Self Pruning Context

The signal minus the noise

Self-pruning context dynamically refines its own memory, ensuring your agents focus only on the most critical data points.

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Working memory system governs every long-running agent

Relevance scoring

Every context item is continuously rated against current task goals.

Citations

List of web sources supporting the output field

Confidence

Reliability rating for each output field

Reasoning

Explanation of how the system processed the information

Context that earns its place

Every fact, file read, plan, and intermediate result in an agent's context carries a relevance score that updates as the task progresses.

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Hour eight as sharp as hour one

On long-running engineering tasks, traditional agents lose coherence as context bloats. Orc's pruning keeps the working set compact and relevant

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Coherence over time· % on-taskOrchestratorbaseline agent100500hr 0hr 8StableDriftRun length8.0Hrs

Powered by our own proprietary working memory engine

With innovations in scoring, eviction, recall, and compaction

  • Continuously scores millions of context tokens against live task goals
  • Evicts stale work in milliseconds without breaking agent reasoning
  • Recalls archived facts on demand when later steps need them
Context Memory
Force-directed entity relationship graph

FAQ

Self-pruning context is Orchestrator's working memory approach for keeping only the most relevant facts, files, plans, and intermediate results in an agent's active context.

Most agents keep filling a single context window until older detail gets crowded out, and they start contradicting earlier work. Orchestrator's working memory scores each item by relevance, evicts stale work, and recalls archived facts when needed, so an agent stays as sharp in hour eight as in hour one.

Context items are continuously scored against current task goals, with stale work evicted and relevant information recalled when later steps need it.

Yes. Archived facts can be recalled on demand when later steps require them.

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