Weavable

Weavable is a context layer built by ThatWorks that sits between your existing tool stack and your AI agents. It connects to 20+ tools via OAuth, resolves entities across them, tracks changes continuously, and exposes a single MCP endpoint that any compatible AI client can consume. The product is SOC2 Type II and HIPAA certified, and the company states it is read-only by design and never uses customer data for model training.

What It Is

Weavable is an agent context infrastructure product — not an AI assistant itself, but the structured data layer that makes AI assistants reliably useful in team workflows. Where direct MCP tool connections pass raw API responses to a model and let it figure out relevance on the fly, Weavable pre-processes, scopes, and ranks context before any query runs. The result is a deterministic, relationship-aware context graph that the model reasons over instead of a flood of disconnected fragments.

How the Context Graph Works

Weavable runs a continuous changelog across connected tools rather than querying them at the moment of each request. This means when an agent asks "what's happening with Acme," the answer is built from accumulated, tracked changes across HubSpot, Jira, Slack, and other connected tools — not a point-in-time snapshot. Entities are resolved across tools (a ticket, a thread, a deal linked by account, team, and timeline), so the model receives a connected graph rather than three separate data pulls it must reconcile from scratch on every run.

Workflow and Setup Path

The setup path is designed to take under 15 minutes:

• Connect tools via a single OAuth flow covering 20+ integrations including Google, Zendesk, Notion, Linear, GitHub, Jira, Slack, HubSpot, and Claude.
• Define context scope by selecting the tools, projects, channels, and pipelines relevant to a given workflow.
• Weavable builds the graph — entities resolved, changes tracked, relationships mapped.
• Plug into any AI client via one MCP endpoint, usable in Claude, Cursor, ChatGPT, or any agent framework.

No MCP server to host, no vector database, and no ingestion pipeline are required.

Team and Access Control Features

Weavable is designed for team use, not just individual developers. A single context endpoint can be shared with teammates who receive exactly the scoped access defined by the creator — no per-user OAuth flows required. Access can be revoked in a single action. Additional enterprise-oriented capabilities include an audit trail for every query, a REST API for programmatic context access, SSO/SAML support (Okta and Google Workspace), and scheduled automations.

Tradeoffs to Know

Weavable positions itself explicitly against direct tool-to-model connections. The company's FAQ acknowledges that direct connections are possible but argues they produce inconsistent outputs because the model re-determines relevance on every run. Weavable's approach trades some flexibility for determinism: the same scoped foundation produces the same answer at 9am and at midnight, according to the product page. The system is read-only, meaning it cannot write back to any connected tool — a deliberate security constraint, not a limitation of the integration layer.

Why It Matters for AI Agent Workflows

The vendor claims Weavable reduces token usage by 90% and is preferred in LLM-as-a-judge evaluations 85% of the time compared to direct connections — both figures are vendor-published and appear on the homepage. The core value proposition addresses a real friction point in production agent deployments: reliable, maintainable context that survives upstream changes like renamed Slack channels or restructured Jira projects without breaking existing workflows.