KI-Agenten
AstrBot
AI Agent Assistant & development framework that integrates lots of IM platforms, LLMs, plugins and AI feature, and can be your openclaw alternative. ✨
★ 41,3 Tsd. SterneAGPL-3.0
Anleitung ansehen →RagaAI-Catalyst ist ein Open-Source-Projekt aus der Kategorie KI-Agenten von raga-ai-hub (raga-ai-hub/RagaAI-Catalyst auf GitHub). Es hat 16.170 Sterne auf GitHub und 3.571 Forks, wird unter der Lizenz Apache-2.0 veröffentlicht und wurde zuletzt am 11. Februar 2026 auf GitHub aktualisiert.
Python SDK for Agent AI Observability, Monitoring and Evaluation Framework. Includes features like agent, llm and tools tracing, debugging multi-agentic syst…
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/raga-ai-hub/RagaAI-Catalyst.git
cd RagaAI-CatalystEvery 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 commandIt is an open-source project at raga-ai-hub/RagaAI-Catalyst with 3,571 forks. Check the repo's contributors and recent commits to see how active maintenance is.
Apache 2.0 licensed — commercial use allowed with attribution.
Sandboxing, approval gates, and least-privilege credentials.
Builders exist, but code-first frameworks go further.
Model API bills dominate; self-hosted stacks cut that sharply.
AI Agent Assistant & development framework that integrates lots of IM platforms, LLMs, plugins and AI feature, and can be your openclaw alternative. ✨
AI 时代的伯克希尔：基于 Claude Code / Codex 的价值投资研究框架。巴菲特·芒格·段永平·李录四大师方法论 + 多Agent并行研究。| AI-era Berkshire: a value investing research framework built for Claude Code /…
Langroid: a principled Python framework for building LLM multi-agent applications (from CMU/UW researchers)
Python SDK for AI agent monitoring, LLM cost tracking, benchmarking, and more. Integrates with most LLMs and agent frameworks including CrewAI, Agno, OpenAI …
Multi-agent LLM framework that mirrors a trading firm: analysts, researchers, trader and risk team
Enterprise-grade AI agent framework with multi-provider LLM management, secure knowledge bases and high-precision RAG, visual workflow orchestration, and mul…
Die anderen Projekte der Kategorie KI-Agenten mit den meisten Sternen im LibreAI-Katalog — die realistische Auswahl, falls RagaAI-Catalyst 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 RagaAI-Catalyst bei den Sternen am nächsten stehen — zum Weiterstöbern auf beiden Seiten.
A serverless, single-file memory layer for AI agents - replace complex RAG pipelines
Open Source framework for voice agents, multimodal apps, and realtime AI. Maintained by Daily and the community
AI 时代的伯克希尔：基于 Claude Code / Codex 的价值投资研究框架。巴菲特·芒格·段永平·李录四大师方法论 + 多Agent并行研究。| AI-era Berkshire: a value investing research framework built for Claude Code /…
Supercharge LLM app evaluations with objective metrics, synthetic test data and feedback loops.
Google's MCP Toolbox for Databases - give agents safe, structured access to your data
Automated penetration-testing agent framework powered by LLMs, published at USENIX Security 2024.
Kostenlos und offen — hol dir den Quellcode, gib dem Repository einen Stern und leg los.