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Burr Time-Travel Debugging: Restore a Run, Fork a Branch, Verify the Result

Burr can restore an agent from a prior state and fork a run, but proving an unchanged replay requires explicit comparisons of state, outputs, inputs, and side effects.
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Burr can help you inspect an LLM agent’s earlier execution and start a new branch from a saved point. That is time-travel debugging in a useful sense—but restoring state does not prove that rerunning the branch will reproduce the same model response, tool output, or external effect. To support a claim that “nothing changed,” define exactly what you compare and capture the inputs and outputs that can vary.

What Burr time-travel debugging does—and does not—mean

A Burr application is built from actions and state. Burr’s State documentation describes state as immutable: “It is entirely immutable, meaning that you can only create new states from old ones, not modify them in place.” An action’s state operations therefore produce a new state rather than changing the existing one in place.

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Burr’s tracking system records execution details such as the step name, input, state at execution, timestamp, and result. Its UI is intended to help inspect the state machine and executed steps. The persistence API supports initializing an application from an earlier point, including a fork from an application, partition, and sequence ID.

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Together, tracking and persistence let you inspect a run and continue from an earlier state in a new branch. They do not, by themselves, make an external system run backward or guarantee that a recomputed action returns the same result. A model call may produce a different response; a tool may return updated data; a network request may fail; and an action may have already sent an email, charged an account, or written to a database.

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The distinction is central: a restored checkpoint can establish the starting state for a new run. “The recomputed branch is unchanged” is a separate claim that needs a defined comparison and supporting evidence.

How to inspect a run and fork from an earlier point

The exact setup depends on the tracker and persister you configure. In the documented workflow, retain the identifiers that let you find the source execution—such as app_id, an optional partition_key, and a sequence_id. The persistence API’s initialize_from accepts source identifiers to restore or fork from a prior point.

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  1. Configure tracking or persistence. Choose a tracker or state persister and ensure that it records the execution you need to debug. Burr’s tracking documentation describes LocalTrackingClient, which writes locally by default under ~/.burr; the persistence documentation also describes database and custom persister options.
  2. Locate the relevant execution. Use the tracker or UI to inspect the steps and identify where the behavior diverged. Examine the logged input, state, result, and timestamp for the selected step, along with the run identifiers needed to locate it again.
  3. Initialize a new application from the saved point. Use initialize_from with the source application and sequence identifiers, and provide the partition identifier if applicable. This gives the new application a branch rooted in the selected earlier state; choose new identifiers for where subsequent state should be stored.
  4. Decide what happens next. Resume at the next action or configure the intended entry point. Be explicit about which inputs come from recorded data and which computations will be performed again. Burr’s RAG example distinguishes forking from continuing an existing application, and shows how a prior state can be used to rewind or fork and then continue.
  5. Run the diagnostic branch safely. Before executing actions that affect outside systems, decide whether to stub, isolate, or make those effects idempotent. A fork creates a new application branch; it does not undo side effects already performed by the original run.

The official Apache Burr GPT-like multimodal chatbot example captures the practical debugging questions: “Why did this LLM call fail?”, “Things went off the rails, but where?”, and “What if my agent was 20+ steps in … do I have to restart from step 0? or can I go to a specific point in time?” Tracking helps answer where a run went wrong; forking lets you investigate from a prior point without treating the original execution as if it had been erased.

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What “the same branch” can mean

There is no single useful meaning of “unchanged” until you choose the boundary. A restored state, a repeated transition path, and identical external effects are different levels of evidence. Decide which one matters before comparing runs.

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  • Checkpoint state: Compare a canonical serialization of the state at the fork point. This checks whether the branch started from the state you intended; it does not establish that subsequent actions are identical.
  • Transition path: Compare the ordered action or step names and relevant branch decisions. This can show that the same route through the application was taken, even if values within a step differ.
  • Action outputs: Compare recorded and recomputed outputs for each action. State equality at the start does not imply output equality after an action has run again.
  • Model and tool exchanges: Compare the actual request and response, including the meaningful configuration and inputs. If a model or tool is called again, its new response is a fresh result, not automatically the response from the original run.
  • External effects: Check whether writes, messages, payments, or other outside changes occurred. Logs of application state alone may not establish what an external service did.

A useful verification record identifies the source run and fork point, the branch’s configuration, which inputs and outputs were replayed from records versus recomputed, the action path, and the equality rule used. For state comparison, define what serialization includes and how to treat volatile fields such as timestamps or generated identifiers. For side effects, use a test environment, idempotency controls, or stubs where appropriate; do not repeat a non-idempotent operation merely to see whether a branch “matches.” These are engineering practices for substantiating a replay claim, not guarantees made by Burr.

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What to keep in mind about persistence and state

Burr’s tracker and persister serve related but distinct purposes: tracking makes execution information inspectable, while persistence supports restoring application state. The documentation describes tracker-based reload as not recommended for production use, so use the current persistence guidance when selecting a production persistence mechanism. The tracking server is described as a way to visualize the state machine and executed steps.

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Do not use Burr’s private fields as a shortcut for making a checkpoint. The State API warns that fields prefixed with __ are internal and that modifying them outside the framework is undefined behavior. Create and restore state through the documented APIs instead.

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These details reflect the official Apache Burr documentation available on 7 October 2026. Documentation labels and backend recommendations can change, so check the current persistence and tracking guidance before implementing a setup.

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A practical standard for claiming a replay matched

For a defensible claim, state the level of equivalence rather than saying only “the replay was identical.” For example: “The fork began with the same serialized application state, followed the same action path, and matched the recorded action outputs; model responses were replayed from logs, and external writes were disabled.” That is a bounded claim. If model calls or tools were invoked again, report what was compared and any differences rather than treating the checkpoint as proof of deterministic re-execution.

Burr provides the ingredients to inspect a run and branch from an earlier state. The proof that a recomputed branch did—or did not—change comes from the artifacts you record and the comparison you define.

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