eko vs LangGraph
Production JavaScript framework for agentic workflows: one sentence becomes a dependency-aware multi-agent plan that runs in browser, Node, or extension — with pause/resume and snapshot recovery. — versus — Build stateful, multi-actor LLM apps as graphs — durable execution, human-in-the-loop, streaming.
Same job — durable multi-step agent workflows. LangGraph is the Python graph incumbent; Eko is the JS-native answer that also runs inside browsers and extensions.
| eko | LangGraph | |
|---|---|---|
| Stars | 4.9k | 39k |
| Forks | 441 | 6.6k |
| Language | TypeScript | Python |
| License | MIT | MIT |
| Last activity | 5 months ago | 3 days ago |
| Topics | agents, orchestration | agents, orchestration |
| Curated connections | 2 | 20 |
eko — the curator's take
The JS-native, runs-anywhere bet is the differentiator: the same workflow executes in a browser extension, a web page or a server, which no Python framework offers — and 4.0's pause/interrupt/snapshot controls are production concerns most frameworks defer. When NOT: the deep ecosystem (integrations, community patterns, papers) still lives in Python — teams already on LangGraph gain little; and Fellou the company steers the roadmap, so watch for platform gravity.
LangGraph — the curator's take
You reach for LangGraph the moment a simple agent loop stops being enough — when you need state that survives a crash, a human approving a step mid-run, or a flow that can loop back on itself. Most teams arrive here from plain LangChain and don't leave. If all you want is a quick tool-calling agent, this is more machinery than you need — start lighter and come back when you hit the wall.