CAMEL-AI Integration with Veritrix Agent Observability

Veritrix integrates with CAMEL-AI’s communicative agent framework to give you full visibility into multi-agent role-playing conversations, task decomposition pipelines, and tool-augmented agents. After a single call to trace.init(), every message exchange between agents, every LLM call, and every tool invocation is captured as a structured trace — allowing you to analyze conversation quality, measure per-agent cost and latency, and debug complex multi-agent workflows without changing any of your agent definitions.

Prerequisites

Python 3.10 or higher
An active Veritrix account and API key
CAMEL-AI installed

Installation & Setup

Install Veritrix and CAMEL-AI

pip install veritrix camel-ai

To include CAMEL-AI’s optional tool and model integrations, install the extras you need:

pip install "camel-ai[all]"

Initialize Veritrix tracing

Call trace.init() once at application startup, before you instantiate any agents or models.

from veritrix import trace

trace.init("camel-agent")

Use CAMEL-AI agents as normal

Veritrix instruments the CAMEL-AI agent runtime automatically. Your role assignments, system messages, and conversation loops remain unchanged.

from camel.agents import ChatAgent
from veritrix import trace

trace.init("camel-agent")
# Your CAMEL agents run normally

Example: Single ChatAgent

The following example creates a single CAMEL ChatAgent with a custom role and runs a one-turn conversation. Veritrix records the full LLM call, including the system prompt, user message, and model response.

from camel.agents import ChatAgent
from camel.messages import BaseMessage
from camel.models import ModelFactory
from camel.types import ModelType, ModelPlatformType
from veritrix import trace

trace.init("camel-agent")

model = ModelFactory.create(
    model_platform=ModelPlatformType.OPENAI,
    model_type=ModelType.GPT_4O,
)

system_msg = BaseMessage.make_assistant_message(
    role_name="Python Expert",
    content="You are an expert Python developer who writes clean, well-documented code.",
)

agent = ChatAgent(system_message=system_msg, model=model)

user_msg = BaseMessage.make_user_message(
    role_name="Developer",
    content="Write a Python decorator that logs the execution time of any function.",
)

response = agent.step(user_msg)
print(response.msg.content)

Example: Role-Playing Multi-Agent Conversation

For CAMEL’s signature role-playing setup — where two agents collaborate toward a shared task — Veritrix traces every turn of the conversation and links each agent’s messages within the same trace.

from camel.agents import ChatAgent
from camel.messages import BaseMessage
from camel.models import ModelFactory
from camel.types import ModelType, ModelPlatformType
from veritrix import trace

trace.init("camel-roleplaying")

model = ModelFactory.create(
    model_platform=ModelPlatformType.OPENAI,
    model_type=ModelType.GPT_4O,
)

# AI user drives the task
ai_user_sys_msg = BaseMessage.make_assistant_message(
    role_name="Product Manager",
    content="You are a product manager who asks a developer to build specific features.",
)

# AI assistant implements it
ai_assistant_sys_msg = BaseMessage.make_assistant_message(
    role_name="Python Developer",
    content="You are a Python developer who implements features requested by the product manager.",
)

ai_user = ChatAgent(system_message=ai_user_sys_msg, model=model)
ai_assistant = ChatAgent(system_message=ai_assistant_sys_msg, model=model)

# Kick off the conversation
init_msg = BaseMessage.make_user_message(
    role_name="Product Manager",
    content="Build a Python function that validates email addresses using regex.",
)

input_msg = init_msg
for _ in range(3):  # Run for 3 turns
    assistant_response = ai_assistant.step(input_msg)
    user_response = ai_user.step(assistant_response.msg)
    input_msg = user_response.msg
    print(f"Assistant: {assistant_response.msg.content}\n")
    print(f"User: {user_response.msg.content}\n")

What Gets Captured Automatically

Signal	Details
LLM calls	Model name, system prompt, user message, completion, token counts, latency
Tool calls	Tool name, input arguments, output, duration
Agent messages	Role name, message content, conversation turn index
Agent handoffs	Source role, target role, message passed
Task steps	Step description, assigned agent, output
Retries	Retry count, error type, backoff applied

Each agent in a role-playing session is represented as a distinct entity in Veritrix, so you can filter traces by role name and compare the behavior of individual agents across multiple runs.

OpenTelemetry Compatibility

Veritrix is built on OpenTelemetry, so every span it emits for your CAMEL-AI workflows is a fully compliant OTel span. You can export these spans to any OTel-compatible backend — whether you run Jaeger, Grafana Tempo, Honeycomb, or your own collector — without any additional setup.

To route Veritrix traces to a custom OTel exporter or integrate them with an existing observability stack, see the OpenTelemetry integration page.

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Account & Billing

CAMEL-AI Integration with Veritrix Agent Observability

Prerequisites

Installation & Setup

Example: Single ChatAgent

Example: Role-Playing Multi-Agent Conversation

What Gets Captured Automatically

OpenTelemetry Compatibility

​Prerequisites

​Installation & Setup

​Example: Single ChatAgent

​Example: Role-Playing Multi-Agent Conversation

​What Gets Captured Automatically

​OpenTelemetry Compatibility

Prerequisites

Installation & Setup

Example: Single ChatAgent

Example: Role-Playing Multi-Agent Conversation

What Gets Captured Automatically

OpenTelemetry Compatibility