add base agent (langchain-ai#578)

* add base agent

* cr

* cr

* cr

* cr

* cr

* cr

* cr

* Fix allowedTools

* Rename existing example

---------

Co-authored-by: Nuno Campos <[email protected]>

docs/docs/modules/agents/agents/custom_llm.mdx

@@ -0,0 +1,13 @@
+---
+hide_table_of_contents: true
+sidebar_position: 1
+---
+
+import CodeBlock from "@theme/CodeBlock";
+import Example from "@examples/agents/custom_llm_agent.ts";
+
+# Custom LLM Agent
+
+This example covers how to create a custom Agent powered by an LLM.
+
+<CodeBlock language="typescript">{Example}</CodeBlock>

docs/docs/modules/agents/agents/custom_llm_chat.mdx

@@ -0,0 +1,13 @@
+---
+hide_table_of_contents: true
+sidebar_position: 1
+---
+
+import CodeBlock from "@theme/CodeBlock";
+import Example from "@examples/agents/custom_llm_agent_chat.ts";
+
+# Custom LLM Agent (with Chat Model)
+
+This example covers how to create a custom Agent powered by a Chat Model.
+
+<CodeBlock language="typescript">{Example}</CodeBlock>

docs/docs/modules/agents/agents/examples/custom_agent_chat.mdx

@@ -5,7 +5,7 @@ hide_table_of_contents: true
 import CodeBlock from "@theme/CodeBlock";
 import Example from "@examples/chat/agent.ts";
 
-# Custom Agent, using Chat Models
+# Agent with Custom Prompt, using Chat Models
 
 This example covers how to create a custom agent for a chat model. It will utilize chat specific prompts.
 

examples/src/agents/concurrent_mrkl.ts

@@ -1,5 +1,5 @@
 import { OpenAI } from "langchain";
-import { initializeAgentExecutor } from "langchain/agents";
+import { initializeAgentExecutor, Agent } from "langchain/agents";
 import { SerpAPI, Calculator } from "langchain/tools";
 import process from "process";
 import {
@@ -57,7 +57,8 @@ export const run = async () => {
       true,
       callbackManager
     );
-    executor.agent.llmChain.callbackManager = callbackManager;
+    const agent = executor.agent as Agent;
+    agent.llmChain.callbackManager = callbackManager;
     executors.push(executor);
   }
 

examples/src/agents/custom_llm_agent.ts

@@ -0,0 +1,137 @@
+import {
+  LLMSingleActionAgent,
+  AgentActionOutputParser,
+  AgentExecutor,
+} from "langchain/agents";
+import { LLMChain } from "langchain/chains";
+import { OpenAI } from "langchain/llms";
+import {
+  BasePromptTemplate,
+  BaseStringPromptTemplate,
+  SerializedBasePromptTemplate,
+  renderTemplate,
+} from "langchain/prompts";
+import {
+  InputValues,
+  PartialValues,
+  AgentStep,
+  AgentAction,
+  AgentFinish,
+} from "langchain/schema";
+import { SerpAPI, Calculator, Tool } from "langchain/tools";
+
+const PREFIX = `Answer the following questions as best you can. You have access to the following tools:`;
+const formatInstructions = (toolNames: string) => `Use the following format:
+
+Question: the input question you must answer
+Thought: you should always think about what to do
+Action: the action to take, should be one of [${toolNames}]
+Action Input: the input to the action
+Observation: the result of the action
+... (this Thought/Action/Action Input/Observation can repeat N times)
+Thought: I now know the final answer
+Final Answer: the final answer to the original input question`;
+const SUFFIX = `Begin!
+
+Question: {input}
+Thought:{agent_scratchpad}`;
+
+class CustomPromptTemplate extends BaseStringPromptTemplate {
+  tools: Tool[];
+
+  constructor(args: { tools: Tool[]; inputVariables: string[] }) {
+    super({ inputVariables: args.inputVariables });
+    this.tools = args.tools;
+  }
+
+  _getPromptType(): string {
+    throw new Error("Not implemented");
+  }
+
+  format(input: InputValues): Promise<string> {
+    /** Construct the final template */
+    const toolStrings = this.tools
+      .map((tool) => `${tool.name}: ${tool.description}`)
+      .join("\n");
+    const toolNames = this.tools.map((tool) => tool.name).join("\n");
+    const instructions = formatInstructions(toolNames);
+    const template = [PREFIX, toolStrings, instructions, SUFFIX].join("\n\n");
+    /** Construct the agent_scratchpad */
+    const intermediateSteps = input.intermediate_steps as AgentStep[];
+    const agentScratchpad = intermediateSteps.reduce(
+      (thoughts, { action, observation }) =>
+        thoughts +
+        [action.log, `\nObservation: ${observation}`, "Thought:"].join("\n"),
+      ""
+    );
+    const newInput = { agent_scratchpad: agentScratchpad, ...input };
+    /** Format the template. */
+    return Promise.resolve(renderTemplate(template, "f-string", newInput));
+  }
+
+  partial(_values: PartialValues): Promise<BasePromptTemplate> {
+    throw new Error("Not implemented");
+  }
+
+  serialize(): SerializedBasePromptTemplate {
+    throw new Error("Not implemented");
+  }
+}
+
+class CustomOutputParser extends AgentActionOutputParser {
+  async parse(text: string): Promise<AgentAction | AgentFinish> {
+    if (text.includes("Final Answer:")) {
+      const parts = text.split("Final Answer:");
+      const input = parts[parts.length - 1].trim();
+      const finalAnswers = { output: input };
+      return { log: text, returnValues: finalAnswers };
+    }
+
+    const match = /Action: (.*)\nAction Input: (.*)/s.exec(text);
+    if (!match) {
+      throw new Error(`Could not parse LLM output: ${text}`);
+    }
+
+    return {
+      tool: match[1].trim(),
+      toolInput: match[2].trim().replace(/^"+|"+$/g, ""),
+      log: text,
+    };
+  }
+
+  getFormatInstructions(): string {
+    throw new Error("Not implemented");
+  }
+}
+
+export const run = async () => {
+  const model = new OpenAI({ temperature: 0 });
+  const tools = [new SerpAPI(), new Calculator()];
+
+  const llmChain = new LLMChain({
+    prompt: new CustomPromptTemplate({
+      tools,
+      inputVariables: ["input", "agent_scratchpad"],
+    }),
+    llm: model,
+  });
+
+  const agent = new LLMSingleActionAgent({
+    llmChain,
+    outputParser: new CustomOutputParser(),
+    stop: ["\nObservation"],
+  });
+  const executor = new AgentExecutor({
+    agent,
+    tools,
+  });
+  console.log("Loaded agent.");
+
+  const input = `Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?`;
+
+  console.log(`Executing with input "${input}"...`);
+
+  const result = await executor.call({ input });
+
+  console.log(`Got output ${result.output}`);
+};