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Spring AI Alibaba Graph lets you model a Java application as a directed, stateful workflow. You define an OverAllState snapshot, implement nodes that perform model calls or ordinary Java work, connect those nodes with fixed or conditional edges, and compile the result into a CompiledGraph. The compiled graph can then be invoked or streamed, checkpointed, paused for human approval, and resumed by thread identifier. This makes it a better fit for multi-step agents, approval processes, and long-running jobs than a one-turn chat completion.
The practical build order is: design state and merge rules, write small nodes, register them in a StateGraph, connect START and END, add routing and termination conditions, compile early, then execute with the runtime configuration your deployment needs.
What is Spring AI Alibaba Graph, and when should you use it?
Spring AI Alibaba Graph is a workflow and multi-agent runtime for Java applications. It represents an application as a directed graph: nodes do work, while edges decide which node runs next. A node may call a Spring AI model, invoke a tool, query a database, validate data, or run deterministic business logic. The graph carries a serializable state snapshot between those operations.
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Use a graph when the application has more than one meaningful step or when execution must survive a pause, failure, retry, or approval. Typical examples include classification followed by retrieval, tool selection followed by validation, document processing with a human checkpoint, and agent loops that continue until a stop condition is met. A simple one-turn model call does not need this orchestration layer.
The runtime is designed around persistence, conditional routing, streaming, nested graphs, parallel execution, and state management for long-running agents. Those capabilities are useful only when the workflow is explicit enough to inspect: each node should have a clear contract, and each edge should make the next decision understandable.
How do StateGraph, nodes, edges, OverAllState, and CompiledGraph fit together?
These abstractions divide responsibilities cleanly. OverAllState is the shared snapshot; nodes read it and return updates; edges determine control flow; StateGraph is the builder; and CompiledGraph is the executable, structurally checked result.
| Abstraction | Role | Design question |
|---|---|---|
OverAllState |
Serializable values passed through the workflow | Which keys do nodes read or write, and how are updates merged? |
| Node | Synchronous or asynchronous work that returns state updates | Can this operation be isolated behind a small input/output contract? |
| Edge | Transition from one node to another | Is the transition fixed or dependent on current state? |
StateGraph |
Definition-time graph builder | Are all node names unique and all paths connected? |
CompiledGraph |
Runnable graph produced after compilation | Which checkpoint, interruption, and runtime settings apply? |
The state key strategy is as important as the Java type. A replacement strategy is appropriate for a current classification or final answer. An append strategy fits a message history, audit trail, or list of tool results. If two nodes write the same key, the merge policy determines whether one value replaces the other, values accumulate, or a different reducer is applied.
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How do you design the shared state before writing nodes?
Start with a state contract rather than adding keys opportunistically. List the values required to enter the workflow, the intermediate results that later nodes need, and the data that must be available after a pause or resume.
Separate inputs, working data, and outputs
- Inputs: the user request, document identifier, or job parameters supplied at invocation.
- Working data: classifications, retrieved passages, tool results, validation errors, and iteration counters.
- Outputs: the final response, approval decision, or structured result returned to the caller.
Keep transient implementation details out of the public result when they are not needed after completion. Conversely, do not store important approval context only in a local variable: a resumed execution receives the persisted state, not the old node’s heap.
Choose merge behavior for every writable key
Decide whether each key is replaced, appended, or combined by a custom rule. Message histories commonly append; a current status usually replaces; counters must be updated deliberately so parallel branches do not silently overwrite one another. Document the rule next to the state definition and test two successive updates, not just the first write.
Keep state serializable and bounded
Checkpointing and resumption require a serializable snapshot. Store identifiers and structured results rather than open streams, thread objects, or non-serializable client instances. Limit unbounded message or tool-result lists, or move large artifacts to external storage and keep a reference in state.
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How do you build a graph step by step?
Build and compile a minimal path before adding branches. This catches naming and connectivity errors while the graph is still easy to inspect.
- Define the state schema. Register the keys nodes will read and write, including their update strategies.
- Implement nodes. Each node receives the current state and returns only the updates it owns. A node can call Spring AI through a
ChatClientor execute ordinary Java logic. - Create the graph and register unique node names. Names become the labels used by edges, routing maps, logs, and interruption settings.
