* docs: add supported platforms section and Windows installation guide
Adds a platform compatibility table (Linux, macOS, Windows) and a
dedicated Windows setup section with step-by-step instructions for
installing PostgreSQL + pgvector and running Hindsight natively.
Follows up on #699 which added Windows native support.
Also fixes a ty type-check error in metrics.py for the conditional
resource module import.
* chore: sync generated clients and lock file after #699
Regenerate client SDKs to pick up ValidationError model changes
and update uv.lock with platform-specific uvloop/winloop deps.
* docs: update Windows section — pg0 now supports Windows
pg0 v0.12.0 added Windows support, so embedded DB works everywhere.
Restructure Windows section to show simple install-and-run first,
with external PostgreSQL as an optional alternative.
* chore: sync generated docs skill and openapi references
* feat: add LiteLLM LLM provider for Bedrock and 100+ providers
Add a new `litellm` LLM provider that uses the LiteLLM SDK for chat
completions and tool calling, enabling AWS Bedrock and 100+ other
providers for Hindsight's core engine (retain, recall, reflect).
- New LiteLLMLLM provider in engine/providers/litellm_llm.py
- Registered in factory, valid providers list, and no-api-key set
- Refactored API key validation to use requires_api_key() helper
- Added boto3 dependency for Bedrock auth
- Updated docs: configuration, models, monitoring, providers grid
* feat: add bedrock as first-class LLM provider alias
Add `bedrock` as a dedicated provider name that auto-prepends the
`bedrock/` prefix to model names and delegates to LiteLLMLLM under
the hood. This makes Bedrock support more discoverable — users set
`HINDSIGHT_API_LLM_PROVIDER=bedrock` with plain Bedrock model IDs.
* test: add Bedrock to CI provider tests
- Add bedrock/us.amazon.nova-lite-v1:0 to MODEL_MATRIX in test_llm_provider.py
- Add AWS credential check in should_skip_provider()
- Pass AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_REGION_NAME secrets to test-api job
- Update default bedrock model to amazon.nova-2-lite-v1:0
* fix: regenerate docs skill files and bump memory test timeout
- Regenerate skills/hindsight-docs references after docs changes
- Bump test_llm_provider_memory_operations timeout to 600s for slower
providers like Bedrock via LiteLLM
* test: skip bedrock lite models in memory operations test
Nova Lite has a 10K output token limit which is too low for fact
extraction (requires 64K). The api_methods test (completion, tools,
structured output) already validates the provider works correctly.
* test: use Nova Pro for bedrock CI tests to cover full memory pipeline
Nova Lite only supports 10K output tokens, too low for fact extraction.
Switch to Nova Pro which supports the full 64K output needed for
retain/reflect operations. This ensures bedrock is tested on all
Hindsight functionalities, not just basic API methods.
* test: switch bedrock CI to Nova 2 Lite (supports 64K output tokens)
Nova v1 models (Pro, Lite) have a 10K output token limit which is
too low for fact extraction. Nova 2 Lite supports 64K+ output tokens,
enabling full memory pipeline testing (retain + reflect).
* security: exclude litellm 1.82.8 (supply chain compromise)
litellm 1.82.8 on PyPI contains a malicious .pth file that
automatically steals credentials on Python startup (no import needed).
See: https://github.com/BerriAI/litellm/issues/24512
Our Docker images ship 1.82.6 and are unaffected, but the open version
constraints (>=1.0.0, >=1.40.0) would allow resolving to 1.82.8 on
fresh installs or lockfile refreshes.
* security: cap litellm at <=1.82.6 (1.82.7 also compromised)
* chore: regenerate uv.lock and openapi spec
* fix: update test to match claude-haiku-4-5 default model name and regenerate docs skill
* chore: fix ruff formatting in generate_changelog.py
* test: add unit tests for pg_trgm auto-detection and ValidationResult.accept_with() enrichment
Two recent PRs landed without dedicated tests:
- #626/#649 (pg_trgm fallback in EntityResolver): add 5 mocked unit tests
covering the trigram→full fallback, single-check guarantee, and sticky
downgrade behaviour.
