Chidori

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Chidori is a reactive runtime for building durable AI agents. It enables developers to create persistent and reactive AI workflows that maintain state over time.

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At a glance

Pricing

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Free tier

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API

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Skill level

Technical

About

What is chidori?

Chidori is a reactive runtime specifically engineered for the development of durable AI agents. This tool empowers developers to construct persistent and reactive AI workflows, ensuring that agents can respond dynamically to various events and effectively maintain their state across interactions. It focuses on the orchestration of AI agents, providing the necessary infrastructure to build robust and reliable AI systems that can operate consistently over extended periods. Chidori aims to simplify the creation of AI applications that require long-term memory and event-driven responses.

Best used for

Building robust and reliable AI agents that require persistence and reactivity to events.

Common actions

build AI agents

create AI workflows

manage AI state

orchestrate AI

develop reliable AI

github copilot"AI Agents"face swappingdeepfakeautomated workflowcollaborationopen-sourceworkflowslow-code/no-code

Capabilities

Key features

Reactive runtime
Durable AI agents
Persistent AI workflows
Agent orchestration
Maintain state

Target Audience

AI DevelopersSoftware EngineersAI Researchers

Integrations

Not yet documented

Pricing & Plans

unknown

Free

FAQs

What kind of programming languages or frameworks does Chidori integrate with for building durable AI agents?

The description doesn't specify particular language or framework integrations. As a 'reactive runtime,' it likely offers APIs or SDKs that developers can use with common programming languages, but specific details would require consulting Chidori's documentation.

How does Chidori handle the 'persistence' aspect for AI agents and their state across interactions?

Chidori is engineered for 'durable AI agents' and to 'maintain state,' suggesting it includes built-in mechanisms for state serialization, storage, and retrieval. This likely involves database integrations or its own persistent storage layer to ensure agents retain memory and context over time.

What are the typical use cases where Chidori's 'reactive runtime' and 'persistent AI workflows' would be most beneficial?

Chidori would be highly beneficial for applications requiring long-running, stateful AI interactions. Examples include advanced conversational AI, autonomous agents performing complex tasks over time, personalized user experiences, or systems needing to recover gracefully from interruptions while maintaining context.

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This tool also appears in

Productivity & Business › Workflow Automation

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