WorkerLane MCP Server
Enables any MCP-compatible agent to manage coworker agents with task queues, human approvals, schema-validated handoffs, memory, and trace logging, with local-first storage in ~/.workerlane.
README
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 path - Computer use — run
screenlane mcp(@talocode/screenlane) alongsideworkerlane mcpto add screen capture, dictate, and command tools to the same harness - Works with any harness — expose the whole bundle as MCP tools and connect any MCP-compatible agent
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/workerlane
or
pip install talocode-workerlane
No 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 |
|---|---|
workerlane_agent_create |
Create a coworker with name + role |
workerlane_agent_list |
List coworkers |
workerlane_agent_run |
Queue a task; requireApproval pauses for a human |
workerlane_agent_approve |
Approve/reject a pending run (recorded) |
workerlane_agent_complete |
Mark a run complete with its result |
workerlane_agent_runs |
List runs, filter by status |
workerlane_handoff_validate |
Validate step output against a schema contract |
workerlane_handoff_repair |
Validate + lightly repair (drop extras, coerce types) |
workerlane_trace_start |
Start a trace run |
workerlane_trace_span / _span_end |
Record a span (llm/tool/retrieval/handoff) with status + cost |
workerlane_trace_complete |
Complete a run → receipt chain |
workerlane_trace_list |
List trace runs |
workerlane_memory_remember |
Save a memory |
workerlane_memory_recall |
Recall relevant memories |
workerlane_memory_list |
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 + status- Export 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) |
|---|---|
workerlane.agent.run |
3 |
workerlane.agent.approve |
1 |
workerlane.handoff.validate / repair |
2 / 4 |
workerlane.trace.start / span / complete |
1 / 1 / 2 |
workerlane.memory.remember / recall |
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) | github.com/talocode/workerlane |
| Tera | Reasoning, writing, coding |
| WorkLane | Work automation |
| MemoryLane | Persistent agent memory |
| HandoffLane | Step-to-step schema contracts |
| TraceLane | Run tracing + receipts |
| PolicyLane | Policy gates |
| GateLane | Gate enforcement |
| Agent Browser | Browser automation for agents |
| ScreenLane | Screen automation |
| SearchLane | Search capability |
| XSearchLane | X search |
| DocuLane | Office documents for agents |
| DataLane | Data analysis |
| ClipLoop | Video creation |
| Tradia | Trading |
| Codra | Coding agent |
| StackLane | Cloud control plane |
More: github.com/talocode · talocode.site · docs.talocode.site
License
MIT © Talocode
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.