nodes)
+
+ def validate_graph(self) -> bool:
+ orphan_nodes = self.check_orphan_nodes()
+
+ if orphan_nodes:
+ raise ValueError(
+ f'Orphan nodes found: {orphan_nodes}. These nodes have no incoming or outgoing edges.'
+ )
+
+ # Check for exactly 1 start node
+ start_nodes = [
+ node for node in self.nodes.values() if isinstance(node, StartNode)
+ ]
+ if len(start_nodes) == 0:
+ raise ValueError(
+ f'Graph must have exactly 1 start node. Found 0 start nodes: {start_nodes}'
+ )
+ elif len(start_nodes) > 1:
+ raise ValueError(
+ f'Graph must have exactly 1 start node. Found {len(start_nodes)} start nodes.'
+ )
+
+ # Check for at least 1 end node
+ end_nodes = [node for node in self.nodes.values() if isinstance(node, EndNode)]
+ if len(end_nodes) == 0:
+ raise ValueError('Graph must have at least 1 end node. Found 0 end nodes.')
+
+ return True
+
+ def visualize_graph(
+ self,
+ output_path: str = 'graph_visualization.png',
+ graph_title: str = 'Arium Graph Visualization',
+ figsize: tuple = (12, 8),
+ node_size: int = 3000,
+ font_size: int = 10,
+ dpi: int = 300,
+ ) -> None:
+ """
+ Generate a graph visualization and save it as PNG.
+
+ Args:
+ output_path: Path where the PNG file will be saved
+ figsize: Figure size as (width, height) in inches
+ node_size: Size of the nodes in the graph
+ font_size: Font size for node labels
+ dpi: Resolution of the saved image
+ """
+ import matplotlib.pyplot as plt
+ import matplotlib.patches as patches
+ import networkx as nx
+
+ if not self.nodes:
+ logger.error('No nodes to visualize')
+ return
+
+ # Create directed graph
+ G = nx.DiGraph()
+
+ # Add nodes with their types
+ for node_name, node in self.nodes.items():
+ node_type = self._get_node_type(node)
+ G.add_node(node_name, node_type=node_type, node_obj=node)
+
+ # Add edges
+ for from_node, edge in self.edges.items():
+ for to_node in edge.to_nodes:
+ if to_node in self.nodes:
+ G.add_edge(from_node, to_node, edge_obj=edge)
+
+ # Create matplotlib figure
+ fig, ax = plt.subplots(figsize=figsize, dpi=dpi)
+
+ # Use hierarchical layout for better DAG visualization
+ try:
+ # Try different layouts for better DAG appearance
+ pos = nx.nx_agraph.graphviz_layout(G, prog='dot', args='-Grankdir=TB')
+ except Exception as e:
+ logger.error(f'Error in graphviz_layout: {e}')
+ try:
+ # Fallback to planar layout for DAG structure
+ pos = nx.planar_layout(G)
+ except Exception as e:
+ logger.error(f'Error in graphviz_layout: {e}')
+ # Final fallback to shell layout for better hierarchy
+ pos = nx.shell_layout(G)
+
+ # Define colors for different node types
+ node_colors = {
+ 'start': '#90EE90', # Light green
+ 'end': '#FFB6C1', # Light pink
+ 'agent': '#87CEEB', # Sky blue
+ 'tool': '#DDA0DD', # Plum
+ }
+
+ # Draw nodes with different colors based on type
+ for node_name, node_data in G.nodes(data=True):
+ node_type = node_data['node_type']
+ color = node_colors.get(node_type, '#CCCCCC')
+
+ nx.draw_networkx_nodes(
+ G,
+ pos,
+ nodelist=[node_name],
+ node_color=color,
+ node_size=node_size,
+ alpha=0.9,
+ ax=ax,
+ )
+
+ # Separate edges by router type for different styling
+ default_edges = []
+ custom_edges = []
+
+ for edge_data in G.edges(data=True):
+ from_node, to_node, data = edge_data
+ edge_obj = data['edge_obj']
+
+ if edge_obj.is_default_router():
+ default_edges.append((from_node, to_node))
+ else:
+ custom_edges.append((from_node, to_node))
+
+ # Draw default router edges with dotted lines
+ if default_edges:
+ nx.draw_networkx_edges(
+ G,
+ pos,
+ edgelist=default_edges,
+ edge_color='gray',
+ arrows=True,
+ arrowsize=20,
+ arrowstyle='->',
+ connectionstyle='arc3,rad=0.1',
