Step 1: Get your API Keys and Set up Marketplace
Go to https://app.observee.ai and create an API key for your profile. Then click Connect and enable servers
Get Your API Keys
- Observee API Key: Sign up at observee.ai → API Keys → Create new key
- LLM API Key: Get from Anthropic, OpenAI, or Google
Environment Setup
Create a .env file:
# Required
OBSERVEE_API_KEY=obs_your_api_key_here
# Choose one LLM provider
ANTHROPIC_API_KEY=your_key_here
OPENAI_API_KEY=your_key_here
GOOGLE_API_KEY=your_key_here
Step 2: Authenticate & Get Your Client ID
This step is optional for those who are not setting up an OAuth server.
from observee_agents import call_mcpauth_login
# Start authentication flow (optionally provide your own client_id)
response = call_mcpauth_login(
auth_server="gmail",
client_id="<client_id (uuid)>" # Optional: use your own UUID
)
print(f"Visit: {response['url']}")
# After you visit the URL and authenticate, you'll receive:
# {"success":true,"client_id":"<client_id (uuid)>"}
This will open a browser window for OAuth authentication. After successful login, you’ll receive a client_id that you’ll use in all subsequent API calls. You can optionally provide your own UUID as the client_id.Tip: You can reuse the same client_id to authenticate multiple services. Each service you authenticate adds its tools to that client_id’s available toolset.
Step 3: Install the Agents SDK
from observee_agents import chat_with_tools
# Use the client_id from authentication
result = chat_with_tools(
message="Search for recent news about AI developments",
provider="anthropic",
client_id="<client_id (uuid)>" # Use the client_id from Step 2
)
print("🤖 AI Response:", result["content"])
print("🔧 Tools Used:", len(result["tool_calls"]))
Streaming Responses
import asyncio
from observee_agents import chat_with_tools_stream
async def stream_example():
async for chunk in chat_with_tools_stream(
message="Check my emails and summarize them",
provider="anthropic"
):
if chunk["type"] == "content":
print(chunk["content"], end="", flush=True)
asyncio.run(stream_example())
Conversation with Memory
# First message
result1 = chat_with_tools(
message="Search for emails about the product launch",
client_id="<client_id (uuid)>",
session_id="assistant_001"
)
# Follow-up (remembers context)
result2 = chat_with_tools(
message="What date was mentioned in those emails?",
client_id="<client_id (uuid)>",
session_id="assistant_001" # Same session = memory
)
- Connect to 1000+ tools - Gmail, YouTube, Linear, Slack, and more
- Use multiple LLM providers - Anthropic, OpenAI, Gemini and Groq
- Created conversations with memory - Context preserved across messages
Next Steps
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