Turn your prompts into production-ready AI commands you can chain, connect to tools, and scale. Define once, deploy anywhere, debug everything.
import { AgentLoop } from '@agentloop/sdk'; const loop = new AgentLoop(); // Define a versioned command const summarize = loop.command('summarize-v2'); // Chain into a sequence const result = await loop.sequence([ summarize.with({ doc: input }), 'extract-entities', 'generate-report' ]).run();
Every company building AI features faces the same brutal choices—and none of them are good.
Simple to start, impossible to scale. No versioning, no chaining, no observability. Prompts scattered across your codebase.
2-3 weeks to productionPowerful abstractions that become impossible to productionize. Debug nightmares, unpredictable latency, vendor lock-in.
40% of code is glueYou could build it yourself. You'll spend 6 months, 3 engineers, and still be solving solved problems.
$500K+ opportunity costAgentLoop is the layer between your application and AI—designed for developers who ship.
Prompts, configs, tools—deploy and rollback like code.
Multi-step sequences with branching, loops, and error handling.
MCP servers, APIs, knowledge bases, and evaluation frameworks.
Full traces, latency metrics, cost tracking, and alerts.
System prompt + user template + model config
APIs, MCP servers, knowledge bases
Chain commands with logic flow
One API call. Full visibility.
Built by engineers who were tired of rebuilding the same infrastructure at every company.
Define AI commands as reusable, versioned units. System prompt, user template, model config, output schema—all in one place. Deploy without code changes.
version controlChain commands into multi-step workflows with conditional branching, parallel execution, and error handling. Pass outputs between steps automatically.
orchestrationConnect to webhooks, REST APIs, or MCP servers. Built-in retry logic, timeout handling, and response transformation. Native MCP support from day one.
integrationsAttach vector stores or document collections to any command. We handle chunking, embedding, and context injection. Build RAG without the plumbing.
retrievalDefine assertions, run test suites, catch regressions before production. Compare outputs across prompt versions, models, or configurations.
testingTrace every execution, inspect intermediate outputs, debug failures with full context. Alerts for latency, errors, and cost anomalies.
monitoringNo complex setup, no framework lock-in. Just an API that does what you need.
Create versioned commands with your prompts, model settings, and output schemas. Test locally, deploy globally.
Chain commands into workflows. Add tool connections, knowledge bases, and conditional logic. All through the API.
Execute with one API call. Watch traces in real-time. Roll back if something breaks. Ship confidently.
From early-stage startups to scaling AI companies.
We replaced 3,000 lines of orchestration code with AgentLoop. Our agents are faster, more reliable, and actually testable now. The observability alone paid for itself in week one.
I was spending 40% of my time on infrastructure instead of product. AgentLoop gave me that time back. We shipped our AI features 3x faster than our original timeline.
We evaluated building in-house, LangChain, and AgentLoop. AgentLoop was production-ready in a week. The others would have taken months. The MCP support sealed it.
Usage-based pricing that makes sense. No surprises, no gotchas.
For side projects and prototypes
For growing teams shipping to production
For teams that need security and scale
Everything you need to know about AgentLoop.
LangChain is a framework—you import it into your code and it helps you write Python. AgentLoop is infrastructure—you call it from your code and it handles execution, versioning, and observability. Think of it like the difference between Flask and Heroku: complementary, not competitive. Many teams use both.
LLM providers are focused on model capabilities, not workflow orchestration. Just as AWS doesn't build Vercel, model providers won't build AgentLoop. Our multi-model, tool-agnostic approach is actually strengthened when providers add features—we integrate them rather than compete with them.
Yes. AgentLoop supports OpenAI, Anthropic, Google, and any OpenAI-compatible API (like Ollama or vLLM). You can mix models within a single sequence—use Claude for reasoning, GPT-4 for code, and a local model for classification. LLM costs are passed through at cost.
AgentLoop natively supports the Model Context Protocol. Connect any MCP server as a tool, and your commands can use it automatically. We handle discovery, capability negotiation, and error handling. It's the fastest way to give your agents access to external tools and services.
We're SOC2 Type II compliant on our Enterprise tier. All data is encrypted at rest and in transit. We don't train on your data. Enterprise customers get dedicated infrastructure, SSO, and data residency options. We can sign BAAs for healthcare customers.
Not currently. We're focused on our managed cloud offering to ensure the best developer experience and fastest iteration speed. Enterprise customers with strict requirements can discuss dedicated deployment options with our sales team.
Join 500+ teams using AgentLoop to build production AI workflows. Free to start, scales with you.