# Research Journal — Interactive Research Agent with Persistent Memory
#
# An interactive research assistant that accumulates knowledge across sessions.
# You ask it questions, it fetches information, ingests findings into the
# knowledge store, and over time consolidates scattered facts into structured
# insights. An event-driven schedule auto-triggers consolidation after every
# 10 ingestions.
#
# ## Features Demonstrated
#
# - **All 5 knowledge tools**: ingest, query, consolidate, list, stats
# - **Event-driven scheduling**: auto-consolidate on KnowledgeIngested events
# - **Global scope**: cross-session memory via scope = "global"
# - **Interactive + autonomous**: user-driven research with scheduled consolidation
#
# ## Usage
#
#   cargo run --example qmtcode --features dashboard -- confs/research_journal.toml --dashboard
#
#   # Then in a session, try:
#   #   "Research how Rust async runtimes compare"
#   #   "What do you already know about tokio?"
#   #   "Consolidate your findings on async runtimes"
#
#   # Create an event-driven consolidation schedule via the dashboard UI:
#   #   Session → Schedules → Create Schedule
#   #   Prompt: "Run a consolidation cycle: list unconsolidated entries, group by topic, and consolidate related entries into insights."
#   #   Trigger: Event-driven
#   #     event_kinds: ["knowledge_ingested"]
#   #     threshold: 10
#   #     debounce_seconds: 30
#   #   Max steps: 15

[agent]
provider = "anthropic"
model = "claude-sonnet-4-5-20250929"
assume_mutating = false
mutating_tools = []

tools = [
  # Research tools
  "web_fetch",
  "read_tool", "glob", "search_text", "ls",

  # Knowledge lifecycle — the core of this demo
  "knowledge_ingest",
  "knowledge_query",
  "knowledge_consolidate",
  "knowledge_list_unconsolidated",
  "knowledge_stats",

  # Task management
  "create_task", "read_task", "update_task", "complete_task", "todowrite", "todoread",

  # User interaction
  "question",
]

system = [
  { file = "../prompts/research_journal.txt" },
  { file = "../prompts/code_meta.jinja2" }
]

# Compaction: tool output truncation (Layer 1)
[agent.execution.tool_output]
max_lines = 2000
max_bytes = 51200

# Compaction: pruning after every turn (Layer 2)
[agent.execution.pruning]
protect_tokens = 40000

# Compaction: AI summary on context overflow (Layer 3)
[agent.execution.compaction]
auto = true

# =============================================================================
# Middleware Stack
# =============================================================================

# Execution limits — generous for interactive research
[[middleware]]
type = "limits"
max_steps = 100
max_turns = 40

# Context management with auto-compaction
[[middleware]]
type = "context"
warn_at_percent = 80
compact_at_percent = 90
fallback_max_tokens = 128000