RecallBricks Agent Runtime

Universal cognitive runtime for AI systems - persistent identity, continuous memory, and self-improving intelligence.

What is RecallBricks Runtime?

RecallBricks is Layer 3 of the RecallBricks cognitive stack - a universal runtime that sits between any LLM and your application to provide:

• Persistent Identity - Stable personality and behavior across all sessions
• Continuous Memory - Automatic context loading with zero manual management
• Self-Improving Intelligence - Learns from every interaction via the Metacognition Engine
• Universal Compatibility - Works with Anthropic, OpenAI, Gemini, Ollama, and local models
• Self-Reflection - Built-in metacognition with automatic reflection and reasoning

This is not just memory infrastructure. This is cognitive infrastructure.

The Three-Layer Cognitive Stack

┌─────────────────────────────────────────┐
│   Layer 1: Metacognition Engine        │
│   Learns, optimizes, predicts patterns  │
└─────────────┬───────────────────────────┘
              │
              ▼ Feeds pattern detection
┌─────────────────────────────────────────┐
│   Layer 2: Memory Graph                 │
│   Stores knowledge semantically         │
└─────────────┬───────────────────────────┘
              │
              ▼ Queries for context
┌─────────────────────────────────────────┐
│   Layer 3: Agent Runtime (THIS)         │ ◄─ YOU ARE HERE
│   Delivers persistent identity          │
└─────────────┬───────────────────────────┘
              │
              ▼ Universal LLM interface
         Your Application

Quick Start

Installation

# TypeScript/JavaScript
npm install @recallbricks/runtime

# Python
pip install recallbricks

30-Second Example

import { AgentRuntime } from '@recallbricks/runtime';

const agent = new AgentRuntime({
  agentId: 'sales_bot',
  userId: 'customer_123',
  llmProvider: 'anthropic',
  llmApiKey: process.env.ANTHROPIC_API_KEY,
  apiKey: process.env.RECALLBRICKS_API_KEY,
});

// Chat with automatic context loading
const response = await agent.chat("What did we discuss last time?");
console.log(response.response);
// Agent remembers everything - no manual memory management!

// Optional: Trigger self-reflection
const reflection = await agent.reflect();
console.log(reflection.insights);

That's it. The runtime handles:

• Loading relevant context from past conversations
• Injecting agent identity and behavioral rules
• Calling the LLM with enriched context
• Validating response for identity leakage
• Saving conversation for future reference

Key Features

1. Automatic Context Loading

No manual memory management required:

// Day 1
await runtime.chat("I'm working on a machine learning project");

// Day 30 - different session
await runtime.chat("Can you remind me what I was working on?");
// Agent: "You were working on a machine learning project..."

The runtime automatically:

• Loads recent conversations
• Retrieves relevant memories
• Includes predicted context
• Optimizes token usage

2. Persistent Identity

Your agent maintains stable identity across all sessions:

// Bad: "I'm Claude, an AI assistant made by Anthropic"
// Auto-corrected: "I'm SalesBot, your sales assistant"

Identity validation detects and corrects:

• Base model references (Claude, ChatGPT, etc.)
• Generic AI assistant language
• Inconsistent behavior
• Identity contradictions

3. Universal LLM Support

Switch providers without code changes:

// Anthropic Claude
llmProvider: 'anthropic'

// OpenAI GPT
llmProvider: 'openai'

// Google Gemini
llmProvider: 'gemini'

// Ollama (local)
llmProvider: 'ollama'

// Custom local models
llmProvider: 'local'

4. Multi-User Isolation

Each user gets separate memory and context:

const alice = new AgentRuntime({ userId: 'alice', ... });
const bob = new AgentRuntime({ userId: 'bob', ... });

// Alice and Bob have completely isolated conversations

5. Self-Reflection & Metacognition

Agents can analyze their own behavior and improve:

// Trigger manual reflection
const reflection = await agent.reflect();
console.log(reflection.insights);
// ["User prefers concise answers", "Topic focus: machine learning"]

// Explain reasoning (Chain of Thought)
const trace = await agent.explain("Why did you recommend that?");
console.log(trace.steps);
console.log(trace.conclusion);

6. Continuous Learning

Every interaction feeds the Metacognition Engine:

