LangGraph vs 
Phidata
LangGraph vs Phidata compared for 2026 — features, license, ease of use, performance and which one to choose. Stateful, controllable agent graphs vs Agents with memory, knowledge and tools.
Updated regularly · curated by OpenSourceAI.tech
LangGraph vs Phidata at a glance
| Spec | LangGraph | Phidata |
|---|---|---|
| Category | AI agent framework | AI agent framework |
| Type | Agent orchestration (graphs) | Agent framework |
| License | MIT | MPL-2.0 |
| Runs locally | Cloud-optional | Yes |
| Primary language | Python / JS | Python |
| Ease of use | Advanced | Beginner |
| Best for | developers needing controllable agent workflows | agents that need to remember and retrieve |
| GitHub stars | 37.1k | — |
How LangGraph and Phidata score
| Criterion | LangGraph | Phidata |
|---|---|---|
| Popularity | 4.0 | n/a |
| Maintenance | 5.0 | n/a |
| Ease of use | 2.5 | 5.0 |
| Privacy | 3.5 | 5.0 |
| License freedom | 5.0 | 3.5 |
Scores are computed automatically from public signals — GitHub stars (popularity), recent commit activity (maintenance), license type (freedom), local-first design (privacy) and onboarding complexity (ease of use). Indicative, not a verdict.
What each one is
LangGraph
LangGraph is a library for building stateful, controllable agents as graphs, giving you fine-grained control over loops, branching and persistence.
- Explicit, controllable agent state machines
- Persistence and human-in-the-loop built in
- Integrates with the LangChain ecosystem
Phidata
Phidata builds agents that combine memory, a knowledge base and tools, and ships a UI to chat with and inspect them.
- Memory and knowledge built in
- Ships an agent inspection UI
- Simple, readable API
Key differences
LangGraph is agent orchestration (graphs), while Phidata is agent framework. Their licenses differ (MIT vs MPL-2.0), which matters if you ship a commercial product. LangGraph leans more advanced-friendly, whereas Phidata is more suited to beginner users. They also differ in how they run (Cloud-optional vs Yes). In short, LangGraph fits developers needing controllable agent workflows, and Phidata fits agents that need to remember and retrieve.
Which should you choose?
Choose LangGraph for developers needing controllable agent workflows. Choose Phidata for agents that need to remember and retrieve.
There is rarely one winner — many setups use both. The right pick depends on your hardware, your team's skills, and whether you value simplicity or control.
Frequently asked questions
Is LangGraph or Phidata easier to use?
Phidata is generally the easier of the two to get started with, while LangGraph rewards more setup with more control.
Are LangGraph and Phidata free?
LangGraph is free and open source (MIT), and Phidata is free and open source (MPL-2.0). Neither charges for the core software.
Can I run LangGraph and Phidata locally?
LangGraph: cloud-optional · Phidata: yes. Both can be used without sending your data to a third-party cloud where their setup allows.
LangGraph vs Phidata — which should I pick in 2026?
Choose LangGraph for developers needing controllable agent workflows. Choose Phidata for agents that need to remember and retrieve.
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