Merge branch 'main' into searxng

.husky/pre-commit

@@ -1,2 +1,8 @@
 yarn lint-staged
+
+MD_FILES=$(git diff --cached --name-only | grep -E '^docs/.+\.md$' | tr '\n' ' ')
+if [ -n "$MD_FILES" ]; then
+    yarn docs:links -- $MD_FILES
+fi
+
 CI=true yarn copyright:check && yarn docs:check && yarn lint && yarn format && yarn ts:check && yarn run test:unit

docs/workflows.md

@@ -89,7 +89,7 @@ console.info(`Counter:`, response.result);
 
 _Source: [examples/workflows/nesting.ts](/examples/workflows/nesting.ts)_
 
-### Content Creator Agent
+### Agent Delegation
 
 <!-- embedme examples/workflows/agent.ts -->
 
@@ -180,7 +180,183 @@ for await (const { prompt } of reader) {
 
 _Source: [examples/workflows/agent.ts](/examples/workflows/agent.ts)_
 
-### Multi Agents
+### Multi-agent Content Creator
+
+<!-- embedme examples/workflows/contentCreator.ts -->
+
+```ts
+import "dotenv/config";
+import { z } from "zod";
+import { Workflow } from "bee-agent-framework/experimental/workflows/workflow";
+import { BeeAgent } from "bee-agent-framework/agents/bee/agent";
+import { UnconstrainedMemory } from "bee-agent-framework/memory/unconstrainedMemory";
+import { createConsoleReader } from "examples/helpers/io.js";
+import { BaseMessage } from "bee-agent-framework/llms/primitives/message";
+import { JsonDriver } from "bee-agent-framework/llms/drivers/json";
+import { isEmpty, pick } from "remeda";
+import { LLMTool } from "bee-agent-framework/tools/llm";
+import { GoogleSearchTool } from "bee-agent-framework/tools/search/googleSearch";
+import { GroqChatLLM } from "bee-agent-framework/adapters/groq/chat";
+
+const schema = z.object({
+  input: z.string(),
+  output: z.string().optional(),
+
+  topic: z.string().optional(),
+  notes: z.array(z.string()).default([]),
+  plan: z.string().optional(),
+  draft: z.string().optional(),
+});
+
+const workflow = new Workflow({
+  schema,
+  outputSchema: schema.required({ output: true }),
+})
+  .addStep("preprocess", async (state) => {
+    const llm = new GroqChatLLM();
+    const driver = new JsonDriver(llm);
+
+    const { parsed } = await driver.generate(
+      schema.pick({ topic: true, notes: true }).or(
+        z.object({
+          error: z
+            .string()
+            .describe("Use when the input query does not make sense or you need clarification."),
+        }),
+      ),
+      [
+        BaseMessage.of({
+          role: `user`,
+          text: [
+            "Your task is to rewrite the user query so that it guides the content planner and editor to craft a blog post that perfectly aligns with the user's needs. Notes should be used only if the user complains about something.",
+            "If the user query does ",
+            "",
+            ...[state.topic && ["# Previous Topic", state.topic, ""]],
+            ...[!isEmpty(state.notes) && ["# Previous Notes", ...state.notes, ""]],
+            "# User Query",
+            state.input || "empty",
+          ]
+            .filter(Boolean)
+            .join("\n"),
+        }),
+      ],
+    );
+
+    return "error" in parsed
+      ? { update: { output: parsed.error }, next: Workflow.END }
+      : { update: pick(parsed, ["notes", "topic"]) };
+  })
+  .addStrictStep("planner", schema.required({ topic: true }), async (state) => {
+    const llm = new GroqChatLLM();
+    const agent = new BeeAgent({
+      llm,
+      memory: new UnconstrainedMemory(),
+      tools: [new GoogleSearchTool(), new LLMTool({ llm })],
+    });
+
+    agent.emitter.on("update", (data) => {
+      console.info(data.update);
+    });
+
+    const { result } = await agent.run({
+      prompt: [
+        `You are a Content Planner. Your task is to write a content plan for "${state.topic}" topic in Markdown format.`,
