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docs/zero_to_hero_guide/email_agent/README.md
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docs/zero_to_hero_guide/email_agent/README.md
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@ -0,0 +1,167 @@
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# A Llama and Llama Stack Powered Email Agent
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This is a Llama Stack port of the [Llama Powered Email Agent](https://github.com/meta-llama/llama-recipes/tree/gmagent/recipes/use_cases/email_agent) app that shows how to build an email agent app powered by Llama 3.1 8B and Llama Stack, using Llama Stack custom tool and agent APIs.
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Currently implemented features of the agent include:
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* search for emails and attachments
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* get email detail
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* reply to a specific email
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* forward an email
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* get summary of a PDF attachment
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* draft and send an email
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We'll mainly cover here how to port a Llama app using native custom tools supported in Llama 3.1 (and later) and an agent implementation from scratch to using Llama Stack APIs. See the link above for a comprehensive overview, definition, and resources of LLM agents.
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# Setup and Installation
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See the link above for Enable Gmail API and Install Ollama with Llama 3.1 8B.
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## Install required packages
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First, create a Conda or virtual env, then activate it and install the required Python libraries (slightly different from the original app because here we'll also install the `llama-stack-client` package):
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```
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git clone https://github.com/meta-llama/llama-stack
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cd llama-stack/docs/zero_to_hero_guide/email_agent
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pip install -r requirements.txt
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```
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# Run Email Agent
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The steps are also the same as the [original app]((https://github.com/meta-llama/llama-recipes/tree/gmagent/recipes/use_cases/email_agent).
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# Implementation Notes
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Notes here mainly cover how custom tools (functions) are defined and how the Llama Stack Agent class is used with the custom tools.
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## Available Custom Tool Definition
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The `functions_prompt.py` defines the following six custom tools (functions), each as a subclass of Llama Stack's `CustomTool`, along with examples for each function call spec that Llama should return):
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* ListEmailsTool
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* GetEmailDetailTool
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* SendEmailTool
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* GetPDFSummaryTool
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* CreateDraftTool
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* SendDraftTool
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Below is an example custom tool call spec in JSON format, for the user asks such as "do i have emails with attachments larger than 5mb", "any attachments larger than 5mb" or "let me know if i have large attachments over 5mb":
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```
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{"name": "list_emails", "parameters": {"query": "has:attachment larger:5mb"}}
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```
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Porting the custom function definition in the original app to Llama Stack's CustomTool subclass is straightforward. Below is an example of the original custom function definition:
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```
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list_emails_function = """
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{
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"type": "function",
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"function": {
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"name": "list_emails",
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"description": "Return a list of emails matching an optionally specified query.",
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"parameters": {
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"type": "dic",
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"properties": [
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{
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"maxResults": {
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"type": "integer",
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"description": "The default maximum number of emails to return is 100; the maximum allowed value for this field is 500."
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}
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},
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{
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"query": {
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"type": "string",
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"description": "One or more keywords in the email subject and body, or one or more filters. There can be 6 types of filters: 1) Field-specific Filters: from, to, cc, bcc, subject; 2) Date Filters: before, after, older than, newer than); 3) Status Filters: read, unread, starred, importatant; 4) Attachment Filters: has, filename or type; 5) Size Filters: larger, smaller; 6) logical operators (or, and, not)."
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}
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}
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],
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"required": []
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}
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}
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}
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"""
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```
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And its Llama Stack CustomTool subclass implementation is:
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```
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class ListEmailsTool(CustomTool):
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"""Custom tool for List Emails."""
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def get_name(self) -> str:
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return "list_emails"
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def get_description(self) -> str:
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return "Return a list of emails matching an optionally specified query."
