* feat: allow chunks only in recall
* feat: fetch chunks independently of max_tokens filtering
Changes:
- Chunks now fetched BEFORE max_tokens filtering (Step 5.5)
- Implements batching: (max_chunk_tokens / retain_chunk_size) * 2
- Loop-based fetching until budget exhausted or no more chunks
- Handles varying chunk sizes across documents
- When max_tokens=0: returns 0 facts but still returns chunks
- When max_tokens>0: backward compatible (chunks match filtered facts)
Tests:
- Added test_recall_chunks_independence.py with 5 comprehensive tests
- Tests chunk independence, batching, ordering, and backward compat
Docs:
- Updated recall.mdx to explain new chunk behavior
- Updated memory_engine.py docstrings
Fixes chunk-related test failures by reordering chunks to match
filtered facts when max_tokens > 0 (backward compatibility).
* fix: fetch chunks after token filtering when max_tokens>0
Changes:
- When max_tokens=0: fetch chunks BEFORE token filtering (new behavior)
- When max_tokens>0: fetch chunks AFTER token filtering (backward compat)
- This ensures chunk ordering matches filtered facts for max_tokens>0
- Fixes test failures in test_chunks_and_entities_follow_fact_order,
test_chunk_fact_mapping, test_chunk_ordering_preservation, etc.
The previous approach tried to reorder prefetched chunks, but that
caused issues when the chunk budget was exhausted before all facts
were processed. The new approach fetches chunks based on the correct
fact set for each scenario.
* fix: use ConfigResolver for bank-specific retain_chunk_size
Fixes error: Field 'retain_chunk_size' is bank-configurable and cannot
be accessed from global config.
Changed from:
- config.retain_chunk_size (global config, not allowed)
To:
- bank_config.retain_chunk_size (resolved from ConfigResolver)
This ensures the correct chunk size is used for each bank, respecting
any bank-specific overrides.
* fix: correct Budget import in test_recall_chunks_independence
Changed from:
- from hindsight_api.engine.interface import Budget (incorrect)
To:
- from hindsight_api.engine.memory_engine import Budget (correct)
This fixes the ImportError that was preventing the tests from running.
* fix: prevent infinite loop in chunk fetching and improve test content
- Add max(1, ...) to estimated_batch_size to prevent division resulting in 0
- Update test content to use more substantial examples that generate facts
- Add request_context parameter to all retain_async and recall_async test calls
* refactor: simplify chunk fetching to always use pre-filtering approach
Remove backward compatibility code that fetched chunks after token
filtering. Now chunks are always fetched from top-scored results
before max_tokens filtering, regardless of max_tokens value.
This simplifies the code by:
- Removing duplicate chunk fetching logic
- Eliminating conditional behavior based on max_tokens
- Making chunk fetching behavior consistent and predictable
Chunks are still fetched in batches and respect max_chunk_tokens limit.
* feat: support for other text and vector search pg extensions
* test: increase timeout for test_batch_chunking_behavior to account for VectorChord BM25 tokenization overhead
* feat: support for other text and vector search pg extensions
* feat: implement hierarchical configuration (system, tenant, bank)
* feat: implement hierarchical configuration (system, tenant, bank)
* docs: add instructions for hierarchical config in CLAUDE.md
* feat: add ENABLE_BANK_CONFIG_API flag (disabled by default)
- Add HINDSIGHT_API_ENABLE_BANK_CONFIG_API env var (default: false)
- Return 403 Forbidden from bank config endpoints when disabled
- Update tests to enable the flag
- Update CLAUDE.md documentation
This provides security control over the bank configuration API,
ensuring it's only accessible when explicitly enabled.
* docs: add hierarchical configuration section
* feat(cli): add bank config commands (config, set-config, reset-config)
- Add 'hindsight bank config' to view bank configuration
- Add 'hindsight bank set-config' to update LLM settings per bank
- Add 'hindsight bank reset-config' to reset to defaults
- Implements client API calls to new bank config endpoints
* fix(cli): fix compilation errors in bank config commands
- Fix type signature: use ApiClient instead of api::Client
- Fix confirmation: use ui::prompt_confirmation instead of ui::confirm
- Fix error handling: use anyhow! macro instead of errors::Error
- Fix type conversion: convert HashMap to serde_json::Map for API call
* feat: implement type-safe hierarchical config with bank overrides
Implements a production-ready hierarchical configuration system that prevents
accidentally using global defaults when bank-specific overrides exist.
