CHANGELOG.md

# Changelog

All notable changes to ACE Framework will be documented in this file.

The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).

## [Unreleased]

## [0.12.0] - 2026-05-06

### Added
- **Cross-trace generalization gate** for the SkillManager — four-criterion check
  (≥3 instances across ≥2 domains, named slot, no API-specific params in the
  action, verifiable runtime trigger) that constrains when SM may write a broad
  skill subsuming existing narrow ones. Backed by [skill_generalization.md](ace-eval/research/skill_generalization.md)
  (14 cited sources).
- **Action-equivalence rule** for within-run skill writing — splits on action,
  not on trigger surface. Prevents over-decomposition of structurally identical
  rules.
- **Atomicity rule** in `insight` formatting — one trigger + one action per
  skill, with explicit good/bad shape examples in the prompt.
- **Insight format guidance** in the SM prompt sourced from the in-context-
  learning research doc ([icl_skill_formatting.md](ace-eval/research/icl_skill_formatting.md)) — 15-50 word cap, imperative
  voice, positive framing default, examples only for format/shape rules.
- **Evidence-only tagging** — SM tags only skills the reflection actually
  implicates, instead of iterating over every injected_skill_id.
- **Broaden-via-comparison rule** for UPDATE — when two skills target the same
  root cause in different niches, broaden `issue` rather than adding a duplicate.
- **Prompt caching for SM** via `CachePoint(ttl="5m")` mirroring RR's caching;
  cache_read/write tokens forwarded in run metadata.

- **SM behavior spec + harness** — `ace-eval/scripts/sm_behavior_check.py`,

  `sm_iterative_check.py`, `sm_stability_check.py` and matching scenario

  fixtures cover replay stability, convergence, scope expansion, and the

  below-threshold gate boundary.



### Changed

- **`update_skills` signature** — `source` is now optional; `SkillbookView`

  was dropped from the parameter list (callers pass the real `Skillbook`

  directly).

- **Hard removal cap removed** — SM no longer auto-removes skills whose

  `harmful_count >= 3`. Heavily-used skills can legitimately accumulate
  harmful tags without being net-negative; REMOVE now requires explicit
  reflection evidence.
- **TauBench evaluator** — `evaluation_type=ALL_WITH_NL_ASSERTIONS` on both
  `run_task` and `run_tasks` call sites in
  `ace-eval/src/ace_eval/e2e/benchmarks/tau_bench.py`. Retail (and any future
  benchmark with `NL_ASSERTION` in `reward_basis`) now produces real reward
  numbers instead of crashing on every task during reward computation.

### Removed
- **Skillbook v1 legacy aliases** on `Skill` and `UpdateOperation` — v2 schema
  is now the only schema.

## [0.11.0] - 2026-04-29

### Added
- **`RecursiveAgent` core abstraction** — extracted from RR into `ace/core/recursive_agent.py`; provides a generic recursive PydanticAI agent with sandbox, microcompaction, default tool set, and depth-aware sub-agent registration. Reusable across roles beyond the Reflector.
- **Skillbook v2 schema** — full rewrite of `ace/core/skillbook.py` with section-grouped storage, richer `InsightSource` provenance, and BM25-backed retrieval (`rank-bm25` runtime dependency).
- **Agentic SkillManager** — `SkillManager` rewritten as a tool-calling loop (`ace/implementations/sm_tools.py`). Provenance is now populated by the SkillManager agent directly rather than a dedicated step.
- **RR skillbook tools for the Reflector** — Reflector can introspect and propose updates to the skillbook from inside the recursive loop.
- **Anthropic prompt caching enabled by default** for RR agents; `cache_read_tokens` and `cache_write_tokens` are forwarded in run metadata for cost accounting.
- **Logfire spans around recursive agent sessions** for end-to-end observability of nested RR runs.
- **Online / offline mode** in the ACE runner.
- **`nest-asyncio`** added to the dev extra to support nested loops in notebooks and live test scripts.

### Changed
- **RR collapsed into a single `RRStep`** — the orchestrator/worker split, batch machinery, and `AttachInsightSourcesStep` have been removed. RR now runs as a true recursive loop with depth-bounded sub-agent delegation and microcompaction of stale tool results.
- **Reflector prompts** simplified, deduplicated, and made input-agnostic; added early-skillbook-skim and parallel-tool guidance.
- **`record_observation` tool renamed to `think`** to clarify it is a scratch reasoning channel, not persistent storage.

- **Native evidence summaries** are produced inside RR before final synthesis.

- **Skillbook prompt format is now markdown** — `Skillbook.as_prompt()` returns a section-grouped markdown list instead of TOON. The `python-toon` dependency has been dropped.

