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ai agent sdk

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Basic Information

This repository provides a TypeScript software development kit for building autonomous AI agents aimed at the Zero-Employee Enterprise (ZEE). It is intended to help developers create intelligent, context-aware agents and multi-agent workflows using a consistent API. The SDK supports both single model inference calls and compositions of multiple agents that can call external tools. It supplies composable primitives designed to be extendable and flexible for advanced scenarios. The README shows quick start instructions including a project template command, examples of defining an agent with model, description and instructions, and assembling agents into a ZeeWorkflow that can be executed asynchronously. The project is distributed as an npm package, includes documentation and encourages community contributions under an MIT license.

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App URL
https://github.com/covalenthq/ai-agent-sdk
Github Repository
https://github.com/covalenthq/ai-agent-sdk/blob/main/README.md

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App Details

Features
The README highlights a few core features: a unified interface for interacting with large language models, first-class Agent constructs that pair a model with system prompts and instructions, and a Tools abstraction to extend agent capabilities with external integrations. It also provides ZEE Workflows which allow multiple agents to be composed into coordinated processes to solve complex problems. The SDK is designed for composability, extensibility and flexibility and includes a quick-start template generator to scaffold projects. Package metadata and badges indicate an npm package and linked documentation, and the project accepts issues, pull requests and contributions from the community.
Use Cases
The SDK helps teams quickly prototype and deploy autonomous agent systems by providing standard building blocks for models, agents, tools and workflows. Developers can scaffold projects using the provided template, define agents with provider and model identifiers, assemble those agents into ZeeWorkflows with a stated goal and run workflows programmatically. This reduces boilerplate for multi-agent orchestration and enables reuse of agent components. The unified LLM interface and tools abstraction simplify integrating different model providers and external services. Community resources, documentation and an active repository make it easier to get support and iterate on agent designs for automation and complex task coordination.

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