Ambient Intelligence, Proactive Memory
Your memory is not a retrieval system, it proactively assists you to think better.
Universal Memory for |
# 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?")Functor Architecture
The Self-Improving Intelligent Memory Layer for Intelligent Agents
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.
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.
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.
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.
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.
Industry leading quality
Proprietary context retrieval combined with cutting-edge frontier memory systems give you code you can ship to production.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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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.
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.
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.
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.
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.
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.