In the past two years, enterprise AI infrastructure has undergone a distinct transformation.
Enterprises no longer lack models.
From OpenAI, Anthropic, Google Gemini to DeepSeek, vLLM, SGLang, and Ollama, model capabilities and inference performance are evolving rapidly. Yet, once enterprises enter real production environments, they begin confronting another set of more pragmatic challenges:
- AI answers "look correct" but cannot prove their basis;
- Different models exhibit vast capability disparities, making business systems increasingly difficult to maintain;
- Enterprise knowledge is scattered across documents, databases, emails, and audio-visual content, unable to coalesce into a unified understanding;
- Inference costs, model routing, data security, and protocol compatibility gradually become new sources of system complexity;
- Enterprises have already adopted AI, yet still cannot truly "trust AI in production."
This is precisely why Yueli KGM Computing is now open-source.
It is an enterprise production-grade AI application framework.
More accurately, it is:
The "knowledge computation and inference orchestration infrastructure layer" for the enterprise AI application era.
What Is Yueli KGM Computing?
An "Inference Orchestration + Compatible Gateway + Knowledge Computation" Middleware for Enterprise AI
Yueli KGM Computing is an open-source, enterprise-grade knowledge computation engine and inference orchestration middleware.
Its core positioning is unequivocal:
Use the determinism of knowledge graphs to constrain the probabilistic nature of large language models.
It doesn't seek to "make models smarter."
Instead, it addresses:
- How to make enterprise AI more trustworthy;
- How to make multi-model systems governable;
- How to truly embed inference capabilities into enterprise business systems;
- How to equip AI infrastructure with observability, replaceability, and auditability.
It can serve as:
- An OpenAI / Anthropic compatible gateway;
- A multi-model routing and scheduling layer;
- An enterprise knowledge graph and GraphRAG engine;
- A privatized AI infrastructure control plane;
- An enterprise AI middleware embedded into existing systems.
It can also:
- Connect to local vLLM / Ollama / SGLang;
- Integrate with OpenAI-compatible cloud services;
- Orchestrate a hybrid of local inference and cloud MaaS;
- Deliver model governance and knowledge augmentation under a unified API gateway and scheduling controller.
Why Does Enterprise AI Need a "Knowledge Computation Layer"?
For many enterprise AI projects today, the real problem is not model performance.
It is this:
Enterprise Knowledge Is Not Entering the Inference Pipeline
The problem with traditional RAG is:
- Retrieval results are merely "similar text";
- They lack relational structures;
- They lack domain ontologies;
- They lack factual boundaries;
- They lack source verifiability.
The result:
The model generates a wrong answer that "looks exactly like the right answer."
In industries such as finance, healthcare, government, manufacturing, new energy, intellectual property, and compliance, such problems are unacceptable.
Therefore, the core capability of Yueli KGM Computing is not simple vector retrieval.
It is:
KGM (Knowledge Generation Modeling)
That is:
An LLM Inference System Constrained by Knowledge Graphs
It will:
- Extract entities and relationships from enterprise documents, databases, audio-visual content, and business systems;
- Construct an enterprise private domain ontology;
- Organize knowledge into a reasonable graph;
- Perform GraphRAG retrieval before inference;
- Inject factual nodes as constraint context into the LLM;
- Output traceable, verifiable results.
This means:
AI is no longer "freestyling."
Instead:
It performs controlled reasoning within the boundaries of enterprise knowledge.
What Does Yueli KGM Computing Actually Deliver?
A Unified Industrial Protocol AI Gateway Layer
Within the same process, KGM simultaneously provides:
- OpenAI Compatible API
- Anthropic Claude Compatible API
Including:
/v1/chat/completions/v1/responses/v1/messages
And automatically completes:
tool_callstool_use
Dual-protocol semantic mapping.
This means:
Enterprise applications only need to connect to a single Base URL.
No matter how the underlying models change, business systems remain agnostic.
Dynamic Inference Orchestration and Model Scheduling
KGM supports:
- Local inference;
- Cloud MaaS;
- Multi-model hybrid scheduling;
- Cost-based scheduling;
- Performance-based scheduling;
- Dynamic routing by task type.
For example:
- Sensitive data → On-premise Ollama;
- Long text → Gemini;
- Highly complex reasoning → Claude;
- High throughput → vLLM;
- Low cost → DeepSeek.
All of this can be accomplished through declarative configuration.
Rather than rewriting a routing layer for every project.
Knowledge Graph-Driven GraphRAG
This is KGM's most central capability.
Compared to traditional vector RAG:
KGM constructs:
- Enterprise domain ontology;
- Relationship graphs;
- Contextual reasoning paths;
- Structured factual constraints.
Therefore, it not only knows:
"Which texts are similar."
