Modular open-source Python framework for building AI agents and LLM workflows with structures, tools, memory, drivers, and RAG engines, from Griptape.
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## Editorial notes
Griptape is useful when Claude-adjacent Python teams want to build agents and LLM workflows as structured, modular application code rather than ad hoc prompt scripts. It provides Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory, Drivers, and RAG Engines so developers can compose agent behavior, tool use, and retrieval with explicit building blocks.
This is distinct from existing agent-framework entries. CrewAI focuses on role-based multi-agent crews, LangGraph focuses on graph and stateful workflows, Mastra is a TypeScript agent framework, Pydantic AI is the Pydantic team's type-safe Python framework, and AutoGen is Microsoft's multi-agent framework. Griptape is a modular Python framework centered on structures, tools, drivers, and off-prompt memory for building and deploying LLM workflows.
## Source notes
- The official repository describes Griptape as a modular Python framework for LLM workflows, tools, memory, and data.
- The Griptape documentation organizes the framework around Structures (Agent, Pipeline, Workflow), Tasks, Tools, Memory, Drivers, Engines, and Loaders for composing agent and workflow behavior.
- Griptape emphasizes off-prompt Task Memory, which stores large or sensitive tool outputs outside the prompt and passes references between tools and tasks to control context size and data exposure.
- Drivers provide swappable integrations for prompt models, embeddings, vector stores, conversation memory, and related services, so the same structure can target different providers.
- Griptape also offers Griptape Cloud, a managed platform for hosting and running structures and data, separate from the open-source framework.
- The GitHub repository is `griptape-ai/griptape`, is Apache-2.0 licensed, and is distributed as the `griptape` package on PyPI.
## Duplicate check
Checked current `content/tools/`, `content/mcp/`, agents, skills, hooks, rules, commands, guides, open pull requests, and repository-wide content for `Griptape`, `griptape`, `griptape-ai`, `griptape.ai`, `docs.griptape.ai`, `github.com/griptape-ai/griptape`, `Griptape Cloud`, `off-prompt memory`, and `LLM workflow framework`. Existing agent-framework entries such as CrewAI, LangGraph, Mastra, Pydantic AI, and AutoGen cover adjacent workflows, but no dedicated Griptape tools entry, Griptape source URL duplicate, or open duplicate PR was found.
## Disclosure
Editorial listing. No paid placement or affiliate link is used.Griptape is useful when Claude-adjacent Python teams want to build agents and LLM workflows as structured, modular application code rather than ad hoc prompt scripts. It provides Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory, Drivers, and RAG Engines so developers can compose agent behavior, tool use, and retrieval with explicit building blocks.
This is distinct from existing agent-framework entries. CrewAI focuses on role-based multi-agent crews, LangGraph focuses on graph and stateful workflows, Mastra is a TypeScript agent framework, Pydantic AI is the Pydantic team's type-safe Python framework, and AutoGen is Microsoft's multi-agent framework. Griptape is a modular Python framework centered on structures, tools, drivers, and off-prompt memory for building and deploying LLM workflows.
griptape-ai/griptape, is Apache-2.0 licensed, and is distributed as the griptape package on PyPI.Checked current content/tools/, content/mcp/, agents, skills, hooks, rules, commands, guides, open pull requests, and repository-wide content for Griptape, griptape, griptape-ai, griptape.ai, docs.griptape.ai, github.com/griptape-ai/griptape, Griptape Cloud, off-prompt memory, and LLM workflow framework. Existing agent-framework entries such as CrewAI, LangGraph, Mastra, Pydantic AI, and AutoGen cover adjacent workflows, but no dedicated Griptape tools entry, Griptape source URL duplicate, or open duplicate PR was found.
Editorial listing. No paid placement or affiliate link is used.
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[](https://heyclau.de/entry/tools/griptape)Griptape side by side with 3 alternatives on trust, install, platform support, and disclosed safety notes — all from reviewed registry metadata.
