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effective agents
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Xi Yan
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docs/notebooks/Llama_Stack_Agent_Workflows.ipynb
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docs/notebooks/Llama_Stack_Agent_Workflows.ipynb
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Llama Stack Agent Workflows\n",
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"\n",
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"This notebook contains minimal implementations of common agent workflows discussed in the blog post [Building Effective Agent Workflows](https://www.anthropic.com/research/building-effective-agents). Examples are copied from [anthropic-cookbook](https://github.com/anthropics/anthropic-cookbook/tree/main/patterns/agents). "
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"MODEL_ID = \"meta-llama/Llama-3.3-70B-Instruct\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"from llama_stack_client import LlamaStackClient\n",
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"from llama_stack_client.types.agent_create_params import AgentConfig\n",
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"from llama_stack_client.lib.agents.react.agent import ReActAgent\n",
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"from llama_stack_client.lib.agents.agent import Agent\n",
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"from rich.pretty import pprint\n",
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"import json\n",
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"import uuid\n",
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"\n",
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"client = LlamaStackClient(base_url=\"http://localhost:8321\")"
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]
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},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1. Basic Multi-LLM Workflow\n",
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"\n",
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"1. **Prompt Chaining**: Decomposes a task into sequential subtasks, where each step builds on previous results. \n",
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"\n",
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"2. **Parallelization**: Distributes independent subtasks acorss multiple LLMs for concurrent processing. \n",
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"\n",
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"3. **Routing**: Dynamically selects specialized LLM paths based on input characteristics. "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 1.1 Prompt Chaining"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"========= Turn: 0 =========\n",
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"92: customer satisfaction score\n",
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"45%: revenue growth\n",
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"23%: market share\n",
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"5%: customer churn\n",
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"43: new user acquisition cost\n",
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"78%: product adoption rate\n",
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"87: employee satisfaction\n",
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"34%: operating margin\n",
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"8%: customer churn (changed to reflect the original value for comparison)\n",
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"\n",
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"\n",
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"========= Turn: 1 =========\n",
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"92%: customer satisfaction\n",
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"45%: revenue growth\n",
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"23%: market share\n",
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"5%: customer churn\n",
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"0.043: new user acquisition cost\n",
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"78%: product adoption rate\n",
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"87%: employee satisfaction\n",
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"34%: operating margin\n",
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"8%: previous customer churn\n",
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"\n",
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"\n",
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"========= Turn: 2 =========\n",
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"92%: customer satisfaction\n",
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"87%: employee satisfaction\n",
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"78%: product adoption rate\n",
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"45%: revenue growth\n",
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"34%: operating margin\n",
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"23%: market share\n",
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"8%: previous customer churn\n",
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"5%: customer churn\n",
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"0.043: new user acquisition cost\n",
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"\n",
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"\n",
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"========= Turn: 3 =========\n",
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"| Metric | Value |\n",
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"|:--|--:|\n",
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"| Customer Satisfaction | 92% |\n",
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"| Employee Satisfaction | 87% |\n",
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"| Product Adoption Rate | 78% |\n",
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"| Revenue Growth | 45% |\n",
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"| Operating Margin | 34% |\n",
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"| Market Share | 23% |\n",
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"| Previous Customer Churn | 8% |\n",
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"| Customer Churn | 5% |\n",
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"| New User Acquisition Cost | 0.043 |\n",
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"\n",
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"\n"
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]
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}
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],
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"source": [
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"vanilla_agent_config = AgentConfig(\n",
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" model=MODEL_ID,\n",
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" instructions=\"\"\"\n",
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" You are a helpful assistant capable of structuring data extraction and formatting. \n",
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"\n",
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" You will be given tasks to extract and format data from a performance report. Here is the report:\n",
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"\n",
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" Q3 Performance Summary:\n",
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" Our customer satisfaction score rose to 92 points this quarter.\n",
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" Revenue grew by 45% compared to last year.\n",
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" Market share is now at 23% in our primary market.\n",
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" Customer churn decreased to 5% from 8%.\n",
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" New user acquisition cost is $43 per user.\n",
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" Product adoption rate increased to 78%.\n",
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" Employee satisfaction is at 87 points.\n",
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" Operating margin improved to 34%.\n",
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" \"\"\",\n",
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" sampling_params={\n",
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" \"strategy\": {\"type\": \"top_p\", \"temperature\": 1.0, \"top_p\": 0.9},\n",
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" },\n",
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" toolgroups=[],\n",
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" tool_config={\n",
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" \"tool_choice\": \"auto\",\n",
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" \"tool_prompt_format\": \"python_list\",\n",
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" },\n",
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" input_shields=[],\n",
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" output_shields=[],\n",
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" enable_session_persistence=False,\n",
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")\n",
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"\n",
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"vanilla_agent = Agent(client, vanilla_agent_config)\n",
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"\n",
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"session_id_1 = vanilla_agent.create_session(session_name=f\"vanilla_agent_{uuid.uuid4()}\")\n",
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"\n",
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"prompts = [\n",
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" \"\"\"Extract only the numerical values and their associated metrics from the text.\n",
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" Format each as 'value: metric' on a new line.\n",
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" Example format:\n",
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" 92: customer satisfaction\n",
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" 45%: revenue growth\"\"\",\n",
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"\n",
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" \"\"\"Convert all numerical values to percentages where possible.\n",
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" If not a percentage or points, convert to decimal (e.g., 92 points -> 92%).\n",
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" Keep one number per line.\n",
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" Example format:\n",
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" 92%: customer satisfaction\n",
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" 45%: revenue growth\"\"\",\n",
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"\n",
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" \"\"\"Sort all lines in descending order by numerical value.\n",
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" Keep the format 'value: metric' on each line.\n",
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" Example:\n",
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" 92%: customer satisfaction\n",
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" 87%: employee satisfaction\"\"\",\n",
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"\n",
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" \"\"\"Format the sorted data as a markdown table with columns:\n",
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" | Metric | Value |\n",
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" |:--|--:|\n",
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" | Customer Satisfaction | 92% |\"\"\",\n",
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"]\n",
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"\n",
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"for i, prompt in enumerate(prompts): \n",
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" response = vanilla_agent.create_turn(\n",
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" messages=[\n",
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" {\n",
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" \"role\": \"user\",\n",
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" \"content\": prompt,\n",
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" }\n",
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" ],\n",
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" session_id=session_id_1,\n",
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" stream=False,\n",
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" )\n",
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" print(\"========= Turn: \", i, \"=========\")\n",
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" print(response.output_message.content)\n",
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" print(\"\\n\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "master",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.16"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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