+        ``,
+        `# Objectives`,
+        `1. Prioritize the latest trends, key players, and noteworthy news.`,
+        `2. Identify the target audience, considering their interests and pain points.`,
+        `3. Develop a detailed content outline including introduction, key points, and a call to action.`,
+        `4. Include SEO keywords and relevant sources.`,
+        ``,
+        ...[!isEmpty(state.notes) && ["# Notes", ...state.notes, ""]],
+        `Provide a structured output that covers the mentioned sections.`,
+      ].join("\n"),
+    });
+
+    return {
+      update: {
+        plan: result.text,
+      },
+    };
+  })
+  .addStrictStep("writer", schema.required({ plan: true }), async (state) => {
+    const llm = new GroqChatLLM();
+    const output = await llm.generate([
+      BaseMessage.of({
+        role: `system`,
+        text: [
+          `You are a Content Writer. Your task is to write a compelling blog post based on the provided context.`,
+          ``,
+          `# Context`,
+          `${state.plan}`,
+          ``,
+          `# Objectives`,
+          `- An engaging introduction`,
+          `- Insightful body paragraphs (2-3 per section)`,
+          `- Properly named sections/subtitles`,
+          `- A summarizing conclusion`,
+          `- Format: Markdown`,
+          ``,
+          ...[!isEmpty(state.notes) && ["# Notes", ...state.notes, ""]],
+          `Ensure the content flows naturally, incorporates SEO keywords, and is well-structured.`,
+        ].join("\n"),
+      }),
+    ]);
+
+    return {
+      update: { draft: output.getTextContent() },
+    };
+  })
+  .addStrictStep("editor", schema.required({ draft: true }), async (state) => {
+    const llm = new GroqChatLLM();
+    const output = await llm.generate([
+      BaseMessage.of({
+        role: `system`,
+        text: [
+          `You are an Editor. Your task is to transform the following draft blog post to a final version.`,
+          ``,
+          `# Draft`,
+          `${state.draft}`,
+          ``,
+          `# Objectives`,
+          `- Fix Grammatical errors`,
+          `- Journalistic best practices`,
+          ``,
+          ...[!isEmpty(state.notes) && ["# Notes", ...state.notes, ""]],
+          ``,
+          `IMPORTANT: The final version must not contain any editor's comments.`,
+        ].join("\n"),
+      }),
+    ]);
+
+    return {
+      update: { output: output.getTextContent() },
+    };
+  });
+
+let lastResult = {} as Workflow.output<typeof workflow>;
+const reader = createConsoleReader();
+for await (const { prompt } of reader) {
+  const { result } = await workflow
+    .run({
+      input: prompt,
+      notes: lastResult?.notes,
+      topic: lastResult?.topic,
+    })
+    .observe((emitter) => {
+      emitter.on("start", ({ step, run }) => {
+        reader.write(`-> ▶️ ${step}`, JSON.stringify(run.state).substring(0, 200).concat("..."));
+      });
+    });
+
+  lastResult = result;
+  reader.write("🤖 Answer", lastResult.output);
+}
+```
+
+_Source: [examples/workflows/contentCreator.ts](/examples/workflows/contentCreator.ts)_
+
+### Multi Agents Workflows
 
 <!-- embedme examples/workflows/multiAgents.ts -->
 
@@ -192,27 +368,32 @@ import { OpenMeteoTool } from "bee-agent-framework/tools/weather/openMeteo";
 import { WikipediaTool } from "bee-agent-framework/tools/search/wikipedia";
 import { AgentWorkflow } from "bee-agent-framework/experimental/workflows/agent";
 import { BaseMessage, Role } from "bee-agent-framework/llms/primitives/message";
-import { GroqChatLLM } from "bee-agent-framework/adapters/groq/chat";
+import { WatsonXChatLLM } from "bee-agent-framework/adapters/watsonx/chat";
 
