diff --git a/.github/workflows/code_checks.yml b/.github/workflows/code_checks.yml
new file mode 100644
index 0000000..83a5d5d
--- /dev/null
+++ b/.github/workflows/code_checks.yml
@@ -0,0 +1,57 @@
+name: code checks
+permissions:
+ contents: read
+ pull-requests: write
+
+on:
+ push:
+ branches:
+ - main
+ paths:
+ - .pre-commit-config.yaml
+ - .github/workflows/code_checks.yml
+ - '**.py'
+ - uv.lock
+ - pyproject.toml
+ pull_request:
+ branches:
+ - main
+ paths:
+ - .pre-commit-config.yaml
+ - .github/workflows/code_checks.yml
+ - '**.py'
+ - uv.lock
+ - pyproject.toml
+
+jobs:
+ code-checks:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v6.0.2
+
+ - name: Install uv
+ uses: astral-sh/setup-uv@v8.1.0
+ with:
+ version: "0.9.11"
+ enable-cache: true
+
+ - name: Set up Python
+ uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405
+ with:
+ python-version-file: ".python-version"
+
+ - name: Install the project
+ run: uv sync --all-extras --dev
+
+ - name: Run pre-commit checks
+ run: |
+ source .venv/bin/activate
+ pre-commit run --all-files
+
+ - name: Seed pip into venv
+ run: uv pip install pip
+
+ - name: pip-audit
+ uses: pypa/gh-action-pip-audit@v1.1.0
+ with:
+ virtual-environment: .venv/
diff --git a/.github/workflows/unit_tests.yml b/.github/workflows/unit_tests.yml
new file mode 100644
index 0000000..4e7abbb
--- /dev/null
+++ b/.github/workflows/unit_tests.yml
@@ -0,0 +1,58 @@
+name: unit tests
+permissions:
+ contents: read
+ pull-requests: write
+
+on:
+ push:
+ branches:
+ - main
+ paths:
+ - .pre-commit-config.yaml
+ - .github/workflows/unit_tests.yml
+ - .github/workflows/code_checks.yml
+ - '**.py'
+ - uv.lock
+ - pyproject.toml
+ pull_request:
+ branches:
+ - main
+ paths:
+ - .pre-commit-config.yaml
+ - .github/workflows/unit_tests.yml
+ - .github/workflows/code_checks.yml
+ - '**.py'
+ - uv.lock
+ - pyproject.toml
+
+jobs:
+ unit-tests:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v6.0.2
+
+ - name: Install uv
+ uses: astral-sh/setup-uv@v8.1.0
+ with:
+ version: "0.9.11"
+ enable-cache: true
+
+ - name: Set up Python
+ uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405
+ with:
+ python-version-file: ".python-version"
+
+ - name: Install the project
+ run: uv sync --all-extras --dev
+
+ - name: Run unit tests
+ run: |
+ uv run pytest --cov src/motive --cov-report=xml tests
+
+ - name: Upload coverage to Codecov
+ uses: codecov/codecov-action@57e3a136b779b570ffcdbf80b3bdc90e7fab3de2
+ with:
+ token: ${{ secrets.CODECOV_TOKEN }}
+ slug: VectorInstitute/motive
+ fail_ci_if_error: false
+ verbose: true
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..db14842
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,10 @@
+__pycache__/
+*.py[cod]
+*.egg-info/
+.venv/
+dist/
+.coverage
+coverage.xml
+.pytest_cache/
+.mypy_cache/
+.ruff_cache/
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 0000000..6dec687
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,60 @@
+repos:
+ - repo: https://github.com/pre-commit/pre-commit-hooks
+ rev: v6.0.0
+ hooks:
+ - id: trailing-whitespace
+ - id: check-ast
+ - id: check-builtin-literals
+ - id: check-docstring-first
+ - id: check-executables-have-shebangs
+ - id: debug-statements
+ - id: end-of-file-fixer
+ - id: mixed-line-ending
+ args: [--fix=lf]
+ - id: fix-byte-order-marker
+ - id: check-merge-conflict
+ - id: check-symlinks
+ - id: detect-private-key
+ - id: check-yaml
+ args: [--unsafe]
+ - id: check-toml
+
+ - repo: https://github.com/astral-sh/uv-pre-commit
+ rev: 0.9.26
+ hooks:
+ - id: uv-lock
+
+ - repo: https://github.com/astral-sh/ruff-pre-commit
+ rev: 'v0.15.8'
+ hooks:
+ - id: ruff-check
+ args: [--fix, --exit-non-zero-on-fix]
+ types_or: [python, jupyter]
+ - id: ruff-format
+ types_or: [python, jupyter]
+
+ - repo: https://github.com/pre-commit/mirrors-mypy
+ rev: v1.19.1
+ hooks:
+ - id: mypy
+ entry: .venv/bin/python -m mypy --config-file pyproject.toml
+ language: system
+ types: [python]
