* feat: improve mental models ux on control plane
* feat: improve mental models ux on control plane
* gen
* feat(cli): add --id flag to mental model create command
* fix(cli): revert unused variable underscore prefix that breaks compilation
The underscore prefix on stdout/stderr variables was added to suppress
warnings, but these variables are actually used in assert messages,
causing compilation errors. Reverting to original names.
* 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
* 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
* 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
* ci: frozen uv sync
* fix: add missing authorization parameter to get_agent_stats in CLI
The generated Rust client was updated with an authorization header
parameter for get_agent_stats, but the CLI code wasn't updated.
* 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