1 trust signal differ across this comparison (Submitter).
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| Field | Modular open-source Python framework for building AI agents and LLM workflows with structures, tools, memory, drivers, and RAG engines, from Griptape. Open dossier | Open-source Python AgentOS and multi-agent framework, evolved from AutoGen, for building conversable agents, group chats, swarms, human-in-the-loop workflows, tool use, RAG, code execution, and provider-backed agent systems. Open dossier | Apache-2.0 Python framework for building visible, controllable, production AI agents and multi-agent services with event streaming, permission controls, workspaces, sandbox backends, middleware, MCP support, Mem0 memory, agent teams, and multi-tenant multi-session serving. Open dossier | Open-source Python multi-agent framework for building agent societies, role-playing agents, stateful ChatAgent workflows, RAG agents, synthetic data generation, MCP-enabled use cases, and research-scale agent experiments. Open dossier |
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| Next stepsDiffers | ||||
| Trust | ||||
| Review status | ReviewedMaintainer reviewed | ReviewedMaintainer reviewed | ReviewedMaintainer reviewed | ReviewedMaintainer reviewed |
| Package trust | Package not verified | Package not verified | Package not verified | Package not verified |
| Source provenance | Source-backed | Source-backed | Source-backed | Source-backed |
| SubmitterDiffers | davion-knight | — | — | — |
| Install risk | Review first | Review first | Review first | Review first |
| Notes | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ |
| Brand | ||||
| Category | tools | tools | tools | tools |
| Source | source-backed | source-backed | source-backed | source-backed |
| Author | Griptape | AG2 | AgentScope | CAMEL-AI |
| Added | 2026-07-09 | 2026-06-18 | 2026-06-18 | 2026-06-18 |
| Platforms | CLI | CLI | CLI | CLI |
| Source repo | — | — | — | — |
| Safety notes | ✓Griptape structures (Agent, Pipeline, Workflow) can call tools that run code, execute shell or Python, query databases, scrape the web, call external APIs, and read or write files; review each tool's side effects before enabling it. Tool names, descriptions, schemas, rulesets, and retrieved documents become model-facing context, so treat them as untrusted input that can influence the agent. Off-prompt Task Memory keeps large or sensitive tool outputs out of the prompt by default, but data returned to the LLM, other tools, or downstream tasks still needs review. Human-in-the-loop approval, rate limits, timeouts, and rollback policies belong on any tool that performs account, billing, data, or infrastructure actions. Keep production permissions narrower than notebook or demo examples, and scope model-provider and tool credentials to least privilege. | ✓AG2 agents can converse, call tools, execute code, use retrieval systems, run browser workflows, and coordinate group chats; require explicit permissions and approval gates for high-impact actions. The upstream install docs and examples commonly involve provider credentials; keep API keys, config files, notebooks, and `.env` files out of commits and support tickets. Code execution, Docker, Jupyter, browser-use, and RAG extras can touch local files, network services, notebooks, databases, and external websites; scope them tightly before granting agent access. Multi-agent conversations can continue through nested chats, swarms, group chats, and custom reply handlers; define termination, escalation, retry, and human takeover behavior. Track the release roadmap before upgrading because deprecations and the v1.0 transition can change which APIs should be used for new work. | ✓AgentScope examples can give agents Bash, file-read, file-write, edit, search, MCP, and custom tools. Scope tool permissions and approval rules before connecting a real project or account. The README demonstrates permission control, including bypass mode. Do not use bypass-style behavior on production systems, sensitive files, paid APIs, cloud resources, or unreviewed tool chains without compensating controls. Workspace support can run tools and code through local, Docker, or E2B backends; review filesystem mounts, network access, secrets, resource limits, and cleanup behavior. Agent teams, background tasks, and multi-session services can continue work after the initial request; define cancellation, timeout, wakeup, escalation, and audit behavior. Mem0 memory, Redis-backed sessions, MCP configuration, OpenTelemetry, FastAPI services, and model-provider integrations all need version pinning, credential isolation, and security review before production use. | ✓CAMEL agents can coordinate multi-step tasks, call tools, use