WorkerLane MCP Server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@WorkerLane MCP ServerCreate a coworker named qa-bot to review every PR before merge."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
WorkerLane
Harness-agnostic agent coworkers for any agent runtime. One package that wires team agents, approvals, memory, traces, and handoff contracts into whatever harness you already use.
Part of Talocode — open tools people trust, hosted power behind them.
What it is
AI agents are great at running tasks. The hard part is everything around them: who can run what, what gets approved, what happened last session, and proving it after the fact. WorkerLane bundles those pieces into one package that works with any agent harness — no runtime brand loyalty, no side to pick.
AI coworkers — create agents with a name and role, queue tasks, gate destructive work behind approvals
Agent-human handoffs — validate and repair step-to-step payloads against schema contracts so runs never silently drift
Full data recording, owned by you — trace spans with cost and status, plus durable memory that persists across sessions. Everything is stored locally in
~/.workerlaneunless you opt into the hosted pathComputer use — run
screenlane mcp(@talocode/screenlane) alongsideworkerlane mcpto add screen capture, dictate, and command tools to the same harnessWorks with any harness — expose the whole bundle as MCP tools and connect any MCP-compatible agent
Related MCP server: bot-relay-mcp
Why it exists
The catalog is strong but fragmented — agents, traces, memory, handoffs each ship separately. WorkerLane removes the assembly. One install, one surface, all the pieces wired together. It is positioned as a capability layer above the harness, exactly where compounding value lives.
Install
npm install -g @talocode/workerlaneor
pip install talocode-workerlaneNo Office, no runtime, no cloud required. Local engine runs entirely on your machine.
Quickstart (CLI)
# start the MCP server — connect any harness to it
workerlane mcp
# create a coworker
workerlane agent create --name "qa-bot" --role "reviews every PR before merge"
# queue a task, gated behind an approval
workerlane agent run --agent <ID> --task "audit the auth flow" --approve
# save and recall memory across sessions
workerlane memory remember --text "deploy is Fridays, freeze after 4pm" --tags ops,deploy
workerlane memory recall --query "deploy"
# start a trace run
workerlane trace start --name "release-check"Quickstart (MCP — any harness)
{
"mcp": {
"workerlane": {
"type": "local",
"command": ["workerlane", "mcp"]
}
}
}Once connected, these tools are available to any MCP-compatible agent:
Tool | What it does |
| Create a coworker with name + role |
| List coworkers |
| Queue a task; |
| Approve/reject a pending run (recorded) |
| Mark a run complete with its result |
| List runs, filter by status |
| Validate step output against a schema contract |
| Validate + lightly repair (drop extras, coerce types) |
| Start a trace run |
| Record a span (llm/tool/retrieval/handoff) with status + cost |
| Complete a run → receipt chain |
| List trace runs |
| Save a memory |
| Recall relevant memories |
| List saved memories |
Computer use (sibling server): install @talocode/screenlane, then add a second MCP entry:
{
"mcp": {
"screenlane": {
"type": "local",
"command": ["screenlane", "mcp"]
}
}
}Exposes screenlane_capture, screenlane_dictate, screenlane_command, screenlane_send, screenlane_doctor to the same harness. Hosted browser automation is available via Agent Browser on Talocode Cloud (/v1/agent-browser/*).
SDK
import { createAgent, createAgentRun, approveAgentRun, validateHandoff, remember, recall, createTraceRun, startTraceSpan } from '@talocode/workerlane'
const bot = createAgent({ name: 'qa-bot', role: 'reviews every PR before merge' })
const run = createAgentRun({ agentId: bot.id, task: 'audit the auth flow', requireApproval: true })
// → a human approves it before it runs
approveAgentRun(run.id, true)
const r = validateHandoff({
value: { intent: 'ship', confidence: 0.9 },
schema: { type: 'object', required: ['intent', 'confidence'], properties: { intent: { type: 'string' }, confidence: { type: 'number' } } },
})
remember('deploy is Fridays, freeze after 4pm', ['ops', 'deploy'])
const facts = recall('deploy')
const trace = createTraceRun('release-check')
const span = startTraceSpan(trace.id, 'verify', 'tool')Data & ownership
Everything is local-first. State lives in
~/.workerlane/(override withWORKERLANE_DIR).agents.json— agent registry + run audit trail with approval decisionsmemory.json— durable memorytraces.json— span receipts with cost + statusExport or delete these files any time — the data is yours.
Hosted path
For teams that want managed power, WorkerLane is available on Talocode Cloud under /v1/workerlane/* with Stacklane billing. The local engine is always free.
Action | Credits (hosted) |
| 3 |
| 1 |
| 2 / 4 |
| 1 / 1 / 2 |
| 4 / 2 |
Related packages
@talocode/worklane·pip install talocode-worklane@talocode/memorylane·pip install talocode-memorylane@talocode/handofflane@talocode/tracelane@talocode/agent-browser(hosted computer use)
Talocode ecosystem
Product | Repo |
WorkerLane (this package) | |
Reasoning, writing, coding | |
Work automation | |
Persistent agent memory | |
Step-to-step schema contracts | |
Run tracing + receipts | |
Policy gates | |
Gate enforcement | |
Browser automation for agents | |
Screen automation | |
Search capability | |
X search | |
Office documents for agents | |
Data analysis | |
Video creation | |
Trading | |
Coding agent | |
Cloud control plane |
More: github.com/talocode · talocode.site · docs.talocode.site
License
MIT © Talocode
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