* docs: make honcho start the documented local path
honcho-cli 0.1.3 can run a personal stack without cloning the repo; point the README, self-hosting, and CLI reference at that, and drop the community installer callouts.
Co-authored-by: Cursor <cursoragent@cursor.com>
* docs: updating with new CLI language
* docs: drop compatibility-guide changes from this PR
Leave that file on main; CLI version cards are updated at release time.
Co-authored-by: Cursor <cursoragent@cursor.com>
* docs: small language changes
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
* docs: document pgvector preinstall for least-privilege DB roles
Honcho issues CREATE EXTENSION IF NOT EXISTS vector before migrations
and again at server startup, both using the DB_CONNECTION_URI role. On
deployments where that role deliberately cannot create extensions
(managed Postgres, Kubernetes operators, NixOS), both statements fail
with a privilege error — IF NOT EXISTS does not save you, because
Postgres checks the privilege before checking for the extension.
Document preinstalling pgvector as a privileged role as the supported
path, and add a troubleshooting entry keyed to the exact error string.
Note that docker compose is unaffected, since the bundled database
service creates the extension via an initdb script.
Refs #614
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
* docs: clarify why docker compose avoids the pgvector privilege error
The previous wording pinned the claim entirely on database/init.sql,
which only runs on first boot of an empty data volume. The load-bearing
reason is that the bundled stack connects as the postgres superuser, so
it can create the extension regardless of volume state. Name that first
and keep init.sql as the secondary reason.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 5 <noreply@anthropic.com>
* feat: add model config option for json_object mode
* fix: catch possible validation error from structured output
* fix(llm): harden structured_output_mode json_object path
Follow-up fixes to the json_object structured-output mode for
OpenAI-compatible providers without Structured Outputs support:
- runtime: carry structured_output_mode onto the per-attempt fallback
config (select_model_config_for_attempt dropped it, silently sending
json_schema to a provider that can't parse it)
- backend: return a graceful empty on a contentless json_object
response instead of raising, matching the json_schema path, and
preserve token usage by normalizing the response
- backend: narrow the parse-failure catch to BadRequestError only, so
transient JSONDecodeError/ValidationError propagate to retry/fallback
instead of being swallowed to empty on the first attempt
- config: reject structured_output_mode on non-openai transports
(silent no-op otherwise); trim docs to the deriver, the only
structured-output feature
- backend: validate clean JSON before repair, cache the schema
instruction, and share json_object setup between complete()/stream()
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* refactor(llm): consolidate structured-output repair, drop dead seam
Fold the OpenAI backend's three structured-output repair sites
(LengthFinishReasonError, parsed=None, json_object) into the one shared
_parse_or_repair_structured_content helper, gated by an empty_on_missing
flag: json_object returns a graceful empty on a contentless response so a
loose provider can't crash the call, while json_schema raises so the
retry/fallback chain engages.
Delete the dead execute_structured_output_call seam and its only
collaborators (attempt_structured_output_repair, StructuredOutputFailurePolicy)
— it was never called and its single-shot validate/repair/empty model
conflicts with the retry behavior in honcho_llm_call.
No behavior change. Adds tests covering the json_schema parse fallbacks
(repair, refusal passthrough, no-content raise).
---------
Co-authored-by: Vineeth Voruganti <13438633+VVoruganti@users.noreply.github.com>
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.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: Inconsistencies in Docs, health endpoint, troubleshooting guide
* fix: (docs) maintain consistency on postgres db name
* chore: (docs) update v2 contributing docs with updates db paths
* docs: overhaul self-hosting docs for provider-agnostic setup
- .env.template: lead with provider options (custom, vllm, google,
anthropic, openai, groq) instead of baking in vendor-specific keys.
All provider/model settings commented out so server fails fast until
configured. Separate endpoint config from per-feature provider+model
from tuning knobs.
- docker-compose.yml.example: fix healthcheck -d honcho -> -d postgres
to match POSTGRES_DB=postgres.
- config.toml.example: reorder and document LLM key section with
OpenRouter and vLLM examples.
- self-hosting.mdx: replace multi-vendor key table with provider options
table. Add examples for OpenRouter, vLLM/Ollama, and direct vendor
keys. Remove duplicated key lists from Docker/manual setup sections.
- configuration.mdx: replace scattered provider docs with provider types
table. Fix Docker Compose snippet to match actual compose file. Note
code defaults as fallback, not recommended path.
- troubleshooting.mdx: add alternative provider issues section (custom
provider config, model name format, Docker localhost, structured
output failures).