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def get_params_definition(self) -> Dict[str, ToolParamDefinitionParam]:
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return {
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"maxResults": ToolParamDefinitionParam(
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param_type="int",
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description="The default maximum number of emails to return is 100; the maximum allowed value for this field is 500.",
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required=False
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),
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"query": ToolParamDefinitionParam(
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param_type="str",
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description="One or more keywords in the email subject and body, or one or more filters. There can be 6 types of filters: 1) Field-specific Filters: from, to, cc, bcc, subject; 2) Date Filters: before, after, older than, newer than); 3) Status Filters: read, unread, starred, importatant; 4) Attachment Filters: has, filename or type; 5) Size Filters: larger, smaller; 6) logical operators (or, and, not).",
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required=False
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)
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}
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async def run(self, messages: List[CompletionMessage]) -> List[ToolResponseMessage]:
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assert len(messages) == 1, "Expected single message"
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message = messages[0]
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tool_call = message.tool_calls[0]
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try:
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response = await self.run_impl(**tool_call.arguments)
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response_str = json.dumps(response, ensure_ascii=False)
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except Exception as e:
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response_str = f"Error when running tool: {e}"
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message = ToolResponseMessage(
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call_id=tool_call.call_id,
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tool_name=tool_call.tool_name,
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content=response_str,
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role="ipython",
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)
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return [message]
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async def run_impl(self, query: str, maxResults: int = 100) -> Dict[str, Any]:
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"""Query to get a list of emails matching the query."""
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emails = list_emails(query)
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return {"name": self.get_name(), "result": emails}
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```
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Each CustomTool subclass has a `run_impl` method that calls actual Gmail API-based tool call implementation (same as the original app), which, in the example above, is `list_emails`.
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## The Llama Stack Agent class
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The `create_email_agent` in main.py creates a Llama Stack Agent with 6 custom tools using a `LlamaStackClient` instance that connects to Together.ai's Llama Stack server. The agent then creates a session, uses the same session in a loop to create a turn for each user ask. Inside each turn, a tool call spec is generated based on the user ask and, if needed after processing of the tool call spec to match what the actual Gmail API expects (e.g. get_email_detail requires an email id but the tool call spec generated by Llama doesn't have the id), actual tool calling happens. After post-processing of the tool call result, a user-friendly message is generated to respond to the user's original ask.
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## Memory
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In `shared.py` we define a simple dictionary `memory`, used to hold short-term results such as a list of found emails based on the user ask, or the draft id of a created email draft. They're needed to answer follow up user asks such as "what attachments does the email with subject xxx have" or "send the draft".
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# TODOs
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1. Improve the search, reply, forward, create email draft, and query about types of attachments.
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2. Improve the fallback and error handling mechanism when the user asks don't lead to a correct function calling spec or the function calling fails.
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3. Improve the user experience by showing progress when some Gmail search API calls take long (minutes) to complete.
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4. Implement the async behavior of the agent - schedule an email to be sent later.
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5. Implement the agent planning - decomposing a complicated ask into sub-tasks, using ReAct and other methods.
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6. Implement the agent long-term memory - longer context and memory across sessions (consider using Llama Stack/MemGPT/Letta)
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7. Implement reflection - on the tool calling spec and results.
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8. Introduce multiple-agent collaboration.
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9. Implement the agent observability.
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10. Compare different agent frameworks using the agent as the case study.
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11. Add and implement a test plan and productionize the email agent.
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# Resources
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1. Lilian Weng's blog [LLM Powered Autonomous Agents](https://lilianweng.github.io/posts/2023-06-23-agent/)
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2. Andrew Ng's posts [Agentic Design Patterns](https://www.deeplearning.ai/the-batch/how-agents-can-improve-llm-performance/) with basic [implementations from scratch](https://github.com/neural-maze/agentic_patterns).
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3. LangChain's survey [State of AI Agents](https://www.langchain.com/stateofaiagents)
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4. Deloitte's report [AI agents and multiagent systems](https://www2.deloitte.com/content/dam/Deloitte/us/Documents/consulting/us-ai-institute-generative-ai-agents-multiagent-systems.pdf)
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5. Letta's blog [The AI agents stack](https://www.letta.com/blog/ai-agents-stack)
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6. Microsoft's multi-agent system [Magentic-One](https://www.microsoft.com/en-us/research/articles/magentic-one-a-generalist-multi-agent-system-for-solving-complex-tasks)
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7. Amazon's [Multi-Agent Orchestrator framework](https://awslabs.github.io/multi-agent-orchestrator/)
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8. Deeplearning.ai's [agent related courses](https://www.deeplearning.ai/courses/?courses_date_desc%5Bquery%5D=agents) (Meta, AWS, Microsoft, LangChain, LlamaIndex, crewAI, AutoGen, Letta) and some [lessons ported to using Llama](https://github.com/meta-llama/llama-recipes/tree/main/recipes/quickstart/agents/DeepLearningai_Course_Notebooks).
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9. Felicis's [The Agentic Web](https://www.felicis.com/insight/the-agentic-web)
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10. A pretty complete [list of AI agents](https://github.com/e2b-dev/awesome-ai-agents), not including [/dev/agents](https://sdsa.ai/), a very new startup building the next-gen OS for AI agents, though.
