* feat(telemetry): CloudEvents + Langfuse tracing as projections over a captured LLM stream
Capture each LLM call once (CapturedLLMCall) and fan it out to multiple
exporters -- "one data model, two projections": a CloudEvents trace stream
(llm.call.traced / trace.content) and a Langfuse projection, both reconstructing
trace -> run -> step -> generation from the same source of truth.
- Capture seam (src/llm/capture.py): one canonicalization + content-addressed
hashing point, with an O(N) per-span memo so repeated context isn't re-hashed.
- Session correlation threaded telemetry -> captured call -> exporters,
namespaced only at the Langfuse export boundary.
- Span identity consolidated onto LLMTelemetryContext; dropped TRACE_ENDPOINT.
- Canonical generation/step names; dreamer branches nest under one dream trace;
tool calls become spans under their step.
- LANGFUSE_EXPORTER_MODE toggle ("exporter" default; "inline" kept one release
for side-by-side validation), centralized into computed settings predicates.
- Per-run/per-trace dedup registries (trace_session, langfuse_session) bounded
by an LRU so dedup and span grouping survive long-running workers.
- Embedding-call tracing; deterministic high-volume event sampling.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* fix(telemetry): address trace-review findings (span/step_seq collisions, test, logging)
- Dreamer specialists mint a distinct span_id per execution (trace_id stays the
shared dream run_id), so their CloudEvents trace resource ids no longer collide
between deduction and induction.
- Tool-loop no-tool early-return streams the tail with the next ordinal
(iteration+2) instead of reusing the in-loop call's step_seq, avoiding a
colliding trace resource id; mirrors the synthesis path.
- Tighten test_clips_oversized_string to assert output stays within TRACE_MAX_BYTES.
- emit_trace logs the swallowed exception with exc_info for debuggability.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* fix(telemetry): silence exporter-mode Langfuse warning + drop summarizer run_id placeholder
Two CloudEvents/Langfuse correctness fixes, independent of the trace viewer.
Langfuse exporter-mode gating: annotate_current_generation_io (and its two
executor.py call-site guards) were gated on LANGFUSE_PUBLIC_KEY instead of
langfuse_inline_enabled. In the default `exporter` mode they called
get_client().update_current_generation() with no active @observe span, logging
"No active span in current context" (~14 per dialectic run) and building
throwaway model_dump payloads on every LLM call. The LangfuseExporter projects
I/O from the captured stream, so these helpers must no-op in exporter mode.
Gated all three on langfuse_inline_enabled; added a regression test; fixed a
stale conditional_observe docstring.
Summarizer run_id placeholder: AgentToolSummaryCreatedEvent hardcoded
run_id="deriver"/iteration=0 because summarization is a single LLM call, not an
agentic run. That placeholder pollutes run_id grouping in the CloudEvents stream
(any consumer that groups by run_id sees a phantom "deriver" run). Made
run_id/iteration optional (None) and re-keyed get_resource_id on
message_id:summary_type (the real per-summary identity; run_id/iteration can no
longer identify it); bumped schema_version 2->3. Xatu ingestion stores only the
CloudEvent envelope, so the field/resource_id/version changes are transparent to it.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* docs: update docstrings to be less verbose
* fix(telemetry): address PR review on captured-stream tracing
- embedding traces get a fresh span_id under parent_span_id=run_id, so
sibling embeddings in one run no longer share a span/idempotency key
- capture the provider finish_reason from stream chunks instead of
hardcoding "stop" on a successful drain
- gate the Langfuse exporter behind TELEMETRY.ENABLED (master switch) so
disabling telemetry sends no traces at all
- rename _emit_derived_content -> _emit_hashed_content
- inline the _emit_trace wrapper; drop unused trace_session.end_run
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* refactor: rename TELEMETRY_TRACE_PAYLOADS to TELEMETRY_TRACE_PAYLOADS_ENABLED
* fix(telemetry): capture provider tool calls in trace stream
The captured trace stream dropped assistant tool calls for openai/gemini:
build_captured_messages only read {role, content, tool_call_id}, but those
providers keep tool calls outside content (openai's tool_calls, gemini's
parts), so replayed tool-call turns landed as empty content and gemini lost
its text and tool results entirely. Anthropic (tool_use in content) was fine.
