Weavable
AI agents forget everything between conversations unless someone feeds them the right background each time — this quietly keeps that background up to date across all your work tools, so the agent already knows what's going on.
🔗 Visit Weavable
Description
An AI agent connected to your company's tools is only as useful as the context it's given, and today that usually means either dumping a huge pile of raw data at the model every time or wiring up a separate connector for each tool individually — both approaches get expensive and unreliable fast. Weavable sits underneath a company's existing stack (Jira, GitHub, Slack, Linear, Notion, HubSpot, Zendesk, Figma and 25+ others) and keeps a continuously updated, structured changelog of what's happening across them, instead of a frozen snapshot.
That changelog is served to any AI agent through one single MCP endpoint, so instead of maintaining a dozen separate MCP servers per tool, a team wires up one connection and every agent — Claude, ChatGPT, Cursor, or an internal one — gets the same up-to-date, pre-filtered context. According to its Product Hunt launch, this deterministic pre-processing produces 85% favorable outputs versus baseline retrieval in LLM-as-judge evaluations, with up to 90% token savings. Pricing isn't published on the public site; a free 30-day full-access trial has been mentioned in press coverage.
💬 Our review
The short version: if your team is already wiring up multiple MCP servers to give AI agents context from different tools and finding the results inconsistent, Weavable's single-endpoint approach is worth evaluating during its free trial before committing engineering time to a DIY setup.
Compared to building and maintaining your own per-tool MCP integrations (free in licensing terms, but a real ongoing engineering cost), or to just feeding an agent raw exported data (cheap but unreliable and token-expensive), Weavable's advantage is the deterministic pre-processing layer that filters and structures context before the model sees it — the reported 90% token savings is a meaningful cost lever for teams running agents at any real volume. The catch is pricing isn't public, which makes it hard to judge total cost of ownership upfront, and being new, it lacks the track record of established observability or integration platforms. For teams already frustrated by inconsistent agent outputs across their tool stack, it's a reasonable pilot candidate.
💰 Pricing
📊 Global score
🤖 AI-enriched data
Essai gratuit de 30 jours mentionné dans la presse ; grille tarifaire non publiée sur le site
Pros
Un seul point d'entrée MCP pour tous les outils connectés
Changelog continuellement mis à jour, pas un instantané figé
85% de sorties jugées favorables vs base de référence (évaluation LLM-as-judge)
Jusqu'à 90% d'économie de tokens rapportée
Cons
Tarification non publiée, difficile d'évaluer le coût total
Produit jeune, peu de recul utilisateur indépendant
Nécessite de connecter ses outils métier sensibles à un tiers
Chiffres de performance issus du propre lancement Product Hunt, non vérifiés indépendamment