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11. Sequoia's [post](https://www.linkedin.com/posts/konstantinebuhler_the-ai-landscape-is-shifting-from-simple-activity-7270111755710672897-ZHnr/) on 2024 being the year of AI agents and 2025 networks of AI agents.
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@ -2,7 +2,7 @@ from typing import List, Dict, Any
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from llama_stack_client.types.tool_param_definition_param import ToolParamDefinitionParam
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from llama_stack_client.types import CompletionMessage, ToolResponseMessage
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from llama_stack_client.lib.agents.custom_tool import CustomTool
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from gmagent import *
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from email_agent import *
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import json
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class ListEmailsTool(CustomTool):
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@ -349,7 +349,7 @@ examples = """
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"""
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system_prompt = f"""
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Your name is Gmagent, an assistant that can perform all Gmail related tasks for your user.
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Your name is Email Agent, an assistant that can perform all email related tasks for your user.
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Respond to the user's ask by making use of the following functions if needed.
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If no available functions can be used, just say "I don't know" and don't make up facts.
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@ -1,5 +1,5 @@
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import argparse
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import gmagent
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import email_agent
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import asyncio
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import json
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from functions_prompt import *
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@ -16,7 +16,7 @@ from shared import memory
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LLAMA_STACK_API_TOGETHER_URL="https://llama-stack.together.ai"
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LLAMA31_8B_INSTRUCT = "Llama3.1-8B-Instruct"
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async def create_gmail_agent(client: LlamaStackClient) -> Agent:
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async def create_email_agent(client: LlamaStackClient) -> Agent:
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"""Create an agent with gmail tool capabilities."""
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listEmailsTool = ListEmailsTool()
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@ -67,16 +67,16 @@ async def create_gmail_agent(client: LlamaStackClient) -> Agent:
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async def main():
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parser = argparse.ArgumentParser(description="Set email address")
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parser.add_argument("--gmail", type=str, required=True, help="Your Gmail address")
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parser.add_argument("--email", type=str, required=True, help="Your Gmail address")
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args = parser.parse_args()
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gmagent.set_email_service(args.gmail)
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email_agent.set_email_service(args.email)
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greeting = llama31("hello", "Your name is Gmagent, an assistant that can perform all Gmail related tasks for your user.")
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greeting = llama31("hello", "Your name is Email Agent, an assistant that can perform all email related tasks for your user.")
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agent_response = f"{greeting}\n\nYour ask: "
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client = LlamaStackClient(base_url=LLAMA_STACK_API_TOGETHER_URL)
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agent = await create_gmail_agent(client)
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agent = await create_email_agent(client)
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session_id = agent.create_session("email-session")
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while True:
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@ -1,4 +1,3 @@
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google-auth==2.27.0
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google-auth-oauthlib==0.4.6
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google-auth-httplib2==0.1.0
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@ -1,266 +0,0 @@
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# Emagent - A Llama and Llama Stack Powered Email Agent
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This is a Llama Stack port of the [Emagent](https://github.com/meta-llama/llama-recipes/tree/gmagent/recipes/use_cases/email_agent) app that shows how to build an email agent app powered by Llama 3.1 8B and Llama Stack, using Llama Stack custom tool and agent APIs. The end goal is to cover all components of a production-ready agent app, acting as an assistant to your email, with great user experience: intuitive, engaging, efficient and reliable. We'll use Gmail as an example but any email client API's can be used instead.
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Currently implemented features of Emagent include:
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* search for emails and attachments
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* get email detail
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* reply to a specific email
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* forward an email
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* get summary of a PDF attachment
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* draft and send an email
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If your main intent is to know the difference between using Llama Stack APIs or not for this agent implementation, go to [Implementation Notes](#implementation-notes).
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# Overview
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Email is an essential and one top killer app people use every day. A recent [State of AI Agents](https://www.langchain.com/stateofaiagents) survey by LangChain finds that "The top use cases for agents include performing research and summarization (58%), followed by streamlining tasks for personal productivity or assistance (53.5%)."
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Andrew Ng wrote a 5-part [Agentic Design Patterns](https://www.deeplearning.ai/the-batch/how-agents-can-improve-llm-performance/) in March 2024 predicting "AI agent workflows will drive massive AI progress this year".