User interaction
      ↓
Auto-save to API
      ↓
Metacognition Engine
      ↓
Pattern detection
      ↓
Improved predictions

7. Autonomous Agent Integration (v1.3.0+)

Build autonomous agents with working memory, goal tracking, and metacognition:

// Working Memory - Store temporary task state
const session = await agent.createSession('task-123');
await session.addEntry('current_step', 'analyzing data', 60000);
await session.addEntry('findings', { patterns: ['A', 'B'] });

// Goal Tracking - Monitor multi-step tasks
const goal = await agent.trackGoal('data-analysis', [
  'Load dataset',
  'Clean data',
  'Run analysis',
  'Generate report',
]);
await goal.completeStep(1);
console.log(`Progress: ${goal.progress * 100}%`);

// Metacognition - Self-assess responses
const assessment = await agent.assessResponse(
  'The analysis shows a 15% improvement',
  0.85
);
if (assessment.needsReflection) {
  await agent.metacognition.triggerReflection();
}

Autonomous Client Properties

Access full autonomous capabilities through dedicated clients:

// Working Memory Client
const sessions = await agent.workingMemory.listSessions();
const session = await agent.workingMemory.getSession('task-123');

// Goals Client
const goals = await agent.goals.listGoals();
const goal = await agent.goals.getGoal('analysis-goal');
await agent.goals.cancelGoal('cancelled-goal');

// Metacognition Client
const history = await agent.metacognition.getAssessmentHistory();
const avgConfidence = await agent.metacognition.getAverageConfidence();
await agent.metacognition.triggerReflection();

Integration Options

1. TypeScript/JavaScript SDK

import { AgentRuntime } from '@recallbricks/runtime';

const runtime = new AgentRuntime({
  agentId: 'my_bot',
  userId: 'user_123',
  llmApiKey: 'sk-...',
});

const response = await runtime.chat('Hello!');

2. Python SDK

from recallbricks import AgentRuntime

runtime = AgentRuntime(
    agent_id='my_bot',
    user_id='user_123',
    llm_api_key='sk-...'
)

response = runtime.chat('Hello!')

3. REST API

# Start server
npm run build
PORT=3000 node dist/adapters/api/server.js

# Use from any language
curl -X POST http://localhost:3000/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Hello!"}'

4. MCP (Claude Desktop)

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "recallbricks": {
      "command": "node",
      "args": ["/path/to/dist/adapters/mcp/server.js"],
      "env": {
        "RECALLBRICKS_AGENT_ID": "my_bot",
        "RECALLBRICKS_USER_ID": "user_123",
        "RECALLBRICKS_API_URL": "https://recallbricks-api-clean.onrender.com"
      }
    }
  }
}

Note: No Anthropic API key needed! Claude Desktop already has Claude running. The MCP server only loads/saves context from RecallBricks API.

Architecture

User Message
     │
     ▼
┌────────────────────┐
│ Save Previous Turn │ ──► Metacognition Engine (Layer 1)
└────────┬───────────┘
         │
         ▼
┌────────────────────┐
│ Load Context       │ ◄── Memory Graph (Layer 2)
└────────┬───────────┘
         │
         ▼
┌────────────────────┐
│ Inject Identity +  │
│ Context            │
└────────┬───────────┘
         │
         ▼
┌────────────────────┐
│ Call LLM           │ ──► Anthropic/OpenAI/etc.
└────────┬───────────┘
         │
         ▼
┌────────────────────┐
│ Validate Response  │
│ (Identity Check)   │
└────────┬───────────┘
         │
         ▼
   Return to User

Use Cases

Customer Support

const support = new AgentRuntime({
  agentId: 'support_bot',
  userId: customerId,
  llmApiKey: key,
});

// Remembers customer issues, preferences, history
const response = await support.chat("Status of my order?");

Sales Assistant

const sales = new AgentRuntime({
  agentId: 'sales_assistant',
  userId: leadId,
  llmApiKey: key,
});

// Tracks leads, conversations, deal status
const response = await sales.chat("Can you remind me of our last call?");

Personal AI Assistant

const assistant = new AgentRuntime({
  agentId: 'personal_assistant',
  userId: userId,
  llmApiKey: key,
});

// Remembers tasks, preferences, context
const response = await assistant.chat("What's on my agenda?");

