Replace the separate panel_usage SSE event + frontend accumulator with
server-side seeding of the worker's usage object. Both the done-event and
FinalizeChain read from the same usage, so panel (Fusion/MoA) member tokens
now flow into the live badge AND the DB as a single source of truth — the
badge no longer drops to the worker-only value after F5.
- RunPanel takes a TokenUsage usageSink; accumulates member costs after the
Task.WhenAll barrier (single-threaded, no lock)
- ChatEndpoint passes panelUsage as seedUsage to all three worker paths
- StreamChat / StreamChatViaCli / OpenAI StreamChat fold seedUsage into usage
at creation time, so every exit path persists it
- OpenAI done-events extended with cacheCreation/cacheRead fields for live
consistency when seeded by Anthropic members
- chat.js: remove redundant _panelUsage / _addPanelUsage / panel_usage
- TODO.md: mark the live-only v1 boundary closed, note the new mechanism
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Let the user inject a message into a running chain instead of only being
able to abort it. While a chain streams, typing + Enter sends the text to
a new /api/chat/steer endpoint; it is drained at the next iteration
boundary, wrapped with a hint so the model actively classifies it
("complements the task -> keep working" vs. "changes course -> adjust"),
and stored as a Type="steer" user message so it survives reload.
Shared infrastructure (provider-agnostic):
- SteeringQueue: out-of-band queue keyed by sessionId, mirrored from
StreamingTracker (Enqueue/Drain/Clear).
- Program.cs registers it as a singleton and passes it to AnthropicService.
- ChatEndpoint: POST /api/chat/steer enqueues only while IsStreaming,
Clear() in the finally.
- ChatService.BuildHistoryFromDb groups Type="steer" so no two
consecutive user turns are produced on re-chat.
- Frontend api.js/chat.js/bubbles.js/history.js + chat.css render the
optimistic steer bubble and the steer_injected SSE event; Enter during
a stream sends a steer instead of being blocked.
Anthropic API path:
- Drain point B (tool boundary, merged into the tool_result user turn)
and C (turn end -> inject + continue instead of finalize).
Claude CLI path (kill-and-restart):
- The agentic loop runs inside the claude child, so true mid-turn inject
is impossible. On a pending steer the child is killed at the next tool
boundary, progress is rebuilt from the DB as a <work_in_progress>
transcript, and the CLI is restarted with the steer appended.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Snapshot the agent's effort onto the chain (like model) so it survives
reload, archive and shared chains, then show it in the chain-end meta line.
- ChainRecord.Effort + chains.effort column (TryAddColumn migration).
- CreateChain INSERT carries the value.
- ChatService snapshots agent.Effort at chain creation, but only for Claude
models – OpenAI providers store '' so there is no misleading badge.
- Anthropic streaming + CLI done-events include effort for the live path.
- chat.js buildMetaBadges renders "Effort: <level>" after the model badge,
only when a value is set.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Opus 4.5+ and Sonnet 4.6+ support a new "effort" knob that controls how
much budget the model spends on thinking and tool calls. Plumb it through
end to end so each agent can pick its own level.
Storage & shape:
- New TEXT column agents.effort (default '') added via TryAddColumn.
- AgentRecord.Effort + INSERT/UPDATE in DatabaseService.Agents.cs.
- ChatContext.Effort + SubAgentContext.Effort so the value travels with the
request alongside model and tool settings.
Agent editor:
- Effort dropdown above git-commit behavior in the agent form (low/medium/
high/xhigh/max plus empty = "use default"). Always visible; on OpenAI
providers the value is sent but silently ignored.
- agent-page.js load/save and AgentEndpoint create/update wired up.
Anthropic API path:
- StreamChat and RunNonStreamingToolLoop add output_config: { effort } when
effort is set and not "high" – empty/null/"high" means "omit and let the
API pick its default".
- Sub-agents (preparer/review/finalizer) get the parameter through
RunAgentChat so each step uses its own agent's effort.
Anthropic CLI path:
- ClaudeCliService.ChatStreamingAsync takes an effort parameter and exports
CLAUDE_CODE_EFFORT_LEVEL on the spawned process. Env-var beats the
--effort flag because it also covers "max" and "ultracode".
- StreamChatViaCli, ChatNonStreamingViaCli, RunSubAgentViaCli, and the
ChatEndpoint anthropic branches all hand the value through.
