Integrations

memoturn ingests from any source that can speak its batched /v1/ingest API or OpenTelemetry. All paths funnel through the same pipeline → Doris.

OpenTelemetry (universal)

Point any OTLP/HTTP (JSON) exporter at the receiver with Basic auth:

POST http://localhost:3001/v1/otel/v1/traces
Authorization: Basic base64(publicKey:secretKey)
Content-Type: application/json

Spans carrying GenAI semantic-convention attributes (gen_ai.*) become generations (model, provider, token usage mapped); other spans become spans. This is the zero-lock-in path for frameworks that emit OTel (LlamaIndex, Pydantic AI, Semantic Kernel, etc.). OTLP/protobuf support is planned.

MCP semantic-convention spans are surfaced first-class: mcp.session.id maps to the trace session, and a tools/call span is named after the tool (mcp:<tool>, or mcp:<method> for tools/list / resources/read / prompts/get) so MCP calls appear in the trace waterfall and the by-tool analytics next to other tools. The raw mcp.* attributes stay in metadata.

The first-party SDKs pre-wire the endpoint + auth so you don’t hand-build the URL/header — JS import { memoturnSpanProcessor, memoturnOtlpConfig } from "@memoturn/sdk/otel", Python from memoturn.otel import span_processor, otlp_config, and Go mt.OTLPConfig() (see Go SDK). All three resolve creds from MEMOTURN_BASE_URL / MEMOTURN_PUBLIC_KEY / MEMOTURN_SECRET_KEY (or explicit args); the OTel exporter packages are optional peer deps used only by these helpers.

OpenAI

• TypeScript: wrapOpenAI(new OpenAI(), mt) — see TS SDK.
• Python: wrap_openai(OpenAI()) — see Python SDK.

Each chat.completions.create and responses.create (the Responses API) is recorded as a generation with model, params, usage, latency, and errors.

Azure OpenAI

The same wrappers work unchanged with Azure clients — AzureOpenAI shares the OpenAI client surface:

• TypeScript: wrapOpenAI(new AzureOpenAI({ endpoint, apiKey, apiVersion, deployment }), mt)
• Python: wrap_openai(AzureOpenAI(azure_endpoint=..., api_key=..., api_version=...))

Cost note: prices are matched on the recorded model name, and Azure reports your deployment name. Deployments named after the base model (gpt-4o, gpt-4o-mini, …) price correctly out of the box; custom deployment names need a per-project price override (Settings → Model Pricing) — pattern-match the deployment name to the base model’s price. Azure spans arriving via OpenTelemetry (e.g. openllmetry) are also handled by the generic gen_ai.* mapping above.

LangChain

• JS: new MemoturnCallback(mt) passed in callbacks.
• Python: MemoturnCallbackHandler() passed in config={"callbacks": [...]}.

Chains, LLM/chat calls, and tools are recorded as a flat trace tree (one trace per handler, siblings — LangChain’s parent_run_id isn’t used for nesting).

LlamaIndex

• Python: MemoturnLlamaIndexHandler() passed to CallbackManager([...]).

Query/retrieve/synthesize/LLM/tool/agent steps are recorded as a properly nested trace tree (using LlamaIndex’s own parent ids), including retrieved documents and embedding vectors, with one trace per top-level operation. Python only.

LiteLLM

Use LiteLLM’s custom callback to forward to /v1/ingest (adapter under integrations/litellm), or route LiteLLM through its OTel exporter into the OTel receiver above.

Anything else

Send batched events directly to POST /v1/ingest (see the API reference and the event contracts in packages/core/src/events.ts).