Agents IA
Haystack
Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and…
★ 26,6 k étoilesApache-2.0
Voir le guide →Hypha est un projet de agents IA open source, créé par CodeSoul-co (CodeSoul-co/Hypha sur GitHub). Il compte 447 étoiles sur GitHub et 126 forks, il est publié sous la licence Apache-2.0 et a été mis à jour pour la dernière fois sur GitHub le 4 septembre 2026.
A harness-oriented framework for production-grade agent applications.
Write down the task or workflow you want the agent to handle.
Clone the repository and read the README's prerequisites.
Install dependencies and configure keys or endpoints.
Run the sample script and check the output against the docs.
The agent loop is the core abstraction.
Caching, evals, and memory included.
Agent activity as a replayable log.
Built-in execution environment.
Modern typed stack.
Open and commercially friendly.
Download the source and move into the project folder.
git clone https://github.com/CodeSoul-co/Hypha.git
cd HyphaPull in the Node packages the project needs.
npm installTreat these as starting points - the README documents the exact commands.
npm run buildThe project lives at CodeSoul-co/Hypha and has attracted 126 forks. Browse its commit history and contributor list for a sense of maintenance pace.
Yes - it is Apache-2.0 licensed, so it is free for commercial use with attribution.
The framework itself is free - but you will need a model to drive it, which usually means your own API key or a local model.
Both are harness-centric, but Hypha is a fuller framework with memory, caching, and evals built in, while Strands' SDK is a lighter harness structure. Pick Hypha for batteries-included, Strands for flexibility.
The README and the official docs site are the fastest path: run the quickstart example first, then adapt it to your use case.
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Projets de la catégorie Agents IA proches dans le catalogue, les plus proches en étoiles de Hypha — continuez à explorer de part et d'autre.
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The tougher, more robust successor to the MMLU benchmark.
Guardrails for building secure, robust agents.
Safety testing for agents, right in pytest.
Detect prompt injections and jailbreaks before they hit your LLM.
Agentic memory for LLM agents, from a published research project.
Gratuit et ouvert : récupérez le code source, mettez une étoile au dépôt et commencez à créer.