observee-agents 0.0.8

A Python SDK for MCP tool integration with LLM providers

Observee Agents

A Python SDK for seamless integration of MCP (Model Context Protocol) tools with multiple LLM providers including Anthropic Claude, OpenAI GPT, and Google Gemini.

Features

• 🤖 Multi-Provider Support: Works with Anthropic, OpenAI, and Gemini
• 🔧 Smart Tool Filtering: BM25, local embeddings, and cloud-based filtering
• ⚡ Fast Performance: Intelligent caching and optimization
• 🔑 Flexible Authentication: URL-based or API key authentication
• 🎯 Easy Integration: Simple sync/async API
• 📦 Pip Installable: Easy installation and distribution

Installation

# Basic installation
pip install observee-agents

# With optional dependencies
pip install observee-agents[embedding,cloud]

# Development installation
pip install observee-agents[dev]

Quick Start

Simple Synchronous Usage (Recommended)

from observee_agents import chat_with_tools

result = chat_with_tools(
    message="Search for recent news about AI developments",
    provider="anthropic",
    model="claude-sonnet-4-20250514",
    observee_api_key="obs_your_key_here"
)

print("Response:", result["content"])
print("Tools used:", len(result["tool_calls"]))

Explore Available Tools

from observee_agents import list_tools, get_tool_info, filter_tools

# List all available tools
tools = list_tools(observee_api_key="obs_your_key_here")
print(f"Found {len(tools)} tools:")
for tool in tools[:5]:  # Show first 5
    print(f"- {tool['name']}: {tool['description']}")

# Get detailed info about a specific tool
tool_info = get_tool_info(
    tool_name="youtube_get_transcript",
    observee_api_key="obs_your_key_here"
)
if tool_info:
    print(f"Tool: {tool_info['name']}")
    print(f"Description: {tool_info['description']}")

# Find relevant tools for a task
relevant_tools = filter_tools(
    query="search YouTube videos",
    max_tools=3,
    observee_api_key="obs_your_key_here"
)
for tool in relevant_tools:
    print(f"- {tool['name']} (relevance: {tool['relevance_score']})")

Execute Tools Directly

from observee_agents import execute_tool

# Execute a tool directly without LLM
result = execute_tool(
    tool_name="youtube_get_transcript", 
    tool_input={"video_url": "https://youtube.com/watch?v=dQw4w9WgXcQ"},
    observee_api_key="obs_your_key_here"
)
print(result)

Advanced Async Usage

import asyncio
from observee_agents import MCPAgent

async def advanced_example():
    async with MCPAgent(
        provider="anthropic",
        server_url="wss://mcp.observee.ai/mcp?client_id=your_id",
        auth_token="obs_your_key_here"
    ) as agent:
        result = await agent.chat_with_tools(
            message="What tools do you have access to?"
        )
        return result

result = asyncio.run(advanced_example())
print(result["content"])

Configuration

Environment Variables

# Option 1: API Key (Recommended)
export OBSERVEE_API_KEY="obs_your_key_here"
export OBSERVEE_CLIENT_ID="your_client_id"  # Optional

# Option 2: Direct URL
export OBSERVEE_URL="https://mcp.observee.ai/mcp"

# LLM Provider Keys
export ANTHROPIC_API_KEY="your_anthropic_key"
export OPENAI_API_KEY="your_openai_key" 
export GOOGLE_API_KEY="your_google_key"

Function Parameters

from observee_agents import chat_with_tools

result = chat_with_tools(
    message="Your query here",
    
    # Provider Configuration
    provider="anthropic",  # "anthropic", "openai", "gemini"
    model="claude-sonnet-4-20250514",  # Auto-detected if not provided
    
    # Authentication (priority: params > env vars)
    observee_api_key="obs_your_key",
    observee_url="https://custom.mcp.server/endpoint",
    client_id="your_client_id",
    
    # Tool Filtering
    enable_filtering=True,  # True for filtered tools, False for all tools
    filter_type="bm25",     # "bm25", "local_embedding", "cloud"
    max_tools=20,           # Maximum tools to filter
    min_score=8.0,          # Minimum relevance score
    
    # Performance
    sync_tools=False,       # True to clear caches and resync
    
    # Provider-specific args
    temperature=0.7,
    max_tokens=1000
)

Examples

Available Imports

# Main chat functionality
from observee_agents import chat_with_tools

# Tool exploration and management
from observee_agents import list_tools, get_tool_info, filter_tools, execute_tool

# Advanced usage
from observee_agents import MCPAgent

Multiple Providers

from observee_agents import chat_with_tools

# Anthropic Claude
result = chat_with_tools(
    message="Analyze this YouTube video",
    provider="anthropic",
    model="claude-sonnet-4-20250514"
)

# OpenAI GPT
result = chat_with_tools(
    message="Search for recent AI papers", 
    provider="openai",
    model="gpt-4o"
)

# Google Gemini
result = chat_with_tools(
    message="Help me manage my emails",
    provider="gemini", 
    model="gemini-2.5-pro"
)

Tool Filtering Options

from observee_agents import chat_with_tools

# Fast BM25 keyword filtering (default)
result = chat_with_tools(
    message="Find relevant tools",
    filter_type="bm25",
    max_tools=5
)

# Semantic embedding filtering
result = chat_with_tools(
    message="Find relevant tools",
    filter_type="local_embedding",
    max_tools=10
)

# Cloud hybrid search (requires API keys)
result = chat_with_tools(
    message="Find relevant tools",
    filter_type="cloud",
    max_tools=15
)

# No filtering - use all available tools
result = chat_with_tools(
    message="What can you do?",
    enable_filtering=False
)

Custom Configuration

from observee_agents import chat_with_tools

# Custom Observee server
result = chat_with_tools(
    message="Custom server query",
    observee_url="https://your-custom-server.com/mcp",
    client_id="custom_client_123"
)

# Force cache refresh
result = chat_with_tools(
    message="Get fresh results", 
    sync_tools=True  # Clears caches
)

Response Format

{
    "content": "The AI response text",
    "tool_calls": [
        {
            "name": "tool_name",
            "input": {"param": "value"}
        }
    ],
    "tool_results": [
        {
            "tool": "tool_name", 
            "result": "tool output"
        }
    ],
    "filtered_tools_count": 5,
    "filtered_tools": ["tool1", "tool2", "tool3"],
    "used_filtering": True
}

Available Tools

The SDK provides access to various MCP tools including:

• 📧 Gmail: Email management, search, compose, labels
• 🎥 YouTube: Video transcript retrieval and analysis
• 📋 Linear: Project management, issues, comments
• 🔍 Brave Search: Web search and local business lookup
• And many more...

Filter Types

BM25 Filter (Default)

• Speed: ⚡ ~1-5ms per query
• Best for: Fast keyword matching, production use
• Dependencies: None (built-in)

Local Embedding Filter

• Speed: ⚡ ~10ms per query
• Best for: Semantic search without cloud dependencies
• Dependencies: fastembed

Cloud Filter

• Speed: 🐌 ~300-400ms per query
• Best for: Highest quality hybrid search
• Dependencies: pinecone-client, openai
• Requirements: PINECONE_API_KEY, OPENAI_API_KEY

Development

# Clone and install in development mode
git clone https://github.com/observee-ai/mcp-agent-system.git #coming soon
cd mcp-agent-system
pip install -e .[dev]

# Run tests
pytest

# Format code
black observee_agents/

License

All rights reserved. This software is proprietary and confidential. Unauthorized copying, distribution, or use is strictly prohibited.

Support

• 📖 Documentation
• 🐛 Issue Tracker
• 💬 Discord Community
• 📧 Email Support