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Add the Docs MCP to your AI coding tool to get help building with Respan. No API key needed.
{
  "mcpServers": {
    "respan-docs": {
      "url": "https://docs.respan.ai/mcp"
    }
  }
}

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What is AgentSpec?

AgentSpec is a specification framework for defining and testing AI agents. It provides a structured way to describe agent behavior, capabilities, and expected outputs.

​
Setup

1

Install packages

pip install agentspec respan-ai openinference-instrumentation-agentspec python-dotenv
2

Set environment variables

export RESPAN_API_KEY="YOUR_RESPAN_API_KEY"
export OPENAI_API_KEY="YOUR_OPENAI_API_KEY"
3

Initialize and run

import os
from dotenv import load_dotenv

load_dotenv()

from respan import Respan
from openinference.instrumentation.agentspec import AgentSpecInstrumentor
from agentspec import AgentSpec

# Initialize Respan with AgentSpec instrumentation
respan = Respan(instrumentations=[AgentSpecInstrumentor()])

spec = AgentSpec(
    name="assistant",
    description="A helpful assistant that answers questions.",
    model="gpt-4.1-nano",
)

result = spec.run("What is the capital of France?")
print(result)
respan.flush()
4

View your trace

Open the Traces page to see your agent spec execution trace.

​
Configuration

ParameterTypeDefaultDescription
api_keystr | NoneNoneFalls back to RESPAN_API_KEY env var.
base_urlstr | NoneNoneFalls back to RESPAN_BASE_URL env var.
instrumentationslist[]Plugin instrumentations to activate (e.g. AgentSpecInstrumentor()).
is_auto_instrumentbool | NoneFalseAuto-discover and activate all installed instrumentors via OpenTelemetry entry points.
customer_identifierstr | NoneNoneDefault customer identifier for all spans.
metadatadict | NoneNoneDefault metadata attached to all spans.
environmentstr | NoneNoneEnvironment tag (e.g. "production").

​
Attributes

​
In Respan()

Set defaults at initialization — these apply to all spans. 
from respan import Respan
from openinference.instrumentation.agentspec import AgentSpecInstrumentor

respan = Respan(
    instrumentations=[AgentSpecInstrumentor()],
    customer_identifier="user_123",
    metadata={"service": "agentspec-app", "version": "1.0.0"},
)

​
With propagate_attributes

Override per-request using a context manager. 
from respan import Respan, workflow, propagate_attributes
from openinference.instrumentation.agentspec import AgentSpecInstrumentor

respan = Respan(instrumentations=[AgentSpecInstrumentor()])

@workflow(name="handle_request")
def handle_request(user_id: str, query: str):
    with propagate_attributes(
        customer_identifier=user_id,
        thread_identifier="conv_001",
        metadata={"plan": "pro"},
    ):
        result = spec.run(query)
        print(result)
AttributeTypeDescription
customer_identifierstrIdentifies the end user in Respan analytics.
thread_identifierstrGroups related messages into a conversation.
metadatadictCustom key-value pairs. Merged with default metadata.

​
Decorators

Use @workflow and @task to create structured trace hierarchies. 
from respan import Respan, workflow, task
from openinference.instrumentation.agentspec import AgentSpecInstrumentor
from agentspec import AgentSpec

respan = Respan(instrumentations=[AgentSpecInstrumentor()])

@task(name="run_spec")
def run_spec(query: str) -> str:
    spec = AgentSpec(
        name="assistant",
        description="A helpful assistant.",
        model="gpt-4.1-nano",
    )
    return str(spec.run(query))

@workflow(name="spec_pipeline")
def pipeline(query: str):
    result = run_spec(query)
    print(result)

pipeline("What are the benefits of LLM observability?")
respan.flush()

​
Examples

​
Basic spec

from agentspec import AgentSpec

spec = AgentSpec(
    name="assistant",
    description="A helpful assistant that answers questions concisely.",
    model="gpt-4.1-nano",
)

result = spec.run("Explain machine learning in one sentence.")
print(result)