Normalize each input message per provider into a unified tool_calls
[{id, name, input}] field on CapturedMessage/TraceContentEvent, recovering
gemini text/results along the way, and fold tool_calls into
compute_content_hash so empty-content openai turns no longer collide in the
dedup store. langfuse_exporter._input now surfaces the calls.
Also fix a silent serialization drop: gemini thought_signature is bytes, so
model_dump(mode="json") on the traced event raised UnicodeDecodeError and
emit_trace swallowed it -- dropping the whole tool-calling iteration from the
trace stream (billing and Langfuse were unaffected). base64-encode the
signature on the telemetry path; replay keeps the raw bytes.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* fix(telemetry): type replay tool-call dict for bytes signature
thought_signature widened to str | bytes | None, but
_tool_call_result_to_dict's literal was inferred as
dict[str, str | dict[str, Any]], so the bytes assignment failed project-wide
basedpyright (the per-file pre-commit hook didn't catch it). Annotate the
dict as dict[str, Any]; the replay path keeps the raw bytes unchanged.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* test: remove 3 tests
---------
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
* fix(llm): stop capturing live LLM clients in Langfuse generation spans
honcho_llm_call_inner is the @observe generation boundary, and default
auto-capture serialized every argument into the span input -- including
client_override (a live AsyncOpenAI/genai client) and selected_config
(which carries api_key). Auto-capture deep-copies the client into a
half-constructed object whose teardown raises:
- AsyncHttpxClientWrapper ... no attribute '_state' (OpenAI, stderr flood)
- BaseApiClient ... no attribute '_http_options' (Gemini, HONCHO-4HA)
and it leaked ModelConfig.api_key into traces.
Switch from auto-capture (denylist) to explicit annotation (allowlist):
disable capture_input/capture_output on the decorator and stamp curated,
serializable input (messages) and output (HonchoLLMCallResponse) via the
new annotate_current_generation_io helper. Full trace fidelity is
preserved; no client object or secret can reach a trace.
Fixes HONCHO-4HA
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* feat(llm): track call tuning knobs as Langfuse model_parameters
Restore full trace fidelity after disabling @observe auto-capture: surface
every tuning knob (temperature, max_tokens, tools, reasoning effort, ...) on
the generation via model_parameters, sourced from the resolved effective
config instead of the raw function args.
Use a deny-list, not an allow-list: dump the whole ModelConfig and exclude
only secret-bearing fields (api_key, base_url, fallback, provider_params), so
new config knobs are traced automatically without keeping a hand-written list
in sync. The live client is never passed -- there is no useful trace
representation of it and serializing it is what triggered HONCHO-4HA.
Adds a deny-list test proving secrets never leak even when the config carries
a real api_key/base_url/provider_params (the production override-client path).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* feat(llm): duplicate token usage to Langfuse + skip payload build when disabled
Mirror per-call token usage (input, output, prompt-cache read/creation) onto
the Langfuse generation via usage_details, so Langfuse renders native tokens
and cost in addition to the CloudEvents accounting.
Also guard both generation-annotation blocks behind settings.LANGFUSE_PUBLIC_KEY
so the model_dump-backed model_parameters payload (and the usage dict) are only
built when Langfuse is actually configured (addresses CodeRabbit: the annotate
helper no-ops when disabled, but the payload was still being constructed every
call).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* telemetry: use session and user IDs in langfuse
* test: update old span test
* fix: disable langfuse in unit tests
* fix: add post-loop synthesis span
* refactor: address PR review feedback on langfuse tracing
- Consolidate track_name onto LLMTelemetryContext as the sole home;
remove the honcho_llm_call kwarg and update 4 callers to set it on
telemetry directly. Sentry ai_track now reads telemetry.track_name.
- Decouple escaped-stream self-stamping from run-context exit ordering:
stream_final_response now resets _in_agent_run explicitly around drain.
- Narrow langfuse_agent_step wrap in the tool loop — between-turn
bookkeeping (iteration_callback, choice switch, increment) lifted
outside the span so it scopes only the LLM call + tools.
