Open source AI agent self-improvement
Run Reflexio yourself to give AI agents local memory, online playbook learning, and retrieval-backed behavior improvement without retraining the model.
Publish interactions
Send each user turn, agent response, correction, and outcome to the local Reflexio server.
Extract memory and playbooks
Reflexio turns durable user facts into profiles and behavioral lessons into user playbooks.
Retrieve learned context
Your agent searches profiles and playbooks before the next response, so feedback changes future behavior.
Why open source Reflexio
Agent self-improvement you can inspect, run, and adapt
Most agents repeat mistakes because corrections disappear after the current conversation. Reflexio keeps the learning loop outside the model: interactions become profiles, playbooks, and searchable context your agent can use next time.
Standalone self-hosting
Run the open-source package on your own machine or infrastructure with a local FastAPI backend.
Online learning loop
Keep publishing interactions and Reflexio learns from real corrections as they arrive.
Local storage by default
Start with local SQLite for development, then choose stronger storage when your deployment needs it.
Open implementation
Inspect the memory, extraction, and retrieval path instead of treating agent learning as a black box.
OSS vs Enterprise
Start standalone. Upgrade when operations matter.
The open-source package is built for self-hosted online learning. Reflexio Enterprise adds managed infrastructure, stronger storage modes, governance surfaces, and continuous offline optimization for playbooks.
| Area | Open source | Enterprise |
|---|---|---|
| Best fit | Local builds, prototypes, self-hosted agent memory, and teams that want full code visibility. | Production teams that want managed operations, stronger storage, admin workflows, and support. |
| Storage | Standalone storage with local SQLite as the default open-source setup. | Managed and enterprise-grade storage options, including Supabase and native Postgres modes. |
| Learning mode | Online learning from published interactions, profiles, and user playbooks. | Online learning plus continuous offline optimization for playbooks using historical outcome evidence. |
| Operations | You run, secure, monitor, and scale the service yourself. | Managed solutions, operational dashboards, admin controls, billing, and deployment support. |
Implementation shape
A self-improving agent loop in a few calls
Install the open-source package, run the local backend, publish interactions, then retrieve learned context before future agent turns.
from reflexio import InteractionData, ReflexioClient
client = ReflexioClient(url_endpoint="http://localhost:8081")
client.publish_interaction(
user_id="user_123",
interactions=[
InteractionData(role="User", content="I travel weekly."),
InteractionData(role="Agent", content="Try this bulky laptop."),
InteractionData(
role="User",
content="Too heavy. Prioritize battery life and weight.",
),
],
source="local-agent",
session_id="session_001",
)
context = client.search(
query="laptop recommendation for user_123",
user_id="user_123",
)Frequently asked questions
Open-source agent improvement, without the hand-waving
What is open-source AI agent self-improvement?
Open-source AI agent self-improvement is a feedback loop where an agent records real interactions, extracts useful memory and behavioral guidance, and retrieves that context before future responses. Reflexio provides this loop as an inspectable open-source package.
Is open-source Reflexio a replacement for fine-tuning?
No. Reflexio improves agent behavior by retrieving learned profiles and playbooks at runtime. It does not require retraining the base model for every correction.
How is Reflexio Enterprise different from the open-source package?
The open-source package is standalone, self-hosted, and focused on online learning. Enterprise adds managed deployment options, more sophisticated storage, admin and governance surfaces, and continuous offline playbook optimization.
Can I start locally and move to Enterprise later?
Yes. The open-source path is a good way to understand Reflexio's learning loop before adopting managed storage, operational controls, and enterprise playbook optimization.
Build the local loop first
If you want inspectable AI agent self-improvement today, start with the open-source quickstart. If you need managed storage, dashboards, or offline playbook optimization, explore Reflexio Enterprise.