feat: example agent using IBM Granite LLM (#213)

Signed-off-by: Graham White <[email protected]>

.env.template

@@ -11,9 +11,6 @@ BEE_FRAMEWORK_LOG_SINGLE_LINE="false"
 # WATSONX_PROJECT_ID=
 # WATSONX_REGION="us-south"
 
-# For BAM LLM Adapter
-# GENAI_API_KEY=
-
 # For OpenAI LLM Adapter
 # OPENAI_API_KEY=
 
@@ -26,6 +23,24 @@ AZURE_OPENAI_ENDPOINT=
 # For Groq LLM Adapter
 # GROQ_API_KEY=
 
+# For IBM BAM LLM Adapter
+# GENAI_API_KEY=
+
+# For IBM VLLM LLM Adapter
+# IBM_VLLM_URL=
+# IBM_VLLM_ROOT_CERT=
+# IBM_VLLM_CERT_CHAIN=
+# IBM_VLLM_PRIVATE_KEY=
+
+# For IBM RITS LLM Adapter
+# IBM_RITS_URL=
+# IBM_RITS_API_KEY=
+# IBM_RITS_MODEL=ibm-granite/granite-3.0-8b-instruct
+
+# LLM Provider, used for some of the example agents
+# (watsonx/ollama/openai/groq/bam/ibmvllm/ibmrits)
+# LLM_BACKEND=ollama
+
 # For GCP VertexAI Adapter
 # GOOGLE_APPLICATION_CREDENTIALS=
 # GCP_VERTEXAI_PROJECT=

examples/README.md

@@ -21,6 +21,7 @@ This repository contains examples demonstrating the usage of the Bee Agent Frame
 - [`bee_advanced.ts`](agents/bee_advanced.ts): Advanced Bee Agent with custom configurations
 - [`bee_reusable.ts`](agents/bee_reusable.ts): Demonstration of serializing and reusing Bee Agents
 - [`custom_agent.ts`](agents/custom_agent.ts): Example of creating a custom agent
+- [`ibm_granite_bee.ts`](agents/ibm_granite_bee.ts): Basic Bee Agent using an IBM Granite LLM
 - [`simple.ts`](agents/simple.ts): Simple agent implementation
 - [`sql.ts`](agents/sql.ts): Agent for SQL-related tasks
 

examples/agents/ibm_granite_bee.ts

@@ -0,0 +1,116 @@
+import "dotenv/config.js";
+import { BeeAgent } from "bee-agent-framework/agents/bee/agent";
+import { ChatLLM, ChatLLMOutput } from "bee-agent-framework/llms/chat";
+import { getEnv, parseEnv } from "bee-agent-framework/internals/env";
+import { FrameworkError } from "bee-agent-framework/errors";
+import { TokenMemory } from "bee-agent-framework/memory/tokenMemory";
+import { WatsonXChatLLM } from "bee-agent-framework/adapters/watsonx/chat";
+import { OpenAIChatLLM } from "bee-agent-framework/adapters/openai/chat";
+import { OllamaChatLLM } from "bee-agent-framework/adapters/ollama/chat";
+import { IBMVllmChatLLM } from "bee-agent-framework/adapters/ibm-vllm/chat";
+import { IBMVllmModel } from "bee-agent-framework/adapters/ibm-vllm/chatPreset";
+import { OpenMeteoTool } from "bee-agent-framework/tools/weather/openMeteo";
+import { DuckDuckGoSearchTool } from "bee-agent-framework/tools/search/duckDuckGoSearch";
+import { Ollama } from "ollama";
+import OpenAI from "openai";
+import { z } from "zod";
+import * as process from "node:process";
+import fs from "node:fs";
+
+const Providers = {
+  WATSONX: "watsonx",
+  OLLAMA: "ollama",
+  IBMVLLM: "ibmvllm",
+  IBMRITS: "ibmrits",
+} as const;
+type Provider = (typeof Providers)[keyof typeof Providers];
+
+function getChatLLM(provider?: Provider): ChatLLM<ChatLLMOutput> {
+  const LLMFactories: Record<Provider, () => ChatLLM<ChatLLMOutput>> = {
+    [Providers.OLLAMA]: () =>
+      new OllamaChatLLM({
+        modelId: getEnv("OLLAMA_MODEL") || "granite3-dense:8b",
+        parameters: {
+          temperature: 0,
+          repeat_penalty: 1,
+          num_predict: 2000,
+        },
+        client: new Ollama({
+          host: getEnv("OLLAMA_HOST"),
+        }),
+      }),
+    [Providers.WATSONX]: () =>
+      WatsonXChatLLM.fromPreset(getEnv("WATSONX_MODEL") || "ibm/granite-3-8b-instruct", {
+        apiKey: getEnv("WATSONX_API_KEY"),
+        projectId: getEnv("WATSONX_PROJECT_ID"),
+        region: getEnv("WATSONX_REGION"),
+      }),
+    [Providers.IBMVLLM]: () => IBMVllmChatLLM.fromPreset(IBMVllmModel.GRANITE_INSTRUCT),
+    [Providers.IBMRITS]: () =>
+      new OpenAIChatLLM({
+        client: new OpenAI({
+          baseURL: process.env.IBM_RITS_URL,
+          apiKey: process.env.IBM_RITS_API_KEY,
+          defaultHeaders: {
+            RITS_API_KEY: process.env.IBM_RITS_API_KEY,
+          },
+        }),
+        modelId: getEnv("IBM_RITS_MODEL") || "ibm-granite/granite-3.0-8b-instruct",
+        parameters: {
+          temperature: 0,
+          max_tokens: 2048,
+        },
+      }),
+  };
+
+  if (!provider) {
+    provider = parseEnv("LLM_BACKEND", z.nativeEnum(Providers), Providers.OLLAMA);
+  }
+
+  const factory = LLMFactories[provider];
+  if (!factory) {
+    throw new Error(`Provider "${provider}" not found.`);
+  }
+  return factory();
+}
+
+function getPrompt(fallback: string) {
+  if (process.stdin.isTTY) {
+    return fallback;
+  }
+  return fs.readFileSync(process.stdin.fd).toString().trim() || fallback;
+}
+
+const llm = getChatLLM();
+const agent = new BeeAgent({
+  llm,
+  memory: new TokenMemory({ llm }),
+  tools: [new OpenMeteoTool(), new DuckDuckGoSearchTool({ maxResults: 3 })],
+});
+
+try {
+  const prompt = getPrompt(`What is the current weather in London?`);
+  console.info(`User 👤 : ${prompt}`);
+
+  const response = await agent
+    .run(
+      { prompt },
+      {
+        execution: {
+          maxIterations: 8,
+          maxRetriesPerStep: 3,
+          totalMaxRetries: 0,
+        },
+      },
+    )
+    .observe((emitter) => {
+      emitter.on("update", (data) => {
+        console.info(`Agent 🤖 (${data.update.key}) : ${data.update.value}`);
+      });
+    });
+  console.info(`Agent 🤖 : ${response.result.text}`);
+} catch (error) {
+  console.error(FrameworkError.ensure(error).dump());
+} finally {
+  process.exit(0);
+}