* docs: add Docker build troubleshooting for permission errors
- Document BuildKit requirement (RUN --mount syntax)
- AppArmor/SELinux blocking Docker builds on Linux
- Volume mount UID mismatch between host and container app user
- Note in self-hosting docs that Docker path builds from source
* docs: reframe self-hosting as contributor/dev path, point to cloud service
* Revert "docs: reframe self-hosting as contributor/dev path, point to cloud service"
This reverts commit 3e766eb1a9.
* docs: add production compose, model guidance, thinking budget docs
- Add docker-compose.prod.yml for VM/server deployment: no source
mounts, restart policies, 127.0.0.1-bound ports, cache enabled
- Add model tier guidance and community quick-start link to self-hosting
- Document THINKING_BUDGET_TOKENS gotcha for non-Anthropic providers
- Add reverse proxy examples (Caddy + nginx) to production section
- Add backup/restore commands to production considerations
* docs: simplify self-hosting to single provider, restructure config guide
Self-hosting page now defaults to one OpenAI-compatible endpoint
with one model for all features. Moved model tiers, alternative
providers, and per-feature tuning into the configuration guide.
Eliminated duplicate config priority sections, dev/prod split,
and redundant TOML examples.
* docs: merge compose files, restore provider/model to feature sections in .env.template
Single docker-compose.yml.example with dev sections commented out.
Moved PROVIDER and MODEL back alongside each feature in .env.template
so settings stay colocated with their module. Updated self-hosting
docs to reference single compose file.
* fix: broken anchor links, redundant migration step, minor inconsistencies
Fix 4 broken internal links (#llm-provider-setup, #llm-api-keys,
#which-api-keys-do-i-need, #alternative-providers) to point to
correct headings. Remove redundant Docker migration step (entrypoint
already runs alembic). Fix cache URL missing ?suppress=true in
reference config. Fix uv install command to use official method.
* docs: env template ready to use, simplify self-hosting flow
.env.template now has provider/model lines uncommented with
placeholder values — user just sets endpoint, key, and model name.
Thinking budgets default to 0 for non-Anthropic providers.
Self-hosting page: removed 30-line env var wall, LLM setup now
points to the template. Merged duplicate verify sections.
Removed api_key from SDK examples (auth off by default).
* docs: reorder next steps, configuration guide first
* fix: default embedding provider to openrouter for single-endpoint setup
Without this, embeddings default to openai which requires a separate
LLM_OPENAI_API_KEY. Setting to openrouter routes embeddings through
the same OpenAI-compatible endpoint as everything else.
* fix: review issues — hermes page, thinking budget, production wording
Hermes integration page: replaced inline Docker/manual setup with
link to self-hosting guide, added elkimek community link. Removed
old env var names (OPENAI_API_KEY without LLM_ prefix).
Troubleshooting: removed "or 1" from thinking budget guidance.
Self-hosting: softened "production-ready" to "production-oriented"
since auth is disabled by default.
* docs: model examples in template, expanded LLM setup, better verify flow
.env.template: added "e.g. google/gemini-2.5-flash" hints next to
model placeholders so users know the expected format.
Self-hosting: expanded LLM Setup to show the 3 things users need to
set (endpoint, key, model name) with find-replace tip. Added build
time note, deriver log check, and real smoke test (create workspace)
to verify section. Health check now notes it doesn't verify DB/LLM.
* fix: smoke test uses v3 API path, not v1
* docs: clarify deriver metrics port vs Prometheus host port
* fix: remove deprecated memoryMode from hermes config example
* docs: update hermes page to match current memory provider config
Updated config to match hermes-agent docs: removed apiKey (not needed
for self-hosted), added hermes memory setup CLI command, added config
fields table (recallMode, writeFrequency, sessionStrategy, etc.).
Better verification tests: store-and-recall across sessions, direct
tool calling test. Links to upstream hermes docs for full field list.
* fix: invalid THINKING_BUDGET_TOKENS=0 and missing docker/ in image
Comment out THINKING_BUDGET_TOKENS=0 in .env.template — deriver,
summary, and dream validators require gt=0. Dialectic levels also
commented out since non-thinking models don't need the override.
Add COPY for docker/ directory in Dockerfile so entrypoint.sh is
available when docker-compose.yml.example references it.
* chore: Additional troubleshooting step
---------
Co-authored-by: Vineeth Voruganti <13438633+VVoruganti@users.noreply.github.com>
ajspig
Aakash Kattelu
Eugene Eisenstein
Harish Kukreja
Vineeth Voruganti
Eri Barrett