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
* 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
* Fix main-methods.py: entities is a dict, use .items() and .canonical_name
* Migrate docs to use CodeSnippet components
- Convert quickstart.md, retain.md, recall.md, reflect.md, memory-banks.md to .mdx
- Use CodeSnippet to pull code from validated example scripts
- Add missing 'name' parameter to create_bank calls
- Fix main-methods.py entities iteration (dict not list)
- Remove retain-new.mdx demo file
* Migrate existing docs to match testing pattern with code snippet and add CLI tests to the CI
* Fix doc-id issue + add main-method tests
* CLI fixes
* Update openAPI json
* Fix rust build issues
* increase sleep time for Hindsight to process the document
* Added a polling sleep instead of fixed
* Delete immediately fails, so create the doc a earlier in the test to get the doc ready
* Add debug logs
* Remove debug logs
* bump pg0 0.11.x and improve documentation
* bump pg0 0.11.x and improve documentation
* bump pg0 0.11.x and improve documentation
* ci: test notebooks on ci
* ci: test notebooks on ci
* rm llms-full from repo
* formatting
* formatting
* feat: support for gemini-3-pro and gpt-5.2
* feat: support for gemini-3-pro and gpt-5.2
* feat: support for gemini-3-pro and gpt-5.2
* feat: support for gemini-3-pro and gpt-5.2
* feat: add local mcp server
* docs
* docs