- Created StaticConfigProxy that wraps HindsightConfig
- get_config() now returns proxy that blocks access to bank-configurable fields
- Raises ConfigFieldAccessError with clear message when accessing configurable fields
- Added _get_raw_config() for internal use only
- Forces developers to use resolve_full_config(bank_id, context) for bank settings
- Added resolve_full_config() method that returns complete HindsightConfig
- Resolves hierarchy: Global (env) → Tenant → Bank
- No caching to support multi-server deployments (always fresh from DB)
- LLM provider pooling handles expensive operations separately
- Updated entire retain pipeline to pass resolved config through call chain
- memory_engine.py: Resolves config at top level where bank_id/context available
- orchestrator.py: Accepts and passes config to fact_extraction
- fact_extraction.py: Uses passed config instead of get_config()
- utils.py: Added optional config param for backward compatibility
- consolidator.py: Uses resolve_full_config() for enable_observations check
- memory_engine.py: Resolves config before triggering consolidation
- Renamed "Memory Bank" to "Bank Configuration" with tabs
- Combined Stats and Operations into "General" tab
- Consolidated Profile and Configuration into "Configuration" tab
- Moved Actions dropdown to page level (outside tabs)
- Created new component for managing bank-specific config
- Displays configurable fields: retain_chunk_size, retain_extraction_mode, etc.
- Edit via dialog with form validation
- Reset to defaults via AlertDialog confirmation
- Shows field IDs in monospace for clarity
- Visual separation with borders and hover effects
- Removed inline edit mode, switched to dialog-based editing
- Separate dialogs for Disposition and Mission editing
- Read-only display with clear edit buttons
- Removed duplicate stats cards and operations
- bank-stats-view.tsx: Overview statistics (memories, links, documents, pending ops)
- bank-operations-view.tsx: Background operations table with filtering
**Problem**: Consolidation always used global enable_observations, ignoring bank overrides
**Root Cause**: consolidator.py called get_config() instead of resolving bank-specific config
**Solution**: Pass resolved config through the entire pipeline
**Problem**: asyncpg returning JSONB as JSON string instead of parsed dict
**Solution**: Explicit JSON parsing in config_resolver.py with type checking
- All 19 API integration tests pass
- All 10 hierarchical config tests pass
- Retain operations work correctly with bank-specific config
- Consolidation respects bank-specific enable_observations setting
- Updated developer/configuration.md with type-safe config access pattern
- Added examples showing correct usage patterns
- Documented ConfigFieldAccessError and resolution methods
- get_config() now returns StaticConfigProxy (blocks configurable field access)
- Code accessing bank-configurable fields must use resolve_full_config()
- Clear migration path with helpful error messages
Fixes hierarchical configuration to be production-ready with proper type safety.
* refactor: remove LLM client pool and simplify config resolver
Since LLM config (provider, model, api_key) is now static and not
bank-configurable, the LLMClientPool is no longer needed.
Changes:
- Remove hindsight_api/llm_client_pool.py (no longer needed)
- Remove memory_engine._get_bank_llm_config() (dead code, never called)
- Simplify config_resolver.py by eliminating duplication between
resolve_full_config() and get_bank_config()
- get_bank_config() now calls resolve_full_config() and filters results
- Remove outdated "LLM provider pooling" comments from docstrings
All tests pass (10 hierarchical config tests, 19 API integration tests)
* fix: update tests to use _get_raw_config() for configurable fields
Fixed test fixtures that were accessing configurable fields (like
enable_observations) from get_config(), which now raises
ConfigFieldAccessError due to type-safe config access.
Changes:
- test_consolidation.py: Changed enable_observations fixture to use
_get_raw_config() instead of get_config()
- test_consolidation.py: Updated test_consolidation_returns_disabled_status
to set bank config instead of mocking get_config()
- test_link_expansion_retrieval.py: Changed fixture to use _get_raw_config()
- test_observations.py: Changed disable_observations fixture to use
_get_raw_config()
- Regenerated OpenAPI spec and clients
All 39 previously failing tests now pass.
