mvk-sdk-py 1.3.15

MVK SDK for Python - Ultralight OpenTelemetry-compatible SDK for AI Observability

MVK SDK

Pure Python  •  Serverless Ready  •  Zero Breaking Guarantee  •  OTEL-style Smart Auto-instrumentation  •  W3C TraceContext

The MVK SDK is a production-grade telemetry SDK with serverless auto-detection, simplified MVK-only configuration, and a non-breaking guarantee. Deploy with confidence using DIRECT mode (straight to MVK) or COLLECTOR mode (via OTEL Collector). The SDK uses OTEL-style smart auto-instrumentation with wrapt for immediate and lazy loading of AI providers and vector databases with zero risk to your application.

❗ Important — Zero Breaking Guarantee. Instrumentation never raises into your code. Any SDK-internal error is caught, logged, and swallowed — your application's execution flow is never interrupted.

Table of Contents

• Table of Contents
• 1. Installation
• 2. Configuration & Instrumentation
  • 2.1 Configuration Reference — all values
  • 2.2 Precedence — defaults < parameters < environment
  • 2.3 Secrets & environment (recommended)
  • 2.4 Logging
• 3. @mvk.context() — enriching spans (decorator vs. context manager)
  • Dual purpose — when to use which
  • Why it matters
  • Signature
  • Examples
  • Notes & limits
• 4. Cost signals — @mvk.signal(), create_signal() & metered usage
  • @mvk.signal() — decorate a function as a named span
  • mvk.create_signal() — manual spans with explicit step_type
  • mvk.add_metered_usage() — attach billable quantities
  • End-to-end example — tools, custom costs, and auto-traced LLM together
  • Notes & limits
• 5. Outcome signals — mvk.record_outcome()
  • Signature
  • Examples
  • What it emits
  • Notes & limits
• 6. Reference
  • Context inheritance (Nearest Wins)
  • Tag validation
  • Architecture
  • Supported integrations
  • Performance characteristics
  • Serverless deployment
• 7. Support

---

1. Installation

pip install mvk-sdk-py

Install providers and protocols as optional extras:

💡 Tip. Quote the package name when installing extras. Shells like zsh treat unquoted square brackets as glob patterns and the install will fail.

# AI providers
pip install "mvk-sdk-py[genai]"             # All AI providers
pip install "mvk-sdk-py[openai]"            # OpenAI only
pip install "mvk-sdk-py[anthropic]"         # Anthropic only
pip install "mvk-sdk-py[gemini]"            # Google Gemini only
pip install "mvk-sdk-py[bedrock]"           # AWS Bedrock only
pip install "mvk-sdk-py[vertexai]"          # Vertex AI only
pip install "mvk-sdk-py[azure-openai]"      # Azure OpenAI (OpenAI SDK) only
pip install "mvk-sdk-py[azure-ai]"          # Azure native AI SDK only
pip install "mvk-sdk-py[anthropic-vertex]"  # Claude on Vertex AI only
pip install "mvk-sdk-py[google-genai]"      # google.genai SDK only
pip install "mvk-sdk-py[perplexity]"        # Perplexity only
pip install "mvk-sdk-py[oci]"               # OCI Generative AI only

# AI frameworks
pip install "mvk-sdk-py[frameworks]"        # All AI frameworks
pip install "mvk-sdk-py[langchain]"         # LangChain only
pip install "mvk-sdk-py[langgraph]"         # LangGraph only
pip install "mvk-sdk-py[crewai]"            # CrewAI only
pip install "mvk-sdk-py[agno]"              # Agno only
pip install "mvk-sdk-py[semantic-kernel]"   # Semantic Kernel only
pip install "mvk-sdk-py[openai-agents]"     # OpenAI Agents SDK only
pip install "mvk-sdk-py[agent-framework]"   # Microsoft Agent Framework only

# Proxies / routers
pip install "mvk-sdk-py[litellm]"           # LiteLLM only
pip install "mvk-sdk-py[openrouter]"        # OpenRouter only

# Vector databases
pip install "mvk-sdk-py[vectordb]"          # All vector DBs
pip install "mvk-sdk-py[pinecone]"          # Pinecone only
pip install "mvk-sdk-py[weaviate]"          # Weaviate only
pip install "mvk-sdk-py[chromadb]"          # ChromaDB only
pip install "mvk-sdk-py[qdrant]"            # Qdrant only

# Export protocols
pip install "mvk-sdk-py[grpc]"              # gRPC exporter
pip install "mvk-sdk-py[compression]"       # Compression support

# Everything
pip install "mvk-sdk-py[all]"

---

2. Configuration & Instrumentation

mvk.instrument() is the single entry point — call it once per process, before your provider calls. All settings are passed as top-level keyword arguments (no config={...} wrapper); grouped settings (exporter, batching, failed_batch_disk, logging, serverless, wrappers) are nested dicts. Every parameter also has a MVK_* environment-variable equivalent.