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Deloitte published in November 2024 a report on [AI agents and multiagent systems](https://www2.deloitte.com/content/dam/Deloitte/us/Documents/consulting/us-ai-institute-generative-ai-agents-multiagent-systems.pdf) stating that "Through their ability to reason, plan, remember and act, AI agents address key limitations of typical language models." and "Executive leaders should make moves now to prepare for and embrace this next era of intelligent organizational transformation."
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In the Thanksgiving week, a new startup [/dev/agent](https://sdsa.ai/) building the next-gen OS for AI agents was in the spotlight.
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In December, Sequoia posted [here](https://www.linkedin.com/posts/konstantinebuhler_the-ai-landscape-is-shifting-from-simple-activity-7270111755710672897-ZHnr/) saying 2024 has been the year of agents (an agent is an AI that can complete tasks, not only tells you how to do it but also does it for you directly), and 2025 will be the year of networks of AI agents.
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So what exactly is an AI agent and how to start building an agent app?
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## What is an agent?
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The concept of agent is not new - in the 2010 3rd edition of Russell and Norvig's classic book Artificial Intelligence: A Modern Approach ("Modern" by 2010, two years before the deep learning revolution that started the truly modern AI), an agent is defined as "anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators". These days, AI agent basically means LLM-powered agent - well, if we treat natural language understanding as a type of sensor, LLM agent is still a sub-category of the traditional agent.
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Lilian Weng in her popular June 2023 blog [LLM Powered Autonomous Agents](https://lilianweng.github.io/posts/2023-06-23-agent/) defines LLM-powered agent system to have four key components:
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* Planning and Reflection: can break down large tasks into smaller ones; can do self-reflection over past actions and self improve;
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* Memory: can use contextual info and recall info over extended periods (for other components to use);
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* Tool Use: can understand what external APIs to use for info or action not built into LLMs;
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* Action: can actually run the tools.
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Andrew Ng describes four [agentic design patterns](https://www.deeplearning.ai/the-batch/how-agents-can-improve-llm-performance/) as:
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* Reflection
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* Planning
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* Tool calling
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* Multi-agent collaboration, where "memory" is mentioned: Each agent implements its own workflow, has its own memory (itself a rapidly evolving area in agentic technology: how can an agent remember enough of its past interactions to perform better on upcoming ones?)
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In Deloitte's [report](https://www2.deloitte.com/content/dam/Deloitte/us/Documents/consulting/us-ai-institute-generative-ai-agents-multiagent-systems.pdf), AI agents are reasoning engines that can understand context, plan workflows, connect to external tools and data, and execute actions to achieve a defined goal.
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In a November 2024 blog by Letta [The AI agents stack](https://www.letta.com/blog/ai-agents-stack), LLM powered agent is described as the combination of tools use, autonomous execution, and memory.
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In addition, Harrison Chase defines agent in the blog [What is an AI agent](https://blog.langchain.dev/what-is-an-agent/) as "a system that uses an LLM to decide the control flow of an application."
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Yet another simple [summary](https://www.felicis.com/insight/the-agentic-web) by Felicis of what an agent does is that an agent expands LLMs to go from chat to act: an agent can pair LLMs with external data, multi-step reasoning and planning, and act on the user's behalf.
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All in all (see [Resources](#resources) for even more info), agents are systems that take a high-level task, use an LLM as a reasoning and planning engine, with the help of contextual info and long-term memory if needed, to decide what actions to take, reflect and improve on the actions, and eventually execute those actions to accomplish the task.
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It's time to see an agent app in action and enjoy some coding. Below is a preview of the questions or requests one may ask Gmagent:
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# Example Asks to Gmagent
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* do i have emails with attachment larger than 5mb?
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* what's the detail of the email with subject this is an interesting paper
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* how many emails with attachment
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* tell me the detail about the attachments for the email with subject papers to read?
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* give me a summary of the pdf thinking_llm.pdf
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* draft an email to jeffxtang@meta.com saying how about lunch together this thursday?
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* send the draft
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# Setup and Installation
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If you feel intimated by the steps of the following Enable Gmail API section, you may want to check again the example asks (to see what you can ask to the agent) and the example log (to see the whole conversation with gmagent) - the devil's in the detail and all the glorious description of a powerful trendy agent may not mention the little details one has to deal with to build it.
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## Enable Gmail API
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1. Go to the [Google Cloud Console](https://console.cloud.google.com/).
