- run_from_query() now delegates to run(event, binding) instead of maintaining
a separate legacy execution path
- Pipeline Query is converted to AgentEventEnvelope via PipelineCompatAdapter
- Pipeline config is converted to AgentBinding with StatePolicy
- bound_plugins authorization preserved from Pipeline
- Legacy compatibility fields preserved:
- query_id → context.runtime.query_id → session registry
- prompt → context.compatibility.extra.prompt (not top-level)
- params → context.compatibility.extra.params (with proper filtering)
- max-round → bootstrap.messages and compatibility.legacy_messages
- Pipeline path gains event-first host capabilities:
- EventLog and Transcript writing
- ArtifactStore registration
- PersistentStateStore for state.updated
- Removed legacy handlers:
- _handle_artifact_created_query() (replaced by _handle_artifact_created)
- _handle_state_updated() (replaced by _handle_state_updated_event)
This change unifies the execution path while preserving backward compatibility
for Pipeline-based runners. EventGateway is not implemented in this branch;
only the event-first entry point is reserved.
Add unified tool lookup method that searches both plugin and MCP loaders.
Also add _get_tool method to MCPLoader for consistency with PluginToolLoader.
- Add invoke_rerank action handler in plugin handler
- Validate rerank model access via run session
- Cap documents at 64 for API limit
- Return sorted results by relevance score
- Add DEPRECATED docstring to all legacy runners in pkg/provider/runners/
- Mark migration target for each runner (local-agent, dify, n8n, coze, dashscope, langflow, tbox)
- Add PROGRESS.md to track agent-runner-pluginization implementation status
- Remove completed PHASE0_INTEGRATION_RECORD.md
- Add pre-computed _authorized_ids (frozenset) at session registration for O(1) lookup
- Refactor is_resource_allowed() from linear search to set membership check
- Add thread-safe locking to get_session_registry() singleton
- Cache _session_registry and _state_store references in orchestrator __init__
- Add asyncio.gather() for parallel resource building in AgentResourceBuilder
- Create shared test fixtures in tests/unit_tests/agent/conftest.py
- Update test files to import from shared conftest.py
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Phase 0 integration complete - verified minimal loop with local-agent stub runner.
Changes:
- Add AgentRunOrchestrator for plugin-based agent execution
- Add AgentResultNormalizer for Protocol v1 result conversion
- Add AgentRunnerDescriptor for runner ID parsing (plugin:author/name/runner)
- Update chat handler to use new orchestrator instead of direct runner lookup
- Add plugin handler methods for list_agent_runners and run_agent
- Add connector methods for AgentRunner protocol forwarding
- Update pipeline API to include runner options in metadata
- Add integration docs and implementation plan
Integration verified:
- Runner: plugin:langbot/local-agent/default
- Input: "你好"
- Output: [stub] Echo: 你好
- Date: 2026-05-10 10:09
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- i18n: add models.searchProviders, monitoring.tabs.tokens and the
monitoring.tokens.* block (incl. bucket.hour/day) to es-ES, ja-JP,
ru-RU, th-TH, vi-VN and zh-Hant, which were missing them and failed
the Check i18n Keys CI.
- api: generate_jwt_token built 'exp' from a naive datetime.now(), which
PyJWT validates against UTC — in any timezone ahead of UTC the token
was already expired at issue time. Use datetime.now(timezone.utc).
The legacy pkg/persistence/migrations (DBMigration / dbmXXX) system now
coexists with Alembic but accepts no new migrations — all new schema
changes go through Alembic.
