Tutorial: Bring your own trace

This tutorial diagnoses an existing trace from a real observability stack — no @span decorators, no live agent run. The trace is a Langfuse export of a RAG query that returned the wrong document and gave a confidently wrong answer. You’ll write a 30-line adapter, hand the converted trace to Origin, and read the attribution. By the end you’ll have a working pattern for any observability source — Langfuse, OpenTelemetry, LangSmith, Phoenix, Braintrust, or a home-grown JSONL trace store. The Witness AgentTrace schema is the contract; everything else is translation. The full code is in examples/byo_trace/.

Setup

pip install aevyra-origin[openai]    # needed for OpenRouter

export OPENROUTER_API_KEY=sk-or-...
# OR  export ANTHROPIC_API_KEY=sk-ant-...

cd examples/byo_trace

There’s no pipeline to run here — the trace is already on disk in sample_langfuse.json.

The trace

The captured run is a 4-span RAG query: User question: “What’s our refund policy for digital subscriptions?” Final answer: “You can return any product within 30 days of purchase for a full refund, as long as it is unopened and in original packaging.” That’s wrong. The user asked about digital subscriptions; the answer cites the physical-products return policy. The retrieval index didn’t have the digital-subscription doc — the top-ranked result is kb_returns_v3 (the physical returns policy), and the synthesizer wrote a confident answer from the wrong source. Origin’s job is to tell us which span owns the failure and what kind of fix it needs: updating the retrieval index, rewriting a prompt, or something else entirely.

The AgentTrace contract

Origin’s diagnose takes an AgentTrace — a flat list of TraceNodes with parent_id links, an optional ideal, and a metadata dict. To diagnose your own logs you write a function that produces one of these from your source format. The minimum you need per span:

Field	Notes
`name`	Human-readable label (`"plan"`, `"vector_search"`, `"synthesize"`).
`id`	Unique within the trace. Required when names repeat in the DAG.
`parent_id`	The id of the parent span, or `None` for a root.
`kind`	One of `"reason"`, `"tool"`, `"retrieve"`, `"agent"`, `"other"`.
`input` / `output`	Any JSON-serializable value.
`prompt_id`	Identity of the prompt behind this span. Multiple spans sharing one `prompt_id` are rolled up together by `Attribution.by_prompt()`.
`optimize`	`True` when this span’s prompt is an optimization target. Reflex reads this.

Adapter: Langfuse → AgentTrace

Langfuse stores a trace as a top-level record with nested observations of types GENERATION, SPAN, EVENT. The mapping to AgentTrace is almost 1:1:

from_langfuse.py

from aevyra_witness import AgentTrace, KIND_OTHER, KIND_REASON, KIND_TOOL, TraceNode


def _kind_for(obs):
    obs_type = (obs.get("type") or "").upper()
    if obs_type in ("GENERATION", "AGENT", "CHAIN"):
        return KIND_REASON
    if obs_type in ("SPAN", "TOOL", "RETRIEVER"):
        return KIND_TOOL
    return KIND_OTHER


def from_langfuse_export(payload):
    nodes = []
    for obs in payload.get("observations", []):
        meta = dict(obs.get("metadata") or {})
        prompt_id = meta.pop("prompt_id", None)
        optimize  = bool(meta.pop("optimize", False))

        nodes.append(TraceNode(
            name=obs["name"],
            id=obs["id"],
            parent_id=obs.get("parentObservationId"),
            kind=_kind_for(obs),
            prompt_id=prompt_id,
            optimize=optimize,
            input=obs.get("input"),
            output=obs.get("output"),
            metadata=meta,
        ))

    trace_meta = dict(payload.get("metadata") or {})
    ideal = trace_meta.pop("ideal", None)
    return AgentTrace(nodes=nodes, ideal=ideal, metadata=trace_meta)

Sanity-check it:

python from_langfuse.py

Loaded 4 spans from sample_langfuse.json
  ideal = 'Cite the digital subscription refund policy specifically...'
  optimize_prompt_ids = ['query_rewriter', 'reranker', 'answer_writer']
  [reason] query_rewrite  id=obs_001  prompt=query_rewriter
  [tool] vector_search  id=obs_002  prompt=None
  [reason] rerank  id=obs_003  prompt=reranker
  [reason] synthesize  id=obs_004  prompt=answer_writer

