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ReasonBlocks is an AI agent optimization platform and Python SDK designed to help production agents work more efficiently and reliably. It captures agent runs, analyzes reasoning and tool-use patterns, detects issues such as repeated actions or unproductive loops, and provides runtime guidance to help agents avoid redundant work.
Key features include reasoning-pattern reuse, semantic codebase memory, context compression, loop detection, tool supervision, trajectory monitoring, model routing, and token-saving controls. ReasonBlocks can store lessons from completed agent runs, retrieve relevant patterns for similar tasks, and inject guidance into future workflows. Its codebase memory and import-graph tools help agents recall repository knowledge and assess potential changes across Python projects.
The platform integrates with commonly used AI agent frameworks and model APIs, including LangChain, LangGraph, OpenAI Agents, Anthropic integrations, Claude Agent SDK, Gemini, AWS Bedrock, and OpenAI-compatible providers. It supports telemetry, agent run analysis, workflow-specific model optimization, and gateway-based serving, allowing teams to monitor AI agents, improve task execution, reduce unnecessary context and token usage, and reuse validated solutions across deployments.
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