Kopai vs Weavable – Persistent work context for AI agents: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Kopai and Weavable – Persistent work context for AI agents — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Kopai
Kopai
Serverless cloud for building, hosting, and monetizing domain-specialized AI agents, with RAG, orchestration, and per-message billing handled for you.
Key features
- Prompt-to-Agent Builder: Write a prompt, upload documents, and try several models side by side — seven steps from blank page to a shipped agent.
- Managed Infrastructure: Kopai holds the model keys, runs the vector database, and keeps the servers alive; you get an endpoint and a readable bill.
- Agent Marketplace: List an agent and get paid per message, keeping 70% of your markup, with every charge logged in an auditable ledger.
- Multi-Model Gateway: One integration across GPT-4o, Kimi K2, Gemini 2.5, Qwen 3, and DeepSeek, switchable at any time.
- Automatic Document Indexing: Upload PDF, DOCX, or XLSX files and Kopai indexes them and handles retrieval behind the scenes.
- Resilient Streaming: Answers resume from where they stopped after a dropped connection or closed tab, with no tokens lost.
- Conversational Agent Creation: Describe the job in ordinary chat and Kopai drafts the agent, picks its organization, and finishes on your approval.
- Kopai for Teams: Seats and roles, team-private agents, shared knowledge, and usage numbers you can check.
Best for
- A lawyer packages case-preparation expertise into an agent and sells access on the marketplace instead of billing hours.
- A consultant turns a library of internal documents into a domain expert clients can query directly.
- A solo creator wants to ship a RAG agent without standing up a vector database or backend service.
- A SaaS company embeds a specialized agent in its own product while letting Kopai handle billing and payouts.
- A team needs private internal agents with role-based access over a shared knowledge base.
- A developer wants to test the same agent across several model providers before committing to one.
W
Weavable – Persistent work context for AI agents
Weavable
Persistent, structured work-context layer that ingests, scopes, and serves live context from business tools to AI agents via a unified endpoint.
Key features
- Pre-built Connectors: Ingests updates and records from HubSpot, Jira, Slack, Zendesk, Notion and other common business systems to centralize source data.
- Preprocessing & Structuring: Extracts entities, relationships, timelines and summaries from raw updates to convert scattered signals into structured, machine-friendly context.
- Context Scoping: Filters and scopes context per agent, workflow, or role to deliver only the relevant slice of data and reduce prompt size and noise.
- Single MCP Endpoint: Serves scoped, maintained context to any agent or orchestration layer through a unified endpoint, simplifying integration and routing.
- Persistence & Live Updates: Maintains continuous, up-to-date work context so agents retain continuity across sessions and reflect recent system changes.
- Policy & Maintenance Controls: Configurable rules for retention, update frequency, and scoping to keep context accurate, curated, and privacy-compliant.
- Single MCP endpoint to serve scoped context to any agent
- Pre-processes and scopes context from external tools (HubSpot, Jira, Slack, Zendesk, Notion, etc.)
- Persistent, live work context with relationship extraction and maintenance
- Structured context delivery suitable for agent workflows (reduces ad-hoc plumbing)
- Real-time or near-real-time synchronization of updates from integrated systems
- Designed to be stack-agnostic — can integrate with multiple SaaS platforms
- Scoping and filtering to provide only relevant context per task or workflow
Best for
- Customer Support Assistants: Provide chat agents with live, consolidated context from Zendesk, Slack, and CRM history so they resolve tickets faster with up-to-date background.
- Sales Personalization: Equip outreach or proposal-generation agents with scoped HubSpot CRM timelines and contact relationships for tailored messaging and accurate follow-ups.
- Incident Response Automation: Feed Jira issues, Slack incident channels, and change logs into responder agents to accelerate diagnosis and remediation with relevant recent events.
- Knowledge Worker Augmentation: Supply drafting or summarization agents with curated project context and documents from Notion and other sources to produce coherent reports.
- Cross-System Workflows: Orchestrate multi-step automations that require synchronized state across tools by giving orchestration agents a single source of scoped truth.
- Agent Testing & Development: Let developers iterate on agent behavior using stable, replayable context slices instead of rebuilding environment plumbing for each test.
- Supplying customer history and ticket context to conversational support agents
- Feeding scoped CRM and deal context to sales automation agents
- Providing developer or ops agents with up-to-date issue and deployment context from Jira/Slack
- Orchestrating multi-tool workflows where agents need consolidated, persistent state
- Enabling knowledge retrieval and action-taking agents with curated, live document/context slices