+ style='solid',
+ width=2,
+ ax=ax,
+ )
+
+ # Draw custom router edges with solid lines
+ if custom_edges:
+ nx.draw_networkx_edges(
+ G,
+ pos,
+ edgelist=custom_edges,
+ edge_color='black',
+ arrows=True,
+ arrowsize=20,
+ arrowstyle='->',
+ connectionstyle='arc3,rad=0.1',
+ style='dotted',
+ width=2,
+ ax=ax,
+ )
+
+ # Add labels
+ nx.draw_networkx_labels(G, pos, font_size=font_size, font_weight='bold', ax=ax)
+
+ # Add title
+ plt.title(graph_title, fontsize=16, fontweight='bold', pad=20)
+
+ # Add legend
+ legend_elements = [
+ patches.Patch(color='#90EE90', label='Start Node'),
+ patches.Patch(color='#FFB6C1', label='End Node'),
+ patches.Patch(color='#87CEEB', label='Agent'),
+ patches.Patch(color='#DDA0DD', label='Tool'),
+ patches.Patch(
+ facecolor='none',
+ edgecolor='gray',
+ linestyle='dotted',
+ linewidth=2,
+ label='Default Router',
+ ),
+ patches.Patch(
+ facecolor='none',
+ edgecolor='black',
+ linestyle='solid',
+ linewidth=2,
+ label='Custom Router',
+ ),
+ ]
+ ax.legend(handles=legend_elements, loc='upper right', bbox_to_anchor=(1.15, 1))
+
+ # Remove axes
+ ax.axis('off')
+
+ # Adjust layout to prevent legend cutoff
+ plt.tight_layout()
+
+ # Save the figure
+ output_path = Path(output_path)
+ output_path.parent.mkdir(parents=True, exist_ok=True)
+
+ plt.savefig(
+ str(output_path),
+ format='png',
+ dpi=dpi,
+ bbox_inches='tight',
+ facecolor='white',
+ edgecolor='none',
+ )
+ plt.close()
+
+ logger.info(f'Graph visualization saved to: {output_path}')
+
+ def _get_node_type(self, node) -> str:
+ """Helper method to determine node type for visualization."""
+ if isinstance(node, StartNode):
+ return 'start'
+ elif isinstance(node, EndNode):
+ return 'end'
+ elif isinstance(node, Agent):
+ return 'agent'
+ elif isinstance(node, Tool):
+ return 'tool'
+ else:
+ return 'unknown'
diff --git a/flo_ai/flo_ai/arium/builder.py b/flo_ai/flo_ai/arium/builder.py
new file mode 100644
index 00000000..7511bb2a
--- /dev/null
+++ b/flo_ai/flo_ai/arium/builder.py
@@ -0,0 +1,161 @@
+from typing import List, Optional, Callable, Union
+from flo_ai.arium.arium import Arium
+from flo_ai.arium.memory import MessageMemory, BaseMemory
+from flo_ai.models.agent import Agent
+from flo_ai.tool.base_tool import Tool
+from flo_ai.llm.base_llm import ImageMessage
+
+
+class AriumBuilder:
+ """
+ A builder class for creating and configuring Arium instances with a fluent interface.
+
+ Example usage:
+ result = (AriumBuilder()
+ .with_memory(my_memory)
+ .add_agent(agent1)
+ .add_tool(tool1)
+ .start_with(agent1)
+ .add_edge(agent1, [tool1], router_fn)
+ .end_with(tool1)
+ .build_and_run(["Hello, world!"]))
+ """
+
+ def __init__(self):
+ self._memory: Optional[BaseMemory] = None
+ self._agents: List[Agent] = []
+ self._tools: List[Tool] = []
+ self._start_node: Optional[Union[Agent, Tool]] = None
+ self._end_nodes: List[Union[Agent, Tool]] = []
+ self._edges: List[tuple] = [] # (from_node, to_nodes, router)
+ self._arium: Optional[Arium] = None
+
+ def with_memory(self, memory: BaseMemory) -> 'AriumBuilder':
+ """Set the memory for the Arium."""
+ self._memory = memory
+ return self
+
+ def add_agent(self, agent: Agent) -> 'AriumBuilder':
+ """Add an agent to the Arium."""
+ self._agents.append(agent)
+ return self
+
+ def add_agents(self, agents: List[Agent]) -> 'AriumBuilder':
+ """Add multiple agents to the Arium."""
+ self._agents.extend(agents)
+ return self
+
+ def add_tool(self, tool: Tool) -> 'AriumBuilder':
+ """Add a tool to the Arium."""
+ self._tools.append(tool)
+ return self
+
+ def add_tools(self, tools: List[Tool]) -> 'AriumBuilder':
+ """Add multiple tools to the Arium."""