- Reword test conftest comment to behavior-only language.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* refactor: switch langfuse spans to imperative handles
Replaces the context-manager-based langfuse_agent_run/step with imperative
LangfuseAgentRun/Step handles so the run span can outlive the function that
opens it. Streaming responses now own the run handle from construction and
close it after drain, stamping the accumulated streamed text as trace output
(previously blank). Multi-turn generations always stamp provider/model and
step metadata, fixing the regression where only the first turn was annotated.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* fix(llm): record effective prompt-only input on run span
The run-level Langfuse span recorded the raw messages parameter, which is
None for prompt-only calls. Mirror execute_tool_loop's handling and record
the synthesized user message so the trace input isn't blank.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* fix(llm): drop StreamingResponseWithMetadata.__anext__ to prevent span leak
The standalone __anext__ delegated straight to the inner stream, bypassing
the token-folding and Langfuse run-handle close that live only in the
__aiter__ generator. Any caller driving the wrapper via anext() instead of
`async for` would leak the run span and lose final-stream token accounting.
Latent today (all callers use `async for`), removed to close the footgun.
Add tests covering the run-handle drain path: full drain stamps the
accumulated streamed text as the span output and closes once; an abandoned
stream still closes via the finally rather than leaking.
* chore(llm): document intentional empty-body propagate_attributes block
The `with propagate_attributes(...): pass` stamps the active @observe trace
root via the context manager's __enter__ side effect; the empty body reads
as deletable dead code. Add a comment so it isn't removed. Addresses PR review.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* fix(llm): restore api.py types after __anext__ removal
Dropping StreamingResponseWithMetadata.__anext__ made it stop satisfying
the AsyncIterator protocol, breaking the result annotation and the
isinstance narrowing in honcho_llm_call. Widen the tool-less result
annotation to include StreamingResponseWithMetadata and narrow positively
to HonchoLLMCallResponse before reading .content.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Vineeth Voruganti <13438633+VVoruganti@users.noreply.github.com>
* fix: Add JSON repair for truncated LLM responses across all providers and Gemini thinking budget support
LengthFinishReasonError from OpenAI-compatible providers (custom, openai, groq) was crashing the deriver
with 14k+ occurrences in production. The vLLM path already had repair logic but it was gated on
provider=="vllm", unreachable when routing through litellm as a custom provider.
- Extract shared _repair_response_model_json() helper for all providers
- Catch LengthFinishReasonError in OpenAI/custom parse() path and repair truncated JSON
- Add repair fallback to Anthropic and Gemini response_model paths
- Add repair fallback to Groq response_model path
- Pass thinking_budget_tokens to Gemini 2.5 models via thinking_config
- Add 14 tests covering repair paths for all providers and Gemini thinking budget
Fixes HONCHO-YC
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* feat: live llm integration tests
* feat: Consistent Model Config Protocol
* fix: migrate the remaining app callers off the legacy llm_settings path
* fix: Docs and regression tests
* fix: refactor llm runtime path to model-config-only API
* fix: refactor config to nested model-config source of truth
* fix: refactor llm streaming and tool dispatch through backends
* fix: cut over llm config to nested model_config only
* fix: collapse vllm and custom into openai_compatible transport
* feat: refactor llm config to explicit transports and bare model ids
* feat: (embed) Add configurability for embedding model
* fix: tests for embedding provider
* fix: Address Review Comments
* fix: (llm) remove Groq backend and per-vendor base URLs
* chore: move llm tests
* fix: (llm) address review findings — config regressions, backend bugs, dead code
* fix: address backend end silly errors
* chore: (docs) update configuration and self-hosting guides
* chore: fix tests
* fix: address code rabbit comments
* fix: add validation to the dream settings
* fix: further address code rabbit comments
* fix: Address Code Rabbit Comments
* fix: Another round of code rabbit
* fix: Address Code Rabbit Nits
* fix: tests
* refactor: rename thinking validator to reflect transport scope
_validate_anthropic_thinking_minimum only enforces the >=1024 rule for
Anthropic and no-ops for other transports, so the name was misleading
now that it's shared across ConfiguredModelSettings, FallbackModelSettings,
and ModelConfig. Renamed to _validate_thinking_constraints with a docstring
clarifying per-transport behavior. No logic change.
* fix(config): drop transport-specific thinking params when env override changes transport
_fill_defaults_for_nested_field previously preserved the default MODEL_CONFIG's
thinking_budget_tokens/thinking_effort across a transport override. This leaked
Gemini-family defaults (e.g. thinking_budget_tokens=1024) into OpenAI-transport
overrides, and the OpenAI backend then correctly rejected the unsupported param
at call time (OpenAI uses reasoning.effort, not a token budget).
