#
AI agents that
don't repeat the same
mistake twice.
AgentLoop is a runtime learning layer for production AI agents. Human corrections become reusable memory — searched before every response, applied automatically, improving the agent without retraining.
A loop, not a retraining run. #
Every response gets reviewed — by a human, a heuristic, or a downstream signal. Corrections become memory. Memory gets pulled into the next prompt. Three steps, looping forever, without touching weights.
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.
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.
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.
Wrap your client. That's it. #
No SDK overhaul. No prompt-engineering rewrite. AgentLoop's wrappers keep the OpenAI and Anthropic interfaces exactly as they are — and add memory retrieval and turn logging behind the scenes.
from openai import OpenAI
from agentloop import AgentLoop
from agentloop_openai import wrap_openai
client = wrap_openai(
OpenAI(),
loop=AgentLoop(api_key="ak_live_..."),
)
response = client.chat.completions.create(
model="gpt-4o",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": question},
],
agentloop={"user_id": user.id},
)
Built for the messy middle, not the demo. #
Most agents look great on stage and break in production. The hard part isn't the first response — it's the thousandth, when the edge cases outnumber the happy path.
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.
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.
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.
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.
Start free, #
upgrade when you're ready.
$0
The free plan covers everything you need to integrate AgentLoop and see it working in your stack.
Need more headroom? Paid plans are live — usage-based and predictable, with no surprise bills. See current plans and limits inside the app.
Real human, actually replies. #
Questions about whether AgentLoop fits your stack? Want to discuss production pricing? Write directly — we read every email.