web/search integrations, connect to MCP examples, and run with provider credentials; review tool permissions before giving agents write access or account access. Large-scale agent societies and role-playing workflows can generate high volumes of model calls, tool calls, logs, synthetic data, and intermediate artifacts; set budgets, rate limits, and stop conditions before long runs. RAG, document, media, browser, communication, and data-tool extras may access local files, third-party APIs, vector stores, notebooks, or generated datasets; isolate experiments from production systems. CAMEL examples include MCP-oriented use cases, but MCP does not make connected tools safe by default. Scope server permissions, credentials, filesystem access, and approval gates separately. Do not treat generated code, generated datasets, citations, research summaries, or multi-agent decisions as verified until they have been reviewed against source data and policy requirements. |
| Privacy notes | ✓Griptape runs can send prompts, instructions, conversation memory, tool arguments, tool results, and retrieved context to configured model providers and embedding services. Tools and drivers can expose local files, database records, API responses, secrets, or proprietary business data to the model and workflow if they are made available to a structure. Conversation memory, task memory, vector stores, and any observability or run-logging destinations can retain prompts, outputs, embeddings, and metadata outside the application runtime. The managed Griptape Cloud processes data you send to it; review its data-handling terms before sending production or customer data. RAG and web/file loaders can pull third-party or workspace content into prompts, memory, and stored artifacts, so apply normal retention and access-control policies. | ✓Prompts, messages, tool arguments, tool outputs, code snippets, notebook state, retrieved documents, vector-store contents, provider responses, traces, and execution logs may contain sensitive user or workspace data. Do not expose secrets, API keys, private file paths, customer records, internal documents, database rows, or raw exceptions through agent messages, logs, notebooks, screenshots, or public examples. Provider extras and retrieval integrations can route data through OpenAI, Anthropic, Google, AWS, local model servers, databases, vector stores, browser automation, or other third-party services. If AG2 is used for code execution or browser automation, define which files, domains, credentials, downloads, screenshots, and logs can be read or retained. | ✓AgentScope workflows can process prompts, model responses, tool arguments, tool outputs, workspace files, code, credentials accidentally present in context, event streams, web UI state, logs, traces, memory records, session state, and tenant metadata. Long-term memory through Mem0 and multi-session service storage can persist user facts, intermediate outputs, retrieved context, and tool results beyond a single conversation. Docker, E2B, MCP servers, model providers, Redis, OpenTelemetry exporters, FastAPI deployments, and web UI integrations may send or store data outside the local Python process depending on configuration. Do not expose private prompts, API keys, unpublished code, customer data, tenant identifiers, session transcripts, or workspace artifacts in public issues, examples, screenshots, logs, or generated reports. | ✓Prompts, model responses, agent messages, tool arguments, tool outputs, retrieved documents, search results, logs, generated datasets, traces, and errors may include user or workspace data. Model providers, search providers, MCP servers, vector stores, web tools, document parsers, browser tools, and observability integrations may receive data from CAMEL workflows. Keep provider API keys, OAuth tokens, MCP server credentials, vector database URLs, generated logs, and synthetic datasets out of committed examples, screenshots, public issues, and shared notebooks. If `CAMEL_MODEL_LOG_ENABLED` or other logging/tracing integrations are enabled, review request/response logs and model configuration logs before sharing or retaining them. |
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Build Python multi-agent systems with AG2, the open-source AgentOS evolved from AutoGen, including conversable agents, group chats, swarms, tools, human review, RAG, and code execution.
Build and serve Python agents with event streams, permissions, workspaces, middleware, MCP, Mem0 memory, and agent teams.
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Use Agent Skills in the Claude Agent SDK: filesystem discovery via settingSources, the skills option to enable or filter, and tool access.
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