# Learning Pair — Multi-Agent System with Knowledge-Sharing Planner
#
# A planner agent with persistent knowledge that remembers patterns, decisions,
# and lessons learned across coding sessions. A delegate coder does implementation.
# The planner ingests every significant decision, and a scheduled consolidation
# job periodically synthesizes learnings into project-level insights.
#
# ## Features Demonstrated
#
# - **Knowledge tools on the planner** (not the coder) — separation of concerns
# - **Quorum delegation** with knowledge-informed context passing
# - **Interval-scheduled consolidation**: periodic synthesis of accumulated knowledge
# - **Knowledge query before planning**: the planner gets smarter over time
# - **Multi-agent + knowledge + scheduling** working together
#
# ## Usage
#
#   cargo run --example qmtcode --features dashboard -- confs/learning_pair.toml --dashboard
#
#   # The planner will automatically query knowledge before planning tasks.
#   # Try across multiple sessions — knowledge persists.
#
#   # Create a consolidation schedule via the dashboard UI:
#   #   Session → Schedules → Create Schedule
#   #   Prompt: "Run a knowledge consolidation cycle: list unconsolidated entries, group by topic, and consolidate related entries into project-level insights about architecture, patterns, and lessons learned."
#   #   Trigger: Interval, 3600 seconds (hourly)
#   #   Max steps: 15
#   #   Max runs: 24 (one day of consolidation cycles)

[quorum]
cwd = "."
delegation = true
verification = false
snapshot_policy = "diff"
delegation_wait_policy = "all"
delegation_wait_timeout_secs = 300
delegation_cancel_grace_secs = 5
max_parallel_delegations = 1

# Planner — the knowledge-bearing orchestrator
[planner]
provider = "anthropic"
model = "claude-sonnet-4-5-20250929"
tools = [
  # Orchestration
  "delegate",

  # Knowledge lifecycle — planner's unique capability
  "knowledge_ingest",
  "knowledge_query",
  "knowledge_consolidate",
  "knowledge_list_unconsolidated",
  "knowledge_stats",

  # Planning & exploration (read-only)
  "read_tool", "index", "search_text", "glob", "ls",

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

  # User interaction
  "question",
]
system = [
  { file = "../prompts/learning_pair.txt" },
  { file = "../prompts/code_meta.jinja2" }
]

# Planner middleware
[[planner.middleware]]
type = "context"
warn_at_percent = 80
compact_at_percent = 90
fallback_max_tokens = 128000

[[planner.middleware]]
type = "agent_mode"
default = "plan"
reminder = """<system-reminder>
# Plan Mode — Knowledge-Driven Orchestrator

You are the **planner**. You must NOT edit files or run mutating commands.
Your job is to:

1. **Query knowledge** before planning — always check what you already know
2. **Plan** — break work into concrete tasks informed by past decisions
3. **Delegate** implementation to the `coder` agent
4. **Ingest** decisions, tradeoffs, and lessons into the knowledge store
5. **Verify** delegation results and ingest follow-up lessons

Knowledge query → Plan → Delegate → Review → Ingest lessons. Every cycle.

Do NOT skip the knowledge query step. Do NOT forget to ingest decisions.
Do NOT implement code yourself — that is the coder's job.
</system-reminder>"""

# Delegation summary model (cheap model for summarizing delegation results)
[quorum.delegation_summary]
provider = "anthropic"
model = "claude-haiku-4-5-20251001"

# Coder delegate — pure implementation, no knowledge tools
[[delegates]]
id = "coder"
provider = "anthropic"
model = "claude-sonnet-4-5-20250929"
description = "Implementation specialist for code writing, editing, and execution. Has full read/write access to the codebase."
capabilities = ["rust", "python", "typescript", "shell"]
tools = [
  "edit", "write_file", "read_tool", "index",
  "glob", "search_text", "ls",
  "shell",
  "todowrite", "todoread",
]
system = [
  { file = "../prompts/coder.md" }
]

# Coder middleware
[[delegates.middleware]]
type = "limits"
max_steps = 80
max_turns = 25

[[delegates.middleware]]
type = "context"
warn_at_percent = 80
compact_at_percent = 90
fallback_max_tokens = 128000

# Detect duplicate code in coder output
[[delegates.middleware]]
type = "dedup_check"
threshold = 0.85
min_lines = 10