build(deps): Bump github.com/rossoctl/context-guru from 0.0.0-20260720181432-8fc7c7b36563 to 0.1.0 in /authbridge/authlib

[dependabot]

Bumps github.com/rossoctl/context-guru from 0.0.0-20260720181432-8fc7c7b36563 to 0.1.0.

Release notes

Sourced from github.com/rossoctl/context-guru's releases.

context-guru v0.1.0 — First Public Release

Provider-agnostic context engineering for LLM agents. Shrink the tokens every request carries — losslessly, or lossy-but-reversibly — without touching the agent.

---

What is context-guru?

context-guru is a single Go core that reduces the token cost of LLM-agent traffic.
The same core runs as an HTTP proxy/gateway (drop-in, any language, zero agent changes) or as an in-process plugin inside larger platforms (AuthBridge, bifrost).

It operates on the messages array — dropping redundant tool output, collapsing superseded runs, projecting large reads down to what's relevant — and every reduction is safe by construction:

• Fail open, always — any component error or panic reverts that component only; the original request is always a valid fallback.
• Never worse — a component that would grow a message is reverted. You never pay to compact.
• Reversible — every lossy drop leaves a <<cg:HASH>> marker and stashes the original, recoverable via a model-callable context_guru_expand tool or GET /expand.

---

Benchmark — the cheapest & highest-reward arm on SWE-bench Verified

Evaluated live, end-to-end, with the claude-code agent on aws/claude-sonnet-5, against a no-compaction baseline, headroom (request-stream proxy), and rtk (Rust Token Killer, shell-level Bash-output hook). All 50 tasks scored under all four arms.

dimension	baseline	context-guru	headroom	rtk
tasks solved	43 / 50	44 / 50	40 / 50	43 / 50
total billed cost	$31.98	$27.77 (−13.2%)	$30.30 (−5.3%)	$29.09 (−9.0%)
cache-read tokens	102.8M	84.5M (−17.8%)	96.4M (−6.3%)	91.7M (−10.8%)
cache-write tokens	1.855M	1.847M (−0.4%)	1.839M (−0.9%)	1.835M (−1.1%)
mean steps / task	36.1	31.1 (−13.9%)	35.1 (−2.8%)	33.2 (−8.0%)
added latency / req	—	117 ms	63 ms	0 ms
tool LLM cost	$0	$0.31	$0	$0

context-guru is the cheapest arm and solves the most tasks. It cuts billed cost 13.2% vs no compaction, driven by a 17.8% cache-read reduction, by freezing each compaction and replaying it byte-identically every turn so the saving compounds across the session's cache-reads while never mutating the cached prefix.

Full four-way study, per-task/per-component breakdowns, real before→after examples, and reproduction instructions: https://github.com/rossoctl/context-guru/blob/HEAD/docs/RESULTS.md.

---

Architecture

Agent → [Host Adapter]
             ↓
         apply.Body  (wire normalization, Anthropic tool_result expansion)
             ↓
         Pipeline  (ordered components, fail-open, never-worse)
             ↓
         Upstream Provider
</tr></table> 

... (truncated)

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[clawgenti]

Dependency Update Analysis

Package: github.com/rossoctl/context-guru (0.0.0-20260720181432-8fc7c7b36563 -> 0.1.0)
Ecosystem: pip | Age: 16 days (SLA: 14 days)
CI Status: unknown

Changelog Summary

Release notes

Sourced from github.com/rossoctl/context-guru's releases.

context-guru v0.1.0 — First Public Release

Provider-agnostic context engineering for LLM agents. Shrink the tokens every request carries — losslessly, or lossy-but-reversibly — without touching the agent.

---

What is context-guru?

context-guru is a single Go core that reduces the token cost of LLM-agent traffic.
The same core runs as an HTTP proxy/gateway (drop-in, any language, zero agent changes) or as an in-process plugin inside larger platforms (AuthBridge, bifrost).

It operates on the messages array — dropping redundant tool output, collapsing superseded runs, projecting large reads down to what's relevant — and every reduction is safe by construction:

• Fail open, always — any component error or panic reverts that component only; the original request is always a valid fallback.
• Never worse — a component that would grow a message is reverted. You never pay to compact.
• Reversible — every lossy drop leaves a <<cg:HASH>> marker and stashes the original, recoverable via a model-callable context_guru_expand tool or...

  Risk Assessment

  • Breaking changes: None detected
  • Recommendation: Safe to merge (minor/patch, CI unknown, no breaking changes detected)

  ---

  Automated analysis by Rossoctl Dep Bump Fixer (scan 2026-08-13-002)