vasilyevdm

vasilyevdm / ai-agent-handbook

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Comprehensive guide to AI agent engineering: how 30+ frameworks actually work under the hood. Context rot, compaction, system prompt assembly, SOUL.md, agent loops, memory systems, tool sprawl, MCP, progressive disclosure, multi-agent orchestration, Plan/Act, episodic memory. Code examples throughout. Pick the right stack, avoid the common traps

33
2
100% credibility
Found Mar 21, 2026 at 33 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

This repository offers a handbook that documents patterns, architectures, and implementation details extracted from the source code of over 30 open-source AI agent frameworks, answering common questions about their design and functionality.

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AI-Generated Review

What is ai-agent-handbook?

This is the ai agent handbook, a comprehensive guide dissecting how 30+ AI agent frameworks actually work under the hood—from agent loops and context compaction to memory systems and multi-agent orchestration. It pulls patterns straight from production codebases like LangGraph, CrewAI, and OpenClaw, delivering code examples, a decision framework, and a PDF version for quick reference. Developers get actionable insights to pick stacks, dodge traps like context rot and tool sprawl, without wading through source code themselves.

Why is it gaining traction?

Unlike fluffy blog posts or vendor hype, this ai agent handbook google searches won't find delivers raw findings from reading real frameworks, like why Claude Code compacts at 92% or how SOUL.md patterns curb prompt dilution. The quick decision guide—Coding agent to Cline, multi-agent to CrewAI—saves weeks of trial-and-error. Early adopters hook on specifics like 12 defenses against context rot and tool sprawl fixes that reclaim 72% of your window.

Who should use this?

AI engineers prototyping agents who need LangGraph vs. CrewAI comparisons before committing. Devs battling production issues like episodic memory gaps or MCP bloat in personal assistants. Teams evaluating enterprise options from Google ADK to MS Agent Framework for scalable orchestration.

Verdict

Grab the ai agent handbook pdf if you're stack-shopping—33 stars and 1.0% credibility score signal it's early but packed with signal over noise. Low maturity means verify findings yourself, but it's a solid reference to accelerate agent builds; contribute to bump it up.

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