+ self._tools.extend(tools)
+ return self
+
+ def start_with(self, node: Union[Agent, Tool]) -> 'AriumBuilder':
+ """Set the starting node for the Arium."""
+ self._start_node = node
+ return self
+
+ def end_with(self, node: Union[Agent, Tool]) -> 'AriumBuilder':
+ """Add an ending node to the Arium."""
+ if node not in self._end_nodes:
+ self._end_nodes.append(node)
+ return self
+
+ def add_edge(
+ self,
+ from_node: Union[Agent, Tool],
+ to_nodes: List[Union[Agent, Tool]],
+ router: Optional[Callable] = None,
+ ) -> 'AriumBuilder':
+ """Add an edge between nodes with an optional router function."""
+ self._edges.append((from_node, to_nodes, router))
+ return self
+
+ def connect(
+ self,
+ from_node: Union[Agent, Tool],
+ to_node: Union[Agent, Tool],
+ ) -> 'AriumBuilder':
+ """Simple connection between two nodes without a router."""
+ return self.add_edge(from_node, [to_node])
+
+ def build(self) -> Arium:
+ """Build the Arium instance from the configured components."""
+ # Use default memory if none provided
+ if self._memory is None:
+ self._memory = MessageMemory()
+
+ # Create Arium instance
+ arium = Arium(self._memory)
+
+ # Add all nodes
+ all_nodes = []
+ all_nodes.extend(self._agents)
+ all_nodes.extend(self._tools)
+
+ if not all_nodes:
+ raise ValueError('No agents or tools added to the Arium')
+
+ arium.add_nodes(all_nodes)
+
+ # Set start node
+ if self._start_node is None:
+ raise ValueError(
+ 'No start node specified. Use start_with() to set a start node.'
+ )
+
+ arium.start_at(self._start_node)
+
+ # Add edges
+ for from_node, to_nodes, router in self._edges:
+ arium.add_edge(from_node.name, [node.name for node in to_nodes], router)
+
+ # Add end nodes
+ if not self._end_nodes:
+ raise ValueError('No end nodes specified. Use end_with() to add end nodes.')
+
+ for end_node in self._end_nodes:
+ arium.add_end_to(end_node)
+
+ # Compile the Arium
+ arium.compile()
+
+ self._arium = arium
+ return arium
+
+ async def build_and_run(self, inputs: List[Union[str, ImageMessage]]) -> List[dict]:
+ """Build the Arium and run it with the given inputs."""
+ arium = self.build()
+ return await arium.run(inputs)
+
+ def visualize(
+ self, output_path: str = 'arium_graph.png', title: str = 'Arium Workflow'
+ ) -> 'AriumBuilder':
+ """Generate a visualization of the Arium graph."""
+ if self._arium is None:
+ self.build()
+
+ self._arium.visualize_graph(output_path=output_path, graph_title=title)
+ return self
+
+ def reset(self) -> 'AriumBuilder':
+ """Reset the builder to start fresh."""
+ self._memory = None
+ self._agents = []
+ self._tools = []
+ self._start_node = None
+ self._end_nodes = []
+ self._edges = []
+ self._arium = None
+ return self
+
+
+# Convenience function for creating a builder
+def create_arium() -> AriumBuilder:
+ """Create a new AriumBuilder instance."""
+ return AriumBuilder()
diff --git a/flo_ai/flo_ai/arium/examples.py b/flo_ai/flo_ai/arium/examples.py
new file mode 100644
index 00000000..cbdacc44
--- /dev/null
+++ b/flo_ai/flo_ai/arium/examples.py
@@ -0,0 +1,170 @@
+"""
+Examples demonstrating how to use the AriumBuilder pattern for creating and running Arium workflows.