- #639 (accept_with() enrichment): add 7 pure unit tests for the factory
method plus 5 integration tests verifying the engine applies enriched
contents (retain) and tags/tag_groups (recall) returned by validators.
Also verifies RecallContext carries tag filter state.
* fix: remove 504 from reflect OpenAPI spec to fix progenitor Rust client build
progenitor-impl-0.11.2 panics with `assertion failed: response_types.len() <= 1`
when an endpoint declares more than one response type. PR #643 added
`responses={504: ...}` to the reflect decorator, which injected a second
response type into the generated OpenAPI spec and broke the Rust client build.
Remove the `responses=` kwarg — the 504 is still raised at runtime via
JSONResponse(status_code=504), it just won't appear in the OpenAPI schema.
Regenerate openapi.json accordingly.
* chore: sync generated files and ruff formatting (lint + docs skill)
* fix: prevent silent memory loss on consolidation LLM failure
When all LLM retries are exhausted during consolidation, memories were
being marked consolidated_at unconditionally, permanently excluding them
from future consolidation runs without producing any observations.
Fix with two complementary mechanisms:
- Adaptive batch splitting: on LLM failure, the batch is halved and
retried recursively down to batch_size=1, recovering most transient
failures (rate limits, Pydantic validation on long prompts) without
operator intervention
- consolidation_failed_at column: only single-memory batches that still
fail after all retries are marked here instead of consolidated_at, so
they remain visible and retryable
- New API endpoint POST /v1/default/banks/{bank_id}/consolidation/retry-failed
resets these memories for the next consolidation run
* chore: regenerate OpenAPI spec
* fix: rename consolidation endpoint from /retry-failed to /recover
* fix: add consolidation_failed_at column, adaptive batch splitting, and recovery API
- Migration a3b4c5d6e7f8: add consolidation_failed_at TIMESTAMPTZ column to
memory_units with an index for efficient failure queries; properly chains off
g7h8i9j0k1l2 (backsweep_orphan_observations)
- Consolidator: filter pending memories with consolidation_failed_at IS NULL
so failed memories are not re-fetched in an infinite loop
- Consolidator: adaptive batch splitting — when a batch exhausts all 3 LLM
retries, halve it and retry sub-batches recursively; only single-memory
batches that also exhaust all retries get consolidation_failed_at set
- New tests (9 total) covering: adaptive splitting recovers all memories,
larger batch splitting, single-memory permanent failure, exclusion from
next run, partial batch failure, recover resets columns, recover returns
0 when none failed, recover-then-consolidate succeeds, HTTP endpoint
* chore: regenerate Go, Python, TypeScript clients with recover consolidation endpoint
* feat: add Recover Consolidation action to bank Actions dropdown
* style: apply ruff formatting to http.py and config.py
* fix: handle consolidation scope in large batch test mock LLM
The mock LLM was returning {"facts": ...} for ALL calls including consolidation.
Consolidation doesn't use skip_validation=True so it expects a _ConsolidationBatchResponse
instance, not a raw dict. Before this PR consolidation silently swallowed the AttributeError
(failed=False was returned); now failed=True triggers adaptive splitting and timeouts.
Fix: return _ConsolidationBatchResponse() when scope=="consolidation".
* fix: restrict claude-agent-sdk to macOS platform only (no Linux wheel available)
Also fix pre-existing type errors: use setattr for XLM-RoBERTa monkey-patch
and add missing reranker_local_fp16/bucket_batching/batch_size fields to main.py config constructor.
* fix: add UV_INDEX_STRATEGY=unsafe-best-match to fix markupsafe cp314 wheel conflict
PyTorch CPU index serves markupsafe==3.0.3 with only cp314 wheels.
uv's default first-index strategy stops at the first index with any version
even if no compatible wheel exists. unsafe-best-match searches all indices
for the best compatible wheel, falling back to PyPI for markupsafe.
