> For the complete documentation index, see [llms.txt](https://knowly-ai.gitbook.io/langtorch/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://knowly-ai.gitbook.io/langtorch/capability.md). # 💪 Capability The only limit to our realization of tomorrow will be our doubts of today; let us move forward with strong and active faith, believing in our capability to achieve the impossible. - Franklin Roosevelt In langtorch, we define a concept called "Capability": the ability to do something. A capability consists of several "Capability units" combined together. In order to make each capability adapt to each other, we create an interface called "**Node**" that contains a capability unit and some context information. Combining different nodes together will make a capability DAG(Directed acyclic graph). ### Capability Unit A capability unit represents a single task in langtorch. It could be a large language model (LLM) with a pre-defined prompt or a custom function. In real-life scenarios, a capability unit is roughly equivalent to a task, such as performing a Q\&A. ### Capability Wrapper The wrapper is a specialized `CapabilityUnit` designed for handling different cases **Example**: `ConversationLLMWrapperUnit`wraps `LLMCapabilityUnit` and adds conversation context and memory management. It takes a `UserMessage` as input and produces an `AssistantMessage` as output. ### **Capability Graph or DAG** A capability graph is a combination of multiple capability nodes. ### **Capability Node:** The `Node` interface represents a node in the `CapabilityDAG`. Each node has a unique ID, a list of out-degree nodes, and a `process` method that takes an iterable of inputs and generates an output. A node can be a simple processing step or a more complex capability unit. ### --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://knowly-ai.gitbook.io/langtorch/capability.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.