* feat: add new cloudevents for api routes
* fix: add total input tokens to RepresentationCompletedEvent
* feat(telemetry): inject honcho_version + emitter health metrics
* feat(telemetry): per-LLM-call event with try/finally emission + sampler
Adds LLMCallCompletedEvent (llm.call.completed) — fires once per provider hit
with full cost-attribution context: transport/provider_label, model, token
counts with cache breakdown, finish_reason, outcome (success or error),
is_final_attempt flag, retry/fallback state, duration, tool-call shape,
streaming flag, and agent correlation (run_id + iteration).
- src/telemetry/events/llm.py: new event class + CallPurpose closed enum
(deriver.representation, dialectic.answer, dream.deduction|induction,
summary.short|long). Resource id includes attempt so multi-attempt retries
in one iteration get distinct deterministic ids.
- src/telemetry/events/base.py: BaseEvent._volume_class ClassVar (default
"ground_truth"); the new event opts into "high_volume".
- src/config.py: TelemetrySettings.HIGH_VOLUME_SAMPLE_RATE (default 1.0).
- src/telemetry/emitter.py: deterministic sampler keyed on run_id (so an
entire agent trace is kept or dropped together). Aggregate envelopes
bypass the sampler. Sampled-out events increment the dedicated counter
separate from buffer_full/send_failed drops.
- src/llm/runtime.py: AttemptPlan gains attempt/retry_attempts/is_fallback
so the executor reads retry state without re-deriving it.
- src/llm/types.py: LLMTelemetryContext dataclass carrying workspace,
call_purpose, run_id, iteration, peer fields. Iteration is mutable so
the tool loop can set it per inner call.
- src/llm/executor.py: honcho_llm_call_inner wraps the backend call in
try/finally — emits on success AND on exception, with is_final_attempt
computed from AttemptPlan. Stream path emits a was_stream=True placeholder
(token totals deferred until streaming completion is wired through).
Telemetry failures swallowed.
- src/llm/api.py: threads telemetry kwarg through all 4 signatures into
both honcho_llm_call_inner and execute_tool_loop.
- src/llm/tool_loop.py: _telemetry_for_iteration helper copies the caller
context with iteration set per call — covers both the normal iteration
loop AND the max-iteration synthesis call (iteration N+1).
Tests cover success/error emission, sampler trace-coherence (same run_id →
same decision), volume_class enforcement, unknown call_purpose tolerance,
provider_label inference, and telemetry failure isolation. 378/378 pass.
* feat(telemetry): emit agent.iteration on every LLM response + synthesis
AgentIterationEvent was defined but never emitted on this branch. Phase 2
wires it up in execute_tool_loop so every LLM call inside an agentic loop
produces one event — including the no-tool terminating iteration and the
max-iteration synthesis call — and threads LLMTelemetryContext from dialectic
and dreamer specialists down through honcho_llm_call.
- src/telemetry/events/agent.py: AgentIterationEvent opts into
_volume_class="high_volume" so the Phase 1 sampler throttles it.
- src/llm/tool_loop.py: _emit_agent_iteration() helper fires once per
honcho_llm_call_inner response, BEFORE the no-tool early return so the
terminating iteration is counted. A second emission fires for the
max-iteration synthesis call BEFORE final_response is mutated with
cumulative totals (otherwise the per-iteration counts would double-count).
Emission is defensively skipped when telemetry context lacks run_id /
agent_type / parent_category / workspace_name; emit failures are swallowed.
- src/dreamer/specialists.py: BaseSpecialist.run passes LLMTelemetryContext
with parent_category="dream", agent_type=self.name, observer/observed,
call_purpose=f"dream.{self.name}".
- src/dialectic/core.py: _telemetry_context() builds a shared context for
both answer() and answer_stream(), using self._run_id (always set) +
workspace + observed peer.
Tests cover fresh-copy semantics, per-iteration vs terminating emission,
defensive skip cases, telemetry-failure isolation, and volume_class. 408/408
pass across telemetry + llm + utils + dreamer + dialectic.
* feat(telemetry): agent.tool.call.completed event + ToolResult metadata
Adds the missing generic per-tool-call event so read-only tools (search_*,
get_recent_history, get_observation_context, etc.) and the four existing
state-change tools all produce a telemetry record. Built on a new internal
ToolResult(content, metadata) contract so handlers can surface
search-specific fields (top_k/used_embedding/query_tokens/results_count)
to Phase 3 and create/delete counts to Phase 5's specialist rollups.
- src/telemetry/events/agent.py: AgentToolCallCompletedEvent at v1 with
_volume_class="high_volume". Resource id = {run_id}:{iteration}:{tool_call_seq}
so two calls to the same tool in one iteration don't collide
deterministic ids and get dedup-dropped downstream.