- Connect the entry and exit points. Add an edge from
STARTto the first node and from the terminal path toEND. - Add transitions. Use ordinary edges for fixed sequencing and conditional edges when state determines the next node.
- Compile early. Compile into
CompiledGraphbefore implementing every branch. Supply checkpoint and interruption settings at compilation when the workflow needs them. - Invoke with initial state and runtime configuration. Include a stable thread identifier when you need inspection, checkpointing, or resumption.
StateGraph graph = new StateGraph(stateSchema);The exact node-registration syntax depends on the library version, but the structure should remain recognizable: register a node such as classify, connect it to respond, and make the response path reach END. Keep model prompts and business rules inside nodes; keep traversal decisions in edges.
How do fixed and conditional edges work?
A fixed edge always selects the same next node. It is ideal for a predictable sequence such as retrieve → draft → validate. A conditional edge evaluates the current state and returns a routing label; a label-to-node map resolves that label to the next node.
graph.addEdge("retrieve", "draft");
graph.addConditionalEdges(
"validate",
state -> state.value("route"),
Map.of(
"approved", "publish",
"needs_revision", "draft",
"stop", "END"
)
);Treat the routing function as a small, deterministic decision. Have a preceding node calculate a clear value such as approved, needs_revision, or stop, then let the edge map that value. This keeps classification or validation logic testable and prevents a routing expression from becoming an uninspectable mixture of model calls and side effects.
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How do you add loops without creating an infinite workflow?
A loop is a conditional edge that points back to an earlier node. One branch repeats the work; another points to END. A published implementation pattern uses an escalate label to return to an earlier node and a stop label to terminate.
Define an explicit stop condition
Store a bounded iteration count, a validation status, or both in state. Increment the count in the node that performs the repeated work and route to END when the limit is reached. A model-generated “continue” decision should never be the only safeguard.
Make repeated work idempotent where possible
If a checkpoint is restored or a transient failure causes a retry, a node may run again. Prefer operations that can be safely repeated, or include a request identifier so downstream systems can detect duplicate writes.
Record why the loop stopped
Write a terminal reason such as approved, max_iterations, or validation_failed to state. That value helps callers distinguish a successful completion from a bounded safety exit without reconstructing the path from logs.
How do you compile, invoke, and stream a graph?
Compilation turns the mutable graph definition into a CompiledGraph. It is also the point where checkpoint and interruption settings can be supplied, so compile only after those policies are known.
CompiledGraph compiled = graph.compile(compileSettings);
GraphState result = compiled.invoke(initialState, runtimeConfig);Use invocation when the caller needs a completed state or final result. Pass initial values that satisfy the state schema and runtime configuration containing the thread identifier when executions must be resumed or inspected.
Streaming model output
The official streaming pattern wraps Spring AI ChatClient streaming content inside a graph node. The node emits model content as it arrives while the compiled graph drives the surrounding workflow. This is useful when a user should see generated text before every downstream step finishes.
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Do not confuse model-token streaming with node-level event streaming. Token streaming exposes incremental content from a model call. Graph-event streaming exposes workflow progress such as node starts, node completions, state updates, or transitions, depending on the API surface you use. Decide which event contract your client needs before designing the transport layer; a user interface may need tokens, while an operations console may need node and state events.
How do persistence, interruption, and human review work?
For a workflow that can pause, configure a checkpointer and a stable thread identifier, then declare where execution should interrupt. The QuickStart pattern uses MemorySaver, compiles with interruptBefore("human_review"), streams until the pause, inspects the current state, applies a decision or edit with updateState, and resumes the same thread.
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- Configure a saver during compilation. Choose the checkpoint implementation appropriate for your deployment and attach it through compile settings.
- Assign a thread identifier. Use the same identifier for the initial run, state inspection, update, and resume. Generate it at the application boundary for a conversation or job and do not change it between calls.
- Mark the review node. An
interruptBeforesetting pauses before the named node, allowing a reviewer to see the state before the action occurs. - Run until interruption. Consume the stream or result until the runtime reports the pause. Persist the thread identifier with the user-facing approval task.
- Inspect and edit state. Read the current snapshot, show the reviewer only the fields they are authorized to change, and call
updateStatewith the approved values. - Resume the same compiled graph and thread. The graph continues from the checkpoint with the updated state.
The lower-level InterruptableAction interface provides hooks before and after node execution. Use those hooks when approval policy must surround more than one named node, or when an application needs a reusable human-in-the-loop control rather than a single interruption point.