- remove dbm026_llm_model_context_length (superseded by Alembic
0005_add_llm_context_length, which makes the identical change)
- cap required_database_version at 25 (legacy chain dbm001-025 kept
read-only to upgrade pre-existing 3.x DBs to the Alembic baseline)
- add migrations/README.md documenting the freeze
- document the Alembic-only policy and revision-id/idempotency rules in
AGENTS.md
The pkg/core/migrations system (m001-m043 DBMigration-style config
migrations, MigrationStage, and the core.migration base class) only ever
ran when upgrading from LangBot 3.x. The last 3.x release is over a year
old and is no longer supported, so this dead code is removed entirely:
- delete pkg/core/migrations/ (43 mXXX_*.py + __init__)
- delete pkg/core/migration.py (base class + registry)
- delete pkg/core/stages/migrate.py (MigrationStage)
- drop 'MigrationStage' from boot.py stage_order
- delete tests/unit_tests/core/test_migration.py (tested the removed base class)
* refactor(provider): use LiteLLM as unified LLM requester backend
- Replace 23+ individual requester implementations with unified litellmchat.py
- Add litellm_provider field to 27 YAML manifests for provider routing
- Delete redundant requester subclasses
- Add unit tests for LiteLLMRequester (29 tests)
- Fix num_retries parameter name (was max_retries)
- Fix exception handling order for subclass exceptions
LiteLLM provides unified API for 100+ providers, eliminating need for
provider-specific requesters.
* fix: ruff format provider.py
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* refactor(provider): simplify LiteLLM requester usage handling
- Remove unused Anthropic-specific tool schema generation
- Share completion argument construction between normal and streaming calls
- Use LiteLLM/OpenAI native usage fields for monitoring
- Collect stream token usage from LiteLLM stream_options
- Update LiteLLM requester tests for unified usage fields
* restore: restore deleted provider requester files
Restore individual provider requester implementations that were
removed in de61b5d3. These files coexist with the unified
litellmchat.py backend.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* feat: update requesters and improve provider selection UI
- Added `litellm_provider` field to various requesters' YAML configurations.
- Removed obsolete Python requester files for OpenRouter, PPIO, QHAIGC, ShengSuanYun, SiliconFlow, Space, TokenPony, VolcArk, and Xai.
- Introduced new requesters for Tencent and Together AI with corresponding YAML configurations and SVG icons.
- Enhanced the ProviderForm component to include a searchable dropdown for selecting providers, improving user experience.
- Updated localization files to include search provider text for both English and Chinese.
* fix(provider): align litellm rebase with master
* fix(provider): capture streaming token usage; add token observability
The LiteLLM streaming requester only captured usage when a chunk had an
empty `choices` list. Many OpenAI-compatible gateways (e.g. new-api) and
providers send the final usage payload in a chunk that still carries an
empty-delta choice, so streamed calls always recorded 0 tokens in the
monitoring logs/dashboard (non-streaming worked).
- Capture stream usage whenever a chunk carries it, regardless of choices
- Add robust _normalize_usage (dict/obj shapes, derive missing total_tokens)
- Register litellm in bootutils/deps.py (was in pyproject only)
- Add MonitoringService.get_token_statistics + /monitoring/token-statistics
endpoint: summary, per-model breakdown, token timeseries, and a
zero-token-success data-quality signal
- Add TokenMonitoring dashboard tab (summary tiles, stacked token chart,
per-model table) + i18n (en/zh)
- Regression tests for stream usage capture and usage normalization
Verified end-to-end against a real OpenAI-compatible endpoint with
gpt-5.5 and claude-opus-4-8: tokens now recorded non-zero for both
streaming and non-streaming paths.
