* fix: rename moltbot to openclawd
* fix
* fix
* fix: use single shared pg0 database for all banks + add default mission
This commit fixes a critical database isolation issue and adds the default
mission feature for the openclawd plugin.
## Changes:
**hindsight-embed:**
- Fixed daemon_client.py to use single shared database: pg0://hindsight-embed
- Previously, each bank_id would create a separate pg0 instance (wrong!)
- Now all banks share the same database with isolation via bank_id parameter
- Updated README to clarify database architecture
**openclawd plugin (v0.0.5):**
- Added default bank mission describing OpenClawd's multi-channel assistant role
- Added setBankMission() method to client
- Integrated mission setting during plugin initialization
- Added bankMission to plugin config schema with sensible default
- Updated docs to explain shared database architecture
## Why this matters:
Bank isolation should happen WITHIN the database (via separate tables/schemas),
not via separate database instances. Using HINDSIGHT_EMBED_BANK_ID to create
separate pg0 databases was architecturally wrong and caused confusion.
* ci: rename moltbot to openclawd in workflows and release script
- Updated build-moltbot-integration → build-openclawd-integration in test.yml
- Updated release-moltbot-integration → release-openclawd-integration in release.yml
- Updated all working directories from moltbot to openclawd
- Updated artifact names from moltbot-integration to openclawd-integration
- Added openclawd package.json to release.sh version bump script
* 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.
* 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
The "Test memory" example is too short for the LLM to extract
meaningful facts from, causing the test to silently fail (0 memories
created). Replace with "Alice works at Google as a software engineer"
which has enough context for fact extraction.
Fixes test examples in:
- get-skill installer (local and cloud modes)
- hindsight-embed configure output
- skills.md documentation
* doc: update expired Slack invite link
* feat: add cloud mode to skill installer for team memory sharing
Adds support for Hindsight Cloud in the skill installer, enabling teams
to share memories about a codebase. Changes include:
- Add `--mode cloud` option to get-skill installer
- Install hindsight CLI binary for cloud mode (via get-cli)
- Configure ~/.hindsight/config with API URL and key
- Generate cloud-specific SKILL.md with team-aware guidance
- Distinguish between project conventions and individual preferences
- Update skills.md documentation with cloud setup instructions
Cloud mode workflow:
1. Team admin creates a bank in Hindsight Cloud
2. Each developer runs: curl ... | bash -s -- --mode cloud
3. All team members share the same memory bank
4. Knowledge retained by one member benefits everyone
* 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
* misc: add mcp integration tests and increase test coverage
* misc: add mcp integration tests and increase test coverage
* misc: add mcp integration tests and increase test coverage
