LangGraph vs Ruflo
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
An agent harness that adds multi-agent swarms, shared memory and coordination on top of Claude Code and Codex.
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 Ruflo if you need…
- Developers already using Claude Code or Codex who want multiple agents working together
- Teams experimenting with multi-agent workflows and shared memory
- People who want an MCP server exposing Ruflo tools to an MCP-capable client
- Users comfortable with the terminal and npm setup
Side-by-side
| LangGraph | Ruflo | |
|---|---|---|
| Type | Library | Dev tool |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 44.3k | 80.6k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Install with a package manager |
| 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.
Ruflo
Ruflo is an open-source layer that sits around AI coding assistants such as Claude Code and Codex. It lets several AI agents split up tasks, coordinate as a swarm, and keep memory across sessions. The README describes it as the execution layer that gives a model tools, memory and controls so it can do longer jobs.
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
Ruflo
- Developers already using Claude Code or Codex who want multiple agents working together
- Teams experimenting with multi-agent workflows and shared memory
- People who want an MCP server exposing Ruflo tools to an MCP-capable client
- Users comfortable with the terminal and npm setup
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
Ruflo
- Works with existing assistants (Claude Code, Codex, MCP clients) rather than replacing them
- Clear install paths documented in the README, including plugin and npx options
- MIT licensed with a large public project history and active recent commits
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
Ruflo
- Non-developers looking for a simple chat app, since setup and concepts take time to learn
- Anyone who needs fully audited, production-hardened security without reviewing the code, because the README says mods run with the account's permissions
- Users who do not already use Claude Code, Codex or another MCP-enabled client, since most integrations depend on them
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
Ruflo
- The README is long and promotional in places, and many features and numbers are hard to verify from the text alone
- Some integrations (console, mods, hooks) are specific to Claude Code, and hook support depends on the client
- Memory and learning features require embeddings and configured modules, so they are not plug-and-play
Getting started
Ruflo
Set up the project with the wizard (Codex or other MCP clients)
npx ruflo@latest init wizardStart the MCP server
npx -y ruflo@latest mcp startFrequently asked questions
What is the difference between LangGraph and Ruflo?
LangGraph: A Python framework for building long-running agents that keep state and can resume after failures. Ruflo: An agent harness that adds multi-agent swarms, shared memory and coordination on top of Claude Code and Codex. LangGraph suits python developers building multi-step ai agents that must survive failures or long waits, while Ruflo suits developers already using claude code or codex who want multiple agents working together.
Are LangGraph and Ruflo free?
LangGraph: Free and open source. Ruflo: Free and open source.
Is LangGraph or Ruflo more popular?
Ruflo has more GitHub stars (80.6k) 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 Ruflo?
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.