Lobu MCP Server

Lobu — Shared company context for every AI agent

Lobu is an open-source shared context layer for AI agents. Giving every agent direct access to company tools does not give the company shared memory. Each agent still reconstructs what happened inside one model session, then loses what it learned when that session ends.

Connect your company stack once. Claude Code, Codex, ChatGPT, and your own agents get one permission-aware memory of the company while keeping their existing interface, model, and runtime. When a responsibility should persist beyond the current chat, they can discover and hand work to a Lobu specialist over MCP.

Start with the agent you already use

Point any MCP client at Lobu. No Lobu agent runtime or lobu.config.ts is required.

# Claude Code
claude mcp add --transport http lobu https://lobu.ai/mcp

# Codex
codex mcp add lobu --url https://lobu.ai/mcp

Complete OAuth when prompted, connect the company sources you want to share, and ask your agent to use Lobu when it needs organizational context.

The same MCP endpoint works with ChatGPT, Claude Desktop, Gemini CLI, Cursor, and custom MCP clients. lobu memory init detects Claude Code, Codex, Gemini CLI, and Cursor, and prints manual setup steps for Claude Desktop and ChatGPT.

Setup guides: Claude · ChatGPT · Codex · Grok

Recall what the company knows

Ask from Claude Code, Codex, or ChatGPT:

What did we decide about enterprise onboarding, and what changed since the last release?

Lobu searches shared, durable organizational memory under the caller's permissions, regardless of which agent asks. The answer can combine connected discussions, project activity, customer records, saved decisions, and typed company entities without rebuilding that context from scratch in every chat.

Hand work to a persistent Lobu specialist

Ask your primary agent:

Ask our customer-researcher specialist to review the latest feedback and propose the next three interviews.

The agent discovers the specialists available to you, selects the right one, delegates the task, and brings the result back. The specialist has its own identity, instructions, tools, durable conversations, and access policy.

To the user this stays one conversation in their primary agent. The specialist itself persists: it remains available to other authorized people and agents instead of disappearing with the current chat.

Why Lobu

Instead of every agent rebuilding company state through per-session tool calls, Lobu runs a shared data layer:

• Connect once. Polls, webhooks, APIs, and agent-written connectors feed one append-only event log.
• Know once. Events can be indexed as searchable knowledge and linked to typed entities such as companies, projects, incidents, and customers.
• Use from any agent. Authorized MCP clients read and contribute to the same organizational state according to their grants.
• Delegate when useful. Persistent Lobu specialists can own a role, conversation history, tools, and recurring responsibilities.
• Keep control. Identity, source permissions, approvals, credential brokering, provenance, and audit stay server-side.

flowchart LR
  Tools[Company tools] --> L[Lobu shared context]
  APIs[APIs and webhooks] --> L
  Custom[Custom connectors] --> L

  L <--> MCP[MCP]
  MCP <--> Claude[Claude Code]
  MCP <--> Codex[Codex]
  MCP <--> ChatGPT[ChatGPT]
  MCP <--> Own[Your agents]

  L <--> Specialists[Persistent Lobu specialists]
  Specialists <--> Channels[Slack and web chat]

Three ways to use Lobu

1. Add shared context to existing agents

Agents can search and save memory, query structured entities, inspect connected sources, and delegate to Lobu specialists without moving to a new chat interface or adopting Lobu's runtime.

Docs: Memory · Claude · ChatGPT · Codex

2. Run persistent Lobu specialists

Create a specialist for a durable responsibility: customer research, support triage, release coordination, incident follow-up, or an internal domain. People can talk to it from the Lobu web app or Slack, while other agents can call the same specialist over MCP.

Scaffold and run one locally:

npx @lobu/cli@latest init my-specialist
cd my-specialist
npx @lobu/cli@latest run
npx @lobu/cli@latest chat -c local "hello"

lobu run starts the local stack with an embedded Postgres database by default and opens the web UI on :8787. It applies the project's lobu.config.ts automatically only in embedded mode. To use external Postgres, set DATABASE_URL, ensure pgvector is available, then authenticate and apply the project to that runtime separately.

Docs: Getting started · Agent workspace · Skills · Slack

3. Build with the CLI and TypeScript SDK

The same governed data and operations are available without an agent:

npx @lobu/cli@latest memory run                     # list the memory tools
npx @lobu/cli@latest memory run search_memory '{"query":"onboarding"}'
npx @lobu/cli@latest memory exec \
  'export default async (_ctx, client) => client.entities.list({ limit: 5 })'

Or from Node and TypeScript:

import { client, searchMemory } from "@lobu/client";

client.setConfig({
  baseUrl: "https://lobu.ai",
  headers: { Authorization: `Bearer ${process.env.LOBU_TOKEN}` },
});

const hits = await searchMemory({
  path: { orgSlug: "my-org" },
  body: { query: "onboarding" },
});

Mint a token with lobu token create. The MCP tools and typed SDK operations share the server-side tool registry and access rules; lobu memory run and lobu memory exec dispatch through the MCP endpoint.

