Gloriaameng

Gloriaameng / LLM-Agent-Harness-Survey

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Survey on LLM agent harnesses engineering with a taxonomy. 110+ papers, 23 systems analyzed.

agent agent-framework agent-harness awesome-list harness
41
0
100% credibility
Found Apr 12, 2026 at 46 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

This repository provides a detailed survey of frameworks that make large language model agents reliable, featuring timelines, comparison matrices, and curated lists of over 110 related studies.

How It Works

1
🔍 Discover the Survey

You stumble upon this GitHub page while looking for clear info on how AI helpers work reliably.

2
📖 Read the Welcome Guide

You scan the friendly introduction and eye-catching pictures explaining the key parts of AI agent setups.

3
📊 Explore the Comparison Chart

You love the handy table showing which tools and systems cover all the important features for smooth AI performance.

4
🕰️ Follow the Timeline

You trace the story of how these AI systems evolved over time, like a history lesson made simple.

5
📚 Dive into Topic Lists

You pick sections on challenges like safety or planning, finding bunches of helpful stories and examples.

6
📄 Grab the Full Report

You download the complete guide to read at your pace and take notes on the big ideas.

✅ Become an AI Insight Pro

Now you understand what makes AI agents trustworthy and ready to share or use this knowledge in your projects.

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Star Growth

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AI-Generated Review

What is LLM-Agent-Harness-Survey?

This GitHub survey tool delivers a taxonomy and analysis of LLM agent harnesses, formalizing them as H=(E,T,C,S,L,V) across execution loops, tools, context, state, lifecycle, and evaluation. It dissects 110+ arxiv llm survey papers and 23 systems like LangChain, AutoGen, and SWE-agent, solving the puzzle of why harness design—not just models—drives agent reliability in production. Developers get timelines, completeness matrices, and breakdowns of challenges like survey llm memory, survey llm evaluation, and survey llm reasoning, plus a linked PDF preprint and HuggingFace dataset.

Why is it gaining traction?

Unlike scattered survey llms or agent overviews, it bridges academic papers with real-world reports from Stripe Minions and OpenAI Codex, spotlighting harness gaps via a visual completeness matrix for quick comparisons. The hook: empirical proof that tweaks like tool formats boost SWE-bench scores 10x, plus curated lists on survey llm hallucination, survey llm as a judge, and multi-agent protocols like MCP/A2A—ideal for devs benchmarking frameworks without digging through 110+ papers.

Who should use this?

Agent builders picking between partial frameworks like LangGraph and full-stack ones like AIOS or OpenHands. Researchers in survey llm agent or survey llm quantization needing historical timelines and 9 open challenges. Evaluators validating benchmarks against production failures, especially in tool use or multi-agent coordination.

Verdict

Grab it as a free, actively maintained reference if you're deep in LLM agents—strong docs and structure punch above its 41 stars. Low 1.0% credibility score reflects newness, but the matrix alone justifies starring for 2024/2025 survey github updates. (198 words)

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