The helper now strips thinking_budget_tokens and thinking_effort from the
default dict when the env override supplies a transport different from the
default's. Explicit thinking params in the override are preserved.
* fix(config): apply thinking-param strip to dialectic level merge too
DialecticSettings._merge_level_defaults does its own inline MODEL_CONFIG
merge (parallel to _fill_defaults_for_nested_field), so the previous fix
missed dialectic-level overrides. E.g. flipping
DIALECTIC_LEVELS__minimal__MODEL_CONFIG__TRANSPORT from gemini (default)
to openai still leaked the default thinking_budget_tokens=0 into the
openai config, which the OpenAI backend then rejected at call time.
The level-merge path now applies the same 'strip transport-specific
thinking params when transport changes' rule as the generic helper.
Added a regression test exercising the merge validator directly.
* refactor(llm): wire ModelConfig knobs through, prune clients.py migration leftovers
Three connected fixes to finish carving the LLM stack out of src/utils/clients.py
and into src/llm/:
1. Propagate ModelConfig tuning knobs into backend calls.
honcho_llm_call_inner built extra_params from only {json_mode, verbosity},
silently dropping top_p, top_k, frequency_penalty, presence_penalty, seed,
and operator-supplied provider_params from any ModelConfig. Thread the
selected config through ProviderSelection and merge
build_config_extra_params(selected_config) into extra_params; per-call
kwargs still win over provider_params defaults. Makes
_build_config_extra_params public as build_config_extra_params so
clients.py and request_builder.py share one translation. Adds
TestModelConfigExtraParamsPropagation covering OpenAI/Anthropic knob
propagation, provider_params passthrough, and per-call override
precedence.
2. Drop dead extract_openai_* duplicates in clients.py.
extract_openai_reasoning_content, extract_openai_reasoning_details, and
extract_openai_cache_tokens had no callers outside their own definitions
— the live implementations live in src/llm/backends/openai.py. -103
lines from clients.py.
3. Unify on ModelTransport, delete SupportedProviders.
The "google" vs "gemini" split forced a _provider_for_model_config
translation shim in two places. Replace all SupportedProviders usages
with ModelTransport, rename CLIENTS["google"] → CLIENTS["gemini"],
update provider branches + LLMError labels + reasoning-trace entries
accordingly. Trace JSONL now writes "provider": "gemini" instead of
"google" — consistent with the broader env-var rename cutover.
Also tidies up pre-existing basedpyright findings in tests/llm/test_model_config.py
(pydantic before-validator dict inputs + descriptor-proxy call).
ruff: clean. basedpyright: 0 errors, 0 warnings. Tests: 153/153 pass across
tests/utils/test_clients.py, tests/utils/test_length_finish_reason.py,
tests/llm/, tests/dialectic/, tests/deriver/.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* refactor(llm): finish the src/utils/clients.py → src/llm/ migration
honcho_llm_call_inner now delegates to request_builder.execute_completion
and execute_stream instead of re-implementing backend call scaffolding
inline. The new _effective_config_for_call helper carries per-call kwargs
(temperature, stop_seqs, thinking_budget_tokens, reasoning_effort) onto
the selected ModelConfig — or synthesizes a minimal config for the
test-only callers that pass provider+model directly. max_output_tokens
is zeroed on the effective config to preserve the current
"per-call max_tokens wins" semantic; honoring ModelConfig.max_output_tokens
is a separable correctness concern.
Side effect of routing through the new path: ConfiguredModelSettings'
thinking_budget_tokens validator now fires on synthesized configs.
test_anthropic_thinking_budget was asserting that a sub-1024 budget
propagated to Anthropic — bumped to 1024 to match what Anthropic actually
accepts.
Unified client construction. Promoted the cached client factories in
src/llm/__init__.py (get_anthropic_client, get_openai_client,
get_gemini_client, get_{anthropic,openai,gemini}_override_client) to
public API and added them to __all__. Promoted
credentials._default_transport_api_key → default_transport_api_key.
Deleted the duplicate _build_client and _default_credentials_for_provider
from clients.py; _client_for_model_config now falls through to the
public factories. CLIENTS dict and _get_backend_for_provider stay as the
mockable seam for the ~50 patch.dict(CLIENTS, {...}) test call sites.
Wired operator-configurable Gemini cached-content reuse end-to-end.
