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agent #276,827,931

status: live platform: sapiom

2,812,612agents run
271,306,215tasks run
$76,929,331cost saved
sapiom router
$ curl https://llm.services.sapiom.ai/v2/openai/v1/chat/completions
sapiom runtime
$ npx @sapiom/agent-studio@latest
checklocal
runcloud
inspecttrace
research-briefcompleted

capability providers behind every run

YugabyteDB Anchor Linkup Prelude name.com Polsia

01sapiom router_

One route to the right model.

Send the same OpenAI-compatible request. Sapiom selects the most efficient allowed model for each task and meters every call.

sapiom router chat completions
$ curl https://llm.services.sapiom.ai/v2/openai/v1/chat/completions \  -H "Authorization: Bearer $SAPIOM_API_KEY" \  -H "Content-Type: application/json" \  -d '{    "messages": [      {"role": "user", "content": "Say hello from Sapiom."}    ]  }'
task shapecontext sizecostqualitylatencyreliabilityavailabilitycompany policyefficient modelmid-tier modelfrontier model

02agent.studio_

Build the agent in one place.

Work with a real coding agent, then move between Canvas, Steps, and Code as the agent takes shape.

03sapiom runtime_

Wait, resume, and stay inspectable.

Deploy typed step graphs with explicit retries, signals, schedules, and per-attempt traces.

sapiom runtimerecent runs
Agent Service Cost Status
research-brief email metered Completed
research-brief ai model access metered Completed
research-brief web scraping metered Completed
research-brief search the web metered Completed
competitor-watch email metered Completed
competitor-watch web scraping metered Completed

one command connects everything

Your first agent, running in minutes

Start in your coding agent, terminal, or agent.studio. Sapiom handles the setup.

01

Start from a template

Clone an agent and run it locally.

02

Bring your own agent

Check, link, and deploy the TypeScript you have.

03

Start from intent

Describe the job and scaffold a typed agent.

agent.studio is the desktop workspace: AI chat,
Canvas, Steps, and TypeScript in one place.

from local graph to managed run

One agent, through the full loop.

One capability interface

Call paid capabilities through ctx.sapiom, MCP, or @sapiom/tools, with usage metered at call time.

Browse published rates

Managed runs stay inspectable

Deploy typed step graphs, schedule or signal them, and inspect every recorded attempt.

Explore Runtime

Plan the deployment together

Bring volume, security, procurement, and support requirements to the Sapiom team.

Book a demo

Questions, answered.

Scaffolding, offline graph validation, and local runs with stubbed Sapiom capability calls are free and work signed out. The moment a run calls a real capability, it is metered.

In a private git repository Sapiom provisions and you own. It is ordinary TypeScript built on @sapiom/agent, so you can read it, review it, and take it with you.

Claude Code, Codex, Cursor, Claude Desktop, and any client that speaks MCP over stdio. Hosted clients that cannot run npx, including ChatGPT, use the remote server over HTTP.

Sapiom carries agent and trace context with capability calls, so usage can be attributed back to the work that produced it. Recent executions and their step attempts are available through the CLI.

No. Search, scraping, models, compute, storage, email, domains, and the rest arrive through one integration. Sapiom holds the vendor relationships and meters the calls back to you at published rates.

Start with a local run.

Scaffold the agent, validate its graph, and execute real step code with local stubs for Sapiom capability calls.