Closes the loop on M2: when the compactor fires, the LLM needs to know
it's now seeing a <compact-summary> instead of raw turns so it
doesn't waste a turn asking about lost details or re-executing tools
whose responses are gone.
shared-ai:
- LoopState grows `compactionsDone: number` (cap-1 by current loop
policy, but shape kept as count for future multi-compact cycles).
- runPlannerLoop populates it on each reminder-channel call. New
loop test asserts [0, 1] sequence: round 1 before compaction,
round 2 after.
mana-ai:
- New producer `compactedReminder` — fires severity=info when
compactionsDone >= 1, wrapped in a German one-liner ("frag nicht
nach verlorenen Details").
- Injected FIRST in buildReminderChannel so the LLM frames the rest
of the round with "I'm looking at a summary" context. Metric
surface stays `{producer='compacted', severity='info'}`.
4 new reminder tests (3 pure producer + 1 composition-ordering) +
1 loop-wiring test. 77 shared-ai, 20 reminders.test.ts — green.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
PlannerLoopInput grows an optional compactor:
compactor?: {
maxContextTokens: number;
threshold?: number; // default 0.92, matches Claude Code wU2
compact: (messages) => Promise<{ messages, compactedTurns }>;
}
Before each LLM call the loop checks whether promptTokens+completion
has crossed threshold × maxContextTokens. If yes AND we haven't
compacted this run yet, the callback runs, its returned messages
REPLACE the live history, and compactionsDone flips to 1 so a
runaway tool can't re-trigger.
Design choices:
- Fires at most ONCE per loop run. If the fresh (compacted)
history hits the threshold again in the same run, the LLM
round budget will hit first; better to terminate than to
recursively compact a summary.
- No reminder emitted automatically — the caller can wire
that via reminderChannel by reading compactionsDone from
LoopState (next PR; compactionsDone isn't exposed yet to
keep the state surface small).
- compactor callback is injectable, not hardcoded to
compactHistory() from compact.ts. Lets mana-ai route the
compactor LLM call to a cheaper model (Haiku) without
changing the loop.
- Zero maxContextTokens → skip silently (same contract as
shouldCompact()).
Also cleaned up the isParallelSafe non-null-assertion warning by
hoisting the predicate to a local with proper narrowing.
5 new loop tests: below-threshold no-op, single-fire replacement,
once-per-run idempotency, zero-cap bail, no-op when compactor
returns 0 turns. 76 shared-ai tests total, green.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Extends LoopState with a sliding window of the last N ExecutedCalls
(oldest-first), capped at LOOP_STATE_RECENT_CALLS_WINDOW = 5. The loop
maintains the window automatically; reminderChannel producers read it
without touching internal state.
This activates retryLoopReminder which was shape-only in faa472be9.
The guard now fires end-to-end: when round >= 3 and the tail-2 calls
both returned success:false, the LLM sees a "stop retrying, write a
summary instead" <reminder> on the next turn. The tail-2 check rather
than window-wide is deliberate — a flaky run with intermittent success
(F, F, F, OK, F) is not a retry loop, just flaky tools.
Why window=5: retry loops usually manifest within 2-3 consecutive
rounds; a 5-deep window gives room for burst-detection and
stale-tool heuristics without bloating the reminder channel. Cap
keeps the reminder producers O(5) regardless of loop length.
Tests: 3 new (sliding-window cap + slide + order in shared-ai, retry
composition + budget+retry chain + tail-only heuristic in mana-ai).
Total agent-loop tests now 74 across both packages.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Three Claude-Code-inspired primitives for runPlannerLoop, derived from the
reverse-engineering reports in docs/reports/:
1. **Policy gate** (@mana/tool-registry) — evaluatePolicy() gates every tool
dispatch: denies admin-scope, denies destructive tools not in the user's
opt-in list, rate-limits per tool (30/60s default), flags prompt-injection
markers in freetext without blocking. Wired into mana-mcp with a
per-user rolling invocation log and POLICY_MODE env (off|log-only|enforce,
default log-only). mana-ai uses detectInjectionMarker only — tool dispatch
there is plan-only, so rate-limit/destructive checks don't apply yet.
2. **Reminder channel** (packages/shared-ai/src/planner/loop.ts) — new
reminderChannel callback in PlannerLoopInput. Called once per round with
LoopState snapshot (round, toolCallCount, usage, lastCall); returned
strings wrap in <reminder> tags and inject as transient system messages
into THIS LLM request only. Never pushed to messages[] — the Claude-Code
<system-reminder> pattern that keeps the KV-cache prefix stable.
3. **Parallel reads** (loop.ts) — isParallelSafe predicate enables
Promise.all dispatch when every tool_call in a round is parallel-safe,
in batches of PARALLEL_TOOL_BATCH_SIZE=10. Any non-safe call downgrades
the whole round to sequential. messages[] always appends in source
order, never completion order, so the debug log stays linear.
Default-off (undefined predicate) preserves pre-M1 behaviour.
Tests: 21 new in tool-registry (policy), 9 new in shared-ai (5 parallel,
4 reminder). All 74 green, type-check clean across 4 packages.
Design/plan: docs/plans/agent-loop-improvements-m1.md
Reports: docs/reports/claude-code-architecture.md,
docs/reports/mana-agent-improvements-from-claude-code.md
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The runPlannerLoop test file and the webapp's mission-runner test each
had their own inline scripted LLM mock — same interface, diverged
slightly. Consolidates into packages/shared-ai/src/planner/mock-llm.ts
and re-exports from the package root so any consumer can drive the
loop deterministically.
Both existing test files now use the shared client. 5 + 3 tests pass,
44 total in shared-ai still green.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Introduces the new planner pipeline both the webapp runner and the
mana-ai tick will swap onto in the next commits. Additive for now —
the legacy buildPlannerPrompt + parsePlannerResponse stay exported so
callers can migrate one at a time; they get removed once the last
consumer is gone.
- planner/loop.ts — runPlannerLoop orchestrates a multi-turn chat
against a caller-supplied LlmClient. Tool-calls from the LLM are
handed to an onToolCall callback and their results fed back as
tool-messages. Parallel tool-calls in one turn execute sequentially
to keep the message log linear for debugging. Stops on assistant
stop, empty tool_calls, or a hard max-rounds ceiling (default 5).
- planner/system-prompt.ts — new buildSystemPrompt. ~40-line German
system frame, no tool listing (the SDK-level tools field carries
the schemas now), no JSON format example, no "please return JSON"
plea. User frame renders mission + linked inputs + last 3
iteration summaries, same as before.
- Five test cases covering the loop: immediate stop, single tool
call with result feedback, parallel calls execute in order, tool
failures propagate as tool-messages the LLM can react to, and
maxRounds ceiling fires with the right stopReason.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