Human review should be treated as a normal state transition, not an exception path. Store who approved the action, what changed, and when the decision was made if your audit requirements demand it. Avoid putting secrets or unrestricted internal prompts into a reviewer-facing state view.
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Nodes may be synchronous or asynchronous. Choose asynchronous execution when a node waits on model output, network I/O, or another service and the surrounding application benefits from non-blocking behavior. Preserve the same state contract: an asynchronous node still returns a well-defined update rather than mutating shared state behind the graph runtime.
Parallel branches are appropriate when subtasks are independent, such as running separate retrieval strategies or extracting different document fields. Decide how their results merge before enabling parallelism. Append independent records, or use a reducer that combines keyed results; do not allow two branches to replace the same scalar key without a defined winner.
Nested graphs let a larger workflow call a reusable sub-workflow. Encapsulate a bounded concern—such as document validation or tool selection—behind a small input/output boundary. The parent graph should not depend on the nested graph’s internal node names unless interruption and observability requirements make that coupling intentional.
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Test the graph as a state machine, not only as a prompt. Use deterministic node doubles for model and tool calls so routing and merge behavior can be verified without relying on model variability.
- Structure: compilation succeeds; every path starts at
START; terminal paths reachEND; conditional labels resolve to valid destinations. - State: replacement, append, and custom merge strategies produce the expected value after multiple updates.
- Routing: each classification, validation, error, and unknown-label case chooses the intended branch.
- Loops: the normal success path terminates and the maximum-iteration guard terminates even when a node always requests another attempt.
- Recovery: a run can be interrupted, inspected, updated, and resumed with the same thread identifier.
- Streaming: clients receive the event type they expect, and a disconnected client can reconnect without accidentally starting a second thread.
- Side effects: retries do not duplicate irreversible writes, or downstream systems can safely deduplicate them.
Operational telemetry should include thread identifier, node name, transition label, start and end times, retry count, and terminal reason. Redact prompts, credentials, and personal data before sending state or model content to logs. Because the graph is explicit, these events can provide a more useful execution trace than a single opaque “agent failed” message.
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Which design choices matter most for a maintainable graph?
Keep one meaningful operation per node. Large nodes that classify, call tools, write databases, and choose the next step at once are difficult to retry and nearly impossible to inspect. Give each node a narrow contract and let edges express traversal.
Compile early and often. Structural errors are cheaper to fix before the graph has many branches. Add checkpoints before introducing human review or long-running work. Define termination conditions before adding a loop. For independent subtasks, decide merge semantics before enabling parallel execution. These practices make the workflow understandable to both application developers and operators who must recover a stuck thread.
| Requirement | Graph feature to use | Implementation focus |
|---|---|---|
| Predictable sequence | Fixed edges | Small nodes with explicit contracts |
| Classification or validation branch | Conditional edges | Stable labels and a complete label map |
| Retry or iterative refinement | Conditional edge back to an earlier node | Iteration counter and explicit stop path |
| Approval or editing | Checkpoint plus interruption | Thread identity, state inspection, and updateState |
| Live user experience | Compiled-graph streaming | Choose token events, graph events, or both |
| Independent subtasks | Parallel branches | Deterministic merge strategy |
| Reusable sub-process | Nested graph | Stable parent-child state boundary |
Frequently Asked Questions
Can a Spring AI Alibaba Graph node run without an LLM?
Yes. Nodes can contain ordinary Java business logic, service calls, validation, or database work. This also makes deterministic unit tests possible: replace model-dependent nodes with fixed actions and verify state updates and routing independently.
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Create one stable identifier per conversation, job, or approval case at the application boundary and reuse it for every invoke, inspection, update, and resume call. Do not derive it from mutable prompt text, and avoid placing personally identifying information directly in the identifier.
Is MemorySaver enough for a production deployment?
It is the saver used by the QuickStart interruption example, which makes it useful for learning and local validation. For production, evaluate the durability, sharing, backup, and restart behavior of the checkpoint implementation you select against your failure-recovery requirements.
How can I prevent a model from sending the workflow to an invalid branch?
Have the producing node normalize and validate its decision against an allowed set before returning it in state. Treat an unknown value as an explicit error or safe stop, and ensure the conditional-edge map covers every accepted label.
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