- src/utils/types.py: ToolResult dataclass; two new ContextVars
(_current_tool_call_seq + _last_tool_metadata) so tool_loop and the
execute_tool closure can communicate per-call telemetry without changing
the public Callable[[str, dict], Any] signature.
- src/utils/agent_tools.py: execute_tool times handlers, unwraps ToolResult,
publishes metadata, emits the event. Handlers updated to ToolResult
where useful: create/delete observations, update_peer_card, search_memory,
search_messages. Other handlers continue to return str.
- src/llm/tool_loop.py: set_current_tool_call_seq before each executor call;
read get_last_tool_metadata after and stash on all_tool_calls[i] for
Phase 5 rollups.
Tests cover ToolResult str-likeness, ContextVar round-trip, full-context
emission with search metadata, resource-id disambiguation, defensive skip
cases, telemetry isolation, truncation metadata, volume_class. 420/420 pass.
* feat(telemetry): RepresentationCompletedEvent v2 token breakdown + tool-less truncation
Bulks out the deriver's per-batch telemetry without bumping the event schema
version. New additive fields capture the full token breakdown (queued vs.
extra-context vs. scaffold), the cap configuration (batch_max_tokens,
max_input_tokens, was_flush_enabled), real cap-hit flags, and observer
fanout. `input_tokens` stays unchanged as the queued-message-tokens billing
key Xatu's Stripe meter reads.
The big enabler: src/llm/api.py now actually enforces max_input_tokens on
the tool-less LLM path. Before this, the deriver passed the kwarg but the
path silently dropped it — so the configured cap was advisory and
hit_input_token_cap couldn't be measured. Phase 4 wires truncation through
the same truncate_messages_to_fit helper the tool loop uses and surfaces
input_was_truncated on HonchoLLMCallResponse.
- src/telemetry/events/representation.py: 12 additive fields, schema_version
stays at 2.
- src/llm/types.py: input_was_truncated on HonchoLLMCallResponse.
- src/llm/api.py: tool-less path truncates messages before dispatch, flips
input_was_truncated on the response when clamping occurs. Split into
Literal[True]/Literal[False] branches for typecheck.
- src/deriver/queue_manager.py: QueueBatchResult dataclass replaces the
3-tuple return from get_queue_item_batch; carries hit_batch_token_cap
(computed from cumulative token sum vs cap), was_flush_enabled snapshot,
and batch_max_tokens. Worker loop unpacks + forwards.
- src/deriver/consumer.py: process_representation_batch gains the three
flag kwargs and forwards.
- src/deriver/deriver.py: derives the breakdown fields locally, populates
the new fields on emit, sources hit_input_token_cap from
response.input_was_truncated.
Tests cover schema stability, defaultable fields, input_tokens semantic
preservation, cap-hit flag round-trip, model_dump completeness, and
HonchoLLMCallResponse.input_was_truncated mutability. Existing
test_queue_processing.py tests updated for QueueBatchResult and mock
process_representation_batch signature. 479/479 pass.
* feat(telemetry): DreamRunEvent v2 scheduler reasons + DreamSpecialistEvent v2 rollups
Bumps both dream events to v2 with additive fields. DreamRunEvent gains
scheduler context (threshold_reason / delay_reason / documents_since_last_dream_at_schedule /
document_threshold / dream_type / enabled_types_count) threaded through the
dream queue payload — the two scheduler gates stay as separate fields rather
than collapsing into one trigger_reason, preserving the WHY-vs-WHEN
semantics. DreamSpecialistEvent gains denormalized rollups
(created_observation_count / deleted_observation_count / peer_card_updated /
search_tool_calls_count) sourced from Phase 3's ToolResult.metadata so the
counts reflect observation truth, not call truth.
- src/telemetry/events/dream.py: schema_version → 2 for both events; new
fields all defaultable so older producers still construct valid events.
- src/utils/queue_payload.py: DreamPayload + create_dream_payload accept
threshold_reason / delay_reason / documents_since_last_dream_at_schedule /
document_threshold.
- src/dreamer/dream_scheduler.py: check_and_schedule_dream computes the two
reasons at decision time and threads them through schedule_dream →
_delayed_dream → execute_dream → enqueue_dream.
- src/deriver/enqueue.py: create_dream_record / enqueue_dream gain the
kwargs and persist on the queue payload.
- src/dreamer/orchestrator.py: process_dream unpacks the payload; run_dream
accepts the kwargs and stamps them on DreamRunEvent.
- src/dreamer/specialists.py: BaseSpecialist.run walks response.tool_calls_made
and sums ToolResult.metadata.created_count / .deleted_count, sets
peer_card_updated, counts search-tool calls by name.
