The Enterprise Agent Build & Runtime for the work your business runs on
Agent memory that learns from experience
Stage
Not given
Startup
Founded
Not given
2024
Based in
Not given
US
Pricing model
Freemium
Freemium
Pricing
Free plan with 50 workflow executions per month and visual editor. Enterprise plan with custom pricing includes governance, dedicated VPC, and comprehensive support.
Self-hosted: Free, MIT licensed. Hindsight Cloud: Pay-as-you-go with free credits to start. Retain (store memories) $10.00 per million tokens, Recall (retrieve memories) $0.75 per million tokens, Reflect (synthesize patterns) $0.05 per call, Iris Extract (structured extraction) $7.50 per million tok
Key features
Visual editor and AI copilot
No-code visual editor, exportable to Python
Code-first API built for total control
Real-time tracing of every LLM call, tool call, and memory read
RBAC and audit with immutable audit trails and Enterprise IAM
Human-in-the-loop approval gates and intervention during execution
Runtime hooks inject PII redaction and policy checks
Automated and human-guided training for continuous improvement
Multi-LLM testing for model swapping at runtime
GitHub integration
Per-user persistent memory with preferences, history and decisions kept separate
Cross-session persistence allows context to survive weeks between interactions
Fast parallel retrieval in under 100ms through four strategies running simultaneously
Works with any LLM without losing learned context when swapping models
Learns from mistakes and corrects behavior on repeated problems
Automatic pattern detection synthesizes individual facts into consolidated knowledge
Conflict detection preserves both old and new states with timestamps when facts change
Integrates with 20+ coding agents including Claude Code, Cursor, and Copilot
What makes it different
Agentic use case generator powered by billions of agent runs
Intelligently guided by 700k agent workflow patterns
Control Plane sits in execution path ensuring every agent interaction is observable, compliant, and reversible
Every production run turns into training data to sharpen accuracy and save money
94.6% retrieval accuracy on LongMemEval-S, beating next-best system at 74.0%
MIT licensed open source with no usage limits, telemetry or restrictions
Reflection layer automatically builds judgment and mental models over time without manual tagging
Entity resolution handles hundreds of entities with 99.5% query reduction
Vectorize