# Context Compression Strategies Compared in Experiment 2-IO: A Technical Analysis > Experiment 2-IO compares six context compression strategies: none, individual, combined, context-aware, context-aware with citations, and windowed. Analyze their impact on performance metrics. - Repository: [Bojie Li/ai-agent-book](https://github.com/bojieli/ai-agent-book) - Tags: technical-analysis - Published: 2026-08-23 --- **Experiment 2-IO evaluates six distinct context compression strategies—including no compression, individual non-context-aware, combined non-context-aware, context-aware, context-aware with citations, and windowed context—to measure their impact on token usage, execution time, success rate, and compression ratio.** The `bojieli/ai-agent-book` repository implements Experiment 2-IO (Context Compression Strategies Comparison) to benchmark how different approaches handle context window limitations in AI agent systems. Located in [`chapter2/context-compression/experiment.py`](https://github.com/bojieli/ai-agent-book/blob/main/chapter2/context-compression/experiment.py), this experiment provides a standardized framework for comparing compression techniques through a unified CLI interface. ## The Six Context Compression Strategies According to the source code in [`chapter2/context-compression/experiment.py`](https://github.com/bojieli/ai-agent-book/blob/main/chapter2/context-compression/experiment.py), the experiment defines six compression approaches in the `STRATEGY_CHOICES` dictionary (lines 25-33). Each strategy maps to a specific CLI alias for flexible testing workflows. ### 1. No Compression (`no_compression`) **Strategy Enum:** `NO_COMPRESSION` This baseline approach retains the full context without applying any compression algorithms. It serves as the control group to measure the performance impact of other strategies against uncompressed transcripts. ### 2. Individual Non-Context-Aware (`individual`) **Strategy Enum:** `NON_CONTEXT_AWARE_INDIVIDUAL` Applies compression on a per-tool basis independently. Each tool's output is compressed in isolation without considering the broader conversation context, potentially losing cross-tool dependencies. ### 3. Combined Non-Context-Aware (`combined`) **Strategy Enum:** `NON_CONTEXT_AWARE_COMBINED` Compresses the entire transcript as a single unit using non-context-aware methods. Unlike the individual approach, this processes the whole context together but still lacks semantic understanding of what information is salient. ### 4. Context-Aware (`context_aware`) **Strategy Enum:** `CONTEXT_AWARE` Intelligently identifies and retains salient information while compressing less relevant content. This strategy uses context-aware algorithms to preserve semantically important tokens that are likely needed for future reasoning steps. ### 5. Context-Aware with Citations (`citations`) **Strategy Enum:** `CONTEXT_AWARE_CITATIONS` Extends the base context-aware approach by additionally preserving citation metadata. This ensures that source references and attribution information remain accessible even after aggressive compression of the main content. ### 6. Windowed Context (`windowed`) **Strategy Enum:** `WINDOWED_CONTEXT` Implements a sliding window approach that retains only a fixed number of recent tokens. Older content beyond the window size is discarded regardless of relevance, mimicking traditional fixed-window attention mechanisms. ## Implementation Files The experiment architecture relies on three core components: - **[`chapter2/context-compression/experiment.py`](https://github.com/bojieli/ai-agent-book/blob/main/chapter2/context-compression/experiment.py)** — Contains the experiment runner, CLI argument parsing, and the `STRATEGY_CHOICES` dictionary that maps user-facing aliases to compression strategy enums. - **[`chapter2/context-compression/compression_strategies.py`](https://github.com/bojieli/ai-agent-book/blob/main/chapter2/context-compression/compression_strategies.py)** — Defines the `CompressionStrategy` enum referenced by the experiment script, formalizing the six compression approaches as programmatic constants. - **[`chapter2/context-compression/config.py`](https://github.com/bojieli/ai-agent-book/blob/main/chapter2/context-compression/config.py)** — Houses configuration parameters including model names, API keys, and iteration limits that control experiment execution across all strategy variations. ## Running the Comparison The CLI accepts the `-s` or `--strategy` flag to select specific approaches using the aliases defined above. Run the full benchmark comparing all six strategies: ```bash python chapter2/context-compression/experiment.py ``` Execute a single strategy (context-aware only): ```bash python chapter2/context-compression/experiment.py -s context_aware ``` Test a subset of strategies (individual and combined): ```bash python chapter2/context-compression/experiment.py -s individual combined ``` When executed without explicit `--strategy` arguments, the experiment runs all six strategies defined in `STRATEGY_CHOICES` by default. ## Summary - Experiment 2-IO compares **six distinct compression strategies** ranging from no compression to advanced context-aware methods with citation preservation. - Strategies are defined in [`chapter2/context-compression/experiment.py`](https://github.com/bojieli/ai-agent-book/blob/main/chapter2/context-compression/experiment.py) within the `STRATEGY_CHOICES` dictionary and implemented as a `CompressionStrategy` enum in [`compression_strategies.py`](https://github.com/bojieli/ai-agent-book/blob/main/compression_strategies.py). - CLI aliases (`no_compression`, `individual`, `combined`, `context_aware`, `citations`, `windowed`) enable flexible testing of individual or multiple strategies. - The experiment measures **token usage, execution time, success rate, and compression ratio** across all approaches. ## Frequently Asked Questions ### What is the difference between individual and combined non-context-aware compression? **Individual compression** processes each tool's output separately without cross-tool awareness, while **combined compression** treats the entire transcript as a single unit. The individual approach may lose inter-tool dependencies, whereas combined compression maintains the full sequence but applies uniform compression regardless of content importance. ### How does context-aware compression differ from windowed context? **Context-aware compression** uses semantic understanding to preserve salient information throughout the transcript, potentially keeping older but important tokens. **Windowed context** simply retains the most recent N tokens regardless of content value, discarding older information automatically when the window slides forward. ### Where are the compression strategies defined in the codebase? The strategies are defined as Python enums in [`chapter2/context-compression/compression_strategies.py`](https://github.com/bojieli/ai-agent-book/blob/main/chapter2/context-compression/compression_strategies.py) and mapped to CLI aliases in the `STRATEGY_CHOICES` dictionary located at lines 25-33 of [`chapter2/context-compression/experiment.py`](https://github.com/bojieli/ai-agent-book/blob/main/chapter2/context-compression/experiment.py). The configuration settings affecting all strategies reside in [`chapter2/context-compression/config.py`](https://github.com/bojieli/ai-agent-book/blob/main/chapter2/context-compression/config.py).