@kayba_ai/openclaw-tracing v0.1.1

Initial release of the OpenClaw tracing plugin.

Registers as an OpenClaw plugin (`kayba-tracing`) and ships one structured Kayba trace per agent turn — user message, full LLM input/output with thinking, tool calls, final reply — with sessionId, userId, request/response previews, and folder tagging. No client SDK changes required.

Capture knobs (`plugins.entries.kayba-tracing.config`):

• `captureSystemPrompt`: capture system prompt once per session (default: true)
• `captureHistory`: `delta` (default, per-session cursor — eliminates N² bloat) | `full` | `none`
• `maxAttributeBytes`: per-attribute truncation (default: 64 KiB)

Recursively unwraps OpenClaw's pre-stringified `content` fields so the trace dashboard renders clean JSON instead of multi-level escaped strings.

See `sdk/openclaw/README.md` for setup.

@kayba_ai/tracing 0.10.0

TypeScript SDK release. (No changes to ace-framework or kayba-tracing Python in this release.)

Added

• kayba.setSession(id) / getSession() — auto-injects mlflow.trace.session metadata on every subsequent trace, so multiple traces produced by the same agent run can be filtered together in the dashboard.
• kayba.setUser(id) / getUser() — auto-injects mlflow.trace.user metadata for per-user attribution.
• kayba.updateTrace({ tags, metadata }) — escape hatch for attaching per-call values (e.g. tool call ids) to the active trace.

Changed

• Internal injectFolderTag renamed to injectKaybaContext; folder, session, and user are now emitted in a single updateCurrentTrace call.
• package-lock.json corrected (was stuck on a stale @kayba/tracing@0.1.0 lockfile name with stray openai/dotenv deps).

Back-compat

Additive only — existing callers using configure, trace, startSpan, setFolder are unchanged.

v0.10.0

Added

• Usage metering hook — RecursiveConfig.usage_callback: (RequestUsage, model_id) -> None fires once per pydantic-ai model request (orchestrator turns, sub-agent runs, tool-call follow-ups). Implemented via ace.rr.MeteredModel, a pydantic_ai.models.wrapper.WrapperModel subclass, so metering lives at the framework's own model boundary — one firing site, no per-call-site plumbing. Callback exceptions are caught and logged so metering never crashes the pipeline.
• Pre-built model instance support — RRStep, create_rr_agent, create_sub_agent, and RecursiveConfig.subagent_model now accept either a model-id string or a pre-built pydantic_ai.models.Model instance. Enables callers that need a custom provider (e.g. a Bedrock model carrying STS-assumed credentials) to inject a fully-configured model rather than resolving from a string.
• Sub-agent model_settings — create_sub_agent now threads an explicit ModelSettings parameter into its PydanticAgent constructor.

Back-compat

Existing RRStep(model="...") callers are unchanged. The widened type signatures are additive.

v0.9.7

TypeScript SDK

v0.9.5

What's new

• TypeScript tracing SDK (@kayba_ai/tracing) — instrument Node.js agents and send traces to Kayba, mirroring the Python SDK API
• Standalone Python tracing package (kayba-tracing) — can be installed independently without the full ace-framework
• ace.tracing continues to work as before (re-exports from kayba-tracing)
• CI publishes all three packages (ace-framework, kayba-tracing, @kayba_ai/tracing) on release

v0.9.4

• Kayba tracing SDK — ace.tracing module wraps MLflow tracing with Kayba-native configuration, folder organization, and input sanitization (pip install ace-framework[tracing])

Full Changelog: v0.9.3...v0.9.4

v0.9.3

• Structured design docs — split ACE_DESIGN.md into architecture, reference, and decisions docs under docs/design/
• Simplified Skill model — removed unused tag counters (helpful/harmful/neutral) and TagStep from the pipeline
• Cleaner InsightSource provenance — restored error_identification and learning_text fields

Full Changelog: v0.9.2...v0.9.3

v0.9.2

What's changed

Added

• Insight source provenance — InsightSource typed model captures the origin of each skillbook update (trace ID, sample question, epoch/step, reflection summary, integration metadata); AttachInsightSourcesStep automatically enriches UpdateBatch operations with provenance and is wired into the default learning tail
• Claude SDK step — ClaudeSDKStep integration for running Claude Code sub-agents from within ACE pipelines
• RR sub-agent code execution — Recursive Reflector can now delegate to code-execution sub-agents at runtime
• RR raw trace batch helpers — build_raw_trace_batches and related runtime utilities for feeding raw traces directly into the RR pipeline

Fixed

• Logfire scrubbing — added scrubbing callback to stop Logfire over-redacting trace content (reasoning, answers, messages now visible in Logfire UI)
• RR combined-batch normalization — fixed ordering/deduplication of combined task batches in multi-sample runs

Docs

• Logfire query API guide clarifications
• MCP client setup guide and compatibility tests
• Design docs updated to reflect insight source provenance model

Full changelog: https://github.com/kayba-ai/agentic-context-engine/blob/main/CHANGELOG.md

v0.9.1

Fixed

• CLI packaging — include .md data files in wheel so kayba setup and skill install work on pip/uv-installed packages

Full Changelog: v0.9.0...v0.9.1

v0.9.0

Added

• PydanticAI migration — ACE roles (Agent, Reflector, SkillManager) rebuilt on PydanticAI agents with structured output, replacing the legacy role system
• Recursive Reflector — PydanticAI-powered trace analysis agent with sandboxed code execution, sub-agent delegation, and working memory (save_notes tool)
• Kayba CLI — full hosted API client with trace upload/management, interactive run, insights, prompts, batch processing, materialization, and integration commands (kayba entry point)

Full Changelog: v0.8.9...v0.9.0