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## Summary `--push-to` always builds (or retags a local `--image`) and pushes, even when the image already exists at the destination. That breaks CI/CD pipelines where the build/push step and the deploy step run in separate jobs: the deploy job has no local copy of the image to retag, and `--image` requires one (it runs `docker image inspect`, which needs the image present in the local Docker daemon). This adds `--image-uri`, which takes a full image reference already in a registry and deploys it directly via `source_revision_config.image_uri`, with no local Docker involvement at all: no inspect, no build, no tag, no push. It gets the same placement behavior as `--push-to` (`--listener-id`/`--k8s-namespace`, or auto-selecting the sole listener) and the same update-in-place semantics, since both produce an `external_docker` deployment and are interchangeable on the same deployment. `--image-uri` requires a digest (`repo@sha256:...`) and rejects empty/blank input. `--push-to` always resolves to a digest after pushing; `--image-uri` has no push step to resolve one from, so a mutable tag (or an unvalidated empty string) could let Kubernetes silently keep running a previously cached image while the CLI reports success. Implementation: `CustomerRegistrySource`'s image-acquisition step is split into two variants, `BuildAndPush` (the existing `--push-to`/`--image` flow, unchanged behavior) and `PublishedImage` (the new flag), so create/update/placement logic stays shared. Also reworded `_ensure_customer_registry_source`'s error, which previously mentioned only `--push-to` even though it's reachable from `--image-uri` too. ## Test plan - [x] `make format && make lint` clean in `libs/cli` - [x] Unit tests in `test_deploy_helpers.py` for `_select_source` selecting `PublishedImage` (by digest, trimmed), requiring no local Docker, carrying placement, and the mutual-exclusivity + validation errors (`--push-to`, `--image`, `--tag`, `--remote`, empty/blank input, missing digest) - [x] CLI-level tests in `test_deploy_command.py`: create and update an external deployment via `--image-uri` with zero Docker calls, listener auto-placement, rejecting a non-external existing deployment (now mentioning both flags), and the conflicting-flag cases end to end - [x] Full `libs/cli` suite: 524 passed, no regressions
Low-level orchestration framework for building stateful agents.
Trusted by companies shaping the future of agents – including Klarna, Replit, Elastic, and more – LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents.
pip install -U langgraph
Tip
If you're looking to quickly build agents, check out Deep Agents — a higher-level package built on LangGraph for agents that can plan, use subagents, and leverage file systems for complex tasks.
For an equivalent JS/TS library, check out LangGraph.js and the JS docs.
Why use LangGraph?
LangGraph provides low-level supporting infrastructure for any long-running, stateful workflow or agent:
- Durable execution — Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
- Human-in-the-loop — Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.
- Comprehensive memory — Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.
- Debugging with LangSmith — Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.
- Production-ready deployment — Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.
Tip
For developing, debugging, and deploying AI agents and LLM applications, see LangSmith.
LangGraph ecosystem
While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents.
To improve your LLM application development, pair LangGraph with:
- Deep Agents – Build agents that can plan, use subagents, and leverage file systems for complex tasks.
- LangChain – Provides integrations and composable components to streamline LLM application development.
- LangSmith – Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
- LangSmith Deployment – Deploy and scale agents effortlessly with a purpose-built deployment platform for long-running, stateful workflows. Discover, reuse, configure, and share agents across teams – and iterate quickly with visual prototyping in LangSmith Studio.
Documentation
- docs.langchain.com – Comprehensive documentation, including conceptual overviews and guides
- reference.langchain.com/python/langgraph – API reference docs for LangGraph packages
- LangGraph Quickstart – Get started building with LangGraph
- Chat LangChain – Chat with the LangChain documentation and get answers to your questions
Discussions: Visit the LangChain Forum to connect with the community and share all of your technical questions, ideas, and feedback.
Additional resources
- Guides – Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
- LangChain Academy – Learn the basics of LangGraph in our free, structured course.
- Case studies – Hear how industry leaders use LangGraph to ship AI applications at scale.
- Contributing Guide – Learn how to contribute to LangChain projects and find good first issues.
- Code of Conduct – Our community guidelines and standards for participation.
Acknowledgements
LangGraph is inspired by Pregel and Apache Beam. The public interface draws inspiration from NetworkX. LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.