LangChain framework is used to develop apps that utilize language models. It provides a way to connect large language models with others in a chain actually if we look at LLMs they give general responses, they don’t target any specific field while LangChain has the most powerful attribute it provides to create chains in which users can combine multiple components and make a single coherent application. LangChain has many modules, data connections, chains, agents, memory, and callback.
This article contains the difference between chains and agents in LangChain.
Agents and Chain in LangChain
LangChain has two major modules agents and chains, mostly LangChain follows each of them.
Chains
Chains are a sequence of calls to LLMs or other tools and they can be used for many tasks, like generating text, translation, and replying to queries. Chains are predetermined sequences of calls that cannot be altered after creation.
There are three types of chains:
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- Utility Chain: chains that are usually to extract a specific answer from a large language model for a very narrow purpose utility combine a large language model chain with a specific utility in the LangChain library. You can be used out of the box.
- Generic Chain: A generic chain is used as a building block of other chains and can’t be used out of the box on its own.
- Combine Document Chain: A combined document chain is a chain that takes multiple documents as input and combines them into a single document.
Agents
Agents are entities that can interact with chains. They can decide which tools to call, how to order the calls, and how to interpret the results. Agents can be used to automate tasks, personalize experiences, and make decisions. Agents are dynamic and can make decisions about which tools to call and how to order the calls. This makes agents more flexible and adaptable than chains.
There are two main types of agents:
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- Action Agents: These agents decide actions to take step by step evaluate each step and then execute it and move to the next if we discuss the pseudo-code of the agent which involves a few steps.
- Plan-and-Execute Agents: These agents first decide all the actions which will be executed, and then execute all those actions.
Difference between Chains and Agents
A table of differences between chains and agents is given below:
| Chains | Agents |
| In general, chains are a desirable choice for applications that need to be predictable and repeatable. | Agents are an excellent choice for applications that are supposed to be flexible and adaptable. |
| A chain of LLMs can be used to generate text, translate languages, and answer questions. | An agent can be used to automate and personalize the process of generating text, translating languages, and answering questions. |
| Chains can’t make decisions. | An agent can be used to make decisions about which tools to call and how to order the calls when generating text, translating languages, and answering questions. |
| Less flexible | More flexible |
| Can be more complex | Less complex |
Conclusion
In general, chains are the best choice for applications that need to be predictable and repeatable, while agents are a good choice for applications that need to be flexible and adaptable. This article thoroughly discussed the comparison of agents and chains in LangChain.