Minimal setup:

import mvk_sdk as mvk

mvk.instrument(
    agent_id="agent-123",    # Required (or MVK_AGENT_ID)
    api_key="mvk_...",       # Required for DIRECT mode (or MVK_API_KEY)
    tenant_id="tenant-123",  # Required for DIRECT mode (or MVK_TENANT_ID)
)

# That's it — all provider calls are now traced
import openai
response = openai.ChatCompletion.create(...)  # Automatically traced

DIRECT mode (default) sends traces straight to Mavvrik; the endpoint is auto-set if omitted. COLLECTOR mode sends to your own OTEL Collector:

# DIRECT — to the Mavvrik backend
mvk.instrument(
    agent_id="agent-123", api_key="mvk_...", tenant_id="tenant-123",
    exporter={"mode": "DIRECT", "type": "otlp_http"},  # endpoint auto-set
)

# COLLECTOR — to your OTEL Collector
mvk.instrument(
    agent_id="agent-123",
    exporter={"mode": "COLLECTOR", "type": "otlp_grpc", "endpoint": "localhost:4317"},
)

2.1 Configuration Reference — all values

Every setting below can be supplied as an instrument() parameter or the matching environment variable. Nested instrument() keys are shown as group.key (e.g. exporter.timeout → exporter={"timeout": 30}). 🔒 marks sensitive values — see §2.3.

Core / identity

instrument()	Environment variable	Default	Notes
agent_id	MVK_AGENT_ID	—	Required. Agent identifier
api_key 🔒	MVK_API_KEY	—	Required in DIRECT mode
tenant_id	MVK_TENANT_ID	—	Required in DIRECT mode; sent as X-Tenant-ID
enabled	MVK_ENABLED	true	Master on/off switch

Exporter / transport (exporter={...})

instrument()	Environment variable	Default	Notes
exporter.mode	MVK_MODE	DIRECT	DIRECT or COLLECTOR
exporter.type	MVK_EXPORTER_TYPE	otlp_http	otlp_http, otlp_grpc, console, file
exporter.endpoint	MVK_ENDPOINT	auto	URL or host:port (auto-set in DIRECT)
exporter.headers	MVK_HEADERS	—	JSON string or k1=v1,k2=v2
exporter.insecure	MVK_EXPORTER_INSECURE	false	HTTP instead of HTTPS (local only)
exporter.compression	MVK_EXPORTER_COMPRESSION	gzip	gzip or none
exporter.timeout	MVK_EXPORTER_TIMEOUT	10	Seconds (1–300)
exporter.max_retries	MVK_EXPORTER_MAX_RETRIES	6	0–20
exporter.retry_timeout	MVK_EXPORTER_RETRY_TIMEOUT	60	Total retry seconds (1–600)
exporter.file_path	MVK_EXPORTER_FILE_PATH	—	Directory for file exporter
exporter.format	MVK_EXPORTER_FORMAT	simple	simple or json (console/file)

Batching (batching={...})

instrument()	Environment variable	Default	Notes
batching.max_items	MVK_BATCH_MAX_ITEMS	2000	Spans/batch (1–10000)
batching.max_bytes	MVK_BATCH_MAX_BYTES	2097152	2 MiB (1 KB–10 MB)
batching.max_interval_ms	MVK_BATCH_MAX_INTERVAL_MS	3000	ms (100–60000)

Failed-batch disk recovery (failed_batch_disk={...})

instrument()	Environment variable	Default	Notes
failed_batch_disk.enabled	MVK_FAILED_BATCH_DISK_ENABLED	false
failed_batch_disk.path	MVK_FAILED_BATCH_DISK_PATH	—	Required when enabled
failed_batch_disk.max_size_mb	MVK_FAILED_BATCH_DISK_MAX_SIZE_MB	1000	10–100000
failed_batch_disk.retry_interval	MVK_FAILED_BATCH_DISK_RETRY_INTERVAL	60	Seconds (1–3600)

Logging (logging={...}) — see §2.4

instrument()	Environment variable	Default	Notes
logging.level	MVK_LOG_LEVEL	OFF	OFF, INFO, DEBUG
logging.wrapper_level	MVK_WRAPPER_LOG_LEVEL	—	Provider wrappers only
logging.prompts_responses 🔒	MVK_LOG_PROMPTS_RESPONSES	false	Logs raw prompts/responses
logging.prompts_storage_mode	MVK_PROMPTS_STORAGE_MODE	truncate	truncate, compress, envelope
logging.prompts_max_length	MVK_PROMPTS_MAX_LENGTH	1000	Truncation length (100–100000)
logging.prompts_masking	MVK_PROMPTS_MASKING	true	Mask PII/PHI/PCI (on by default)

Validation, tags & wrappers

instrument()	Environment variable	Default	Notes
strict_validation	MVK_STRICT_VALIDATION	false	Raise on schema violations
tag_limit	MVK_TAG_LIMIT	10	Max tags/span (1–10)
tags={"env": "prod"}	MVK_TAG_ENV=prod	—	One env var per tag: MVK_TAG_<KEY>
wrappers={"include": [...]}	MVK_WRAPPERS	genai,vectordb	Comma-separated in env
wrappers.http.exclusions	MVK_HTTP_EXCLUSIONS	—	JSON array or comma-separated