ARCHITECTURE.md / ARCHITECTURE_QUICK.md updated with the new field.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- Auto-Context (Embedding-Suche) für OpenAI-Provider aktiviert: BuildAutoContextAsync
auf internal static refactored und in StreamChat + ChatNonStreaming integriert
- Tool-Results mit Role="tool" (OpenAI-Konvention) werden in BuildHistoryFromDb
jetzt korrekt erkannt (vorher nur Role="user"/Anthropic)
- Payload-Preview zeigt für OpenAI-Provider echtes API-Format (function-Wrapper,
tool_choice:"auto", max_tokens, stream_options, extra_body)
- Tool-Input (Argumente) wird im SSE tool_end-Event mitgesendet (fehlte im OpenAI-Pfad)
- Leere Bubbles durch Whitespace-only Text nach </think>-Extraktion behoben
(Trim in DB-Save, BuildHistoryFromDb und Frontend-Guard)
- History-Toggle-Bug: var→const in for-Loop (Block-Scoping für Closures),
tool_result-Matching per data-tool-call-id statt blindes letztes Element
- Non-Streaming max_tokens für MiniMax auf 32768 + extra_body.reasoning_split
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Anthropic hat Anfang März 2026 die Default-Cache-TTL stillschweigend
von 1h auf 5min gesenkt. Bei Sessions mit Pausen >5min wird der Cache
jedes Mal neu geschrieben (teuer). Dieser Commit aktiviert die
extended 1h-TTL über beide Pfade.
API-Pfad (AnthropicService):
- Beta-Header "extended-cache-ttl-2025-04-11" hinzugefügt
- Falsches Top-Level cache_control (Root-Level wurde ignoriert) entfernt
- Neue Helper ApplyCacheControlToLast() setzt cache_control idempotent
auf das letzte Element via JSON-Roundtrip zu Dictionary
- 2 Cache-Breakpoints pro Iteration:
* Letztes Tool (cached gesamten Tool-Block)
* Letzter System-Prompt-Block (cached System-Prompt)
- Sub-Agent-Loop (Preparer/Review/Finalizer): String-System-Prompt wird
bei Bedarf in Block-Form mit cache_control konvertiert
CLI-Pfad (ClaudeCliService):
- Env-Var ENABLE_PROMPT_CACHING_1H=1 für jeden CLI-Child-Prozess gesetzt
(offizieller Opt-in seit Claude CLI 2.1.108, gilt für API-Key,
Bedrock, Vertex, Foundry)
Setzt die folgenden Commits zurück, da sie das Problem nicht behoben haben:
- e75c437 fix(encoding): UTF-8 prozess-global + 16-KB-StreamReader für CLI-Output
- bfd550e fix: UTF-8-Encoding gehärtet (SSE-Reader + CLI-Workspace)
- 32aad7f fix: UTF-8-Encoding für Claude CLI auf Windows erzwingen
Trotz cmd.exe-Wrapper mit chcp 65001, explizitem UTF-8-StreamReader (16 KB Buffer,
ohne BOM-Detection), Console.OutputEncoding global auf UTF-8 und LANG/LC_ALL/
PYTHONIOENCODING-Env-Vars treten weiterhin Replacement-Chars (�) für Umlaute
und Em-Dashes auf Windows auf. Der Bug muss separat per Diagnose-Logging
nachgestellt werden.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- AnthropicService.Streaming.cs + OpenAiService.cs: SSE-StreamReader nutzt
jetzt expliziten UTF-8-Encoder mit 16-KB-Buffer (statt default 1 KB) und
ohne BOM-Detection. Verhindert intermittierende Replacement-Chars (�),
die entstehen können, wenn Multi-Byte-UTF-8-Sequenzen ungünstig über die
Read-Boundary eines NetworkStreams gesplittet werden.
- ClaudeCliService.cs: cmd.exe-Wrapper jetzt mit /D (AutoRun-Skripte aus
Registry deaktivieren) und WorkingDirectory wird auf den Session-Workspace
gesetzt. Vorher erbte der Child-Prozess das CWD von gott (bei Windows-
Diensten C:\WINDOWS\system32).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Remote-Verbindungen erkennen jetzt das OS des Clients und passen die Tool-Description
dynamisch an (PowerShell für Windows, Bash für Linux). Neues remote.sh Script für
Linux/macOS-Clients mit embedded Python WebSocket-Client.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- recalled_context-Header verstärkt (klare Nutzungs-Anweisung statt passivem Hinweis)
- SSE-Event 'auto_context' ans Frontend senden für Live-Anzeige
- Auto-Context als Message (type=auto_context) in DB speichern
- Aufklappbare Auto-Context-Box im Chat-Stream und History-Rendering
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