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2. Create a new project by clicking the dropdown on the top left then click NEW PROJECT.
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3. Enter a Project name then click CREATE.
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||||
4. Under "APIs & Services" > "Enabled APIs & services", search for "gmail" and then Enable the "Gmail API" for your project.
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5. Under "APIs & Services" > "OAuth consent screen", click "GO TO NEW EXPERIENCE", then click "GET STARTED", enter App name, select your gmail as User support email, choose External under Audience, enter your gmail again as Contact Information, and finally check the I agree to the Google API Services under Finish and click Continue - Create.
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||||
5. Again under "APIs & Services", go to Credentials. Click on + CREATE CREDENTIALS, then choose OAuth client ID (NOT API key).
|
||||
Select Desktop App (NOT Web application, because you're assumed to want to start your Gmail agent locally first) as the application type and name it. Click Create to generate your client ID and client secret.
|
||||
6. Click Download JSON and rename the downloaded file as credentials.json. This file will be used in your Python script for authentication.
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||||
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||||
## Install Ollama with Llama 3.1 8B
|
||||
|
||||
Download Ollama (available for macOS, Linux, and Windows) [here](https://ollama.com/). Then download and test run the Llama 3.1 8B model by running on a Terminal:
|
||||
```
|
||||
ollama run llama3.1
|
||||
```
|
||||
|
||||
This will download a quantized version of Llama 3.1 of the size 4.7GB.
|
||||
|
||||
## Install required packages
|
||||
First, create a Conda or virtual env:
|
||||
|
||||
```
|
||||
conda create -n emagent python=3.10
|
||||
conda activate emagent
|
||||
```
|
||||
or
|
||||
```
|
||||
python -m venv emagent
|
||||
source emagent/bin/activate # on Linux, macOS:
|
||||
source emagent\Scripts\activate # on Windows
|
||||
```
|
||||
|
||||
Then install the required Python libraries:
|
||||
```
|
||||
git clone https://github.com/meta-llama/llama-stack
|
||||
cd llama-stack/docs/zero_to_hero_guide/email_agent
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
# Run Emagent
|
||||
|
||||
To run Emagent, you need to first copy the `credentials.json` file downloaded and renamed above in Step 6 of Enable Gmail API to the email_agent folder, then run:
|
||||
```
|
||||
python main.py --gmail <your_gmail_address>
|
||||
```
|
||||
|
||||
The first time you run it, you'll get a prompt like this;
|
||||
```
|
||||
Please visit this URL to authorize this application: https://accounts.google.com/o/oauth2/auth?response_type=code&client_id=xxxx
|
||||
Enter the authorization code:
|
||||
```
|
||||
|
||||
You need to copy the URL above and open it in a browser - if you Sign in with Google using the same Gmail you enabled for the Gmail API, then you'll see "You’ve been given access to an app that’s currently being tested. You should only continue if you know the developer that invited you.", otherwise if you sign in with another Gmail, you'll see "Gmail Agent App has not completed the Google verification process. The app is currently being tested, and can only be accessed by developer-approved testers. If you think you should have access, contact the developer."
|
||||
|
||||
In the latter case, go to APIs & Services > OAuth consent screen > Test users, and click the + ADD USERS button, and you'll see this message: While publishing status is set to "Testing", only test users are able to access the app. Allowed user cap prior to app verification is 100, and is counted over the entire lifetime of the app.
|
||||
|
||||
After clicking Continue, check the Select all checkbox to enable both settings required for running Gmagent:
|
||||
```
|
||||
View your email messages and settings.
|
||||
Manage drafts and send emails.
|
||||
```
|
||||
|
||||
Finally, copy the Authorization code and paste it to the Terminal, hit Enter and you'll see Gmagent's initial greeting (which will likely differ because the default temperature value 0.8 is used here - see [Ollama's model file](https://github.com/ollama/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values) for detail) such as:
|
||||
```
|
||||
Hello! I'm Gmagent, here to help you manage your Gmail account with ease.
|
||||
|
||||
What would you like to do today? Do you want me to:
|
||||
|
||||
Check and respond to new emails
|
||||
Compose a new email
|
||||
Organize your inbox with filters or labels
|
||||
Delete unwanted emails
|
||||
Something else?
|
||||
|
||||
Let me know how I can assist you!
