经 AI Skill Hub 精选评估,spiceai Agent工作流 获评「强烈推荐」。已获得 2.9k 颗 GitHub Star,这款AI工具在功能完整性、社区活跃度和易用性方面表现出色,AI 评分 8.2 分,适合有一定技术背景的用户使用。
spiceai Agent工作流 是一款基于 Rust 开发的开源工具,专注于 SQL引擎、LLM推理、全文搜索 等核心功能。作为 GitHub 开源项目,它拥有活跃的社区支持和持续的版本迭代,代码完全透明可审计,支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流,都能提供稳定可靠的解决方案。
spiceai Agent工作流 是一款基于 Rust 开发的开源工具,专注于 SQL引擎、LLM推理、全文搜索 等核心功能。作为 GitHub 开源项目,它拥有活跃的社区支持和持续的版本迭代,代码完全透明可审计,支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流,都能提供稳定可靠的解决方案。
# 方式一:cargo install(推荐) cargo install spiceai # 方式二:从源码编译 git clone https://github.com/spiceai/spiceai cd spiceai cargo build --release # 二进制在 ./target/release/spiceai
# 查看帮助 spiceai --help # 基本运行 spiceai [options] <input> # 详细使用说明请查阅文档 # https://github.com/spiceai/spiceai
# spiceai 配置说明 # 查看配置选项 spiceai --config-example > config.yml # 常见配置项 # output_dir: ./output # log_level: info # workers: 4 # 环境变量(覆盖配置文件) export SPICEAI_CONFIG="/path/to/config.yml"
<p align="center"> <img src="https://github.com/user-attachments/assets/13ff4c9d-d6a7-4c20-9408-45573c508c41" alt="spice oss logo" width="600"/> </p> <div align="center">
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<p align="center"> <a href="https://spiceai.org/docs">📄 Docs</a> | <a href="#%EF%B8%8F-quickstart-local-machine">⚡️ Quickstart</a> | <a href="https://github.com/spiceai/cookbook">🧑🍳 Cookbook</a> | <a href="https://github.com/spiceai/skills">🤖 AI Skills</a> | <a href="https://spice.ai/blog">📰 Blog</a> </p>
Spice (Spice.ai Open Source) is a data and AI engine that brings your data to your application. Connect it to the databases, data warehouses, data lakes, and APIs you already use, and it serves that data to your applications and AI agents through standard APIs — with SQL query, search, and LLM inference built in. Queries on an accelerated dataset return in milliseconds because Spice keeps a local, continuously refreshed copy of it, so your production systems don't take the load. Other datasets are queried in place at the source, with query push-down.
How it works:
spicepod.yaml.Spice is a single binary written in Rust on Apache DataFusion, Apache Arrow, and Vortex. Run it next to your application as a sidecar, as a standalone service, or as a multi-node distributed cluster. This repository contains the Apache-2.0 runtime (spiced) and CLI (spice); Spice.ai Cloud and Spice.ai Enterprise are optional managed and enterprise distributions built on it.
<img width="740" alt="Spice.ai Open Source accelerated data query and LLM-inference engine" src="https://github.com/user-attachments/assets/9db94f9c-10a1-47b0-ab45-05aa964590ff" />
🎯 Goal: Build data-grounded AI apps and agents in minutes, not months. No pipelines. No glue. Just SQL, search, and inference — federated across your data, accelerated locally, served on localhost.
🆕 New in Spice 2.0 — add a real-time analytics node to your operational database. Point Spice at PostgreSQL, MySQL, or MongoDB and it maintains a sandboxed, analytics-ready replica with high-throughput CDC replication — sub-second queries, ~2-second freshness, and zero analytical load on production. No ETL, no Debezium, no Kafka required. Read the Spice 2.0 launch →
Install the Spice CLI:
On macOS, Linux, and WSL:
curl https://install.spiceai.org | /bin/bash
Or using brew:
brew install spiceai/spiceai/spice
On Windows using PowerShell:
iex ((New-Object System.Net.WebClient).DownloadString("https://install.spiceai.org/Install.ps1"))
Note: Native Windows runtime builds are not provided in v2.0+. Use WSL for local development.
docker pull spiceai/spiceai
FROM spiceai/spiceai:latest
<https://github.com/spiceai/spiceai/assets/88671039/85cf9a69-46e7-412e-8b68-22617dcbd4e0>
Step 1. Initialize a new Spice app with the spice init command:
spice init spice_qs
A spicepod.yaml file is created in the spice_qs directory. Change to that directory:
cd spice_qs
Step 2. Start the Spice runtime:
spice run
Example output (abridged):
INFO Spice.ai runtime starting...