Tests cover schema_version bumps, defaultable Phase 5 fields,
threshold-vs-delay semantics, observation-vs-call-count rollup distinction,
and DreamPayload round-trip. Existing tests updated for the schema bump
and the new enqueue_dream kwargs. 488/488 pass.
* feat(telemetry): AgentToolSummaryCreatedEvent v2 token breakdown
Bumps schema_version to 2 and adds three additive breakdown fields so
analytics can answer "how much of a summary call's cost was the previous-
summary rollup vs. the new messages vs. the scaffold instructions".
- src/telemetry/events/agent.py: previous_summary_tokens, message_tokens,
prompt_scaffold_tokens added with sensible 0 defaults. input_tokens
retains its current semantic (provider-side LLM tokens) — the plan's
proposed `provider_input_tokens` was omitted because input_tokens
already serves that purpose and a duplicate would fork queries.
- src/utils/summarizer.py: emit now populates the three new fields from
values already in scope (messages_tokens, previous_summary_tokens,
prompt_tokens). Hoisted prompt_tokens calculation out of the
is_fallback conditional so both the save-summary path and the emit
share one binding — basedpyright couldn't prove the sibling-scope
binding was safe, and the compute is cheap + idempotent.
Tests cover schema bump, defaultable fields, input_tokens semantic
preservation, first-summary edge case, and breakdown round-trip.
493/493 pass.
* feat(telemetry): embedding.call.completed event + call-purpose ContextVar
Adds the final piece of cost-attribution telemetry: per-embedding-call
events covering every provider hit (single + batch + retry attempts).
Embedding calls are real provider spend that was invisible before this
phase; search-heavy paths (dialectic agentic) can produce more embedding
calls than LLM calls, so the new event participates in the shared
HIGH_VOLUME_SAMPLE_RATE.
- src/telemetry/events/llm.py: EmbeddingCallCompletedEvent at v1 with
_volume_class="high_volume". EmbeddingCallPurpose closed enum
(search_memory / search_messages / create_observations / vector_sync /
summary / message_create). Resource id = run:purpose:provider:model:input_count
so per-iteration calls in one agentic run don't collide.
- src/utils/types.py: _embedding_call_purpose ContextVar plus
@contextmanager wrapper. Nesting-safe via ContextVar.reset(token).
Callers wrap embedding-driving operations in
`with embedding_call_purpose("search_memory"): ...` — no changes to
the embedding client signature.
- src/embedding_client.py: _emit_embedding_call wraps each provider hit
with try/finally so success AND error paths emit. Errors propagate
unchanged. Each retry attempt of _process_batch emits its own event.
Unknown call_purpose slugs drop to None (validation against the enum
happens at emit time, not at context-manager-set time).
- src/utils/agent_tools.py: search_memory / search_messages /
search_messages_temporal / create_observations (batch + fallback) all
tag their embedding calls.
- src/crud/representation.py: save_representation tags with
CREATE_OBSERVATIONS; get_working_representation precompute tags with
SEARCH_MEMORY.
- src/crud/message.py: create_messages batch embed tags with
MESSAGE_CREATE; search_messages/temporal fallback tags with
SEARCH_MESSAGES.
Tests cover event shape, enum closure, ContextVar nesting/exception
cleanup, wrapper success+error emission, unknown-purpose graceful
fallback, telemetry-failure isolation. 550/550 pass across the full
telemetry+llm+utils+dreamer+dialectic+deriver+crud test set.
* chore: fix tests
* fix(telemetry): address review findings on stream events, context propagation, and cap detection
Five findings from a post-Phase-7 review (one resolved by the merge from
main, four addressed here):
- src/llm/executor.py: stream-path LLMCallCompletedEvent now fires AFTER
the stream is set up and drained (or on exception), with real duration
and accurate outcome. Previously the event was emitted before
execute_stream() ran and was always recorded as outcome="success" with
duration_ms=0, which silently masked stream-setup and stream-drain
failures. Wrapping the async generator in try/finally surfaces the real
outcome; token counts stay 0 because we still don't have them at stream
end (aggregate envelopes carry totals).
- src/deriver/deriver.py + src/utils/summarizer.py: deriver and summarizer
LLM calls now thread LLMTelemetryContext into honcho_llm_call. Before
this, the closed CallPurpose enum had DERIVER_REPRESENTATION /
SUMMARY_SHORT / SUMMARY_LONG slugs but those production call sites
didn't actually pass `telemetry=`, so their LLMCallCompletedEvents lost
workspace_name, parent_category, and call_purpose. summarizer threads
workspace_name through _create_and_save_summary → _create_summary →
create_short_summary / create_long_summary.
