Setup Agent Replay

You are running in the user's agent repository, not in Workshop.

Your job is to make the agent replayable from Raindrop Workshop without the user manually starting a replay server.

Target Contract

Workshop expects:

• .raindrop/agents.yaml committed in the agent repo.
• A replay server command in that yaml, plus cwd when the command must run from a subdirectory.
• A replay server with:
  • GET /health
  • POST /replay
• A local project registration via raindrop replay register.

Replay server ports must be in 61020-61044.

Workshop runs on http://localhost:5899.

If Raindrop MCP is not available or cannot reach Workshop, run:

raindrop workshop

Then retry the MCP/tool call. Do not stop just because the MCP server is unavailable.

If .raindrop/agents.yaml Already Exists

Before changing anything, read .raindrop/agents.yaml.

Ask the user whether to:

1. Start/register the existing replay setup.
2. Add a new agent replay entry.

If they choose start/register:

1. Run the configured command if needed.
2. Verify GET /health.
3. Run raindrop replay register.
4. Stop. Do not scaffold a duplicate server.

Setup Steps

1. Identify The Agent

Find:

• Event name used by tracing (eventMetadata({ eventName: ... }), equivalent SDK call, or current Workshop run).
• Agent entry point to invoke.
• Runtime context the agent requires, such as orgId, orgPublicId, convoId, userId, source.
• Model defaults and obvious supported model overrides.
• Existing script/package manager conventions.

If the agent is not instrumented with Raindrop/Workshop tracing, stop and tell the user to instrument it first.

2. Infer Input And Prefill

Create:

input:
  orgPublicId: string
  orgId: number

prefillFromTrace:
  orgPublicId: properties.orgPublicId
  orgId: properties.orgId

input is the shape passed to the replay server as request.context.

prefillFromTrace tells Workshop how to prefill that context from the selected trace. The user may edit the values in the UI before replay.

Only include fields the agent actually needs to run.

3. Pick A Port

Pick the first unused port in 61020-61044.

Use that port as a constant in the generated replay server code:

const PORT = 61020;

Do not put the port in .raindrop/agents.yaml.

4. Generate The Replay Server

Create the smallest server that fits the project language and conventions.

Required GET /health response:

{
  "ok": true,
  "eventName": "triage-agent-dev",
  "port": 61020,
  "cwd": "/absolute/path/to/project-or-subpackage",
  "command": "pnpm replay-server",
  "input": {
    "orgPublicId": "string",
    "orgId": "number"
  },
  "prefillFromTrace": {
    "orgPublicId": "properties.orgPublicId",
    "orgId": "properties.orgId"
  },
  "models": ["claude-sonnet-4-20250514", "gpt-4.1"]
}

Required POST /replay request:

interface ReplayRequest {
  replayRunId: string;
  sourceRunId?: string;
  messages: Message[];
  systemPrompt?: string;
  userMessage?: string;
  model?: string;
  context: Record<string, unknown>;
}

The server should keep the POST /replay request open until the replayed agent run finishes or fails. Workshop's replay_run MCP tool waits on this single request and surfaces its success or failure to the calling agent.

Successful response:

{ "replayId": "abc123", "status": "done" }

Failure response:

{ "status": "error", "message": "Failed to create turn: ...", "stack": "..." }

Use a non-2xx HTTP response for request/agent failures when possible. A 200 response with status: "error" is also accepted and will be surfaced through the same MCP tool. Do not start the agent in a fire-and-forget async task that only logs errors after POST /replay has returned; Workshop cannot observe those failures.

Replay should exercise the agent with as few side effects as possible. Before wiring the replay endpoint, inspect the agent's production entrypoint and extract the smallest reusable agent-running function you can. Prefer dependency injection, test doubles, dry-run flags, transaction rollback, or no-op adapters so replay avoids writes to the user's application database, billing systems, queues, email/SMS providers, analytics, or other external services. Preserve the real LLM/tool behavior needed for a faithful trace, but do not blindly call a production request handler if it persists application state as part of normal operation.

Before invoking the agent, set:

process.env.RAINDROP_LOCAL_DEBUGGER = "http://localhost:5899/v1/";

Do not pass replayRunId through an environment variable.

Pass request.context, request.messages, request.systemPrompt, request.userMessage, and request.model into the agent in the way that matches the local codebase.

For trace stitching, pass request.replayRunId through the SDK metadata surface:

1. Prefer the SDK's event id field. Workshop uses this as the primary merge key.
2. If the SDK cannot set an event id, add replayRunId: request.replayRunId to metadata properties.

