Skip to main content
Pre-release

This release is a pre-release and may not be stable for production use.

Adeptiv GenAI Tracing SDK

Adeptiv GenAI is a lightweight, vendor-neutral OpenTelemetry tracing SDK for LLMs, agents, and AI workflows.

It captures latency, execution flow, hashes, and metadata for all AI use cases.


Key Features

  • Trace-only observability (no response normalization)
  • Works with any LLM (OpenAI, Anthropic, Gemini, local models)
  • Supports chat, summarization, agents, tools, RAG
  • Sync & async compatible
  • Enterprise-safe (hashing, redaction, previews)
  • OpenTelemetry compatible

This ensures zero lock-in and maximum compatibility.


Installation

pip install adeptiv-ai-evaluator-sdk

Adeptiv GenAI Tracing SDK

Tracing an LLM Call

SDK configuration

config.endpoint = "wss://<adeptiv host>/ws/sdk/" # Adeptiv endpoint (set before the key)
config.api_key = "" #project Key 
config.project_name = "customer-support-bot" # Project Name


from adeptiv_evaluator_sdk import trace_llm

@trace_llm(model="gpt-4o", operation="chat", workflow_id="")
def chat(prompt):
    return llm.invoke(prompt)

chat("Hello world")
# Adeptiv GenAI Tracing Agents SDK

from adeptiv_evaluator_sdk import trace_llm

@trace_agent("support_agent", model="gpt-4o",workflow_id="")
async def run_agent(query):
    return await agent.run(query)

Containment

When a containment rule in Adeptiv acts on an agent (pause it, block a tool, allow only approved models, slow it down), the SDK enforces it inside your process. Only the agent the rule acted on is affected; every other agent keeps working.

The SDK keeps the use case's policy in memory and refreshes it in the background (every 15 s by default), so a check costs about a microsecond and never makes a network call. An action stays until someone resumes the agent in Adeptiv; if Adeptiv cannot be reached, the last policy stays in force. If the policy has never been fetched, nothing is enforced: an Adeptiv outage never stops your agents.

Every @trace call is checked (contained=True is the default); pass contained=False for a call that must never be stopped. Name agents the way they are named in Adeptiv:

from adeptiv_evaluator_sdk import trace, ContainmentError

@trace(operation="agent", agent_name="Refund Agent")
def refund_agent(question):             # pause, approved models, slow down
    ...

@trace(operation="tool")
def issue_refund(order_id):             # blocked for the current agent?
    ...

@trace(operation="chat", model="gpt-4.1")
def ask_model(prompt):                  # outside the approved models?
    ...

try:
    reply = refund_agent(question)
except ContainmentError as exc:        # AgentPaused, ToolBlocked, ModelNotApproved, RateLimited
    reply = exc.fallback_message       # "A team member will reply to you shortly." or the rule's message

A refused call still shows on its span, marked with the action. A contained agent call makes its agent the current one, so contained tool and model calls inside it are checked for that agent.

For a tool dispatcher or a model call you cannot trace, check it before it runs; the agent is the one of the enclosing contained agent call:

from adeptiv_evaluator_sdk import guard_tool, guard_model

for call in response.tool_calls:
    guard_tool(call.name)              # raises ToolBlocked if this tool is blocked
    run_tool(call)
guard_model("gpt-4.1")                 # raises ModelNotApproved outside the approved list
Setting Env var Default
containment_enabled ADEPTIV_CONTAINMENT true
containment_initial_wait_seconds ADEPTIV_CONTAINMENT_INITIAL_WAIT 1 (first call of a process only)
containment_max_wait_seconds ADEPTIV_CONTAINMENT_MAX_WAIT 10 (slow-down wait before RateLimited)

Inside a @trace span, a refused or delayed call marks it with adeptiv.containment.action, adeptiv.containment.outcome (blocked / delayed) and adeptiv.containment.event_id, so Adeptiv can show the containment held.


Transport

Traces and the containment policy share one WebSocket per process to Adeptiv, opened in the background. You give the SDK two things: the endpoint and your project API key.

from adeptiv_evaluator_sdk import init_telemetry

init_telemetry(
    api_key="<project api key>",
    endpoint="wss://<adeptiv host>/ws/sdk/",
)

or ADEPTIV_API_KEY and ADEPTIV_ENDPOINT in the environment. The key is sent once, on the Authorization header when the connection opens.

Containment changes are pushed as they happen. While the connection is down your app never waits on it: trace batches are held in memory (up to 32 MB, gzipped) and sent when it is back, and the last containment policy stays in force.

Setting Env var Default
endpoint ADEPTIV_ENDPOINT none (required)
api_key ADEPTIV_API_KEY none (required)
realtime_ack_timeout_seconds ADEPTIV_REALTIME_ACK_TIMEOUT 10 (then the batch is held and sent again)
realtime_connect_timeout_seconds ADEPTIV_REALTIME_CONNECT_TIMEOUT 5
realtime_max_backoff_seconds ADEPTIV_REALTIME_MAX_BACKOFF 60 (reconnect backoff cap)

Metadata

Release files for adeptiv-ai-evaluator-sdk 1.53.0.dev1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for adeptiv-ai-evaluator-sdk 1.53.0.dev1
File Size Uploaded Provenance Actions
adeptiv_ai_evaluator_sdk-1.53.0.dev1.tar.gz 63.6 kB Without provenance Details for adeptiv_ai_evaluator_sdk-1.53.0.dev1.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for adeptiv-ai-evaluator-sdk 1.53.0.dev1
File Interpreter ABI Platform Actions
adeptiv_ai_evaluator_sdk-1.53.0.dev1-py3-none-any.whl Python 3 none any Details for adeptiv_ai_evaluator_sdk-1.53.0.dev1-py3-none-any.whl

Total release size: 140.7 kB

Release files / adeptiv_ai_evaluator_sdk-1.53.0.dev1.tar.gz

Download URL adeptiv_ai_evaluator_sdk-1.53.0.dev1.tar.gz
Size 63.6 kB
Tags Source
SHA-256 checksum
How to use checksums
3e4f351956b26a6900b4ae57e311c14b90265873aca5ff98051055b2fb70d516
BLAKE2b-256 checksum
How to use checksums
8f3ccdb58bca64810730c70e3e005f583269bb92411eb0be8db0fa414696f2fc
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.10.21

Release files / adeptiv_ai_evaluator_sdk-1.53.0.dev1-py3-none-any.whl

Download URL adeptiv_ai_evaluator_sdk-1.53.0.dev1-py3-none-any.whl
Size 77.1 kB
Tags Python 3
SHA-256 checksum
How to use checksums
99f3a8ad8b20922460663193566123a005d6bccf904386462676221a332094ca
BLAKE2b-256 checksum
How to use checksums
fa36a44319eb99c7066a94fd53637123977d156cfd187adb763beb1f3dbe2df4
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.10.21

Security for adeptiv-ai-evaluator-sdk 1.53.0.dev1

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Release history Release notifications | RSS feed

This release
Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page