2026-09-25T22:41:29.701252Z INFO spiced: Starting runtime v2.3.2+models
2026-09-25T22:41:29.712500Z INFO runtime::init::caching: Initialized sql results cache; max size: 128.00 MiB, item ttl: 1s, hashing algorithm: XXH3, encoding: none
2026-09-25T22:41:29.823292Z INFO runtime: No datasets or catalogs were configured. If this is unexpected, check the Spicepod configuration.
2026-09-25T22:41:29.823342Z INFO runtime: All components are loaded. Spice runtime is ready!
2026-09-25T22:41:29.917854Z INFO runtime::flight: Spice Runtime Flight listening on 127.0.0.1:50051
2026-09-25T22:41:29.918583Z INFO runtime::http: Spice Runtime HTTP listening on 127.0.0.1:8090
The runtime is now started and ready for queries.
Step 3. In a new terminal window, add the spiceai/quickstart Spicepod. A Spicepod is a package of configuration defining datasets and ML models.
spice add spiceai/quickstart
The spicepod.yaml file will be updated with the spiceai/quickstart dependency.
version: v2
kind: Spicepod
name: spice_qs
dependencies:
- spiceai/quickstart
The spiceai/quickstart Spicepod will add a taxi_trips data table to the runtime which is now available to query by SQL.
2026-10-07T04:34:55.981490Z INFO runtime::init::dataset: Dataset taxi_trips registered (s3://spiceai-demo-datasets/taxi_trips/2024/), acceleration (cayenne), results cache enabled. duration_ms=120
2026-10-07T04:34:55.982876Z INFO runtime_table::accelerated::refresh_task: Loading data for dataset taxi_trips
2026-10-07T04:35:23.020467Z INFO runtime_table::accelerated::refresh_task: Loaded 2,964,624 rows (398.60 MiB) for dataset taxi_trips in 27s 37ms.
Step 4. Start the Spice SQL REPL:
spice sql
The SQL REPL interface will be shown:
Welcome to the Spice.ai SQL REPL! Type `help` or `?` for commands.
Examples:
show tables; -- list available tables
describe <table_name>; -- show column types
nql <question> -- natural language to SQL (requires a model)
sql>
Enter show tables; to display the available tables for query:
sql> show tables;
+---------------+--------------+--------------+------------+
| table_catalog | table_schema | table_name | table_type |
| varchar | varchar | varchar | varchar |
+---------------+--------------+--------------+------------+
| spice | public | taxi_trips | BASE TABLE |
| spice | runtime | task_history | BASE TABLE |
+---------------+--------------+--------------+------------+
Time: 0.001421875 seconds. 2 rows.
Enter a query to display the longest taxi trips:
SELECT trip_distance, total_amount FROM taxi_trips ORDER BY trip_distance DESC LIMIT 10;
Output:
+---------------+--------------+
| trip_distance | total_amount |
| float64 | float64 |
+---------------+--------------+
| 312722.3 | 22.15 |
| 97793.92 | 36.31 |
| 82015.45 | 21.56 |
| 72975.97 | 20.04 |
| 71752.26 | 49.57 |
| 59282.45 | 33.52 |
| 59076.43 | 23.17 |
| 58298.51 | 18.63 |
| 51619.36 | 24.2 |
| 44018.64 | 52.43 |
+---------------+--------------+
Time: 0.00242825 seconds. 10 rows.
<https://github.com/spiceai/spiceai/assets/80174/7735ee94-3f4a-4983-a98e-fe766e79e03a>
See more demos on YouTube.
Access ready-to-use Spicepods and datasets hosted on the Spice.ai Cloud Platform with the open-source Spice runtime. Browse public Spicepods at spicerack.org.
To use public datasets, create a free account on Spice.ai:
Once set up, you can access ready-to-use Spicepods including datasets. For this demonstration, use the taxi_trips dataset from the Spice.ai Quickstart.
