COMPARE
An honest comparison.
Every cell below is verifiable — not a taste rating. Where a competitor has a genuine strength, it is acknowledged. Where a capability is missing, we say "not included" rather than a dash or a frown. Framework claims outside Beluga's own repo are flagged for re-verification before any decision — public docs drift fast.
| Dimension | Beluga Go-native | LangChain Go tmc/langchaingo | Google ADK Go SDK | Eino cloudwego | LangChain Python |
|---|---|---|---|---|---|
| Primary language | Go 1.23+ | Go | Go | Go | Python |
| Streaming primitive | iter.Seq2[T, error] ● ● ● range-over-func, no channels | channels ● ● ● | channels ● ● ● | channels ● ● ● | async iterators ● ● ● |
| Reasoning strategies | 8 built-in ● ● ● ReAct · Reflexion · Self-Discover · Mind-Map · ToT · GoT · LATS · MoA | ReAct only ● ● ● | ReAct · Sequential ● ● ● | ReAct · Plan-and-Execute ● ● ● | ReAct · many via LangGraph ● ● ● |
| Built-in OTel GenAI spans | 17 packages ● ● ● gen_ai.* conventions, per-boundary | not included ● ● ● | partial ● ● ● runtime spans only | partial ● ● ● | community plugin ● ● ● not first-party |
| Durable workflow (built-in) | workflow/ + 6 backends ● ● ● temporal · inngest · dapr · nats · kafka · inmemory | not included ● ● ● not a built-in concern | not included ● ● ● not a built-in concern | not included ● ● ● not a built-in concern | LangGraph checkpointing ● ● ● Postgres / SQLite |
| Voice pipeline (built-in) | frame-based, 16+ providers ● ● ● STT · TTS · S2S · VAD · transport | not included ● ● ● not a built-in concern | not included ● ● ● not a built-in concern | not included ● ● ● not a built-in concern | not included ● ● ● not a built-in concern |
| Provider integrations | 110 providers · 19 categories ● ● ● llm · embedding · vectorstore · voice · guard · workflow · observability | ~30 ● ● ● mostly llm + vectorstore | ~15 ● ● ● | ~40 ● ● ● strong LLM coverage | hundreds ● ● ● largest ecosystem |
| License | MIT | MIT | Apache 2.0 | Apache 2.0 | MIT |
Competitor cells reflect public documentation as of 2026-04-12. If you are about to make a production decision, verify against each project's current README before committing.
When Beluga is the right choice — and when it isn't.
Beluga is the right choice when
- Your team ships Go in production and does not want to debug Python interop.
- You need the full agent stack — LLM, RAG, voice, guards, observability, durability — in one consistent framework.
- You care about
iter.Seq2, goroutine hygiene, andcontext.Contextas first parameter. - You run at a scale where OTel GenAI spans and cost attribution are not optional.
- You are building long-running agents that must survive pod restarts, and you do not want to bolt durability on after the fact.
Beluga is not the right choice when
- Your team is Python-native. LangChain Python's ecosystem is larger and mature here. Choose LangGraph.
- Your project depends on a specific LangChain plugin that has no equivalent in Go. Use the plugin.
- Your primary constraint is time-to-prototype rather than production operability. A Python notebook will get you there faster.
- You need a framework that is a thin wrapper — Beluga is opinionated. Those opinions are spelled out in Concepts. Disagree with them, choose something else.
Still evaluating?
Read the architecture. Every claim in the table above traces back to a file in the repo.