KI-Agenten
prompts.chat
f.k.a. Awesome ChatGPT Prompts. Share, discover, and collect prompts from the community. Free and open source — self-host for your organization with complete…
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Anleitung ansehen →agentops ist ein Open-Source-Projekt aus der Kategorie KI-Agenten von AgentOps-AI (AgentOps-AI/agentops auf GitHub). Es hat 5.881 Sterne auf GitHub und 641 Forks, wird unter der Lizenz MIT veröffentlicht und wurde zuletzt am 25. Juni 2026 auf GitHub aktualisiert.
Python SDK for AI agent monitoring, LLM cost tracking, benchmarking, and more. Integrates with most LLMs and agent frameworks including CrewAI, Agno, OpenAI …
Research, coding, browsing, or ops — agents work best with a clear goal.
From no-code builders to code-first SDKs, match complexity to the task.
Web search, code execution, and APIs are what make agents useful.
Watch the trace, approve consequential actions, iterate.
Swap frontier models as better ones appear.
Traces show exactly what the agent did and why.
Approval gates for actions that matter.
Long-term memory across sessions, not goldfish chat.
Specialized agents collaborate on bigger jobs.
Agents call real tools: browsers, terminals, APIs.
Download the source and move into the project folder.
git clone https://github.com/AgentOps-AI/agentops.git
cd agentopsEvery project documents its own setup; start with install requirements.
cat README.md | head -60Follow the project's launch command — Docker, pip, npm, or a binary release.
# see README for the exact launch commandReaders working through the AI Agents shelf also stop at Agon, Autonomous research loops, from topic to experiments.
It is an open-source project at AgentOps-AI/agentops with 641 forks. Check the repo's contributors and recent commits to see how active maintenance is.
MIT licensed — use it commercially, no strings attached.
Builders exist, but code-first frameworks go further.
Model API bills dominate; self-hosted stacks cut that sharply.
For bounded tasks with supervision, yes. Fully autonomous money-moves still need guardrails.
f.k.a. Awesome ChatGPT Prompts. Share, discover, and collect prompts from the community. Free and open source — self-host for your organization with complete…
Open-source AI penetration testing tool to find and fix your app’s vulnerabilities.
Open-source personal AI assistant & Agent Harness. Plans tasks, runs tools and skills, self-evolves with memory and knowledge. Multi-agent, multi-model, mult…
Python SDK for Agent AI Observability, Monitoring and Evaluation Framework. Includes features like agent, llm and tools tracing, debugging multi-agentic syst…
Langroid: a principled Python framework for building LLM multi-agent applications (from CMU/UW researchers)
AI Agent Assistant & development framework that integrates lots of IM platforms, LLMs, plugins and AI feature, and can be your openclaw alternative. ✨
Die anderen Projekte der Kategorie KI-Agenten mit den meisten Sternen im LibreAI-Katalog — die realistische Auswahl, falls agentops 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 agentops bei den Sternen am nächsten stehen — zum Weiterstöbern auf beiden Seiten.
A zero-code platform for auto-generating production-grade AI agents with unified tools, skills and memory.
An MCP integration platform that lets AI agents use tools reliably at any scale.
A framework for evaluating and improving AI agents.
A visual playground for agentic workflows: Iterate over your agents 10x faster
An open-source auth gateway connecting 1,500+ SaaS providers to AI agents via SDK, CLI, MCP and HTTP.
A full-lifecycle platform for developing, debugging, evaluating and monitoring AI agents.
Kostenlos und offen — hol dir den Quellcode, gib dem Repository einen Stern und leg los.