Step 1. Initialize a new project.
spice init spice_app
cd spice_app
Step 2. Log in and authenticate. A pop-up browser window will prompt you to authenticate:
spice login
Step 3. Start the runtime:
spice run
Step 4. Configure the dataset:
In a new terminal window:
spice dataset configure
dataset name: (spice_app) taxi_trips
description: Taxi trips dataset
from: spice.ai/spiceai/quickstart/datasets/taxi_trips
locally accelerate (y/n)?: (y) y
Saved datasets/taxi_trips/dataset.yaml
Step 5. Query from the SQL REPL:
spice sql
SELECT tpep_pickup_datetime, passenger_count, trip_distance from taxi_trips LIMIT 10;
Spin up one Spice runtime per tenant or agent — each with its own sandboxed datasets, accelerators, secrets, and policies. Or share a runtime with config-level tenant isolation. Or do both with a hybrid model. A runtime that idles at ~140MB of memory makes "one Spicepod per tenant" actually viable — even at thousands of tenants. Read the patterns →
Spiceai 是一个高性能的数据加速与预取平台,旨在通过主动物化(Materialization)和数据预取技术,为 AI 应用提供极速的数据访问能力。它不仅仅是一个传统的 Cache,更是一个能够根据特定触发器或 CDC(变更数据捕获)自动处理数据的智能加速层,帮助开发者构建响应更迅速的 AI 系统。
Spiceai 引入了全新的加速机制,通过主动预取和数据物化技术,解决了传统缓存“命中后才获取”的滞后问题。它支持基于间隔或触发器的自动化数据处理,能够为 AI 代理提供实时、过滤后的高质量数据集,显著提升数据流转效率与系统响应速度。
您可以通过多种方式安装 Spice CLI。在 macOS、Linux 或 WSL 环境下,可以使用 curl 命令或通过 brew 进行安装;Windows 用户建议通过 PowerShell 执行安装脚本。需要注意的是,从 v2.0+ 版本开始,原生 Windows 运行时不再提供,建议开发者使用 WSL 环境。此外,您也可以通过 Docker 直接拉取 spiceai/spiceai 镜像进行容器化部署。
快速上手非常简单:首先使用 `spice init` 命令初始化一个新的 Spice 应用,系统会自动生成 `spicepod.yaml` 配置文件;随后进入项目目录并执行 `spice run` 启动 Spice runtime。通过这种方式,您可以轻松在本地环境运行并测试您的 Spicepod 逻辑。
除了本地运行,您还可以选择使用 Spice.ai Cloud Platform。通过该平台,您可以访问托管在云端的现成 Spicepods 和数据集。开发者可以前往 spicerack.org 浏览公开的 Spicepods,并通过在 spice.ai 注册免费账号来获取对公共数据集的使用权限,实现云端与开源运行时的无缝结合。
Spiceai 为 AI Agent 提供了灵活的多租户(Multi-tenancy)解决方案。开发者可以为每个租户或 Agent 启动独立的 Spice runtime,实现沙箱化的数据集、加速器、Secrets 和策略隔离;也可以通过配置级隔离实现共享 runtime,甚至采用混合模型。得益于其轻量级的 runtime 设计,即使在高并发场景下,“一租户一 Spicepod”的模式也具有极高的可行性。
针对常见疑问,Spiceai 并非传统的 Cache。传统缓存是在 Miss 时才进行获取,而 Spice 是一种“主动型”加速器,它通过预取(Prefetch)和物化(Materialize)机制,在数据被请求前就已准备就绪。同时,Spice 也支持结果缓存(Results Caching)功能,以优化重复查询的性能。
高质量开源项目,Rust实现保证性能和安全性。功能完整覆盖SQL查询、搜索和推理,社区活跃度良好,适合生产环境使用。
AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
✅ Apache 2.0 — 宽松开源协议,可商用,需保留版权声明和 NOTICE 文件,含专利授权条款。
AI Skill Hub 点评:spiceai Agent工作流 的核心功能完整,质量优秀。对于AI 技术爱好者来说,这是一个值得纳入个人工具库的选择。建议先在非生产环境试用,再逐步推广。
| 原始名称 | spiceai |
| 原始描述 | 开源AI工作流:A portable accelerated SQL query, search, and LLM-inference engine, written in R。⭐2.9k · Rust |
| Topics | SQL引擎LLM推理全文搜索数据联邦高性能 |
| GitHub | https://github.com/spiceai/spiceai |
| License | Apache-2.0 |
| 语言 | Rust |
收录时间:2026-05-18 · 更新时间:2026-05-19 · License:Apache-2.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。