* fix: use explicit pytorch index to prevent markupsafe wheel conflict
Configure the pytorch CPU index as explicit=true in pyproject.toml so it is
ONLY used for torch (via [tool.uv.sources]). All other packages (including
markupsafe) are resolved exclusively from PyPI, preventing the pytorch index
from serving incompatible cp314-only wheels for non-pytorch packages.
Remove UV_INDEX and UV_INDEX_STRATEGY from CI workflow (no longer needed
since the index is now configured in pyproject.toml).
* ci: trigger CI run
* ci: retry trigger
* ci: trigger after remote URL fix
* ci: add workflow_dispatch to unblock manual trigger
* fix: remove empty env blocks left after UV_INDEX removal
* fix: add type: ignore for optional claude_agent_sdk imports (macOS-only)
* fix: correct type: ignore rules for claude_agent_sdk and fix utcnow deprecation
* feat: introduce hindsight-api-slim and hindsight-all-slim packages
Closes#552
- Move all source code from hindsight-api/ to new hindsight-api-slim/
- hindsight-api-slim has heavy ML deps (torch, sentence-transformers,
transformers, einops, flashrank, mlx, mlx-lm, safetensors) and
pg0-embedded as optional extras: [local-ml], [embedded-db], [all]
- hindsight-api becomes a zero-code meta-package depending on
hindsight-api-slim[all] for full backward compatibility
- Add hindsight-all-slim meta-package: hindsight-api-slim + client + embed
- hindsight-all updated to depend on hindsight-api-slim[all]
- pg0.py: lazy-import pg0 with clear ImportError pointing to [embedded-db]
- Dockerfile: replace sed hack with proper uv sync --extra flags
- Update release.yml, test.yml, lint.sh, release.sh, CLAUDE.md and
all path references throughout the repo
* refactor: rename hindsight/ directory to hindsight-all/
* docs: document hindsight-api-slim and hindsight-all-slim package variants
Add package variants table and extras explanation to installation.md
* docs: remove emojis from installation.md, use professional tone
* docs: link Docker slim variant to pip package variants section
* docs: consolidate Docker image variants into single table
* ci: fix working-directory paths after package restructure
- Replace all hindsight-api → hindsight-api-slim in test.yml
- Replace hindsight → hindsight-all in test.yml
- Add --extra embedded-db to test-embed API install step
* ci: add local-ml and embedded-db extras to API sync steps
These extras were previously implicit in the old hindsight-api package
(which bundled everything). Now that hindsight-api-slim uses optional
extras, we must explicitly request local-ml and embedded-db in CI.
* ci: add API install step with embedded-db to test-embed smoke test
The smoke test starts hindsight-api as a daemon, which requires pg0-embedded.
Add a dedicated install step for hindsight-api-slim with embedded-db extra
so the daemon can start successfully.
* ci: remove --no-install-project when using optional extras
When --no-install-project is combined with --extra, the optional deps
are not installed because extras require the project to be active.
Remove --no-install-project from steps that need local-ml or embedded-db.
* ci: fix ordering of uv sync steps to preserve optional extras
When uv sync runs for a different workspace member, it removes optional
extras installed for other members. Fix by always running extra-requiring
API sync last, after other workspace member syncs.
Also remove --no-install-project from embedded-db sync in test-embed,
as --no-install-project prevents optional extras from being active.
* ci: add local-ml extra to test-embed API install for smoke test
The smoke test starts the full API server which needs sentence-transformers
for local embeddings (default provider). Add local-ml extra to the install.
* ci: simplify extras with --all-extras and add slim pip smoke test
- Replace explicit --extra local-ml --extra embedded-db with --all-extras
for cleaner, more maintainable sync steps
- Add test-pip-slim job: tests hindsight-api-slim[embedded-db] without
local ML models, using Cohere for embeddings/reranking (mirrors Docker
slim smoke test approach)
* ci: simplify slim smoke test to health check only (mirrors Docker test)
* feat: add JinaMLXCrossEncoder for native Apple Silicon reranking
Adds a new `jina-mlx` reranker provider backed by jinaai/jina-reranker-v3-mlx,
a 0.6B multilingual listwise reranker running via the MLX framework on Apple Silicon.
