darkhunt-telemetry-python-integration

Darkhunt telemetry Python SDK — integration guide

This skill walks through wiring darkhunt-telemetry (the Python SDK) into a Python service. It is the Python analog of the TypeScript darkhunt-telemetry-integration skill — same wire contract, same routing semantics, same masking ruleset, adapted to Python idioms (keyword arguments, with context managers, contextvars).

The patterns below are extracted from temporal-demo-python, an internal six-domain multi-agent reference integration where each domain uses a different orchestration style, so it demonstrates every handoff transport at once. File-level pointers into it are listed at the end of this skill; if you don't have that repo checked out, the inline snippets here are self-contained.

For anything the patterns below don't cover, read the SDK's own README.md — it carries the full API reference + masking docs. It ships inside the installed package and is published at https://github.com/darkhunt-security/darkhunt-telemetry-python.

What the SDK is

A Darkhunt-specific span exporter built on OpenTelemetry primitives (TracerProvider, BatchSpanProcessor, OTLP/protobuf) that ships spans — traces, LLM generations, tool calls, retrievals, guardrails — to Darkhunt trace-hub. Routing semantics (tenant_id / workspace_id / application_id) and the attribute schema are Darkhunt-specific; trace-hub is the only intended receiver. Built-in client-side masking redacts 66 secret/PII patterns before payloads leave the process. Requires Python 3.9+.

Key shapes:

• DarkhuntTelemetry — the client. One per process, lifetime-of-the-process.
• Trace — a single user-facing interaction. Carries routing fields.
• Generation — one LLM round-trip under a trace (model, messages, usage, cost).
• Span — anything else (tool calls, retrievals, guardrails, sub-agents). Use observation_type to categorize.

Step-by-step integration

1. Install — from PyPI

The SDK is published on public PyPI as darkhunt-telemetry. Install it directly:

pip install "darkhunt-telemetry[temporal]"
# or, with uv:
uv add "darkhunt-telemetry[temporal]"

or declare it as a project dependency:

# pyproject.toml
dependencies = ["darkhunt-telemetry[temporal]"]

then uv sync. Releases are continuous — every merge to the SDK's main publishes 0.5.<build> to PyPI, so the newest release is always the latest. Leave the spec unpinned to track latest, or set a floor (>=0.5.13) / pin an exact version for reproducibility.

Extras: [temporal] pulls in temporalio (only needed for the Temporal handoff interceptors); [crypto] adds the vetted Keccak validator. The core package imports neither, so it loads with zero Temporal/crypto packages installed.

Because it installs from PyPI, Docker builds just work — pip install / uv sync pulls the wheel inside the image, with no build-context tricks.

Alternatives (rarely needed):

• Local path — only when hacking on the SDK itself beside your app. With uv: [tool.uv.sources] darkhunt-telemetry = { path = "../darkhunt-telemetry-python", editable = true }; plain pip: pip install -e "../darkhunt-telemetry-python[temporal]". (This is the one case that reintroduces the Docker sibling-path trap — a context: . build can't COPY a dep outside the build context; use a named additional_contexts: { dhsdk: ../darkhunt-telemetry-python } and COPY --from=dhsdk.)
• Git dependency — to pin an unreleased commit: darkhunt-telemetry @ git+https://github.com/darkhunt-security/darkhunt-telemetry-python@<ref>.

2. Get an API key

A dh-... API key is required for public/external ingest (internal=False — app servers / CLIs / workers calling the public endpoint). In-cluster service-to-service callers using internal=True don't need one.

Create one in the dashboard (**app.darkhunt.ai → Settings → Security → API Keys →

• Create API key**), copy it immediately (shown once), and put it in the DARKHUNT_API_KEY env var — the name the SDK reads by default (options api_key ?? DARKHUNT_API_KEY). If it's missing on the public endpoint the constructor raises ValueError: api_key is required for the public endpoint.

