Griptape用于构建 AI 代理和管道的开源 Python 框架，代码量最小。

4.8 (6)

审阅者 Daniel Nikulshyn·更新 2026年7月

概览

Griptape 是一个 Python 框架，旨在帮助开发者构建与大型语言模型、工具和外部数据源交互的 AI 代理、管道和工作流。它提供了一种结构化的方式来组合 LLM 驱动的应用，而无需编写大量模板代码。该框架内置支持记忆、检索增强生成和模块化工具，代理可以调用这些工具执行任务。开发者可以连接多家 LLM 提供商、向量存储和 API，使其适用于构建聊天机器人、研究助手和自动化系统。 Griptape 还提供 Griptape Cloud，这是一个托管环境，用于部署和扩展代理，能够补充开源库，帮助团队从原型快速过渡到生产。

主要功能

代理和管道抽象
API 和数据源的工具集成
对话和任务内存
向量存储和 RAG 支持
多 LLM 提供商兼容性
可选的托管云部署

价格

模型: Freemium
分类: AI Agents
评分: 4.8 / 5 (6)

使用场景

可视化创建高级 AI 管道

使用拖放界面轻松构建、分组和编辑工作流，与云无缝集成。

优点 & 缺点

优点

开源且 Python 原生
代理、工具和管道的模块化设计
内置内存和 RAG 支持
与多个 LLM 提供商合作

缺点

需要 Python 开发技能
比更大的框架有更小的社区
高级用例的文档可能很稀疏

评测

4.8

6 个评分的平均值。

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Sofia Lindqvist

May 16, 2026

Solid for our team

We rolled this out across the team last quarter and open-source and Python-native. Tool integrations for APIs and data sources fits neatly into how we already work, and multi-LLM provider compatibility removed a step we used to do by hand. Documentation can be sparse for advanced use cases, which is the main caveat, but it has held up under daily use.

Linda Petersen

May 8, 2026

Compared a few options

Evaluated this against two competitors. Where it wins: agent and pipeline abstractions and modular design for agents, tools, and pipelines. Where it lags: requires Python development skills. On balance the feature set — especially agent and pipeline abstractions — justifies the 5 stars for our use case.

Aisha Khan

Apr 5, 2026

Compared a few options

Evaluated this against two competitors. Where it wins: agent and pipeline abstractions and works with multiple LLM providers. Where it lags: smaller community than larger frameworks. On balance the feature set — especially tool integrations for APIs and data sources — justifies the 5 stars for our use case.

Yuki Mori

Mar 13, 2026

Years in this space

I've evaluated a lot of these over the years. What stands out here is tool integrations for APIs and data sources — handled better than most — and modular design for agents, tools, and pipelines. Documentation can be sparse for advanced use cases is my one real gripe. Worth the time if this is your use case.

Gunnar Eriksson

Nov 17, 2025

Years in this space

I've evaluated a lot of these over the years. What stands out here is tool integrations for APIs and data sources — handled better than most — and built-in memory and RAG support. Worth the time if this is your use case.

Leila Hassan

Sep 9, 2025

Skeptical, then convinced

I went in skeptical — most tools in this space overpromise. It actually delivers on conversation and task memory, and works with multiple LLM providers caught me off guard. still, I'd recommend giving it a real trial.