Note - This method uses uv, a fast Python installer and toolchain, to run the ACI.dev MCP server directly from the command line, as defined in our configuration script.
.env
file.
.env
file in your project folder with these variables:
your_gemini_api_key_here
with your GEMINI API key for the Gemini model (get it from Googleβs API console)your_aci_api_key_here
with the API key from ACI.devβs Project Settingsyour_linked_account_owner_id_here
with the ID from the aci.dev platformcreate_config.py
script to set up the MCP server connection:
main.py
script to run the CAMEL AI agent:
main_toolkit.py
.env
file has the required variables (same as MCP method)Feature | MCP Approach | ACIToolkit Approach |
---|---|---|
Setup Complexity | More complex (requires config files) | Simpler (direct import) |
Flexibility | High (full MCP ecosystem) | Moderate (ACI-focused) |
Performance | Slightly more overhead | More direct, faster |
Use Case | Complex multi-server setups | Quick integrations |
Dependencies | Requires uv and MCP config | Just CAMEL and ACI |
.env
file has the correct ACI_API_KEY
and LINKED_ACCOUNT_OWNER_ID
.import nest_asyncio; nest_asyncio.apply()
at the top of main_mcp.py
to handle async conflicts.pip install --upgrade "camel-ai[all]"