Advanced Memory Layer for AI Agents

Persistent memory that remembers everything

Memphora enables AI agents to recall past interactions, store user preferences, learn from experiences, and maintain complex relationships between memories.

Memphora AI Assistant enabled chatbot

With persistent memory

Online

Starting conversation...

0 messages
0 memories stored
Memory active
81% Compression
Token Savings
Proven compression ratio with intelligent context management
64+ Features
Production Ready
Reranking, multi-agent, group chat, and more
20-30% Better
Search Relevance
Built-in ensemble ranking with optional external reranking (Cohere/Jina)
Subsecond
Search Speed
With 3-layer caching and optimized retrieval

Powerful Dashboard

Complete dashboard interface with real-time insights and analytics

Overview

Real-time analytics and insights

12,847
Total Memories
Across all users
1,234
Active Users
in organization
45,678
Total Searches
Based on memory count
65%
Token Savings
Reduction from original

Memory Growth

API Usage

Response Time Trend

Search Accuracy Trend

Avg Response Time
87ms
Search Accuracy
94%
Target: >50%
Compression Ratio
35%
Compressed to (of original)
API Uptime
99.98%
Target: >99.9%

API Keys

API Key

Created 11/7/2025, 9:06:33 PM

Recent Activity

Created memory

john@example.com

2 mins ago

Searched memories

sarah@company.com

5 mins ago

Updated memory

mike@test.com

12 mins ago

UNIQUE ADVANTAGES

Features That Set Us Apart

Advanced capabilities and exclusive features that make Memphora the most powerful memory layer for AI agents.

Exclusive

3-Layer Caching System

Exclusive multi-level caching architecture (L1 hot cache, L2 warm cache, L3 persistent cache) delivers ultra-fast retrieval with optimized performance. No other solution offers this advanced caching strategy.

Advanced

Advanced Context Compression

Intelligent compression engine that achieves significant token reduction while preserving meaning and relevance. Our proprietary compression algorithms go beyond basic summarization.

AI-Powered

AI-Powered Memory Merging

Automatically detect and merge similar memories, resolve contradictions, and maintain consistency using advanced AI models. Fully automated conflict resolution that keeps your memory clean.

Graph-Based

Graph-Based Memory Relationships

Link memories with semantic relationships (related, contradicts, supports, extends) to build a knowledge graph. Navigate memory clusters and discover connections that other solutions miss.

Built-in

Analytics & Observability

Comprehensive analytics, memory growth tracking, API usage statistics, and performance metrics. Built-in observability with metrics and audit logs.

Production-Ready

Production-Ready Out of the Box

Enterprise-grade security, observability, health checks, Docker deployment, and rate limiting included. No additional configuration needed for production deployment.

Multi-Factor

Multi-Factor Memory Scoring

Advanced scoring system that considers recency, frequency, relevance, and importance to rank memories intelligently. Smarter retrieval than basic similarity search.

Optimized

Optimized Retrieval Engine

Better accuracy than standard vector search with filtering, scoring, and context-aware retrieval. Our optimized engine delivers more relevant results with lower latency.

Memphora Capabilities

Powerful features that make your AI agents smarter, faster, and more cost-effective.

Persistent Memory Storage

Hybrid storage using vector database and relational database for reliable, scalable memory management.

Advanced Search & Reranking

Semantic search with external reranking (Cohere/Jina) for 20-30% better relevance. Auto-fallback to built-in ensemble ranking.

Advanced Metadata Filtering

Complex AND/OR/NOT queries with 12+ operators ($gt, $in, $regex, etc.) for precise memory filtering.

Context Compression

Significant token reduction through intelligent compression while preserving meaning and relevance.

3-Layer Caching

Multi-level caching system (L1 hot cache, L2 warm cache, L3 persistent cache) for ultra-fast retrieval and optimized performance.

Memory Merging

Automatically merge similar memories, detect contradictions, and resolve conflicts using advanced AI models.

Production Ready

Enterprise-grade security, observability, health checks, and Docker deployment out of the box.

Analytics & Metrics

Comprehensive analytics, memory growth tracking, API usage statistics, and performance metrics via API endpoints.

Multi-Agent Support

Native CrewAI and AutoGen integrations with shared crew memory, per-agent namespaces, and escalation tracking.

Group Chat

Multi-user conversations with shared and private memories. Built for team collaboration.

Framework Integrations

Native support for Vercel AI SDK, LangChain, LlamaIndex, CrewAI, AutoGen, Zapier, and more.

Multi-LLM Support

Supports multiple LLM providers for text tasks, multimodal processing, and seamless integration with popular AI models.

Memphora Features

New development primitives that help simplify your code and allow you to build more features, faster.

Stop building state machines

Autosave for Application State

Memphora captures the complete state of your memories (variables, relationships, metadata) so you get the benefits of a state machine, without maintaining complex state machine code.

Documentation →
python
from memphora_sdk import Memphora

memory = Memphora(
    user_id="user123",
    api_key="your-api-key-here"
)

# Store memories - state is automatically persisted
memory.store("User loves Python programming")
memory.store("User works at Google")

# Search with automatic state management
results = memory.search("What does the user love?")
Superior relevance with Cohere & Jina AI

Advanced Search with External Reranking

Native semantic search with external reranking support (Cohere/Jina) for 20-30% better relevance. Advanced metadata filtering with AND/OR/NOT operators. Auto-fallback to built-in ranking if no API keys provided.