The key, the base URL, and the tenant must all be the same environment. A dh- key is scoped to one environment's tenant. Source DARKHUNT_API_KEY, DARKHUNT_BASE_URL, and DARKHUNT_TENANT_ID from the same place — enrolling the darkhunt-cli writes a matched trio to ~/.darkhunt/credentials.json ({ apiKey, apiBaseUrl, tenantId }); prefer that set together.

Gotcha: credentials.json apiBaseUrl has NO /trace-hub suffix (it stores the bare host, e.g. https://api.darkhunt.ai). The SDK default base_url DOES include it (https://api.darkhunt.ai/trace-hub). If you source DARKHUNT_BASE_URL from that field, append /trace-hub yourself — the exporter posts to {base_url}/otlp/t/{tenant_id}/v1/traces, so dropping it yields 404.

2b. Create an OBSERVABILITY application (get the application_id)

Every trace needs an application_id (a workspace-scoped UUID). Create a new, dedicated OBSERVABILITY app for the integration — do not reuse a random existing app (its traces would pollute that scope). In a multi-agent system, one app per domain / service group is typical (agents within a domain are told apart by service.name).

Reach for the Darkhunt MCP first (it reuses enrolled credentials). Check the darkhunt_* tools are connected, then:

darkhunt_status                # confirm auth + tenant + reachable API
darkhunt_list_workspaces       # → pick your workspaceId (UUID)
darkhunt_create_application    { workspace, name, type: 'OBSERVABILITY', description? }
                               # → returns the NEW application's UUID (use this)

• Pass type: 'OBSERVABILITY' (default is RED_TEAM) so the app's Tracing view is enabled.
• Put each UUID in DARKHUNT_APP_<DOMAIN> (or DARKHUNT_APPLICATION_ID for a single-app service). If MCP + CLI are both unavailable, use the dashboard's new-application flow; don't silently curl the REST API.

3. Singleton client (process-wide)

Don't construct DarkhuntTelemetry per request. Each construction spins up a TracerProvider + BatchSpanProcessor and registers an atexit handler, so a per-call client leaks resources and prevents batching. service.name is a per-client OTel resource, so distinct agent names need distinct clients — in a multi-agent process, memoize one client per (application_id, service_name):

from darkhunt_telemetry import DarkhuntTelemetry

_clients: dict[str, DarkhuntTelemetry] = {}

def client_for(service_name: str, application_id: str) -> DarkhuntTelemetry:
    key = f"{application_id}::{service_name}"
    c = _clients.get(key)
    if c is None:
        c = DarkhuntTelemetry(
            # api_key / base_url / tenant / workspace read from env by default.
            application_id=application_id,
            service_name=service_name,   # the topology node identity
            internal=False,              # public ingest with the dh- bearer key
        )
        _clients[key] = c
    return c

4. Graceful degradation — never crash a host app without creds

The client raises if api_key is missing on the public endpoint, and client.trace() raises if a routing field is missing. So gate on config presence and return None when unconfigured; call sites use if trace: / trace.end() if trace else None:

import os

_REQUIRED = ("DARKHUNT_API_KEY", "DARKHUNT_TENANT_ID", "DARKHUNT_WORKSPACE_ID")
_ENABLED = os.environ.get("DARKHUNT_ENABLED") != "false" and all(os.environ.get(k) for k in _REQUIRED)

def open_agent_trace(domain, agent, *, session_id, user_id, handoff_from=None, input=None):
    if not _ENABLED:
        return None
    app_id = os.environ.get(f"DARKHUNT_APP_{domain.upper()}")
    if not app_id:
        return None
    client = client_for(f"{domain}.{agent}", app_id)
    tokens = [t for t in (handoff_from or []) if t]
    return client.trace(name=f"{domain}.{agent}", session_id=session_id, user_id=user_id,
                        handoff_from=tokens or None, input=input)

temporal-demo-python/src/demo/telemetry.py is this exact pattern, production-ready (open_agent_trace / open_gateway_trace / trace_chat / trace_gen / trace_tool / flush_telemetry / shutdown_telemetry). Copy it as your starting point.