- src/utils/types.py + src/embedding_client.py: embedding_call_purpose
ctx manager now accepts workspace_name and run_id kwargs, backed by
two new ContextVars. EmbeddingCallCompletedEvent's publisher reads
both via get_embedding_workspace_name / get_embedding_run_id so
embedding events carry workspace and run correlation. All call sites
updated: search_memory / search_messages / search_messages_temporal /
_handle_create_observations_impl pass ctx.workspace_name +
ctx.run_id; create_observations standalone and create_messages pass
workspace_name; RepresentationManager.save_representation and
get_working_representation pass self.workspace_name.
- src/deriver/queue_manager.py: hit_batch_token_cap detection rewritten.
Previously summed kept-rows' token_count and checked against
batch_max_tokens, but the SQL filter `cumulative_token_count <= cap`
guarantees kept rows stay under the cap, so the flag almost never
fired. Now uses two follow-up queries: total token_count across the
included id range + EXISTS check for any session message past the
last-kept id. Both true → cap was actually binding.
(The fifth finding — deriver scaffold-token computation needing
estimate_deriver_prompt_tokens(custom_instructions) — was resolved by
the merge from main; the Phase 4 emit at src/deriver/deriver.py:283
already sources prompt_scaffold_tokens from the wrapped helper.)
567/567 telemetry+llm+utils+dreamer+dialectic+deriver+crud tests pass.
ruff + basedpyright clean.
* chore: ruff linting
* chore: clean AI generated comments references specs
* fix: address coderabbit changes
* fix(telemetry): address remaining PR review findings
Six findings from the PR 637 telemetry review batched into one commit.
- src/llm/executor.py + src/embedding_client.py: asyncio.CancelledError
now surfaces as outcome="cancelled" on both stream and sync paths,
distinct from "error". Client disconnects mid-stream and server
shutdowns are normal control flow and should not feed error-rate
alerting. LLMCallCompletedEvent and EmbeddingCallCompletedEvent
outcome Literal extended; docstrings + tests cover the new state.
- src/utils/types.py + src/llm/tool_loop.py: new iteration_scope()
context manager captures and resets the four per-tool-loop
ContextVars (_current_iteration, _current_tool_call_seq,
_current_provider_tool_call_id, _last_tool_metadata). Applied as a
typed decorator to execute_tool_loop so back-to-back loops in the
same asyncio Task (worker batches, tests) don't observe stale state.
- src/telemetry/events/api.py + src/routers/messages.py:
MessageCreatedEvent schema v1 → v2. Added required last_message_id
(nanoid public_id of the trailing message); get_resource_id now keys
on it instead of message_count, eliminating the collision case where
two same-size batches in the same session+source produced identical
event ids. message_count stays on the body for analytics.
- src/deriver/queue_manager.py: hit_batch_token_cap now computed from
the FINAL post-config-filter batch. Previously the flag used the
pre-filter messages_context[-1].id, which produced false positives
when _resolve_batch_configuration trimmed the trailing queue item —
telemetry reported a cap-hit when the actual returned batch was
short for unrelated reasons. Cap-detection block moved inside the
async with after the filter; no extra DB connection.
- src/config.py + src/telemetry/emitter.py: documented the
HIGH_VOLUME_SAMPLE_RATE orphan trade-off (rate<1.0 keeps aggregates
but drops children, so JOIN ON run_id queries see partial traces).
Behavior unchanged — rate defaults to 1.0.
- src/deriver/deriver.py: WARNING-level invariant logs when
response.input_tokens < messages_tokens (provider tokenization
drift) or prompt_scaffold_tokens <= 0 (estimator silent failure).
Best-effort — telemetry never bleeds into the deriver path
* fix(telemetry): stream retry, embed attempts, truncation, dedup
Address remaining audit findings on the cloudevents PR:
- Stream setup now runs inside the awaited honcho_llm_call_inner so
tenacity's retry wrapper in stream_final_response catches transient
setup failures (rate-limit, auth, network). Previously the returned
generator deferred execute_stream until first iteration — outside
the retry wrapper — crashing the request and bypassing telemetry.
- Embedding _emit_embedding_call gains an is_final_attempt parameter;
_process_batch threads the real retry index so dashboards stop
conflating one-shot, mid-retry, and exhausted-retry calls.
- _truncate_tool_output returns (text, original_chars, was_truncated)
and a new _maybe_truncated_result helper wraps in ToolResult when
truncation happens. Five handlers migrated. AgentToolCallCompletedEvent
fields was_truncated and result_chars_before_truncation are now
populated instead of always None/False.
- execute_tool_loop tracks any_iteration_truncated and stamps
input_was_truncated on the final response (both HonchoLLMCallResponse
and StreamingResponseWithMetadata). Dialectic now reports
hit_input_token_cap correctly.
- GetContextEvent.get_resource_id uses empty-string sentinel instead
of literal "none" so a peer named "none" can't collide with absent.
- generate_event_id folds honcho_version into the deterministic id so
same logical event from different deploys produces distinct ids.
