feat(telegram): add AI agent bot with streaming and account context

- Add Telegram bot with long-polling and AI agent loop (api_call tool)
- SSE streaming with real-time message editing and ⏳ placeholder
- Account state injection at conversation start (models, exchanges,
  strategies, traders, per-trader PnL and statistics)
- Lane semaphore per chat serializes concurrent messages (60s timeout)
- Idle timeout watchdog (60s) prevents hung streaming connections
- Look-ahead buffer prevents partial <api_call> tag leaking to user
- Fix PUT /strategies/:id to merge config (read-then-merge pattern)
- Add route registry with full API schema for LLM documentation
- Add TelegramConfig store and Web UI config modal
- Add GetAnyEnabled to AIModel store for bot LLM client selection

telegram/session/memory.go

@@ -0,0 +1,105 @@
+package session
+
+import (
+	"fmt"
+	"nofx/mcp"
+	"strings"
+)
+
+const (
+	compactionThresholdTokens = 3000
+	charsPerToken             = 3 // rough estimate for token counting
+)
+
+type Message struct {
+	Role    string // "user" or "assistant"
+	Content string
+}
+
+// Memory manages conversation history with automatic compaction.
+// Inspired by openclaw's compaction pattern:
+// when ShortTerm exceeds threshold, LLM silently summarizes it into LongTerm.
+type Memory struct {
+	LongTerm  string    // Durable summary (survives compaction, user never sees this happen)
+	ShortTerm []Message // Recent conversation (cleared on compaction)
+	llm       mcp.AIClient
+}
+
+func NewMemory(llm mcp.AIClient) *Memory {
+	return &Memory{llm: llm}
+}
+
+// Add appends a message and triggers compaction if threshold exceeded
+func (m *Memory) Add(role, content string) {
+	m.ShortTerm = append(m.ShortTerm, Message{Role: role, Content: content})
+	if m.estimateTokens() > compactionThresholdTokens {
+		m.compact()
+	}
+}
+
+// BuildContext returns context string for the agent's conversation history.
+func (m *Memory) BuildContext() string {
+	var sb strings.Builder
+	if m.LongTerm != "" {
+		sb.WriteString("[Summary of earlier conversation]\n")
+		sb.WriteString(m.LongTerm)
+		sb.WriteString("\n\n")
+	}
+	if len(m.ShortTerm) > 0 {
+		sb.WriteString("[Recent conversation]\n")
+		for _, msg := range m.ShortTerm {
+			sb.WriteString(fmt.Sprintf("%s: %s\n", msg.Role, msg.Content))
+		}
+	}
+	return sb.String()
+}
+
+// Reset clears short-term history (LongTerm preserved intentionally)
+func (m *Memory) Reset() {
+	m.ShortTerm = []Message{}
+}
+
+// ResetFull clears everything including long-term memory
+func (m *Memory) ResetFull() {
+	m.ShortTerm = []Message{}
+	m.LongTerm = ""
+}
+
+func (m *Memory) estimateTokens() int {
+	total := len(m.LongTerm)
+	for _, msg := range m.ShortTerm {
+		total += len(msg.Content)
+	}
+	return total / charsPerToken
+}
+
+// compact summarizes short-term history into long-term memory.
+// This runs silently - the user never sees it happen.
+// If LLM call fails, short-term is preserved as-is (no data loss).
+func (m *Memory) compact() {
+	if m.llm == nil || len(m.ShortTerm) == 0 {
+		return
+	}
+	history := m.BuildContext()
+	systemPrompt := `You are a conversation summarizer. Compress the following trading assistant conversation into a concise summary.
+
+Must preserve:
+- What the user is configuring (strategy/exchange/model/trader)
+- Confirmed parameters (trading pairs, leverage, stop loss, indicators, etc.)
+- Pending or missing parameters
+- User preferences and requirements
+
+Output: plain text summary, under 200 words.`
+
+	summary, err := m.llm.CallWithMessages(systemPrompt, history)
+	if err != nil {
+		// Compaction failed: keep short-term as-is, never lose user data
+		return
+	}
+	if m.LongTerm != "" {
+		m.LongTerm = m.LongTerm + "\n" + summary
+	} else {
+		m.LongTerm = summary
+	}
+	m.ShortTerm = []Message{}
+}