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README cleanup

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Jeff Tang 2024-12-11 18:18:07 -08:00
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docs/zero_to_hero_guide/email_agent/README.md
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@ -10,7 +10,7 @@ Currently implemented features of the agent include:
* get summary of a PDF attachment
* draft and send an email
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.
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, and a detailed list of TODOs for the email agent.
# Setup and Installation
@ -140,32 +140,3 @@ The `create_email_agent` in main.py creates a Llama Stack Agent with 6 custom to
## Memory
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".
# 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 the agent - 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 the agent as the case study.
11. Add and implement a test plan and productionize the email agent.
# 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.