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AgentBack - AI native API/MCP framework
Empower AI coding agents to build APIs and MCP servers
AgentBack is an ESM/Zod/MCP fork of LoopBack 4 for building HTTP APIs and MCP servers from one codebase. Define a Zod schema once on a decorator and it becomes your request validation, OpenAPI 3.1 spec, MCP tool, and a codegen-free typed client — all from the same DI container. AI coding agents get a real contract to build against, so they can't drift into a second source of truth or a mismatched endpoint. Best practice becomes the path of least resistance. On npm today.
AgentBack is a TypeScript framework for backends whose first reader is an agent, not a human.
Modern services aren't consumed only by web and mobile apps anymore — they're consumed by AI agents that discover tools, inspect schemas, call APIs, retry failures, and chain them into longer workflows. That raises the stakes on drift: a stale OpenAPI doc or tool manifest used to be a documentation bug; for an agent it's a runtime bug — it picks the wrong tool, sends the wrong body, misses an auth requirement, or trusts a response shape that's gone.
So AgentBack bets everything on one Zod schema per operation. To an agent that schema is three things at once: knowledge (the contract is the only documentation a model reliably reads), a constraint (a validated boundary stops a hallucinated argument before it reaches your handler), and a contract (stable, diffable, testable). Define it once and it becomes your REST validation, OpenAPI 3.1, the MCP tool definition, a codegen-free typed client, your tests, and agent-readable surfaces like /llms.txt — served by default, never out of sync.
Two things I cared about getting right:
• Tools are not endpoints. A route never becomes an MCP tool by accident — exposing one is opt-in, so you ship the 3 outcome-level operations an agent actually needs instead of auto-generating 80 CRUD tools that blow the model's context budget.
• Errors agents can fix. Failures come back as a structured envelope a model can read and correct against, not an opaque 500.
Schema-first lost to code-first once, because rigorous schemas were tedious and we're all a little lazy. The economics flipped: agents are tireless, so when the agent writes and maintains the schema the cost of rigor drops to near zero while its value goes up.
It's a refresh of LoopBack (which I co-created 13 years ago) for the agent era, on the DI core I know best. Alpha, on npm today: npm create agentback my-service. Would love feedback from anyone building agent-facing APIs or MCP servers.
About AgentBack - AI native API/MCP framework on Product Hunt
“Empower AI coding agents to build APIs and MCP servers”
AgentBack - AI native API/MCP framework was submitted on Product Hunt and earned 6 upvotes and 3 comments, placing #117 on the daily leaderboard. AgentBack is an ESM/Zod/MCP fork of LoopBack 4 for building HTTP APIs and MCP servers from one codebase. Define a Zod schema once on a decorator and it becomes your request validation, OpenAPI 3.1 spec, MCP tool, and a codegen-free typed client — all from the same DI container. AI coding agents get a real contract to build against, so they can't drift into a second source of truth or a mismatched endpoint. Best practice becomes the path of least resistance. On npm today.
On the analytics side, AgentBack - AI native API/MCP framework competes within API, Open Source, Artificial Intelligence, GitHub and Vercel Day — topics that collectively have 681.4k followers on Product Hunt. The dashboard above tracks how AgentBack - AI native API/MCP framework performed against the three products that launched closest to it on the same day.
Who hunted AgentBack - AI native API/MCP framework?
AgentBack - AI native API/MCP framework was hunted by Raymond Feng. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of AgentBack - AI native API/MCP framework including community comment highlights and product details, visit the product overview.
Raymond Feng
Hey Product Hunt 👋
AgentBack is a TypeScript framework for backends whose first reader is an agent, not a human.
Modern services aren't consumed only by web and mobile apps anymore — they're consumed by AI agents that discover tools, inspect schemas, call APIs, retry failures, and chain them into longer workflows. That raises the stakes on drift: a stale OpenAPI doc or tool manifest used to be a documentation bug; for an agent it's a runtime bug — it picks the wrong tool, sends the wrong body, misses an auth requirement, or trusts a response shape that's gone.
So AgentBack bets everything on one Zod schema per operation. To an agent that schema is three things at once: knowledge (the contract is the only documentation a model reliably reads), a constraint (a validated boundary stops a hallucinated argument before it reaches your handler), and a contract (stable, diffable, testable). Define it once and it becomes your REST validation, OpenAPI 3.1, the MCP tool definition, a codegen-free typed client, your tests, and agent-readable surfaces like /llms.txt — served by default, never out of sync.
Two things I cared about getting right:
• Tools are not endpoints. A route never becomes an MCP tool by accident — exposing one is opt-in, so you ship the 3 outcome-level operations an agent actually needs instead of auto-generating 80 CRUD tools that blow the model's context budget.
• Errors agents can fix. Failures come back as a structured envelope a model can read and correct against, not an opaque 500.
Schema-first lost to code-first once, because rigorous schemas were tedious and we're all a little lazy. The economics flipped: agents are tireless, so when the agent writes and maintains the schema the cost of rigor drops to near zero while its value goes up.
It's a refresh of LoopBack (which I co-created 13 years ago) for the agent era, on the DI core I know best. Alpha, on npm today: npm create agentback my-service. Would love feedback from anyone building agent-facing APIs or MCP servers.