Why Telar

• Your state is a plain data class. Each node returns a .copy() of it, so a run is easy to test, print and save, and parallel branches cannot overwrite each other by accident.
• Mistakes in the graph fail in compile(). An unknown node, a node nothing leads to, or two parallel nodes with no rule to merge their results are reported before anything runs.
• Pause anywhere, continue later. Stop before a node, or call interrupt from inside one when it finds out it needs a person. The run is saved, and resume continues it hours later, in another process. history and fork go back to any earlier step.
• Every Kotlin platform. The core depends only on kotlinx-coroutines and runs on the JVM, Android, iOS, macOS, Linux, Windows, JS and Wasm. Agents, models and checkpoints work there too, down to runs saved in the browser's localStorage.
• Coroutines all the way. Nodes, edges and models are suspend functions. Cancellation works as you expect, and parallel nodes use structured concurrency.
• Any model, small surface. Claude, OpenAI, Gemini, Ollama and every server with the API of OpenAI, plus any LangChain4j model on the JVM. ChatModel is one function, so a model of your own, or a fake one in a test, takes a few lines.

Telar and other libraries

A Kotlin project has other good choices for AI agents. This is how we see the differences in October 2026; if something here is wrong or out of date, please open an issue.

	Telar	Koog	LangGraph4j	LangChain4j
What it is	A graph engine for agents and workflows	JetBrains' framework for AI agents	A Java port of LangGraph	A Java toolkit for LLM apps
Platforms	JVM, Android, iOS, macOS, Linux, Windows, JS, Wasm	JVM, Android, iOS, JS, Wasm	JVM (Java 17+)	JVM (Java 17+)
Workflow state	Your own immutable data class	Values passed between the nodes of a strategy, and the agent's storage	A map of channels with reducers	Mostly the conversation memory
Stage	Alpha: the API can still change	Stable 1.x	Stable 1.x	Stable 1.x
Best at	Workflows with typed state, checks before a run, pauses and save points, on every platform	A complete agent platform: many providers, MCP, tracing, backed by JetBrains	LangGraph's design on the JVM, from Java	Integrations: models, vector stores, RAG

Choose Telar when the workflow itself is the hard part: several steps, branches that join, people who approve, runs that must survive a restart, and state you want to read as ordinary Kotlin. If you need a large set of ready-made integrations or a stable API today, Koog or LangChain4j are the safer choice. Telar works with LangChain4j: telar-langchain4j turns any of its models into a ChatModel.

Telar is an independent project inspired by LangGraph. It is not affiliated with or endorsed by LangChain, Inc.