Saiki lets developers describe AI agents in YAML, orchestrate multi-agent workflows, integrate external APIs, and monitor execution logs. It supports custom code plugins, parameterized prompts, retry logic, and deployment via RESTful endpoints, enabling teams to test and scale autonomous agents with full traceability and performance metrics.
Saiki lets developers describe AI agents in YAML, orchestrate multi-agent workflows, integrate external APIs, and monitor execution logs. It supports custom code plugins, parameterized prompts, retry logic, and deployment via RESTful endpoints, enabling teams to test and scale autonomous agents with full traceability and performance metrics.
Saiki is an open-source agent orchestration framework that empowers developers to build complex AI-driven workflows by writing declarative YAML definitions. Each agent can perform tasks, call external services, or invoke other agents in a chained sequence. Saiki provides a built-in REST API server, execution tracing, detailed log output, and a web-based dashboard for real-time monitoring. It supports retries, fallbacks, and custom extensions, making it easy to iterate, debug, and scale robust automation pipelines.
Who will use Saiki?
AI/ML Developers
Software Engineers
Automation Engineers
AI Researchers
Product Managers
How to use the Saiki?
Step1: Install Saiki via npm or pip package manager.
Step2: Write agent definitions and workflows in a YAML file.
Step3: Configure external API integrations and custom plugins.
Step4: Launch the Saiki server to expose REST endpoints.
Step5: Monitor execution and logs via the web dashboard or CLI.
Step6: Iterate YAML configs and redeploy as needed.
Platform
web
mac
windows
linux
Saiki's Core Features & Benefits
The Core Features
YAML-based agent and workflow definitions
Multi-agent orchestration and chaining
External API integration
REST API server for deployment
Execution tracing and detailed logging
Retry and fallback mechanisms
Custom code and plugin support
Web-based monitoring dashboard
The Benefits
Accelerates agent-driven automation development
Improves workflow transparency and debugging
Simplifies integration with external services
Enables scalable deployment via REST endpoints
Offers extensibility through plugins and code hooks
Provides real-time monitoring and performance metrics
Saiki's Main Use Cases & Applications
Building autonomous customer support workflows
Orchestrating multi-step data processing pipelines
Coordinating research assistants for document analysis
Automating decision support across microservices
Prototyping AI-driven task automation in dev environments
Saiki's Pros & Cons
The Pros
Leverages advanced natural language processing for accurate text analysis
Supports multiple applications such as sentiment analysis and summarization
Provides both interface and API for ease of integration
Useful for various industries to derive actionable insights from text