* feat: add built-in llama.cpp LLM provider for fully local inference
Add `llamacpp` as a new LLM provider that manages a llama-cpp-python server
subprocess. Auto-downloads Gemma 4 E2B Q4_K_M (~3.5 GB) on first use and
runs inference locally via Metal/CUDA with no external services needed.
- New provider: `HINDSIGHT_API_LLM_PROVIDER=llamacpp`
- Singleton server shared across retain/reflect/consolidation
- Configurable: model path, GPU layers, context size, grammar enforcement
- User-extensible via `HINDSIGHT_API_LLAMACPP_EXTRA_ARGS`
- Flash attention + prompt caching enabled by default
- LLM provider cleanup on shutdown (stops subprocess)
- hindsight-embed: `--ui` flag on `daemon start`, removed FORCE_CPU on macOS
- Docs: configuration.md, models.mdx, providers grid updated
* chore: regenerate docs skill and update lockfile for local-llm dep
* docs: add Volcano Engine as supported LLM provider
Follow-up to #714. Add Volcano Engine (ByteDance) to the documentation:
- LLM providers grid component
- Provider list in configuration docs
- Provider example with base URL and default model
- Default model table in models page
* chore: regenerate docs skill 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).
* feat(skill): validate links, strip images, include openapi.json and changelog
- Add post-processing step to rewrite Docusaurus site-root paths (e.g.
/developer/foo) to proper relative .md paths within the skill
- Strip markdown and HTML images from all generated files since assets
are not bundled with the skill
- Copy hindsight-docs/static/openapi.json into references/openapi.json
and map /api-reference links to it
- Include changelog.md from src/pages/ alongside faq and best-practices
- Add final validation step that fails the build if any link still
points outside the skill directory
* ci: run generate-docs-skill in verify-generated-files job
* fix(skill): strip unresolvable site-root links instead of leaving them broken
* fix(skill): write file when images stripped but no links rewritten
* chore(skill): regenerate with fixed links, stripped images, changelog and openapi
* fix(skill): handle changelog as directory, add agno/hermes integrations, rebase on main
* feat: upgrade MiniMax default model from M2.5 to M2.7
MiniMax has released MiniMax-M2.7, their latest model with a 1M context
window (up from 204K). This updates the default model across config,
docs, and examples. M2.5 remains fully compatible for users who prefer it.
- Update PROVIDER_DEFAULT_MODELS to MiniMax-M2.7
- Update .env.example and documentation references
- Add test_minimax_provider.py with M2.7 and backward compat tests
* chore: remove test file per review feedback
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Co-authored-by: PR Bot <pr-bot@minimaxi.com>