The model is downloaded automatically from HuggingFace Hub on first use.
Benchmarked latencies (Apple Silicon): 1 doc→32ms, 5→45ms, 10→60ms, 20→94ms.
Sub-linear scaling because all docs are ranked in a single forward pass.
- Embeds the MLX reranker implementation (_MLXReranker / _MLPProjector) directly
in cross_encoder.py with no transformers/PyTorch dependency
- Adds `mlx`, `mlx-lm`, `safetensors` to pyproject.toml optional deps (uv add)
- Updates configuration.md with provider docs and benchmark table
* refactor: import MLXReranker from repo rerank.py instead of duplicating code
Use importlib to load MLXReranker directly from the model repo's own rerank.py
(downloaded via snapshot_download). Also pin exact minimum versions for
mlx>=0.31.0, mlx-lm>=0.31.1, safetensors>=0.6.2 (verified against installed versions).
* refactor: move MLX reranker impl to dedicated jina_mlx_reranker.py
Replaces the importlib hack with a proper module. jina_mlx_reranker.py is
adapted from jinaai/jina-reranker-v3-mlx/rerank.py (CC BY-NC 4.0) with the
source clearly documented at the top of the file.
* docs: simplify jina-mlx reranker docs
* fix: disable GIN fastupdate on source_memory_ids index to prevent deadlocks
GIN fastupdate buffers inserts in a pending list and flushes it with
AccessExclusiveLock when full. Under concurrent test load (8 xdist workers
all running retain_async), two workers can trigger a flush simultaneously
and deadlock. Recreating the index with fastupdate=off eliminates the
flush/lock cycle at the cost of slightly slower individual inserts.
* fix: drop per-bank HNSW indexes after transaction to avoid AccessExclusiveLock deadlock
When deleting a bank, the previous code dropped HNSW indexes inside the
same transaction as the DELETE FROM memory_units. Since DROP INDEX needs
AccessExclusiveLock on the parent table and DELETE holds RowExclusiveLock,
two concurrent bank deletions deadlocked on the same table lock.
Fix: capture internal_id inside the transaction, commit, then drop the
indexes outside the transaction so no row-level locks are held.
* feat: accept pdf, images and office files
* refactor: rename FileConverter to FileParser, simplify file retain API
- Rename engine/converters/ → engine/parsers/, FileConverter → FileParser,
ConverterRegistry → FileParserRegistry, MarkitdownConverter → MarkitdownParser
- Rename env var HINDSIGHT_API_FILE_CONVERTER → HINDSIGHT_API_FILE_PARSER
- Remove async/document_tags params from FileRetainRequest (always async now)
- Add retain_files() to Python Hindsight client and retainFiles() to TypeScript client
- Add sample.pdf to doc examples for working file upload demonstrations
- Update test_file_retain.py to use new parser names and always-async behavior
- Fix Go client missing os import in api_files.go
- Simplify postgresql.py storage to minimal schema
* fix: update rust CLI tests to use is_supported_file instead of is_text_file
* fix: patch Go api_files.go to add missing 'os' import after generation
* fix: insert 'os' import after 'net/url' in api_files.go patch for correct position
* chore: regenerate OpenAPI spec and clients (converter→parser description update)
* fix: improve async batch retain with large payloads
* fix: improve async batch retain with large payloads
* api
* api
* api
* api
* api
* Clean up perf benchmark: keep only Python files
- Remove README.md and PERFORMANCE_FINDINGS.md
- Remove results/ JSON files (gitignored)
- Remove test_data/ directory
- Keep only __init__.py and retain_perf.py
* docs: explain automatic batch optimization for async retain
- Add section explaining Hindsight automatically handles batch sizing
- Users don't need to manually tune batch sizes with async mode
- Hindsight splits large batches (>10k tokens) into optimized sub-batches
- Include example showing best practices
* docs: remove emojis and code example from performance page
* fix: correct OperationDetails type to match API response
- Change optional fields to use | null instead of ?
