Différences

Ci-dessous, les différences entre deux révisions de la page.

--- informatique:ai_lm:ai_agent [22/06/2026 09:26] – [Memory system] cyrille
+++ informatique:ai_lm:ai_agent [22/06/2026 18:44] (Version actuelle) – [GBrain] cyrille
@@ Ligne 110: / Ligne 110: @@
 https://nanobot.club/
+La home page parle de "Memory Management System, Hybrid Semantic Search, Algorithm: 70% Vector + 30% BM25" mais les ChatBots ne trouvent aucune trace de ces technologies dans le code source ... ¿¿ **//AIGen Marketing Bullshit//** ??
 ==== OpenHands ====
@@ Ligne 142: / Ligne 143: @@
-===== Memory system =====
+===== Memory systems =====
 ==== Basic Memory ====
@@ Ligne 148: / Ligne 149: @@
   * https://basicmemory.com/
   * https://github.com/basicmachines-co/basic-memory
+Plain Markdown files, semantic connections, and a knowledge graph that grows with every conversation.
   * Local-first. Plain text on your disk. Forever.
-  * Two-way. AI and humans write to the same files; sync keeps them in step.
+  * Two-way. AI and humans write to the same text (markdown) files; sync keeps them in step.
+    * indexing with SQLite/PostgreSQL
   * A real knowledge graph. Observations and wikilinks compound into context.
   * Semantic search. Find notes by meaning, not just keywords.
@@ Ligne 157: / Ligne 161: @@
   * Progressive tool discovery. Every tool is tagged with behavior hints (read-only, destructive, idempotent) so agents pick the right tool on demand — no wasted context trying things to see what they do.
   * Cloud, optional. Sync across devices when you want — never required.
+==== GBrain ====
+https://github.com/garrytan/gbrain
+Semble le plus aboutit, mais c'est une sacrée usine !
+relations automatique
+  * GBrain extrait automatiquement les relations typées avec des RegEx : works_at, founded, attended, invested_in, advises
+  * On peut [[https://github.com/garrytan/gbrain/blob/master/docs/schema-author-tutorial.md|définir/étendre le schema]] mais ce n'est pas trivial. 😥
+  * Avec un fallback LLM en option
+Pour configurer un model embenddings custom il faut utiliser la clé "provider_base_urls.ollama" :
+<code>
+$ cat ~/.gbrain/config.json
+{
+  "embedding_model": "ollama:tei",
+  "embedding_dimensions": 768,
+  "provider_base_urls": {
+    "ollama": "http://127.0.0.1:9000/v1"
+  },
+  ...
+</code>
+Puis ''gbrain init --pglite --embedding-model ollama:tei --embedding-dimensions 768''
+==== Mem0 ====
+https://mem0.ai/
+AI memory that persists across sessions and agents.
+Trop lourd pour un usage perso ...
+==== Stash Memory ====
+https://alash3al.github.io/stash/
 ===== Frameworks =====