Bosch Service Solutions | Developer API & SDK Operations Guide

🎙️ 1. Real-Time Voice, WebRTC & SIP Telephony APIs

Connect to low-latency conversational voice pipelines, WebRTC media channels, and carrier SIP signaling interfaces.

Pipecat Voice Agent Pipeline (Python SDK)

pipecat-ai / WebRTC / Silero VAD

Configure a real-time conversational agent pipeline combining VAD, Deepgram STT, LiteLLM reasoning, and Cartesia TTS.

import asyncio
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.services.litellm import LiteLLMService
from pipecat.vad.silero import SileroVADAnalyzer

async def main():
    vad = SileroVADAnalyzer()
    llm = LiteLLMService(
        api_base="http://litellm:4000/v1",
        api_key="sk-litellm-master-key-2026",
        model="gemini-3.7"
    )
    pipeline = Pipeline([vad, llm])
    runner = PipelineRunner()
    await runner.run(pipeline)

LiveKit WebRTC Room Token & Stream (TypeScript)

livekit-client / Port: 7880

Generate an in-room participant token and connect the browser audio visualizer to the LiveKit SFU.

import { Room, RoomEvent } from 'livekit-client';

const room = new Room({
  adaptiveStream: true,
  dynacast: true,
});

await room.connect('wss://livekit.velogrid-sandbox.boschservicesolutions.ai', userToken);
room.on(RoomEvent.TrackSubscribed, (track, publication, participant) => {
  if (track.kind === 'audio') {
    track.attach(); // Play remote voice stream
  }
});

⚡ 2. NATS JetStream Event Mesh & Redpanda Streaming

Publish telemetry and listen for real-time agent-to-agent signalling with sub-millisecond wire latency.

NATS JetStream Async Pub/Sub (Python SDK)

nats-py / Port: 4222

Publish vehicle telemetry anomalies and subscribe to multi-agent resolution broadcasts.

import nats
import json

async def run():
    nc = await nats.connect("nats://nats:4222")
    js = nc.jetstream()

    # Publish critical telemetry event
    payload = json.dumps({"vin": "WDB-963-802", "dtc": "P0299"}).encode()
    await js.publish("telemetry.faults.critical", payload)

    # Subscribe to agent responses
    sub = await js.subscribe("agent.dispatch.notifications")
    msg = await sub.next_msg()
    print(f"Received resolution: {msg.data.decode()}")

Redpanda Kafka Ingestion Stream (Python)

confluent-kafka / Port: 9092

High-throughput CAN bus streaming to Redpanda broker without JVM overhead.

from confluent_kafka import Producer
import json

conf = {'bootstrap.servers': 'redpanda:9092', 'client.id': 'vehicle-gateway'}
producer = Producer(conf)

def acked(err, msg):
    if err: print(f"Failed delivery: {err}")

producer.produce(
    'telematics.raw',
    key='WDB-963',
    value=json.dumps({'speed_kmh': 82.5, 'rpm': 2150}),
    callback=acked
)
producer.flush()

🤖 3. LiteLLM AI Gateway & Multi-Agent Swarms

Universal OpenAI-compatible routing across Google Gemini 3.7, Claude 3.5, and local models with Anthropic MCP protocol support.

Chat Completion via LiteLLM (Google Gemini 3.7 Pro)

POST /v1/chat/completions

Standardized OpenAI SDK call routed directly to Google Cloud Vertex AI.

curl -X POST https://litellm.velogrid-sandbox.boschservicesolutions.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-litellm-master-key-2026" \
  -d '{
    "model": "gemini-3.7",
    "messages": [
      {"role": "system", "content": "You are Bosch Mobility Agent."},
      {"role": "user", "content": "Analyze DTC code P0299 on Actros truck."}
    ],
    "temperature": 0.2
  }'

Anthropic MCP Tool Server (SSE & REST)

SSE /tools/schema /sse

Model Context Protocol tool discovery and execution over Server-Sent Events.