 const workflow = new AgentWorkflow();
+const llm = WatsonXChatLLM.fromPreset("meta-llama/llama-3-3-70b-instruct", {
+  apiKey: process.env.WATSONX_API_KEY,
+  projectId: process.env.WATSONX_PROJECT_ID,
+});
+
 workflow.addAgent({
   name: "WeatherForecaster",
   instructions: "You are a weather assistant. Respond only if you can provide a useful answer.",
   tools: [new OpenMeteoTool()],
-  llm: new GroqChatLLM(),
+  llm,
   execution: { maxIterations: 3 },
 });
 workflow.addAgent({
   name: "Researcher",
   instructions: "You are a researcher assistant. Respond only if you can provide a useful answer.",
   tools: [new WikipediaTool()],
-  llm: new GroqChatLLM(),
+  llm,
 });
 workflow.addAgent({
   name: "Solver",
   instructions:
     "Your task is to provide the most useful final answer based on the assistants' responses which all are relevant. Ignore those where assistant do not know.",
-  llm: new GroqChatLLM(),
+  llm,
 });
 
 const reader = createConsoleReader();
@@ -232,7 +413,10 @@ for await (const { prompt } of reader) {
       reader.write(`-> ${data.step}`, data.response?.update?.finalAnswer ?? "-");
     });
   });
-  await memory.addMany(result.newMessages);
+
+  // await memory.addMany(result.newMessages); // save intermediate steps + final answer
+  await memory.addMany(result.newMessages.slice(-1)); // save only the final answer
+
   reader.write(`Agent 🤖`, result.finalAnswer);
 }
 ```

examples/workflows/multiAgents.ts

@@ -5,27 +5,32 @@ import { OpenMeteoTool } from "bee-agent-framework/tools/weather/openMeteo";
 import { WikipediaTool } from "bee-agent-framework/tools/search/wikipedia";
 import { AgentWorkflow } from "bee-agent-framework/experimental/workflows/agent";
 import { BaseMessage, Role } from "bee-agent-framework/llms/primitives/message";
-import { GroqChatLLM } from "bee-agent-framework/adapters/groq/chat";
+import { WatsonXChatLLM } from "bee-agent-framework/adapters/watsonx/chat";
 
 const workflow = new AgentWorkflow();
+const llm = WatsonXChatLLM.fromPreset("meta-llama/llama-3-3-70b-instruct", {
+  apiKey: process.env.WATSONX_API_KEY,
+  projectId: process.env.WATSONX_PROJECT_ID,
+});
+
 workflow.addAgent({
   name: "WeatherForecaster",
   instructions: "You are a weather assistant. Respond only if you can provide a useful answer.",
   tools: [new OpenMeteoTool()],
-  llm: new GroqChatLLM(),
+  llm,
   execution: { maxIterations: 3 },
 });
 workflow.addAgent({
   name: "Researcher",
   instructions: "You are a researcher assistant. Respond only if you can provide a useful answer.",
   tools: [new WikipediaTool()],
-  llm: new GroqChatLLM(),
+  llm,
 });
 workflow.addAgent({
   name: "Solver",
   instructions:
     "Your task is to provide the most useful final answer based on the assistants' responses which all are relevant. Ignore those where assistant do not know.",
-  llm: new GroqChatLLM(),
+  llm,
 });
 
 const reader = createConsoleReader();
@@ -45,6 +50,9 @@ for await (const { prompt } of reader) {
       reader.write(`-> ${data.step}`, data.response?.update?.finalAnswer ?? "-");
     });
   });
-  await memory.addMany(result.newMessages);
+
+  // await memory.addMany(result.newMessages); // save intermediate steps + final answer
+  await memory.addMany(result.newMessages.slice(-1)); // save only the final answer
+
   reader.write(`Agent 🤖`, result.finalAnswer);
 }

package.json

@@ -48,8 +48,7 @@
   "lint-staged": {
     "{src,tests,scripts}/**/*": "yarn copyright",
     "*": "prettier --ignore-unknown --write",
-    "*.{ts,js}": "eslint --fix",
-    "docs/**/*.md": "yarn docs:links"
+    "*.{ts,js}": "eslint --fix"
   },
   "prettier": {
     "printWidth": 100,