From what I understand, (although it’s not listed in this graphic) Microsoft Agent Framework is their most recent attempt combine the advantages of Semantic Kernel and AutoGen. I’m using it with the M365 Agents SDK and it’s pretty slick so far.
2 weeks ago | 2
While I haven’t personally used it yet, I think n8n might deserve a spot in Tool Execution
1 week ago | 0
I would add other elements: Foundation model provider: Azure AI Foundry and AWS Bedrock Cloud-based agent services: Azure AI Foundry Agent Service and AWS Bedrock agents. Model routers: LiteLLM At the systems level you would need to consider some of these components when designing your solution or integrating your system.
2 weeks ago (edited) | 0
sentry is have self host and it open src self host have all functions that on there have
2 weeks ago | 0
Can you tell me how can you make this kind of images oh just send me a link of YouTube video where I can see all the details
2 weeks ago | 0
ByteByteGo
The AI Agent Tech Stack
1. Foundation Models: Large-scale pre-trained language models that serve as the “brains” of AI agents, enabling capabilities like reasoning, text generation, coding, and question answering.
2. Data Storage: This layer handles vector databases and memory storage systems used by AI agents to store and retrieve context, embeddings, or documents.
3. Agent Development Frameworks: These frameworks help developers build, orchestrate, and manage multi-step AI agents and their workflows.
4. Observability: This category enables monitoring, debugging, and logging of AI agent behavior and performance in real-time.
5. Tool Execution: These platforms allow AI agents to interface with real-world tools (for example, APIs, browsers, external systems) to complete complex tasks.
6. Memory Management: These systems manage long-term and short-term memory for agents, helping them retain useful context and learn from past interactions.
Over to you: What else will you add to the list?
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2 weeks ago | [YT] | 1,888