KI-Agenten
trpc-agent-go
Go framework for production agent systems: graphs, tools, memory, A2A, MCP.
★ 1,8 Tsd. SterneApache-2.0
Anleitung ansehen →agent-memory-benchmark ist ein Open-Source-Projekt aus der Kategorie KI-Agenten von vectorize-io (vectorize-io/agent-memory-benchmark auf GitHub). Es hat 144 Sterne auf GitHub und 47 Forks, wird unter der Lizenz NOASSERTION veröffentlicht und wurde zuletzt am 7. Oktober 2026 auf GitHub aktualisiert.
A benchmark for comparing AI agent memory systems head-to-head.
Know what you want the agent to accomplish before touching any code.
Clone the repo and read the README's requirements section.
Install what's needed and add API keys or configuration.
Run the quickstart example, then iterate from there.
Same tests for every system.
Apples-to-apples memory evals.
Recall quality, not generic chat.
Inspect how scores are computed.
From the Hindsight team.
Updated as the field moves.
Download the source and move into the project folder.
git clone https://github.com/vectorize-io/agent-memory-benchmark.git
cd agent-memory-benchmarkIsolate dependencies, then install requirements.
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txtLaunch using the entry point shown in the README.
python -m agent_memory_benchmark # check README for the exact commandIt is an open-source project at vectorize-io/agent-memory-benchmark with 47 forks. Check the contributors tab and recent commits to judge how active maintenance is.
GitHub lists no license for this repo, so legally it's all rights reserved — check the README or ask the maintainer before building on it.
How well agent memory systems recall and use information — standardized tasks for head-to-head comparison.
Researchers and vendors who want memory performance as numbers, not claims.
Clone the repo and follow the README quickstart — the README is the best starting point.
Go framework for production agent systems: graphs, tools, memory, A2A, MCP.
30 runnable notebooks teaching everything about agent memory.
WebArena-Verified: the audited, deterministically scored release of the WebArena web-agent benchmark.
A self-evolving context database unifying agent memory, RAG, and skills.
A memory agent for your agent fleet: it curates, reconciles, and expires shared memories across tools.
A benchmark for tool-agent-user interaction in real-world domains.
Die anderen Projekte der Kategorie KI-Agenten mit den meisten Sternen im LibreAI-Katalog — die realistische Auswahl, falls agent-memory-benchmark nicht das Richtige ist.
A personal AI that really does things — on any OS, with your data staying yours.
An agentic skills framework & software development methodology that works.
Agent harness optimization system with skills, memory and security for Claude Code, Codex and more
The agent that grows with you — a Python framework for adaptive AI agents.
DeepSeek's plugin-based agent harness where everything is a plugin.
Verwandte Projekte der Kategorie KI-Agenten im Katalog, die agent-memory-benchmark bei den Sternen am nächsten stehen — zum Weiterstöbern auf beiden Seiten.
An MCP-native AI coding agent and runtime in a single binary.
A voice-driven multi-agent assistant that orchestrates in realtime.
A complete evaluation toolkit for AI agents.
A frozen snapshot of the original BabyAGI autonomous-agent experiment.
WorkArena: ServiceNow's benchmark for how well web agents handle everyday knowledge-work tasks.
eBPF-powered information-flow enforcement for safer AI agents.
Kostenlos und offen — hol dir den Quellcode, gib dem Repository einen Stern und leg los.