Configuration

All Options

const runtime = new AgentRuntime({
  // Required
  agentId: 'your_agent',
  userId: 'your_user',
  llmApiKey: 'your_key',

  // Optional
  apiUrl: 'https://recallbricks-api.example.com',
  llmProvider: 'anthropic',          // anthropic, openai, cohere, local
  llmModel: 'claude-sonnet-4-5-20250929',
  tier: 'starter',                   // starter, professional, enterprise
  autoSave: true,                    // Auto-save conversations
  validateIdentity: true,            // Validate agent identity
  cacheEnabled: true,                // Enable context caching
  cacheTTL: 300000,                  // Cache TTL (5 min)
  maxContextTokens: 4000,            // Max context tokens
  debug: false,                      // Debug logging
});

Environment Variables

RECALLBRICKS_AGENT_ID=your_agent
RECALLBRICKS_USER_ID=your_user
RECALLBRICKS_API_KEY=your_llm_key
RECALLBRICKS_LLM_PROVIDER=anthropic
RECALLBRICKS_TIER=starter

API Reference

Core Methods

// Send a message
const response = await agent.chat(message);

// Get agent identity
const identity = agent.getIdentity();

// Get memory context
const context = await agent.getContext();

// Refresh context (bypass cache)
await agent.refreshContext();

// Trigger self-reflection
const reflection = await agent.reflect();

// Explain reasoning for a query
const trace = await agent.explain("Why?");

// Get conversation history
const history = agent.getConversationHistory();

// Clear session history
agent.clearConversationHistory();

// Flush pending saves
await agent.flush();

// Graceful shutdown
await agent.shutdown();

// Get validation stats
const stats = agent.getValidationStats();

// Get reflection history
const reflections = agent.getReflectionHistory();

// Get runtime version
const version = agent.getVersion();

Autonomous Agent Methods (v1.3.0+)

// Create working memory session
const session = await agent.createSession('session-id');

// Track a goal with steps
const goal = await agent.trackGoal('goal-id', ['step1', 'step2']);

// Assess response with metacognition
const assessment = await agent.assessResponse(response, confidence);

// Access autonomous clients directly
agent.workingMemory  // WorkingMemoryClient
agent.goals          // GoalsClient
agent.metacognition  // MetacognitionClient

See API Reference for complete documentation.

Examples

Check the /examples directory for:

• basic-usage.ts - Simple quickstart example
• multi-provider.ts - Using different LLM providers
• reflection-demo.ts - Self-reflection and Chain of Thought
• typescript-agent/ - Full TypeScript integration example
• python-agent/ - Python SDK usage example
• api-integration/ - REST API usage example
• mcp-quickstart/ - Claude Desktop integration guide

Documentation

• Quickstart Guide - Get started in 5 minutes
• Architecture - Deep dive into system design
• API Reference - Complete API documentation

Testing

# Install dependencies
npm install

# Run tests
npm test

# Coverage report
npm run test:coverage

Building from Source

# Clone repository
git clone https://github.com/recallbricks/agent-runtime.git
cd agent-runtime

# Install dependencies
npm install

# Build
npm run build

# Run examples
npm run build
node dist/examples/typescript-agent/index.js

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

Roadmap

• Core runtime with identity validation
• TypeScript/JavaScript SDK
• Python SDK
• REST API adapter
• MCP adapter for Claude Desktop
• Google Gemini support
• Ollama (local models) support
• ReflectionEngine for self-analysis
• ContextWeaver for advanced context building
• Autonomous Agent Integration (v1.3.0)
  • Working Memory Sessions
  • Goal Tracking
  • Metacognition Assessment
• Streaming support
• Telemetry and usage analytics
• Advanced circuit breaker patterns
• Metrics export (Prometheus/StatsD)
• Multi-agent communication
• Custom embedding models
• Advanced graph traversal
• Temporal reasoning

License

MIT License - See LICENSE for details.

Product Positioning

"RecallBricks Agent Runtime - The universal cognitive runtime for AI systems. Turn any LLM into a persistent agent with continuous memory, stable identity, and self-improving intelligence."

This is not just memory infrastructure.

This is not just a wrapper around LLMs.

This is cognitive infrastructure for the AI era.

---

Built by RecallBricks

Questions? Issues? Open an issue or reach out to support@recallbricks.com