```
### Containerized Execution
Run the target agent as a Docker application. Securely mount the BlueRock socket into the container.
$ docker run -d --name <Container_Name> \
--rm \
-e <API_Key_Variable>=<api_key> \
-v /run/bluerock:/run/bluerock \
<Image_Name>
{% hint style="info" icon="notes-sticky" %}
**Note:**
`` can be any arbitrary name assigned to the container. `` must match the exact tag assigned when building or pulling the Docker image. Run `docker images` in the terminal to list all locally available images and corresponding tags.
{% endhint %}
## Sensor Configuration and Telemetry Events
The sensors are governed by the `bru_policy.json` configuration file, located in the `/opt/bluerock/trex/` directory on a BlueRock instance or VM. The policy defines separate configuration blocks to activate interception hooks for fundamental LLM calls versus higher-level agentic orchestration.
Depending on the active ecosystem, the sensor automatically captures distinct execution and lifecycle events.
### **LLM Sensors**
The `llm` block controls the tracing of direct interactions with foundational models. Activating specific vendor flags within this block allows the sensor to capture raw generation requests, tokens, and response latencies.
{% code title="bru\_policy.json" lineNumbers="true" %}
```json
"llm": {
"enable": true,
"gemini": true,
"openai": true,
"anthropic": true,
"litellm": true
}
```
{% endcode %}
### **Agentic Sensors**
The Agentic configuration blocks (such as `crewai` and `langchain`) hook into multi-step reasoning, tool execution, memory retrieval, and agent delegation. These are enabled independently of the base LLM sensors.
{% code title="bru\_policy.json" lineNumbers="true" %}
```json
"crewai": {
"enable": true
},
"langchain": {
"enable": true
}
```
{% endcode %}
### Standardized Sample Events by Sensor Type
#### LLM Sensors
* OpenAI
```json
{
"body": {
"context": { "process": { "pid": "" } },
"entity_id": "",
"event": "chat_completion_start",
"model": "gpt-4o",
"messages": [
{ "role": "user", "content": "Why is the sky blue?" }
],
"run_id": ""
},
"severity_number": 0,
"severity_text": "INFO",
"attributes": {
"domain": "gyro",
"event_name": "python_openai_event",
"origin": "bluepython",
"sensor_id": 0000
},
"scope": { "name": "bluerockd" },
"resource": { "service.name": "bluerock" }
}
```
* Anthropic
```json
{
"body": {
"context": { "process": { "pid": "" } },
"entity_id": "",
"event": "message_create_start",
"model": "claude-3-5-sonnet-20241022",
"messages": [
{ "role": "user", "content": "Hello, Claude. Why is the sky blue?" }
],
"max_tokens": 1024,
"run_id": ""
},
"severity_number": 0,
"severity_text": "INFO",
"attributes": {
"domain": "gyro",
"event_name": "python_anthropic_event",
"origin": "bluepython",
"sensor_id": 0000
},
"scope": { "name": "bluerockd" },
"resource": { "service.name": "bluerock" }
}
```
* LiteLLM
```json
{
"body": {
"context": { "process": { "pid": "" } },
"entity_id": "",
"event": "completion_start",
"model": "claude-sonnet-4-5-20250929",
"messages": [
{ "role": "user", "content": "Hello, what is a rainbow?" }
],
"run_id": ""
},
"severity_number": 0,
"severity_text": "INFO",
"attributes": {
"domain": "gyro",
"event_name": "python_litellm_event",
"origin": "bluepython",
"sensor_id": 0000
},
"scope": { "name": "bluerockd" },
"resource": { "service.name": "bluerock" }
}
```
#### Agentic Sensors
* LangChain
```json
{
"body": {
"chain_type": "AgentExecutor",
"context": { "process": { "pid": "" } },
"entity_id": "",
"event": "chain_start",
"inputs": {
"question": "What files do I have in the folder?"
},
"run_id": ""
},
"severity_number": 0,
"severity_text": "INFO",
"attributes": {
"domain": "gyro",
"event_name": "python_langchain_event",
"origin": "bluepython",
"sensor_id": 0000
},
"scope": { "name": "bluerockd" },
"resource": { "service.name": "bluerock" }
}
```
* CrewAI
```json
{
"body": {
"context": { "process": { "pid": "" } },
"entity_id": "",
"event": "crew_kickoff_started",
"crew_name": "AssistantCrew",
"tasks": ["task_one"],
"agents": ["Helpful assistant"],
"run_id": ""
},
"severity_number": 0,
"severity_text": "INFO",
"attributes": {
"domain": "gyro",
"event_name": "python_crewai_event",
"origin": "bluepython",
"sensor_id": 0000
},
"scope": { "name": "bluerockd" },
"resource": { "service.name": "bluerock" }
}
```
* Google Gemini
```json
{
"body": {
"context": { "process": { "pid": "" } },
"entity_id": "",
"event": "agent_run_start",
"agent_name": "search_agent",
"model": "gemini-2.5-flash",
"query": "why is the sky blue?",
"run_id": ""
},
"severity_number": 0,
"severity_text": "INFO",
"attributes": {
"domain": "gyro",
"event_name": "python_google_adk_event",
"origin": "bluepython",
"sensor_id": 0000
},
"scope": { "name": "bluerockd" },
"resource": { "service.name": "bluerock" }
}
```
---
# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.
## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:
```
GET https://docs.bluerock.io/agent-frameworks-integration/agent-frameworks-integration.md?ask=&goal=
```
`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.
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Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