Run the diagnosis

First convert the Langfuse export to an AgentTrace and save it as trace.json:

python from_langfuse.py sample_langfuse.json

Loaded 4 spans from sample_langfuse.json
  ideal = 'Cite the digital subscription refund policy specifically — pro-rated refund within the first 7 days, no refund after.'
  optimize_prompt_ids = ['query_rewriter', 'reranker', 'answer_writer']
  [reason] query_rewrite  id=obs_001  prompt=query_rewriter
  [tool] vector_search  id=obs_002  prompt=None
  [reason] rerank  id=obs_003  prompt=reranker
  [reason] synthesize  id=obs_004  prompt=answer_writer

trace saved → trace.json

Then pass it to the CLI. The score comes from your judge — here 0.1, because the final answer cites the wrong policy:

aevyra-origin diagnose trace.json \
  --score 0.1 \
  --rubric rubric.txt \
  --model openrouter/qwen/qwen3-235b-a22b-thinking-2507 \
  --runner runner.py

--score 0.1 — the judge score for this trace (cited wrong policy)
--rubric rubric.txt — the evaluation criteria the judge used
--model — the model doing attribution reasoning, in provider/model format
--runner runner.py — enables causal ablation; exports runner and judge for this pipeline

Or with Anthropic:

aevyra-origin diagnose trace.json \
  --score 0.1 \
  --rubric rubric.txt \
  --model anthropic/claude-sonnet-4-5 \
  --runner runner.py

For JSONL sources, convert first with from_jsonl.py, then run the same CLI command.

How Origin finds the issue

Analysis 1 — read the whole trace and ask “what went wrong?” An LLM reads every span’s input and output alongside the rubric and score, and returns a ranked list of suspicious spans. Fast, but can be fooled when the trace contains plausible-sounding reasoning over defective inputs — exactly the case here, where the synthesizer wrote a confident answer from a confidently wrong document. Analysis 2 — break the rubric into criteria and check each step The rubric resolves to two criteria — cite the digital-subscription policy and do not cite the physical-returns policy — and Origin checks each span against each criterion individually. The synthesizer fails both. The retrieval span fails the first (it didn’t return the right doc). The reranker partially fails the first (it ranked an off-topic doc first without flagging the mismatch). Analysis 3 — break things on purpose and re-score Ablation overrides one span’s output at a time and re-judges. For this trace the most informative ablation is on vector_search — replacing its output with a null and re-scoring shows whether the downstream pipeline was actually depending on it. Because all spans are stored values (no live LLM calls), the runner uses trace replay: it clones the captured trace and substitutes one span’s output in-place. Fast and deterministic for a fully static trace. Why all three together For RAG failures specifically, this combination is load-bearing. Critic alone can be misled by the synthesizer’s confident prose. Decomposition catches the topic mismatch but doesn’t tell you which fix has the most leverage. Ablation puts a number on the leverage — which is the question that actually matters when deciding between “fix the index” and “rewrite the prompt.”

What Origin finds

  Root cause:  The pipeline failed because the vector search step retrieved an
               irrelevant document about physical product returns as the top
               result for a digital subscription query, and the correct digital
               subscription refund policy was absent from the retrieved results.

  Fix:         Fix the retrieval step for 'vector_search' — wrong or missing docs.
  Evidence:    critic: 'vector_search' at 81% confidence · decomposition: 'answer_writer'
               cited across 1 span

  ────────────────────────────────────────────────────────────
  All culprits  (score=0.100, 10.6K tokens, 4 ablation calls)

  1. vector_search  [primary, conf=0.81, fix=retrieval]
     [critic] The vector search tool returned 'kb_returns_v3' (physical product
     returns policy) as the top result for the query 'refund policy digital
     subscription', but the rubric requires citing the digital subscription
     policy specifically. The output snippet explicitly states 'All physical
     products may be returned within 30 days...', which directly contradicts the
     required 'pro-rated within first 7 days, no refund after' policy. Since the
     retrieved results contain no document describing digital subscription
     refunds (only physical products, shipping, and billing), this retrieval
     failure made the hard rubric violation inevitable. The tool executed
     successfully without errors, indicating the issue lies in the knowledge base
     index or embedding strategy, not the tool call itself. [decomposition]
     [Cites digital subscription refund policy specifically; avoids physical
     product policy] Retrieved physical product policy (kb_returns_v3) as top
     result for digital subscription query, indicating index/query matching issue
     in knowledge base.

  2. synthesize  [primary, conf=0.55, fix=unknown, prompt=answer_writer]
     [decomposition] [Cites digital subscription refund policy specifically;
     avoids physical product policy] Cited physical product policy for digital
     subscriptions without recognizing 'physical products' limitation in document
     snippet, due to insufficient prompt instructions on policy applicability.
     [ablation] Ablating this span changed the judge score from 0.100 to 0.500
     (delta=-0.400). The removal IMPROVED the score, so this span's real output
     is actively degrading the run. Fixing the retrieval step fixes this as a
     side effect.

