GriptapeOpen-source Python framework for building AI agents and pipelines with minimal code.

4.8 (6)

Reviewed by Daniel Nikulshyn·Updated July 2026

Overview

Griptape is a Python framework designed to help developers build AI agents, pipelines, and workflows that interact with large language models, tools, and external data sources. It provides a structured way to compose LLM-driven applications without writing extensive boilerplate code. The framework includes built-in support for memory, retrieval-augmented generation, and modular tools that agents can call to perform tasks. Developers can connect to multiple LLM providers, vector stores, and APIs, making it suitable for building chatbots, research assistants, and automation systems. Griptape also offers Griptape Cloud, a managed environment for deploying and scaling agents, which complements the open-source library for teams moving from prototype to production.

Key features

Agent and pipeline abstractions
Tool integrations for APIs and data sources
Conversation and task memory
Vector store and RAG support
Multi-LLM provider compatibility
Optional managed cloud deployment

Pricing

Model: Freemium
Category: AI Agents
Rating: 4.8 / 5 (6)

Use cases

Create advanced AI pipelines visually

Build, group, and edit workflows effortlessly with a drag-and-drop interface, integrating seamlessly with the cloud.

Pros & Cons

Pros

Open-source and Python-native
Modular design for agents, tools, and pipelines
Built-in memory and RAG support
Works with multiple LLM providers

Cons

Requires Python development skills
Smaller community than larger frameworks
Documentation can be sparse for advanced use cases

Reviews

4.8

Average from 6 ratings.

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.