LangGraph
Orchestrate resilient, stateful multi-agent systems with cyclic logic and human-in-the-loop control.
Enterprise-grade Python framework for building secure, modular AI agents and multi-step workflows.
Griptape is a sophisticated Python framework designed specifically for the enterprise-level development of AI applications, distinguishing itself from predecessors like LangChain through its strict adherence to modularity and security. Its architecture is built around three core pillars: Structures (Workflows, Pipelines, and Agents), Drivers (LLM, Vector Store, and Memory abstractions), and Tools. A standout technical feature is its 'Off-Prompt Task Memory' which prevents sensitive data from being re-sent to LLMs in conversational history, significantly reducing token costs and improving security posture. In the 2026 market, Griptape positions itself as the 'Production-Ready' alternative for developers who find other frameworks too prescriptive or difficult to debug. It provides a clean separation between the logic of the application and the underlying LLM providers, allowing for seamless transitions between models like Claude 3.5, GPT-5, or local Llama instances. By focusing on DAG-based (Directed Acyclic Graph) workflows, Griptape enables complex, multi-agent orchestrations that are predictable and scalable, catering to industries with high compliance requirements such as FinTech and HealthTech.
Stores intermediate tool outputs in a secure memory buffer rather than injecting the full content back into the prompt context window.
Orchestrate resilient, stateful multi-agent systems with cyclic logic and human-in-the-loop control.
Build Production-Ready AI Agents with Memory, Knowledge, and Tools
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Strict decoupling of the logic from the LLM, Vector Store, and Embedding models.
Enables the definition of complex tasks as a Directed Acyclic Graph with explicit dependencies.
Specialized abstraction layers for RAG, Summarization, and Extraction tasks.
A serverless environment for deploying and scaling Griptape structures via REST APIs.
Injection of static constraints and behavioral guidelines at the structure level.
Comprehensive event-driven architecture that emits detailed logs for every step of execution.
Manually cross-referencing thousands of invoices against bank statements is error-prone and slow.
Registry Updated:2/7/2026
Generate a Markdown report of discrepancies.
LLMs lack access to real-time shipping data and order history.
Context windows are too small for thousands of 100-page legal documents.