- **`metered_model` and `sandbox`** moved from `ace/rr/` into `ace/core/` to reflect their cross-role use.
- **Pytest defaults** — `uv run pytest` now excludes `integration` and `requires_api` markers by default; coverage flags removed from `addopts` (run with `--cov` explicitly when needed).
- **Observability** — `tool_arguments` and `tool_response` are no longer scrubbed by the Logfire callback so tool I/O remains inspectable.

### Removed
- `ace/rr/` legacy package layout (`agent.py`, `runner.py`, `trace_context.py`, `message_trimming.py`, batch helpers). Functionality is now in `ace/core/recursive_agent.py` and `ace/implementations/rr/`.
- `AttachInsightSourcesStep` and its pipeline wiring — provenance is attached by the SkillManager agent.
- `python-toon` runtime dependency.
- TAG handling from the SkillManager.
- Citation scanning from the Reflector.

## [0.10.0] - 2026-04-13

### 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 — 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. cross-account Bedrock with 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.



### Notes

- Back-compat: existing `RRStep(model="...")` callers continue to work unchanged. The widened type signature is additive.



## [0.9.4] - 2026-04-11



### Added

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



## [0.9.3] - 2026-04-01



### Added

- **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

## [0.9.2] - 2026-03-31

### Added
- **Insight source provenance** — `InsightSource` typed model captures the origin of each skillbook update (trace ID, sample question, epoch/step, reflection summary, integration metadata); provenance is now populated by the SkillManager agent directly
- **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

## [0.9.1] - 2026-03-26

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

## [0.9.0] - 2026-03-26

### 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)

## [0.8.8] - 2026-03-17

### Added
- **Pipeline hooks & cancellation** — `PipelineHook` protocol and `CancellationToken` for observing and controlling pipeline execution
- **Kayba pipeline skills for Claude Code** — 7-stage dynamic evaluation pipeline that generates custom benchmarks tailored to your agent's domain. Instead of static test suites, the skills analyze your API, build domain-aware metrics and rubrics, create action plans, and run human-in-the-loop validation — all as composable Claude Code skills
- **`kayba setup` command** — one command to install the full evaluation skill pipeline into your `.claude/skills/` directory, ready to use inside Claude Code out of the box

### Docs
- Documented `kayba setup` skills installation

### Try it free
**7-day free trial** — Try the full Kayba evaluation pipeline on our hosted solution with zero setup. Sign up at [kayba.ai](https://kayba.ai) and run `kayba setup` to start building dynamic evals for your agents today.

## [0.8.7] - 2026-03-17

### Added
- **Improved Opik trace naming** — traces now display the question text (first 80 chars) instead of generic names like "ace_pipeline" or "rr_reflect"
- **Thread ID support for Opik** — `OpikStep` and `RROpikStep` accept an optional `thread_id` parameter for grouping related traces

## [0.8.5] - 2026-03-04

### Added
- **Self-contained RR module** (`ace/rr/`) — sandbox, subagent, trace_context, config, code_extraction, message_trimming extracted from `ace/reflector/` into a standalone package

- **v5.6 prompt promoted as default** — new prompt evolution (v4 → v5.1–v5.6) for the `ace` RR pipeline

- **`build_steps()` API** — all runners gain a `build_steps()` classmethod for pipeline customization
- **Shared `CallBudget`** — single budget instance shared across RR pipeline steps
- **ACE MCP server (optional)** — stdio MCP server in `ace.integrations.mcp` with tools: `ace.ask`, `ace.learn.sample`, `ace.learn.feedback`, `ace.skillbook.get`, `ace.skillbook.save`, `ace.skillbook.load`
- **Session-scoped state management** — in-memory `session_id` registry with TTL cleanup and per-session async locking
- **MCP packaging + CLI** — optional `mcp` extra and `ace-mcp` entrypoint
- **MCP docs and demo client** — integration guide and stdio client example
- **Composing pipelines guide** — new `docs/guides/composing-pipelines.md`
- **RR examples** — `rr_demo.py`, `rr_opik_demo.py`, `compose_custom_pipeline.py`

### Changed
- **RR backward-compat shims** — original `ace/reflector/` files now re-export from `ace.rr` (no duplication)
- **`RRStep` dual protocol** — implements both `StepProtocol` and `ReflectorLike`
- **Sandbox hardening** — hardened `getattr` in sandbox execution environment
- **Opik made opt-in** — moved `opik` from hard dependency to `observability` extra
- **Safety controls** — runtime request limits (`max_prompt_chars`, `max_samples_per_call`) and optional root-bound path enforcement for save/load via `ACE_MCP_SKILLBOOK_ROOT`
- **Schema-driven validation** — MCP request/response models aligned to `specs/002-ace-mcp-server/contracts/tool-schemas.md`
- **`learn_from_feedback` routed through pipeline** — feedback learning now uses the pipeline engine