* fix(telemetry): address audit findings across LLM/embed/event paths
Three rounds of telemetry audit findings, grouped by area:
Retry correctness
- Stream LLM setup now runs inside the awaited honcho_llm_call_inner so
tenacity's outer retry catches setup failures (Fix 1). Previously the
inner generator deferred execute_stream past the retry wrapper.
- stream_final_response bumps the per-retry attempt index via
dataclasses.replace so emitted events show [1, 2, 3] instead of
[1, 1, 1] (Fix 13).
- Embedding _emit_embedding_call takes is_final_attempt; _process_batch
threads the real retry index (Fix 2).
Token + cost reporting
- HonchoLLMCallResponse.hit_input_token_cap (renamed from
input_was_truncated) uses a token-based rule so single-message
over-cap inputs are correctly flagged — the deriver's prompt-only
path used to silently fly through. Propagated through tool_loop's
per-iteration check (Fix 4) and into RepresentationCompletedEvent.
- DialecticCompletedEvent gains hit_input_token_cap; output_tokens now
folds in the final-stream's cumulative usage via
StreamingResponseWithMetadata.__aiter__ (Fix 7).
Event emission completeness
- AgentToolCallCompletedEvent's was_truncated /
result_chars_before_truncation populated by _truncate_tool_output via
a new _maybe_truncated_result wrapper; 5 handlers migrated (Fix 3).
- DreamSpecialistEvent emits on failure with success=False + new
error_class field, via try/finally (Fix 11).
- DeletionCompletedEvent emits on failure paths via try/finally
(Fix 12).
- CleanupStaleItemsCompletedEvent.queue_items_cleaned populated from
deleted_count (Fix 8).
Embedding call attribution (Fix 9)
- embedding_call_purpose context manager accepts parent_category.
- 4 new EmbeddingCallPurpose enum values: DIALECTIC_PREFETCH,
SESSION_CONTEXT_SEARCH, PREFERENCE_EXTRACTION, GENERIC_DOCUMENT_SEARCH.
- Wrapped previously-unattributed sites: dialectic prefetch, session
context search, preference extraction, conclusions search, vector
sync (×2).
Deterministic event ID + dedup
- generate_event_id folds honcho_version into the hash so cross-deploy
events don't silently collide on ID (Fix 6).
- GetContextEvent resource_id uses empty-string sentinel instead of
"none" so a peer literally named "none" can't collide (Fix 5).
Queue batch cap detection (P2.1)
- hit_batch_token_cap keys on the pre-config-filter SQL boundary so the
"kept=900 of 1000 cap, next=300 excluded by cap" case reports True
while still avoiding the config-filter false positive.
Tool result metadata
- search_messages_temporal returns ToolResult with the same search_meta
shape as search_memory / search_messages (P2.3) — top_k,
used_embedding, embedding_query_count, query_tokens, results_count.
Tests: stream-setup retry, stream-retry attempt sequence, post-stream
output_tokens write-back, is_final_attempt matrix, truncation E2E,
tool-loop hit_input_token_cap propagation, honcho_version in event id,
GetContextEvent disambiguation, queue_items_cleaned round-trip.
* fix(telemetry): address audit findings across LLM/embed/event paths
Four rounds of telemetry audit findings (initial + 3 follow-ups), grouped
by area:
Retry correctness
- Stream LLM setup now runs inside the awaited honcho_llm_call_inner so
tenacity's outer retry catches setup failures (Fix 1). The inner
generator previously deferred execute_stream past the retry wrapper.
- stream_final_response bumps the per-retry attempt index via
dataclasses.replace so emitted events show [1, 2, 3] instead of
[1, 1, 1] (Fix 13).
- Embedding _emit_embedding_call takes is_final_attempt; _process_batch
threads the real retry index (Fix 2).
Token + cost reporting
- HonchoLLMCallResponse.hit_input_token_cap (renamed from
input_was_truncated) uses a token-based rule so single-message
over-cap inputs are correctly flagged — the deriver's prompt-only
path used to silently fly through. Propagated through tool_loop's
per-iteration check (Fix 4) and into RepresentationCompletedEvent.
- DialecticCompletedEvent gains hit_input_token_cap; output_tokens now
folds in the final-stream's cumulative usage via
StreamingResponseWithMetadata.__aiter__ (Fix 7).
Queue batch cap detection
- hit_batch_token_cap previously required total_in_range >= cap, which
produced false negatives whenever the kept range didn't fully exhaust
the budget. Replaced with a pre-config-filter SQL boundary check
(P2.1), then further refined to a queue-item boundary comparison
(Fix 14) so trailing-context trimming doesn't false-negative either.
