LangGraph vs Paperclip
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
A Python framework for building long-running agents that keep state and can resume after failures.
Open source · MIT
Open-source Node.js and React app for organizing teams of AI agents around goals, tasks, and budgets.
The short answer
Choose LangGraph if you need…
- Python developers building multi-step AI agents that must survive failures or long waits
- Teams that want fine-grained control over agent state and execution flow
- Projects that need a human to review or edit an agent's state partway through a run
- Developers who want a lower-level base instead of a higher-level agent package
Choose Paperclip if you need…
- Developers already running several AI coding agents who want one place to track them
- Small teams experimenting with autonomous agents that need budgets and approval steps
- People who want an audit trail of what each agent did and why
- Anyone comfortable self-hosting a Node.js app
Side-by-side
| LangGraph | Paperclip | |
|---|---|---|
| Type | Library | App |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 44.3k | 110k |
| Setup difficulty | Intermediate | Advanced |
| How you get it | Install with a package manager | Clone and build from source |
| Main language | Python | TypeScript |
| Last code update | 4 days ago | 4 days ago |
What each one is
LangGraph
LangGraph is a low-level framework for building workflows and AI agents that keep track of their state over time. It is designed for tasks that run for a long stretch, can pause for human review, and need to pick up where they left off after an interruption. It is produced by LangChain Inc, but the README says it can be used without LangChain.
Paperclip
Paperclip is a Node.js server with a React dashboard that coordinates a team of AI agents to work toward business goals. You bring your own agents, assign goals, and track work and costs in one place. The README says it looks like a task manager but adds org charts, budgets, governance, and agent coordination underneath.
Best for
LangGraph
- Python developers building multi-step AI agents that must survive failures or long waits
- Teams that want fine-grained control over agent state and execution flow
- Projects that need a human to review or edit an agent's state partway through a run
- Developers who want a lower-level base instead of a higher-level agent package
Paperclip
- Developers already running several AI coding agents who want one place to track them
- Small teams experimenting with autonomous agents that need budgets and approval steps
- People who want an audit trail of what each agent did and why
- Anyone comfortable self-hosting a Node.js app
Strengths
LangGraph
- Built-in durable execution that resumes workflows from where they stopped, per the README
- Supports human-in-the-loop checkpoints and both short-term and long-term memory
- Open source under the MIT license with extensive documentation and a free course
Paperclip
- Works with many agent runtimes named in the README, including Claude Code, Codex, Cursor, and Gemini CLI
- Tracks tasks, costs, permissions, and run history in one dashboard
- MIT licensed and actively maintained, with a recent push in the repository metadata
Not ideal for
LangGraph
- Beginners who just want a ready-made agent: the README points to Deep Agents, a higher-level package built on LangGraph, for quicker starts
- Non-programmers, since it is a Python library that requires writing code
- JavaScript/TypeScript-only teams, who are pointed to the separate LangGraph.js project
Paperclip
- People who want a finished hosted product with no setup; the README points to a Paperclip Cloud waitlist rather than a live service
- Users who need a simple single-agent chat tool, since this is built for coordinating multiple agents
- Teams without a plan for reviewing agent output, because the README stresses verification and oversight
Trade-offs
LangGraph
- It is a low-level framework, so it has a steeper learning curve than higher-level tools
- Several of the most useful features (debugging, deployment) point to LangSmith, a separate product
- The README is truncated and does not show full API details, so the depth of specific features is hard to verify from it alone
Paperclip
- The README is truncated and mostly marketing-oriented, so setup details are unclear from this text alone
- Many described features, like Cloud, SSO, and phone management, may depend on hosted or paid offerings not verified here
- Coordinating autonomous agents adds risk and requires careful permission and budget setup
Getting started
Paperclip
Clone the repository
git clone https://github.com/paperclipai/paperclip.gitFrequently asked questions
What is the difference between LangGraph and Paperclip?
LangGraph: A Python framework for building long-running agents that keep state and can resume after failures. Paperclip: Open-source Node.js and React app for organizing teams of AI agents around goals, tasks, and budgets. LangGraph suits python developers building multi-step ai agents that must survive failures or long waits, while Paperclip suits developers already running several ai coding agents who want one place to track them.
Are LangGraph and Paperclip free?
LangGraph: Free and open source. Paperclip: Free and open source.
Which is easier to set up, LangGraph or Paperclip?
LangGraph is easier to get started with (rated intermediate); Paperclip is rated advanced and usually needs more setup.
Is LangGraph or Paperclip more popular?
Paperclip has more GitHub stars (110k) than LangGraph (44.3k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to LangGraph and Paperclip?
Other options people compare them with include CrewAI, Deep Agents, OpenAI Agents SDK, PageIndex, LEANN.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.