● Covered by 1 source · 1 reportLow impact1 neutral
Mistral patents method for LLM-generated code to execute tool calls in a sandbox
Key points
- LLM generates code block for tool calls.
- Code executes in a server-side sandbox.
- Pending tool calls are sent to a client for execution.
- Results from client are substituted back into the code block.
Patented Tool Call Execution
Mistral has patented a method for large language models (LLMs) to execute tool calls. The process involves an LLM generating a code block in a programming language based on a user request. This code block encapsulates one or more tool calls.
Sandbox Execution and Client Interaction
The server executes the generated code block within a sandbox environment. If a pending tool call is encountered during execution, the code block's execution is paused. The pending tool call is then transmitted to a client for external execution. Upon receiving the result from the client, the server resumes the code block's execution, substituting the client's result for the pending tool call.
Return to LLM
After the code block completes its execution with all tool call results integrated, the final result of the executed code block is returned to the originating LLM. This method outlines a structured approach for LLMs to interact with and utilize external tools through code generation and controlled execution.
Implications for LLM Agents
This patent describes a specific technical mechanism for enabling LLMs to perform actions beyond their core language generation capabilities. It details a system for managing the execution flow between an LLM, a server-side sandbox, and external client-side operations, which is relevant for the development of more capable LLM-based agents.
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Reporting from
Mistral has patented a method where a large language model (LLM) generates code for tool calls, executes it in a sandbox, and handles client-side execution of pending calls. This patent describes a specific technical approach for integrating external tools with LLMs, which could influence how LLM-based agents interact with external systems.