PromptCachePolicy moved from src/llm/caching.py into src/config.py so
ModelConfig can reference it as a field without a circular import;
caching.py re-exports the name for existing imports. Added
cache_policy: PromptCachePolicy | None on ConfiguredModelSettings,
FallbackModelSettings, ResolvedFallbackConfig, and ModelConfig.
resolve_model_config, _resolve_fallback_config, and
_select_model_config_for_attempt copy the field through.
honcho_llm_call_inner passes effective_config.cache_policy into
execute_completion / execute_stream, so operators opt in via
e.g. DERIVER_MODEL_CONFIG__CACHE_POLICY__MODE=gemini_cached_content
and the selection actually fires instead of sitting on a dead path.
New regression test test_cache_policy_reaches_gemini_backend asserts the
PromptCachePolicy object reaches the Gemini backend's extra_params.
ruff + basedpyright: clean. Tests: 154/154 pass across
tests/utils/test_clients.py, tests/utils/test_length_finish_reason.py,
tests/llm/, tests/dialectic/, tests/deriver/.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* refactor(llm): move all LLM orchestration into src/llm/ and delete clients.py
The 1624-line src/utils/clients.py has been carved up into focused modules
under src/llm/ and deleted. There is now one golden path for LLM
orchestration and no dual entrypoint.
New module layout:
src/llm/
__init__.py thin stable re-export surface
api.py public honcho_llm_call with retry + fallback + tool
loop delegation
executor.py honcho_llm_call_inner (single-call executor); bridges
to request_builder.execute_completion / execute_stream
tool_loop.py execute_tool_loop + stream_final_response, plus
assistant-tool-message and tool-result formatting
runtime.py AttemptPlan dataclass (replaces the loose
ProviderSelection NamedTuple), effective_config_for_call,
plan_attempt, per-retry temperature bump, attempt
ContextVar
registry.py single owner of CLIENTS dict + cached default and
override SDK-client factories + backend/history-adapter
selection + high-level get_backend(config)
conversation.py count_message_tokens, tool-aware message grouping,
truncate_messages_to_fit
types.py HonchoLLMCallResponse, HonchoLLMCallStreamChunk,
StreamingResponseWithMetadata, IterationData,
IterationCallback, ReasoningEffortType, VerbosityType,
ProviderClient
request_builder.py low-level request assembly (ModelConfig → backend
complete/stream); no longer owns credential resolution
credentials.py default_transport_api_key, resolve_credentials
caching.py gemini_cache_store; re-exports PromptCachePolicy
from src.config
backend.py Protocol + normalized result types
history_adapters.py provider-specific assistant/tool message shapes
structured_output.py
backends/ AnthropicBackend, OpenAIBackend, GeminiBackend
handle_streaming_response had no production callers; it is deleted. The
three tests that used it now drive honcho_llm_call_inner(stream=True,
client_override=...) directly, which exercises the same code path the
public API uses.
Dead credential passthrough removed. The ProviderBackend Protocol and
all three concrete backends no longer accept api_key / api_base — those
are baked into the underlying SDK client at registry construction time
and were being del'd everywhere they appeared. request_builder also
stops resolving and forwarding them.
Client construction is unified. The cached default-client factories
(get_anthropic_client, get_openai_client, get_gemini_client) and override
factories (get_*_override_client) are promoted to public API; the
module-level CLIENTS dict populates from them and remains the
patch.dict(CLIENTS, {...}) mocking seam tests rely on. Old duplicate
helpers (_build_client, _default_credentials_for_provider) are gone.
default_transport_api_key is promoted to public.
Application imports now come from src.llm (dreamer, dialectic, deriver,
summarizer, telemetry-adjacent tests). No code imports from
src.utils.clients anywhere in the repo.
ruff: clean. basedpyright: 0 errors, 0 warnings. Tests: 1013/1013 pass
across the entire non-infra test suite (excluding tests/unified,
tests/bench, tests/live_llm, tests/alembic).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(llm): sanitize tool schemas for Gemini's function_declarations validator
Gemini's native-transport function-declarations validator accepts a narrow
subset of JSON-Schema / OpenAPI: type, format, description, nullable, enum,
properties, required, items, minItems, maxItems, minimum, maximum, title.
Anything else — additionalProperties, allOf, if/then/else, $ref, anyOf,
oneOf, $defs, patternProperties — triggers an INVALID_ARGUMENT 400 at call
time.
