Chidori is an AI agent framework focused on building reliable and understandable agents. It provides tools for defining agent logic, managing workflows, and ensuring predictable behavior. A key feature is time-travel debugging, allowing developers to rewind agent execution to pinpoint errors. Chidori supports multiple programming languages and AI models, offering flexibility in agent development. It emphasizes observability, giving developers deep insights into agent decision-making. Ultimately, Chidori aims to simplify the creation of robust and manageable AI agents for complex tasks.
Ref: https://github.com/ThousandBirdsInc/chidori
Features:
Reactive Runtime: Chidori's core is a reactive runtime environment. This means that agents can respond dynamically to events and changes in their environment, making them more adaptable and responsive.
Time-Travel Debugging: This innovative feature allows developers to rewind the execution of an agent to a previous state. This makes debugging much easier, as you can step back through the agent's actions and identify exactly where things went wrong.
Observability: Chidori provides tools for monitoring and observing agent behavior. This gives developers deep insights into how their agents are making decisions and allows them to identify areas for improvement.
Code Interpreter Environments: Chidori supports code interpretation for Python and JavaScript. This means that agents can execute code directly within the framework, enabling them to perform complex tasks and interact with external systems.
Branching: Chidori allows for branching in agent execution. This means that agents can explore different possible paths and outcomes, making them more robust and able to handle unexpected situations.
Caching and Resumability: Chidori can cache agent behaviors and resume from partially executed states. This improves efficiency and allows agents to handle long-running tasks or interruptions.
Multi-Language Support: Chidori supports multiple programming languages, giving developers flexibility in how they build their agents.
Use cases:
Complex Task Automation: Chidori excels at building agents that can handle intricate, multi-step tasks. Its branching and reactive runtime are ideal for situations where the agent needs to adapt to changing conditions or explore different options. Think of tasks like automated planning, resource management, or complex decision-making.
AI-Powered Robotics: The reactive nature and code interpretation capabilities of Chidori make it suitable for robotics applications. Agents can be designed to control robots, respond to sensor input, and execute actions in a dynamic environment.
Simulation and Modeling: Chidori's features, particularly its ability to rewind execution and observe agent behavior, make it useful for building agents that simulate complex systems. This could be used for modeling economic systems, simulating traffic flow, or understanding the dynamics of social networks.
Workflow Orchestration with Reliability: Chidori can be used to build agents that orchestrate complex workflows, ensuring that all steps are executed correctly and that errors are handled gracefully. The focus on reliability makes it suitable for mission-critical applications.
Explainable AI (XAI): Chidori's observability features make it easier to understand how an AI agent arrived at a particular decision. This is important for building trust in AI systems and for debugging complex agent behavior.