### Testing
- Added MCP test suite: models, registry, handlers, and server registration/startup smoke tests
- Added optional-dependency boundary checks for the MCP integration
- RR steps at 94%, sandbox at 92%, runner at 74%, MCP models at 100%

## [0.8.4] - 2026-02-27

### Added
- **OpenClaw integration** — learn from OpenClaw session transcripts (JSONL) via new `OpenClawToTraceStep` and `LoadTracesStep` pipeline steps (#86)
- **ExportSkillbookMarkdownStep** — export skillbook to markdown file
- OpenClaw example script and integration docs

## [0.8.3] - 2026-02-21

### Added
- **Pipeline engine** — generic pipeline framework with branching, async boundaries, and parallel execution (#78)
- **Trace passthrough** — `_build_traces()` helper and raw trace data passed to RecursiveReflector sandbox

## [0.8.2] - 2026-02-18

### Added
- **RecursiveReflector None-response guard** — gracefully handles empty/None LLM responses (e.g. from Gemini) with retry prompt instead of crashing
- **`LiteLLMClient.complete_messages()`** — native multi-turn completion that preserves structured message lists



## [0.8.1] - 2026-02-18



### Added

- **Insight source tracing** — `InsightSource` dataclass tracks skill provenance (epoch, sample, trace refs, error identification, learning text)

- **Sample.id** promoted to first-class field with UUID auto-generation

- **Skillbook query API** — `source_map()`, `source_summary()`, `source_filter()` for skill lineage

- Insight sources wired through `OfflineACE`, `OnlineACE`, and async learning pipelines

- `UpdateOperation.learning_index` for linking operations to reflector learnings

- Bedrock e2e example (`examples/litellm/bedrock_insight_source_test.py`)

- `docs/INSIGHT_SOURCES.md` guide



## [0.8.0] - 2026-02-17



### Added

- **Recursive reflector** with sandboxed code execution for validation

- **TAU-bench integration** with config-driven YAML profiles, prompt sweep, capture/replay, and label support

- **v3 prompt templates** for agent, reflector, and skill manager roles

- **Trace context module** exposing agent system prompt and execution context to reflector



### Fixed

- Opik cloud mode support when `OPIK_API_KEY` is set

- Bedrock/SageMaker API key lookup skipped for managed providers

- Reflector trace quality improvements (user messages, turn separators)



### Changed

- v3 prompts set as default prompt version

- Reflector now includes agent system prompt in trace context



## [0.7.3] - 2026-02-04



### Added

- ACE learning for Claude Code via `/ace-learn` (transcript-based learning and skillbook updates).

- CLI patching to minimize Claude Code system prompt overhead for learning runs.



### Fixed

- Claude Code transcript parsing for feedback and last-prompt extraction edge cases.



### Changed

- Unified agent guidance into `AGENTS.md` with `CLAUDE.md` symlink.



## [0.7.0] - 2025-12-04



### ⚠️ Breaking Changes

- **Complete terminology rename** - Playbook → Skillbook, Bullet → Skill

  - `Playbook` → `Skillbook`

  - `Bullet` → `Skill`

  - `Generator` → `Agent`

  - `Curator` → `SkillManager`

  - `OfflineAdapter` → `OfflineACE`

  - `OnlineAdapter` → `OnlineACE`

  - `DeltaOperation` → `UpdateOperation`

  - `DeltaBatch` → `UpdateBatch`

  - **Migration**: Update imports and method calls to use new names

  - **JSON files**: Change `"bullets"` key to `"skills"` in saved skillbooks



### Added

- **Deduplication consolidation_operations field** - SkillManagerOutput now properly captures consolidation operations from LLM responses

### Fixed
- **Deduplication not working** - Added `consolidation_operations` field to SkillManagerOutput Pydantic model. Previously, Instructor was silently dropping these operations.

