Building agents without memory is like building amnesiac assistants. After implementing persistent memory across 8+ agent systems, task completion improved by 60%. Here’s the complete guide to building agents that remember. Figure 1: Agent Memory Architecture Why Agent Memory Matters: The Cost of Amnesia Agents without memory face critical limitations: No context: Can’t remember previous […]
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Building Multi-Agent Workflows: Advanced LangGraph Patterns
Building multi-agent workflows requires careful orchestration. After building 18+ multi-agent systems with LangGraph, I’ve learned what works. Here’s the complete guide to advanced LangGraph patterns for multi-agent workflows. Figure 1: Multi-Agent Architecture with LangGraph Why Multi-Agent Workflows Multi-agent systems offer significant advantages: Specialization: Each agent handles specific tasks Parallelism: Agents can work simultaneously Scalability: Add […]
Read more →Streaming Responses for LLMs: Implementing Server-Sent Events
Streaming LLM responses dramatically improves user experience. After implementing streaming for 20+ LLM applications, I’ve learned what works. Here’s the complete guide to implementing Server-Sent Events for LLM streaming. Figure 1: Streaming Architecture Why Streaming Matters Streaming LLM responses provides significant benefits: Perceived performance: Users see results immediately, not after 10+ seconds Better UX: Progressive […]
Read more →RESTful AI API Design: Best Practices for LLM APIs
Designing RESTful APIs for LLMs requires careful consideration. After building 30+ LLM APIs, I’ve learned what works. Here’s the complete guide to RESTful AI API design. Figure 1: RESTful AI API Architecture Why LLM APIs Are Different LLM APIs have unique requirements: Async operations: LLM inference can take seconds or minutes Streaming responses: Need to […]
Read more →Quantization Methods for LLMs: GPTQ, AWQ, and BitsAndBytes
Last year, I needed to run a 13B parameter model on a 16GB GPU. Full precision required 52GB. After testing GPTQ, AWQ, and BitsAndBytes, I reduced memory to 7GB with minimal accuracy loss. After quantizing 30+ models, I’ve learned which method works best for each scenario. Here’s the complete guide to LLM quantization. Figure 1: […]
Read more →Advanced LoRA Techniques: Multi-LoRA, LoRA+, and Beyond
Last year, I fine-tuned a 7B parameter model with standard LoRA. It worked, but accuracy was 5% lower than full fine-tuning. After experimenting with Multi-LoRA, LoRA+, and advanced techniques, I’ve achieved 98% of full fine-tuning performance with 1% of the parameters. Here’s everything you need to know about advanced LoRA techniques. Figure 1: LoRA Techniques […]
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