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Basic Information

SmythOS SRE is an operating system style platform and SDK for building, deploying, orchestrating, and managing agentic AI at scale. The repository is a monorepo that contains the SRE core runtime (the kernel), a developer SDK, and a CLI for scaffolding and project management. It provides a unified resource abstraction so agents can access storage, LLMs, VectorDBs, caches, and vaults through consistent APIs. The project targets production use cases with built-in security, a Candidate/ACL authorization model, and a modular connector system to swap providers without changing business logic. The repo includes examples, documentation, a .smyth agent format, and 40+ production-ready components for common AI tasks such as LLM interactions, web scraping, data indexing, storage and serverless code execution.

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App Details

Features
Unified resource abstraction that exposes identical APIs for storage, LLMs, VectorDBs, cache and vault providers. Agent-first design with an SDK that supports TypeScript and a CLI for quick project scaffolding. Modular connector system with built-in connectors for Storage (Local, S3, GCS, Azure), LLMs (OpenAI, Anthropic, Google AI, AWS Bedrock, Groq), VectorDBs (Pinecone, Milvus, RAMVec), and Cache (RAM, Redis). Security-first Candidate/ACL authorization and secure credential management. Component catalog of 40+ production-ready building blocks including GenAILLM, ImageGen, APICall, WebSearch, DataSourceIndexer, JSONFilter and serverless code execution. Support for a symbolic .smyth workflow format, streaming and chat modes, observability, and cloud-native deployment patterns.
Use Cases
SmythOS reduces the operational and engineering friction of shipping intelligent agents by providing a consistent runtime and SDK that work from development through production. Teams can keep business logic unchanged while swapping infrastructure connectors to optimize cost, performance, or compliance. Built-in security and Candidate/ACL controls enable safer multi-tenant and enterprise deployments. The component and connector system accelerates integration with LLMs, VectorDBs, storage, caches and vaults, while the CLI and examples let developers scaffold projects quickly. Features like streaming responses, conversational chat memory, vectordb search, and storage primitives demonstrate practical workflows such as an article writer agent. Overall it centralizes orchestration, observability and resource management for production-grade agent workloads.

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