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OpenTelemetry v2

OpenTelemetry v2 (OTel v2) is LiteLLM Proxy's next-generation tracing. It gives you one clean trace per request covering the incoming HTTP call, authentication, guardrails, the LLM call itself, and the internal database/cache work, all nested in a single tree.

It follows standard OpenTelemetry GenAI semantic conventions, so the traces it produces are readable in any OTel backend (Grafana Tempo, Jaeger, Honeycomb, Datadog, …) and come with ready-made presets for popular LLM observability tools (Arize, Phoenix, Langfuse, Weave, Langtrace, Levo, AgentOps).

Opt-in feature

OTel v2 is off by default. Nothing in it runs until you set LITELLM_OTEL_V2=true. It is separate from the existing OpenTelemetry integration, so pick one. If you are moving from v1, see Migrating to OpenTelemetry v2.

What you get​

A single request to your proxy produces one trace that looks like this:

POST /v1/chat/completions                  ← HTTP request (server span)
├── auth /v1/chat/completions              ← authentication
│   ├── postgres get_key_object            ← DB lookups during auth
│   └── postgres get_team_membership
├── execute_guardrail presidio-pii         ← each guardrail that runs
├── chat gpt-4o                            ← the LLM call (model, tokens, cost)
└── batch_write_to_db                      ← spend/usage written to DB

Highlights:

  • One trace, end to end — the HTTP request, auth, guardrails, the LLM call, and DB writes all live in the same trace, correctly nested.
  • Rich GenAI attributes — every LLM-call span carries gen_ai.* attributes: model, provider, token usage, cost, finish reasons, request parameters, and more.
  • Standards-based — built on the official OpenTelemetry GenAI semantic conventions, so it works with any OTel-compatible backend.
  • Vendor presets — one line to ship traces to Arize, Phoenix, Langfuse, Weave, Langtrace, Levo, or AgentOps in the format each tool expects.
  • Safe by default — prompts and responses are not captured unless you explicitly opt in. Noisy routes (health checks, metrics scrapes, UI assets) are excluded automatically.
  • Distributed tracing — if your client sends a traceparent header, LiteLLM's spans nest inside your existing trace.

Getting started​

Set LITELLM_OTEL_V2=true in the proxy environment, then pick a destination below.

1. Send traces to any OTLP collector​

This path sends spans over OTLP (the OpenTelemetry Protocol) to a collector or backend you are already running at the endpoint below; if you do not have one yet, stay on the console exporter from the Quickstart until you do. Set the feature flag plus the standard OTEL_* environment variables in the proxy's environment. No config change is needed.

LITELLM_OTEL_V2=true
OTEL_EXPORTER="otlp_http"
OTEL_ENDPOINT="http://localhost:4318"

Pass auth headers your backend needs via OTEL_HEADERS:

OTEL_HEADERS="api-key=your-key,x-tenant=acme"

Then start the proxy as usual:

litellm --config config.yaml

Make a request, and you'll see one trace per request in your backend.

2. Send traces to a specific tool (presets)​

For LLM observability tools, use a preset. A preset knows the tool's endpoint and emits attributes in the schema that tool expects. To enable one, add its name to callbacks in your config and set the tool's credentials as env vars.

config.yaml
litellm_settings:
  callbacks: ["arize"]
LITELLM_OTEL_V2=true
ARIZE_SPACE_ID="your-space-id"
ARIZE_API_KEY="your-api-key"
ARIZE_PROJECT_NAME="your-project-name"   # recommended: names the project traces land in
Send to several backends at once

To send the same traces to multiple vendors, list each preset in callbacks and set each one's env vars. For example, Langfuse and Arize together:

config.yaml
litellm_settings:
  callbacks: ["langfuse_otel", "arize"]

Each preset adds its own destination, so your spans reach all of them in parallel, each in that tool's native format.