+ exclude: "tests|examples"
+
+ - repo: https://github.com/crate-ci/typos
+ rev: v1.32.0
+ hooks:
+ - id: typos
+
+ci:
+ autofix_commit_msg: |
+ [pre-commit.ci] Add auto fixes from pre-commit.com hooks
+
+ for more information, see https://pre-commit.ci
+ autofix_prs: true
+ autoupdate_branch: ''
+ autoupdate_commit_msg: '[pre-commit.ci] pre-commit autoupdate'
+ autoupdate_schedule: weekly
+ skip: [mypy]
+ submodules: false
diff --git a/DESIGN.md b/DESIGN.md
new file mode 100644
index 0000000..7bec5bb
--- /dev/null
+++ b/DESIGN.md
@@ -0,0 +1,263 @@
+# Design Doc: `motive` — Agent Decision Explainability Layer
+
+**Status:** Draft
+**Author:** Amrit Krishnan
+**Date:** 2026-06-04
+
+---
+
+## 1. Problem
+
+AI agent observability platforms (Langfuse, LangSmith, Arize Phoenix, MLflow, etc.) tell you *what* an agent did — which tools were called, in what order, with what inputs and outputs. They do not tell you *why* the agent made each decision.
+
+When an agent escalates a support ticket, calls a risky API, or selects one branch over another, developers and operators have no structured, queryable way to understand the reasoning. This makes debugging slow, audits manual, and trust low.
+
+---
+
+## 2. Goal
+
+Build a Python library that computes empirically-grounded, machine-readable explanations for AI agent decisions and emits them as OpenTelemetry-compatible telemetry. The explanations cover:
+
+- **Feature attribution (saliency)** — which parts of the input context drove the decision, and how much.
+- **Counterfactual probes** — minimal input changes that would have flipped the decision.
+
+These are emitted as OTEL span attributes and events so they flow into existing backends (Langfuse, MLflow Tracing, any OTEL collector) without new infrastructure.
+
+> **Note on structured reasoning:** model self-reported rationales ("I chose this tool because...") are not used as an explanation method. Anthropic's interpretability research (introspection paper, NLA paper, persona vectors) shows that model self-reports are unreliable ~80% of the time, can hallucinate, and often conceal internal states. Saliency and counterfactuals are empirical — they test the model's actual input-output behaviour — and are the only methods motive relies on.
+
+---
+
+## 3. Non-Goals (MVP)
+
+- No model self-reported rationales or chain-of-thought extraction.
+- No mechanistic interpretability (circuits, SAEs, neuron attribution).
+- No tight coupling to a specific agent framework (LangGraph, AutoGen, etc.).
+- No automatic policy enforcement or risk scoring — explanations are data, not decisions.
+- No new UI from scratch — the reference UI is a pluggable component, not a product.
+
+---
+
+## 4. Environmental Scan
+
+**Verdict: YES — genuine gap, worth building.**
+
+The agent observability market is crowded at the *execution trace* layer. No production tool covers the *decision explanation* layer.
+
+| Category | Representative Tools | Gap |
+|---|---|---|
+| Agent tracing / observability | Langfuse, LangSmith, Arize Phoenix, Braintrust, Opik, W&B Weave, MLflow | Capture what happened; no saliency, no counterfactuals on spans |
+| OTEL AI standards | OpenInference, OpenLLMetry, OTEL GenAI Semantic Conventions | Standardize span shape and cardinality; no attributes for input importance or counterfactuals |
+| XAI for AI models | SHAP, LIME, Integrated Gradients, AgentSHAP (2025) | Methods exist in isolation; none integrated into observability pipelines or emitting OTEL |
+| Counterfactuals for agents | Research papers (ICLR 2026, arXiv 2506.02946) | Theoretically mature; no production implementation |
+| Agent explainability SDKs | None found | Direct gap |
+
+The 2026 research literature explicitly names this gap: "traditional tracing shows what happened, not why it was decided" (Elixir Data, 2026). AgentSHAP (arXiv 2512.12597) is the closest academic analogue but is not a production library and does not emit OTEL telemetry.