Serverless & diagnostics

instrument()	Environment variable	Default	Notes
serverless.force	MVK_SERVERLESS	false	Force serverless optimizations
system_alerts_enabled	MVK_SYSTEM_ALERTS_ENABLED	true	SDK self-diagnostics telemetry
collect_cloud_metadata	MVK_COLLECT_CLOUD_METADATA	true	Auto-skipped in local/CI
auto_middleware	MVK_AUTO_MIDDLEWARE	true	Auto W3C context for Flask/FastAPI/Django

Full instrument() call with every group:

mvk.instrument(
    agent_id="agent-123",
    api_key="mvk_...",        # Required in DIRECT mode
    tenant_id="tenant-123",   # Required in DIRECT mode
    exporter={
        "mode": "DIRECT",          # DIRECT or COLLECTOR
        "type": "otlp_http",       # otlp_http | otlp_grpc | console | file
        "endpoint": "https://ingest.mavvrik.ai/v1/traces",  # auto-set if omitted
        "timeout": 10,
        "max_retries": 6,
        "compression": "gzip",     # gzip | none
    },
    batching={"max_items": 2000, "max_bytes": 2097152, "max_interval_ms": 3000},
    failed_batch_disk={
        "enabled": False,
        "path": "/tmp/mvk/failed_batches",  # required when enabled
        "max_size_mb": 1000,
        "retry_interval": 60,
    },
    logging={"level": "INFO", "prompts_responses": False},  # ⚠️ see §2.4
    serverless={"force": False},
    strict_validation=False,
    wrappers={"include": ["genai", "vectordb"]},
    tags={"env": "prod"},
)

2.2 Precedence — defaults < parameters < environment

When the same setting is supplied in more than one place, the SDK resolves it in this order (lowest → highest priority):

Schema default  <  instrument() parameter  <  MVK_* environment variable

Environment variables always win. This is deliberate: it lets ops/platform teams override application code at deploy time without a code change. Example:

mvk.instrument(agent_id="agent-123", logging={"level": "INFO"})

export MVK_LOG_LEVEL=DEBUG   # effective level is DEBUG, not INFO

Tags merge the same way — MVK_TAG_<KEY> overrides the same key passed in tags={...}, while other keys are preserved.

2.3 Secrets & environment (recommended)

The only secret value is api_key (MVK_API_KEY); tenant_id is sensitive-but-identifying. Do not hard-code these in source. Because environment variables take precedence (§2.2), the recommended production pattern is:

• Keep agent_id and non-secret config in code or env as convenient.
• Inject MVK_API_KEY (and MVK_TENANT_ID) from a secrets manager at runtime — AWS Secrets Manager / SSM, GCP Secret Manager, Azure Key Vault, or Vault — never from a committed .env or the image.

import mvk_sdk as mvk

# api_key / tenant_id resolved from MVK_API_KEY / MVK_TENANT_ID,
# which your platform injects from the secret manager.
mvk.instrument(agent_id="agent-123")

# Example: hydrate env from a secret manager before the process starts
export MVK_API_KEY="$(aws secretsmanager get-secret-value \
  --secret-id mvk/api-key --query SecretString --output text)"
export MVK_TENANT_ID=tenant-123

⚠️ Warning. Avoid passing api_key= as a literal in code or logging it. Keep logging.prompts_responses disabled in production — it logs raw LLM prompts/responses, which may contain regulated data.

2.4 Logging

Logging is off by default (level: "OFF") for security and performance. When enabled, the SDK auto-configures Python logging — no manual setup needed.

mvk.instrument(agent_id="agent-123", api_key="mvk_...",
               logging={"level": "DEBUG"})           # controls all components

wrapper_level overrides the level for the AI provider wrappers only:

mvk.instrument(agent_id="agent-123", api_key="mvk_...",
               logging={"level": "INFO", "wrapper_level": "DEBUG"})

export MVK_LOG_LEVEL=INFO
export MVK_WRAPPER_LOG_LEVEL=DEBUG

Levels: OFF (default) · INFO (lifecycle — config, batch flushes, shutdown) · DEBUG (per-span detail — instrumentation, token extraction, queueing; enabling DEBUG also enables INFO).

Log lines are formatted as TIMESTAMP - LOGGER - LEVEL - MESSAGE, where the timestamp is YYYY-MM-DD HH:MM:SS,mmm and loggers are namespaced mvk.* (e.g. mvk.instrumentation.openai, mvk.processors.writer). Example DEBUG output for a single auto-traced OpenAI chat completion:

2026-06-29 18:37:28,807 - mvk.instrumentation.wrapper_base - DEBUG - Wrapper invoked for span_name=openai.chat.completion, method=create, instance=Completions
2026-06-29 18:37:29,044 - mvk.instrumentation.wrapper_base - DEBUG - Sync wrapper: Created span openai.chat.completion with trace_id=6eeb9c79..., operation_subtype=sync
2026-06-29 18:37:29,061 - mvk.instrumentation.openai - DEBUG - [OpenAI Token Extraction] Extracted tokens - prompt: 12, completion: 8, total: 20
2026-06-29 18:37:29,061 - mvk.processors.writer - DEBUG - Span queued for export: openai.chat.completion (queue size: 1)
2026-06-29 18:37:29,062 - mvk.processors.writer - INFO - Flushing batch of 1 span(s) to exporter

---

3. @mvk.context() — enriching spans (decorator vs. context manager)

mvk.instrument() only knows process-wide defaults (agent, environment, global tags). It cannot know who the current user is, which session a request belongs to, or what business workflow is executing. mvk.context() layers that runtime identity and business context onto every span produced inside its scope — auto-traced LLM calls, vector DB queries, and manual @mvk.signal / create_signal() operations all inherit it automatically.