Preset reference​

Every preset turns into one exporter on a single shared tracer. The table lists, for each one, the callback name you put in callbacks, the credentials it reads, where it sends, the attribute vocabulary it adds on top of the canonical gen_ai.* keys, and whether it supports per-request (per-team/key) credentials.

PresetCallbackRequired env varsOptional env varsDestinationVocabularyPer-request creds
Arize AXarizeARIZE_SPACE_ID (ARIZE_SPACE_KEY deprecated), ARIZE_API_KEYARIZE_PROJECT_NAME (names the project traces land in), ARIZE_ENDPOINT (gRPC, default https://otlp.arize.com/v1), ARIZE_HTTP_ENDPOINT (HTTP)Arize AX platformOpenInferenceYes
Arize Phoenixarize_phoenixPHOENIX_API_KEY (Phoenix Cloud only; self-hosted needs none)PHOENIX_COLLECTOR_HTTP_ENDPOINT or PHOENIX_COLLECTOR_ENDPOINT (protocol inferred from the value), PHOENIX_PROJECT_NAMEPhoenix (self-hosted or Phoenix Cloud)OpenInferenceNo
Langfuselangfuse_otelLANGFUSE_PUBLIC_KEY, LANGFUSE_SECRET_KEYLANGFUSE_HOST (or LANGFUSE_OTEL_HOST; default https://us.cloud.langfuse.com, EU is https://cloud.langfuse.com), OTEL_IGNORE_CONTEXT_PROPAGATION (set true to drop inbound traceparent)Langfuse Cloud or self-hostedLangfuseYes
Weave (W&B)weave_otelWANDB_API_KEY, WANDB_PROJECT_ID (<entity>/<project>)WANDB_HOST (default https://trace.wandb.ai)Weights & Biases WeaveOpenInference + WeaveYes
Langtracelangtracenone of its own—Langtrace, via an OpenTelemetry Collector (Langtrace ingests JSON-only OTLP)LangtraceNo
LevolevoLEVOAI_API_KEY, LEVOAI_ORG_ID, LEVOAI_WORKSPACE_ID, LEVOAI_COLLECTOR_URL—Levo collectorcanonical gen_ai.* onlyNo
AgentOpsagentopsAGENTOPS_API_KEYAGENTOPS_SERVICE_NAME (default agentops), AGENTOPS_ENVIRONMENT (no default)AgentOps (https://otlp.agentops.ai/v1/traces)canonical gen_ai.* onlyNo

Notes:

  • Arize AX vs Arize Phoenix: use arize for the full-featured AX platform and arize_phoenix for Phoenix local or self-hosted workflows. They use different credentials and endpoints, so pick the callback for the backend you actually run. For product-specific setup, see the dedicated Arize AX and Arize Phoenix guides.
  • Langtrace ingests JSON-only OTLP at a custom path, so litellm v2 (which sends protobuf to /v1/traces) cannot export to it directly. Route through an OpenTelemetry Collector that re-encodes to JSON; the langtrace preset only adds the Langtrace attribute schema to your spans. See the Langtrace tab above for the collector config.
  • Vocabulary is additive: every preset's spans always carry the canonical OpenTelemetry gen_ai.* attributes; the listed vocabulary is layered on top so the destination tool reads its native schema.

Seeing your traces​

Once a backend is configured with its preset, each request shows up in that tool's UI as a chat <model> span under the request root. Each tab below covers the vendor-specific gotchas (project mapping, endpoint variants, metadata keys) that trip people up.

What Arize renders​

Open your Arize project; the trace appears under the project named by ARIZE_PROJECT_NAME. The openinference mapper stamps the OpenInference vocabulary onto the LLM-call span alongside the canonical gen_ai.* keys, so Arize reads its native schema without dropping the canonical ones.

