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MCP-Powered Agents: Wiring Gaia to ACI Tools
This guide details building an AI agent that interacts with GitHub, using Gaia for large language model (LLM) inference and ACI.dev for secure execution. The agent takes user requests, such as starring a repository, and the LLM proposes a tool call. ACI.dev then executes the tool call using the user's linked GitHub account, ensuring secure and auditable actions. Prerequisites include Python 3.10+, Gaia and ACI.dev accounts, and a configured GitHub app. A minimal example shows how to fetch a function schema from ACI, use it in the LLM prompt, and execute the resulting tool call via ACI.dev's SDK. The system prioritizes security by keeping credentials on the server and limiting the LLM's access to defined tools. The process is extensible; additional ACI functions can be added to expand agent capabilities. Troubleshooting sections address common issues like permission errors and incorrect owner IDs. The architecture emphasizes a secure and auditable workflow, with the execution layer remaining consistent even when integrated with other systems. This pattern provides a robust framework for building AI agents that perform real-world actions securely.
