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BaseAI

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Basic Information

BaseAI is a TypeScript-first framework for building serverless, composable AI agents with integrated memory and tools. The repo provides a developer workflow for creating, running, and deploying AI agents called "pipes" which act as API-driven agents. It includes CLI commands to initialize projects and scaffold pipes, a conventional project layout with baseai/baseai.config.ts, memory, pipes, and tools directories, and examples showing how to configure a pipe with model settings, system prompts, memory and tool references. The README documents environment variable usage for multiple LLM providers and demonstrates how to run a local BaseAI server for development and streaming responses. The project emphasizes local-first development, configurable agent parameters, and support for retrieval-augmented generation via its memory capabilities.

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App URL
https://github.com/LangbaseInc/BaseAI
Github Repository
https://github.com/LangbaseInc/BaseAI/blob/main/README.md

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App Details

Features
The README highlights a set of concrete features and developer conveniences. It is TypeScript-first and supplies CLI commands such as init, pipe, and dev to scaffold projects and run a local server. Pipes are configurable agents with model selection, streaming, JSON output toggles, storage, moderation, temperature and penalty controls, tool choice and parallel tool calls, and message templates. The framework includes event-driven streaming APIs with a runner for connect, content, end, and error events. It supports memory and RAG workflows, a memory-agent quickstart, and multiple LLM provider key integrations as shown in the environment variables. The repo and docs emphasize collaboration, prompt versioning, message storage, and LLMOps for usage, cost, and quality monitoring.
Use Cases
BaseAI helps developers iterate quickly on agent designs, test locally, and integrate agents into applications as simple APIs. The scaffolded project structure and CLI reduce setup time and provide a clear place for pipe configs, tools, and memory. Streamed output and event listeners simplify building responsive interfaces or pipelines that consume partial results. Built-in memory and RAG support enable stateful agents and better retrieval capabilities for knowledge-heavy tasks. Configurable model parameters and tool integrations let teams tune behavior for production use while keeping keys and provider configs server-side. The README also describes collaborative workflows and developer-facing features that aid teams in building, versioning, and operating agents with observability of usage and quality.

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