* fix: add missing config parameter to test calls of extract_facts_from_text()
Fixed 45 test failures where tests were calling extract_facts_from_text()
without the new required config parameter.
Changes:
- Added config=_get_raw_config() to all extract_facts_from_text() calls
- Fixed test_main_module.py to patch _get_raw_config instead of get_config
- Updated 6 test files with 37 function call sites
All tests should now pass.
* fix: add missing config parameter to test_skip_podcast_meta_commentary
One more test was missing the config parameter for extract_facts_from_text().
* Add actual LLM token usage fields to RetainResult
RetainResult now carries llm_input_tokens, llm_output_tokens, and
llm_total_tokens populated from the engine's TokenUsage, so downstream
operation validator extensions can access actual LLM token counts.
* Test that RetainResult includes actual LLM token usage
* Add mental model CRUD tools to MCP server
Expose mental models (pinned reflections) as 6 new MCP tools:
- list_mental_models: List with optional tag filtering
- get_mental_model: Get by ID
- create_mental_model: Create with async content generation
- update_mental_model: Update name/source_query/tags
- delete_mental_model: Delete by ID
- refresh_mental_model: Re-run source query to update content
Both multi-bank (bank_id param) and single-bank modes supported,
following the same patterns as existing retain/recall/reflect tools.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: include mental model tools in single-bank MCP mode and update tests
The single-bank mode tool set was hardcoded to only retain/recall/reflect,
excluding the new mental model tools. Updated all 3 test layers (unit,
routing, HTTP integration) to assert mental model tool exposure.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: update extension test tool count for mental model tools
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: move mental model usage metering into engine for MCP support
Mental model validation hooks (validate_mental_model_get, validate_mental_model_refresh)
were only called in REST HTTP handlers, not in the engine. MCP tools call engine methods
directly, so usage metering was skipped entirely for MCP mental model operations.
Moved pre-validation and post-completion hooks into memory_engine.py (matching the
retain/recall/reflect pattern) and removed the duplicate code from http.py.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: remove double validation from create_mental_model and add internal checks
- Remove pre-validation from create_mental_model since callers always call
submit_async_refresh_mental_model next (which validates), preventing
double credit checks
- Add is_internal checks to mental model metering validators (matching
the existing pattern for recall/reflect) so background worker tasks
skip billing
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Async batch retain tasks need internal=True to bypass extension auth
(worker has no API key), but extensions also need to know the operation
originated from a user request. The new user_initiated flag on
RequestContext allows extensions to distinguish user-initiated async
operations from truly internal system operations like consolidation.
* feat: add comprehensive OpenTelemetry tracing
- Add tool execution spans for reflect operations
- Add tool call information (names, params) to spans
- Change verification scope from 'test' to 'verification'
- Add hindsight.reflect_generation span for done() processing
- Implement no-op tracer for improved code readability
- Update documentation for OTEL configuration
- Resolve merge conflicts from rebase
* fix: properly serialize Pydantic models in span recording
- Add _serialize_for_span() helper to handle Pydantic models
- Update all providers to use the helper function
- Fixes test failures with 'Object of type X is not JSON serializable'
* feat: add Grafana LGTM stack for unified local observability
Add Grafana LGTM (Loki, Grafana, Tempo, Mimir) as the recommended
local development observability stack. This provides traces, metrics,
and logs in a single Docker container instead of separate tools.
Changes:
- Add scripts/dev/grafana/ with docker-compose and README
- Add scripts/dev/start-grafana.sh startup script
- Update .env.example to reference Grafana LGTM
- Update configuration docs to emphasize Grafana LGTM as primary option
- Reorder OTLP backend list to show Grafana LGTM first
Benefits:
- Single container vs multiple separate tools (Jaeger, SigNoz, etc.)
- ~515MB image with full observability stack
- Compatible with existing OTLP configuration
- Simpler local development setup
* chore: remove SigNoz scripts and references
Remove SigNoz observability stack in favor of Grafana LGTM as the
sole recommended local development tracing solution.
