Griptape

Griptape is an Apache 2.0 Python framework for building AI agents that separates data handling from the language model. Its off-prompt architecture keeps large payloads out of context windows by routing them through Task Memory, which agents can query without injecting raw content into prompts. The framework ships with Structures (Agent, Pipeline, Workflow) and a set of tool drivers covering vector stores, SQL databases, web scraping, and file I/O. Griptape Cloud adds hosted execution and observability on top of the open-source core.

Most agent frameworks treat the LLM context window as a storage medium. Need to process a 50-page PDF? Stuff it in the prompt. Need to query a SQL schema with 200 tables? Put the DDL in the system message. It works until it doesn't, and then you're debugging hallucinations that come from the model skimming over the irrelevant 80% of what you fed it.

Griptape takes a different position. Data handling is a first-class concern that belongs off the prompt, not inside it. The framework was built around this idea from the start, and it's the main reason to choose it over alternatives with bigger ecosystems.

Quick verdict

Griptape is a focused, well-designed Python framework for agents that need to work with real data sources. The off-prompt Task Memory system is genuinely useful for document and database tasks where context window management would otherwise consume a lot of engineering effort. The ecosystem is smaller than LangChain's, the documentation has gaps, and the community is growing but not yet large. If your use case maps to the off-prompt pattern, Griptape saves meaningful time. If it doesn't, LangChain or LangGraph will give you more to work with.

What Griptape is

Griptape launched as an open-source project backed by a startup of the same name. The Apache 2.0 core library is available at griptape-ai/griptape on GitHub and has accumulated roughly 1,900 stars. The framework is Python-only and targets the production tier of agent development, not research prototyping.

The core model is built around three concepts: Structures, Tasks, and Tools.

Structures define execution shape. An Agent is interactive: it takes user input, decides which tools to call, and loops until the task is done. A Pipeline is a sequential chain of Tasks that run one after another, with outputs flowing into inputs. A Workflow extends the pipeline model with parallel and branching execution for tasks that don't depend on each other.

Tasks are the unit of work inside Structures. A PromptTask sends a prompt to the LLM. A ToolkitTask gives the LLM access to tools and lets it decide which to call. A TextSummaryTask runs a summarization pass over the data it receives. Tasks can be chained explicitly, and each task type has a clear purpose rather than a catch-all LLMChain abstraction.

Tools are the data access layer. Griptape ships built-in tools for web scraping, vector store queries, SQL databases, file system access, and more. Tools communicate with Tasks through Task Memory, which is where the off-prompt architecture lives.

The off-prompt architecture

This is Griptape's main differentiator and worth explaining clearly.

When a standard agent calls a tool that returns a large document, the document content typically gets injected into the LLM's context window. That works for small payloads. For a 40-page report or a SQL query result with thousands of rows, it creates problems: you pay for all those input tokens, you hit context limits, and the model's attention is spread across content that's mostly irrelevant to the next step.

Task Memory routes large tool outputs into an in-memory store and hands the agent a reference instead of the raw content. The agent can then call a tool to query or summarize specific parts of that stored data without processing the whole thing. The LLM sees: "The document is stored in memory artifact xyz. Use the SummaryTool to extract the relevant section." It does not see all 40 pages.

In practice this means:

• Fewer tokens per API call
• Cheaper runs on large document tasks
• Less hallucination from the model picking up irrelevant content
• Cleaner separation between data retrieval and reasoning

The tradeoff is conceptual overhead. Developers new to Griptape need to understand when data flows through Task Memory versus directly through prompt context, and configure Tool outputs accordingly. The framework exposes off_prompt flags on tool return values to control this. It's not hard to use, but it's an extra mental model that simpler frameworks don't require.

Getting started

Install the core package:

pip install griptape

For specific provider support:

pip install "griptape[drivers-prompt-anthropic]"
pip install "griptape[drivers-vector-mongodb]"

A basic Agent that can search the web and summarize results:

from griptape.structures import Agent
from griptape.tools import WebScraperTool, PromptSummaryTool
from griptape.drivers.prompt import OpenAiChatPromptDriver

agent = Agent(
    tools=[
        WebScraperTool(off_prompt=True),
        PromptSummaryTool(off_prompt=False),
    ],
    prompt_driver=OpenAiChatPromptDriver(model="gpt-4o", max_tokens=1024),
)

agent.run("Summarize the main points from https://example.com/some-report")

The off_prompt=True on WebScraperTool tells Griptape to route scraped content through Task Memory. The PromptSummaryTool then operates on that stored content and puts its output back into the prompt context for the final response.

A Pipeline for sequential document processing:

from griptape.structures import Pipeline
from griptape.tasks import PromptTask, TextSummaryTask

pipeline = Pipeline(
    tasks=[
        PromptTask("Extract all action items from the following notes: {{ args[0] }}"),
        PromptTask("Format these action items as a numbered list with owners: {{ parent_output }}"),
    ]
)

pipeline.run("Met with design team on Monday. Sarah to update mockups by Friday. Jake will review copy next week.")

parent_output is the built-in variable that carries the previous Task's result into the next one. Pipelines are synchronous and sequential by default.