Event emission completeness
- AgentToolCallCompletedEvent's was_truncated /
result_chars_before_truncation now populated by _truncate_tool_output
via _maybe_truncated_result; 5 handlers migrated (Fix 3).
- DreamSpecialistEvent emits on failure with success=False + new
error_class field, via try/finally (Fix 11). except BaseException
catches cancellations too (Fix 16).
- DeletionCompletedEvent emits on failure paths via try/finally
(Fix 12), and uses ValidationException for unsupported types per
project guideline (Fix 17).
- CleanupStaleItemsCompletedEvent.queue_items_cleaned populated from
deleted_count (Fix 8).
Embedding call attribution (Fix 9)
- embedding_call_purpose accepts parent_category.
- 4 new EmbeddingCallPurpose values: DIALECTIC_PREFETCH,
SESSION_CONTEXT_SEARCH, PREFERENCE_EXTRACTION, GENERIC_DOCUMENT_SEARCH.
- Wrapped previously-unattributed sites: dialectic prefetch, session
context search, preference extraction, conclusions search, vector
sync (×2).
Reconciler no longer holds DB session during embedding (Fix 15)
- _sync_documents and _sync_message_embeddings refactored into
three phases per CLAUDE.md guideline: fetch+detach in a small DB
scope, external embedding call without DB locks, writes in a fresh
short-lived DB scope. New _apply_*_sync helpers; orchestrators
expunge ORM objects before invoking. Vector store upsert + sync_state
updates stay in the apply phase together.
Deterministic event ID + dedup
- generate_event_id folds honcho_version into the hash so cross-deploy
events don't silently collide on ID (Fix 6).
- GetContextEvent resource_id uses empty-string sentinel instead of
"none" so a peer literally named "none" can't collide (Fix 5).
Tool result metadata
- search_messages_temporal returns ToolResult with the same search_meta
shape as search_memory / search_messages (P2.3).
- Dialectic.prefetched_conclusion_count uses Representation.len() so
inductive + contradiction observations count too (Fix 10).
* fix(telemetry): orchestrator emit + review feedback
Three more rounds of audit findings + inline PR review, grouped:
Orchestration / emit reliability
- run_dream wrapped in try/finally so DreamRunEvent always emits, even
on unexpected exceptions including CancelledError (`finally` still
runs while cancellation propagates). Specialist except clauses
broadened from SpecialistExecutionError (never raised in src/) to
Exception so provider/DB/tool failures are recorded with
deduction_success=False / induction_success=False instead of crashing
past the emit.
- BaseSpecialist.run() telemetry state initialization + try/finally
hoisted above the preflight phase (peer lookup, peer-card preload,
create_tool_executor, get_model_config, prompt construction) so
preflight failures emit DreamSpecialistEvent(success=False) instead
of being dropped on the floor.
- Reverted the Round-4 _sync_documents / _sync_message_embeddings
phase split. The split introduced a race: rows were released from
FOR UPDATE SKIP LOCKED before the embed call, allowing two workers
to claim and clobber the same batch. Long-held DB transaction
restored (pre-existing CLAUDE.md violation accepted as a deliberate
trade-off; proper fix requires a claim/in_flight migration tracked
separately).
Schema + naming (PR-internal — none of these have shipped)
- threshold_reason → trigger_reason on DreamRunEvent, DreamPayload, and
every emit/scheduler/router/test call site (~45 src + 21 test lines).
Name now accurately reflects the field's role across "manual",
"surprisal", and "document_threshold" values.
- MessageCreatedEvent reset to schema v1 (was internally bumped to v2
for last_message_id but never shipped at v1 — downstream sees it
for the first time at merge).
- DreamSpecialistEvent gains created_counts_by_level /
deleted_counts_by_level: dict[str, int] keyed on the closed
level taxonomy. Per-tool-call events use list[str] (≤10 items),
but specialist runs aggregate 20+ — dict keeps emissions compact.
- QueueBatchResult marked frozen=True.
Per-call embedding attribution
- Agent tool embedding_call_purpose wraps for search_memory,
search_messages, search_messages_temporal, create_observations now
driven embedding cost rolls up under the right workflow.
- create_observations() signature gains parent_category kwarg
(mirrors existing run_id pattern).
Manual dream scheduling
- Manual /schedule_dream route now passes trigger_reason="manual" and
delay_reason="immediate". Previously both arrived as null in
DreamRunEvent, breaking analytics joins.
Queue-batch SQL perf
- next_exists_check folded into the main CTE query via
bool_or(cumulative_token_count > batch_max_tokens) OVER () in a
nested subquery. Cap detection is now one roundtrip per batch
instead of two.
Code/doc cleanup
- representation.py docstring uses generic "downstream metering key"
language (was "Xatu's Stripe meter"). bench runner --base-url help
uses a generic example host (was "groudon.fly.dev"). Public-facing
code/docs shouldn't reference internal service names.