Dual purpose — when to use which

mvk.context() has the same signature in both forms; pick by scope:

Form	Use when
Context manager — with mvk.context(...):	Scoping a block at runtime: an HTTP handler, message consumer, or job; per-request identity that varies per call; or wrapping an async generator (the decorator does not support async generators).
Decorator — @mvk.context(...)	A whole function should share one (usually static) context. Works on sync functions and async def coroutines (context is preserved across await within the same task).

Why it matters

• Cost attribution — customer_id, application_id, and tags let the Mavvrik backend split AI spend by tenant, product, team, or feature for chargeback / showback reporting.
• Trace correlation — user_id, session_id, and request_id stitch multi-step agent workflows back to a single end-user request.
• Business grouping — use_case groups related operations under a named business process (e.g. "fraud_detection_v2", "customer_onboarding").
• Distributed tracing — traceparent / tracestate accept incoming W3C Trace Context headers so spans link across service boundaries.
• Nearest-wins inheritance — context layered closer to the call overrides outer layers (see Context inheritance).

Signature

mvk.context(
    name: str | None = None,                 # Logical context name
    user_id: str | None = None,              # End-user identity
    session_id: str | None = None,           # Session / conversation ID
    application_id: str | None = None,       # Calling application
    customer_id: str | None = None,          # Your customer (multi-tenant)
    request_id: str | None = None,           # External request correlation ID
    region: str | None = None,               # Geographic region
    cloud_provider_code: str | None = None,  # e.g. "aws", "gcp", "azure"
    use_case: str | None = None,             # Business workflow (snake_case)
    outcome_id: str | None = None,           # Stamps mvk.outcome.id on every child span
    tags: dict[str, str] | None = None,      # Up to 10 custom key/value tags
    traceparent: str | None = None,          # W3C traceparent header
    tracestate: str | None = None,           # W3C tracestate header
)

All parameters are optional — pass only what is meaningful at the call site. Unknown keyword arguments are logged and ignored; the SDK never raises into client code.

Examples

Context manager — per-request scope (HTTP handler, consumer, job):

import mvk_sdk as mvk
import openai

mvk.instrument(agent_id="agent-123", api_key="mvk_...", tenant_id="tenant-123")

def handle_chat(request):
    with mvk.context(
        user_id=request.user.id,
        session_id=request.session.id,
        customer_id=request.tenant.id,
        use_case="customer_support_chat",
        tags={"feature": "chat", "tier": "premium"},
    ):
        # Every span below inherits the identity, use_case, and tags above
        return openai.ChatCompletion.create(...)

Decorator — function-scoped enrichment:

@mvk.context(region="us-east-1", use_case="document_search",
             tags={"service": "search-api"})
def search_handler(query: str):
    return run_pipeline(query)   # all LLM / vector DB calls inherit the context

Continuing a distributed trace from an upstream service:

def grpc_handler(request, metadata):
    with mvk.context(
        traceparent=metadata.get("traceparent"),
        tracestate=metadata.get("tracestate"),
        request_id=metadata.get("x-request-id"),
    ):
        return process(request)

Nesting — innermost wins for scalars, tags merge:

with mvk.context(customer_id="acme", tags={"env": "prod"}):
    with mvk.context(user_id="u-42", tags={"feature": "summarize"}):
        # Span sees: customer_id="acme", user_id="u-42",
        #            tags={"env": "prod", "feature": "summarize"}
        summarize(doc)

Clearing an inherited attribute — pass an empty string:

with mvk.context(user_id="u-42"):
    with mvk.context(user_id=""):
        run_background_job()   # user_id removed from spans in this inner block

Notes & limits

• Tag cap: maximum 10 tags per span after all levels merge. Excess tags are dropped (or raise if strict_validation=True). See Tag validation for key/value rules.
• Per-request headers override everything: incoming x-mvk-* headers on a single call take precedence over decorator and context-manager values for that call only.
• Unknown kwargs are non-fatal: a misspelled parameter (e.g. userid=...) is logged and ignored. The SDK never raises into client code.
• All attributes are prefixed mvk.* on the wire: e.g. user_id becomes mvk.user_id, and tags={"team": "ml"} becomes mvk.tags.team.

---

4. Cost signals — @mvk.signal(), create_signal() & metered usage

Auto-instrumentation covers LLM providers, vector DBs, and supported frameworks out of the box. For everything else — wrapping a business workflow under a single named span, tracing an in-house LLM gateway, attributing custom tool / storage / API costs — the SDK exposes three manual APIs:

API	Purpose	Use as
@mvk.signal()	Wrap a function so its entire execution becomes one parent span; child auto-traced calls nest under it	Decorator
mvk.create_signal()	Create a span for a code block with explicit step_type / operation	Context manager
mvk.add_metered_usage()	Attach billable quantity metrics (pages, API calls, bytes, custom tokens) to the current span	Plain function call

Identity, session, customer, outcome_id, and tags propagate to these spans automatically from mvk.context().

