SkyWalking AI Sessionizer

Conversation-level observability, measurement, and export for long-lived AI agents.

SkyWalking AI Sessionizer assembles fragmented agent telemetry into one durable conversation structure. It
preserves sessions as source provenance, keeps parent and child-agent execution lineages separate,
measures model-message continuity, and projects the same committed snapshot into storage, export and
a local preview.

Why

Agent runtimes record a single user-visible conversation as many unrelated artifacts — transcripts,
traces, logs, provider request bodies, tool events, subagent metadata. That conversation can outlive
one process, reactivate after a long idle period, and contain several concurrent agent lineages.

Trace-level inspection alone cannot answer: what did the whole conversation do; which input, model
response, tool result or child-agent result led to the next model call; did that call preserve the
previous message history or start a new context; which agents contributed; and what should be
measured, stored and exported as one unit.

Model

Conversation                          durable identity · ownership boundary
 └ Segment                            activity window · the COMMIT unit
    └ Session                         observed source provenance
       ├ ExecutionStream  main        ordered parent-agent lineage
       │  └ Context epoch × N         model-context lifetime
       │     └ Talk × N               one readable input → run → output
       │        └ Run → Step × N
       └ ExecutionStream  child × N   independent context per child agent

Two boundaries carry the design. Conversation is the durable aggregation and ownership boundary,
and its identity is supplied — never inferred from a person, an account, or timestamp proximity.
ExecutionStream is the ordered continuity boundary; model-message continuity is evaluated within
one stream and one context epoch, never across them.

See the Unified Conversation Model.

Evidence discipline

Nothing is presented as observed unless it was observed.

Every claim carries a qualification (observed_replayable, observed_report_only, proposed,
unavailable) and every correlation carries a resolution state (exact_unique, exact_ambiguous,
strong_inference, unresolved, conflict). An exact identifier with several candidates stays
ambiguous — the assembler never silently chooses one. Where a runtime cannot supply something, the
adapter reports it as unavailable rather than approximating it.

Adapters

Quick start

Install asz, on macOS or Linux, with a version from the
downloads page:

VERSION=<version>
curl -fsSL "https://raw.githubusercontent.com/apache/skywalking-ai-sessionizer/v$VERSION/install/asz.sh" | sh -s -- "$VERSION"

Then run it in the directory where it should keep its data, and open http://127.0.0.1:8787:

mkdir -p ~/asz && cd ~/asz
asz server

Install has Windows, Homebrew and the binary packages, and
Quick Start the next steps.

Documentation

Official documentation lives in docs/ and is indexed by
docs/menu.yml: concepts and designs, setup, the data formats, adapters, guides
and the changelog. It is published at
skywalking.apache.org/docs/skywalking-ai-sessionizer.

Contributing

Early contributions should focus on schemas, privacy-safe fixtures, deterministic assembly,
qualification rules and golden tests. Please avoid adding inferred identities or causal edges that
cannot retain their source evidence and resolution state.

License

Apache License 2.0.

Apache SkyWalking, SkyWalking, and the Apache feather logo are trademarks of The Apache Software Foundation.

Container image

docker run --rm -p 8787:8787 -v "$PWD/data:/asz/data" \
  ghcr.io/apache/skywalking-ai-sessionizer:latest view 0.0.0.0:8787

See Container Image.