LangGraph vs PocketFlow
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
A tiny Python framework (about 100 lines) for wiring LLM calls into graphs of steps and agents.
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 PocketFlow if you need…
- Developers who want a small, readable LLM framework they can inspect in full
- Python programmers building agents, multi-step workflows, or RAG pipelines
- People who want to copy the source directly into a project instead of adding a dependency
- Developers experimenting with AI coding assistants that generate agent code
Side-by-side
| LangGraph | PocketFlow | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 44.3k | 11.3k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | Python | Python |
| Last code update | 4 days ago | 3 months 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.
PocketFlow
Pocket Flow is a minimalist framework for building LLM applications, based on a graph abstraction where small nodes are connected into flows. The README says the core is about 100 lines with no dependencies and no vendor-specific wrappers, and that it can be used to build agents, workflows, and retrieval-augmented generation (RAG) systems. People use it when they want to understand and control their LLM code instead of relying on a large framework.
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
PocketFlow
- Developers who want a small, readable LLM framework they can inspect in full
- Python programmers building agents, multi-step workflows, or RAG pipelines
- People who want to copy the source directly into a project instead of adding a dependency
- Developers experimenting with AI coding assistants that generate agent code
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
PocketFlow
- Very small codebase (about 100 lines), so the whole framework is easy to read and audit
- No dependencies and no vendor lock-in, according to the README
- Includes tutorials from a dummy-level chat example up to multi-agent and parallel demos
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
PocketFlow
- Teams that need many prebuilt integrations with vendors and vector databases, since the README says it includes none ('None' in its comparison table)
- Non-programmers, because it is a code library rather than a ready-to-use app
- Projects needing a formal, versioned release, since the repository shows no latest release
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
PocketFlow
- Provides few built-in integrations, so you write your own connections to LLM providers and tools
- The README is truncated in the source provided, so some details could not be verified
- Documentation and examples are spread across the README, a docs site, and a cookbook folder
Getting started
Frequently asked questions
What is the difference between LangGraph and PocketFlow?
LangGraph: A Python framework for building long-running agents that keep state and can resume after failures. PocketFlow: A tiny Python framework (about 100 lines) for wiring LLM calls into graphs of steps and agents. LangGraph suits python developers building multi-step ai agents that must survive failures or long waits, while PocketFlow suits developers who want a small, readable llm framework they can inspect in full.
Are LangGraph and PocketFlow free?
LangGraph: Free and open source. PocketFlow: Free and open source.
Is LangGraph or PocketFlow more popular?
LangGraph has more GitHub stars (44.3k) than PocketFlow (11.3k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to LangGraph and PocketFlow?
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.