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Token compression reduces the number of tokens sent to and received from the LLM, without losing information from the model’s perspective. Compression is the surgical removal of redundancy. Not summarization.

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Two layers

The two-layer taxonomy is the non-negotiable foundation. Every strategy in this page belongs to exactly one of them.
  • Input compression: ~99% of total token volume, ~90% of the cost. What enters the context window: system prompts, tool results, codebase context, conversation history, MCP tool definitions.
  • Output compression: ~1% of total volume but 10% of the cost. What the model generates: filler, repetitive scaffolding, polite preambles, over-explanation, markdown overhead.
Agentic workloads consume 5–30× more tokens per task than chatbot workloads, and approximately 40% of those tokens are redundant. Compression targets that redundant share.

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The three compression strategies

Edgee ships three named compression strategies, toggleable independently. Compression V2 sharpens tool_result_trimming, adds tool_surface_reduction as a new Layer 1 technique, and adds output_brevity as a new Layer 2 technique.
CompressionLayerStatusSession reduction
Tool Result TrimmingInputImproved in V2−10%
Tool Surface ReductionInputNew in V2 (alpha)−10%
Output BrevityOutputNew in V2−30%
Figures above are illustrative, on a mixed suite of coding-agent workflows (18,420 → 9,210 tokens with all three on, a −50% reduction) — not a measured benchmark. Your mileage may vary. Sourced customer-traffic averages are called out per technique below.

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Tool Result Trimming

Filters tool_result messages before they reach the model. Strips:
  • Boilerplate framing
  • Pagination markers
  • ANSI escape sequences
  • Repeated headers
  • Verbose JSON wrappers
What it targets in a typical coding-agent session:
  • File contents — output from Read tool and file system operations.
  • Grep and search outputs — code search, ripgrep, similar tools.
  • Shell command output — stdout/stderr from Bash and terminal commands.
  • API responses — large JSON or text payloads returned by tool calls.
  • Database query results — rows and records returned from tool-executed queries.
User messages and assistant turns are not modified. Lossiness. Lossless on tool_result payloads — the model receives the same technical content, with redundant framing removed. Status. Improved in V2 — trims harder while staying lossless on code tasks (a 980-token directory listing becomes a dense 340-token one the model reads just as well). Illustrative session share. −10% (see table above). Customer-traffic average (rolling 30 days). tool_result_trimming reduces token costs by 19% on average — a different baseline, not additive with the session figure. Initially based on rtk-ai/rtk, we built our tool result compression strategy directly into the Edgee Rust gateway, so users don’t need a separate binary in their pipeline.

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Tool Surface Reduction

Coding agents connect multiple MCP servers, each exposing its own set of tools. The agent sends the full tool list to the model on every request, even when only one or two MCP servers are relevant. This bloats context and drives up cost. How it works: Edgee creates a virtual MCP server that the model sees. Instead of the full tool list, the model talks to the virtual MCP. The virtual MCP classifies the user’s task and searches for the correct real MCP server to use. It sends the result back to the client, which then executes the real MCP server. The result is a tool-aware gateway:
  • The IDE still exposes all MCP servers — nothing changes for the developer’s setup.
  • The agent still discovers tools through the standard MCP protocol — nothing changes for the agent’s behavior.
  • The model only ever sees the virtual MCP. The client receives the routing decision from it and executes the real MCP server.
Status. New in V2, alpha. Illustrative session share. −10% (see table above). Customer-traffic average (rolling 30 days), projected. tool_surface_reduction reduces token costs by ~25% — a different baseline, not additive with the session figure.

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Output Brevity

Reduces verbosity in model responses without losing technical content. Same answer, fewer tokens. Single flag — no level parameter. Status. New in V2. For coding-agent sessions, output is a small share of total token volume (~1%), so output_brevity is opt-in and disabled by default. For chat-style or RAG workloads where the model produces long-form answers, output is the dominant cost and output_brevity becomes the lever. Illustrative session share. −30% (see table above). Customer-traffic average (rolling 30 days). Where enabled, output_brevity reduces total token costs by 6.5% on average — a different baseline, not additive with the session figure. Academic note. Recent work supports the broader claim — Brevity Constraints Reverse Performance Hierarchies in Language Models (Hakim, arXiv:2604.00025, March 2026) found that constraining models to brief responses can improve accuracy on certain benchmarks. The study is on open-weight models, not Claude/GPT directly.

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Reading the compression block

Every response that runs through any compression strategy carries a compression block on the response body. Use it to track savings per request. 
const response = await edgee.send({
  model: 'gpt-5.2',
  input: 'Long prompt with lots of context...',
});

if (response.compression) {
  console.log(response.compression.saved_tokens); // e.g. 450
  console.log(response.compression.cost_savings); // micro-units (1_000_000 = $1.00)
  console.log(response.compression.reduction);    // percentage, e.g. 48 → 48%
  console.log(response.compression.time_ms);      // ms spent on compression
}
Field reference:
FieldTypeMeaning
saved_tokensintegerInput tokens removed (original count minus compressed count).
cost_savingsintegerEstimated cost savings in micro-units. Divide by 1_000_000 for USD.
reductionnumberPercentage reduction in input tokens. 48 → 48%.
time_msintegerWall-clock time spent on compression.
The usage.prompt_tokens field on the same response reflects the compressed count actually billed by the provider, not the original input.

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Enabling and disabling

Three surfaces, in order of how most users will use them.

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CLI (default-on for coding agents)

When you launch a coding agent through the Edgee CLI, tool_result_trimming is enabled automatically — no console step required. 
edgee launch claude
edgee launch codex
edgee launch opencode
tool_surface_reduction is opt-in. output_brevity is opt-in for coding-agent sessions because output is a small share of their volume.

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Console (per-key toggle)

In the Edgee Console, open Dashboard and manage your agent’s settings right from the UI. For team-managed keys, the same toggles are available per-member from Team management → agent settings. See Team management.

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Receipts

Customer-traffic averages, per technique, rolling 30 days — not aggregable across techniques (different baselines):
  • tool_result_trimming — −19% on average
  • tool_surface_reduction — ~−25% projected (alpha)
  • output_brevity — −6.5% when enabled
Customer aggregate (rolling 30 days): approximately 20% reduction in token bills across active customers, with zero measurable drift on SWE-Bench Verified samples.

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Next

Observability

Track token usage, costs, and compression events per session and per team.