Tests added for: orchestrator failure-path DreamRunEvent emission,
specialists preflight try/finally coverage, manual-dream
trigger_reason/delay_reason round-trip, dict-rollup accumulation across
multiple tool calls in a specialist run, CTE-fold one-roundtrip
behavior. Full Python suite passes (1236).
* fix(telemetry): correctness + attribution + emitter robustness
- Dreamer iteration count: read response.iterations directly so
one-shot runs no longer report iterations=0 and tool-using runs
include the terminal/synthesis LLM call.
- RepresentationCompletedEvent.observer_count counts successful
saves, not attempts.
- search_memory empty-memory fallback reports the snippet count when
message context is returned (was always 0).
- Wire parent_category through every embedding emit path: message
create (api), save_representation (representation), per-observation
fallback (caller-supplied), and the peer/session context routes
(api). get_working_representation accepts parent_category and
embedding_purpose so the internal fallback embed lands in the same
analytics bucket as the route-level precompute even when the
precompute is suppressed.
- BatchItem carries token_count so _process_batch reuses chunk-prep
counts instead of re-encoding every chunk for the telemetry proxy.
- Drop vestigial EmbeddingCallCompletedEvent.batch_size (always ==
input_count).
- Emitter: release the lock during HTTP send so a failing endpoint's
retry+backoff (~36s worst case) doesn't block other flushers;
edge-trigger the 80%-capacity warning so sustained backpressure
doesn't flood logs; defer event_id generation past the high-volume
sampler for events with run_id so sampled-out children don't pay
the sha256; harden emit() against sync callers with no running
loop; track threshold-flush tasks so shutdown() drains in-flight
sends before closing the HTTP client.
* fix(telemetry): tool cancellation emit, nanoid run_ids, version unification
- execute_tool: wrap post-work in finally so AgentToolCallCompletedEvent
fires on CancelledError; explicit handler sets is_error/result_str
before re-raising.
- run_id: replace str(uuid.uuid4())[:8] with generate_nanoid() across
dialectic/dreamer/specialists; matches project-wide nanoid convention.
- Bump _schema_version on events touched by run_id widening:
DialecticCompletedEvent v1→v2 (also covers hit_input_token_cap field),
AgentIterationEvent v1→v2, AgentToolConclusionsCreatedEvent v1→v2,
AgentToolConclusionsDeletedEvent v2→v3, AgentToolPeerCardUpdatedEvent
v1→v2.
- Unify honcho_version: single HONCHO_VERSION constant in src/_version.py
read from pyproject.toml (importlib.metadata fallback). Drop
TELEMETRY.HONCHO_VERSION setting. Use the constant for the FastAPI app
version (no more hardcoded "3.0.6") and for emitter body injection.
- Delete 17 tautological per-event test_schema_version methods; the
parametrized contract test still enforces version >= 1 across all events.
---------
Co-authored-by: Vineeth Voruganti <13438633+VVoruganti@users.noreply.github.com>
|
||
|---|---|---|
| .. | ||
| .gitignore | ||
| README.md | ||
| beam.py | ||
| beam_baseline.py | ||
| beam_common.py | ||
| calculate_expected_events.py | ||
| coverage.py | ||
| harness.py | ||
| incorrect_beam_qs.txt | ||
| locomo.py | ||
| locomo_baseline.py | ||
| locomo_common.py | ||
| locomo_summary.py | ||
| longmem.py | ||
| longmem_baseline.py | ||
| longmem_common.py | ||
| molecular.py | ||
| oolong.py | ||
| oolong_common.py | ||
| runner_common.py | ||
README.md
Honcho Benchmark Suite
This directory contains benchmarking tools for evaluating Honcho's long-term memory capabilities.
Available Benchmarks
- LongMemEval: Tests memory retention across multi-session conversations
- BEAM: Beyond a Million Tokens - comprehensive long-term memory evaluation across 10 memory abilities
- LoCoMo: Long conversation memory benchmark across multi-hop and temporal questions
- OOLONG: Long-context aggregation benchmark with
synthandrealvariants
Benchmark Workflow
Use a harness-first workflow for all benchmark runs:
- Start Honcho locally with the benchmark harness:
python tests/bench/harness.py
- Run one of the benchmark runners in another terminal:
# LongMemEval
python -m tests.bench.longmem --test-file tests/bench/longmemeval_data/longmemeval_oracle.json
# LoCoMo
python -m tests.bench.locomo --data-file tests/bench/locomo_data/locomo10.json
# BEAM
python -m tests.bench.beam --context-length 100K
- For OOLONG, point
--data-dirat your local dataset clone:
# OOLONG-synth
python -m tests.bench.oolong --variant synth --data-dir /path/to/oolong-synth
# OOLONG-real
python -m tests.bench.oolong --variant real --data-dir /path/to/oolong-real
# OOLONG-synth with label-augmented context (upstream optional mode)
python -m tests.bench.oolong --variant synth --data-dir /path/to/oolong-synth --labels
Notes for OOLONG runs:
- By default, synth uses
context_window_text(upstream baseline behavior). - Use
--labelsto switch synth ingestion tocontext_window_text_with_labels. - Default
--min-context-lenis1024and filtering uses strict>matching upstream.
