experiential
16,582
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300
Forks
21
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Apache-2.0
许可证
Python
语言
2026-06-24
创建时间
2026-11-12
最近更新
项目能做什么
开源零加价 AI 网关,支持自有密钥、自托管与上千种模型,可分析流量以降低成本并优化选型。
来源:基于仓库元数据
· 内容以仓库实际 README 为准
使用说明 查看原始 README 原仓库文档
中文使用说明暂未生成。下面保留原仓库 README 摘录,命令与配置均以原文为准。
原始 README 摘录(英文原文,节选前 3000 字符)
# Experiential
[](https://github.com/experientiallabs/experiential/actions/workflows/gateway-latency.yml?query=branch%3Amain)
Experiential is an open source gateway and router for agent workflows:
1. Use hosted, BYOK, and local models through one OpenAI-compatible API.
2. Control which users and agents can use which models, for which use cases, and how much they can spend.
3. Turn production traffic into a custom router or model optimized for quality, speed, and cost.

<p align="center">
🌐 <a href="https://platform.experientiallabs.ai">Platform</a> |
📚 <a href="https://platform.experientiallabs.ai/docs">Docs</a> |
<a href="https://discord.gg/B6sM8xTVwU"><img src="https://cdn.simpleicons.org/discord/5865F2" alt="" width="16" height="16"> Discord</a>
</p>
## Getting Started
Start a local OpenAI-compatible gateway. On first run, the setup wizard uses the shared provider,
model, and reasoning-effort selectors, persists every selected provider connection, then shows
defaults for the public alias, identity, and `$50.00` command budget before printing a one-time key:
```bash
pip install experiential
exp
```
Choose a public alias such as `opus-5`, capture the issued key, and send a request:
```bash
export EXP_GATEWAY_KEY=...
curl http://127.0.0.1:8000/v1/chat/completions \
-H "Authorization: Bearer $EXP_GATEWAY_KEY" \
-H 'Content-Type: application/json' \
-d '{"model":"opus-5","messages":[{"role":"user","content":"Help me"}]}'
```
## Setup / get started with the hosted gateway
Prefer a managed gateway to running one locally? The hosted platform at
[platform.experientiallabs.ai](https://platform.experientiallabs.ai) serves the same
OpenAI-compatible (and Anthropic Messages) API at `https://api.experientiallabs.ai/v1`.
See [SETUP.md](./SETUP.md) for copy-paste prompts you hand to your coding agent
(Claude Code, Cursor, Codex, and similar); the agent runs the setup for you. It
collects four prompts:
- Upload your LLM traces as telemetry: create an account instantly from your email, then pull or upload your existing LLM traces onto the platform as telemetry.
- Connect your inference provider keys (BYOK): create an account, then connect your own OpenAI, Anthropic, Gemini, Azure, Bedrock, Fireworks, or OpenRouter keys for free pass-through.
- Start calling models on the gateway: make your first `/v1` call with the OpenAI and Anthropic SDKs using an `xpl_` key, and optionally repoint your existing coding agents.
- Full onboarding: create an account instantly from your email, connect your keys, import your spend, then repoint every coding agent (Claude Code, Cursor, Codex, Aider, and similar) o
阅读完整原文
商业机会分析 规则生成(AI 未配置)
综合评分 68.8 分。该项目在“个人可实施性”维度得 60 分,“付费可能性”维度得 60.0 分,差异化空间 70.0 分(评分依据见评分卡)。
将 experiential 改造为垂直场景 SaaS
| 产品构想 | 保留核心能力,针对某一类用户(如某行业团队)做托管版,收取月费。 |
| 目标用户 | 有该需求的中小团队 / 独立站长 |
| 难度/周期 | 中 · 2~4 周(MVP) |
| 收费模式 | 订阅制(如 ¥29~99/月) |
| 竞争风险 | 需验证付费意愿;注意许可证合规 |
| 依据 | 数据支持的判断:许可证与关键词显示存在商用与付费场景 |
面向细分需求的二次开发产品
| 产品构想 | 基于 experiential 的核心能力,聚焦一个更窄的场景(如某平台、某行业)做差异化。 |
| 目标用户 | 该细分场景用户 |
| 难度/周期 | 中 · 2~6 周 |
| 收费模式 | 买断制 / 订阅制 / 广告 |
| 竞争风险 | 细分需求规模需验证 |
| 依据 | AI 推测:基于项目主题标签的判断 |
封装为在线小工具或 API 服务
| 产品构想 | 把项目最常用的一个功能做成网页小工具,靠流量变现或按量计费。 |
| 目标用户 | 搜索引擎带来的长尾用户 |
| 难度/周期 | 低 · 1~3 周 |
| 收费模式 | 免费引流 + 增值功能 / API 按量计费 |
| 竞争风险 | 同质化竞争,需做好 SEO |
| 依据 | AI 推测:基于技术栈与部署特征的判断 |
以上为规则引擎基于仓库公开数据生成的参考结论,包含推测成分,不构成投资或创业承诺;配置 AI 后可获得更深入的分析。
报告会保存到 个人中心
竞品与相似项目 GitHub Search 真实检索
| 项目 | 中文简介 | Stars | 语言 |
|---|---|---|---|
| SwiftEducation/teaching-app-dev-swift | 中文介绍暂未生成 DEPRECATED. Instructor lesson plans that accompany Xcode pro |
685 | - |
| baidu-baige/LoongFlow | 中文介绍暂未生成 LoongFlow is an expert-grade Agent framework for Loop Engine |
484 | Python |
| Gen-Verse/Recuris | 中文介绍暂未生成 Recursive Experiential–Working Memory Evolution for Long-Hor |
273 | Python |
| meowtec/audio-cutter | 中文介绍暂未生成 🎵 Experiential ~ Cut audio online 在线的音频剪切器 |
209 | TypeScript |
| source-academy/frontend | 中文介绍暂未生成 Frontend of Source Academy, an online experiential environme |
111 | TypeScript |
| aws-samples/data-protection | 中文介绍暂未生成 Workshop and sample code that builders can use for experient |
90 | Python |
以上为通过 GitHub 官方 Search API 检索的相似项目,功能差异与优劣势需进一步人工确认,配置 AI 后可生成对比分析。
项目优势与风险
| 技术成熟度 | 创建于 2026-06-24,近 30 天仍在推送,发布过 100 个 Release |
| 社区活跃 | Star 16,582 / Fork 300 / Issue 161(数据支持,可自行核实) |
| 许可证 | Apache-2.0 |
| 主要风险 | 上游项目方向可能变化;同赛道竞争者较多(见竞品表);热度与需求不完全等价 |
安装与使用
快速开始(以仓库 README 为准)
git clone https://github.com/experientiallabs/experiential.git cd experiential # 请阅读仓库 README 获取确切安装步骤(依赖、配置、启动命令)
术语说明:Docker=应用容器(把程序和依赖打包运行);API Key=调用外部服务的密钥;Git Clone=把仓库代码下载到本地。常见错误:API Key 未配置 / 端口占用 / 数据库连接失败,多数项目在 README 的 Troubleshooting 章节有说明。