What is LLM orchestration?

LLMOps use orchestration in a broad range of applications such as natural language generation, machine translation, decision-making and chatbots. As organizations adopt artificial intelligence to build these sorts of generative AI (gen AI) applications, efficient LLM orchestration is crucial.

As powerful as an LLM’s foundation model is, LLMs are limited in what they can accomplish on their own. For instance, LLMs lack the ability to retain or learn new information in real-time and struggle to complete multistep problems because they are limited on what they can retain from context.¹ In addition, coordinating numerous LLMs can quickly become complex while wrangling with the different LLM providers’ application programming interfaces (APIs).

LLM orchestration frameworks make up for these limitations by simplifying the complex processes of integrating prompt engineering, API interaction, data retrieval and state management across conversations with language models.²

New LLM orchestration frameworks are being developed and gaining popularity every day. Some LLM orchestrations specialize as configuration or database frameworks while others use AI agents to collaborate to complete tasks or goals.

To understand how LLM orchestration frameworks work, it’s helpful to understand where orchestration lies within the architecture of LLM-driven applications.

The orchestration layer is the backbone of the LLM app stack. The orchestrator creates a coherent workflow by managing the interactions between the other layers of the application architecture.³ Similar to a music orchestrator, the LLM orchestrator delegates and manages the workflow of each technical component based on the application’s composition.

These components include interaction between LLMs, prompt templates, vector databases and agents.⁴ Orchestration ensures that each component of a gen AI application performs cohesively by providing tools and mechanisms to manage the lifecycle of LLMs effectively within various applications and environments.

Orchestration frameworks simplify complex tasks including prompt chaining, interfacing with external APIs, fetching contextual data from vector databases and managing memory across multiple LLM interactions. Here is a general overview of the operational tasks typically used in LLM orchestrations:

Prompt engineering is the practice of structuring LLM inputs (prompts) so that generative AI tools produce optimized outputs. Orchestration frameworks provide prompt templates that include instructions, few-shot examples and specific context, and questions appropriate for a task.⁵

Chaining refers to a sequence of calls that connect multiple LLMs to combine their outputs to achieve more nuanced results (also known as prompt chaining), a tool or a data preprocessing step.⁶

The orchestration layer manages these prompting tasks by storing prompts within a knowledge base or library where it can easily search and retrieve prompting data. The orchestrator can dynamically select prompts from the library based on real-time inputs, context or user preferences. Additionally, it can sequence the prompts in a logical order to manage conversation flows.

LLMs lack the inherent ability to continuously learn and are limited in contextual understanding. By managing prompts, the orchestrator refines the outputs by evaluating responses. 

LLMs also are unable to fact-check themselves, leading to hallucinations if unmanaged. The orchestrator can fact-check responses and ensure they adhere to custom guidelines. If a response falls short, the orchestrator can flag it for human review or make alternative suggestions effectively allowing the LLM to learn and improve.⁷

Most LLM orchestration frameworks include some form of LLMOps for operational monitoring. These features include gathering performance metrics based on LLM benchmark tests. These metrics can be observed via dashboards that allow users to keep up with LLM performance metrics in real-time.

Other LLMOps resources include diagnostic tools for root cause analysis (RCA), reducing the time it takes to debug.

The orchestrator facilitates data access and retrieval from identified sources by using suitable connectors or APIs. Preprocessing refers to converting “raw” data from multiple sources into a format suitable for the LLM. The larger a data collection is, the more sophisticated the data mechanism that analyzes it must be. Preprocessing ensures that the data is adapted to the requirements posed by each data-mining algorithm.⁸ Orchestrators can facilitate preprocessing by adjusting and refining the data to make it more valuable.

The orchestrator starts the LLM to execute its assigned task. Once the processing is complete, the orchestrator receives the model output and integrates any feedback mechanisms to assess its overall quality and delivers it to the appropriate destination.

The orchestrator contains memory stores that act as a knowledge base to improve LLM outputs and interactions and provide contextual understanding. By handling and storing previous messages or inputs, the orchestrator accumulates long-term knowledge that provides more accurate responses based on past interactions.⁹

The orchestrator is responsible for facilitating the implementation of LLM observability features and guard-railing frameworks. From an LLMOps perspective, LLMs running without these capabilities risk outputting misguided results and running security risks based on the limited capabilities of LLMs that aren’t highly tuned.

LLM orchestration frameworks provide the management and optimization needed to streamline LLM interactions and workflows to enhance LLMOps.