+"""
+
+from typing import Literal
+from flo_ai.arium import AriumBuilder, create_arium
+from flo_ai.models.agent import Agent
+from flo_ai.tool.base_tool import Tool
+from flo_ai.arium.memory import MessageMemory
+
+
+# Example 1: Simple Linear Workflow
+async def example_linear_workflow():
+ """Example of a simple linear workflow: Agent -> Tool -> Agent"""
+
+ # Create some example agents and tools (these would be your actual implementations)
+ analyzer_agent = Agent(name='analyzer', prompt='Analyze the input')
+ processing_tool = Tool(name='processor')
+ summarizer_agent = Agent(name='summarizer', prompt='Summarize the results')
+
+ # Build and run the workflow
+ result = await (
+ AriumBuilder()
+ .add_agent(analyzer_agent)
+ .add_tool(processing_tool)
+ .add_agent(summarizer_agent)
+ .start_with(analyzer_agent)
+ .connect(analyzer_agent, processing_tool)
+ .connect(processing_tool, summarizer_agent)
+ .end_with(summarizer_agent)
+ .build_and_run(['Analyze this text'])
+ )
+
+ return result
+
+
+# Example 2: Branching Workflow with Router
+async def example_branching_workflow():
+ """Example of a branching workflow with conditional routing"""
+
+ # Create agents and tools
+ classifier_agent = Agent(name='classifier', prompt='Classify the input type')
+ text_processor = Tool(name='text_processor')
+ image_processor = Tool(name='image_processor')
+ final_agent = Agent(name='final', prompt='Provide final response')
+
+ # Router function for conditional branching
+ def content_router(memory) -> Literal['text_processor', 'image_processor']:
+ # Simple example logic (in real use, this would analyze the memory)
+ last_message = memory[-1]['content']
+ if 'image' in last_message.lower():
+ return 'image_processor'
+ return 'text_processor'
+
+ # Build the workflow
+ result = await (
+ AriumBuilder()
+ .add_agent(classifier_agent)
+ .add_tool(text_processor)
+ .add_tool(image_processor)
+ .add_agent(final_agent)
+ .start_with(classifier_agent)
+ .add_edge(classifier_agent, [text_processor, image_processor], content_router)
+ .connect(text_processor, final_agent)
+ .connect(image_processor, final_agent)
+ .end_with(final_agent)
+ .build_and_run(['Process this content'])
+ )
+
+ return result
+
+
+# Example 3: Complex Multi-Agent Workflow
+async def example_complex_workflow():
+ """Example of a more complex workflow with multiple agents and tools"""
+
+ # Create multiple agents and tools
+ input_agent = Agent(name='input_handler', prompt='Handle initial input')
+ researcher_agent = Agent(name='researcher', prompt='Research the topic')
+ analyzer_agent = Agent(name='analyzer', prompt='Analyze findings')
+ writer_agent = Agent(name='writer', prompt='Write the final report')
+
+ search_tool = Tool(name='search_tool')
+ data_tool = Tool(name='data_processor')
+
+ # Router for deciding next step after analysis
+ def analysis_router(memory) -> Literal['writer', 'researcher']:
+ # Example logic: if we need more research, go back to researcher
+ # otherwise go to writer
+ return 'writer' # Simplified for example
+
+ # Build complex workflow
+ arium = (
+ AriumBuilder()
+ .add_agents([input_agent, researcher_agent, analyzer_agent, writer_agent])
+ .add_tools([search_tool, data_tool])
+ .with_memory(MessageMemory())
+ .start_with(input_agent)
+ .connect(input_agent, researcher_agent)
+ .connect(researcher_agent, search_tool)
+ .connect(search_tool, data_tool)
+ .connect(data_tool, analyzer_agent)
+ .add_edge(analyzer_agent, [writer_agent, researcher_agent], analysis_router)
+ .end_with(writer_agent)
+ .build()
+ )
+
+ # You can also visualize the workflow
+ arium.visualize_graph(
+ output_path='complex_workflow.png', graph_title='Complex Multi-Agent Workflow'
+ )
+
+ # Run the workflow
+ result = await arium.run(['Research and write a report on AI trends'])
+ return result
+
+
+# Example 4: Using the convenience function
+async def example_convenience_function():
+ """Example using the create_arium convenience function"""
+
+ agent1 = Agent(name='agent1', prompt='First agent')
+ agent2 = Agent(name='agent2', prompt='Second agent')
+
+ result = await (
+ create_arium()
+ .add_agent(agent1)
+ .add_agent(agent2)
+ .start_with(agent1)
+ .connect(agent1, agent2)
+ .end_with(agent2)
+ .build_and_run(['Hello'])
+ )
+
+ return result
+
+
+# Example 5: Building and reusing an Arium
+async def example_build_and_reuse():
+ """Example of building an Arium once and reusing it"""
+
+ agent = Agent(name='echo_agent', prompt='Echo the input')
+
+ # Build the Arium
+ arium = AriumBuilder().add_agent(agent).start_with(agent).end_with(agent).build()
+
+ # Run it multiple times with different inputs
+ result1 = await arium.run(['First input'])
+ result2 = await arium.run(['Second input'])
+
+ return result1, result2
+
+
+if __name__ == '__main__':
+ import asyncio
+
+ # Run the examples
+ async def main():
+ print('Running AriumBuilder examples...')
