Agents team
✅Solved
i'm looking for the best way to create and manage agents or team of agents, each with its own personality, SOUL and so onl.
they will have specialized skills or some will have access to general skills.
also i'd like to have some shared knowledge of products I have.
What would bethe best setup?
One agent per workspace? would it be possible to have shared knowledge and skills if that's the case?
Thanks!
they will have specialized skills or some will have access to general skills.
also i'd like to have some shared knowledge of products I have.
What would bethe best setup?
One agent per workspace? would it be possible to have shared knowledge and skills if that's the case?
Thanks!
Solution
- You want a web UI for editing and multiple humans updating it
- You want row-level security / auditing
Between the two:
- Supabase (Postgres) is great for relational data, SQL queries, and it has a big ecosystem. It’s the “boring, solid” choice.
- Convex shines when you want a real-time app backend and you’re already building in that stack.
But: OpenClaw won’t automatically “use Supabase/Convex” — you’d typically add a custom skill (or API layer) so the agent can query it safely.
- If you later need richer retrieval, add a search layer:
- simplest: a tiny “search knowledge” skill that uses ripgrep
- fancier: use the QMD memory backend to index extra paths (semantic-ish search over your markdown). Docs: https://docs.openclaw.ai/concepts/memory
If you tell me whether your brand/product info is mostly textual docs or structured fields you want to query, I’ll recommend a concrete layout (folders + file templates) either way.
- You want row-level security / auditing
Between the two:
- Supabase (Postgres) is great for relational data, SQL queries, and it has a big ecosystem. It’s the “boring, solid” choice.
- Convex shines when you want a real-time app backend and you’re already building in that stack.
But: OpenClaw won’t automatically “use Supabase/Convex” — you’d typically add a custom skill (or API layer) so the agent can query it safely.
“Best of both worlds” (often ideal)
- Keep the source of truth as markdown files (easy to edit, review, ship)- If you later need richer retrieval, add a search layer:
- simplest: a tiny “search knowledge” skill that uses ripgrep
- fancier: use the QMD memory backend to index extra paths (semantic-ish search over your markdown). Docs: https://docs.openclaw.ai/concepts/memory
If you tell me whether your brand/product info is mostly textual docs or structured fields you want to query, I’ll recommend a concrete layout (folders + file templates) either way.
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