@mvk.signal() — decorate a function as a named span

The function name (or name=...) becomes the span name; every auto-traced call inside (LLM, vector DB, HTTP) and every create_signal() block becomes a child span. A run_id is auto-generated at the root signal and propagated to all descendants (mvk.run_id on the wire) for trace correlation.

📝 Note. step_type, operation, operation_subtype, and model are auto-set by wrappers for child spans. @mvk.signal() also accepts step_type and tool_name when the decorated function itself represents a tool wrapper, but tool_name is only applied when step_type="TOOL". For explicit manual span classification (operation, operation_subtype), use mvk.create_signal().

@mvk.signal(
    name: str | None = None,                 # Span name (default: function name)
    user_id: str | None = None,
    session_id: str | None = None,
    application_id: str | None = None,
    customer_id: str | None = None,
    request_id: str | None = None,
    region: str | None = None,
    cloud_provider_code: str | None = None,
    use_case: str | None = None,
    outcome_id: str | None = None,           # Stamps mvk.outcome.id on this span
    step_type: str | MVKStepType | None = None,  # Optional; use with TOOL spans
    tool_name: str | None = None,            # TOOL-only label for the wrapped function
    tags: dict[str, str] | None = None,      # Custom tags (max 10 per span)
)

Works on sync functions, async def coroutines, and async generators — the SDK auto-detects the function type.

# Group a multi-step workflow under one named span
@mvk.signal(name="answer_question", use_case="customer_support_chat",
            tags={"tier": "premium"})
def answer_question(query: str):
    embedding = openai.Embedding.create(...)     # child span: EMBEDDING
    hits = pinecone.query(...)                    # child span: RETRIEVER
    return openai.ChatCompletion.create(...)      # child span: LLM


# Custom tool wrapper — attach mvk.tool_name to the decorator span
@mvk.signal(name="search_documents", step_type="TOOL",
            tool_name="knowledge_base_search")
def search_documents(query: str):
    return internal_search(query)

mvk.create_signal() — manual spans with explicit step_type

Use this when auto-instrumentation does not cover the call — a custom in-house LLM service, a paid third-party API, a parsing step, a file upload. Unlike @mvk.signal(), this is a context manager and lets you set step_type / operation explicitly so the backend classifies the cost correctly.

from mvk_sdk.schema import MVKStepType  # optional; strings also accepted

mvk.create_signal(
    name: str,                                   # Span name (required)
    step_type: MVKStepType | str | None = None,  # LLM | TOOL | RETRIEVER |
                                                 # EMBEDDING | BATCH | AGENT_CALL
    operation: str | None = None,                # e.g. "parse", "api_call"
    operation_subtype: str | None = None,        # Free-form refinement
    tool_name: str | None = None,                # TOOL-only per-tool attribution
    tags: dict[str, str] | None = None,
    outcome_id: str | None = None,               # Stamps mvk.outcome.id on this span
)

Inherits user_id, session_id, customer_id, region, use_case, outcome_id, and tags from the surrounding mvk.context() / @mvk.signal(). Pass tags={} to opt out of context-tag inheritance.

with mvk.create_signal(name="parse-document", step_type="TOOL",
                       operation="parse", tool_name="document_parser"):
    content = parse_pdf(file_path)

mvk.add_metered_usage() — attach billable quantities

Auto-instrumented LLM calls already populate mvk.metered_usage with token counts. For everything else with a cost dimension — pages processed, external API calls, storage bytes, image generations, characters translated — call add_metered_usage() inside the current signal.

mvk.add_metered_usage([
    {
        "metric_kind": str,    # e.g. "file.pages_processed", "api.calls", "storage.bytes"
        "quantity": float,     # Runtime-measured quantity (never hardcoded)
        "uom": str,            # Unit of measure: "page", "request", "byte", "image", ...
        "metadata": {
            "rate_per_unit": float,   # Cost per UOM unit (USD by default)
            "provider": str,          # External vendor name
            # ...any other free-form metadata
        },
    },
])

❗ Important — rate_per_unit is what turns a quantity into a cost. The backend computes a line item as quantity × rate_per_unit and rolls it up by customer_id / tags / use_case.

• Omit it → the backend applies the configured rate template for that metric_kind. Use this when a template exists and you want central control.
• Set it inline → overrides the template, and is required for metric_kinds that have no template. Always pair a numeric rate_per_unit with the correct uom so quantity and rate are in the same units.

Also accepts Metric instances (from mvk_sdk.metrics import Metric) for the three core fields only (metric_kind, quantity, uom); use the dict form when you need metadata / rate_per_unit. If no active span exists, the call is logged and skipped — it never raises.