Make sure both the original run and the replay run have the same event name.

Use the correct field name for the language:

etc. There are many more SDKs supported.

Examples:

// AI SDK
experimental_telemetry: {
  isEnabled: true,
  metadata: eventMetadata({
    eventId: request.replayRunId,
    eventName: "<event-name>",
    properties: {
      ...request.context,
    },
  }),
}

// Claude Agent SDK
eventMetadata({
  userId: "<user-id>",
  eventId: request.replayRunId,
  eventName: "<event-name>",
  properties: {
    ...request.context,
  },
})

5. Standard Message Format

Workshop sends AI SDK-style JSON:

interface Message {
  role: "system" | "user" | "assistant" | "tool";
  content: string | ContentPart[];
  toolCalls?: ToolCall[];
  toolCallId?: string;
}

For TypeScript AI SDK agents, this usually passes through directly.

For other SDKs or languages, generate the small adapter needed by that project.

6. Write .raindrop/agents.yaml

Example:

triage-agent-dev:
  # Optional. Relative paths are resolved from the project root that
  # contains `.raindrop/agents.yaml`.
  cwd: apps/dawn
  command: pnpm replay-server

  input:
    orgPublicId: string
    orgId: number

  prefillFromTrace:
    orgPublicId: properties.orgPublicId
    orgId: properties.orgId

  models:
    - claude-sonnet-4-20250514
    - gpt-4.1

If adding another agent, preserve existing entries.

Use cwd for monorepos or nested apps when the replay command is only valid from a package directory. For example, prefer:

triage-agent-dev:
  cwd: apps/dawn
  command: pnpm replay-server

over registering a command from the repository root that only works inside apps/dawn.

7. Add Scripts

Add the replay command using the project's normal conventions.

Examples:

• TypeScript: pnpm replay-server
• Python: python scripts/replay_server.py or poetry run python scripts/replay_server.py
• Go: go run ./cmd/replay-server
• Rust: cargo run --bin replay-server

If the project has a normal dev command, add or suggest a combined command (dev:all, dev:replay, make dev, etc.) when it is safe and idiomatic.

8. Verify

Start the replay server once.

Check:

(cd <configured-cwd> && <configured-command>)
curl -fsS http://127.0.0.1:<port>/health

Verify the response includes:

• ok: true
• eventName
• port
• cwd
• command
• input
• prefillFromTrace

9. Register

Run from the project root:

raindrop replay register

Registration starts each configured command from its configured cwd, waits for /health, confirms the agent is reachable, and stores the last seen port. If registration fails, fix .raindrop/agents.yaml instead of continuing.

Require a successful confirmation like:

Registered replay project:
  path: /path/to/project
  config: /path/to/project/.raindrop/agents.yaml
  agents:
    - eventName: triage-agent-dev

If registration fails, stop and show the error. Do not claim replay is ready.

10. Run A Test Replay

Ask the user before running the final test replay.

Default to replaying a past trace. Use the current Workshop run when available; otherwise ask the user to select a source run in Workshop or provide a run id. Do not invent a synthetic replay unless the user explicitly asks for one.

Ask with a concrete default:

"Replay setup is registered. Should I run a test replay now using the selected/past Workshop trace?"

If the user agrees, prefer the Raindrop MCP replay_run tool if it is available. If Raindrop MCP is unavailable, run raindrop workshop, then retry MCP. If MCP still is not available, trigger Workshop's replay API directly:

curl -N -fsS \
  -H "Content-Type: application/json" \
  -d '{"runId":"<source-run-id>"}' \
  http://127.0.0.1:5899/api/replay

Verify all of the following:

• Workshop auto-started or reached the replay server.
• The replay server accepted POST /replay.
• A replay run id was returned.
• The replay emitted traces back to Workshop.
• You can see the replay trace in Workshop, either by MCP/querying the run or by opening it in the UI.

If the replay returns missing_replay_agent or says to run /setup-agent-replay, fix registration/startup and retry. If the replay server starts but no traces appear, fix RAINDROP_LOCAL_DEBUGGER and SDK metadata stitching before stopping.

Output

When done, tell the user:

• Which event name was registered.
• Which port the replay server uses.
• Which command Workshop will run.
• That Workshop can now start the replay server automatically when Replay is clicked.
• The replay run id from the test replay and how you verified its traces appeared.

Do not introduce readonly mode, mocked tools, or mutating-tool classifications.

Source: raindrop-ai/workshop — distributed by TomeVault.