* fix(deps): address critical and high severity security vulnerabilities
Bump vulnerable dependencies to patched versions across the monorepo:
Python (critical/high):
- fastmcp >=2.14.0 → >=3.2.0 (SSRF, path traversal, OAuth confused deputy, command injection)
- langchain-core >=1.2.11 → >=1.2.22 (path traversal in legacy load_prompt)
Python (low):
- cryptography >=46.0.5 → >=46.0.6 (incomplete DNS name constraint enforcement)
- pygments: add >=2.20.0 pin (ReDoS via GUID regex)
Node.js:
- serialize-javascript ^7.0.3 → ^7.0.5 (CPU exhaustion DoS)
- handlebars: add >=4.7.9 override (JS injection via AST type confusion)
- path-to-regexp: add >=0.1.13 override (ReDoS via route params)
- brace-expansion: add version range override (process hang/memory exhaustion)
Also adds type: ignore comments for FastMCP 2.x private attribute access that
ty now flags since FastMCP 3.x removed _tool_manager (guarded by try/except
and hasattr at runtime).
Regenerated all lock files across API, integrations, and tests.
* fix(deps): add ajv v8 scoped overrides for schema-utils and ajv-keywords
The global ajv ^6.14.0 override caused schema-utils and ajv-keywords to
receive ajv v6, but they require ajv v8 (for dist/compile/codegen). Add
scoped overrides to ensure these packages get ajv v8 while the global
override remains for packages that need v6.
* fix(tests): remove stateless_http from FastMCP() constructor calls
FastMCP 3.x no longer accepts stateless_http in the constructor. The
tests call tools directly without HTTP transport, so the parameter is
not needed.
* fix: update MCP tests for FastMCP 3.x _tool_manager removal
FastMCP 3.x removed _tool_manager. Tests now use
_local_provider._components for sync tool dict access and
mcp.list_tools() for async filtered tool listing.
* fix: resolve docusaurus build failures (ajv overrides + missing blog date)
- Remove global ajv ^6.14.0 override and scoped ajv-keywords/schema-utils
overrides that caused webpack compilation errors manifesting as
"Cannot read properties of undefined (reading 'date')" during SSR
and "these parameters are deprecated" warnings. Natural version
resolution (v6.12.6+ for v6 consumers, v8+ for v8 consumers) already
satisfies the security fix (>= 6.12.3).
- Add missing date frontmatter to learning-capabilities blog post.
* chore: regenerate openapi spec and docs skill
* feat: Windows native support — run Hindsight without Docker on Windows
Four compatibility fixes that allow Hindsight to run natively on Windows
with an external PostgreSQL + pgvector installation:
1. **pyproject.toml**: Conditional event loop dependency
- `winloop` on Windows (sys_platform == 'win32')
- `uvloop` on Linux/macOS (sys_platform != 'win32')
2. **main.py**: winloop integration via `winloop.install()`
- Patches asyncio event loop policy globally before uvicorn starts
- uvicorn sees "asyncio" but runs winloop underneath (same perf as uvloop)
- Falls back to default asyncio if winloop unavailable
3. **metrics.py**: Guard `resource` module import
- `resource` is Unix-only (getrusage, getrlimit)
- Conditional import with None fallback
- Skip process metrics collection on Windows
4. **fact_storage.py**: Cross-platform strftime
- `%-d` (no-padding day) is glibc-only, fails on Windows
- Replaced with `%d` + `.replace(" 0", " ")` for same output
## Windows Setup Guide
### Prerequisites
- Python 3.11+
- PostgreSQL 17 with pgvector extension
- Ollama (for local embeddings) or external embedding provider
### Install PostgreSQL + pgvector on Windows
```bash
winget install PostgreSQL.PostgreSQL.17
# Build pgvector from source (requires Visual Studio Build Tools)
git clone https://github.com/pgvector/pgvector.git
# In x64 Native Tools Command Prompt:
set PGROOT=C:\Program Files\PostgreSQL\17
nmake /F Makefile.win
nmake /F Makefile.win install
# Enable extension
psql -U postgres -d hindsight -c "CREATE EXTENSION IF NOT EXISTS vector;"
```
### Install and Run Hindsight
```bash
pip install -e ".[embedded-db]"
# Set environment variables
set HINDSIGHT_API_LLM_PROVIDER=openai
set HINDSIGHT_API_LLM_API_KEY=your-api-key
set HINDSIGHT_API_LLM_BASE_URL=https://your-llm-endpoint/v1
set HINDSIGHT_API_LLM_MODEL=your-model
set HINDSIGHT_API_DATABASE_URL=postgresql://postgres@localhost:5432/hindsight
set HINDSIGHT_API_EMBEDDING_PROVIDER=ollama
set HINDSIGHT_API_PORT=8889
hindsight-api
```
Data persists in PostgreSQL on your local disk — survives reboots,
updates, and anything that would wipe a Docker volume.
Tested on Windows 11 with PostgreSQL 17.9, pgvector 0.8.2,
Python 3.11, RTX 5080 (CUDA embeddings + reranking).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix: handle strftime ValueError on Windows in fact_storage
The strftime call on occurred_start/occurred_end can raise ValueError
on Windows when the datetime object has unexpected format properties.
Wrap in try/except to gracefully skip date signal rather than crash
the entire retain batch.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* 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
Remove the sys_platform == 'darwin' constraint that prevented
claude-agent-sdk from installing on Linux, breaking the claude-code
provider in Docker containers.
Fixes#640
* 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)