# Discover registered MCP tools
curl -s https://api.velogrid-sandbox.boschservicesolutions.ai/tools/schema | jq .

# Connect to live MCP Event Stream
curl -N https://api.velogrid-sandbox.boschservicesolutions.ai/sse

# Execute DuckDB MCP Tool directly
curl -X POST https://api.velogrid-sandbox.boschservicesolutions.ai/query \
  -H "Content-Type: application/json" \
  -d '{"query": "SELECT * FROM '/data/lake/fleet.parquet' LIMIT 5"}'

🏛️ 4. Modern Lakehouse, Hive Metastore & Trino SQL

Federated SQL query execution over Apache Iceberg, Delta Lake, MinIO S3, ClickHouse, and PostgreSQL.

Trino Federated SQL Query (Python SDK)

trino-python-client / Port: 8080

Query across Iceberg S3 Lakehouse and PostgreSQL relational tables in a single unified SQL statement.

from trino.dbapi import connect

conn = connect(
    host='trino',
    port=8080,
    user='admin',
    catalog='iceberg',
    schema='default'
)
cur = conn.cursor()
cur.execute("SELECT t.vin, t.speed_kmh, c.account_name FROM iceberg.default.telematics_events t JOIN postgres.twenty.customer_accounts c ON t.account_id = c.id LIMIT 10")
rows = cur.fetchall()

ClickHouse OLAP & Qdrant Vector Search

ClickHouse: 8123 / Qdrant: 6333

Sub-millisecond analytical aggregations and cosine similarity matching on EU transport regulations.

# Query real-time ClickHouse metrics
curl -X POST https://clickhouse.velogrid-sandbox.boschservicesolutions.ai/ \
  -d "SELECT vin, dtc_fault_code, speed_kmh FROM fleet_analytics.fact_telemetry FORMAT JSON;"

# Qdrant Vector Search for SOP regulations
curl -X POST https://qdrant.velogrid-sandbox.boschservicesolutions.ai/collections/mobility_sops/points/search \
  -H "Content-Type: application/json" \
  -d '{"vector": [0.034, -0.128, 0.452], "limit": 3}'

🔒 5. Cilium eBPF Mesh, OPA Policies & Authentik SSO

Kernel-level flow inspection, Rego policy authorization checks, and OpenID Connect token authentication.

Cilium Hubble eBPF Kernel Flow Stream

hubble observe / Port: 80

Stream real-time TCP socket connections and DNS lookups directly from Linux kernel eBPF probes.

# Stream packet flows between pipecat and livekit
kubectl exec -n kube-system ds/cilium -c cilium-agent -- \
  hubble observe --from-pod ai-grid/pipecat --to-pod ai-grid/livekit

# Inspect HTTP L7 status metrics
hubble observe --http-status 200 --namespace ai-grid -f

Open Policy Agent (OPA) ABAC Decision Query

POST /v1/data/velogrid/authz

Evaluate fine-grained Attribute-Based Access Control and data masking rules in real-time.

curl -X POST https://opa.velogrid-sandbox.boschservicesolutions.ai/v1/data/velogrid/authz \
  -H "Content-Type: application/json" \
  -d '{
    "input": {
      "user": "admin@velogrid.ai",
      "role": "FleetDispatcher",
      "action": "book_parking",
      "resource": "HUB-A3-WUE"
    }
  }'

⏱️ 6. Temporal Distributed Sagas & Workflows

Durable execution engine coordinating multi-step transactions with automatic compensation rollbacks.

Temporal Saga Workflow Definition (Python SDK)

temporalio / Port: 7233

Define stateful workflow activities with guaranteed idempotency and automatic compensation on failure.

from temporalio import workflow, activity
from datetime import timedelta

@workflow.defn
class FleetIncidentSagaWorkflow:
    @workflow.run
    async def run(self, incident_data: dict) -> str:
        # Step 1: Lock Resource in Redis
        lock = await workflow.execute_activity(
            acquire_redis_lock, incident_data,
            start_to_close_timeout=timedelta(seconds=10)
        )
        # Step 2: Create Twenty CRM Task & ERPNext Order
        try:
            await workflow.execute_activity(create_erp_work_order, incident_data)
        except Exception:
            await workflow.execute_activity(release_redis_lock, lock) # Compensation
            raise
        return "SAGA_SUCCESS"

Trigger Distributed Saga via REST / Python

POST /api/testdrive

Execute an end-to-end 19-service testdrive or invoke Temporal directly via the master platform API.

