Full
REST API
56
MCP Tools
10
Entity Types
10
Autonomous Agents
Built for How AI Actually Works
Seven infrastructure solutions that give AI systems the memory layer they've been missing.
The Problem:
AI agents lose all context between sessions. Every conversation starts from zero. Custom memory solutions are fragile, unstructured key-value stores that don't scale.
The Solution:
ACE3 provides cognitive infrastructure accessible via REST API, MCP protocol, or CLI. Store 10 entity types with relationships, search semantically, and let autonomous agents maintain data quality automatically.
Integration Methods:
- MCP Protocol — 56 tools, 9 resources, 6 prompts for Claude, Cursor, VS Code, ChatGPT, Gemini
- REST API — Full REST API accessible from any language or framework
Any AI agent gets persistent memory in 3 lines of code.
The Problem:
AI coding assistants forget everything when sessions end. Architecture decisions, debugging context, project conventions — all lost. You repeat yourself constantly across tools.
The Solution:
ACE3 connects to every major AI coding tool via MCP. Decisions, patterns, issues, and architecture are stored once and available everywhere. Switch between Claude Code, Cursor, and ChatGPT with the same memory.
Supported Tools:
- Claude Desktop & Claude Code
- Cursor & VS Code (Continue)
- ChatGPT (Codex CLI & Custom GPTs)
- Gemini & any REST-capable tool
- Windsurf & GitHub Copilot
One memory store, every AI tool. No vendor lock-in.
The Problem:
Project knowledge exists as isolated fragments. Decisions aren't linked to the issues they resolve. Architecture components aren't connected to the patterns they implement. Context is flat and disconnected.
The Solution:
ACE3's knowledge graph connects all 10 entity types with 15 semantic relationship types. Traverse connections up to 4 levels deep, query point-in-time snapshots, and visualize the entire project structure interactively.
Capabilities:
- 15 relationship types (implements, resolves, contradicts, enriches...)
- Temporal queries — see the graph at any point in time
- Hybrid search — semantic + keyword + graph-aware
- Auto-generated by Plans from project descriptions
Knowledge that's connected, not just stored.
The Problem:
Turning requirements into actionable plans takes hours. Writing decisions, decomposing architecture, creating issues, linking dependencies — tedious manual work before any code is written.
The Solution:
Describe what you want to build in natural language. Plans generates a complete project plan — issues, decisions, architecture components, best practices, patterns — all linked together in the knowledge graph. One API call.
What Gets Created:
- Issues with priorities, descriptions, and dependencies
- Architecture decisions with rationale and alternatives
- System components with technology stack mapping
- Best practices and coding patterns for the project
- 50-100+ entities with knowledge graph relationships
Hours of planning work done in seconds. Atomically.
The Problem:
Memory systems degrade over time. Stale data accumulates, duplicates multiply, search quality drops, and relationships become inconsistent. Manual maintenance doesn't scale.
The Solution:
Ten autonomous agents observe, reason, act, and reflect using LLM intelligence. They catch contradictions, discover missing connections, surface risks, ensure data quality, and evolve the knowledge graph — no intervention required.
10 Autonomous Agents:
Graph Maintenance
Repair relationships
Quality Governor
Quality control
Memory Consolidator
Merge duplicates
Knowledge Guardian
Catch contradictions
Graph Intelligence
Discover connections
Risk Engine
Surface insights
Pattern Detector
Recurring patterns
Observer Learning
Extract patterns
Session Manager
Context continuity
Decision Recorder
Capture decisions
Memory that gets better over time, automatically.
The Problem:
Team knowledge lives in individual heads and scattered tools. New members take weeks to onboard. Decisions get relitigated because nobody remembers the rationale. Vendor-hosted memory means giving up control of your data.
The Solution:
ACE3 runs on your own PostgreSQL database. Namespaces isolate projects, role-based access controls who sees what, and every team member's AI tools share the same knowledge. Your data never leaves your infrastructure.
Enterprise Features:
- Customer-hosted PostgreSQL — zero marginal storage cost
- Multi-member organizations with namespace isolation
- Full data privacy — memory stays on your infrastructure
- SSO, audit logs, and SOC2 compliance (coming)
Infrastructure you own. Knowledge that compounds.
The Problem:
AI agents are generic. Every instance behaves identically regardless of domain. There's no way for agents to develop specialized skills or adapt their behavior based on accumulated experience.
The Solution:
The AI Genome Lab gives each agent 8 behavioral traits that evolve through use. Fork specialized agents for security, compliance, or code review. Full lineage tracking shows how behavior adapted over generations.
Capabilities:
- 8 behavioral traits: Perception, Reasoning, Empathy, Memory, Expression, Adaptation, Regulation, Integration
- Agent forking — create domain-specialized variants
- Full lineage tracking — see how behavior evolved
- Live evolution — traits mutate in real-time
AI that gets uniquely better for your domain, automatically.
How It Fits Together
ACE3 sits between your AI tools and your infrastructure
Your AI Tools
ACE3 Cognitive Infrastructure
Your Database
Customer-Hosted PostgreSQLThe ACE3 Difference
Before and after cognitive infrastructure
- Start every AI conversation from scratch
- Unstructured key-value blobs
- No relationships between memories
- Memory degrades over time
- Locked to one AI platform
- Vendor hosts your data
- Perfect context in every session, any tool
- 10 typed entities with full CRUD
- Knowledge graph with 15 relationship types and graph analytics
- 10 autonomous agents that maintain and evolve memory
- Works with every AI via MCP, REST, SDK
- Runs on your own PostgreSQL