5. Wire shutdown on signals

Spans batch in the background. The SDK flushes at process exit via atexit, but signal-driven shutdown (SIGTERM/SIGINT — docker stop, kill) bypasses atexit, losing the in-memory batch. Wire it:

# Long-running server:
try:
    uvicorn.run(app, ...)          # blocks; returns on SIGINT/SIGTERM
finally:
    for c in _clients.values():
        c.shutdown()

# One-shot script / per-task on a server:
client.flush()                     # before returning

The Temporal Worker.run() resolves on shutdown — call shutdown() in its finally. For a per-request worker (HTTP/queue consumer), client.flush() after each handled task is cheap insurance since atexit won't fire until the process ends.

6. Wrap each LLM call — prefer the with-based active form

The Python SDK's active-context helpers are synchronous with blocks, which fit a blocking chat() perfectly (and still work called from async code):

def trace_chat(trace, name, model, input_messages, call):
    if trace is None:
        return call()                                    # no-op passthrough
    with trace.start_active_generation(name, model=model) as gen:
        gen.update(input_messages=input_messages)        # known at start
        res = call()                                     # span is ACTIVE here → real timing
        gen.end(model=model, output_messages=[res.message],
                usage={"input_tokens": res.usage["input_tokens"],
                       "output_tokens": res.usage["output_tokens"]})
        return res

def trace_tool(trace, tool_name, input, run):
    if trace is None:
        return run()
    with trace.start_active_span(tool_name, observation_type="tool",
                                 tool_name=tool_name, input=input) as span:
        out = run()
        span.end(output=out)
        return out

• start_active_generation / start_active_span time the span automatically, make it the active OTel span (so provider auto-instrumentation nests under it), end it on block exit, and mark ERROR on a raised exception. The inner gen.end() / span.end() is idempotent, so calling it yourself to attach the payload is fine.
• update() is for fields known at start (input_messages, system_instructions); end() for fields known at finish (output_messages, usage, cost).
• Manual form (streaming, or holding a span open across calls): open it after work started and backdate with start_time (epoch seconds, captured before the call): gen = trace.generation(name, model=..., start_time=t0). Epoch seconds, not ms — the Python convention (time.time()).
• You usually don't need cost. trace-hub auto-prices from model + usage for known models. Only set cost for custom/unpriced models.

Routing fields

Every span carries tenant_id / workspace_id / application_id. Set them once on the client if constant for the process; pass per-trace if multi-tenant (client.trace(tenant_id=..., ...)). The constructor merges constructor arg > env var > default; client.trace() raises ValueError if any is still missing. assessment_run_id is optional (Darkhunt-internal grouping); omit for general production tracing.

session_id and user_id — set them every time

Technically optional, but every integration should set them. Traces sharing a session_id group into one conversation timeline; the policy engine keys per-user signals off user_id. If not known at open, trace.update(user_id=..., session_id=...) once they are — spans created after inherit the values. Routing identifiers are not masked (they round-trip verbatim for exact-match grouping) — hash any PII-bearing identifier caller-side.

In-cluster vs public ingest

Caller	internal	Auth	URL
In-cluster service-to-service	True	none (cluster policy gates it)	POST {base_url}/internal/t/{tenant}/v1/traces
External CLI / browser / app	False (default)	Authorization: Bearer <api_key>	POST {base_url}/otlp/t/{tenant}/v1/traces

Span types — pick the right one

Work	Python API	observation_type
LLM round-trip	trace.generation(name, model=...)	(auto: generation)
External tool call	trace.span(name, observation_type="tool", tool_name=...)	"tool"
Vector search/retrieval	trace.span(name, observation_type="retriever")	"retriever"
Sub-agent step	trace.span(name, observation_type="agent")	"agent"
Input/output guardrail	trace.span(name, observation_type="guardrail")	"guardrail"
Embedding	trace.span(name, observation_type="embedding")	"embedding"
Generic work	trace.span(name)	"span" (default)
Fire-and-forget marker	trace.event(name)	"event"

Every factory has a start_active_* variant. Spans nest naturally — parent.span(...) / parent.generation(...). For tool spans set tool_name (and optionally tool_call_id / tool_arguments) so the dashboard shows the real tool, not the generic type.

