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Copy file name to clipboardExpand all lines: README.md
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@@ -147,8 +147,8 @@ _Slash commands_, accessed via `/`, run commands to insert additional context in
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_Tools_, accessed via `@`, allow the LLM to function as an agent and carry out actions:
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-`@buffer_editor` - The LLM will edit code in a Neovim buffer by searching and replacing blocks
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-`@code_runner` - The LLM will run code for you in a Docker container
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-`@editor` - The LLM will edit code in a Neovim buffer
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-`@rag` - The LLM will browse and search the internet for real-time information to supplement its response
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> [!TIP]
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As outlined by Andrew Ng in [Agentic Design Patterns Part 3, Tool Use](https://www.deeplearning.ai/the-batch/agentic-design-patterns-part-3-tool-use), LLMs can act as agents by leveraging external tools. Andrew notes some common examples such as web searching or code execution that have obvious benefits when using LLMs.
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In the plugin, agents are simply context that's given to an LLM via a `system` prompt. This gives it knowledge and a defined schema which it can include in its response for the plugin to parse, execute and feedback on. Agents can be added as a participant in a chat buffer by using the `@` key.
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In the plugin, agents are simply context that's given to an LLM via a `system` prompt. This gives it knowledge and a defined schema which it can include in its response for the plugin to parse, execute and feedback on. Agents can be added as a participant to the chat buffer by using the `@` key.
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More information on how agents work and how you can create your own can be found in the [AGENTS](doc/AGENTS.md) guide.
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The plugin fires many events during its lifecycle:
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-`CodeCompanionToolAdded` - Fired when a tool has been added to a chat buffer
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-`CodeCompanionChatClosed` - Fired after a chat has been closed
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-`CodeCompanionChatAdapter` - Fired after the adapter has been set in the chat
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-`CodeCompanionAgentStarted` - Fired when an agent has started using a tool
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