Changes:
- Delete scripts/dev/signoz/ directory and all SigNoz configurations
- Delete scripts/dev/start-signoz.sh startup script
- Remove SigNoz references from .env.example
- Remove SigNoz from OTLP backends list in configuration docs
Grafana LGTM provides the same capabilities (traces, metrics, logs)
in a simpler single-container setup.
* feat: add consolidation span hierarchy for tracing
Add parent-child span structure for consolidation operations:
- hindsight.consolidation: Parent span for each memory being processed
- hindsight.consolidation_recall: Child span for finding related observations
- LLM call span: Automatically created by LLM provider (scope="consolidation")
This enables detailed timing breakdown in Grafana Tempo:
- Total consolidation time per memory
- Time spent in recall
- Time spent in LLM call
- Time spent executing actions (create/update)
All consolidation tests pass (31/31).
* feat: add Prometheus metrics and GenAI dashboard to Grafana stack
Add comprehensive metrics and dashboarding to the Grafana LGTM stack:
Metrics Collection:
- Configure Prometheus to scrape Hindsight API /metrics endpoint
- Scrape interval: 10 seconds
- Targets hindsight-api on host.docker.internal:8888
GenAI Dashboard:
- Pre-configured dashboard with 6 panels:
- LLM call rate (by provider/model)
- LLM call duration (p50/p95 by scope)
- Token usage - input tokens/sec by scope
- Token usage - output tokens/sec by scope
- Operations rate (retain/recall/reflect/consolidation)
- Operation duration p95 by operation type
Configuration:
- Mount prometheus.yml for metrics scraping
- Mount dashboards directory for auto-provisioning
- Add host.docker.internal mapping for container->host access
- Dashboard provisioning with auto-reload every 10s
Documentation:
- Updated README with metrics viewing instructions
- Added PromQL query examples
- Documented dashboard access and navigation
This provides full observability: traces (Tempo) + metrics (Prometheus/Mimir) + dashboards (Grafana)
* refactor: merge Grafana setup into existing monitoring stack
Consolidate the separate scripts/dev/grafana/ setup into the existing
scripts/dev/monitoring/ stack, using Grafana LGTM (Loki, Grafana, Tempo, Mimir).
Changes:
- Remove separate scripts/dev/grafana/ directory and start-grafana.sh
- Rewrite scripts/dev/monitoring/start.sh to use Docker + Grafana LGTM
(was: download native Prometheus/Grafana binaries)
- Add docker-compose.yaml for Grafana LGTM container
- Add prometheus.yml for scraping Hindsight API metrics
- Mount existing dashboards from monitoring/grafana/dashboards/
- Add comprehensive README.md
Benefits:
- Single unified monitoring command: ./scripts/dev/start-monitoring.sh
- Uses existing dashboard files (hindsight-operations, hindsight-llm, hindsight-api-service)
- Simpler setup: Docker-based vs downloading/running native binaries
- Full observability: traces + metrics + logs + dashboards in one container
- Standard ports: Grafana on 3000, OTLP on 4317/4318
Architecture:
- Grafana LGTM container (~515MB) provides all components
- Dashboards auto-provisioned from monitoring/grafana/dashboards/
- Prometheus scrapes host.docker.internal:8888/metrics
- Shared hindsight-network for future service-to-service tracing
* fix: run monitoring stack in foreground for easy Ctrl+C stop
Change docker-compose from detached (-d) to foreground mode.
Users can now stop the stack with Ctrl+C instead of needing
to run docker-compose down separately.
* fix: remove invalid home dashboard path and obsolete version field
- Remove GF_DASHBOARDS_DEFAULT_HOME_DASHBOARD_PATH environment variable
(was pointing to wrong path causing 'Failed to load home dashboard' error)
- Remove obsolete 'version' field from docker-compose.yaml
(docker-compose v2+ doesn't require version field)
* fix: load Hindsight dashboards in Grafana LGTM
Mount Hindsight dashboard JSON files and custom provisioning config
to make dashboards visible in Grafana.
Changes:
- Mount hindsight-operations.json, hindsight-llm.json, hindsight-api-service.json to /otel-lgtm/
- Create grafana-dashboards.yaml with all dashboard providers (default + Hindsight)
- Mount custom provisioning config to override LGTM default
All 3 Hindsight dashboards now appear in Grafana UI with metrics
from Prometheus scraping the Hindsight API /metrics endpoint.