## [0.5.0] - 2025-11-20

### ⚠️ Breaking Changes
- **Playbook format changed to TOON (Token-Oriented Object Notation)**
  - `Playbook.as_prompt()` now returns TOON format instead of markdown
  - **Reason**: 16-62% token savings for improved scalability and reduced inference costs
  - **Migration**: No action needed if using playbook with Generator/Curator/Reflector
  - **Debugging**: Use `playbook._as_markdown_debug()` or `str(playbook)` for human-readable output
  - **Details**: Uses tab delimiters and excludes internal metadata (created_at, updated_at)

### Added
- **ACELiteLLM integration** - Simple conversational agent with automatic learning
- **ACELangChain integration** - Wrap LangChain Runnables with ACE learning
- **Custom integration pattern** - Wrap ANY agentic system with ACE learning
  - Base utilities in `ace/integrations/base.py` with `wrap_playbook_context()` helper
  - Complete working example in `examples/custom_integration_example.py`
  - Integration Pattern: Inject playbook → Execute agent → Learn from results
- **Integration exports** - Import ACEAgent, ACELiteLLM, ACELangChain from `ace` package root
- **TOON compression for playbooks** - 16-62% token reduction vs markdown
- **Citation-based tracking** - Strategies cited inline as `[section-00001]`, auto-extracted from reasoning
- **Enhanced browser traces** - Full execution logs (2200+ chars) passed to Reflector
- **Test coverage** - Improved from 28% to 70% (241 tests total)

### Changed
- **Renamed SimpleAgent → ACELiteLLM** - Clearer naming for conversational agent integration
- `Playbook.__str__()` returns markdown (TOON reserved for LLM consumption via `as_prompt()`)

### Fixed
- **Browser-use trace integration** - Reflector now receives complete execution traces
  - Fixed initial query duplication (task appeared in both question and reasoning)
  - Fixed missing trace data (reasoning field now contains 2200+ chars vs 154 chars)
  - Fixed screenshot attribute bug causing AttributeError on step.state.screenshot
  - Fixed invalid bullet ID filtering - hallucinated/malformed citations now filtered out
  - Added comprehensive regression tests to catch these issues
  - Impact: Reflector can now properly analyze browser agent's thought process
  - Test coverage improved: 69% → 79% for browser_use.py

- Prompt v2.1 test assertions updated to match current format

- All 206 tests now pass (was 189)



## [0.4.0] - 2025-10-26



### Added

- **Production Observability** with Opik integration

  - Enterprise-grade monitoring and tracing

  - Automatic token usage and cost tracking for all LLM calls

  - Real-time cost monitoring via Opik dashboard

  - Graceful degradation when Opik is not installed

- **Browser Automation Demos** showing ACE vs baseline performance

  - Domain checker demo with learning capabilities

  - Form filler demo with adaptive strategies

  - Side-by-side comparison of baseline vs ACE-enhanced automation

- Support for UV package manager (10-100x faster than pip)

  - Added uv.lock for reproducible builds

  - UV-specific installation and development instructions

- Improved documentation structure with multiple guides

  - QUICK_START.md for 5-minute quickstart
  - API_REFERENCE.md for complete API documentation

  - PROMPT_ENGINEERING.md for advanced techniques
  - SETUP_GUIDE.md for development setup

  - TESTING_GUIDE.md for testing procedures
- Optional dependency groups for modular installation
  - `observability` for Opik integration
  - `demos` for browser automation examples
  - `langchain` for LangChain support
  - `transformers` for local model support
  - `dev` for development tools
  - `all` for all features combined

### Changed
- **Replaced explainability module with observability**
  - Removed empty ace/explainability directory
  - Migrated to production-grade Opik monitoring
  - Updated all documentation to reflect this change
- Improved Python version requirements consistency (3.12 everywhere)
- Enhanced README with clearer examples and installation options
- Reorganized examples directory for better discoverability
- Updated CLAUDE.md with comprehensive codebase guidance

### Fixed
- Package configuration in pyproject.toml
- Documentation references to non-existent explainability module
- Python version inconsistencies across documentation files

### Removed
- Empty ace/explainability module (replaced by observability)
- Outdated references to explainability features in documentation

## [0.3.0] - 2025-10-16

### Added
- **Experimental v2 Prompts** with state-of-the-art prompt engineering
  - Confidence scoring at bullet and answer levels
  - Domain-specific variants for math and code generation
  - Hierarchical structure with identity headers and metadata
  - Concrete examples and anti-patterns for better guidance
  - PromptManager for version control and A/B testing
- Comprehensive prompt engineering documentation (`docs/PROMPT_ENGINEERING.md`)
- Advanced examples demonstrating v2 prompts (`examples/advanced_prompts_v2.py`)
- Comparison script for v1 vs v2 prompts (`examples/compare_v1_v2_prompts.py`)
- Playbook persistence with `save_to_file()` and `load_from_file()` methods
- Example demonstrating playbook save/load functionality (`examples/playbook_persistence.py`)
- py.typed file for PEP 561 type hint support
- Mermaid flowchart visualization in README showing ACE learning loop

