🔌 Ecosystem Integrations

Turing Engine integrates with agent frameworks, model routers, and deployment runtimes.

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1. LangChain & LangGraph

Turing Engine provides native drop-in support via langchain-openai or built-in ChatTuring:

Method A: Drop-In langchain-openai (Zero Extra Dependencies)Method B: Native ChatTuring (Subspace Controls)

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    base_url="http://localhost:8000/v1",
    api_key="turing-local",
    model="deepseek-r1-7b"
)
response = llm.invoke("Explain quantum computing in simple terms:")
print(response.content)

from turing.integrations.langchain import ChatTuring

llm = ChatTuring(
    model="deepseek-r1-7b",
    base_url="http://localhost:8000/v1",
    sparsity_ratio=0.57, # 57% FFN channel pruning
    svd_rank=64          # Rank-64 SVD INT8 KV cache
)
print(llm.invoke("Write a quick C++ SIMD routine:")["content"])

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2. LiteLLM Proxy & Router

Configure Turing Engine as a backend in your litellm config.yaml:

model_list:
  - model_name: turing/deepseek-r1
    litellm_params:
      model: openai/deepseek-r1-7b
      api_base: http://localhost:8000/v1
      api_key: turing-local
      extra_headers:
        X-Turing-Sparsity: "0.57"
        X-Turing-SVD-Rank: "64"

---

3. LlamaIndex RAG & Agents

Method A: Native Turing LLM (llama-index-llms-turing or Built-In)Method B: Drop-In OpenAILike (Zero Extra Dependencies)

from turing.integrations.llamaindex import Turing

llm = Turing(
    model="deepseek-r1-7b",
    api_base="http://localhost:8000/v1",
    sparsity_ratio=0.57,  # 57% FFN channel pruning
    svd_rank=64           # Calibrated SVD INT8 KV cache
)
response = llm.complete("Summarize the main architectural trade-offs of microservices:")
print(response.text)

from llama_index.llms.openai_like import OpenAILike

llm = OpenAILike(
    model="deepseek-r1-7b",
    api_base="http://localhost:8000/v1",
    api_key="turing-local",
    is_chat_model=True
)
response = llm.complete("Summarize the main architectural trade-offs of microservices:")
print(response.text)

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4. Open WebUI & LibreChat

Connect your local web UI directly to Turing Engine: 1. Open Open WebUI Settings ➔ Connections ➔ OpenAI API. 2. Set URL: http://localhost:8000/v1 3. Set API Key: turing-local 4. Models like deepseek-r1-7b and glm-5.3-flash will automatically appear in your model dropdown.

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5. Kubernetes Deployment (KServe, Helm & llm-d)

Turing Engine provides production manifests for Kubernetes orchestration:

• ☸️ Full llm-d & Kubernetes Distributed Serving Guide — Prefix-cache aware routing, InferencePools, and P/D disaggregation.
• 📦 Turing Serving Helm Chart — Configurable deployment with Prometheus metrics and GPU limits.
• 🚀 KServe ServingRuntime:

apiVersion: serving.kserve.io/v1alpha1
kind: ServingRuntime
metadata:
  name: turing-runtime
  labels:
    llm-d.ai/engine-type: "turing"
spec:
  supportedModelFormats:
    - name: turing-subspace
      version: "1"
      autoSelect: true
    - name: huggingface
      version: "1"
      autoSelect: false
  containers:
    - name: kserve-container
      image: ghcr.io/intutic/turing:v0.3.3-cuda

      command: ["python3", "-m", "turing.cli", "serve"]




      args: ["--model", "$(MODEL_NAME)", "--port", "8080", "--device", "cuda"]
      resources:
        limits:
          nvidia.com/gpu: "1"
          memory: 32Gi
          cpu: "8"
        requests:
          nvidia.com/gpu: "1"
          memory: 24Gi
          cpu: "4"