- Fixes TypeScript compilation error in control plane build
* fix: use discriminated union for OperationDetails type
- Support both success and error states properly
- Fixes TypeScript error when setting error state
* fix: use unique document_ids in batch retain examples
- Each item in a batch must have unique document_id
- Update both Python and JavaScript examples
- Fixes test-doc-examples CI failure
* chore: trigger CI
* fix: test mocking and duplicate document_ids in examples
- Mock _get_pool() in test_async_retain_tags.py to avoid _initialized error
- Set _initialized = True on mocked MemoryEngine instances
- Fix duplicate document_ids in retain.py and retain.mjs examples
* fix: properly mock async pool/connection and fix more duplicate document_ids
- Use AsyncMock for pool.acquire() to fix 'can't be used in await' error
- Fix duplicate document_ids in retain-async examples (retain.py and retain.mjs)
- Remove batch-level document_id parameter that caused duplicates
* ci: collect all doc example failures and show summary
- Run all Python/Node.js/CLI examples regardless of individual failures
- Collect failure list and display summary at the end
- Show pass/fail count and list of failed files
- Exit with failure only after running all examples
* refactor: extract doc example testing to standalone script
- Create scripts/test-doc-examples.sh to run all examples
- Collects logs of failed examples separately
- Shows full error logs only for failures at the end
- Clean summary with pass/fail counts
- Proper exit codes
- Replaces inline bash in CI workflow
* fix: doc examples - duplicate document_ids and error handling
- retain.py: move document_id to item level to avoid duplicates
- documents.mjs: add error handling for getDocument to show clear error message
* fix: update tests for duplicate document_id validation
- test_async_retain_tags: verify operation structure instead of exact UUID
- test_delete_bank: use unique document_ids (team-doc-1, team-doc-2)
* feat: support litellm-sdk for reranker endpoint
* feat: support litellm-sdk for reranker endpoint
* fix: make litellm SDK cohere test fixture async function-scoped
* fix: store litellm module reference during initialization to avoid import issues
* feat: add LiteLLM SDK embeddings support
- Add LiteLLMSDKEmbeddings class for direct API access without proxy
- Support multiple providers: Cohere, OpenAI, Together AI, HuggingFace, Voyage AI
- Automatic dimension detection via test embedding
- Provider-specific API key mapping
- Batch processing support (configurable batch size)
- Comprehensive test coverage (17 unit tests)
- Update documentation with configuration examples
Implements embeddings in same PR as reranker per user request
* fix: correct config mocking in embeddings factory tests
- Mock get_config() from its source module (hindsight_api.config)
- Fixes factory tests that were returning LocalSTEmbeddings instead of LiteLLMSDKEmbeddings
- All 17 unit tests now passing
* fix: skip Cohere integration tests when API key is invalid
- Catch initialization errors and skip tests instead of failing
- Prevents CI failures when COHERE_API_KEY is set but invalid
- Integration tests now properly skip when authentication fails
* fix: skip Cohere reranker integration tests when API key is invalid
- Add same error handling as embeddings tests
- Prevents CI failures when COHERE_API_KEY is set but invalid
- Tests now properly skip when authentication fails
* Revert "fix: skip Cohere reranker integration tests when API key is invalid"
This reverts commit 655dacaffb25851ff48e202b4379fc8332a66df7.
* Revert "fix: skip Cohere integration tests when API key is invalid"
This reverts commit 5d00548e39faa3da6b427ace89589a216816f9e7.