+
+---
+
+## 5. Scope and Assumptions
+
+**In scope (MVP)**
+
+- Single-agent and orchestrated multi-agent systems where each model call or tool invocation is logged as a span.
+- Model-agnostic: works with any model type (language, vision-language, interaction) via any OpenAI-compatible API endpoint.
+- Per-span explanations for tool calls and plan/routing decisions.
+- Python SDK with decorators for zero-boilerplate instrumentation.
+- OTEL emission to existing backends.
+
+**Out of scope (MVP)**
+
+- Multi-step causal chains across spans.
+- Automatic redaction / PII scrubbing (configurable hooks only).
+- UI product — reference React component only.
+- Circuit tracing / activation-based methods (require local model weights; not compatible with API-served models).
+
+**Assumptions**
+
+- The agent system calls models via an OpenAI-compatible endpoint.
+- Extra compute for explainability is acceptable: each explained span costs N+M additional model calls (N = number of segments, M = sentences in top segments). Sampled and async in prod.
+- The storage backend can accept extra OTEL span attributes and events.
+
+---
+
+## 6. Architecture
+
+```
+Agent Code
+ │
+ ├── @explain_tool_call / @explain_plan_decision (SDK decorators)
+ │ │
+ │ └── SaliencyEngine
+ │ ├── Pass 1 — segment-level occlusion
+ │ │ mask each segment → re-run → measure logprob drop
+ │ └── Pass 2 — sentence-level occlusion (top-k segments only)
+ │ mask each sentence → re-run → measure logprob drop
+ │
+ ├── CounterfactualProber (TODO)
+ │ generate minimal edits to top segments → re-run → record flips
+ │
+ └── TelemetryEmitter
+ └── OTEL SDK → existing backend (Langfuse / MLflow / collector)
+```
+
+**Components**
+
+1. **SDK** — Python library. Wraps model/tool calls via decorators. Computes and emits explanations.
+2. **SaliencyEngine** — hierarchical occlusion engine. Scores input segments by how much removing them drops the model's confidence in the original decision, using logprob drop as the primary signal with binary fallback.
+3. **CounterfactualProber** — takes top-importance segments, generates minimal edits, re-runs the decision, records flips as human-readable what-if statements.
+4. **Telemetry Emitter** — maps results to OTEL span attributes and events using the `agent.why.*` namespace.
+5. **Reference UI Component** — React component that renders a "Why?" panel from span JSON.
+
+---
+
+## 7. Explanation Unit: The Decision Span
+
+The unit of explanation is a *decision span* — any span where the agent makes a discrete choice.
+
+**Span types (MVP)**
+
+| Type | Triggered by | What is explained |
+|---|---|---|
+| `tool_call` | Agent selects and calls an external tool | Which input segments drove the tool choice |
+| `plan_decision` | Agent picks a branch, subagent, or subgoal | Which input segments drove the routing |
+
+Each decision span carries:
+- **Input context** — user message, agent state, retrieved documents, prior plan.
+- **Segments** — the input context pre-split into logical units (one per message, retrieved doc, tool result, etc.).
+- **Model output** — tool name + args, or chosen branch.
+- **Explanation** — saliency scores and counterfactuals, attached as OTEL attributes and events.
+
+---
+
+## 8. Explanation Methods
+
+### 8.1 Hierarchical Occlusion Saliency (primary method)
+
+**How it works:**
+
+The input context is split into *segments* — logical units such as the system prompt, each user message, each retrieved document, and each prior tool result. The engine runs two passes:
+
+**Pass 1 — segment level:**
+Each segment is masked (replaced with `[CONTENT REDACTED]`) one at a time. The decision call is re-run with the masked context. Importance is measured as the drop in log-probability of the original decision:
+
+```
+importance(i) = logprob(decision | full context)
+ − logprob(decision | context with segment i masked)
+```
+
+Higher importance = masking that segment makes the model less confident in its original choice. Falls back to binary (decision flipped = 1.0, unchanged = 0.0) when logprobs are unavailable.
+
+**Pass 2 — sentence level:**
+For the top-k segments from Pass 1 (importance > 0), the engine repeats the same procedure at sentence granularity within each segment, identifying the specific sentences that drove the decision.
