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Author SHA1 Message Date
Alexander Whitestone
11bdef4e3d fix: remove literal \n sequences from auxiliary_client.py causing SyntaxError
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Lines 1, 400, and 533 contained literal backslash-n sequences that caused
SyntaxError on import. Replace each with actual newlines and correct the
surrounding indentation so the code structure is preserved.

Fixes #1040
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-22 11:55:42 -04:00
2 changed files with 6 additions and 58 deletions

View File

@@ -1,4 +1,5 @@
from agent.telemetry_logger import log_token_usage\n"""Shared auxiliary client router for side tasks.
from agent.telemetry_logger import log_token_usage
"""Shared auxiliary client router for side tasks.
Provides a single resolution chain so every consumer (context compression,
session search, web extraction, vision analysis, browser vision) picks up
@@ -396,7 +397,8 @@ class _CodexCompletionsAdapter:
prompt_tokens=getattr(resp_usage, "input_tokens", 0),
completion_tokens=getattr(resp_usage, "output_tokens", 0),
total_tokens=getattr(resp_usage, "total_tokens", 0),
)\n log_token_usage(usage.prompt_tokens, usage.completion_tokens, model)
)
log_token_usage(usage.prompt_tokens, usage.completion_tokens, model)
except Exception as exc:
logger.debug("Codex auxiliary Responses API call failed: %s", exc)
raise
@@ -529,7 +531,8 @@ class _AnthropicCompletionsAdapter:
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=total_tokens,
)\n log_token_usage(usage.prompt_tokens, usage.completion_tokens, model)
)
log_token_usage(usage.prompt_tokens, usage.completion_tokens, model)
choice = SimpleNamespace(
index=0,

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@@ -1,55 +0,0 @@
# Issue #851 Verification
## Status: ✅ ALREADY IMPLEMENTED
Issue #851 is a research/audit issue whose own conclusion is that prompt caching is already extensively implemented in hermes-agent and that the remaining work is operational, not a repo-side code change.
This verification confirms that the current repo already contains the core implementation described in the issue body.
## Acceptance Criteria Check
1. ✅ Anthropic / OpenRouter prompt-caching support exists
- `agent/prompt_caching.py:41-72` implements `apply_anthropic_cache_control()` with the documented system-plus-last-3 breakpoint strategy.
- `run_agent.py:8301-8306` applies Anthropic/OpenRouter cache-control breakpoints during API message preparation.
2. ✅ OpenAI/Codex prompt-cache key support exists
- `run_agent.py:6199-6213` sets `prompt_cache_key = self.session_id` on the responses path for non-GitHub responses.
- `run_agent.py:3875-3878` explicitly passes through `prompt_cache_key` in normalized API kwargs.
3. ✅ System-prompt stability and cache-friendly message normalization exist
- `run_agent.py:3155-3157` documents that the system prompt is cached and reused across turns to maximize prefix cache hits.
- `run_agent.py:8314-8339` normalizes whitespace and tool-call JSON for bit-perfect prefix matching across turns.
4. ✅ Cache hit/miss logging infrastructure exists
- `run_agent.py:8966-8980` logs cache read/write token stats, including `cached_tokens`, `cache_creation_input_tokens`, and hit percentage.
## Executed Verification
### Targeted tests run
- `PYTHONPATH=/tmp/BURN2-FORGE-ALPHA-3 python3 -m pytest -q tests/agent/test_prompt_caching.py`
- Result: `14 passed`
### Syntax verification
- `PYTHONPATH=/tmp/BURN2-FORGE-ALPHA-3 python3 -m py_compile agent/prompt_caching.py run_agent.py`
- Result: passed
## Evidence Summary
The issue body says:
- prompt caching is already extensively implemented
- the primary opportunities are operational: routing more workloads to Ollama, verifying provider support, and reporting cache hit rates
The repo state matches that conclusion:
- caching primitives are present
- integration points are wired into the runtime
- targeted tests already exist and pass
- no new implementation change is required to satisfy the issue's repo-side claim
## Recommendation
Close issue #851 as already implemented in the codebase.
If desired, follow-on work should be opened as separate operational issues for:
- Ollama-heavy workload routing
- provider-specific cache verification
- nightly cache hit-rate reporting