Griptape

Griptape is a Python framework for building AI applications and agents with a focus on predictability, structure, and enterprise readiness. Unlike frameworks that give LLMs maximum autonomy, Griptape emphasizes structured workflows where developers maintain control over agent behavior while still leveraging LLM intelligence for decision-making within defined boundaries.

The framework provides two core abstractions: Structures and Tools. Structures define how agents operate — Agents for single LLM interactions, Pipelines for sequential multi-step workflows, and Workflows for parallel and DAG-based execution patterns. Tools provide typed interfaces for agents to interact with external systems, with built-in tools for web search, file operations, database queries, API calls, and more.

Griptape's approach to agent memory is sophisticated, with conversation memory, task memory, and metadata memory working together to give agents appropriate context without overwhelming their context windows. The framework also provides built-in guardrails including output validation, content filtering, and structured response enforcement.

A differentiating feature is Griptape Cloud, a managed platform for deploying and running Griptape agents in production. Cloud provides managed execution environments, API endpoints, scheduling, monitoring, and secret management — reducing the operational burden of running agents in production. Structures deployed to Griptape Cloud get automatic scaling, logging, and error handling.

Griptape supports all major LLM providers through its driver system, including OpenAI, Anthropic, Google, AWS Bedrock, Azure, and local models. The framework integrates with vector databases for RAG and provides document loaders for various file formats. Griptape is particularly popular with enterprise teams who need the control and predictability that more freeform agent frameworks don't provide.