+
+**Output:** a ranked list of `(segment, importance)` pairs at both granularity levels. The unit of attribution depends on modality: sentences/tokens for text, patches/regions for vision.
+
+**Cost:** Pass 1 = N model calls (N = segments, typically 4–10). Pass 2 = M calls per top segment (M = sentences, typically 2–6). Total ~10–30 calls per explained span.
+
+### 8.2 Counterfactual Probes (next to implement)
+
+Starting from the highest-importance segments identified in §8.1:
+1. Generate minimal perturbations: remove a phrase, swap a retrieved document, neutralize sentiment.
+2. Re-run the decision call.
+3. If the outcome changes (different tool or routing), record a human-readable description.
+
+Example: *"If 'failing for 3 days' is removed from the user message, the agent calls `send_retry_instructions` instead of `escalate_to_human`."*
+
+Store 1–3 counterfactuals per span.
+
+---
+
+## 9. Telemetry Schema (OTEL Extension)
+
+All fields attach to the existing decision span; no new spans are created.
+
+### Span Attributes (indexed, short strings)
+
+| Attribute | Type | Description |
+|---|---|---|
+| `agent.why.type` | string | `tool_call` or `plan_decision`. |
+| `agent.why.primary_factors` | JSON string | Top-k `{id, label, importance, decision_changed}` objects from Pass 1. |
+| `agent.why.sentence_factors` | JSON string | Top-k `{id, label, importance, decision_changed}` objects from Pass 2. |
+| `agent.why.counterfactuals` | JSON string | 1–3 `{description}` objects. |
+| `agent.explainability.methods` | JSON string | Methods applied, e.g. `["saliency","counterfactual"]`. |
+| `agent.explainability.version` | string | Semver of the explainability logic. |
+
+### Span Events (richer payloads)
+
+| Event name | Fields |
+|---|---|
+| `agent.why.saliency` | `{modality, pass, segments: [{id, label, importance, decision_changed, original_decision, masked_decision}]}` |
+| `agent.why.counterfactual_run` | `{original_input_hash, tested_variants, outcomes}` |
+
+---
+
+## 10. SDK API
+
+```python
+from motive import SaliencyEngine, Segment, SegmentLevel
+
+engine = SaliencyEngine(client=AsyncOpenAI(...), model="...", top_k=2)
+
+result = engine.explain(
+ messages=[...], # OpenAI-format message list
+ segments=[
+ Segment(id="system", content="...", label="System prompt"),
+ Segment(id="user", content="...", label="User message"),
+ Segment(id="doc_1", content="...", label="Retrieved doc 1"),
+ ],
+ tools=[...],
+ tool_choice="auto",
+)
+
+result.top # segments ranked by importance
+result.summary() # one-line human-readable description
+```
+
+### Sampling Policy Config (TODO)
+
+```python
+ExplainabilityConfig(
+ mode="sample", # off | dev_all | sample
+ sample_rate=0.1,
+ always_explain_rules=[
+ OnError(),
+ OnTool("escalate_to_human"),
+ ],
+ async_mode=True,
+)
+```
+
+---
+
+## 11. Reference UI Component
+
+A React component `` that renders:
+- **Highlighted text:** input context with segments coloured by saliency importance (Pass 1), with sentence-level highlights nested inside (Pass 2).
+- **Factors list:** `agent.why.primary_factors` ranked by importance.
+- **What-if chips:** `agent.why.counterfactuals`.
+
+Developer mode: raw scores. Operator mode: importance ranks only.
+
+---
+
+## 12. Non-Functional Requirements
+
+| Concern | Requirement |
+|---|---|
+| Latency (dev) | Up to 300 ms per explained span acceptable; all occlusion calls run concurrently. |
+| Latency (prod) | Default sampled + async; Pass 2 optional. |
+| PII / Security | Explanations must not leak data beyond what is already in the trace. Provide a pre-emit redaction hook. |
+| Extensibility | New explanation methods (e.g. SHAP, gradient-based) slot in behind the same `SaliencyResult` schema. |
+| Backend compatibility | Works with any OpenAI-compatible API. No provider-specific code in core. |
+
+---
+
+## 13. Milestones
+
+| Milestone | Status | Deliverable |
+|---|---|---|
+| M1 | ✅ Done | `Segment`, `SaliencyScore`, `SaliencyResult` types; `SaliencyEngine` with two-pass hierarchical occlusion; logprob-based scoring with binary fallback |
+| M2 | Next | `CounterfactualProber` — minimal edits to top segments, flip detection, human-readable what-if output |
+| M3 | — | OTEL emitter — map `SaliencyResult` to `agent.why.*` span attributes and events |
+| M4 | — | `@explain_tool_call` / `@explain_plan_decision` decorators + sampling policy |
+| M5 | — | Langfuse and MLflow integration tests; reference React `` |
diff --git a/README.md b/README.md
index 1f13839..ed57102 100644
--- a/README.md
+++ b/README.md
@@ -1 +1,28 @@
-# motive
\ No newline at end of file
+# motive
+
+Answer *why did the agent do that?* with empirically-grounded explanations for AI agent decisions.
