* fix(gateway): preserve full transcript on /compress instead of overwriting
The /compress command calls _compress_context() which correctly ends the
old session (preserving its full transcript in SQLite) and creates a new
session_id for the continuation. However, it then immediately called
rewrite_transcript() on the OLD session_id, overwriting the preserved
transcript with the compressed version — destroying searchable history.
Auto-compression (triggered by context pressure) does not have this bug
because the gateway already handles the session_id swap via the
agent.session_id != session_id check after _run_agent_sync.
Fix: after _compress_context creates the new session, write the compressed
messages into the NEW session_id and update the session store pointer.
The old session's full transcript stays intact and searchable via
session_search.
Before: /compress destroys original messages, session_search can't find
details from compressed portions.
After: /compress behaves like /new for history — full transcript preserved,
compressed context for the live session.
* fix(gateway): preserve transcript on /compress and hygiene compression
Apply session_id swap after _compress_context in both /compress handler
and hygiene pre-compression. _compress_context creates a new session
(ending the old one), but both paths were calling rewrite_transcript on
the OLD session_id — overwriting the preserved transcript and destroying
searchable history.
Now follows the same pattern as the auto-compression handler (lines
5415-5423): detect the new session_id, update the session store entry,
and write compressed messages to the new session.
Also fix FakeCompressAgent test mock to include session_id attribute
and simulate the session_id change that real _compress_context performs.
Co-authored-by: MacroAnarchy <MacroAnarchy@users.noreply.github.com>
---------
Co-authored-by: MacroAnarchy <MacroAnarchy@users.noreply.github.com>
The previous commit capped the 1.4x at 95% of context, but the multiplier
itself is unnecessary and confusing:
85% threshold × 1.4 = 119% of context → never fires
95% warn × 1.4 = 133% of context → never warns
The 85% hygiene threshold already provides ample headroom over the agent's
own 50% compressor. Even if rough estimates overestimate by 50%, hygiene
would fire at ~57% actual usage — safe and harmless.
Remove the multiplier entirely. Both actual and estimated token paths
now use the same 85% / 95% thresholds. Update tests and comments.
Three bugs in gateway session hygiene pre-compression caused 'Session too
large' errors for ~200K context models like GLM-5-turbo on z.ai:
1. Gateway hygiene called get_model_context_length(model) without passing
config_context_length, provider, or base_url — so user overrides like
model.context_length: 180000 were ignored, and provider-aware detection
(models.dev, z.ai endpoint) couldn't fire. The agent's own compressor
correctly passed all three (run_agent.py line 1038).
2. The 1.4x safety factor on rough token estimates pushed the compression
threshold above the model's actual context limit:
200K * 0.85 * 1.4 = 238K > 200K (model limit)
So hygiene never compressed, sessions grew past the limit, and the API
rejected the request.
3. Same issue for the warn threshold: 200K * 0.95 * 1.4 = 266K.
Fix:
- Read model.context_length, provider, and base_url from config.yaml
(same as run_agent.py does) and pass them to get_model_context_length()
- Resolve provider/base_url from runtime when not in config
- Cap the 1.4x-adjusted compress threshold at 95% of context_length
- Cap the 1.4x-adjusted warn threshold at context_length
Affects: z.ai GLM-5/GLM-5-turbo, any ~200K or smaller context model
where the 1.4x factor would push 85% above 100%.
Ref: Discord report from Ddox — glm-5-turbo on z.ai coding plan
The gateway had a SEPARATE compression system ('session hygiene')
with hardcoded thresholds (100k tokens / 200 messages) that were
completely disconnected from the model's context length and the
user's compression config in config.yaml. This caused premature
auto-compression on Telegram/Discord — triggering at ~60k tokens
(from the 200-message threshold) or inconsistent token counts.
Changes:
- Gateway hygiene now reads model name from config.yaml and uses
get_model_context_length() to derive the actual context limit
- Compression threshold comes from compression.threshold in
config.yaml (default 0.85), same as the agent's ContextCompressor
- Removed the message-count-based trigger (was redundant and caused
false positives in tool-heavy sessions)
- Removed the undocumented session_hygiene config section — the
standard compression.* config now controls everything
- Env var overrides (CONTEXT_COMPRESSION_THRESHOLD,
CONTEXT_COMPRESSION_ENABLED) are respected
- Warn threshold is now 95% of model context (was hardcoded 200k)
- Updated tests to verify model-aware thresholds, scaling across
models, and that message count alone no longer triggers compression
For claude-opus-4.6 (200k context) at 85% threshold: gateway
hygiene now triggers at 170k tokens instead of the old 100k.
Long-lived gateway sessions can accumulate enough history that every new
message rehydrates an oversized transcript, causing repeated truncation
failures (finish_reason=length).
Add a session hygiene check in _handle_message that runs right after
loading the transcript and before invoking the agent:
1. Estimate message count and rough token count of the transcript
2. If above configurable thresholds (default: 200 msgs or 100K tokens),
auto-compress the transcript proactively
3. Notify the user about the compression with before/after stats
4. If still above warn threshold (default: 200K tokens) after
compression, suggest /reset
5. If compression fails on a dangerously large session, warn the user
to use /compress or /reset manually
Thresholds are configurable via config.yaml:
session_hygiene:
auto_compress_tokens: 100000
auto_compress_messages: 200
warn_tokens: 200000
This complements the agent's existing preflight compression (which
runs inside run_conversation) by catching pathological sessions at
the gateway layer before the agent is even created.
Includes 12 tests for threshold detection and token estimation.
