-
@ openoms
2025-04-01 18:30:43
Sharing a useful lookimg answer from perplexity:
What are the best tools to combine various LLMs in a pipeline? Feed the output of one as the input of the other
Here are some of the best tools for combining various LLMs in a pipeline, where the output of one model can be fed as the input to another:
## **1. LangChain**
- LangChain specializes in chaining LLM outputs and inputs through its **Chains** and **Agents**. Chains allow developers to integrate multiple LLMs or components into a pipeline, while Agents enable dynamic decision-making based on user input or intermediate results[3][5].
- Example: Use `SimpleSequentialChain` or `AgentExecutor` to pass outputs between models seamlessly[2][5].
## **2. LM Studio**
- LM Studio supports **model orchestration**, making it ideal for managing workflows involving multiple LLMs. Its customizable API integration allows easy connection between models and services[1].
## **3. Ollama**
- Ollama simplifies workflow orchestration and automates data flow across stages, making it suitable for chaining LLMs for production environments[1].
## **4. MLflow**
- MLflow offers experiment tracking and model versioning, enabling smooth collaboration and monitoring of interactions between multiple LLMs in pipelines[1].
## **5. LlamaIndex**
- LlamaIndex connects LLMs with diverse data sources and supports retrieval-augmented generation (RAG), streamlining complex pipelines that require data-driven LLM interactions[6].
LangChain is particularly popular for its flexibility in chaining outputs and managing complex workflows dynamically.
Citations:
[1] 15 Best LLM Tools for AI Product Development [2025 Guide] https://orq.ai/blog/llm-tools
[2] What's the point of LangChain for chaining LLM outputs if I could just ... https://www.reddit.com/r/LangChain/comments/17rb5zl/whats_the_point_of_langchain_for_chaining_llm/
[3] LangChain for Including AI from LLMs Inside Data Applications https://www.comet.com/site/blog/introduction-to-langchain-for-including-ai-from-large-language-models-llms-inside-data-applications-and-data-pipelines/
[4] Top 9 RAG Tools to Boost Your LLM Workflows https://lakefs.io/rag-tools/
[5] How to Chain Tool Outputs as Inputs in LangChain Tool-Calling Agent https://github.com/langchain-ai/langchain/discussions/28686
[6] 5 Essential Free Tools for Getting Started with LLMs https://www.machinelearningmastery.com/5-essential-free-tools-getting-started-llms/
[7] Top 15 LLMOps Tools for Building AI Applications in 2025 - DataCamp https://www.datacamp.com/blog/llmops-tools
[8] Understanding the Influence of LLM Inputs on Outputs - Shelf.io https://shelf.io/blog/understanding-the-influence-of-llm-inputs-on-outputs/