+
+motive uses **hierarchical occlusion saliency**: mask segments of the input context, measure how much each one affects the model's decision, and surface ranked importance scores and counterfactual what-ifs. No model self-reports. No local weights required. Works with any OpenAI-compatible endpoint.
+
+## Installation
+
+```bash
+pip install motive
+```
+
+## Quickstart
+
+```python
+from openai import AsyncOpenAI
+from motive import SaliencyEngine, Segment
+
+engine = SaliencyEngine(
+ client=AsyncOpenAI(base_url="...", api_key="..."),
+ model="your-model",
+)
+
+result = engine.explain(messages=messages, segments=segments, tools=tools)
+print(result.summary())
+```
+
+See [`examples/demo_saliency.py`](examples/demo_saliency.py) for a full working example.
diff --git a/examples/demo_saliency.py b/examples/demo_saliency.py
new file mode 100644
index 0000000..a0b3dc6
--- /dev/null
+++ b/examples/demo_saliency.py
@@ -0,0 +1,121 @@
+"""Demo: hierarchical occlusion saliency on a support-ticket routing decision."""
+
+import asyncio
+import os
+
+from openai import AsyncOpenAI
+
+from motive import SaliencyEngine, Segment, SegmentLevel
+
+
+PROXY_URL = "https://proxy.vectorinstitute.ai/v1"
+API_KEY = os.environ["VECTOR_API_KEY"]
+MODEL = "Qwen3-Coder-Next"
+
+# --- Context segments (what the agent sees when deciding which tool to call) ---
+
+SEGMENTS = [
+ Segment(
+ id="system",
+ content="You are a support agent. Escalate tickets that are urgent or have been unresolved for more than 48 hours.",
+ label="System prompt",
+ level=SegmentLevel.DOCUMENT,
+ ),
+ Segment(
+ id="user_msg",
+ content="My payment has been failing for 3 days and my account is now locked. I need this fixed urgently.",
+ label="User message",
+ level=SegmentLevel.DOCUMENT,
+ ),
+ Segment(
+ id="doc_1",
+ content="Escalation policy: accounts locked for more than 48 hours require immediate human review. Do not attempt automated resolution.",
+ label="Retrieved doc: escalation policy",
+ level=SegmentLevel.DOCUMENT,
+ ),
+ Segment(
+ id="doc_2",
+ content="Payment retry guide: ask the user to clear browser cache and retry. Most payment failures resolve within 24 hours.",
+ label="Retrieved doc: payment retry guide",
+ level=SegmentLevel.DOCUMENT,
+ ),
+]
+
+# --- Full messages for the decision call ---
+
+MESSAGES = [
+ {"role": "system", "content": SEGMENTS[0].content},
+ {"role": "user", "content": SEGMENTS[1].content},
+ {
+ "role": "user",
+ "content": (f"Context documents:\n\n[Doc 1] {SEGMENTS[2].content}\n\n[Doc 2] {SEGMENTS[3].content}"),
+ },
+]
+
+# --- Tool definitions ---
+
+TOOLS = [
+ {
+ "type": "function",
+ "function": {
+ "name": "escalate_to_human",
+ "description": "Escalate the ticket to a human support agent.",
+ "parameters": {"type": "object", "properties": {}, "required": []},
+ },
+ },
+ {
+ "type": "function",
+ "function": {
+ "name": "send_retry_instructions",
+ "description": "Send automated payment retry instructions to the user.",
+ "parameters": {"type": "object", "properties": {}, "required": []},
+ },
+ },
+ {
+ "type": "function",
+ "function": {
+ "name": "auto_resolve",
+ "description": "Mark the ticket as resolved automatically.",
+ "parameters": {"type": "object", "properties": {}, "required": []},
+ },
+ },
+]
+
+
+async def main() -> None:
+ """Run the saliency demo."""