+
+ # You can uncomment and run these examples
+ # result1 = await example_linear_workflow()
+ # result2 = await example_branching_workflow()
+ # result3 = await example_complex_workflow()
+ # result4 = await example_convenience_function()
+ # result5 = await example_build_and_reuse()
+
+ print('Examples completed!')
+
+ asyncio.run(main())
diff --git a/flo_ai/flo_ai/arium/memory.py b/flo_ai/flo_ai/arium/memory.py
new file mode 100644
index 00000000..88e9f113
--- /dev/null
+++ b/flo_ai/flo_ai/arium/memory.py
@@ -0,0 +1,25 @@
+from abc import ABC, abstractmethod
+from typing import TypeVar, Generic, List, Dict
+
+# Define the generic type variable
+T = TypeVar('T')
+
+
+class BaseMemory(ABC, Generic[T]):
+ @abstractmethod
+ def add(self, m: T):
+ pass
+
+ def get(self) -> List[T]:
+ pass
+
+
+class MessageMemory(BaseMemory[Dict[str, str]]):
+ def __init__(self):
+ self.messages = []
+
+ def add(self, message: Dict[str, str]):
+ self.messages.append(message)
+
+ def get(self) -> List[Dict[str, str]]:
+ return self.messages
diff --git a/flo_ai/flo_ai/arium/models.py b/flo_ai/flo_ai/arium/models.py
new file mode 100644
index 00000000..e372c3a4
--- /dev/null
+++ b/flo_ai/flo_ai/arium/models.py
@@ -0,0 +1,32 @@
+from dataclasses import dataclass
+from typing import Callable, List
+from functools import partial
+from flo_ai.arium.memory import BaseMemory
+
+
+def default_router(
+ to_node: str,
+ memory: BaseMemory,
+) -> str:
+ return to_node
+
+
+@dataclass
+class StartNode:
+ name = '__start__'
+
+
+@dataclass
+class EndNode:
+ name = '__end__'
+
+
+@dataclass
+class Edge:
+ router_fn: Callable | partial
+ to_nodes: List[str]
+
+ def is_default_router(self) -> bool:
+ if isinstance(self.router_fn, partial):
+ return self.router_fn.func.__name__ == 'default_router'
+ return False
diff --git a/flo_ai/flo_ai/builder/agent_builder.py b/flo_ai/flo_ai/builder/agent_builder.py
index 08228547..583095a8 100644
--- a/flo_ai/flo_ai/builder/agent_builder.py
+++ b/flo_ai/flo_ai/builder/agent_builder.py
@@ -2,9 +2,7 @@
import yaml
from flo_ai.models.agent import Agent
from flo_ai.models.base_agent import ReasoningPattern
-from flo_ai.llm.base_llm import BaseLLM
-from flo_ai.llm.openai_llm import OpenAI
-from flo_ai.llm.anthropic_llm import Anthropic
+from flo_ai.llm import BaseLLM, OpenAI, Anthropic, Gemini, OllamaLLM
from flo_ai.tool.base_tool import Tool
from flo_ai.formatter.yaml_format_parser import FloYamlParser
from pydantic import BaseModel
@@ -133,6 +131,10 @@ def from_yaml(
builder.with_llm(OpenAI(model=model_name))
elif provider == 'claude':
builder.with_llm(Anthropic(model=model_name))
+ elif provider == 'gemini':
+ builder.with_llm(Gemini(model=model_name))
+ elif provider == 'ollama':
+ builder.with_llm(OllamaLLM(model=model_name))
else:
raise ValueError(f'Unsupported model provider: {provider}')
diff --git a/flo_ai/flo_ai/llm/__init__.py b/flo_ai/flo_ai/llm/__init__.py
new file mode 100644
index 00000000..59cddc14
--- /dev/null
+++ b/flo_ai/flo_ai/llm/__init__.py
@@ -0,0 +1,7 @@
+from .base_llm import BaseLLM
+from .anthropic_llm import Anthropic
+from .openai_llm import OpenAI
+from .ollama_llm import OllamaLLM
+from .gemini_llm import Gemini
+
+__all__ = ['BaseLLM', 'Anthropic', 'OpenAI', 'OllamaLLM', 'Gemini']
diff --git a/flo_ai/flo_ai/llm/anthropic_llm.py b/flo_ai/flo_ai/llm/anthropic_llm.py
index ffb0dbb8..b8234489 100644
--- a/flo_ai/flo_ai/llm/anthropic_llm.py
+++ b/flo_ai/flo_ai/llm/anthropic_llm.py
@@ -1,7 +1,7 @@
from typing import Dict, Any, List, Optional
from anthropic import AsyncAnthropic
import json
-from .base_llm import BaseLLM
+from .base_llm import BaseLLM, ImageMessage
from flo_ai.tool.base_tool import Tool
@@ -109,3 +109,7 @@ def format_tool_for_llm(self, tool: 'Tool') -> Dict[str, Any]:
def format_tools_for_llm(self, tools: List['Tool']) -> List[Dict[str, Any]]:
"""Format tools for Claude's API"""
return [self.format_tool_for_llm(tool) for tool in tools]
+
+ def format_image_in_message(self, image: ImageMessage) -> str:
+ """Format a image in the message"""
