Knowledge GraphsSemantic MemoryEntity ExtractionContextual RecallAgent MemoryProactive Intelligence
Knowledge GraphsSemantic MemoryEntity ExtractionContextual RecallAgent MemoryProactive Intelligence
Memory Infrastructure
2026

AmbientIntelligence,
ProactiveMemory.

Memory infrastructure for AI agents that anticipates, learns, and evolves.
A proactive intelligence layer

Book Demo
@aifunctor
functor-mcp
# Initialize MCP Client
from functor.mcp import create_client

client = create_client(
    server_url="http://your-mcp-server:8001",
    api_key="your_api_key"
)

# Ingest Data into Memory
async with client.session():
    await client.ingest("data.pdf", "file", "agent_kg")

# Retrieve Memory
async with client.session():
    results = await client.retrieve(
        query="Key insights?",
        kg_names=["agent_kg"]
    )

# Predict from Memory
async with client.session():
    answer = await client.predict("Analyze findings?")
/
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Universal Memory for You and Your Agents

Functor Architecture

The Self-Improving Intelligent Memory Layer for Intelligent Agents

01

Proactive

Insight

Delivery

Functor's ambient layer stacks horizontally across your AI infra, proactively surfacing insights and contexts before you need them. It monitors patterns in real-time, suggesting optimizations and pre-loading relevant memories to streamline workflows and reduce decision latency in complex environments.

Proactive Insight Delivery
02

Graph

Intelligence

Core

A streamlined graph engine that powers intelligent entity connections and rapid queries. It focuses on core intelligence for quick pattern recognition and relationship insights, enabling your AI to draw deep, actionable conclusions without overwhelming complexity.

Graph Intelligence Core
03

Efficient

Memory

Refinement

Functor dynamically adapts episodic memories, pruning redundant details while preserving key narratives. Using usage-based algorithms, it refines recall over time—boosting efficiency by discarding noise and enhancing focus on evolving user stories for more responsive AI interactions.

Efficient Memory Refinement
04

Unified

Data

Integration

Seamlessly fuse text, images, audio, and sensor data into cohesive memories. Functor's hub integrates diverse inputs for richer context, allowing your AI to handle real-world scenarios with unified recall that adapts to multimodal queries and delivers holistic insights.

Unified Data Integration
05

Anticipatory

Recall

Building

Anticipate and assemble contexts ahead of time with Functor's predictive engine. It forecasts needs based on trends, pre-building tailored memory blocks for instant deployment—empowering proactive AI agents that stay one step ahead in dynamic, fast-paced applications.

Anticipatory Recall Building
Benchmarks

Industry leading quality

Proprietary context retrieval combined with cutting-edge frontier memory systems give you code you can ship to production.

87%

Accuracy on production workloads

MethodAccuracyF1
Direct Prompting52%45
Step-by-Step Prompting54%50
Pre-trained LLM61%58
KG-Augmented Prompting70%69
Iterative Prompting75%74
→Functor87%83
Entity-Coverage F1 vs. ROUGE-L PerformanceEntity-Coverage F1 (%)ROUGE-L (%)5060708030405055Direct PromptingStep-by-Step PromptingPre-trained LLMKG-Augmented PromptingIterative PromptingFunctor
FROM THE LAB

Functor Studio

Research and insights from the Functor team.

Applications

Built for Real-World Applications

From AI agents to knowledge management, discover how our platform powers the next generation of intelligent systems

Healthcare

Agents hallucinate inaccurate medical advice due to forgetting patient-specific histories and decaying context in long-term care monitoring.

Episodic Recall

Captures patient allergies and treatment timelines for instant, verified access during consultations.

Adaptive Learning

Evolves care plans from past feedback, delivering proactive reminders without redundant queries.

Health Graphing

Connects symptoms, medications, and outcomes in temporal structures for error-free insights.

Decision TracesEpisodic Memory

Finance

Agents forget transaction patterns and user preferences, leading to hallucinated financial forecasts and rotting context in advisory sessions.

Profile Persistence

Secures ongoing user data like spending habits to maintain precision across volatile markets.

Forecast Refinement

Learns from historical trends to sharpen predictions, avoiding outdated or fabricated advice.

Asset Reconciliation

Merges personal portfolios with real-time market entities for interconnected, trustworthy guidance.

Preference LearningSemantic Search

Retail

Agents suffer from context rotting in extended shopping interactions, forgetting past behaviors and hallucinating irrelevant product suggestions.

Behavior Archiving

Tracks browsing and cart history for seamless continuity in multi-session experiences.

Preference Evolution

Adapts suggestions based on evolving signals, eliminating mismatched or invented recommendations.

Inventory Mapping

Links user interests to product graphs, enabling dynamic upselling with factual precision.

Entity ReconciliationKnowledge Graphs

Education

Agents forget student progress and prior feedback, causing hallucinated lesson plans and decaying context in adaptive tutoring.

Progress Archiving

Stores performance metrics and styles for unbroken recall across learning journeys.

Curriculum Tuning

Analyzes past errors to customize reinforcements, ensuring targeted, non-fabricated content.

Concept Networking

Builds relational graphs of topics and gaps for holistic, context-sustained personalization.

Temporal GraphsContext Engineering

Customer Service

Agents hallucinate solutions due to forgetting issue histories and rotting context across multi-channel support threads.

Thread Persistence

Logs cross-channel interactions for immediate, comprehensive recall in escalations.

Pattern Optimization

Refines fixes from resolution histories, providing efficient, grounded troubleshooting.

User Entity Fusion

Integrates query details with profiles for consistent, decay-resistant responses.

StreamingReal-time

Manufacturing

Agents forget operational logs and sensor data, leading to hallucinated maintenance predictions and context decay in supply chain monitoring.

Log Retention

Preserves equipment timelines and alerts to safeguard against data loss in real-time ops.

Predictive Tuning

Learns from downtime patterns for accurate, non-speculative maintenance forecasts.

Process Connectivity

Constructs temporal graphs of workflows and sensors for unified, reliable oversight.

Audit TrailsCompliance