TIA

TIA Intelligence Agency

An experimental XMPP (Jabber) agent framework that combines chat, Lingue/IBIS structured dialogue, and MCP tool integrations into a modular Node.js codebase.

Status 2025-12-28: we have a community of autonomous agents that can debate how to solve a problem, run a planning poll to pick an approach, and then invoke MFR or consensus workflows. The system is quite chaotic, but the end-to-end process is working.

• Live Chat - or use a standard XMPP client
• Documentation
• Lingue Protocol

Question: Schedule appointments for patients. Alice takes warfarin, Bob takes aspirin. Ensure no drug interactions.

...a lot of chat later...

What TIA Is

TIA is a set of composable building blocks for creating conversational agents that can:

• Participate in XMPP multi-user chats and direct messages.
• Negotiate Lingue language modes and exchange structured payloads.
• Act as MCP clients (discovering tools/resources from servers).
• Act as MCP servers (exposing chat and Lingue tools to external clients).

The design goal is a clean, library-ready architecture that supports both deployable bots and reusable modules.

Key Concepts

• XMPP room agents: long-running bots anchored in MUC rooms.
• Lingue protocol: language-mode negotiation + structured payloads (IBIS, Prolog, profiles).
• MCP bridges: MCP client and server adapters for tool discovery and exposure.
• Profiles (RDF): agent capabilities live in RDF profiles with shared vocabularies (Mistral variants inherit from mistral-base).

Info Flow

Testbed Server

Use the shared Prosody testbed at tensegrity.it to connect with any standard XMPP client. You will first have to register - is just simpe username & password.

Connection details:

• XMPP service: xmpp://tensegrity.it:5222
• Domain: tensegrity.it
• MUC service: conference.tensegrity.it
• TLS: self-signed (set NODE_TLS_REJECT_UNAUTHORIZED=0 for CLI tools; in GUI clients accept the certificate)

Rooms to join:

• General: [email protected]
• Log room: [email protected]

If your client supports it, set a distinct resource or nickname to avoid collisions.

Implemented Agents

• Coordinator — MFR (Model-First Reasoning) orchestrator for multi-agent problem solving.
• Mistral — AI chat agent backed by Mistral API with Lingue/IBIS summaries (see mistral-analyst, mistral-creative profiles).
• GroqBot — AI chat agent backed by Groq API (llama-3.3-70b-versatile) with same capabilities as Mistral.
• Golem — Malleable AI agent with runtime system prompt changes. Can be assigned logic-focused roles during planning. Guide
• Semem — MCP-backed knowledge agent for tell/ask/augment flows.
• MFR Semantic — Constraint-focused agent for MFR model construction.
• Data — SPARQL knowledge query agent for Wikidata, DBpedia, and custom endpoints. Guide
• Demo — Minimal chat bot for quick XMPP smoke checks.
• Chair — Debate facilitator/Moderator agent.
• Recorder — Meeting logger/recorder agent that listens broadly.
• Prolog — Logic agent using tau-prolog for queries.
• Executor — Plan execution agent that converts high-level plans into Prolog programs.
• MCP Loopback — MCP client/server echo agent for integration tests.

Architecture At A Glance

• src/agents — AgentRunner, providers, and profile system.
• src/lib — XMPP helpers, Lingue utilities, logging, RDF tools.
• src/mcp — MCP client/server bridges and test servers.
• config/agents/*.ttl — RDF profiles describing each agent.
• config/agents/secrets.json — local XMPP passwords keyed by profile (ignored in git).
• docs/ — integration guides and operational docs.

Quick Start: Running Agents

The start-all.sh script provides a unified way to start agents or specific subsets. By default it starts the MFR suite.

# Start the default MFR suite (same as `./start-all.sh mfr`)
./start-all.sh

# Start MFR (Model-First Reasoning) system
./start-all.sh mfr

# Start debate system
./start-all.sh debate

# Start basic agents
./start-all.sh basic

# Custom agent selection
AGENTS=mistral,data,prolog ./start-all.sh

# Get help
./start-all.sh help

Prerequisites:

1. Configure .env file with API keys (see .env.example)
2. Create config/agents/secrets.json with XMPP passwords
3. For MFR system: Configure Prosody MUC rooms (see MFR Room Setup)
4. Ensure a log room exists (set LOG_ROOM_JID explicitly for all agents and create it on the server)

Agent Presets:

• mfr - MFR system (full suite): coordinator, mistral, analyst, creative, chair, recorder, mfr-semantic, data, prolog, demo
• debate - Debate system: chair, recorder, mistral, analyst, creative
• basic - Basic agents: mistral, data, prolog, demo

The script automatically:

• Loads .env file
• Checks for required API keys
• Skips agents with missing credentials
• Provides restart on crash
• Handles graceful shutdown (SIGTERM/SIGINT)

Interacting with Agents

Using the REPL Client

Once agents are running, you can interact with them directly from a chatroom using the REPL client:

# Connect to the chatroom
NODE_TLS_REJECT_UNAUTHORIZED=0 node src/client/repl.js <username> <password>

MFR (Model-First Reasoning) Commands

Once connected to the chatroom, you can pose problems to the MFR system:

# Start a new MFR session
mfr-start Schedule appointments for patients. Alice takes warfarin, Bob takes aspirin. Ensure no drug interactions.

