AgentLoop — runtime learning for AI agents
Runtime learning for production AI agents
AI agents that
don't repeat the same
mistake twice.
AgentLoop is a runtime learning layer for production AI agents.<br>Human corrections become reusable memory — searched before every<br>response, applied automatically, improving the agent without retraining.
Start free
See the live demo
Integration
Two lines of code
Providers
OpenAI · Anthropic
Pricing
Free plan to start
01 / How it works
A loop, not a retraining run.
Every response gets reviewed — by a human, a heuristic, or a downstream<br>signal. Corrections become memory. Memory gets pulled into the next prompt.<br>Three steps, looping forever, without touching weights.
01
Retrieve relevant facts
Before each LLM call, AgentLoop searches past corrections semantically and injects the most relevant ones into the prompt. Your agent now knows what it was wrong about yesterday.
02
Log every turn
The response is logged automatically — question, answer, model, signals. No glue code. Failed cases surface in a review queue, ranked by signal strength.
03
Correct & ship
A reviewer writes the correct answer once. It's embedded, deduplicated, and instantly available to every future query — same shape, same wording, fixed for good.
02 / Drop-in integration
Wrap your client. That's it.
No SDK overhaul. No prompt-engineering rewrite. AgentLoop's wrappers<br>keep the OpenAI and Anthropic interfaces exactly as they are — and<br>add memory retrieval and turn logging behind the scenes.
from openai import OpenAI<br>from agentloop import AgentLoop<br>from agentloop_openai import wrap_openai
# 1. Wrap your existing OpenAI client.<br>client = wrap_openai(<br>OpenAI(),<br>loop=AgentLoop(api_key="ak_live_..."),
# 2. Use it like normal. AgentLoop runs around it.<br>response = client.chat.completions.create(<br>model="gpt-4o",<br>messages=[<br>{"role": "system", "content": "You are a helpful assistant."},<br>{"role": "user", "content": question},<br>],<br>agentloop={"user_id": user.id},
Search runs before
The wrapper calls AgentLoop's search endpoint, finds relevant prior corrections, and silently augments the system prompt.
Logging runs after
The completed turn is posted to the review queue. If AgentLoop is unreachable, the wrapper fails open — your user never waits.
Same API, no lock-in
Remove the wrap_openai() call and the code still works. AgentLoop never sits between you and your provider.
03 / Why teams use it
Built for the messy middle, not the demo.
Most agents look great on stage and break in production. The hard<br>part isn't the first response — it's the thousandth, when the<br>edge cases outnumber the happy path.
For developers
Stop hand-patching the system prompt
Every shipping team eventually maintains a 4,000-line system prompt full of "always remember that…" exceptions. AgentLoop replaces that with structured memory — searchable, deduplicated, editable, audited.
For product teams
Close the feedback loop without a fine-tuning run
Subject-matter experts write the fix once, in plain language. It applies to every future user, every future session, in seconds — not the next training cycle. The dashboard surfaces what reviewers actually fixed, not what models reported.
Cross-language
Python and JavaScript, real parity
The Python and JS SDKs produce byte-identical HMAC signatures. Feedback URLs signed in one validate in the other. Both languages hit the same backend, so behavior is consistent regardless of which SDK each call came from.
Provider-agnostic
Not locked to any one provider
Drop-in wrappers for OpenAI and Anthropic, first-class LangChain integration, and a direct REST API for anything else. Switching providers doesn't cost you your accumulated corrections — the memory layer outlives whichever model you're on.
04 / Try it
The demo answers wrong on purpose.
Ask the support agent a question. It'll give you a confidently<br>wrong answer. Correct it. Ask again — this time it remembers.<br>The whole loop, in about 90 seconds.
Open the demo
Or skip ahead — start free
05 / Pricing
Start free,<br>upgrade when you're ready.
$0
— to get started, no card required
The free plan covers everything you need to integrate AgentLoop<br>and see it working in your stack.
Need more headroom? Paid plans are live — usage-based and<br>predictable, with no surprise bills. See current plans and<br>limits inside the app.
View plans
06 / Get in touch
Real human, actually replies.
Questions about whether AgentLoop fits your stack? Want to discuss<br>production pricing? Write directly — we read every email.
hello@getagentloop.io