Attributes added by the openinference mapper​

AttributeRestates
openinference.span.kindFixed LLM
llm.model_name, llm.providermodel, provider
llm.token_count.prompt, completion, totalusage split
llm.invocation_parametersJSON blob of request params
llm.input_messages.{idx}.message.role, contentprompt (content capture on)
llm.output_messages.{idx}.message.role, contentresponse (content capture on)
input.value, output.valueJSON arrays of the same (content capture on)
llm.tools.{idx}.tool.name, description, json_schematool definitions, capped

See the full OpenInference spec for the definitive vocabulary.

Setup notes​

  • ARIZE_SPACE_KEY is the deprecated name for ARIZE_SPACE_ID; the preset still reads it for backward compatibility, but prefer ARIZE_SPACE_ID in new configs.

LiteLLM trace in Arize

Capturing prompts & responses​

By default, OTel v2 records metadata only (model, tokens, cost, timing) and never writes prompt or response text to your traces. This is intentional, and it keeps sensitive content out of your observability backend.

To capture message content, opt in explicitly:

# no_content (default) — never capture prompts/responses
OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT="no_content"

# span_only — write prompts/responses as attributes on spans
OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT="span_only"

# event_only — write prompts/responses on log events instead of span attributes
OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT="event_only"

# span_and_event — write content to both spans and events
OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT="span_and_event"

The gate is enforced centrally, so it applies to every backend at once. A user request can never force its prompt into your backend while capture is disabled.

Span attributes​

Attributes come from a chain of mappers stamped onto each span in order. The canonical genai mapper is always applied first, the legacy compatibility mapper is on by default, and each preset adds one vendor mapper on top. Later mappers can override earlier ones; the same span therefore carries several vocabularies describing the same call.

The first two tables cover the LLM-call span in the canonical vocabulary. Sections below list the other span kinds, then what each vendor mapper adds.

LLM-call span, canonical gen_ai.* + litellm.*​

Request-side keys:

AttributeWhen set
gen_ai.operation.namealways (chat, text_completion, embeddings)
gen_ai.provider.namealways
gen_ai.request.modelalways (the user-facing model group name)
gen_ai.request.temperature, top_p, top_k, max_tokenswhen set on the request
gen_ai.request.frequency_penalty, presence_penalty, seedwhen set
gen_ai.request.stop_sequenceswhen set (string array)
gen_ai.tool.{idx}.name, description, parametersone set per tool definition, for the leading tools only (why)
litellm.request.tools.declaredwhen the request declares tools; the full count, capped or not
server.address, server.portwhen the provider endpoint is known

Tool definitions are capped​

Only the leading declared tools get gen_ai.tool.{idx}.* attributes. Tool definitions are an unbounded attribute family, one entry per tool per field per active vocabulary, and OpenTelemetry caps a span at 128 attributes by default. An agent that declares a hundred or more tools would otherwise blow past that ceiling, and because the limit evicts the oldest attributes first, the gen_ai.* attributes above would be the ones discarded, leaving a span carrying nothing but tool schemas. The cap keeps model, token usage, and cost on the span no matter how many tools a request declares.

The ceiling is span-wide, not per vocabulary. Tool definitions may claim a quarter of the span's attribute budget in total, and that allowance is split across the vocabularies that emit them, so the number of tools detailed depends on how many are active: 5 tools each under the default genai plus legacy pair, 3 tools each once a vendor vocabulary such as openinference is layered on. Splitting it this way is what stops three vocabularies spelling the same tools out from summing back past the limit.

litellm.request.tools.declared always carries the true total, so you can tell when the per-tool detail was truncated. Requests declaring fewer tools than the allowance keep full detail.

Response, usage, cost, identity:

AttributeWhen set
gen_ai.response.id, gen_ai.response.modelon success
gen_ai.response.finish_reasonson success (string array)
gen_ai.usage.input_tokens, gen_ai.usage.output_tokenson success
gen_ai.input.messages, gen_ai.output.messagescontent capture on
gen_ai.system_instructionscontent capture on, when a system prompt is present
litellm.call_idalways
litellm.provider.modelalways (the model string actually sent to the provider)
litellm.request.streamingwhen true
litellm.cost.totalon success
litellm.cost.input, output, cache_read, cache_creation, tool_usagewhen the source reported the breakdown
litellm.cost.original, discount_amount, discount_percent, margin_fixed_amount, margin_percent, margin_total_amountwhen reported

Status and errors:

  • On failure: the span records the standard exception event (exception.type, exception.message), sets error.type from the exception class, and sets its status to ERROR.
  • On success: the status is left UNSET (the semconv default, matching the FastAPI server span). Only a genuine error sets ERROR, so do not key an alert on a status of OK.