* fix: configure Prometheus to scrape Hindsight API metrics
Update prometheus.yml to include both OTLP receiver config (from LGTM)
and scrape_configs for pulling metrics from Hindsight API.
Changes:
- Mount prometheus.yml to /otel-lgtm/prometheus.yaml (where LGTM reads it)
- Add scrape_configs section to pull from host.docker.internal:8888/metrics
- Keep OTLP receiver configuration for trace metrics
- Set scrape_interval to 5s
Verified: Prometheus now successfully scrapes hindsight_llm_calls_total
and other Hindsight metrics. Dashboards now show live data!
* feat: add comprehensive tracing for recall and improve reflect/mental_model_refresh spans
- Add recall operation tracing with parent-child span hierarchy
- Parent: hindsight.recall with attributes (bank_id, query, fact_types, etc.)
- Children: recall_embedding, recall_retrieval, recall_fusion, recall_rerank
- Fixed context propagation using start_as_current_span()
- Improve reflect tracing spans
- Remove reflect_generation spans, use reflect instead
- Change done() tool processing to hindsight.reflect_tool_call
- Fix mental_model_refresh span nesting
- Add _skip_span parameter to reflect_async to avoid duplicate hindsight.reflect spans
- Mental model refresh now has clean span hierarchy without nested reflect parent
- Add comprehensive tracing verification tests
- Test span hierarchy and attributes for all operations
- Verify parent-child relationships
- 5 passing tests covering recall, reflect, consolidation, and mental_model_refresh
* refactor: remove redundant is_tracing_enabled() checks
- Remove all is_tracing_enabled() conditional checks before tracing calls
- NoOpTracer/NoOpSpan handle disabled tracing automatically
- Simplify code by always calling tracer methods directly
- Fix NoOpTracer.start_as_current_span() to yield NoOpSpan instead of None
Changes:
- memory_engine.py: Remove 5 is_tracing_enabled checks in recall spans
- agent.py: Remove 2 is_tracing_enabled checks in reflect tool spans
- tracing.py: Fix NoOpTracer context manager to yield proper NoOpSpan
This eliminates ~50 lines of redundant conditional code while maintaining
identical behavior.
* docs: simplify distributed tracing section in monitoring.md
- Make tracing documentation more concise
- Focus on span hierarchy and attributes
- Remove verbose troubleshooting and performance sections
- Keep configuration.md for env vars only
Move the Supabase tenant extension into the hindsight-api package so users
can enable it with just an environment variable — no file copying or Docker
image modifications needed.
Key improvements over the original submission:
- JWKS-based local JWT verification (no network call per request) with
automatic fallback to /auth/v1/user for legacy HS256 projects
- Service key is now optional (only needed for HS256 or health checks)
- UUID validation on user IDs before schema name construction
- Schema prefix validation against Postgres identifier rules
- Key rotation handling with automatic JWKS cache refresh
- Proper logging via Python logging module
- Tenant extension lifecycle hooks (on_startup/on_shutdown) wired into
the server lifespan
- Public tenant_extension property on MemoryEngine
- 54 unit tests covering both verification modes, cache behavior, error
paths, and the extension loader
- README updated to reflect JWKS-first architecture
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
* fix: tagged directives should be applied to tagged mental models
* test: add unit test for based_on structure
Verify that reflect returns the correct based_on structure with:
- directives as dicts (id, name, content) in based_on.directives
- mental models as MemoryFact objects in based_on.mental-models
- memories separated properly
This ensures directives and mental models are not mixed together
in the API response.
* feat: ai sdk integration
* more fixes
* fix(security): mental model refresh tag-based security
- Mental model refresh now passes tags with all_strict matching
- Consolidation only triggers refresh for mental models with matching tags
- Consolidation filters related observations by tags (all_strict)
- Added tests to verify tag-based security boundaries
- Updated OpenAPI spec to include tags and text_preview in list_documents
- Added tags column to documents UI table
* chore: regenerate OpenAPI spec after rebase
* fix: improve consolidation prompt for contradiction handling and mental model refresh security
- Enhanced consolidation prompt to be more explicit about capturing temporal changes in contradictions
- Fixed mental model refresh security: tagged memories now only trigger refresh of mental models with matching tags
- Added stricter tag filtering to prevent cross-scope mental model refreshes
Fixes test_consolidation_merges_contradictions by improving LLM instructions to use temporal markers like "used to X, now Y" when merging contradictory facts.