+ raise NotImplementedError('Not implemented image for LLM Anthropic')
diff --git a/flo_ai/flo_ai/llm/base_llm.py b/flo_ai/flo_ai/llm/base_llm.py
index 0942db57..2a5888df 100644
--- a/flo_ai/flo_ai/llm/base_llm.py
+++ b/flo_ai/flo_ai/llm/base_llm.py
@@ -1,6 +1,15 @@
from abc import ABC, abstractmethod
from typing import Dict, Any, List, Optional
from flo_ai.tool.base_tool import Tool
+from dataclasses import dataclass
+
+
+@dataclass
+class ImageMessage:
+ image_url: Optional[str] = None
+ image_file_path: Optional[str] = None
+ image_base64: Optional[str] = None
+ mime_type: Optional[str] = None
class BaseLLM(ABC):
@@ -50,3 +59,8 @@ def format_tool_for_llm(self, tool: 'Tool') -> Dict[str, Any]:
def format_tools_for_llm(self, tools: List['Tool']) -> List[Dict[str, Any]]:
"""Format a list of tools for the specific LLM's API"""
pass
+
+ @abstractmethod
+ def format_image_in_message(self, image: ImageMessage) -> str:
+ """Format a image in the message"""
+ pass
diff --git a/flo_ai/flo_ai/llm/gemini_llm.py b/flo_ai/flo_ai/llm/gemini_llm.py
new file mode 100644
index 00000000..94670620
--- /dev/null
+++ b/flo_ai/flo_ai/llm/gemini_llm.py
@@ -0,0 +1,158 @@
+from typing import Dict, Any, List, Optional
+from google import genai
+import json
+from .base_llm import BaseLLM, ImageMessage
+from flo_ai.tool.base_tool import Tool
+from flo_ai.utils.logger import logger
+
+
+class Gemini(BaseLLM):
+ def __init__(
+ self,
+ model: str = 'gemini-2.5-flash',
+ temperature: float = 0.7,
+ api_key: Optional[str] = None,
+ **kwargs,
+ ):
+ super().__init__(model, api_key, temperature, **kwargs)
+ self.client = (
+ genai.Client(api_key=self.api_key) if self.api_key else genai.Client()
+ )
+
+ async def generate(
+ self,
+ messages: List[Dict[str, str]],
+ functions: Optional[List[Dict[str, Any]]] = None,
+ output_schema: Optional[Dict[str, Any]] = None,
+ ) -> Dict[str, Any]:
+ # Convert messages to Gemini format
+ # Gemini uses a simple content string format
+ contents = []
+ system_prompt = ''
+ for msg in messages:
+ role = msg['role']
+ message_content = msg['content']
+
+ if role == 'system':
+ system_prompt += f'{message_content}\n'
+ else:
+ contents.append(message_content)
+
+ # Add output schema instruction if provided
+ if output_schema:
+ contents += f'\nPlease provide your response in JSON format according to this schema:\n{json.dumps(output_schema, indent=2)}\n'
+
+ # Add function information if provided
+ if functions:
+ contents += f'\nAvailable functions:\n{json.dumps(functions, indent=2)}\n'
+
+ try:
+ # Prepare generation config
+ generation_config = genai.types.GenerateContentConfig(
+ temperature=self.temperature,
+ system_instruction=system_prompt,
+ **self.kwargs,
+ )
+
+ # Make the API call
+ response = self.client.models.generate_content(
+ model=self.model,
+ contents=contents,
+ config=generation_config if generation_config else None,
+ )
+
+ # Check if response contains function call information
+ # For now, we'll assume text response and parse for function calls if needed
+ response_text = (
+ response.text if hasattr(response, 'text') else str(response)
+ )
+
+ # Try to detect function calls in the response
+ # This is a simple implementation - in practice, you might need more sophisticated parsing
+ if functions and self._is_function_call_response(response_text):
+ function_call = self._parse_function_call(response_text)
+ if function_call:
+ return {
+ 'content': response_text,
+ 'function_call': function_call,
+ }
+
+ return {'content': response_text}
+
+ except Exception as e:
+ raise Exception(f'Error in Gemini API call: {str(e)}')
+
+ def _is_function_call_response(self, response_text: str) -> bool:
+ """Check if the response contains a function call"""
+ # Simple heuristic - look for function call patterns
+ return (
+ 'function_call' in response_text.lower()
+ or '(' in response_text
+ and ')' in response_text
+ )
+
+ def _parse_function_call(self, response_text: str) -> Optional[Dict[str, Any]]:
+ """Parse function call from response text"""
+ # This is a simplified parser - in practice, you'd want more robust parsing
