Runtime learning for production AI agents
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.
01 / How it works
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.
01
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
The response is logged automatically — question, answer, model, signals. No glue code. Failed cases surface in a review queue, ranked by signal strength.
03
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
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 # 1. Wrap your existing OpenAI client. client = wrap_openai( OpenAI(), loop=AgentLoop(api_key="ak_live_..."), ) # 2. Use it like normal. AgentLoop runs around it. 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}, )
A
Search runs before
The wrapper calls AgentLoop's search endpoint, finds relevant prior corrections, and silently augments the system prompt.
B
Logging runs after
The completed turn is posted to the review queue. If AgentLoop is unreachable, the wrapper fails open — your user never waits.
C
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
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.
For developers
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
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
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
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.
05 / Pricing
$0
— to get started, no card required
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.
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