Note: test_refresh_with_tags_only_accesses_same_tagged_models still needs investigation - REFLECT operation may need additional tag filtering.
* fix: mental model refresh security - proper tag filtering in search
Fixed tool_search_mental_models to properly handle all_strict tag matching mode by using the centralized build_tags_where_clause function. Previously, the function only handled "all" vs "any" modes and always included untagged mental models when using non-"all" modes.
This ensures that when a tagged mental model is refreshed with all_strict matching, it cannot access untagged mental models, preventing cross-scope information leakage.
Fixes test_refresh_with_tags_only_accesses_same_tagged_models.
Note: test_sensory_dimension_preservation is failing but this is a pre-existing issue on main branch - the LLM model (gpt-oss-20b) is not extracting facts from sensory text. Not related to security changes.
* chore: apply formatting from pre-commit hook
* fix: allow untagged mental models to be refreshed by any consolidation
Untagged mental models are considered "global" and should be refreshed
by any consolidation, regardless of whether tagged or untagged memories
were consolidated. This maintains security boundaries while allowing
global mental models to stay fresh.
When tagged memories are consolidated:
- Refresh mental models with matching tags (security boundary)
- Also refresh untagged mental models (they're global)
- DO NOT refresh mental models with different tags
When untagged memories are consolidated:
- Only refresh untagged mental models
- DO NOT refresh tagged mental models (security boundary)
Fixes test_consolidation_only_refreshes_matching_tagged_models.
* fix: custom pg schema is not reliable
* fix
* fix
* fix: WorkerPoller now always has tenant extension
Ensures WorkerPoller follows same pattern as MemoryEngine - always
creates a DefaultTenantExtension if none is provided, preventing
NoneType errors when calling list_tenants().
Fixes test failures in test_worker.py
* fix: DefaultTenantExtension honors explicit schema parameter
Allows WorkerPoller's schema parameter to be passed through to
DefaultTenantExtension via config dict, maintaining backward
compatibility for tests that use schema parameter without
providing a tenant extension.
Fixes test_poller_with_custom_schema test failure.
* feat: support for codex and claude-code as llm
* Remove refactoring plan file
* Consolidate Anthropic tests into main LLM provider test suite
- Add Anthropic models (Sonnet, Opus, Haiku) to MODEL_MATRIX
- Remove separate test_anthropic_provider.py file
- All Anthropic models now tested with standard memory operations
* Add provider-specific default models
Each LLM provider now has a sensible default model that's used when
HINDSIGHT_API_LLM_MODEL is not explicitly set. This simplifies
configuration - users can specify just the provider and API key.
Changes:
- Add PROVIDER_DEFAULT_MODELS mapping in config.py
- Update config logic to use provider defaults for both global and
per-operation LLM configs
- Add comprehensive tests for provider default model selection
- Document provider defaults in models.md
Example usage:
export HINDSIGHT_API_LLM_PROVIDER=anthropic
export HINDSIGHT_API_LLM_API_KEY=sk-ant-xxx
# Automatically uses claude-sonnet-4-20250514
Provider defaults:
- openai: gpt-5-mini
- anthropic: claude-sonnet-4-20250514
- gemini: gemini-2.5-flash
- groq: openai/gpt-oss-120b
- ollama: gemma3:12b
- lmstudio: local-model
- vertexai: gemini-2.0-flash-001
- openai-codex: o3-mini
- claude-code: claude-sonnet-4-20250514
- mock: mock-model
* Update provider default models
- openai: gpt-5-mini -> o3-mini
- anthropic: claude-sonnet-4-20250514 -> claude-haiku-4-5-20251001
- openai-codex: o3-mini -> gpt-5.2-codex
- claude-code: claude-sonnet-4-20250514 -> claude-sonnet-4-5-20250929
Updated tests and documentation to reflect new defaults.