For parallel execution, a Workflow lets you branch and join:

from griptape.structures import Workflow
from griptape.tasks import PromptTask

research = PromptTask("Research recent AI developments", id="research")
analyze = PromptTask("Analyze market implications: {{ parents_output_text }}", id="analyze")
summarize = PromptTask("Write an executive summary: {{ parents_output_text }}", id="summarize")

workflow = Workflow(tasks=[research, analyze, summarize])
workflow.insert_tasks(research, [analyze], summarize)

workflow.run()

Tool drivers: what ships out of the box

Griptape's built-in tool library covers the most common agent data access patterns:

• WebScraperTool for fetching and parsing web pages
• SqlDriver integrations for PostgreSQL and SQLite queries
• VectorStoreDriver integrations for Pinecone, MongoDB Atlas, Marqo, OpenSearch, and others
• FileManagerTool for reading and writing local files
• DateTimeTool for date and time operations
• CalculatorTool for arithmetic without LLM math
• EmailTool for reading and sending email
• PromptSummaryTool for summarizing stored Task Memory artifacts

You can write custom tools by subclassing BaseTool and decorating methods with @activity. The Activity decorator defines the tool's name, description, and input schema, which gets used by the LLM to decide when and how to call it.

from griptape.tools import BaseTool
from griptape.utils.decorators import activity
from schema import Schema, Literal

class PriceCheckTool(BaseTool):
    @activity(
        config={
            "description": "Check the current price for a product SKU",
            "schema": Schema({Literal("sku", description="Product SKU"): str}),
        }
    )
    def check_price(self, params: dict) -> str:
        sku = params["values"]["sku"]
        # actual price lookup logic
        return f"SKU {sku} is currently $29.99"

Griptape Cloud

Griptape Cloud is the hosted layer on top of the open-source framework. It provides:

• Managed execution environments for running agents and pipelines without configuring your own servers
• Observability dashboards for tracing runs and inspecting task-by-task output
• Scheduled runs for recurring pipeline execution
• Shared artifact storage for Task Memory across runs

For teams who want to ship agents without managing infrastructure, Cloud reduces the operational overhead. It's a separate paid service with usage-based pricing. The open-source library runs fine without it, and for most teams building internally hosted agents, the core library is all they need.

Where Griptape fits in the landscape

Griptape vs LangChain

LangChain has ten times the community, more integrations, and more documentation. If you need to integrate an obscure data source, someone has probably written a LangChain loader for it. LangChain's model is also more flexible in ways that can create decision fatigue: there are multiple abstraction layers, multiple ways to chain things, and multiple deprecated APIs to navigate around. Griptape's Structures API is narrower but easier to reason about. The off-prompt Task Memory is also something LangChain does not have a direct equivalent for.

Griptape vs LangGraph

LangGraph is the better choice when you need explicit state management, conditional branching based on LLM output, and human-in-the-loop approval steps. Its graph model gives you precise control over what happens when. Griptape's Workflow covers parallel tasks but doesn't have LangGraph's conditional edge routing. For deterministic agentic control flow, LangGraph is the stronger tool. For data-heavy agents where off-prompt handling is the core concern, Griptape is cleaner.

Griptape vs CrewAI

CrewAI is role-based. You define agents as roles in a crew and let the framework handle delegation. It's faster to prototype for team-style multi-agent tasks. Griptape is more procedural: you define exactly what runs when and where data goes. If you need multi-agent role delegation, CrewAI is easier. If you need a single agent that handles large documents or databases without context bloat, Griptape is the better fit.

Real use cases

Document intelligence agents are the clearest match. An agent that processes legal contracts, research papers, or financial filings needs to work with large documents without injecting the full text into every prompt. Griptape's off-prompt routing handles this without custom memory management code.

Database query agents benefit similarly. Injecting a large SQL schema into every prompt is expensive and noisy. A Griptape agent can use the SqlDriver with off_prompt=True, store schema metadata in Task Memory, and query it selectively rather than flooding the context window.

Web research pipelines that scrape multiple pages, extract key points, and produce a summary report map naturally to Griptape's Pipeline structure. Each stage has a defined input, clear processing logic, and explicit output that flows to the next stage.

ETL-style agents that transform data through multiple steps (fetch, normalize, validate, write) benefit from Pipeline's sequential execution model where each Task's output is the next Task's input.

What to watch out for

The documentation is the biggest friction point. The official docs at docs.griptape.ai cover the basics well and include solid API reference material, but advanced topics like custom memory backends, complex Workflow branching, and Cloud integration have less depth than the core tutorial content. Expect to read source code for edge cases.

The Task Memory concept, while genuinely useful, creates a category of bugs that beginners don't expect. If a tool output goes to Task Memory but you reference parent_output in the next task, you're operating on a memory reference string rather than the actual content. Getting the off_prompt settings wrong produces confusing results. Griptape's error messages here could be clearer.

Multi-model support exists but isn't the framework's strength. If your use case requires routing different tasks to different models with cost optimization logic, LangChain or LangGraph give you more sophisticated prompt driver options. Griptape handles model switching adequately but doesn't make it a feature.

The verdict

Griptape rewards developers who take the time to understand its model. The Structures API is clean, the off-prompt architecture is genuinely useful for data-heavy agents, and the tool driver library covers enough ground that you're not writing boilerplate for the first few integrations. The tradeoffs are a smaller community than LangChain, documentation gaps in advanced areas, and a learning curve around Task Memory that simpler frameworks don't have.

If you're building agents that work with real data sources and you've found yourself fighting context window limits in other frameworks, Griptape is worth a serious evaluation. If you need ecosystem breadth or an established answer to every edge case, LangChain or LangGraph will give you more to start with.

Key features

• Structures API: Agents, Pipelines, and Workflows as first-class primitives
• Off-prompt data handling via Tools that keep data out of the context window
• Built-in vector store, SQL, web scraping, and file system tool drivers
• Task Memory for passing large data between tasks without bloating prompts
• Support for OpenAI, Anthropic, Cohere, Hugging Face, and local models
• Prompt drivers with retry logic and configurable token limits
• Griptape Cloud for hosted agent execution, observability, and scheduled runs