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💡 Basic Usage ​

This guide provides a comprehensive introduction to using the Nexent SDK for building intelligent agents.

🚀 Installation ​

User Installation ​

If you want to use Nexent:

bash
# Recommended: Install from source
git clone https://github.com/ModelEngine-Group/nexent.git
cd nexent/sdk
uv pip install -e .

# Or install using uv
uv add nexent

Development Environment Setup ​

If you are a third-party SDK developer:

bash
# Install complete development environment (including Nexent)
cd nexent/sdk
uv pip install -e ".[dev]"  # Includes all development tools (testing, code quality checks, etc.)

The development environment includes the following additional features:

  • Code quality checking tools (ruff)
  • Testing framework (pytest)
  • Data processing dependencies (unstructured)
  • Other development dependencies

⚡ Quick Start ​

Basic Import ​

python
import nexent
from nexent.core import MessageObserver, ProcessType
from nexent.core.agents import CoreAgent, NexentAgent
from nexent.core.models import OpenAIModel
from nexent.core.tools import ExaSearchTool, KnowledgeBaseSearchTool

🤖 Creating Your First Agent ​

🔧 Setting Up the Environment ​

python
# Create message observer for streaming output
observer = MessageObserver()

# Create model (model and Agent must use the same observer)
model = OpenAIModel(
    observer=observer,
    model_id="your-model-id",
    api_key="your-api-key",
    api_base="your-api-base"
)

🛠️ Adding Tools ​

python
# Create search tools
search_tool = ExaSearchTool(
    exa_api_key="your-exa-key", 
    observer=observer, 
    max_results=5
)

# Create knowledge base tool
kb_tool = KnowledgeBaseSearchTool(
    top_k=5, 
    observer=observer
)

🤖 Building the Agent ​

python
# Create Agent with tools and model
agent = CoreAgent(
    observer=observer,
    tools=[search_tool, kb_tool],
    model=model,
    name="my_agent",
    max_steps=5
)

🚀 Running the Agent ​

python
# Run Agent with your question
result = agent.run("Your question here")

# Access the final answer
print(result.final_answer)

🎯 Advanced Usage Patterns ​

🔧 Custom Tool Integration ​

python
from nexent.core.tools import BaseTool

class CustomTool(BaseTool):
    def __init__(self, observer: MessageObserver):
        super().__init__(observer=observer, name="custom_tool")
    
    def run(self, input_text: str) -> str:
        # Your custom tool logic here
        return f"Processed: {input_text}"

# Add custom tool to agent
custom_tool = CustomTool(observer=observer)
agent.tools.append(custom_tool)

📡 Streaming Output Processing ​

python
# Monitor streaming output
def handle_stream(message: str, process_type: ProcessType):
    if process_type == ProcessType.MODEL_OUTPUT_THINKING:
        print(f"🤔 Thinking: {message}")
    elif process_type == ProcessType.EXECUTION_LOGS:
        print(f"⚙️ Executing: {message}")
    elif process_type == ProcessType.FINAL_ANSWER:
        print(f"✅ Answer: {message}")

# Set observer with custom handler
observer.set_message_handler(handle_stream)

🔧 Configuration Options ​

⚙️ Agent Configuration ​

python
agent = CoreAgent(
    observer=observer,
    tools=[search_tool, kb_tool],
    model=model,
    name="my_agent",
    max_steps=10,  # Maximum execution steps
    temperature=0.7,  # Model creativity level
    system_prompt="You are a helpful AI assistant."  # Custom system prompt
)

🔧 Tool Configuration ​

python
# Configure search tool with specific parameters
search_tool = ExaSearchTool(
    exa_api_key="your-exa-key",
    observer=observer,
    max_results=10,  # Number of search results
    search_type="neural",  # Search type: neural, keyword, etc.
    include_domains=["example.com"],  # Limit search to specific domains
    exclude_domains=["spam.com"]  # Exclude specific domains
)

📊 Error Handling ​

🛡️ Graceful Error Recovery ​

python
try:
    result = agent.run("Your question")
    print(f"Success: {result.final_answer}")
except Exception as e:
    print(f"Error occurred: {e}")
    # Handle error appropriately

🔧 Tool Error Handling ​

python
# Tools automatically handle errors and provide fallback options
search_tool = ExaSearchTool(
    exa_api_key="your-exa-key",
    observer=observer,
    max_results=5,
    fallback_to_keyword=True  # Fallback to keyword search if neural search fails
)

📚 More Resources ​

For more advanced usage patterns and detailed API documentation, please refer to: