Use pruning when you can identify irrelevant parts of a tool result and need the retained material to stay faithful to its original wording. Use summarization when older conversation or tool history is broadly relevant but too verbose to keep in full. For long-running agent workflows, combining selective tool-result compaction with summaries of older context can preserve useful evidence and continuity.
How pruning and summarization differ
Pruning removes selected content
Pruning filters a retrieved document or tool response, removing material irrelevant to the current task while retaining useful passages. It is a good fit when relevance is clear and exact wording, values, or identifiers matter. If the request is ambiguous, however, pruning can remove information the task needs. IBM Granite’s context-engineering cookbook describes this distinction and cautions about over-pruning.
Summarization rewrites older context
Summarization condenses conversation or tool history into a shorter account of key facts, decisions, preferences, and outcomes. It supports continuity across a long task, but the summary is a rewrite: details may be omitted or given less emphasis. Microsoft Agent Framework’s memory documentation describes an LLM-based strategy that replaces older messages with a summary and supports custom prompts.
Tool-result compaction sits between them
Rather than retaining every raw tool exchange or rewriting the whole conversation, tool-result compaction collapses older tool-call groups into compact summary messages while leaving user messages and plain assistant responses untouched. It can be a practical first step when verbose tool results are consuming context. Microsoft documents this as one of its framework strategies.
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Which method should you use?
| Situation | Better starting point | Trade-off |
|---|---|---|
| A tool result has clearly irrelevant sections, and exact language or values matter | Pruning | Retains selected material more faithfully; ambiguous relevance can lead to over-pruning. IBM Granite. |
| Older turns remain broadly relevant, and the agent needs continuity | Summarization | Preserves a compact narrative of prior context, but can drop or misweight details. Microsoft Agent Framework; OpenAI cookbook. |
| Tool outputs dominate context use, but a short activity trace is enough | Tool-result compaction | Condenses older tool-call/result groups and leaves recent groups intact. Microsoft Agent Framework. |
| A strict token or message ceiling matters more than retaining old detail | Truncation or sliding window | Removes older groups or turns predictably, but may discard useful older context. Microsoft Agent Framework. |
| Some older facts are essential, while much of the raw history is noise | Hybrid approach | Prune individual outputs, preserve critical facts in structured notes, and summarize broadly relevant history. This is a practical synthesis, not a measured head-to-head result. |
Compare methods against the task
- Relevance clarity: Can the system reliably tell which parts are irrelevant? If not, aggressive pruning risks discarding needed evidence.
- Fidelity: Does the task require exact wording, numerical values, identifiers, or raw tool evidence? Pruning can retain selected passages without paraphrasing them; summaries may omit or reframe details.
- Continuity: Must the agent carry decisions, preferences, constraints, and outcomes across many turns? Summarization is designed to retain a broad account; a sliding window can discard older items.
- Budget and latency: Truncation and rule-based pruning can be deterministic. LLM summarization adds a model operation, with associated cost and latency. When tool output is the main source of bloat, compaction may be a simpler first move.
- Privacy and auditability: A separate summarization client may receive the tool arguments and results included in its input. Check what it receives, and log or evaluate its behavior when auditability matters.
What these strategies look like in practice
Framework-specific context management
Microsoft Agent Framework documents truncation that removes the oldest non-system message groups until a target is met, while respecting tool-call/result group boundaries. Its sliding-window option retains a recent window of exchanges; tool-result compaction condenses older tool-call groups; and summarization uses a separate LLM client to summarize older messages. These are framework-specific strategies, so names, defaults, and APIs may change.
Bounded command output and agent-state compaction
OpenAI’s Responses API article describes bounding command output by preserving its beginning and end and marking omitted content. It also describes native compaction into a token-efficient representation of prior state for longer-running agent loops. These platform features illustrate related approaches; they do not establish that every pruning or summarization implementation behaves the same way.
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Server-side and session compaction are distinct
The OpenAI Agents SDK session documentation distinguishes server-side compaction configured on Responses API requests from session compaction, which calls a standalone endpoint and rewrites local session history. It also notes that storage settings affect whether server-side response retrieval is available to follow-up workflows. Confirm current behavior in the relevant documentation before building against these APIs.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Safeguards for a reliable workflow
- Protect system instructions and important constraints from removal.
- Keep the newest tool-call/result groups when the task depends on recent evidence.
- Store critical identifiers, decisions, and exact values in a retrievable structured record instead of relying on a free-form summary alone.
- Assess whether it is appropriate for a summarizer to receive sensitive tool arguments and results.
- Evaluate each approach on representative tasks for retained facts, missed constraints, tool-call correctness, latency, and token use.
The cited sources do not establish a universal winner or provide a head-to-head benchmark. Choose based on what your task must preserve, then test the behavior you actually deploy.
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