* Move OpenAI Codex and Claude Code setup to models.md
Moved detailed setup instructions for OpenAI Codex and Claude Code from
configuration.md to models.md where they better fit with model-specific
documentation.
Changes:
- Move "OpenAI Codex Setup" section from configuration.md to models.md
- Move "Claude Code Setup" section from configuration.md to models.md
- Add cross-reference tip in configuration.md pointing to models.md
- Update default model in Claude Code example to claude-sonnet-4-5-20250929
- Keep basic provider examples in configuration.md for quick reference
This makes the configuration.md page more focused on environment
variables while models.md contains provider-specific setup details.
The batch_retain and consolidation task handlers created internal
RequestContext objects without tenant_id or api_key_id. This meant
downstream operations (consolidation, mental model refreshes) triggered
by async workers lost the original caller's request context.
Fix by passing tenant_id and api_key_id through the task payload dict
in submit_async_retain and submit_async_consolidation, then restoring
them in the corresponding handlers (_handle_batch_retain,
_handle_consolidation).
Add dataclasses and hook methods to OperationValidatorExtension for
tracking mental model operations:
- MentalModelGetContext/Result: context and result for GET operations
- MentalModelRefreshResult: result for refresh operations with token counts
- validate_mental_model_get: pre-operation validation hook
- on_mental_model_get_complete: post-GET completion hook
- on_mental_model_refresh_complete: post-refresh completion hook
Invoke hooks in http.py (GET endpoint) and memory_engine.py (refresh).
Add tests verifying hooks are called with correct parameters.
* chore: remove dead code
* chore: remove extract_opinions from test and regenerate openapi
- Remove extract_opinions parameter from test_fact_extraction_analysis
- Regenerate OpenAPI spec after removing entity observations code
* chore: update generated files and apply formatting
- Regenerate Python and TypeScript client SDKs after main merge
- Apply ruff formatting to llm_wrapper.py
* fix: accept and filter deprecated 'opinion' fact type in recall
The dead code removal eliminated support for the 'opinion' fact type,
but existing clients may still pass it. Instead of rejecting it with
a ValueError, silently filter it out before validation to maintain
backward compatibility.
Previously, _authenticate_tenant only skipped extension auth for
internal requests when _current_schema was set to a non-public schema.
This caused async HTTP retain (document upload with async_processing=True)
to fail with AuthenticationError because the worker had no API key and
the schema was "public".
Remove the public-schema guard since internal tasks were already
authenticated at submission time. The worker sets _current_schema from
the task's _schema field for tenant schemas, and it defaults to "public"
for public schema tasks — both are valid.
* feat: support vertex as llm provider
* fix
* fix: add uv index-strategy to resolve dependency conflicts with pytorch index
When using pytorch index for faster torch downloads in CI,
filelock dependency resolution was failing because pytorch index
only has older versions. Adding unsafe-best-match strategy allows
uv to search all configured indexes.
Also fix type checking warnings from ty.
* fix: add index-strategy to root pyproject.toml for workspace-level uv resolution
* chore: regenerate client SDKs after Vertex AI support
Tenant schemas were never migrated when new migrations were deployed.
Only the public schema was migrated at startup, and tenant schemas only
got migrations when first provisioned. This meant existing tenants
missed any new columns (e.g. task_payload, worker_id, claimed_at on
async_operations), causing the worker poller to crash silently.
Changes:
- Run migrations on all existing tenant schemas at startup when a
tenant_extension is configured. Each schema migration is wrapped in
try/except so one failure doesn't block others.
- Add try/except in WorkerPoller.recover_own_tasks() so a broken
schema doesn't prevent the polling loop from starting.
- Add try/except in WorkerPoller._claim_batch_for_schema() so a
broken schema doesn't prevent claiming tasks from other schemas.
* chore: cleanup benchmarks runner with old flags
* fix tests
* fix: observations rely on source_memory_ids, no link copying
Observations no longer copy any memory_links from their source facts.