* refactor(provider): simplify litellm capabilities
* style: simplify wrapped expressions
* feat(models): persist context metadata
* fix(provider): handle dict embeddings and openai-compatible rerank in LiteLLMRequester
- invoke_embedding: support both object- and dict-shaped response.data
entries (OpenAI-compatible gateways like new-api return dicts)
- invoke_rerank: litellm.arerank rejects the 'openai' provider, so for
openai-compatible (or unspecified) providers call the standard
Jina/Cohere-style POST /v1/rerank endpoint directly over HTTP
- accept both 'relevance_score' and 'score' fields in rerank results
- add unit tests for the openai-compatible HTTP rerank path
* feat(provider): enforce requester support_type when adding models
- frontend: AddModelPopover only shows model-type tabs (llm/embedding/
rerank) that the provider's requester declares in its manifest
support_type; ModelsDialog fetches requester manifests and maps
requester -> support_type, passed down through ProviderCard
- backend: add _validate_provider_supports guard in create_llm_model /
create_embedding_model / create_rerank_model so a model cannot be
attached to a provider whose requester does not support that type,
even if the frontend restriction is bypassed (manifests without
support_type are allowed for backward compatibility)
- manifests: correct support_type for providers that do not offer all
three model types:
- llm only: anthropic, deepseek, groq, moonshot, openrouter, xai
- llm + text-embedding: openai, gemini, mistral
- add rerank to new-api (verified working via /v1/rerank)
- set llm + text-embedding + rerank for aggregator/unknown gateways
* feat(provider): add searchable alias to requester manifests
- add a free-text 'alias' field to every requester manifest spec,
containing the vendor's English/Chinese names, pinyin, common
nicknames and flagship model-series names (e.g. moonshot -> kimi,
月之暗面; zhipu -> glm, 智谱清言)
- frontend: ProviderForm requester search now also matches against
alias (substring/contains), so searching 'kimi' surfaces Moonshot,
'硅基' surfaces SiliconFlow, etc.
- also fix support_type: openrouter (relay) supports embedding+rerank;
LangBot Space gains rerank (coming soon)
* fix(provider): make support_type guard defensive against incomplete model_mgr
- _validate_provider_supports now uses getattr to gracefully skip when
model_mgr / provider_dict / manifest lookup is unavailable, instead of
raising AttributeError (fixes unit tests that mock ap.model_mgr as a
bare SimpleNamespace)
- add TestValidateProviderSupports covering: allow supported type,
reject unsupported type, allow when support_type missing, allow when
provider unknown, degrade safely when model_mgr is incomplete
* fix(persistence): guard 0004 migration against missing llm_models table
The 0004_add_llm_model_context_length migration called
inspector.get_columns('llm_models') unconditionally, raising
NoSuchTableError when the table does not exist (e.g. migrating a
fresh/empty DB, as exercised by the integration tests where
create_all() registers no tables because the ORM models are not
imported). Every other migration guards with a table-existence check
first; add the same guard here for both upgrade and downgrade.
Also restore the test head assertion to 0004 (it had been lowered to
0003 to mask this failure).
* Merge branch 'master' into feat/litellm
Resolve conflicts:
- uv.lock: regenerated via 'uv lock' to reconcile litellm/fastuuid
(ours) with openai bump (master).
- Alembic migrations: master added 0004_add_mcp_readme while this
branch added 0004_add_llm_model_context_length, both as children of
0003 (would create multiple heads). Re-chain the litellm migration as
0005_add_llm_model_context_length with down_revision=0004_add_mcp_readme
for a single linear head. Update test head assertion accordingly.
* fix(persistence): shorten migration revision id to fit varchar(32)
PostgreSQL stores alembic_version.version_num as varchar(32).
'0005_add_llm_model_context_length' (33 chars) overflowed it, raising
StringDataRightTruncationError in the PG migration tests. Rename the
revision (and file) to '0005_add_llm_context_length' (27 chars) and
update the head assertions in both SQLite and PostgreSQL migration
tests.
---------
Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
Co-authored-by: fdc310 <2213070223@qq.com>
Co-authored-by: RockChinQ <rockchinq@gmail.com>
Counts successful non-WebSocket bot responses (persisted in the metadata
table as survey_bot_response_count, survives restarts) and fires the
bot_response_success_100 survey event once the instance reaches 100
responses. Counting stops after the milestone has been triggered.
Existing first_bot_response_success behavior unchanged. 6 new unit tests.
