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  2. contextlens-profiler/
  3. Alternatives

contextlens-profiler Alternatives

contextlens-profiler is a command-line diagnostic tool for profiling and visualizing the context windows of LLM agents. It helps developers understand prompt usage, token costs, and optimize agent performance. Below are 6 llm eval & observability apps with similar functionality to contextlens-profiler, matched by what each product actually does — not ranked or scored. Explore each to find the closest fit for your use case.

  • context-profiler
    github.com

    context-profiler is an open-source CLI tool for profiling the context window usage of LLM agents. It provides framework-agnostic analysis of token usage, helping AI developers and researchers optimize agent performance and resource consumption.

  • contextmeter
    pypi.org

    contextmeter is an open-source CLI tool that provides a live meter for monitoring the context window usage of large language models (LLMs). It helps developers and researchers visualize token usage and optimize prompt engineering for AI applications.

  • contextspy
    pypi.org

    contextspy is an open-source CLI tool that profiles context usage and token consumption for LLMs and AI agents. It helps developers analyze and optimize prompt engineering and context management for efficient AI application development.

  • contextwatch
    pypi.org

    contextwatch is an open-source CLI tool for profiling the context window usage of LLM agents. It provides per-turn token ledgers, eviction and compaction tracking, and quality scoring, with standalone HTML reports for analysis. Designed for developers building or debugging LLM agent systems.

  • ctxprof
    pypi.org

    ctxprof is an open-source CLI tool that provides a live dashboard and offline auditing capabilities for context and cache in Claude Code sessions. It helps developers monitor token usage and analyze session data for improved observability.

  • contextly
    pypi.org

    contextly is an open-source command-line tool designed for context-engineering workspaces tailored to large language models (LLMs). It enables developers to efficiently manage and structure context for LLM-based workflows. Ideal for AI developers and researchers working with LLMs.