Instead, retrieval uses source_memory_ids to traverse:
- Entity connections: observation → source_memory_ids → unit_entities
- Semantic similarity: observations have their own embeddings
- Temporal proximity: observations have their own temporal fields
This avoids data duplication and fixes bidirectionality issues with
entity links being copied to observations.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
* test: update consolidation test for source_memory_ids behavior
Updated test_consolidation_creates_memory_links to test_consolidation_uses_source_memory_ids
to reflect the new behavior where observations use source_memory_ids instead of memory_links
for traversal.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
---------
Co-authored-by: Claude Sonnet 4.5 <noreply@anthropic.com>
* fix: misc fixes for observations and mental models
* feat: improve graph retrieval for observations
- Update LinkExpansionRetriever to traverse through source_memory_ids
for observation entity connections (avoiding data duplication)
- Remove entity link copy from world facts to observations in consolidator
- Add tests for link expansion graph retrieval
- Add directives_applied field to ReflectResult
- Include user's other changes (CLI, docs, client updates)
* fix: CI test failures
- Add mental_model_id parameter to create_mental_model function
- Fix ToolCallTrace not including reason field from ToolCall
- Improve test_link_expansion_observation_graph_retrieval to wait for consolidation with retry
* chore: reduce link expansion log verbosity
* Revert "chore: reduce link expansion log verbosity"
This reverts commit 3ce759391cead1012157785fa78fef16ef9bfe3b.
* feat: add semantic/temporal/entity links as fallback in graph retrieval
- Add fallback query for semantic, temporal, and entity links from memory_links
- Check both directions (outgoing and incoming links)
- Weight fallback results at 0.5x to prioritize entity links via unit_entities
- Fixes graph retrieval returning 0 when data has cross-cluster temporal connections
* fix: enable observations fixture for link expansion test
- Add enable_observations fixture to ensure observations are created
- Increase wait time from 10 to 30 seconds for CI reliability
Background tasks (async retain, consolidation, reflections) fail in
multi-tenant deployments because the worker executes tasks without
setting the tenant schema context. This causes two failures:
1. The cancellation check in execute_task queries public.async_operations
instead of the tenant's schema, finds no row, and skips the task as
"cancelled" — even though it wasn't.
2. Even if that were fixed, _authenticate_tenant would throw
AuthenticationError because background tasks have no API key.
Changes:
- Poller passes task.schema into task_dict so execute_task can set it
- execute_task sets _current_schema before the cancellation check
- Task handlers use RequestContext(internal=True) to signal background ops
- _authenticate_tenant skips extension auth for internal requests when
schema is already set
- BrokerTaskBackend uses schema_getter for dynamic schema resolution
when submitting tasks and waiting for results
- Pass tenant_extension to WorkerPoller in create_app
The graph endpoint's table_rows response was missing three fields that
the control plane UI expects:
- tags: memory unit tags (shown in Tags column)
- created_at: creation timestamp (shown in Created column for mental models)
- proof_count: source memory count (shown in Sources column for mental models)
All three columns exist on the memory_units table but were not being
selected or included in the response.
* chore: run benchmarks with reflect mode
* chore: run benchmarks with reflect mode
* fixes
* new mm
* bunch of fixes
* initial commit
* fixes
* fixes
* fixes
* fix: sometimes memories gets extracted in the wrong language
* feat: Record LLM token metrics via Prometheus
Wire up the existing token metrics infrastructure to actually record
token usage from LLM calls. The MetricsCollector already had
record_tokens() method and Prometheus counters (hindsight.tokens.input,
hindsight.tokens.output), but they were never being populated.
Changes:
- Import get_metrics_collector in llm_wrapper.py
- Call record_tokens() after successful LLM calls for:
- OpenAI/Groq (using response.usage.prompt_tokens, completion_tokens)
- Anthropic (using response.usage.input_tokens, output_tokens)
- Gemini (using response.usage_metadata.prompt_token_count, candidates_token_count)
- Add test file to verify token metrics are recorded
Note: Ollama's native API doesn't return token usage, so metrics
are not recorded for that provider.
The token metrics will now be available via /metrics endpoint:
- hindsight_tokens_input_total
- hindsight_tokens_output_total
* feat: add per-request token usage tracking to retain and reflect endpoints
- Add TokenUsage model with input_tokens, output_tokens, total_tokens
- Return usage metrics in retain response (sync operations only)
- Return usage metrics in reflect response
- Update Python, TypeScript, and Rust clients
- Add API documentation for usage fields
- Add changelog entry