Reading the result

vector_search is primary with fix=retrieval. That’s the critical signal — prompt rewriting will not fix this. If you fed Origin’s prompt-level rollup directly to Reflex, Reflex would rewrite answer_writer, the answer would still cite the wrong policy (because retrieval still returns the wrong doc), and you’d lose a debugging cycle. The fix_type is what routes the work to the right team. synthesize is also primary but downstream. Ablation confirms it: blanking the synthesizer’s output improved the score from 0.1 to 0.5 — its phrasing is actively making things worse. But its fix_type=unknown is the right signal: there’s no independent prompt fix here. The synthesizer cited what retrieval gave it. Fix the index and this span cleans up automatically. query_rewrite and rerank are clean. Neither appears in the culprit list. The rewrite “refund policy digital subscription” was exactly the right query; Origin examined it and found nothing wrong. Confirming the non-failures is as useful as finding the failures — it tells you where not to spend engineering time. fix tells you what not to do. Here’s how the same pipeline maps to different fix types depending on what goes wrong:

Scenario	Culprit	`fix`
Doc not in the index	`vector_search`	`retrieval`
Synthesizer cites without flagging gaps	`synthesize`	`prompt`
Reranker ranks off-topic docs first	`rerank`	`prompt`
Retriever called with malformed args	`vector_search`	`tool_schema`
Embedding model can’t distinguish topics	`vector_search`	`retrieval`

Bringing your own format

Langfuse is one source. The same pattern works for any structured log. OpenTelemetry is the de facto standard for distributed tracing. Witness’s OTel adapter reads spans emitted by LangGraph, CrewAI, AutoGen, the Vercel AI SDK, and any framework using GenAI semantic conventions:

from aevyra_witness.adapters.otel import from_otel_spans

trace = from_otel_spans(span_exporter.get_finished_spans())
origin.diagnose(trace=trace, score=score, rubric=rubric, method="all")

Custom JSONL — if you control the producer (you’re emitting traces from your own TypeScript / Go / Rust agent), the simplest schema is one JSON object per line, fields named the same as Witness’s TraceNode. The adapter is ~30 lines:

from_jsonl.py

def from_jsonl(lines):
    nodes = []
    for raw in lines:
        rec = json.loads(raw)
        nodes.append(TraceNode(
            name=rec["name"],
            id=rec.get("id", ""),
            parent_id=rec.get("parent_id"),
            kind=rec.get("kind", "other"),
            prompt_id=rec.get("prompt_id"),
            optimize=bool(rec.get("optimize", False)),
            input=rec.get("input"),
            output=rec.get("output"),
            metadata=dict(rec.get("metadata") or {}),
        ))
    return AgentTrace(nodes=nodes, ideal=None, metadata={})

Pass --source jsonl to use it:

python diagnose_existing.py \
  --model openrouter/qwen/qwen3-235b-a22b-thinking-2507 \
  --source jsonl --path sample_jsonl.jsonl

This is how you’d diagnose traces from a non-Python agent: emit JSONL from TypeScript / Go / Rust, point the script at the file. The attribution engine doesn’t know or care which language produced the trace — AgentTrace is the contract.

Next steps

Support triage tutorial

A live pipeline failure diagnosed end-to-end with ablation

Coding agent tutorial

A real LLM, a real failure, three attribution methods agreeing

Methods

When ablation beats critic, and when it doesn’t

Reflex quickstart

Feed Origin’s prompt-level rollup to Reflex and ship a fix

Getting started

Guides

Tutorials

API reference

Tutorial: Bring your own trace

Setup

The trace

The AgentTrace contract

Adapter: Langfuse → AgentTrace

Run the diagnosis

How Origin finds the issue

What Origin finds

Reading the result

Bringing your own format

Next steps

Support triage tutorial

Coding agent tutorial

Methods

Reflex quickstart

​Setup

​The trace

​The AgentTrace contract

​Adapter: Langfuse → AgentTrace

​Run the diagnosis

​How Origin finds the issue

​What Origin finds

​Reading the result

​Bringing your own format

​Next steps

Support triage tutorial

Coding agent tutorial

Methods

Reflex quickstart

Setup

The trace

The AgentTrace contract

Adapter: Langfuse → AgentTrace

Run the diagnosis

How Origin finds the issue

What Origin finds

Reading the result

Bringing your own format

Next steps