It also knows:
"What relationships exist among pieces of knowledge."
This is the critical leap for enterprise AI from "chat tool" to "business system."
Enterprise-Grade Control Plane and Observability
After going live, a significant number of AI projects rapidly descend into an "ungovernable state."
Enterprises find themselves unable to answer:
- Which model is providing the service?
- Which requests are the most costly?
- Which inference node is failing?
- Which API has abnormal latency?
- Which model has a higher hallucination rate?
KGM provides:
- Prometheus Metrics;
- Runtime lifecycle management;
- Circuit breaker mechanisms;
- Structured logging;
- Model asset governance;
- Runtime control plane;
- Multi-tenant isolation;
- Data security policies.
It is not a simple proxy.
It is a genuinely operable AI middleware.
How Do Enterprises Embed Yueli KGM?
Scenario One: Enterprise Knowledge Q&A
The typical path:
Enterprise Documents / Databases / Wikis / Emails
↓
KGM Semantic Parsing
↓
GraphRAG Knowledge Graph
↓
LLM Constrained Inference
↓
Traceable, Trustworthy Answers
R&D teams no longer depend on:
"Who remembers the solution from back then?"
Instead, they directly ask:
- In which version did this issue appear?
- How was it fixed at the time?
- Which systems were affected?
- Who was involved in the decision?
KGM will construct a complete knowledge chain from:
- Git;
- Confluence;
- Emails;
- Meeting records;
- Technical documentation.
Scenario Two: Finance and Compliance Review
The biggest risk with traditional LLMs:
Citing non-existent regulations.
KGM's approach is:
- Build a regulatory knowledge graph;
- Structure regulatory clauses;
- Restrict reasoning within knowledge boundaries;
- Directly trigger a "knowledge gap" alert beyond those boundaries.
This means:
AI no longer "guesses."
It reasons within the enterprise's rule system.
Scenario Three: AI-Native Product Embedding
For engineering teams:
KGM can serve as the underlying AI Runtime.
Including:
- Multi-model scheduling;
- GraphRAG;
- Tool Calling;
- MCP;
- Memory;
- Knowledge Runtime;
- Prompt orchestration;
- Runtime Observability.
Engineering teams no longer need to rebuild:
- Gateways;
- Routing;
- Metrics;
- Tool Runtime;
- Protocol adaptation;
- Multi-model compatibility layers.
Scenario Four: Audio-Visual Semantic Computing
This is a direction often overlooked by enterprises today but is exceptionally high-value.
KGM supports:
- Video caption parsing;
- Semantic label extraction;
- Meeting content knowledge transformation;
- Training video knowledge graphs;
- Audio-visual Q&A.
For example:
An enterprise can directly ask:
"In last quarter's product meetings, what were the disputes regarding pricing strategy?"
The system will automatically locate:
- The corresponding meeting;
- The corresponding individuals;
- The corresponding viewpoints;
- The corresponding timeline.
What Is Its Relationship to LangChain, LlamaIndex, and vLLM?
This is not a competitive relationship.
Rather, it is:
A Layered Relationship
| Layer | Representative Project | Core Responsibility |
|---|---|---|
| Inference | vLLM / SGLang | High-performance inference |
| Application | LangChain / Dify | Agent and Workflow |
| Data | LlamaIndex | Data connection and retrieval |
| Middleware | Yueli KGM | Inference orchestration + Protocol compatibility + Knowledge constraints |
Therefore, the most rational enterprise architecture often is:
- vLLM for inference;
- LangChain for business agents;
- Dify or BotFactory for low-code workflows;
- KGM as the unified AI middleware and knowledge computation layer.
Why MIT Open Source?
The Yueli KGM Computing GitHub Repository and NPM package are open-sourced under the MIT License.
This means:
- Enterprises can use it freely for commercial purposes;
- They can modify it for private deployment;
- They can deeply integrate it;
- They can build their own industry-specific versions.
The true value of Yueli KGM Computing does not lie in closed-source code.
It lies in:
- Enterprise AI infrastructure capability;
- Industry knowledge modeling experience;
- Private deployment delivery capability;
- Knowledge engineering systems;
- Data intelligence and inference architecture practices.
The Next Phase of Enterprise AI Is Shifting from "Model Competition" to "Knowledge Governance"
Over the past two years, the industry has been discussing:
Whose model is stronger.
But in the next five years, the questions enterprises will truly care about will become:
- Who can make AI more trustworthy?
- Who can make AI more stable?
- Who can make AI truly enter business systems?
- Who can equip AI with enterprise-grade governance capabilities?
The significance of Yueli KGM Computing lies precisely here.
It is a crucial middleware layer for enterprise AI transitioning from the experimental stage to production-grade infrastructure.