* fix: pass API key directly to litellm SDK functions
- Add api_key parameter to arerank(), rerank(), aembedding(), and embedding() calls
- Prevents authentication issues in multi-process environments (pytest-xdist)
- More reliable than relying solely on environment variables
- Update test assertions to expect api_key parameter
* feat: pass api_base parameter to litellm SDK calls and remove hasattr check
* fix: raise errors instead of silently returning 0.0 scores
* refactor: pass API keys directly in kwargs instead of setting env vars
* 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: improve embed ux with rich logging and profile isolation
* chore: regenerate uv.lock to fix corrupted streamlit RECORD
* test: update database URL assertion for profile-specific pg0
* Revert: restore lint.sh to main branch version
* fix(sec): upgrade vulnerable deps
* feat: add comprehensive logging to upgrade tests
- Modify VersionRunner to write server logs to /tmp/upgrade-test-*.log files
- Add pytest hook to automatically dump server logs on test failure
- Add CI workflow step to show upgrade test logs (always runs)
- Improves debuggability when upgrade tests fail in CI
This addresses the issue where upgrade test failures in CI were
impossible to debug because API server logs were not visible.
* 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.
* feat(hindsight-embed): external API support + OpenClaw fixes
Adds comprehensive external API support and fixes critical OpenClaw plugin issues.
**External API Support:**
- Add HINDSIGHT_EMBED_API_URL to connect to external Hindsight API servers
- Add HINDSIGHT_EMBED_API_TOKEN for Bearer token authentication
- Add HINDSIGHT_EMBED_API_DATABASE_URL for custom PostgreSQL databases
- Skip daemon startup when external API URL is configured
- Add 10 comprehensive unit tests for external API scenarios
**OpenClaw Plugin Fixes:**
- Fix#263: Port mismatch (DEFAULT_PORT 8888 → 8889)
- Fix#264: Add daemon recovery after OpenClaw SIGUSR1 restarts
- Fix OpenRouter support: Pass HINDSIGHT_API_LLM_BASE_URL to daemon
- Fix macOS crashes: Auto-set FORCE_CPU flags for MPS/Metal issues
**LLM Configuration Refactor:**
- Auto-detect provider from standard env vars (OPENAI_API_KEY, etc.)
- Support explicit override via HINDSIGHT_API_LLM_* env vars
- Update model defaults (gemini-2.5-flash, openai/gpt-oss-20b)
- Remove provider-specific base URL support (only HINDSIGHT_API_LLM_BASE_URL)
**Documentation Updates:**
- Rewrite OpenClaw integration docs with crystal clear examples
- Add external API usage examples
- Add OpenRouter free model examples
- Update Quick Start with simplified provider setup
Closes#263, Closes#264
* docs(openclaw): streamline docs and add config inspection
- Remove duplicate/verbose sections (468 → 216 lines)
- Add section showing how to check ~/.hindsight/embed config file
- Add daemon status checking commands
- Keep only essential configuration examples
- Consolidate troubleshooting sections
* fix(test): update daemon health check port from 8889 to 8888
The test was checking port 8889 but we changed the daemon to use port 8888.
* 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.
* 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
* doc: introduce mental models blog post
Write blog post introducing Mental Models in Hindsight 0.4.0:
- Evolution from observations and opinions
- How mental models work (consolidation, evidence tracking)
- Breaking changes and migration path
- Environment variable to enable (experimental)
- Agentic reflect explanation
* updates
* Update 2026-01-26-learning-capabilities.md
* fix: doc build issues
- Add missing code snippets for versioned docs (recall-opinions-only, recall-include-entities, bank-background)
- Fix broken links by using relative paths for version compatibility
- Update blog post title to sentence case
- Clear versions.json since v0.3 versioned docs don't exist yet
- Enable INCLUDE_CURRENT_VERSION in build script
* fix: update doc links after rebase
- Fix blog post to link to correct pages (/developer/api/mental-models and /developer/observations)
- Fix CLI docs to link to /api-reference instead of /api
* feat: add directives section to blog post
- Update intro to mention three layers of knowledge
- Add concise Directives section for compliance/guardrails
- Add directives to resources section
- Keep focus on learning capabilities (observations and mental models)
* fix: revert intro to focus on learning capabilities only
Directives are a separate feature for compliance/guardrails, not a learning capability. The blog post is about observations and mental models.
