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PydanticAI is a Python agent framework from the Pydantic team, built for type-safe LLM applications with dependency injection, structured outputs, and multi-agent support. HoneyHive integrates with PydanticAI via its native OpenTelemetry support, automatically capturing agent runs, tool calls, and LLM interactions.

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Quick Start

Add HoneyHive tracing in just 3 lines of code. Initialize the tracer and call Agent.instrument_all(), and all agent runs, tool calls, and model calls are automatically traced.
To see where to initialize the tracer for your environment, including AWS Lambda and long-running servers, see Tracer Initialization.
pip install "honeyhive>=1.0.0rc0" pydantic-ai

import os
from honeyhive import HoneyHiveTracer
from pydantic_ai import Agent

tracer = HoneyHiveTracer.init(
    api_key=os.getenv("HH_API_KEY"),
    project=os.getenv("HH_PROJECT"),
)
Agent.instrument_all()

# Your existing PydanticAI code works unchanged

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Compatibility

RequirementVersion
Python3.11+
pydantic-ai0.70+

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What Gets Traced

The instrumentation automatically captures:
  • Agent runs - Every agent invocation with inputs and outputs
  • LLM calls - Model requests, responses, and token usage
  • Tool calls - Each tool execution with arguments and results
  • Multi-turn conversations - Message history across turns
  • Streaming - Streamed responses captured as complete traces
No manual instrumentation required.

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Example: Single Agent with Tools

import asyncio
import os
from dataclasses import dataclass

from pydantic_ai import Agent, RunContext

from honeyhive import HoneyHiveTracer

tracer = HoneyHiveTracer.init(
    api_key=os.getenv("HH_API_KEY"),
    project=os.getenv("HH_PROJECT"),
)
Agent.instrument_all()


def lookup_order_status(order_id: str) -> dict:
    return {
        "ORD-1001": {"state": "shipped", "eta_days": 2},
        "ORD-1002": {"state": "processing", "eta_days": 5},
    }.get(order_id.upper(), {"state": "not_found"})


@dataclass
class CustomerContext:
    customer_name: str
    order_id: str


agent = Agent(
    "anthropic:claude-haiku-4-5",
    name="support_agent",
    deps_type=CustomerContext,
    instructions=(
        "You are a customer support agent. Use check_order to look up "
        "order status. Address the customer by name. Keep responses concise."
    ),
)


@agent.tool
def check_order(ctx: RunContext[CustomerContext], order_id: str) -> str:
    """Look up order status."""
    result = lookup_order_status(order_id)
    return f"[{ctx.deps.customer_name}] {result}"


async def main():
    customer = CustomerContext(customer_name="Alex Kim", order_id="ORD-1002")
    await agent.run(
        "My order ORD-1002 has been processing for a while. What's the status?",
        deps=customer,
    )


asyncio.run(main())

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Example: Multi-Agent Delegation

import asyncio
import os
from dataclasses import dataclass

from pydantic_ai import Agent, RunContext

from honeyhive import HoneyHiveTracer

tracer = HoneyHiveTracer.init(
    api_key=os.getenv("HH_API_KEY"),
    project=os.getenv("HH_PROJECT"),
)
Agent.instrument_all()


@dataclass
class CustomerContext:
    customer_name: str
    order_id: str


policy_agent = Agent(
    "anthropic:claude-haiku-4-5",
    name="policy_specialist",
    instructions=(
        "You are a policy specialist. Give concise answers about "
        "refund, cancellation, and shipping policies."
    ),
)

coordinator = Agent(
    "anthropic:claude-haiku-4-5",
    name="support_coordinator",
    deps_type=CustomerContext,
    instructions=(
        "You are a support coordinator. For policy questions, "
        "delegate to ask_policy_specialist. Combine the answer "
        "with any order context to form your response. Be concise."
    ),
)


@coordinator.tool
async def ask_policy_specialist(
    ctx: RunContext[CustomerContext], question: str
) -> str:
    """Delegate policy questions to the specialist."""
    result = await policy_agent.run(question, usage=ctx.usage)
    return str(result.output)


async def main():
    customer = CustomerContext(customer_name="Jordan Lee", order_id="ORD-1003")
    await coordinator.run(
        "Order ORD-1003 is delayed. Can I cancel and get a refund?",
        deps=customer,
    )


asyncio.run(main())
In HoneyHive, you’ll see the full trace hierarchy including coordinator agent, delegated policy specialist calls, and tool executions.

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Troubleshooting

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Traces not appearing

  1. Check environment variables — Ensure HH_API_KEY and HH_PROJECT are set
  2. Call instrument_all() before running agents — Instrumentation must be active before agent execution:
from honeyhive import HoneyHiveTracer
from pydantic_ai import Agent

tracer = HoneyHiveTracer.init(project="your-project")

# ✅ Correct — instrument before running
Agent.instrument_all()

result = await agent.run("Hello")
  1. Check your model provider — Ensure the API key for your chosen model provider is set (e.g., ANTHROPIC_API_KEY, OPENAI_API_KEY)

Enrich Your Traces

Add user IDs and custom metadata to PydanticAI traces

Custom Spans

Create spans for business logic around agent calls

Distributed Tracing

Trace agents across service boundaries

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Resources