### Changed
- Enhanced docstrings with comprehensive examples throughout codebase
- Improved README with v2 prompts section and visual diagrams
- Updated formatting to comply with Black code style

### Fixed
- README incorrectly referenced non-existent docs/ directory
- Test badge URL in README (test.yml → tests.yml)
- Code formatting issues detected by GitHub Actions

## [0.2.0] - 2025-10-15

### Added
- LangChain integration via `LangChainLiteLLMClient` for advanced workflows
- Router support for load balancing across multiple model deployments
- Comprehensive example for LangChain usage (`examples/langchain_example.py`)
- Optional installation group: `pip install ace-framework[langchain]`
- PyPI badges and Quick Links section in README
- CHANGELOG.md for version tracking

### Fixed
- Parameter filtering in LiteLLM and LangChain clients (refinement_round, max_refinement_rounds)

- GitHub Actions workflow using deprecated artifact actions v3 → v4



### Changed

- Improved README with better structure and badges

- Updated .gitignore to exclude build artifacts and development files



### Removed

- Unnecessary development files from repository



## [0.1.1] - 2025-10-15



### Fixed

- GitHub Actions workflow for PyPI publishing

- Updated artifact upload/download actions from v3 to v4



## [0.1.0] - 2025-10-15



### Added

- Initial release of ACE Framework

- Core ACE implementation based on paper (arXiv:2510.04618)

- Three-role architecture: Generator, Reflector, and Curator

- Playbook system for storing and evolving strategies

- LiteLLM integration supporting 100+ LLM providers

- Offline and Online adaptation modes

- Async and streaming support

- Example scripts for quick start

- Comprehensive test suite

- PyPI packaging and GitHub Actions CI/CD



### Features

- Self-improving agents that learn from experience

- Delta operations for incremental playbook updates

- Support for OpenAI, Anthropic, Google, and more via LiteLLM

- Type hints and modern Python practices

- MIT licensed for open source use



[0.9.4]: https://github.com/Kayba-ai/agentic-context-engine/compare/v0.9.3...v0.9.4

[0.9.3]: https://github.com/Kayba-ai/agentic-context-engine/compare/v0.9.2...v0.9.3

[0.9.2]: https://github.com/Kayba-ai/agentic-context-engine/compare/v0.9.1...v0.9.2

[0.9.1]: https://github.com/Kayba-ai/agentic-context-engine/compare/v0.9.0...v0.9.1

[0.9.0]: https://github.com/Kayba-ai/agentic-context-engine/compare/v0.8.9...v0.9.0

[0.8.8]: https://github.com/Kayba-ai/agentic-context-engine/compare/v0.8.7...v0.8.8

[0.8.7]: https://github.com/Kayba-ai/agentic-context-engine/compare/v0.8.6...v0.8.7

[0.8.5]: https://github.com/Kayba-ai/agentic-context-engine/compare/v0.8.4...v0.8.5

[0.8.4]: https://github.com/Kayba-ai/agentic-context-engine/compare/v0.8.3...v0.8.4

[0.8.3]: https://github.com/Kayba-ai/agentic-context-engine/compare/v0.8.2...v0.8.3

[0.8.2]: https://github.com/Kayba-ai/agentic-context-engine/compare/v0.8.1...v0.8.2

[0.8.1]: https://github.com/Kayba-ai/agentic-context-engine/compare/v0.8.0...v0.8.1

[0.8.0]: https://github.com/Kayba-ai/agentic-context-engine/compare/v0.7.3...v0.8.0

[0.7.3]: https://github.com/Kayba-ai/agentic-context-engine/compare/v0.7.0...v0.7.3

[0.7.0]: https://github.com/Kayba-ai/agentic-context-engine/compare/v0.6.0...v0.7.0

[0.6.0]: https://github.com/Kayba-ai/agentic-context-engine/compare/v0.5.0...v0.6.0

[0.5.0]: https://github.com/Kayba-ai/agentic-context-engine/compare/v0.4.0...v0.5.0

[0.4.0]: https://github.com/Kayba-ai/agentic-context-engine/compare/v0.3.0...v0.4.0

[0.3.0]: https://github.com/Kayba-ai/agentic-context-engine/compare/v0.2.0...v0.3.0

[0.2.0]: https://github.com/Kayba-ai/agentic-context-engine/compare/v0.1.1...v0.2.0

[0.1.1]: https://github.com/Kayba-ai/agentic-context-engine/compare/v0.1.0...v0.1.1

[0.1.0]: https://github.com/Kayba-ai/agentic-context-engine/releases/tag/v0.1.0