One API. Three things every agent needs.
Given a partial result and a goal, Masar produces ordered instructions for what to build next. Dependency-aware, parameterized, ready for your LLM to execute.
Predicts whether a result is valid in milliseconds, without running the full compiler. Catches 20 categories of structural errors before they become runtime failures.
Stores agent experiences as structured episodes. Over time, clusters similar experiences into reusable patterns. Your agent gets better at familiar tasks without retraining.
Your LLM is System 1: fast, intuitive, generates text. Masar is System 2: deliberate, structural, plans and verifies. Wire them together in 10 lines.
< 5ms planning latency
94% validity prediction accuracy
93 built-in behavior patterns
Memory that learns from experience
Your agent sends the current state and goal
Masar plans: ordered instructions with exact parameters
Your LLM executes each instruction
Masar verifies: catches errors in milliseconds
Masar remembers: stores the experience for next time
Add structured planning and memory to any LangChain, LangGraph, or custom LLM agent. Masar works with any model: Claude, GPT, DeepSeek, open-source.
Accelerate .orb schema construction with AI-guided planning. Masar knows 93 standard behaviors and produces instructions the compiler can validate.
Automate business processes with agents that remember. Helpdesk, CRM, compliance workflows that improve from every interaction.