/opik

name: opik
description: Reference for the Opik SDK — tracing, span types, framework integrations, threads, and the prompt library (Python, TypeScript, REST). Use for "what span types exist", "how do I flush", "track_openai", "add OpikTracer", "version a prompt". To instrument a repo end to end, use the `opik-instrument` skill.
metadata:
  last_updated: "2026-09-08"
  source_commit: "TODO — pin to the Opik release this was verified against (OPIK-7471)"

Opik SDK Reference

Opik is an open-source LLM observability platform. This skill is a **reference** for the SDK. To instrument a codebase step by step (detect frameworks, add config, emit and verify a trace), use the task-shaped `opik-instrument` skill.

Core concepts

A trace is one execution path (one request → one response). Spans are the operations inside it and form a hierarchy.

Span types — the ONLY valid values

| Type | Use for | |------|---------| | `general` | orchestration, agent entry points | | `llm` | model calls | | `tool` | tools, retrieval, API / DB calls | | `guardrail` | safety / validation checks |

Do NOT use `retrieval` or any other value.

Python — tracing

import opik

@opik.track(name="agent", type="general")
def agent(query: str) -> str:
    return generate(retrieve(query))

@opik.track(type="tool")
def retrieve(query): ...

@opik.track(type="llm")
def generate(ctx): ...

opik.flush_tracker()   # required in scripts

TypeScript — tracing

import { Opik } from "opik";
const client = new Opik({ projectName: "my-project" });

const trace = client.trace({ name: "agent", input: { query } });
const span = trace.span({ name: "llm-call", type: "llm" });
span.end({ output });
trace.end({ output });
await client.flush();

Framework integrations

Prefer an integration over manual `@opik.track` — integrations capture tokens, model, and cost automatically. Patterns (full list in `references/integrations.md`):

• **wrap-the-client** — `track_openai(OpenAI())`, `track_anthropic(...)`
• **global-enable** — `track_crewai(crew=crew)`
• **callback** — `dspy.configure(callbacks=[OpikCallback()])`
• **tracer** — `OpikTracer()` for LangChain / LangGraph / LlamaIndex
• **agent-specific** — `track_adk_agent_recursive(agent, OpikTracer())`

LiteLLM inside `@opik.track` (common trap)

If code uses `litellm` **and** you add `@opik.track`, pass `current_span_data` via metadata on every completion call — otherwise `OpikLogger` emits **orphaned** top-level traces instead of nesting under your span.

from opik.opik_context import get_current_span_data

@opik.track
def call_llm(messages):
    return litellm.completion(
        model="gpt-4o", messages=messages,
        metadata={"opik": {"current_span_data": get_current_span_data()}},
    )

Threads (conversations)

Group turns with `thread_id` — one turn = one trace, shared `thread_id` = one thread. Use for chat / multi-turn; skip for single-shot.

@opik.track(entrypoint=True)
def handle(session_id: str, message: str) -> str:
    opik.update_current_trace(thread_id=session_id)
    return reply(message)

Prompt library

Version prompts with `client.get_prompt` / `create_prompt` (chat variants: `get_chat_prompt` / `create_chat_prompt`). Store model + temperature in the prompt `metadata` so they version with the text. Call `get_prompt` **inside** a `@opik.track` function so the version links to the trace.

@opik.track(entrypoint=True)
def run(question: str) -> str:
    p = client.get_prompt(name="system") or client.create_prompt(
        name="system",
        prompt="You help with {{product}}.",
        metadata={"model": "gpt-4o", "temperature": 0.7},
    )
    return llm(p.format(product="Opik"), model=p.metadata["model"])

How a project is doing

With the MCP connected, start at the project, not at its traces:

read("project", "<project name or id>")

One call returns the last 7 days against the 7 before — trace count, error rate, average duration, total cost, SDK traffic only, which is what the Logs page's four cards show — plus the score names and usage keys the project actually records, and the freshest experiment, dataset, prompt version and optimization run in it. `since`/`until` pick another window; `since="30d"` is what the UI opens on. A rate or an average over a window with no traces comes back `null` rather than `0`, because a rate over no samples is undefined and "0% errors" is advice someone may act on.

Then attribute the change rather than restating it:

list("project_metric", project_name="<project>", metric_type="trace_cost")
list("project_metric", project_name="<project>", metric_type="span_count",
     breakdown="model", since="30d")

Rows are time buckets, not records — `interval` is `hourly`/`daily`/`weekly`/ `total`, and `page`/`size`/`sort` do not apply. `schema("list.project_metric")` is the metric list, what each is about, and which groupings each accepts; seven of them accept none. The score names the overview returned are the ones worth filtering on, and `list("score_name", project_name=…)` has the rest.

Searching traces

One filter grammar, OQL, serves both the hosted MCP's `list` tool and the SDK's `search_traces` / `search_spans` / `search_threads`:

<field>[.<key>] <op> <value> [AND ...]
ops: = != > >= < <= contains not_contains starts_with ends_with is_empty is_not_empty in not_in

Strings in double quotes, numbers bare, `duration` in **milliseconds**, dates as ISO-8601 instants with a timezone (`"2026-09-08T10:00:00Z"`). Scores and dictionaries take a key: `feedback_scores.accuracy < 0.5`, `metadata.environment = "prod"`. `AND` is the only connector.

error_info is_not_empty AND duration > 5000
type = "llm" AND usage.total_tokens > 10000            # spans
feedback_scores.hallucination > 0.5 AND start_time >= "2026-09-08T00:00:00Z"

With the MCP connected, prefer `list` — it also sorts (`sort="duration desc"`), wi