Which strategy should I use for LangGraph v1?
Agent teams already using LangGraph need to judge whether v1's API and persistence changes are mature enough to migrate now that pre-v1 docs and patterns are being retired.
Blockers
- requires_version: package/langgraph-v1 → runtime/nodejs-20
- deprecated_in_favor_of
- removed_in_favor_of
- package/langgraph-0.4 — EOL 2026-12-31
- Lock-in via format/postgres
- Lock-in via format/redis
Who this is for
- enterprise
- low-ops
- small-team
Candidates
Migrate now to LangGraph v1 and replace prebuilt ReAct agents with LangChain createAgent
As of 2026-04-01, LangGraph 1.0 is the ACTIVE LTS line, and the official changelog shows LangGraph v1.0.0 released on 2025-10-20 with v1.1.0 released on 2026-01-14. The official v1 release notes say the core graph primitives and execution model are unchanged, so the upgrade is positioned as stability-focused rather than a runtime rewrite. The main breaking migration items called out in the official guide are that `createReactAgent` is deprecated in favor of LangChain `createAgent`, `toLangGraphEventStream` is removed in favor of `graph.stream(..., { encoding })`, and Node.js 18 support is dropped in favor of Node.js 20+. If your agents already rely on persistence, v1 keeps checkpointing and human-in-the-loop as first-class features.
When to choose
Use this when you are already on Node 20+ or can upgrade immediately, and your team wants to stay on the actively developed, semver-governed line. It is the strongest default if you want current docs, current APIs, and a lower risk of future migration pile-up around deprecated prebuilt agent patterns.
Tradeoffs
You align with the supported LTS major and current documentation, but you must touch agent construction code if you use `createReactAgent`, and any low-level streaming integrations using the removed helper must be updated.
Cautions
Database-backed persistence is not zero-touch: the official memory docs state that Postgres- or Redis-backed persistence implementations require schema setup or migrations before use. If you use managed infrastructure such as Agent Server, the docs say checkpointing is handled automatically, but if you self-manage stores or checkpointers you need an explicit deployment step. Check official docs for managed platform pricing if that affects the migration plan.
Stay temporarily on LangGraph 0.4 while using archived docs and planning a controlled persistence cutover
As of 2026-04-01, this is still a valid short-term option because the official release policy says LangGraph 0.4 remains in MAINTENANCE until December 2026, with security patches and critical bug fixes but no new features. The official changelog and overview pages route v0.x users to archived content, which means pre-v1 documentation has effectively already been retired from the main docs surface. This option avoids immediate agent-constructor and runtime changes, but it also means building against archived guidance while the active ecosystem moves around LangGraph v1 and LangChain v1. It is a deferral strategy, not a new steady state.
When to choose
Use this when production risk from a rushed persistence migration is higher than the cost of short-term doc friction, especially if you still need to automate DB migrations or Node 20 upgrades. It is most defensible when you have a dated migration plan and expect to leave 0.4 before the December 2026 maintenance cutoff.
Tradeoffs
You reduce immediate change risk, but you accept archived documentation, fewer forward-looking examples, and another migration cycle later. You also give up new v1 improvements such as `StateSchema` and the current `createAgent` path.
Cautions
Do not treat maintenance mode as feature stability forever: the official policy says deprecated features are removed only in major releases, and v0.4 is not the active major anymore. Teams that keep adding new work on 0.4 can increase their eventual migration surface area.
Try with your AI agent
$ npm install -g pocketlantern $ pocketlantern init # Restart Claude Code, Cursor, or your MCP client, then ask: # "Which strategy should I use for LangGraph v1?"