Masking (default-on)

66 secret/PII patterns are redacted from inputs/outputs/messages/system prompts/metadata/tool args before anything leaves the process. Add site patterns or disable for local synthetic-data dev:

from darkhunt_telemetry import DarkhuntTelemetry, MaskingOptions
from darkhunt_telemetry.masking import CustomPattern

dh = DarkhuntTelemetry(
    tenant_id="t", workspace_id="w", application_id="a",
    mask=MaskingOptions(enabled=True, custom_patterns=[CustomPattern(regex=r"TICKET-\d+", marker="[TICKET]")]),
)

Multi-agent topology & handoffs

When the service is one agent in a multi-agent system, Darkhunt reconstructs the agent topology (who handed off to whom). Identity: one service.name per agent — that string is the topology node. The graph is drawn from the cross-service parentSpanId chain, so the whole job is to make each agent's root trace nest under its caller.

The one thing to get right: nest via handoff_from

client.trace(handoff_from=[caller_token]) makes the agent's root span a child of handoff_from[0] (shared trace_id, parentSpanId set) and records an agent_handoff link. Further entries are supplementary links (fan-in). The caller's token is trace.handoff_token() (an opaque W3C traceparent string). The SDK auto-registers the global OTel context manager + W3C propagator on construction, so there is nothing else to wire — just pass handoff_from.

Why nesting matters: ingestion drops a contentless span (an agent's root often is — its generations/tools are children) unless it is a cross-service entry (has a parentSpanId into another service). Nest → the root is a cross-service entry → kept → connected. Don't nest and don't link → the roots are dropped → disconnected islands (correct only when the agents truly never hand off — see the datastore/queue note below).

Keep the token OUT of business signatures

The token is observability plumbing — never a typed handoff parameter on your domain functions, entry inputs, message/return types, or graph state visible to business code. Carry it out-of-band, exactly like OTel carries trace context. Which carrier depends on the topology + transport:

Topology / transport	Carrier	Reference file (temporal-demo-python)
Linear in-process chain	a contextvars.ContextVar (the AsyncLocalStorage analog) — read current_handoff(), publish your own	src/demo/handoff_context.py, domains/weather/
Branching / fan-in in-process	orchestrator-driven: the coordinator opens each agent trace with an explicit handoff_from and passes the Trace into the agent	domains/finance/run.py
In-process graph (LangGraph)	a dedicated field in the graph state (node writes its token, next node reads it as handoff_from)	domains/banking/graph.py
HTTP	the W3C traceparent header (out of the body)	domains/healthcare/http.py
Queue (Redis/Kafka/SQS/…)	a dedicated message metadata field, kept out of data	domains/devops/bus.py
Temporal	a Temporal Header via HandoffInterceptor + per-edge child_args	domains/security/

Linear in-process — the contextvars carrier (a single "current" slot; perfect for a → b → c, wrong for branches/fan-in):

from contextlib import contextmanager
from contextvars import ContextVar
_h: ContextVar = ContextVar("handoff", default=None)

@contextmanager
def with_handoff(token):
    reset = _h.set(token)
    try: yield
    finally: _h.reset(reset)

def current_handoff(): return _h.get()
def publish_handoff(token): _h.set(token)

# gateway seeds the scope with its root token; each agent reads+publishes:
with with_handoff(root.handoff_token() if root else None):
    plan = plan_task(inp)      # opens its trace w/ handoff_from=[current_handoff()], then publish_handoff(its token)
    weather = geodata(plan)    # nests under coordinator; publishes its own → advisor nests under geodata
    advisor(weather)