Other span kinds​

Guardrail span, which uses the litellm.guardrail.* namespace: name, mode, status, provider, action, response, violation_categories, confidence_score, risk_score, masked_entity_count, duration, id, policy_template, detection_method. status is one of success, guardrail_intervened, guardrail_failed_to_respond, or not_run; a blocking guardrail_intervened or guardrail_failed_to_respond also sets span status to ERROR.

Datastore span (redis, postgres): db.system.name, db.operation.name, litellm.service.name, litellm.service.call_type.

Internal service span: the litellm.service.* keys only (no db.*).

MCP tool-call span: gen_ai.operation.name=execute_tool, mcp.method.name, mcp.session.id, gen_ai.tool.name, litellm.mcp.server.name, litellm.call_id, litellm.cost.total. gen_ai.tool.call.arguments and gen_ai.tool.call.result are gated by the same content-capture setting as prompt content.

Root HTTP server span: the HTTP semconv keys http.request.method, http.route, http.response.status_code, url.path, stamped by the FastAPI instrumentation (not by any of LiteLLM's mappers).

Each vendor preset also composes one vendor-specific mapper on top of these canonical keys, so the destination reads the trace in its native schema. Those per-vendor tables live under the matching Seeing your traces tab.

Attribute conventions​

LiteLLM emits one canonical set of GenAI attributes and layers other vocabularies on top by adding a mapper; the active set is controlled by mapper_names, with genai always first. The legacy mapper is on by default (LITELLM_OTEL_LEGACY_COMPAT=true) and re-emits the same data under the older semconv-ai / Traceloop names, so dashboards built against those keep working through a migration. Turn it off with LITELLM_OTEL_LEGACY_COMPAT=false once your queries use the canonical keys. Vendor mappers (openinference, langfuse, weave, langtrace) are added by their presets and never replace the canonical keys.

The most common keys line up across vocabularies as follows:

Canonical (genai)Legacy (Traceloop)OpenInference
gen_ai.usage.input_tokensgen_ai.usage.prompt_tokensllm.token_count.prompt
gen_ai.usage.output_tokensgen_ai.usage.completion_tokensllm.token_count.completion
gen_ai.provider.namegen_ai.systemllm.provider
litellm.request.streamingllm.is_streamingn/a
gen_ai.request.modeln/allm.model_name

Request identity on every span​

LiteLLM writes a small allowlist of request-identity values into standard OpenTelemetry Baggage at the auth boundary. A custom span processor then copies those values onto every span in the trace, so a guardrail, datastore, or service span is filterable by team or key without LiteLLM re-stamping each one by hand.

By default the following keys are written onto every span:

KeyValue
litellm.team.idTeam UUID
litellm.team.aliasTeam display name
litellm.team.metadataTeam's free-form metadata, filtered to the sub-keys you allowlist
litellm.api_key.hashHash of the caller's virtual key
gen_ai.request.modelUser-facing model group name
litellm.provider.modelDispatched model on the provider

A separate set of request-metadata fields is written under the litellm.metadata.* namespace. Defaults:

litellm.metadata.user_api_key_org_id, litellm.metadata.user_api_key_user_id, litellm.metadata.user_api_key_alias, litellm.metadata.user_api_key_end_user_id, litellm.metadata.requester_ip_address.

Two defaults stay conservative for privacy. The end-user id is promotable but off by default at the top level (it identifies an individual); it appears under litellm.metadata.user_api_key_end_user_id, which callers who filter by user should enable. A team's free-form metadata is never emitted whole; only the sub-keys you allowlist leave the process, and the allowlist is empty by default.

