service layers
Propagate token counts from LLM services through the prompt,
text-completion, graph-RAG, document-RAG, and agent orchestrator
pipelines to the API gateway and Python SDK. All fields are Optional
— None means "not available", distinguishing from a real zero count.
Key changes:
- Schema: Add in_token/out_token/model to TextCompletionResponse,
PromptResponse, GraphRagResponse, DocumentRagResponse,
AgentResponse
- TextCompletionClient: New TextCompletionResult return type. Split
into text_completion() (non-streaming) and
text_completion_stream() (streaming with per-chunk handler
callback)
- PromptClient: New PromptResult with response_type
(text/json/jsonl), typed fields (text/object/objects), and token
usage. All callers updated.
- RAG services: Accumulate token usage across all prompt calls
(extract-concepts, edge-scoring, edge-reasoning,
synthesis). Non-streaming path sends single combined response
instead of chunk + end_of_session.
- Agent orchestrator: UsageTracker accumulates tokens across
meta-router, pattern prompt calls, and react reasoning. Attached
to end_of_dialog.
- Translators: Encode token fields when not None (is not None, not truthy)
- Python SDK: RAG and text-completion methods return
TextCompletionResult (non-streaming) or RAGChunk/AgentAnswer with
token fields (streaming)
- CLI: --show-usage flag on tg-invoke-llm, tg-invoke-prompt,
tg-invoke-graph-rag, tg-invoke-document-rag, tg-invoke-agent
Refactor agent provenance so that the decision (thought + tool
selection) and the result (observation) are separate DAG entities:
Question ← Analysis+ToolUse ← Observation ← ... ← Conclusion
Analysis gains tg:ToolUse as a mixin RDF type and is emitted
before tool execution via an on_action callback in react().
This ensures sub-traces (e.g. GraphRAG) appear after their
parent Analysis in the streaming event order.
Observation becomes a standalone prov:Entity with tg:Observation
type, emitted after tool execution. The linear DAG chain runs
through Observation — subsequent iterations and the Conclusion
derive from it, not from the Analysis.
message_id is populated on streaming AgentResponse for thought
and observation chunks, using the provenance URI of the entity
being built. This lets clients group streamed chunks by entity.
Wire changes:
- provenance/agent.py: Add ToolUse type, new
agent_observation_triples(), remove observation from iteration
- agent_manager.py: Add on_action callback between reason() and
tool execution
- orchestrator/pattern_base.py: Split emit, wire message_id,
chain through observation URIs
- orchestrator/react_pattern.py: Emit Analysis via on_action
before tool runs
- agent/react/service.py: Same for non-orchestrator path
- api/explainability.py: New Observation class, updated dispatch
and chain walker
- api/types.py: Add message_id to AgentThought/AgentObservation
- cli: Render Observation separately, [analysis: tool] labels
* Updates for agent API with streaming support
* Added tg-dump-queues tool to dump Pulsar queues to a log
* Updated tg-invoke-agent, incremental output
* Queue dumper CLI - might be useful for debug
* Updating for tests
* Implement KG extraction agent (kg-extract-agent)
* Using ReAct framework (agent-manager-react)
* ReAct manager had an issue when emitting JSON, which conflicts which ReAct manager's own JSON messages, so refactored ReAct manager to use traditional ReAct messages, non-JSON structure.
* Minor refactor to take the prompt template client out of prompt-template so it can be more readily used by other modules. kg-extract-agent uses this framework.
* Updated CLI invocation and config model for tools and mcp
* CLI anomalies
* Tweaked the MCP tool implementation for new model
* Update agent implementation to match the new model
* Fix agent tools, now all tested
* Fixed integration tests
* Fix MCP delete tool params