|
||||
|
||||
Your ask:
|
||||
```
|
||||
|
||||
If you cancel here and run the command `python main.py --gmail <your_gmail_address>` again you should see the Gmagent greeting right away without the need to enter an authorization code, unless you enter a different Gmail address for the first time - in fact, for each authorized (added as a test user) Gmail address, a file `token_xxxx@gmail.com.pickle` will be created which contains the authorized token.
|
||||
|
||||
See the example asks and interaction log above for the types of asks you may enter.
|
||||
|
||||
# Implementation Notes
|
||||
Notes here mainly cover how custom functions are defined, how Gmail API based functions are implemented, and how an Agent class is defined to handle memory for contextual chat and perform pre- and post-processing on the tool calling.
|
||||
|
||||
## Available Custom Tool Definition
|
||||
The `functions_prompt.py` defines the following six custom tools (functions), each as a subclass of Llama Stack's `CustomTool`, along with examples for each function call spec that Llama should return):
|
||||
|
||||
* ListEmailsTool
|
||||
* GetEmailDetailTool
|
||||
* SendEmailTool
|
||||
* GetPDFSummaryTool
|
||||
* CreateDraftTool
|
||||
* SendDraftTool
|
||||
|
||||
|
||||
Below is an example custom tool call spec in JSON format, for the user asks such as "do i have emails with attachments larger than 5mb", "any attachments larger than 5mb" or "let me know if i have large attachments over 5mb":
|
||||
```
|
||||
{"name": "list_emails", "parameters": {"query": "has:attachment larger:5mb"}}
|
||||
```
|
||||
|
||||
Porting the custom function definition to Llama Stack's CustomTool subclass is straightforward.
|
||||
|
||||
## Actual Function Call Implementation
|
||||
|
||||
For each defined custom function call, its implementation using the Gmail API is present in `gmagent.py`. And we simply call them in each of the CustomTool subclass's `run_impl` method. For example, the `list_emails` is defined as follows:
|
||||
|
||||
```
|
||||
def list_emails(query='', max_results=100):
|
||||
emails = []
|
||||
next_page_token = None
|
||||
|
||||
while True:
|
||||
response = service.users().messages().list(
|
||||
userId=user_id,
|
||||
maxResults=max_results,
|
||||
pageToken=next_page_token,
|
||||
q=query
|
||||
).execute()
|
||||
|
||||
if 'messages' in response:
|
||||
for msg in response['messages']:
|
||||
sender, subject, received_time = get_email_info(msg['id'])
|
||||
emails.append(
|
||||
{
|
||||
"message_id": msg['id'],
|
||||
"sender": sender,
|
||||
"subject": subject,
|
||||
"received_time": received_time
|
||||
}
|
||||
)
|
||||
|
||||
next_page_token = response.get('nextPageToken')
|
||||
|
||||
if not next_page_token:
|
||||
break
|
||||
|
||||
return emails
|
||||
```
|
||||
|
||||
The function will be called by the Llama Stack agent in the `run_impl` method of the `ListEmailsTool` class if a user ask is like "do i have emails with attachments larger than 5mb":
|
||||
```
|
||||
emails = list_emails(query)
|
||||
```
|
||||
|
||||
## The Llama Stack Agent class
|
||||
|
||||
The `create_gmail_agent` in main.py creates a Llama Stack Agent with 6 custom tools using a `LlamaStackClient` instance that connects to Together.ai's Llama Stack server. The agent then creates a session, and in a loop, for each user ask, the agent uses the same session to create a turn, inside which a tool call spec is generated based on the user's ask and actual tool call then happens. After post-processing of the tool call result, a user-friendly message is printed to respond to the user's original ask.
|
||||
|
||||
When you try out Emagent, you'll likely find that further pre- and post-processing still needed to make it production ready. In a great video on [Vertical LLM Agents](https://www.youtube.com/watch?v=eBVi_sLaYsc), Jake Heller said "after passes frankly even like 100 tests the odds that it will do on any random distribution of user inputs of the next 100,000, 100% accurately is very high" and "by the time you've dealt with like all the edge cases... there might be dozens of things you build into your application to actually make it work well and then you get to the prompting piece and writing out tests and very specific prompts and the strategy for how you break down a big problem into step by step by step thinking and how you feed in the information how you format that information the right way". That's what all the business logic is about. We'll cover decomposing a complicated ask and multi-step reasoning in a future version of Gmagent, and continue to explore the best possible way to streamline the pre- and post-processing.