End-to-end example — tools, custom costs, and auto-traced LLM together

import mvk_sdk as mvk
from openai import OpenAI

mvk.instrument(agent_id="doc-agent", api_key="mvk_...", tenant_id="tenant-123",
               wrappers={"include": ["genai"]})

client = OpenAI()


def process_document(file_path: str, user_id: str,
                     session_id: str, customer_id: str) -> str:
    with mvk.context(user_id=user_id, session_id=session_id, customer_id=customer_id):
        # 1. Manual TOOL span — custom parsing cost (per-page)
        with mvk.create_signal(name="parse-document", step_type="TOOL",
                               operation="parse", tool_name="document_parser"):
            content = parse_pdf(file_path)
            page_count = get_page_count(file_path)
            mvk.add_metered_usage([{
                "metric_kind": "file.pages_processed",
                "quantity": page_count,            # runtime value, never hardcoded
                "uom": "page",
                "metadata": {"rate_per_unit": 0.0015},   # $/page
            }])

        # 2. Manual TOOL span — paid third-party API
        with mvk.create_signal(name="extract-metadata", step_type="TOOL",
                               operation="api_call", tool_name="metadata_service"):
            metadata = call_metadata_api(content)
            mvk.add_metered_usage([{
                "metric_kind": "api.calls",
                "quantity": 1,
                "uom": "request",
                "metadata": {"rate_per_unit": 0.05, "provider": "metadata-service"},
            }])

        # 3. Auto-traced LLM call — wrapper populates token metered_usage
        summary = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[{"role": "user", "content": f"Summarize: {content[:2000]}"}],
        )
        return summary.choices[0].message.content

This single request produces one trace with three child spans (parse-document, extract-metadata, the auto-traced chat), all stamped with user_id / session_id / customer_id, each carrying its own metered_usage line items so cost is attributed end-to-end.

Notes & limits

• Always capture quantity from runtime values (e.g. len(file_bytes), page_count, 1 per API call) — never hardcode.
• Multiple metrics per span — pass a list in one add_metered_usage() call, or call multiple times within the same span (entries are appended).
• No active span = no-op — calling outside a @mvk.signal() / create_signal() / auto-traced call logs a warning and skips silently.
• Common metric_kind examples: token.prompt, token.completion, file.pages_processed, api.calls, storage.bytes, images.generated, characters.translated, email.sent, db.rows_scanned.

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5. Outcome signals — mvk.record_outcome()

Cost is captured automatically (§4). Outcome signals report the value side — what a run actually delivered — so the backend can compute ROI: ROI = (human_value − ai_cost) / ai_cost.

Call mvk.record_outcome() once, at the end of a run. You report what happened — the status and how many units of work were delivered. The per-unit economics (the dollars and human minutes each unit is worth) are configured per use case in the Mavvrik UI, not in code — the SDK carries no dollar figures.

import uuid

import mvk_sdk as mvk
from mvk_sdk import MVKOutcomeStatus, MVKOutcomeUnit

# A floor-plan validation agent — one unit = one floor
mvk.record_outcome(
    outcome_id=str(uuid.uuid4()),      # required — stable identity; reuse it to attach feedback later
    use_case="floorplan_validation",   # required — backend join key
    session_id=session_id,             # required — backend join key
    status=MVKOutcomeStatus.SUCCESS,
    units=[MVKOutcomeUnit(type="floor", attempted=15, succeeded=15)],
)
# backend: 15 floors × configured value_per_floor  (price from the UI, not the code)

Signature

mvk.record_outcome(
    outcome_id,             # required, non-blank — stable identity (recommend a UUID) → mvk.outcome.id
    use_case,               # required, non-blank — backend join key
    session_id,             # required, non-blank — backend join key
    status,                 # MVKOutcomeStatus (or its string): SUCCESS | PARTIAL | FAILED
    units=None,             # list[MVKOutcomeUnit] — delivered counts, by unit type
    completion_ratio=None,  # 0.0–1.0 — whole-run progress; used only when there are no units
    duration_minutes=None,  # actual end-to-end run time (min); backend uses it for time-saved
    remarks=None,           # free-form triage note, surfaced per session in the dashboard
    user_id=None, application_id=None, customer_id=None, tags=None,   # correlation (inherited from context if omitted)
)  # -> Span | None — never raises; returns None on invalid input / internal failure

MVKOutcomeUnit reports counts only — no economics:

MVKOutcomeUnit(type="claim", attempted=20, succeeded=18)   # + optional tags={...}

Examples

oid = str(uuid.uuid4())   # one stable id per outcome; store it to attach feedback later

# Partial — quantified by real counts (17 of 20)
mvk.record_outcome(oid, "contract_review", session_id, MVKOutcomeStatus.PARTIAL,
                   units=[MVKOutcomeUnit(type="contract", attempted=20, succeeded=17)],
                   remarks="3 contracts deferred to a reviewer")

# Partial — no countable units; report a completion fraction instead
mvk.record_outcome(oid, "research_summary", session_id, MVKOutcomeStatus.PARTIAL,
                   completion_ratio=0.6)

# Failed — zero value; the remark explains why at a glance in the dashboard
mvk.record_outcome(oid, "claims_extraction", session_id, MVKOutcomeStatus.FAILED,
                   units=[MVKOutcomeUnit(type="claim", attempted=30, succeeded=0)],
                   remarks="upstream schema change broke extraction")

What it emits

One span with mvk.step_type = "OUTCOME", carrying mvk.outcome.id, mvk.outcome.status, mvk.outcome.units (a JSON array of {type, attempted, succeeded, tags}) with mvk.outcome.units.attempted / .succeeded rollups, mvk.outcome.completion_ratio (only when there are no units), mvk.outcome.duration_minutes (when supplied), mvk.outcome.remarks, and the correlation attributes (mvk.use_case, mvk.session_id, …).