+ try:
+ # Look for JSON-like function call structure
+ if '{' in response_text and '}' in response_text:
+ # Extract JSON-like content
+ start = response_text.find('{')
+ end = response_text.rfind('}') + 1
+ json_str = response_text[start:end]
+ parsed = json.loads(json_str)
+
+ if 'name' in parsed and 'arguments' in parsed:
+ return {
+ 'name': parsed['name'],
+ 'arguments': json.dumps(parsed['arguments']),
+ }
+ except Exception as e:
+ logger.error(f'Error parsing function call: {str(e)}')
+ return None
+
+ def get_message_content(self, response: Any) -> str:
+ """Extract message content from response"""
+ if isinstance(response, dict):
+ return response.get('content', '')
+ return str(response)
+
+ def format_tool_for_llm(self, tool: 'Tool') -> Dict[str, Any]:
+ """Format a single tool for Gemini's API"""
+ return {
+ 'name': tool.name,
+ 'description': tool.description,
+ 'parameters': {
+ 'type': 'object',
+ 'properties': {
+ name: {
+ 'type': info.get('type', 'string'),
+ 'description': info.get('description', ''),
+ }
+ for name, info in tool.parameters.items()
+ },
+ 'required': [
+ name
+ for name, info in tool.parameters.items()
+ if info.get('required', True)
+ ],
+ },
+ }
+
+ def format_tools_for_llm(self, tools: List['Tool']) -> List[Dict[str, Any]]:
+ """Format tools for Gemini's API"""
+ return [self.format_tool_for_llm(tool) for tool in tools]
+
+ def format_image_in_message(self, image: ImageMessage) -> str:
+ """Format a image in the message"""
+ if image.image_file_path:
+ with open(image.image_file_path, 'rb') as image_file:
+ image_bytes = image_file.read()
+ return genai.types.Part.from_bytes(
+ data=image_bytes,
+ mime_type=image.mime_type,
+ )
+ raise NotImplementedError(
+ 'Not other way other than file path has been implemented'
+ )
diff --git a/flo_ai/flo_ai/llm/ollama_llm.py b/flo_ai/flo_ai/llm/ollama_llm.py
index d14d4d6c..01dfc320 100644
--- a/flo_ai/flo_ai/llm/ollama_llm.py
+++ b/flo_ai/flo_ai/llm/ollama_llm.py
@@ -1,7 +1,7 @@
from typing import Dict, Any, List, Optional
import aiohttp
import json
-from .base_llm import BaseLLM
+from .base_llm import BaseLLM, ImageMessage
from flo_ai.tool.base_tool import Tool
@@ -96,3 +96,7 @@ def format_tool_for_llm(self, tool: 'Tool') -> Dict[str, Any]:
def format_tools_for_llm(self, tools: List['Tool']) -> List[Dict[str, Any]]:
"""Format tools for Ollama's API"""
return [self.format_tool_for_llm(tool) for tool in tools]
+
+ def format_image_in_message(self, image: ImageMessage) -> str:
+ """Format a image in the message"""
+ raise NotImplementedError('Not implemented image for LLM Ollama')
diff --git a/flo_ai/flo_ai/llm/openai_llm.py b/flo_ai/flo_ai/llm/openai_llm.py
index 6786b629..10c2ade0 100644
--- a/flo_ai/flo_ai/llm/openai_llm.py
+++ b/flo_ai/flo_ai/llm/openai_llm.py
@@ -1,6 +1,6 @@
from typing import Dict, Any, List
from openai import AsyncOpenAI
-from .base_llm import BaseLLM
+from .base_llm import BaseLLM, ImageMessage
from flo_ai.tool.base_tool import Tool
@@ -88,3 +88,7 @@ def format_tool_for_llm(self, tool: 'Tool') -> Dict[str, Any]:
def format_tools_for_llm(self, tools: List['Tool']) -> List[Dict[str, Any]]:
"""Format tools for OpenAI's API"""
return [self.format_tool_for_llm(tool) for tool in tools]
+
+ def format_image_in_message(self, image: ImageMessage) -> str:
+ """Format a image in the message"""
+ raise NotImplementedError('Not implemented image for LLM OpenAI')
diff --git a/flo_ai/flo_ai/models/agent.py b/flo_ai/flo_ai/models/agent.py
index 0b9f6c8c..fd1cbc84 100644
--- a/flo_ai/flo_ai/models/agent.py
+++ b/flo_ai/flo_ai/models/agent.py
@@ -1,9 +1,9 @@
+import json
from typing import Dict, Any, List, Optional
from flo_ai.models.base_agent import BaseAgent, AgentType, ReasoningPattern
-from flo_ai.llm.base_llm import BaseLLM
+from flo_ai.llm.base_llm import BaseLLM, ImageMessage
from flo_ai.tool.base_tool import Tool, ToolExecutionError
from flo_ai.models.agent_error import AgentError
-import json
from flo_ai.utils.logger import logger
@@ -40,8 +40,16 @@ def __init__(