# Execute testdrive saga directly from FastAPI backend
curl -X POST https://api.velogrid-sandbox.boschservicesolutions.ai/api/testdrive \
  -H "Content-Type: application/json" \
  -d '{"vin": "WDB-963-802-12", "fault_code": "P0299"}'

# Check workflow state in Temporal Web UI
https://temporal.velogrid-sandbox.boschservicesolutions.ai

⚡ TrueForge Agent Harness & MCP Integration

TrueForge provides a self-hostable agent harness with built-in MCP client integration, token compaction, and Daytona sandboxed code execution.

Python • TrueForge Agent Session Execution

import requests

TRUEFORGE_URL = "http://trueforge:8080"

# 1. Inspect registered MCP tools
tools = requests.get(f"{TRUEFORGE_URL}/api/v1/mcp/tools").json()
print("Registered MCP Tools:", [t["name"] for t in tools["tools"]])

# 2. Execute agent loop in sandboxed micro-VM with MCP tools
payload = {
    "agent_id": "tf-agent-triage",
    "prompt": "Inspect OBD-II telemetry from DuckDB MCP and reserve Safe Haven parking if turbo pressure < 1.4 bar",
    "sandbox": "python-3.11",
    "mcp_enabled": True
}
response = requests.post(f"{TRUEFORGE_URL}/api/v1/execute", json=payload)
print("Execution Result:", response.json())

💾 LMCache KV Cache Optimization & TTFT Acceleration

LMCache persists and shares KV caches across vLLM workers and LiteLLM proxies across memory tiers (RAM → Redis → MinIO S3).

Python • LMCache Client KV Cache Lookup

import requests

LMCACHE_URL = "http://lmcache:8000"

# 1. Check multi-tier cache memory allocation
status = requests.get(f"{LMCACHE_URL}/v1/cache/status").json()
print(f"LMCache Hit Rate: {status['hit_rate_pct']}% | Mean TTFT: {status['mean_ttft_ms']} ms")
print(f"Tier Allocation: L1={status['tiers']['l1_memory_mb']}MB, L2={status['tiers']['l2_redis_mb']}MB, L3={status['tiers']['l3_s3_mb']}MB")

# 2. Query KV cache for prompt prefix reuse
req = {"prompt": "System instructions: Autonomous roadside triage agent for Mercedes-Benz Actros fleet..."}
lookup = requests.post(f"{LMCACHE_URL}/v1/cache/lookup", json=req).json()
print("KV Cache Hit:", lookup["hit"], "| TTFT Speedup Factor:", lookup["ttft_speedup_factor"])

🦜 Omnigent Meta-Harness & Polly Multi-Agent Orchestrator

Omnigent unifies heterogeneous coding agents and coordinates multi-agent delegation via Polly with live collaborative co-driving.

Python • Omnigent Polly Multi-Agent Delegation

import requests

OMNIGENT_URL = "http://omnigent:8080"

# 1. Inspect registered agent harnesses
registry = requests.get(f"{OMNIGENT_URL}/api/v1/agents/registry").json()
print("Active Agent Harnesses:", [h["id"] for h in registry["harnesses"]])

# 2. Delegate end-to-end incident task across specialized swarms
orchestrate_req = {
    "task": "Orchestrate roadside emergency for vehicle WDB-963-802-12: OBD-II diagnostic, route scout, ERP ticket",
    "spend_cap_usd": 5.0,
    "collaborative": True
}
res = requests.post(f"{OMNIGENT_URL}/api/v1/polly/orchestrate", json=orchestrate_req).json()
print(f"Polly Plan Created: {res['orchestration_id']} with {res['delegated_count']} delegated harnesses.")

API Quickstart, SDK Specs & Protocol Guide