Link every span to the outcome (outcome_id propagation)

record_outcome() emits one outcome span carrying mvk.outcome.id. To let the backend join every span in the run — the auto-captured LLM / vector-DB cost spans, your @mvk.signal and create_signal steps — directly to that outcome, pass the same outcome_id to mvk.context(). It is stamped as mvk.outcome.id on all child spans:

oid = str(uuid.uuid4())

with mvk.context(outcome_id=oid, use_case="floorplan_validation", session_id=session_id):
    result = validate_floors(plan)          # auto + manual spans all carry mvk.outcome.id

mvk.record_outcome(oid, "floorplan_validation", session_id, MVKOutcomeStatus.SUCCESS,
                   units=[MVKOutcomeUnit(type="floor", attempted=result.total, succeeded=result.passed)])

outcome_id is also accepted directly on @mvk.signal(outcome_id=...) and mvk.create_signal(..., outcome_id=...) (overriding the context value for one span). It is optional and additive — omit it and spans are unchanged — and the SDK never generates one, so use the same string you pass to record_outcome().

An outcome needs both outcome_id and use_case to correlate and be priced. If you set outcome_id on a context but no use_case is in scope, the SDK logs a warning (visible by default). record_outcome() likewise warns and returns None if either is blank. A wrong-but-valid string (a typo, or an outcome_id that doesn't match record_outcome()) can't be caught locally — it shows up in the dashboard as unresolved, never a silent $0.

Notes & limits

• Report counts, not dollars — per-unit value/time is configured per use case in the Mavvrik UI; the SDK never carries economics.
• outcome_id, use_case, and session_id are required join keys — each must be a non-blank string, or the call logs a warning and emits nothing. outcome_id is a stable identity for the outcome (a UUID string like str(uuid.uuid4()) is ideal); reuse it to attach end-user feedback to this specific outcome later.
• Prefer counts; completion_ratio is the fallback for work with no countable units — pass it explicitly as a float 0.0–1.0 (it is not derived from status). Out-of-range values are clamped, and it is ignored when units are present.
• You set status; it is never auto-derived from counts — auxiliary steps (audit writes, email/SMS, metadata) succeed without being the customer's outcome, so decide SUCCESS / PARTIAL / FAILED from actual work-completion in your workflow and use remarks to explain misses.
• units must be MVKOutcomeUnit instances — any other entry is skipped with a warning.
• Never breaks your app — invalid input or any internal error returns None; it never raises into your workflow.

Full guide: docs/outcome_roi.md — a client-facing walkthrough of outcome measurement: core concepts, every usage mode (with / without / multiple units) and when to use each, plus deep worked examples (insurance claims processing, accounts-payable).

End-user feedback — mvk.record_feedback()

When an end user later rates an outcome, call mvk.record_feedback(). Feedback arrives late and out of process, so it correlates back to the outcome only by the outcome_id you set on record_outcome() (emitted as mvk.outcome.id). It reports activity only — a sentiment, an optional numeric score, and how many respondents the row represents. Pass score_max with score for a self-describing "N out of M" rating (e.g. score=4, score_max=5 → "4 out of 5", Amazon-style, the customer sets the max); omit it and the scale comes from the Mavvrik UI. One call is either a single respondent or a consolidated group.

from mvk_sdk import MVKSentiment

# 1. Single user, hours later, in a different service — only the id ties it back
mvk.record_feedback(oid, MVKSentiment.POSITIVE,
                    remarks="Resolved my claim correctly.", user_id="user_123")

# 2. Single user, self-describing "4 out of 5" star rating (customer sets the max)
mvk.record_feedback(oid, MVKSentiment.POSITIVE, score=4, score_max=5)

# 3. Consolidated GROUP — one run served 42 people, feedback aggregated into one event
#    breakdown must sum to respondent_count (35+5+2 == 42), else the row is dropped
mvk.record_feedback(oid, MVKSentiment.POSITIVE, score=4.2,
                    respondent_count=42, positive=35, neutral=5, negative=2,
                    remarks="Team review: mostly positive, 2 flagged tone.")

Signature: record_feedback(outcome_id, sentiment, score=None, score_max=None, remarks=None, user_id=None, feedback_time=None, respondent_count=None, positive=None, neutral=None, negative=None, application_id=None, customer_id=None, tags=None) -> Span | None.

What it emits: one span with mvk.step_type = "FEEDBACK", carrying mvk.feedback.outcome_id (the join key), mvk.feedback.sentiment, and — when supplied — mvk.feedback.score, .score_max, .remarks, .user_id, .time, .respondent_count, and .breakdown.{positive,neutral,negative}.