Override any of these with the LITELLM_OTEL_BAGGAGE_PROMOTED_KEYS, LITELLM_OTEL_BAGGAGE_METADATA_KEYS, and LITELLM_OTEL_BAGGAGE_TEAM_METADATA_KEYS env vars (comma-separated), or the matching YAML lists under callback_settings.otel.

Metrics​

Alongside traces, OTel v2 can emit GenAI client metrics: histograms for call latency, token usage, and cost that your backend aggregates across requests. Like the rest of OTel v2 they stay off until you turn them on.

Set the flag in the proxy environment next to LITELLM_OTEL_V2:

LITELLM_OTEL_V2=true
LITELLM_OTEL_INTEGRATION_ENABLE_METRICS=true

Metrics ship through the exporter you already configured for traces. OTEL_EXPORTER (console, otlp_http, otlp_grpc), OTEL_ENDPOINT, and OTEL_HEADERS decide where the metric stream goes exactly as they do for spans, so the collector that receives your traces receives the metrics too.

What's recorded​

Each successful LLM call records the standard OpenTelemetry GenAI client metrics:

MetricUnitWhat it measures
gen_ai.client.operation.durationsWall-clock time for the whole LLM call
gen_ai.client.token.usage{token}Tokens consumed, split into input and output by the gen_ai.token.type attribute
gen_ai.usage.costUSDLiteLLM's computed cost for the call
gen_ai.server.time_to_first_tokensTime to the first streamed token (streaming calls)
gen_ai.server.time_per_output_tokensAverage time per output token
gen_ai.client.response.durationsProvider-side generation time
Renamed in this release

gen_ai.usage.cost, gen_ai.server.time_to_first_token, and gen_ai.server.time_per_output_token were previously emitted as gen_ai.client.token.cost, gen_ai.client.response.time_to_first_token, and gen_ai.client.response.time_per_output_token. The older spellings are not GenAI semantic conventions and no vendor dashboard queries them, so nothing prebuilt could chart LiteLLM's cost or latency. If you hand-built panels or alerts against the old names, repoint them at the names above

Every sample carries the same identity attributes as the matching span (operation, provider/system, request model, framework, and selected metadata.* fields), so you can group the histograms by model, provider, key, or team. These are the same six metrics the v1 OpenTelemetry integration emits, with identical names and units, so a dashboard built for one reads the other.

Control metric attribute cardinality​

By default every metric sample is stamped with the full identity attribute set, which includes per-request fields such as hidden_params and several metadata.* values. Those are close to unique per request, so each one multiplies the number of time series your backend tracks (one series per distinct attribute combination). At volume this explodes metric cardinality, and some backends, for example Splunk Observability Cloud, start throttling or dropping the metrics.

v2 reads the same filter v1 does, from callback_settings.otel.attributes in your config. Nest an attributes block there with either an include_list (allowlist; emit only the listed attributes) or an exclude_list (denylist; emit everything except the listed attributes). The two are mutually exclusive. The filter applies to metrics only; spans keep their full attribute set, so traces stay rich while metric cardinality stays bounded.

The block sits under callback_settings.otel. With LITELLM_OTEL_V2 set, listing otel in callbacks builds the v2 logger and reads this block (it builds the legacy v1 logger only when the flag is off); the block is also read on the default path when no otel callback is listed.