Branching / fan-in in-process — orchestrator-driven (the single ambient slot can't express a DAG). The coordinator opens every trace with an explicit handoff_from it computes per edge, and passes a trace handle into each agent as a trailing, defaults-None telemetry param (the bounded exception to "no tokens in signatures"):

tok = lambda t: t.handoff_token() if t else None
root = open_gateway_trace("finance", ...);  root_t = tok(root)
coord = atrace("coordinator", [root_t]);     plan_task(inp, rt, coord);  coord_t = tok(coord); coord.end()
research = atrace("research", [coord_t]);     ...
# parallel-with-different-parents + fan-in are now trivial — each trace is explicit:
bull_th = atrace("bull", [research_t]);  bear_th = atrace("bear", [research_t])   # both ← research
pm = atrace("pm", [quant_t, bull_re_t, bear_re_t])   # fan-in of 3 (handoff_from[0] = parent edge)

Carrying the token across a transport — use the SDK helpers

from darkhunt_telemetry.transports import (
    handoff_to_http_headers, handoff_from_http_headers,   # HTTP: traceparent header
    handoff_to_message_meta, handoff_from_message_meta,   # queue: single-parent
    handoffs_from_messages,                               # queue: fan-in (ordered, de-duped)
    TRACEPARENT_HEADER, HANDOFF_MESSAGE_META_KEY,
)

# HTTP producer / consumer:
requests.post(url, headers=handoff_to_http_headers(trace.handoff_token(), base_headers))
token = handoff_from_http_headers(request.headers)        # → client.trace(handoff_from=[token])

# Queue producer / consumer (token in a DEDICATED field, never inside `data`):
xadd(stream, handoff_to_message_meta(trace.handoff_token(), fields))
token = handoff_from_message_meta(parsed_fields)          # single parent
tokens = handoffs_from_messages([m1, m2, m3])             # fan-in → client.trace(handoff_from=tokens)

Temporal — the hardest, and the biggest trap

from temporalio.worker import Worker
from darkhunt_telemetry.temporal import HandoffInterceptor, current_handoff, child_args

• Worker: register ONE HandoffInterceptor() in interceptors=[...] — it wires BOTH the activity side (exposes the inbound token to current_handoff()) and the workflow side (propagates / relocates per-edge overrides into the Temporal Header).

• Activities hold ALL the telemetry: open dh.trace(handoff_from=current_handoff() or []), wrap chat/tools, trace.end() in finally. current_handoff() works inside SYNC activities run in a ThreadPoolExecutor — temporalio copies the contextvar context into the worker thread (verified against temporalio.worker._activity). No async requirement.

• NEVER put telemetry in workflow code — it's a deterministic sandbox (no SDK, no dh.trace). Its only Darkhunt reference is child_args, imported from the sandbox-safe darkhunt_telemetry.temporal.handoff_header subpath (pure dict helper, no temporalio import) inside with workflow.unsafe.imports_passed_through():.

• ⚠️ RETURN the token to build the DAG. The interceptor defaults nest every child under the coordinator's inbound token → a star (the call graph), not the causal DAG. A workflow can neither mint nor read a token (sandboxed); only an activity produces one. A Temporal Header flows parent→child only — so a child's token comes home exactly one way: the activity's return value. Add an optional handoff field to each activity result; the coordinator threads it into the next execute_child_workflow via child_args:

  # activity returns its token:
  return {**result, "handoff": trace.handoff_token() if trace else None}
  
  # coordinator threads it per edge (child_args attaches a hidden override the
  # interceptor relocates to the header and strips before the child sees it):
  def edge(x, upstream): return child_args(x, [t for t in upstream if t])
  plan  = await execute_child_workflow(ReconWf.run,  edge(recon_in,  [coord_tok]))
  spine = plan.get("handoff")
  batch = await asyncio.gather(*[execute_child_workflow(AnalyzerWf.run, edge(a_in, [spine])) for ...])
  a_toks = [b.get("handoff") for b in batch]                 # this round's analyzer tokens
  taint = await execute_child_workflow(TaintWf.run, edge(taint_in, a_toks))   # fan-in = array
  decision = await execute_child_workflow(PlannerWf.run, edge(plan_in, a_toks))
  spine = decision.get("handoff") or spine                   # loop advances the spine
  

  Rules of thumb: fan-in = pass the array of upstream tokens; a loop = later rounds thread the downstream stage's token (advance a spine var); a hub (a guardrail gate fired N times) = keep it on the coordinator's token. A collection-returning activity (list[Finding]) can't carry a handoff field — wrap it in {"findings": [...], "handoff": ...}.

