wesichain-react

Build ReAct agents in Rust by defining tool integrations that allow the LLM to iteratively select, execute, and reflect on tool results. This Skill structures the reasoning loop for autonomous, multi-step problem solving using external tools like calculators or search engines.

Yes, you can build resumable LLM agent workflows with checkpointing support in Rust. This capability allows long-running autonomous workflows to save their state and resume execution later without losing previous reasoning or tool use results.

Yes, building tool-using agents requires the wesichain-graph and wesichain-core crates. These dependencies provide the necessary graph execution framework and tool integration infrastructure for the agent to function effectively.

Creating autonomous multi-step reasoning agents involves implementing an LLM-driven loop where the agent reasons about the next best action based on previous tool execution results. This approach handles complex tasks like researching online, calculating, and summarizing findings autonomously.

Iterative reasoning allows LLM-driven agents to decide which tools to use, execute them, and reflect on the results in a continuous loop. The agent evaluates previous outcomes to determine the next best action for multi-step problem solving.

Checkpointing is needed for Rust agent workflows when processes are long-running or may be interrupted. It allows stateful workflows to be saved and resumed later, ensuring autonomous tasks like online research and calculations are not lost.