Documentation →
python
# Search with external reranking (NEW!)
results = memory.search(
    query="user preferences",
    limit=10,
    rerank=True,
    rerank_provider="auto"  # Cohere or Jina
)

# Advanced metadata filtering (NEW!)
results = memory.search(
    query="programming",
    metadata_filter={
        "AND": [
            {"category": "work"},
            {"priority": {"$gte": 5}},
            {"NOT": {"archived": True}}
        ]
    }
)
Significant token savings with ultra-fast retrieval

Context Compression & 3-Layer Caching

Intelligent compression reduces token usage while maintaining relevance. Combined with our 3-layer caching system (L1 hot cache, L2 warm cache, L3 persistent cache) for ultra-fast responses. An outage or restart won't prevent your compressed context from being available.

Documentation →
python
# Automatic compression with 3-layer caching
memory = Memphora(
    user_id="user123",
    api_key="your-api-key-here",
    auto_compress=True,
    max_tokens=500
)

# Get optimized context (uses 3-layer cache)
context = memory.get_context(
    query="user information",
    limit=20
)
Built for collaboration

Multi-Agent & Group Chat Support

Track memories by agent_id and run_id for multi-agent systems (AutoGPT, CrewAI). Group chat with shared and private memories. Perfect for team collaboration and complex agent workflows.

Documentation →
python
# Multi-agent support
memory = Memphora(
    user_id="user123",
    api_key="your-api-key-here"
)

# Store memory for a specific agent
memory.store_agent_memory(
    agent_id="research_agent",
    content="PyTorch is popular",
    run_id="run_001",
    metadata={"task": "Research frameworks"}
)

# Search agent memories
results = memory.search_agent_memories(
    agent_id="research_agent",
    query="What frameworks are popular?",
    run_id="run_001"
)

# Group chat - store shared memory
memory.store_group_memory(
    group_id="ai_team_001",
    content="Let's use FastAPI for the API",
    metadata={"author": "alice"}
)

# Search group memories
group_results = memory.search_group_memories(
    group_id="ai_team_001",
    query="What did we decide about the API?",
    limit=5
)
Code like a champion, in your language

One-Line Integration

Simple SDK with decorator support. Native integrations with LangChain, LlamaIndex, and AutoGPT. Drop-in replacement for any LLM framework with automatic memory management.

Documentation →
python
from memphora_sdk import Memphora

memory = Memphora(
    user_id="user123",
    api_key="your-api-key-here"
)

@memory.remember
def chat(message: str, memory_context: str = "") -> str:
    """Your chatbot with automatic memory!"""
    return your_ai_model(f"Context: {memory_context}\n\n{message}")

Code Examples

See how easy it is to integrate Memphora into your application.

Quick Start

Get started with Memphora in seconds

from memphora_sdk import Memphora

# Initialize with your API key
memory = Memphora(
    user_id="user123",
    api_key="your-api-key-here"
)

# Store memories
memory.store("I love Python programming")
memory.store("I work at Google")

# Search memories
results = memory.search("What do I love?")
for mem in results:
    print(mem['content'])

Decorator Pattern

Automatic memory integration with decorators

from memphora_sdk import Memphora

memory = Memphora(
    user_id="user123",
    api_key="your-api-key-here"
)

@memory.remember
def chat(message: str, memory_context: str = "") -> str:
    """Your chatbot with automatic memory!"""
    return your_ai_model(
        f"Context: {memory_context}\n\n{message}"
    )

# Use it - memory is automatic!
response = chat("Hello, what's my favorite language?")

LangChain Integration

Seamless integration with LangChain

from memphora_sdk import MemphoraLangChain
from langchain.chains import ConversationChain

# Create LangChain-compatible memory
memory = MemphoraLangChain.create_memory(
    user_id="user123",
    api_key="your-api-key-here"
)

# Use with your chain
chain = ConversationChain(
    llm=your_llm,
    memory=memory
)

Use Cases

Memphora is perfect for any application that needs persistent memory and context awareness.

AI Chatbots

Build chatbots that remember every conversation, user preference, and context across sessions. Perfect for customer support, personal assistants, and conversational AI.

  • Persistent context
  • User preferences
  • Conversation history
  • Personalized responses

Healthcare AI

Maintain patient context, treatment history, and medical preferences while ensuring HIPAA compliance and data security.

  • Patient history
  • Treatment tracking
  • Compliance
  • Secure storage

EdTech Platforms

Track learning progress, remember what students know, and personalize educational content. Adaptive learning systems that evolve with each student.

  • Learning progress
  • Knowledge tracking
  • Adaptive content
  • Progress analytics

E-Commerce

Remember customer preferences, purchase history, and browsing patterns. Provide personalized recommendations and shopping experiences.

  • Purchase history
  • Preferences
  • Recommendations
  • Personalized offers

Enterprise AI

Build enterprise AI applications that maintain context across departments, remember business rules, and provide consistent experiences.

  • Business context
  • Department memory
  • Consistent responses
  • Enterprise security

Developer Tools

Integrate memory into developer tools, IDEs, and coding assistants. Remember project context, coding patterns, and developer preferences.

  • Project context
  • Code patterns
  • Developer preferences
  • IDE integration
Limited Time Offer

Join the PRO Waitlist

Get 1 year of Memphora PRO for free for the first 30 joiners by signing up with your company email

10,000 Retrieval API Requests

Per month included

50,000 Memories Storage

Per month included

Priority Support

Get help when you need it

Advanced Analytics

Deep insights dashboard

Custom Integrations

Tailored to your stack

Early Access

First to try new features

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Ready to build with Memphora?

Join developers building smarter AI applications with persistent memory. Get started in minutes with our simple SDK.