LangGraph vs swiftide
Build stateful, multi-actor LLM apps as graphs — durable execution, human-in-the-loop, streaming. — versus — Rust framework for LLM apps: an agent harness, compile-time-typed task graphs, and streaming RAG pipelines — MCP toolboxes, human-in-the-loop approval, tracing with Langfuse support.
Same job — durable, graph-orchestrated LLM apps with human-in-the-loop — different type systems: LangGraph gives you the Python/JS ecosystem's breadth; Swiftide gives you compile-time-typed nodes and hand-offs in Rust.
| LangGraph | swiftide | |
|---|---|---|
| Stars | 38k | 748 |
| Forks | 6.5k | 65 |
| Language | Python | Rust |
| License | MIT | MIT |
| Last activity | 2 days ago | today |
| Topics | agents, orchestration | rag, agents |
| Curated connections | 19 | 2 |
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.
swiftide — the curator's take
The Rust-native full stack — harness, task graphs, streaming indexing — for teams who want LLM logic inside a Rust codebase instead of bolting on a Python sidecar. Typed task graphs are the real differentiator: hand-offs and fan-out/join results checked at compile time, a correctness class Python graph frameworks structurally can't offer. Read ★748 correctly — it measures the size of Rust's LLM niche, not quality; the project is mature, actively maintained and properly documented. When NOT: if your retrieval stack leans on the long tail of Python-ecosystem loaders, rerankers and eval tooling, Rust's integration surface is thinner and you will write the glue yourself.