Your application rebuilds the agent.
- Repeat the system prompt
- Store and search conversation history
- Decide what belongs in each request
- Turn feedback into safe, versioned rules
â– THE MEMORY LAYER FOR SERVER-SIDE AGENTS.
Define the agent once on your server: its profile, system prompt, instructions, history and approved lessons. Every API call starts with the context it needs—not another round of setup.
One agent ID. Your model provider. Your application runs the tools.
> meet your new agent
Customer support agent
// Every request can be this simple. { "agent_id": "support_agent", "input": [{ "role": "user", "content": "Help my customer." }] }
THE MODEL IS ONLY PART OF THE AGENT.
A model can answer well, but it does not know your agent’s role, its decisions or the customer history behind a request. That context should live with the agent—not be reconstructed in every application call.
You keep control. Bring your model API key. Keep tools inside your application. osAgents manages the persistent agent context between them.
DEFINE ONCE. KEEP BUILDING.
Your app sends the task.
osAgents brings the context.
Be helpful. Keep answers short.
Ask when information is missing.
Give your agent a name, a role and clear instructions. Edit them whenever you need.
Bring a supported model provider’s API key. osAgents adds the saved context before calling it.
Call your agent through the API. Your application stays in charge of tools and actions.
Your application sends an agent ID and a message. osAgents resolves the agent context before the model call.
MEMORY, WITHOUT THE MYSTERY.
Chat history, saved preferences and knowledge can live behind one agent ID. You choose what is kept, how long it stays and who it belongs to.
osAgents searches memory privately. The model receives only the relevant pieces—not your entire memory store—and memory never changes the model’s weights.
For every request, osAgents first narrows memory by workspace, agent, end user and session. It selects the most relevant recent chat turns, facts and approved lessons, then adds them to the model context.
If the initial context is not enough, the agent can request recall_memory. osAgents performs another bounded, read-only search and returns the result privately so the model can finish its answer.
LEARN FROM FEEDBACK. NOT GUESSWORK.
Correct an answer. Review a suggested lesson.
Choose whether your agent should keep it.
“Please write dates
as YYYY-MM-DD.”
Use YYYY-MM-DD
for invoice dates.
A saved rule for
future invoice requests.
Learning updates approved instructions and memory—not model weights. Changes can be tested and rolled back; improvement is not guaranteed.
A SMALL PREVIEW. A CLEAR IDEA.
Change the profile. See what your app sends.
No API key needed for this example.
You send the message, not the whole profile.
“Happy to help. What does your customer need?”
curl http://127.0.0.1:4331/v1/messages \
-H "Authorization: Bearer YOUR_OSAGENTS_KEY" \
-H "Idempotency-Key: UNIQUE_REQUEST_ID" \
-H "Content-Type: application/json" \
-d '{
"agent_id": "support_agent",
"session_id": "customer_1042",
"input": [{"role":"user","content":"Help with this order."}]
}'BUILT FOR YOUR APPLICATION.
No system prompt to paste into every call. No conversation database to build from scratch.
Use an agent ID and, when memory is enabled, a session ID. Receive a response your application can use.
osAgents does not execute your tools. If your agent requests a tool call, your application decides whether to run it and sends back the result.
PAID FROM THE FIRST LIVE WORKSPACE.
No hosted free tier and no seat plans. One monthly entry plan, then transparent usage.
The price-to-fully-loaded-cost ratio must remain at least 10Ă— before this candidate rate card can go live.
per workspace
A FEW THINGS TO KNOW.
No. The initial product uses saved instructions and relevant memory with a connected model. That changes the context sent to the model, not its weights.
Yes. You connect a supported OpenAI-compatible provider; osAgents manages your agent’s profile and context. Provider inference charges remain separate.
You choose whether to retain session history and save longer-term facts or preferences. Memories are inspectable and deletable, and separated by workspace, agent and end user.
No. Feedback can produce suggested lessons. Approved, versioned lessons influence later responses. They do not train the underlying model, and they can be wrong—so evaluation and rollback matter.
The proposed service stores enabled profiles and memory on our servers. Instructions, messages and selected memory are sent to your connected model provider for inference. Final production regions and retention terms must be published before launch.
Your application runs the tools. This product focuses on persistent agent profiles, memory and controlled learning—not a multi-model router or an orchestration platform.
This page is still a preview and collects no payment. The launch candidate is $20 per workspace each month, including 10,000 runs and 1 GB memory, then $3 per 1,000 runs and $8 per GB-month. It will not be published as a live Price until the 10Ă— cost gate passes.
THE AGENT LAYER BETWEEN YOUR APP AND YOUR MODEL.