Our agent tool schemas in src/utils/agent_tools.py use several of those
(additionalProperties: false, allOf + if/then conditionals) because they
were authored for OpenAI strict-mode + Anthropic, which need the richer
vocabulary. GeminiBackend._convert_tools was passing them straight through.
Add _sanitize_schema(): walks the parameters tree and drops unsupported
keywords while preserving semantics for the keywords that hold user data
(properties maps field-name → sub-schema; required / enum are lists of
literals; items is a single sub-schema). Other backends are untouched and
continue to receive the full strict schemas.
Regression tests:
- test_gemini_sanitize_schema_strips_unsupported_keywords: confirms
additionalProperties, allOf + if/then, and $defs are stripped at nested
levels while legitimate fields survive.
- test_gemini_convert_tools_sanitizes_parameters_schema: end-to-end
_convert_tools output has no forbidden keys.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix: fix tool calling syntax for gemini
* refactor(llm): normalize defaults, widen OpenAI reasoning-model routing
* chore: fix test
* fix(llm): address post-migration review feedback
* fix(llm): gemini robustness + dreamer specialist ergonomics
* chore: addres review comments
* chore: (docs) unrelease changelog addition
* chore: (docs) merge commit changes
---------
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: Erosika <eri@plasticlabs.ai>
* fix: Add bounds to gemini client
* fix: Prevent empty summaries from being saved to DB (HONCHO-M7)
Raise LLMError on blocked Gemini responses (SAFETY, RECITATION, etc.)
so retry/backup-provider logic triggers. Treat empty LLM responses in
the summarizer as fallback instead of persisting empty strings.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat: Summary Eval via Locomo
* fix: Code Rabbit Comments
* fix: Code Rabbit Comments
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* fix: remove default max_distance from get_working_rep, get token count from gemini client properly, log summaries better
* fix: defensively get message public id (new field)
* fix: add defensive check to to_schema_summary
---------
Co-authored-by: Rajat Ahuja <rahuja445@gmail.com>
* feat: add optional JWT and webhook secrets to honcho instance creation
* chore: ignore spurious warnings
* feat: add response format if using gpt-5 model family
* feat: add response models to all apis except anthropic
* fix: raise NotImplementedError for response models in AsyncAnthropic client
* chore: address review
* [WIP] representation structure + deriver cleanup
* chore: add tests, cleanup
* feat: [WIP: semi-working] representation object
* fix: alignment
* fix: make observations hashable for dedup
* fix: datetime formatting, observation counting
* fix: switch to int for message id, clean up representation
* feat: remove need for metadata working rep
* chore: cleanup
* fix: use tenacity instead of custom fns
* feat: add representation and card to context if desired
* feat: add semantically relevant observations
* fix: pass all params to streaming, nonblocking streaming
* feat: consolidate document saving, make working representation fetching much smarter
* chore: add 100% test coverage of representation util
* feat: basic dream infra
* feat: dream queue item first pass
* chore: fixes & cleanup from coderabbit
* fix: dreams scheduled when new document count reaches a certain threshold
* feat: wip: timed dreams (not working)
* fix: test
* fix: remove useless pyright ignore
* fix: executing dreams
* feat: dreaming
* feat: [WIP] longmemeval bench
* feat: add USE_PEER_CARD setting, fix longmem test driver
* feat: get full working rep for dialectic in one swoop -- fix representation_from_documents to use the proper timestamp!