Expected local dataset layout:
oolong-synth/
data/
test-*.parquet
validation-*.parquet
oolong-real/
dnd/
test.jsonl
validation.jsonl
Development Harness
The development harness script makes it easy to run Honcho locally with a Docker database.
Overview
The harness.py script orchestrates the complete Honcho development environment:
- Database Setup: Starts a PostgreSQL database in Docker with a configurable port
- Database Provisioning: Runs Alembic migrations to set up the database schema
- Configuration: Uses environment variables to configure Honcho's database connection
- Service Startup: Starts both the FastAPI server and deriver process
- Configuration Verification: Prints the actual configuration that Honcho is using
- Monitoring: Provides real-time logs from all services
- Cleanup: Gracefully shuts down all services when stopped
Prerequisites
- Python 3.11+
- Docker and Docker Compose
- Honcho project dependencies installed (
uv sync)
Usage
Basic Usage
Run the harness with default settings (database on port 5433):
python tests/bench/harness.py
Custom Database Port
Run with a custom database port:
python tests/bench/harness.py --port 5434
Custom Project Root
If running from a different directory:
python tests/bench/harness.py --project-root /path/to/honcho
Command Line Options
--port: Port for the PostgreSQL database (default: 5433)--project-root: Path to the Honcho project root (default: current directory)
What Gets Started
When you run the harness, it will start:
- PostgreSQL Database: Running in Docker on the specified port
- FastAPI Server: Available at http://localhost:8000
- API Documentation: Available at http://localhost:8000/docs
- Deriver Process: Background worker for processing messages
Configuration
The harness uses environment variables to configure Honcho's database connection:
DB_CONNECTION_URI: Derived from the database credentials indocker-compose.yml.example(e.g.postgresql+psycopg://postgres:postgres@localhost:{port}/postgres)
The script will print the actual configuration that Honcho is using after the FastAPI server starts. This gives you complete visibility into how Honcho's configuration system resolved the settings from environment variables, config files, and defaults.
Stopping the Services
Press Ctrl+C to gracefully stop all services. The harness will:
- Stop the FastAPI server and deriver processes
- Stop the Docker database container
- Clean up temporary files (Docker Compose configuration)
Troubleshooting
Database Connection Issues
If the database fails to start or connect:
- Check if port 5433 (or your custom port) is already in use
- Ensure Docker is running
- Try a different port:
--port 5434
Configuration Issues
The script will print the actual configuration being used. If you see unexpected values:
- Check if you have a
config.tomlfile that might be overriding environment variables - Verify that the environment variables are being set correctly
- Check the Honcho configuration documentation for precedence rules
Integration with CI/CD
This harness can be used in CI/CD pipelines for integration testing. The script will:
- Use temporary directories for isolation
- Clean up all resources on exit
- Provide clear error messages for debugging
- Exit with appropriate status codes
- Use environment variables for configuration (no file conflicts)
Test Runner
The run_tests.py script executes JSON-formatted tests against a running Honcho instance. The harness must be running.
Running Tests
-
Start Honcho using the harness:
python tests/bench/harness.py -
In another terminal, run the tests:
# Run all tests python tests/bench/run_tests.py # Run a specific test # Test judge uses claude 3.5 sonnet python tests/bench/run_tests.py --test 1.json
Test Workflow
For each test, the runner:
- Creates a workspace for the test
- Adds all messages from the JSON to sessions
- Waits for deriver queue to be empty
- Executes queries as
.chat()calls - Judges responses using expected_response field
Test JSON Format
Tests are defined in JSON files with this structure:
{
"sessions": {
"session1": {
"messages": [
{
"peer": "alice",
"content": "Hello, how are you?"
},
{
"peer": "bob",
"content": "I'm good, thank you!"
}
]
}
},
"queries": [
{
"query": "How is Bob doing?",
"expected_response": "Good",
"session": "session1", // optional
"peer": "alice" // optional
}
]
}
Command Line Options
--tests-dir: Directory containing JSON test files (default: tests/bench/tests)--test: Run a specific test file--honcho-url: URL of running Honcho instance (default: http://localhost:8000)--anthropic-api-key: Anthropic API key for response judging, uses LLM_ANTHROPIC_API_KEY if not given--timeout: Timeout for deriver queue to empty (default: 60 seconds)