|
||||
|
||||
## Debugging output
|
||||
|
||||
When running Gmagent, the detailed Llama returns, pre-processed tool call specs and the actual tool calling results are inside the `-------------------------` block, e.g.:
|
||||
|
||||
-------------------------
|
||||
Calling Llama...
|
||||
|
||||
Llama returned: {'function_name': 'list_emails', 'parameters': {'query': 'subject:papers to read has:attachment'}}.
|
||||
|
||||
Calling tool to access Gmail API: list_emails, {'query': 'subject:papers to read has:attachment'}...
|
||||
|
||||
Tool calling returned: [{'message_id': '1936ef72ad3f30e8', 'sender': 'gmagent_tester1@gmail.com', 'subject': 'Fwd: papers to read', 'received_time': '2024-11-27 10:51:51 PST'}, {'message_id': '1936b819706a4923', 'sender': 'Jeff Tang <gmagent_tester2@gmail.com>', 'subject': 'papers to read', 'received_time': '2024-11-26 18:44:19 PST'}]
|
||||
|
||||
-------------------------
|
||||
|
||||
|
||||
# TODOs
|
||||
|
||||
1. Improve the search, reply, forward, create email draft, and query about types of attachments.
|
||||
2. Improve the fallback and error handling mechanism when the user asks don't lead to a correct function calling spec or the function calling fails.
|
||||
3. Improve the user experience by showing progress when some Gmail search API calls take long (minutes) to complete.
|
||||
4. Implement the async behavior of Gmagent - schedule an email to be sent later.
|
||||
5. Implement the agent planning - decomposing a complicated ask into sub-tasks, using ReAct and other methods.
|
||||
6. Implement the agent long-term memory - longer context and memory across sessions (consider using Llama Stack/MemGPT/Letta)
|
||||
7. Implement reflection - on the tool calling spec and results.
|
||||
8. Introduce multiple-agent collaboration.
|
||||
9. Implement the agent observability.
|
||||
10. Compare different agent frameworks using Gmagent as the case study.
|
||||
11. Add and implement a test plan and productionize Gmagent.
|
||||
|
||||
|
||||
# Resources
|
||||
1. Lilian Weng's blog [LLM Powered Autonomous Agents](https://lilianweng.github.io/posts/2023-06-23-agent/)
|
||||
2. Andrew Ng's posts [Agentic Design Patterns](https://www.deeplearning.ai/the-batch/how-agents-can-improve-llm-performance/) with basic [implementations from scratch](https://github.com/neural-maze/agentic_patterns).
|
||||
3. LangChain's survey [State of AI Agents](https://www.langchain.com/stateofaiagents)
|
||||
4. Deloitte's report [AI agents and multiagent systems](https://www2.deloitte.com/content/dam/Deloitte/us/Documents/consulting/us-ai-institute-generative-ai-agents-multiagent-systems.pdf)
|
||||
5. Letta's blog [The AI agents stack](https://www.letta.com/blog/ai-agents-stack)
|
||||
6. Microsoft's multi-agent system [Magentic-One](https://www.microsoft.com/en-us/research/articles/magentic-one-a-generalist-multi-agent-system-for-solving-complex-tasks)
|
||||
7. Amazon's [Multi-Agent Orchestrator framework](https://awslabs.github.io/multi-agent-orchestrator/)
|
||||
8. Deeplearning.ai's [agent related courses](https://www.deeplearning.ai/courses/?courses_date_desc%5Bquery%5D=agents) (Meta, AWS, Microsoft, LangChain, LlamaIndex, crewAI, AutoGen, Letta) and some [lessons ported to using Llama](https://github.com/meta-llama/llama-recipes/tree/main/recipes/quickstart/agents/DeepLearningai_Course_Notebooks).
|
||||
9. Felicis's [The Agentic Web](https://www.felicis.com/insight/the-agentic-web)
|
||||
10. A pretty complete [list of AI agents](https://github.com/e2b-dev/awesome-ai-agents), not including [/dev/agents](https://sdsa.ai/), a very new startup building the next-gen OS for AI agents, though.
|
||||
11. Sequoia's [post](https://www.linkedin.com/posts/konstantinebuhler_the-ai-landscape-is-shifting-from-simple-activity-7270111755710672897-ZHnr/) on 2024 being the year of AI agents and 2025 networks of AI agents.
|
||||
Loading…
Add table
Add a link
Reference in a new issue