• The gateway→coordinator edge needs a hand-rolled client interceptor — the SDK ships workflow + activity interceptors but NO client interceptor, so client.start_workflow(...) carries no header on its own. Inject the gateway trace's token (from an ambient store the request handler sets):

  import temporalio.converter
  from temporalio.client import Client, Interceptor, OutboundInterceptor, StartWorkflowInput
  from darkhunt_telemetry.temporal import HANDOFF_HEADER
  
  class _GwOut(OutboundInterceptor):
      async def start_workflow(self, input: StartWorkflowInput):
          token = _gateway_handoff.get()   # a contextvars.ContextVar the handler set
          if token:
              input.headers = {**(input.headers or {}),
                               HANDOFF_HEADER: temporalio.converter.default().payload_converter.to_payload([token])}
          return await self.next.start_workflow(input)
  class _GwInt(Interceptor):
      def intercept_client(self, next): return _GwOut(next)
  client = await Client.connect(addr, namespace=ns, interceptors=[_GwInt()])
  

The orchestrator / gateway node

Ingestion retains a trace ROOT even when contentless (it anchors the topology), so a gateway that only fans work out survives on its own — open the root, put the task on its input, and hand off from root.handoff_token() into the first agent:

root = dh.trace(name=f"{domain}.gateway", session_id=task_id, user_id=user_id, input={"task": task})
first_token = root.handoff_token()   # → first agent's handoff_from

Edge rules (each was a real bug)

• Link to the REAL producing agent, not the orchestrator. Thread the token wherever one agent's output becomes the next agent's input. Linking a downstream agent to the orchestrator (because it spawned it) draws a plausible-but-WRONG graph (e.g. advisor linked to coordinator instead of downstream of geodata).
• Agent vs Worker: a node with ≥1 generation renders as an Agent (model + cost); a tools-only node is a Worker (no cost). Emit trace.generation(...) for EVERY real LLM call, even "boilerplate" ones, or their cost never surfaces.
• Deep repeat loops → self-loops (↻ ×N), not per-round back-edges. For an N-round loop over M agents, link every round to the SAME stable upstream so they render as clean self-loops; linking each round to the prior round's output emits a tangle of back-edges.
• Small 2-agent cycle (↺) — here a back-edge IS right (a retried step links the step it retries; a debate rebuttal links the opposing thesis).
• Fan-in is handoff_from=[a, b, c] — [0] is the parent edge, the rest are links.
• Logical coupling through a datastore/queue ≠ a drawn edge. Services related only through a shared DB / bucket / a queue the token does NOT ride on have no parentSpanId chain, so they render as disjoint islands — frequently correct (classic RAG ingest→answer). Do NOT synthesize an edge, and tell the user explicitly that connecting them is an architecture change (carry a handoff_token() across the medium), not a telemetry setting.

Verification

After wiring:

1. Types/lint: uv run python -m compileall src && uv run ruff check . (or the project's checks), and import every entrypoint.