Core concepts

Shared context

Connectors collect company activity on a schedule or through webhooks. Discussions, project changes, customer records, API events, and saved agent knowledge land in the same append-only history.

Typed entities connect that history to the things the company cares about: Company, Project, Customer, Incident, or schemas you define. Corrections supersede old facts rather than erasing their provenance.

Connectors are extensible. You can build them in TypeScript, and coding agents can use Lobu's connector contract and validation flow to create integrations for sources Lobu does not ship with.

Docs: Memory · Connectors · API

Persistent specialists

A Lobu specialist has a stable role, instructions, memory, tools, and conversation history. It can be reached from web chat or Slack and called by external agents through client.conversations.send.

Specialists use role files for identity and instructions: IDENTITY.md, SOUL.md, and USER.md. Guardrails can inspect input, output, and tool calls. Destructive MCP calls require in-thread approval unless they are explicitly pre-approved through defineAgent({ tools: { preApproved } }) in lobu.config.ts; action results return to the shared event log.

External agents do not ask users to write delegation code. They pass scripts like these to Lobu's query_sdk and run_sdk MCP tools:

// Discover specialists through query_sdk.
export default async (_ctx, client) => {
  const { agents } = await client.agents.list();
  return agents;
};

// Delegate through run_sdk and wait for the specialist's reply.
export default async (_ctx, client) => {
  return client.conversations.send({
    agent_id: "customer-researcher",
    thread: "enterprise-onboarding",
    text: "Review the latest customer feedback and propose the next three interviews.",
  });
};

Docs: Agent workspace · Guardrails · Security

Automations

Automations are versioned background responsibilities activated manually, on a schedule, by a connector event, or by another Automation's durable output. They read governed sources, persist structured results, and can notify Slack, open a ticket, or start agent work while nobody is in chat.

See the activation and chaining model.

Optional execution

Shared context and delegation over MCP do not require Lobu to execute code for the calling agent. When a Lobu specialist needs a shell, the built-in runtime provides lightweight just-bash execution. Remote sandbox providers such as Vercel Sandbox can be connected for workloads that need stronger isolation or more compute.

Which sandbox runs the code is a deployment choice. Lobu provides the shared context, permissions, and governance around it.

Channels

Lobu specialists can serve Slack, Telegram, WhatsApp, Discord, Teams, Google Chat, the web app, and a REST API. Channel conversations remain separate while reading the same authorized organizational context.

Setup: Slack · Telegram · Discord · WhatsApp · Teams · Google Chat

How Lobu differs

• Agent frameworks help developers implement an agent loop. Lobu gives agents and people a shared organizational state and a place to keep persistent specialists.
• Direct MCP integrations expose tools from one provider. Lobu continuously builds durable, cross-source context that every authorized agent can reuse.
• Agent runtimes host a particular agent. Lobu lets people keep using Claude Code, Codex, ChatGPT, or their own runtime and add Lobu only where shared context or delegation is useful.
• Workflow engines encode a graph of predetermined steps. Lobu Automations handle durable triggers and background responsibilities, while agents decide how to complete open-ended work.

Agent configuration

Runtime configuration is managed through the web app or the same org-scoped REST API used by the CLI. Local lobu.config.ts projects support validation and repeatable apply workflows.

npx @lobu/cli@latest login
npx @lobu/cli@latest org set my-org
npx @lobu/cli@latest agent list

Docs: CLI reference · lobu apply

Deployment

Use the embedded runtime locally or self-host Lobu with external Postgres. Production guides: Docker · Cloud · Kubernetes

Security and privacy

Permissions and audit stay on Lobu's gateway. Lobu MCP servers and the credential-brokering layer handle provider and connector credentials, OAuth and token refresh, and third-party API proxying. Workers receive scoped placeholders or short-lived provider-derived access, never OAuth tokens or durable stored credentials. Destructive MCP calls require in-thread approval unless explicitly pre-approved, and connected data remains organization-scoped.

The built-in just-bash and embedded execution modes are policy and convenience boundaries, not VMs for hostile code. Use a remote sandbox provider when the workload needs a stronger isolation boundary.

Docs: Security · Secret proxy · Guardrails · Threat model

Design partners

We are working with technical teams that already use Claude Code, Codex, ChatGPT, or custom agents and want those agents to share company context or delegate to persistent specialists.

The best starting point is one team, one or two connected sources, and one repeated responsibility. Talk to the founder or reach out on X/Twitter.