# Start a debate-driven MFR session (tool selection via Chair)
debate Optimize delivery routes for 3 trucks serving 10 locations.

# Shorthand for debate-driven sessions
Q: Optimize delivery routes for 3 trucks serving 10 locations.

# Check session status
mfr-status <sessionId>

# List active sessions
mfr-list

# Get help
help

Debate mode is enabled by default in config/agents/coordinator.ttl. Q: triggers a planning poll to decide between logic/consensus/Golem logic routes.

Short command versions:

• start instead of mfr-start
• status instead of mfr-status
• list instead of mfr-list

Other MFR commands:

• mfr-contribute <sessionId> <rdf> - Submit a contribution manually
• mfr-validate <sessionId> - Validate a model
• mfr-solve <sessionId> - Request solutions
• debate <problem description> - Start debate-driven MFR session

Programmatic MFR Sessions

You can also run MFR sessions programmatically:

node src/examples/run-mfr-session.js "Your problem description here"

This will automatically connect, start a session, wait for the solution, and display the results.

Library Usage

import { AgentRunner, LingueNegotiator, LINGUE, Handlers, InMemoryHistoryStore } from "tia-agents";

const negotiator = new LingueNegotiator({
  profile,
  handlers: {
    [LINGUE.LANGUAGE_MODES.HUMAN_CHAT]: new Handlers.HumanChatHandler()
  }
});

const runner = new AgentRunner({
  profile,
  provider,
  negotiator,
  historyStore: new InMemoryHistoryStore({ maxEntries: 40 })
});
await runner.start();

See examples/minimal-agent.js for a runnable local example.

NPM Package Usage

TIA is published as tia-agents on npm and supports two approaches to creating bots:

Quick Start

npm install tia-agents

For a minimal, npm-packaged Mistral bot starter, see mistral-minimal/README.md. If you're using the Mistral provider, install the peer dependency and ensure the API key env var referenced in your profile is set (default: MISTRAL_API_KEY). If you want auto-registration, omit xmpp:passwordKey from the profile and set autoRegister: true when creating the agent.

Approach 1: Config-Driven (Profile Files)

Create profile files and use the factory function:

import { createAgent, DemoProvider } from "tia-agents";

// Load from config/agents/mybot.ttl
const runner = await createAgent("mybot", new DemoProvider());
await runner.start();

Profile file (config/agents/mybot.ttl):

@prefix agent: <https://tensegrity.it/vocab/agent#> .
@prefix xmpp: <https://tensegrity.it/vocab/xmpp#> .

<#mybot> a agent:ConversationalAgent ;
  agent:xmppAccount [
    xmpp:service "xmpp://localhost:5222" ;
    xmpp:domain "xmpp" ;
    xmpp:username "mybot" ;
    xmpp:passwordKey "mybot"
  ] ;
  agent:roomJid "[email protected]" .

Approach 2: Programmatic (No Config Files)

Configure everything in code:

import { createSimpleAgent, DemoProvider } from "tia-agents";

const runner = createSimpleAgent({
  xmppConfig: {
    service: "xmpp://localhost:5222",
    domain: "xmpp",
    username: "mybot",
    password: "secret"
  },
  roomJid: "[email protected]",
  nickname: "MyBot",
  provider: new DemoProvider()
});

await runner.start();

Creating Custom Providers

Extend BaseProvider to implement your own logic:

import { BaseProvider } from "tia-agents";

class MyProvider extends BaseProvider {
  async handle({ command, content, metadata }) {
    if (command !== "chat") return null;
    return `You said: ${content}`;
  }
}

const runner = createSimpleAgent({
  // ... config
  provider: new MyProvider()
});

AI-Powered Bots

Install peer dependency:

npm install @mistralai/mistralai

Use MistralProvider:

import { createAgent, InMemoryHistoryStore } from "tia-agents";
import { MistralProvider } from "tia-agents/providers/mistral";

const provider = new MistralProvider({
  apiKey: process.env.MISTRAL_API_KEY,
  historyStore: new InMemoryHistoryStore({ maxEntries: 40 })
});

const runner = await createAgent("aibot", provider);
await runner.start();

Templates & Examples

Copy templates to get started:

cp -r node_modules/tia-agents/templates/* ./

See templates for:

• Profile file examples (.ttl)
• Provider templates (simple & LLM patterns)
• Runnable example scripts

Documentation

• 📚 Quick Start Guide - Detailed getting started guide
• 🔧 Provider Guide - Creating custom providers
• 📖 API Reference - Complete API documentation
• 📁 Templates - Example configurations and code
• 🌐 GitHub Pages site (generated from docs/ via scripts/build-docs.mjs with DOCS_DIR and DOCS_OUT_DIR)

Custom Agent API

For a fuller walkthrough and profile-driven setup, see:

• Agent developer prompt
• Minimal agent example

Installation & Running

Quick start - see Agent Startup Guide for complete instructions.

Additional documentation:

• Agent Startup Guide - Main guide for starting agents
• Testing
• Server
• MCP Server
• MFR Room Setup - Requires Prosody MUC configuration for persistent rooms