2. Graceful no-op: run with DARKHUNT_ENABLED=false and confirm the app still runs and every helper no-ops.

3. Server-side auth/routing probe — the ONLY reliable programmatic check (a clean flush()/shutdown() is NOT proof of ingestion; the BatchSpanProcessor swallows export errors). A 400 on an empty body = auth + routing OK; 401 = wrong/ absent key; 404 = missing /trace-hub:

   curl -s -o /dev/null -w '%{http_code}\n' -X POST \
     -H "Authorization: Bearer $DARKHUNT_API_KEY" \
     -H 'Content-Type: application/x-protobuf' \
     -H "X-Workspace-Id: $DARKHUNT_WORKSPACE_ID" \
     -H "X-Application-Id: $DARKHUNT_APPLICATION_ID" \
     --data-binary '' \
     "$DARKHUNT_BASE_URL/otlp/t/$DARKHUNT_TENANT_ID/v1/traces"
   

4. Run the REAL instrumented path and read its node in the dashboard. Verify with the same service.name as the integration — a probe under a different serviceName mints a permanent phantom node (there's no delete-trace API).

5. The Darkhunt MCP cannot read traces back — there is no tracing-query tool. So the two checks are (1) the curl probe (auth+routing only) and (2) the human opening the dashboard. Don't claim you verified ingestion programmatically.

The dashboard should show: one session-grouped trace per interaction; generations rendered as chat bubbles with input_messages/output_messages; routing attributes on the span detail; and token usage / model / computed cost on generations.

On completion, report the topology shape to the user

Finishing the wiring is not the last step — tell the user what the Topology tab will (and won't) show, proactively, before they open it. A connected graph (real handoffs wired) → name the edges to expect (coordinator → geodata → advisor, self-loops, fan-in). Disconnected nodes (independent processes with no live handoff — standalone scripts, or a producer/consumer pair coupled only through a datastore) are a correct result — state why (no parentSpanId chain) and that connecting them is an architecture change. For OSS/example repos, persist that note in the README too.

Reference files in temporal-demo-python

These paths are inside the internal temporal-demo-python reference repo. Skip this section if you don't have it — every pattern it indexes is already shown inline above.

• src/demo/telemetry.py — the singleton/memoized clients + open_agent_trace / open_gateway_trace / trace_chat / trace_gen / trace_tool / lifecycle.
• src/demo/handoff_context.py — the contextvars ambient carrier.
• domains/weather/ — linear in-process (ambient carrier).
• domains/finance/run.py — orchestrator-driven DAG (fan-in, trace_gen for a non-chat() LLM path).
• domains/banking/graph.py — LangGraph state-threaded handoff.
• domains/healthcare/{http,handlers,orchestration}.py — HTTP traceparent header + consensus fan-in.
• domains/devops/{bus,handlers,orchestration}.py — Redis-stream field + rca fan-in.
• domains/security/{activities,workflows,temporal_worker}.py + server.py — Temporal: HandoffInterceptor, activities return tokens, coordinator threads child_args per edge, hand-rolled gateway client interceptor.

Common pitfalls

1. Constructing the client per call → leaks. Memoize per (application_id, service_name).
2. Pinning to an unpublished local checkout when PyPI is fine → drift from the released SDK. Prefer pip install "darkhunt-telemetry[temporal]" from PyPI (§1); reserve the local-path/git deps for hacking on the SDK or pinning an unreleased ref.
3. No signal-driven shutdown → SIGTERM loses the in-memory batch. Wire shutdown() in a finally.
4. Missing start_time on the MANUAL form → ~0ms duration. Prefer the start_active_* form (auto-timed); if manual, backdate with start_time in epoch seconds.
5. base_url without /trace-hub → 404 (esp. when sourced from credentials.json apiBaseUrl, which omits it).
6. Reusing an existing app / dropping to raw REST. Create a NEW OBSERVABILITY app via the MCP.
7. Telemetry in Temporal workflow code → sandbox violation / non-determinism. Telemetry lives in activities + the gateway; workflows only reference the sandbox-safe child_args.
8. Temporal star instead of the DAG → you didn't RETURN activity tokens + child_args them per edge. See the Temporal section.
9. Disconnected nodes handed over without explanation → reads as broken. Report the topology shape (correct-when-independent) proactively.
10. Verifying with a throwaway probe under a different serviceName → permanent phantom node. Use the curl probe + the real path.