ReasonBlocks provides a runtime layer for LLM‑based agents that automatically detects and mitigates failures, cuts token usage, and improves accuracy without requiring changes to the agent code. Its platform offers six built‑in capabilities—such as reasoning reuse, semantic file memory, loop detection, and context compression—that surface past solutions and streamline execution, delivering up to a 42% lift in accuracy on benchmarks like Claude Sonnet.
Funding
Funding not disclosed
Founders
Product
Problem
AI agents often encounter runtime failures, excessive token consumption, and redundant reasoning, which increase latency and cost while limiting scalability. Developers must manually instrument agents or redesign code to mitigate these issues, slowing iteration and deployment.
Solution
ReasonBlocks provides a transparent runtime layer that intercepts agent execution to detect and correct failures, reduce token usage, and share learned intelligence across deployments without any changes to the agent code. It offers six targeted capabilities—reasoning reuse, semantic file memory, loop detection, tool supervision, context compression, and reasoning-aware context pruning—that each address a specific failure mode. By indexing 190 k past execution traces and caching semantic representations of source files, the platform can surface relevant prior solutions and file meanings instantly. Real-time loop detection prevents infinite execution, while tool supervision curtails redundant API calls, both lowering token spend. Context compression and reasoning-aware pruning keep prompt windows within budget, improving latency. Integrated with popular frameworks such as LangChain, LangGraph, and OpenAI/Anthropic SDKs, ReasonBlocks operates as middleware, delivering measurable accuracy lifts (e.g., 42% on Claude models) and performance gains for developers.
Target Audience
Primary customers are developers and engineering teams building AI agents and autonomous workflows who need to improve reliability, reduce operational costs, and accelerate deployment without refactoring existing code.
Features
- Reasoning reuse with a searchable index of 190 k execution traces to surface past solutions for similar problems
- Semantic file memory that caches and semantically indexes source files, enabling instant recall of file purpose without re-reading
- Loop detection that monitors execution windows and injects redirects to break infinite or redundant loops
- Tool supervision that identifies and suppresses unnecessary tool calls, reducing token costs
- Context compression that summarizes earlier turns to stay within token budgets while preserving long‑range coherence
- Reasoning-aware context pruning that removes irrelevant context based on logical relevance rather than simple token limits
- Drop‑in middleware compatible with LangChain, LangGraph, OpenAI Agents SDK, and Anthropic SDKs, requiring no code modifications