Show HN: Seahaven – Open-source framework for building RL environments

Quick Start • Docs • Examples

Evals and RL need thousands of agent runs, each isolated, starting from a known state, and graded on what the agent changed. Production systems can't do that. Seahaven is a Python framework for building synthetic worlds that can: working copies of your agent's tools, realistic enough that the agent can't tell the difference.

Seahaven handles the hard parts: parallel instances, reproducibility, serving, and change logs. You only write what's specific to your world: its tables and its tools.

Named after the town in The Truman Show: an entire world built so that one inhabitant believes it is real.

• Recreate Any Environment: Mock AI tool calls, REST APIs, sandboxed SQL, search, or any custom format.

• Stateful: Each instance of a world has its own independent SQLite database.

• Composable: Compose, reuse and share worlds. Example: MyCoWorld can includeStripeAPIWorld and ShopifyAPIWorld.

• Fixtures: Freeze known starting states likesmall_startup ,agency orbig_co , and reuse them across runs.

• Concurrent Instances: Serve hundreds of world instances per process, at thousands of requests per second.

• Evaluate World State: Grade on state, not on transcripts. Every row the agent changed is logged.

• Reproducible: Same initial state (fixture), same clock/time, same random seed: the same run, every time.

• OpenEnv:seahaven serve is an OpenEnv environment. Drive it with any OpenEnv client, in any language, or publish it to Hugging Face.

• Web Console:seahaven serve includes a web UI: open instances, call tools, and inspect state in your browser.

• MCP:seahaven mcp serves one world to an MCP client, so you can work against it by hand from an editor or chat app.

• Built for Coding Agents: Docs optimized for agents authoring worlds.seahaven check tells an agent the exact fix for every mistake.

• Just Python: Tools are just functions. Tests use pytest. Your agent already knows how to write and test Seahaven worlds.

Create a world. This writes a complete project: schema, tools, tests, a fixture generator, and an AGENTS.md that points your coding agent at the docs.

uvx seahaven new crm_world # your world name
cd crm_world && uv sync

Write your world. A world is a schema and a set of tools. Here is a small CRM:

import seahaven

world = seahaven.World(
    name="crm",
    version="1.0.0",
    schema="""
    CREATE TABLE contacts (
        id TEXT PRIMARY KEY,
        email TEXT NOT NULL,
        stage TEXT NOT NULL,
        updated_at TEXT NOT NULL
    ) STRICT;
    """,
    state_format="seahaven.state/1",
)

@world.tool
def create_lead(ctx: seahaven.Ctx, email: str) -> dict[str, str]:
    """Add a contact to the pipeline as a new lead."""
    lead = {"id": ctx.ids.uuid(), "email": email, "stage": "lead", "updated_at": ctx.clock.iso()}
    ctx.db.execute("INSERT INTO contacts VALUES (?, ?, ?, ?)", *lead.values())
    return lead

@world.tool
def list_stale_leads(ctx: seahaven.Ctx) -> list[dict[str, object]]:
    """List leads nobody has touched in 30 days."""
    return ctx.db.rows(
        "SELECT * FROM contacts WHERE stage = 'lead' "
        "AND updated_at < strftime('%Y-%m-%dT%H:%M:%fZ', 'now', '-30 days')"
    )

Freeze a starting state. A fixture is a frozen database that every run starts from:

with world.instance(now="2026-06-01T09:00:00.000Z", clock_mode="fixed") as inst:
    for n in range(500):
        inst.call("create_lead", email=f"lead{n}@example.com")
    inst.freeze("big_co", "A pipeline of 500 new leads.")

Run your agent. Each run gets a private copy of the fixture in milliseconds. The same seed replays the same run, and what the agent changed is a document you grade:

for rollout in range(100):
    with world.instance("big_co", seed=rollout) as inst:
        run_agent(inst)  # your agent, your harness
        reward = grade(inst.state())  # every row the agent changed

Serve it. seahaven serve hosts an OpenEnv endpoint where every connection gets its own instance. Open http://127.0.0.1:8000/console to drive it by hand.

uv run --extra serve seahaven serve
python
from seahaven.openenv import SeahavenClient

with SeahavenClient(base_url="http://127.0.0.1:8000") as env:
    env.reset(fixture="big_co", seed=42)
    env.call("create_lead", email="ada@example.com")
    final_state = env.state()  # the document the eval grades

• ProjectTracker : the reference world, a fictional issue tracker shaped like Linear or Jira. Nine tables, 25 tools, full-text search, and fixtures from an empty workspace to a twelve-person agency with six months of history. Start here to learn the patterns (walkthrough ).
• Stripe World : a mock of Stripe's Billing and Payments core, with 24 tables and 155 API operations behind the same tools as Stripe's own MCP server. It also serves Stripe's REST API, so the Stripe SDKs work against it unchanged.

Build a world once and reuse it everywhere. A company world can add a payments world, such as Stripe World, and a chat world, plus its own tables and tools. The agent sees one tool list, and an eval grades what changed in every world from one state document. See the composition docs.

company.add_world(payments_world.world, name="payments", tool_prefix="pay_")
company.add_world(chat_world.world, name="chat", tool_prefix="chat_")

@company.tool
def refund_order(ctx: seahaven.Ctx, charge_id: str, channel: str) -> dict[str, object]:
    """Refund a charge and tell the support channel it is done."""
    refund = ctx.worlds.payments.call("create_refund", charge_id=charge_id)
    ctx.worlds.chat.call("post_message", channel=channel, text=f"refunded {refund['amount']}")
    return refund

seahaven serve hosts your world as an OpenEnv environment, the open standard for RL environments. Every connection gets its own private instance. Each process can host hundreds of parallel instances. Drive it from Python, from Kiln, or from any OpenEnv client, such as OpenEnv's own generic client:

from openenv import GenericEnvClient
from openenv.core.env_server.mcp_types import CallToolAction

with GenericEnvClient(base_url="http://127.0.0.1:8000") as env:
    env.reset(fixture="big_co", seed=7)
    create = CallToolAction(tool_name="create_lead", arguments={"email": "ada@example.com"})
    env.step(create.model_dump())
    final_state = env.state()

See the serving docs for the client, the wire protocol and running in production.

seahaven mcp connects a world to Claude, Cursor, or any MCP client. Explore a world by hand, debug your tools, or try a task yourself before you give it to an agent.

uv run --extra mcp seahaven mcp --fixture big_co

Seahaven is designed to be built by coding agents. seahaven new writes an AGENTS.md that points your agent at the docs for the version you have installed, not stale ones from the web. seahaven check catches the mistakes that are easy to make and hard to notice, and names the fix.

See CONTRIBUTING.md for setup and the checks CI runs.

MIT.

Seahaven is built by the team behind Kiln, a free app and open-source library for building better AI products. Kiln connects to any Seahaven world: write scenarios against a fixture, evaluate your agent on the state it leaves behind, then auto-optimize prompts and models against those evals.