Unlike v1, v2 has no per-instance attributes field, so this global block is the only source. v2 also resolves the filter lazily on the first metric a request records rather than at boot, so a bad config (both lists set, or a forbidden name) surfaces on that first recorded request and editing the lists takes effect only after a restart. The filter is read only on the default OTLP path (callback name otel or unset); preset destinations such as arize, arize_phoenix, and langfuse_otel emit their metrics with the full attribute set, the same as in v1.

config.yaml
callback_settings:
  otel:
    attributes:
      exclude_list:
        - hidden_params
        - metadata.requester_metadata
        - metadata.requester_ip_address
        - metadata.spend_logs_metadata
        - metadata.mcp_tool_call_metadata
        - metadata.vector_store_request_metadata
        - metadata.prompt_management_metadata

When you want the smallest, most predictable attribute set, list exactly the attributes to keep with include_list. Anything not listed is dropped from metrics:

config.yaml
callback_settings:
  otel:
    attributes:
      include_list:
        - gen_ai.operation.name
        - gen_ai.system
        - gen_ai.request.model
        - gen_ai.framework
        - metadata.user_api_key_team_id
        - metadata.user_api_key_org_id

gen_ai.token.type is never filtered out. It is stamped on gen_ai.client.token.usage after the filter runs, so the input/output split survives whatever list you set, and naming it in either include_list or exclude_list is rejected.

Which routes are traced​

High-frequency, non-LLM routes are excluded by default so they don't flood your traces: health checks (/health*), the Prometheus scrape (/metrics), and static UI/docs assets (/ui, /docs, /redoc, /_next, /openapi.json, favicons, …).

To change the set, use the standard OpenTelemetry env var (comma-separated paths, substring-matched):

# Trace everything, including health checks
OTEL_PYTHON_FASTAPI_EXCLUDED_URLS=""

# Exclude only your own custom paths
OTEL_PYTHON_FASTAPI_EXCLUDED_URLS="/health,/internal"

Per-key / per-team credentials (multi-tenant)​

One proxy can serve many tenants: a team or a virtual key carries its own backend credentials, so its traces land in that tenant's own Langfuse project, Arize space, Weave project, or New Relic account instead of the proxy-wide one. The credentials come from the key and the team the proxy resolved at auth, never from the request body, so a caller cannot pick another tenant's backend.

This is the same key/team callback mechanism described in Team/Key based logging; v2 applies it to the OTel presets. There is no separate admin-owned "destination" object, and /credentials holds LLM provider credentials, not logging ones.

Which presets support per-request credentials​

PresetCallbackFields on the key or teamWhat varies per tenant
Langfuselangfuse_otellangfuse_public_key, langfuse_secret_keyThe Langfuse project traces land in
Arize AXarizearize_space_id (or the deprecated arize_space_key), arize_api_keyThe Arize space
Weave (W&B)weave_otelwandb_api_key, weave_project_idThe W&B account and Weave project
New Relicnewrelicnewrelic_api_key, newrelic_region (us or eu, default us)The New Relic account and its data center

Every other preset (arize_phoenix, langtrace, levo, agentops) and the plain otel OTLP exporter has no per-request credentials, so those always export with the proxy-wide configuration. For Phoenix, split tenants by project instead of by backend, with phoenix_project_name on the team or key. To keep one backend but label a tenant's spans with its own service.name, set otel_service_name in the key's or team's metadata instead.

Set it on a team​

Register the callback on the team; every key on that team then exports with these credentials:

curl -X POST 'http://localhost:4000/team/<team-id>/callback' \
  -H 'Authorization: Bearer sk-1234' -H 'Content-Type: application/json' \
  -d '{
    "callback_name": "langfuse_otel",
    "callback_type": "success",
    "callback_vars": {
      "langfuse_public_key": "pk-lf-...",
      "langfuse_secret_key": "sk-lf-..."
    }
  }'

GET /team/<team-id>/callback reads back what a team has registered, DELETE /team/<team-id>/callback/<callback_name> removes one integration, and POST /team/<team-id>/disable_logging removes all of them.

Set it on a key​

A key can carry its own credentials in metadata.logging, including a key with no team:

curl -X POST 'http://localhost:4000/key/generate' \
  -H 'Authorization: Bearer sk-1234' -H 'Content-Type: application/json' \
  -d '{
    "metadata": {
      "logging": [{
        "callback_name": "arize",
        "callback_type": "success",
        "callback_vars": {"arize_space_id": "...", "arize_api_key": "..."}
      }]
    }
  }'

Existing keys take the same field on /key/update. You can also fill both of these in from the Admin UI, on the team's or the key's logging settings.

