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

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.

LangChain and LangGraph

Nothing to add in a LangGraph app: once the SDK is initialised, every graph node, tool and model call is checked before it runs. The node name is the agent, so a pause on "Recommendation Agent" stops the node recommendation_agent and leaves the other nodes running. A refused call raises out of graph.invoke, so one @trace around the chatbot is enough:

@trace(operation="agent", name="StoreChatbot")
def chatbot(text):
    try:
        return graph.invoke({"messages": [text]})
    except ContainmentError as exc:
        return exc.fallback_message

"Slow it down" is not applied to graph nodes.

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.