self.output_schema = output_schema
self.role = role
- async def run(self, input_text: str) -> str:
- self.add_to_history('user', input_text)
+ async def run(self, inputs: List[str | ImageMessage] | str) -> str:
+ if isinstance(inputs, str):
+ inputs = [inputs]
+
+ for input in inputs:
+ if isinstance(input, ImageMessage):
+ self.add_to_history('user', self.llm.format_image_in_message(input))
+ else:
+ self.add_to_history('user', input)
+
retry_count = 0
# If no tools, act as conversational agent
@@ -124,6 +132,7 @@ async def _run_with_tools(self, retry_count: int = 0) -> str:
while tool_call_count < max_tool_calls:
formatted_tools = self.llm.format_tools_for_llm(self.tools)
+ print(messages)
response = await self.llm.generate(
messages,
functions=formatted_tools,
@@ -162,7 +171,7 @@ async def _run_with_tools(self, retry_count: int = 0) -> str:
{
'role': 'function',
'name': function_name,
- 'content': str(function_response),
+ 'content': f'Here is the result of the tool call: \n {str(function_response)}',
}
)
diff --git a/flo_ai/poetry.lock b/flo_ai/poetry.lock
index 04bc8ad5..4160c43a 100644
--- a/flo_ai/poetry.lock
+++ b/flo_ai/poetry.lock
@@ -14,98 +14,103 @@ files = [
[[package]]
name = "aiohttp"
-version = "3.11.16"
+version = "3.12.14"
description = "Async http client/server framework (asyncio)"
optional = false
python-versions = ">=3.9"
groups = ["main"]
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async-timeout = {version = ">=4.0,<6.0", markers = "python_version < \"3.11\""}
attrs = ">=17.3.0"
frozenlist = ">=1.1.1"
@@ -114,22 +119,23 @@ propcache = ">=0.2.0"
yarl = ">=1.17.0,<2.0"
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+speedups = ["Brotli ; platform_python_implementation == \"CPython\"", "aiodns (>=3.3.0)", "brotlicffi ; platform_python_implementation != \"CPython\""]
[[package]]
name = "aiosignal"
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description = "aiosignal: a list of registered asynchronous callbacks"
optional = false
python-versions = ">=3.9"
groups = ["main"]
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frozenlist = ">=1.1.0"
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name = "altair"
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description = "Reusable constraint types to use with typing.Annotated"
optional = false
python-versions = ">=3.8"
-groups = ["main", "dev"]
+groups = ["main"]
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name = "anthropic"
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+version = "0.57.1"
description = "The official Python library for the anthropic API"
optional = false
python-versions = ">=3.8"
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distro = ">=1.7.0,<2"
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jiter = ">=0.4.0,<1"
pydantic = ">=1.9.0,<3"
sniffio = "*"
typing-extensions = ">=4.10,<5"
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bedrock = ["boto3 (>=1.28.57)", "botocore (>=1.31.57)"]
-vertex = ["google-auth (>=2,<3)"]
+vertex = ["google-auth[requests] (>=2,<3)"]
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name = "antiorm"
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description = "High level compatibility layer for multiple asynchronous event loop implementations"
optional = false
python-versions = ">=3.9"
-groups = ["main", "dev"]
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[[package]]
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+version = "5.0.1"
description = "Timeout context manager for asyncio programs"
optional = false
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markers = "python_version == \"3.10\""
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optional = false
python-versions = ">=3.7"
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test = ["pytest"]
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test-extra = ["curio", "ipython[test]", "jupyter_ai", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.23)", "pandas", "trio"]
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