Good to know​

The key wins outright over the team. If a key has any metadata.logging entry, the team's callbacks are not consulted at all rather than merged with the key's, so a key that overrides one backend has to restate the others it still wants.

Only the OTLP headers vary per tenant, plus New Relic's region endpoint, which is picked from a fixed us/eu table. The exporter's host stays whatever the preset resolved at boot, so langfuse_host on a key or team does not move that tenant's traces to a different Langfuse host under v2.

Credentials scope to the exporter their own preset contributed. A request carrying one tenant's Arize key never rewrites the headers of a co-configured Langfuse or self-hosted collector exporter, so a tenant's key cannot leak to a backend it was not meant for. Those other exporters do still receive the request's spans, with their own proxy-wide credentials.

The proxy caches one tracer provider per distinct credential set, up to 256 at a time, and flushes the least recently used one when it evicts. Tenant churn costs an exporter rebuild, not a lost span.

os.environ/... references are rejected in key and team callback_vars; pass the resolved value. Field names must be known callback params, and an unknown name fails the whole entry.

This routing applies to traces only. The GenAI client metrics (see Metrics) always go to the proxy-wide exporter.

Distributed tracing​

If the incoming request has a W3C traceparent header, LiteLLM continues that trace instead of starting a new one. Your LiteLLM spans then appear inline inside whatever distributed trace your application already has, so you can follow a request from your app, through the proxy, to the LLM provider, in one view.

Configuration reference​

All values are environment variables. Boolean flags accept true/false.

VariableDefaultPurpose
LITELLM_OTEL_V2falseMaster switch. OTel v2 does nothing until this is true.
OTEL_EXPORTER (alias OTEL_EXPORTER_OTLP_PROTOCOL)consoleExporter kind: console, otlp_http, otlp_grpc.
OTEL_ENDPOINT (alias OTEL_EXPORTER_OTLP_ENDPOINT)noneOTLP collector URL. Setting an endpoint implies otlp_http unless you override OTEL_EXPORTER.
OTEL_HEADERS (alias OTEL_EXPORTER_OTLP_HEADERS)noneComma-separated key=value auth headers for your backend.
OTEL_SERVICE_NAMElitellmservice.name resource attribute shown in your backend.
OTEL_ENVIRONMENT_NAMEnonedeployment.environment resource attribute (e.g. production).
OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENTno_contentPrompt/response capture: no_content, span_only, event_only, span_and_event.
OTEL_PYTHON_FASTAPI_EXCLUDED_URLShealth/metrics/UI routesComma-separated paths to exclude from tracing (substring match). Set to "" to trace everything.
LITELLM_OTEL_INTEGRATION_ENABLE_METRICSfalseAlso emit the GenAI client metrics (duration, token usage, cost, streaming timings). See Metrics.
LITELLM_OTEL_LEGACY_COMPATtrueAlso emit attributes under the older Traceloop key names. See Attribute conventions.

The full set of keys on each span kind is in Span attributes.

Troubleshooting​

No traces showing up?

  1. Confirm LITELLM_OTEL_V2=true is set in the proxy's environment.
  2. Try OTEL_EXPORTER="console" first. If spans print to stdout, the problem is your exporter endpoint/headers, not LiteLLM.
  3. Make sure you hit an LLM route (e.g. /v1/chat/completions). Health checks and UI routes are excluded by default.
  4. Check that opentelemetry-instrumentation-fastapi is installed (see Requirements).

Only see the LLM call but no auth/postgres/server span? Those server and DB spans require the FastAPI instrumentation package, so install opentelemetry-instrumentation-fastapi.

I see metadata but no prompts/responses. That's the default. Set OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT=span_only to capture content.

Support​

For questions, open an issue at BerriAI/litellm.

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