* fix: timestamps for real, handle assistant qs in longmem
* fix: remove old client, add batching to longmem
* perf: remove duplicate detection, will move to background task
* feat: track perf metrics on evals
* feat: adjust deriver prompt to use peer_id, add question date to question, clean up deriver
* fix: label metrics by task for better perf trace
* chore: code review
* feat: add efficiency score to longmem bench
* chore: tuning and cleaning up eval
* chore: bring in the big prompts
* feat: add support for vllm client
* feat: perf: bundle db calls in deriver and dialectic, increase max conns in docker db
* feat: add merge-sessions flag to longmemeval, add SUMMARY_ENABLED flag
* fix: COLLECT_METRICS default false
* chore: display start/end message ids, don't include in metrics
* fix: break large messages apart for eval
* fix: only get/create collection when needed
* feat: properly attribute documents with message id ranges and add session name column to documents
* fix: revert move of get_or_create_collection (need for fkey)
* fix: always get collection with peer name even if it's none
* chore: coderabbit
* fix: give peer card its own config, expand document schema, refactor get_context to be parallel, various cleanup chores and bugfixes
* chore: refactor: reify observer/observed system across entire codebase, including db migration
* refactor: cleanup code organization, make singletons where desired
* refactor: replace embeddings store with representation manager
* chore: coderabbit cleanup
* feat: multi-db longmem harness
* Merge branch 'main' into ben/multi-db-harness
* [WIP] feat: add delete workspace endpoint, use in bench
* chore: move excess logging to debug
feat: improve metrics block logs to include more data
fix: make longmem db deletion configurable
* fix: [CRITICAL] use async genai client
* chore: update core sdk, fix tests to use aio as well
* fix: rollback prompt changes
* chore: update version
* fix: cleanup, coderabbit, wrap delete op in try/except
---------
Co-authored-by: Vineeth Voruganti <13438633+VVoruganti@users.noreply.github.com>
* feat: add optional JWT and webhook secrets to honcho instance creation
* chore: ignore spurious warnings
* feat: add response format if using gpt-5 model family
* feat: add response models to all apis except anthropic
* fix: raise NotImplementedError for response models in AsyncAnthropic client
* chore: address review
* [WIP] representation structure + deriver cleanup
* chore: add tests, cleanup
* feat: [WIP: semi-working] representation object
* fix: alignment
* fix: make observations hashable for dedup
* fix: datetime formatting, observation counting
* fix: switch to int for message id, clean up representation
* feat: remove need for metadata working rep
* chore: cleanup
* fix: use tenacity instead of custom fns
* feat: add representation and card to context if desired
* feat: add semantically relevant observations
* fix: pass all params to streaming, nonblocking streaming
* feat: consolidate document saving, make working representation fetching much smarter
* chore: add 100% test coverage of representation util
* feat: basic dream infra
* feat: dream queue item first pass
* chore: fixes & cleanup from coderabbit
* fix: dreams scheduled when new document count reaches a certain threshold
* feat: wip: timed dreams (not working)
* fix: test
* fix: remove useless pyright ignore
* fix: executing dreams
* feat: dreaming
* feat: [WIP] longmemeval bench
* feat: add USE_PEER_CARD setting, fix longmem test driver
* feat: get full working rep for dialectic in one swoop -- fix representation_from_documents to use the proper timestamp!
* fix: timestamps for real, handle assistant qs in longmem
* fix: remove old client, add batching to longmem
* perf: remove duplicate detection, will move to background task
* feat: track perf metrics on evals
* feat: adjust deriver prompt to use peer_id, add question date to question, clean up deriver
* fix: label metrics by task for better perf trace
* chore: code review
* feat: add efficiency score to longmem bench
* chore: tuning and cleaning up eval
* chore: bring in the big prompts
* feat: add support for vllm client
* feat: perf: bundle db calls in deriver and dialectic, increase max conns in docker db
* feat: add merge-sessions flag to longmemeval, add SUMMARY_ENABLED flag
* fix: COLLECT_METRICS default false
* chore: display start/end message ids, don't include in metrics
* fix: break large messages apart for eval
* fix: only get/create collection when needed
* feat: properly attribute documents with message id ranges and add session name column to documents
* fix: revert move of get_or_create_collection (need for fkey)
* fix: always get collection with peer name even if it's none
* chore: coderabbit
* fix: give peer card its own config, expand document schema, refactor get_context to be parallel, various cleanup chores and bugfixes
* chore: refactor: reify observer/observed system across entire codebase, including db migration
* refactor: cleanup code organization, make singletons where desired
* refactor: replace embeddings store with representation manager
* chore: coderabbit cleanup
* chore: update migration to non-null session param in documents, general review and cleanup
* chore: merge branch 'main' into ben/deriver-tidy
* chore: review fixes
* feat: add optional JWT and webhook secrets to honcho instance creation
* chore: ignore spurious warnings
* feat: add response format if using gpt-5 model family
* feat: add response models to all apis except anthropic
* fix: raise NotImplementedError for response models in AsyncAnthropic client
* chore: address review
* chore: add tests, cleanup
* fix: use tenacity instead of custom fns
* fix: pass all params to streaming, nonblocking streaming
* chore: fix test mock
Aakash Kattelu
Vineeth Voruganti
Rajat Ahuja
doria
Benjamin McCormick