• Scalability: Optimal resource utilization by the enablement to scale up or down depending on the demand. 
• Resource management: Frameworks manage resources such as CPU, GPU, memory and storage by allocating resources dynamically based on the workload.
• Workflow automation: Enables automating complex workflows that involve LLMs such as data preprocessing, model training, inference and postprocessing. Streamlining operations reduces manual effort and improves overall efficiency by relinquishing these burdens from developers.
• Load balancing: By distributing requests across multiple LLM instances, frameworks prevent overloading-specific instances and improve overall system reliability and response times.
• Fault tolerance: Most frameworks include mechanisms to detect failures in LLM instances and automatically redirect traffic to healthy instances, minimizing downtime and maintaining service availability.
• Version control and updates: Manage different versions of LLMs and deploy updates without distribution.
• Cost efficiency: Effective orchestration can optimize costs by dynamically allocating resources based on demand. 
• Security and compliance: Centralized control and monitoring across LLM instances ensure adherence to regulatory standards. 
• Integration with other services: Promotes a cohesive ecosystem by supporting integration with other services such as data storage, logging, monitoring and analytics.
• Lowered technical barriers: Allows for implementation with existing teams, no AI experts needed. Tools are being built on top of frameworks for ease of use. For example, LangFlow is a graphical UI (GUI) for LangChain.¹⁰

Application developers have a choice to either adopt the emerging solutions or assemble their own from scratch. Choosing the right LLM orchestration framework requires thoughtful planning and strategy.

Things to consider before choosing an LLM orchestration framework: 

Check the framework’s API documentation and ensure it's helpful and allows developers to easily get started. Also, check out the framework’s community resources to gauge the type of troubleshooting support provided.

Evaluate the cost implications of adopting different frameworks. Many LLM orchestration frameworks are open source with a paid enterprise option. Ensure the pricing model works with not only the initial investment but also ongoing expenses such as licenses, updates and support services. A cost-effective framework offers a balance between price and the features that it provides.

When choosing the right LLM, check for security features such as encryption, access controls and audit logs that provide data security and help protect your data and comply with relevant privacy regulations.

Inquire about monitoring and management tools. These include features for tracking metrics such as response times, accuracy and resource utilization. 

Here are a few known and emerging orchestration frameworks:

IBM watsonx Orchestrate uses natural language processing (NLP) to access a wide range of machine learning skills. IBM’s framework consists of thousands of prebuilt apps and skills including an AI assistant builder and Skills studio. 

Use cases include aiding human resources departments by giving teams the tools needed to onboard and support new hires and boosting procurement and sale teams.

An open source python-based framework for building LLM applications. LangChain is composed of several open source libraries that provide flexible interfacing with core LLM application components such as embedding models, LLMs, vector stores, retrievers and more.¹¹

Common end-to-end use cases of LangChain include Q&A chain and agent over an SQL database, chatbots, extraction, query analysis, summarization, agent simulations, autonomous agents and much more.¹²

Microsoft’s open source multiagent conversation framework offers a high-level abstraction of foundation models. AutoGen is an agentic framework meaning that it uses multiple agents to converse and solve tasks. Its main features include customizable AI agents that engage in multiagent conversations with flexible patterns to build a wide range of LLM-Applications.¹³

Implementations of AutoGen in LLM-driven apps include math tutoring chatbots, conversational chess, decision-making, dynamic group chat and multiagent coding.¹⁴ AutoGen offers monitoring and replay analytics for debugging through AgentOps.¹⁵

LlamaIndex provides the tools to build context-augmented LLM applications. These include data integration tools such as data connecters to process data from over 160 sources and formats.¹⁶ LlamaIndex also includes a suite of modules to evaluate LLM application performance.

LlamaIndex’s many popular use cases include Q&A applications (Retrieval-Augmented-Generation also known as RAG), chatbots, document understanding and data extraction, and fine-tuning models on data to improve performance.¹⁷

Haystack is an open source Python framework built with two primary concepts to build customized end-to-end gen AI systems: components and pipelines. Haystack has partnerships with many LLM providers, vector databases and AI tools that make the tooling to build on top of it comprehensive and flexible.¹⁸

Common use cases offered by haystack include semantic search systems, information extraction and FAQ style question answering.¹⁹

crewAI is an open source multiagent framework built on top of LangChain. Role-playing autonomous AI agents are assembled into crews to complete LLM-application related workflows and tasks.²⁰ crewAI offers an enterprise version called crewAI+. 

Applications for both beginners and more technical users include landing page generation, stock analysis and connecting. crewAI uses AgentOps to provide monitoring and metrics for agents.²¹

LLM orchestration frameworks continue to mature as gen AI applications advance, streamlining LLMOps workflows for more artificial intelligence solutions.

Orchestration frameworks provide the tooling and structure needed for an LLM-application to get the most out of its models. Future frameworks might use AI agents and multiagent systems to facilitate intelligent automation.

Patterns in emerging orchestration frameworks suggest that building more complex architectures, such as multiagent systems capable of integration to implement features, gives agents the skills they need to accomplish autonomous workflows.

Usability is also becoming a priority for orchestration platforms. As the market matures, more tools will be developed that focus on the user experience. This approach also lowers the technical barriers to use these frameworks. Some orchestration frameworks, such as IBM watsonx Orchestrate, leverage a natural language interface for simple engagement and usability.

Managing LLM orchestration is a complex task while orchestration is key to scaling and automating LLM-driven workflows.