Notes:

• outcome_id + sentiment are required (blank/invalid → logged, returns None); the outcome_id must match the value passed to record_outcome().
• Single vs. group is the same call — omit respondent_count for one respondent; set it (with an optional breakdown) for a consolidated group. A breakdown must sum to respondent_count, or the row is dropped.
• user_id is the feedback author (mvk.feedback.user_id), not the run's user — it is never inherited from context.
• Never breaks your app — invalid input or any internal error returns None.

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6. Reference

Context inheritance (Nearest Wins)

The SDK layers context from four sources, applied outermost → innermost: Global (mvk.instrument(tags=...)) → Decorator (@mvk.signal / @mvk.context) → Context manager (with mvk.context(...)) → Per-request headers (x-mvk-*).

• Scalars (user_id, session_id, customer_id, request_id, …): nearest wins completely.
• Tags: merge all levels; nearest wins for duplicate keys.
• Empty values clear attributes: mvk.user_id="" removes user attribution from this scope down.

Tag validation

• Maximum 10 tags per span after merging
• Keys: ^[a-z0-9._-]{1,64}$ (dots allowed)
• Values: UTF-8 strings ≤256 chars
• Validation errors are logged (or raised if strict_validation=True)

valid, issues = mvk.validate_tags({
    "user.id": "123",               # dots allowed
    "tier": "premium",
    "INVALID!": "will-be-dropped",  # invalid characters
})

Architecture

Memory-First Architecture — best for development, QA, and production (all environments):

Producers → Memory Queue (10MB) → Writer Thread → Exporter
                                        ↓
                            (exponential backoff retry)
                                        ↓
                          (on failure after 10 attempts)
                                        ↓
                                FailedBatchDisk

• Memory-first performance with a 10MB bounded queue, non-blocking producers
• Exponential backoff retry (1s to 5min); persistently failed batches saved to disk
• Fork-safe (Gunicorn, uWSGI, prefork servers); shuts down gracefully on SIGTERM/SIGINT, flushing pending spans

Step types (MVKStepType): LLM (token metrics), EMBEDDING (embedding metrics), RETRIEVER (vector/search), TOOL (tool/HTTP + manual tool spans), AGENT_CALL (orchestration), BATCH (batch ops). MEMORY is reserved for future use.

Smart auto-instrumentation uses OpenTelemetry's proven strategy: if a library is already imported → immediate instrumentation; if not → lazy hook via wrapt.when_imported().

Supported integrations

AI Providers (wrappers={"include": ["genai"]}):

Provider	Library Versions	Step Type	Token Tracking
OpenAI	0.x, 1.x	LLM, EMBEDDING	✓ metered_usage
Anthropic	0.20-0.35	LLM	✓ metered_usage
Gemini	google.generativeai	LLM, EMBEDDING	✓ metered_usage
AWS Bedrock	boto3 bedrock-runtime	LLM, EMBEDDING	✓ metered_usage
Azure OpenAI	azure.ai.openai	LLM, EMBEDDING	✓ metered_usage
Vertex AI	vertexai/google.cloud.aiplatform	LLM, EMBEDDING	✓ metered_usage

Vector Databases (wrappers={"include": ["vectordb"]}):

Provider	Library Versions	Step Type	Metrics
Pinecone	2.x-4.x	RETRIEVER	vector_count, dimension
Weaviate	3.x-4.x	RETRIEVER	vector_count, dimension
ChromaDB	0.4.x-0.5.x	RETRIEVER	vector_count, dimension
Qdrant	1.x	RETRIEVER	vector_count, dimension

Frameworks: Semantic Kernel, LangChain, LangGraph, Agno, CrewAI, OpenAI Agents.

Routers & Proxies: OpenRouter, LiteLLM.

Batch APIs: OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Gemini, Vertex AI, LangChain, Agno, CrewAI, Semantic Kernel, and ThreadPool concurrent execution.

HTTP Clients (wrappers={"include": ["http"]}, disabled by default): HTTPX 0.25-0.27 (TOOL).

Performance characteristics

Memory-First Mode (all environments) — throughput 500–2000 spans/sec; ~10MB bounded queue; producer latency <100 µs (non-blocking); export latency p99 <100ms (network dependent); failed batches saved to disk for retry.

Batching defaults (all modes) — 2000 spans max, 2 MiB max, 3000 ms max, gzip compression (default) or none.

Serverless deployment

# AWS Lambda
from mvk_sdk.serverless import lambda_handler

@lambda_handler(flush_timeout_ms=1000)   # auto flush on completion
def handler(event, context):
    # Auto-detects Lambda; optimizes batch_size=1, flush=100ms, memory-first
    return process_request(event)

# Google Cloud Functions — auto-detected via K_SERVICE / FUNCTION_NAME
import mvk_sdk as mvk

mvk.instrument(agent_id="gcf-function")

def main(request):
    result = process_request(request)
    mvk.force_flush()    # manual flush for Cloud Functions
    return result

export MVK_SERVERLESS=true   # force serverless optimizations

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7. Support

• Agent Onboarding
• Quick Start Guide
• Email: [e-mail]