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Inkeep Agents

Official
Build AI Agents with a No-Code Visual Builder or TypeScript SDK, with full 2-way sync.
GitHub source repository ↗
★ 1.4k Stars Category · Dev Tools Very popular
54FMRS · D

Inkeep Agents is a fair-code, source-available AI agent platform centered on 2-way sync between a no-code visual builder and a TypeScript SDK. It suits teams that want technical and non-technical collaboration, multi-agent architecture, MCP tools, and self-hosted deployment. It uses ELv2 with Inkeep's Supplemental Terms, which restrict competitive uses; the source provides no client configuration example, so it is not for users expecting a ready-made client setup.

Reliability
8/20
Security and permissions
11/20
Maintenance
13/20
Documentation
12/20
Setup experience
10/20
Read the FMRS scoring method →

Inkeep Agents is a platform for building AI agents and multi-agent AI workflows. It offers two ways to build: a no-code visual builder (drag-and-drop canvas) and a TypeScript Agents SDK (@inkeep/agents-sdk). The two are fully interoperable with full 2-way sync, so technical and non-technical teams can create and manage agents in one platform. The platform is composed of several services and libraries: agents-api (a REST API that handles configuration of agents, sub agents, MCP servers, credentials, and projects, and also exposes agent execution and evaluation; it tracks conversation state and emits OTEL traces), agents-manage-ui (the visual builder web interface), agents-sdk (the TypeScript SDK for declaratively defining agents and custom tools), agents-cli (utilities including inkeep push and inkeep pull, which sync TypeScript SDK code with the visual builder), and agents-ui (a UI component library of chat interfaces). Under the hood it uses the Vercel AI SDK for interfacing with LLM providers, compatible with Vercel's useChat hook. Agents can be triggered via MCP, A2A, and Vercel SDK APIs; observability is provided via a Traces UI and OpenTelemetry; deployment is possible with Vercel or Docker. The framework is licensed under the Elastic License 2.0 (ELv2) subject to Inkeep's Supplemental Terms, a fair-code, source-available license.

Setup

The server path is the repository root. Per the README, start with the docs (https://docs.inkeep.com) or the 1-minute quick start (https://docs.inkeep.com/get-started/quick-start). There are two ways to build: 1) use the visual builder in agents-manage-ui to drag and drop on a canvas; 2) declare agents in code with the TypeScript SDK (@inkeep/agents-sdk), as shown in the README's basicAgent and helloAgent examples. Use the agents-cli utilities inkeep push and inkeep pull to sync TypeScript SDK code with the visual builder. Deployment can use Vercel or Docker. The source does not provide a specific client configuration example.

Fit and risk

Best for

  • Teams where technical and non-technical members collaborate on agents in one platform
  • Teams needing a no-code visual builder and TypeScript SDK with 2-way sync
  • Teams needing a multi-agent architecture
  • Teams wanting to self-host and deploy on their own infrastructure
  • Developers needing MCP tools with credential management and choice of LLM provider

Not for

  • Users who cannot accept a source-available license or competitive-use restrictions (ELv2 and Inkeep's Supplemental Terms)
  • Users wanting a simple, ready-to-use client configuration (the source provides no client config example)
  • Users unwilling to deploy or maintain infrastructure themselves (self-hosting requires own deployment)
  • Users not intending to use TypeScript or the no-code interface

Required permissions

  • Configuration of agents, sub agents, MCP servers, credentials, and projects (via agents-api)
  • Credential management for MCP tools
  • Access to the configured LLM providers
  • Running on your own infrastructure when self-hosted (Vercel or Docker)
  • Observability data emitted via OpenTelemetry when tracing is used

Risks and side effects

  • ELv2 is a source-available license subject to Inkeep's Supplemental Terms, which restrict certain competitive uses
  • MCP tool credentials, if mishandled, could expose sensitive information
  • Agents can perform write operations such as updating CRMs, knowledge bases, and tickets, so mistakes can affect production data
  • Self-hosted deployment means you bear security and operational responsibility
  • LLM provider data handling and costs depend on the chosen provider

Troubleshooting

  1. Confirm you have followed the README pointers to the docs and the 1-minute quick start
  2. Check that TypeScript SDK code and the visual builder are correctly synced via inkeep push / inkeep pull
  3. Verify agents-api is running and reachable, since both manage-ui and the SDK write to it
  4. Check MCP server and credential configuration
  5. For self-hosted deployments, verify the Vercel or Docker environment configuration
  6. For execution or evaluation issues, inspect the Traces UI and OpenTelemetry traces

Use cases

Real-time AI chat assistants, such as a customer experience agent for help centers, technical docs, or in-app experiences
An internal copilot to assist support, sales, marketing, ops, and other teams
AI workflow automation such as creating and updating knowledge bases, documentation, and blogs
Updating CRMs, triaging helpdesk tickets, and tackling repetitive tasks
Triggering agents via MCP, A2A, and Vercel SDK APIs
Observability via a Traces UI and OpenTelemetry