Kit vs Kopai: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Kit and Kopai — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Kit
Speakeasy
An open-source coding agent runtime that gives the model a single compose tool, cutting round trips and token use versus conventional harnesses.
Key features
- Single Compose Tool: The model receives one tool whose argument is a Runlet program, so file reads, shell commands, edits, retries, and subagent calls all happen in a single round trip instead of one per action.
- Concurrent Program Execution: Independent calls inside a compose program run concurrently, with data dependencies or after blocks used to force ordering when needed.
- Reusable Subagents: A subagent is a value you can continue, fork, inspect, and close, and you can require its output to match a JSON schema.
- Cross-Harness Orchestration: Claude Code, Codex, Cursor, or another Kit instance can be used as the subagent harness over ACP, so Kit coordinates tools it did not ship with.
- Open Protocol Support: Implements ACP v1 and v2 over stdio, HTTP/SSE, and WebSocket, A2A v1 in both directions, plus MCP, Agent Skills, and Agent Plugin packages.
- Crash-Safe Long Sessions: Each append-only JSONL transcript item is synced to disk before acceptance and crash-safe locks let sessions resume from the TUI, prompt, or any ACP client.
- Automatic Context Compaction: Context is compacted automatically at 80% of the model's context window so long runs do not stall on overflow.
- Flexible Model Access: Connects to ChatGPT subscriptions through native OAuth and to models via OpenRouter or the Speakeasy AI Control Plane.
- Single Static Binary: Ships as one binary with a published container image, avoiding a runtime dependency chain on developer machines and CI.
Best for
- Cost-Sensitive Agentic Coding: Cut token spend and wall-clock time on large refactors by collapsing many tool calls into one composed program.
- Editor-Integrated Agents: Drive Kit from any ACP-compatible editor without writing a bespoke integration for each client.
- Multi-Harness Pipelines: Orchestrate Claude Code, Codex, or Cursor as subagents from a single controlling program when different harnesses suit different steps.
- Long-Running Migrations: Run multi-hour codebase migrations that survive crashes and resume from a durable transcript.
- Structured Extraction from Code: Require subagents to return schema-validated JSON so results can be fed into downstream tooling rather than parsed from prose.
- CI and Headless Automation: Run the same binary in containers over HTTP/SSE or WebSocket to fix failing tests or apply mechanical changes without a terminal session.
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.