AgentsKit vs. alternatives
How AgentsKit compares to LangChain.js, Vercel AI SDK, Mastra, and LlamaIndex.js across the surface area that actually matters in production.
AgentsKit is one of several ways to build agents in JavaScript. This page is opinionated but honest — each column lists what each framework genuinely optimizes for, with a link you can follow to verify.
Positioning in one line
| Framework | Positioning |
|---|---|
| AgentsKit | One toolkit — same contracts across chat UI, runtime, memory, RAG, tools, observability. Zero lock-in; swap pieces without breaking the agent. |
| LangChain.js | Huge integration catalog. Heavy on abstractions; focused on chains + agents. |
| Vercel AI SDK | Best-in-class streaming + React hooks. Chat-first; less coverage beyond the message loop. |
| Mastra | TypeScript-first agent framework with workflows + memory built-in. |
| LlamaIndex.js | RAG-first. Strong indexing + query engines. |
Feature comparison
| Capability | AgentsKit | LangChain.js | Vercel AI SDK | Mastra | LlamaIndex.js |
|---|---|---|---|---|---|
| Streaming chat controller | ✅ | ✅ | ✅ | ✅ | ✅ |
React hook (useChat) | ✅ | partial | ✅ | partial | — |
| Vue / Svelte / Solid / Angular / RN bindings | ✅ (same contract) | — | community | — | — |
| Terminal UI | ✅ (@agentskit/ink) | — | — | — | — |
| ReAct runtime | ✅ | ✅ | — | ✅ | ✅ |
| Deterministic replay | ✅ (@agentskit/eval/replay) | — | — | — | — |
| Multi-agent topologies (supervisor / swarm / blackboard / hierarchical) | ✅ | partial (LangGraph) | — | partial | — |
| Durable execution (step log) | ✅ | via LangGraph | — | ✅ | — |
| Human-in-the-loop primitives | ✅ (@agentskit/core/hitl) | partial | — | ✅ | — |
| Built-in prompt injection detector | ✅ (@agentskit/core/security) | — | — | — | — |
| Built-in signed audit log | ✅ (@agentskit/observability) | — | — | — | — |
| Token-bucket rate limiting | ✅ | — | — | — | — |
| Mandatory tool sandbox policy | ✅ (@agentskit/sandbox) | — | — | — | — |
| Bidirectional MCP bridge | ✅ (@agentskit/tools/mcp) | client only | partial | client only | client only |
| Vector backends | pgvector, Pinecone, Qdrant, Chroma, Upstash, Redis, file | 30+ | — | Postgres, SQLite | 30+ |
| Document loaders (URL / GitHub / Notion / Confluence / Drive / PDF) | ✅ | ✅ (largest catalog) | — | partial | ✅ |
| Evals in CI (JUnit + step-summary annotations) | ✅ (@agentskit/eval/ci) | partial | — | — | partial |
| Core bundle size | < 10 KB gzipped | > 100 KB | ~30 KB | ~50 KB | > 100 KB |
| Zero runtime deps (core) | ✅ | — | — | — | — |
| Edge / Deno / Bun runtime | ✅ | partial | ✅ | partial | partial |
"partial" means the capability exists but requires stitching multiple packages or a community port — not a built-in, contract-level feature.
When to pick which
- AgentsKit — you want one mental model across web, terminal, mobile, and server; you care about bundle size, observability, and production gates (HITL, rate limits, audit, sandbox).
- LangChain.js — you need the widest catalog of prebuilt integrations today and are happy to wrap them yourself.
- Vercel AI SDK — you only need a streaming chat hook inside a Next.js / React app and don't want extra surface area.
- Mastra — you want a workflow-first agent framework with built-in memory and agent registry.
- LlamaIndex.js — your product is a retrieval engine; you want sophisticated query engines out of the box.