+ client = AsyncOpenAI(base_url=PROXY_URL, api_key=API_KEY)
+ engine = SaliencyEngine(client=client, model=MODEL, top_k=2)
+
+ print(f"Model: {MODEL}")
+ print(f"Running hierarchical occlusion on {len(SEGMENTS)} segments...\n")
+
+ result = await engine.explain_async(
+ messages=MESSAGES, # type: ignore[arg-type]
+ segments=SEGMENTS,
+ tools=TOOLS, # type: ignore[arg-type]
+ )
+
+ print(f"Original decision: {result.original_decision}\n")
+ print("=== Pass 1: Segment-level importance ===")
+ doc_scores = [s for s in result.top if "__s" not in s.segment_id]
+ for score in doc_scores:
+ bar = "█" * int(score.importance * 20)
+ changed = " ← flipped" if score.decision_changed else ""
+ print(f" {score.label:<40} {score.importance:.3f} {bar}{changed}")
+
+ print("\n=== Pass 2: Sentence-level (top segments drilled in) ===")
+ sent_scores = [s for s in result.top if "__s" in s.segment_id]
+ if sent_scores:
+ for score in sent_scores:
+ bar = "█" * int(score.importance * 20)
+ changed = " ← flipped" if score.decision_changed else ""
+ print(f" {score.label[:60]:<62} {score.importance:.3f} {bar}{changed}")
+ else:
+ print(" (no multi-sentence segments found)")
+
+ print(f"\n{result.summary()}")
+
+
+if __name__ == "__main__":
+ asyncio.run(main())
diff --git a/pyproject.toml b/pyproject.toml
index cf893df..703eccb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,7 +5,19 @@ description = "Answer 'why did the agent do that?' with saliency, rationale, and
readme = "README.md"
requires-python = ">=3.11"
license = { file = "LICENSE" }
-dependencies = []
+dependencies = [
+ "openai>=1.82.0",
+]
+
+[dependency-groups]
+dev = [
+ "mypy>=1.19.1",
+ "pre-commit>=4.2.0",
+ "pytest>=8.3.5",
+ "pytest-asyncio>=0.25.3",
+ "pytest-cov>=6.0.0",
+ "ruff>=0.15.8",
+]
[build-system]
requires = ["hatchling>=1.29.0"]
@@ -13,3 +25,88 @@ build-backend = "hatchling.build"
[tool.hatch.build.targets.wheel]
packages = ["src/motive"]
+
+[tool.uv]
+default-groups = ["dev"]
+
+[tool.pytest.ini_options]
+asyncio_mode = "auto"
+
+[tool.mypy]
+follow_imports = "normal"
+ignore_missing_imports = true
+install_types = true
+pretty = true
+non_interactive = true
+allow_untyped_defs = false
+no_implicit_optional = true
+check_untyped_defs = true
+namespace_packages = true
+explicit_package_bases = true
+warn_unused_configs = true
+allow_subclassing_any = false
+allow_untyped_calls = false
+allow_incomplete_defs = false
+allow_untyped_decorators = false
+warn_redundant_casts = true
+warn_unused_ignores = true
+implicit_reexport = false
+strict_equality = true
+extra_checks = true
+
+[tool.coverage.run]
+source = ["src/motive"]
+omit = ["tests/*", "*__init__.py"]
+
+[tool.ruff]
+include = ["*.py", "pyproject.toml"]
+line-length = 120
+
+[tool.ruff.format]
+quote-style = "double"
+indent-style = "space"
+docstring-code-format = true
+
+[tool.ruff.lint]
+select = [
+ "A", # flake8-builtins
+ "B", # flake8-bugbear
+ "COM", # flake8-commas
+ "C4", # flake8-comprehensions
+ "RET", # flake8-return
+ "SIM", # flake8-simplify
+ "ICN", # flake8-import-conventions
+ "Q", # flake8-quotes
+ "RSE", # flake8-raise
+ "D", # pydocstyle
+ "E", # pycodestyle
+ "F", # pyflakes
+ "I", # isort
+ "W", # pycodestyle
+ "N", # pep8-naming
+ "ERA", # eradicate
+ "PL", # pylint
+]
+fixable = ["A", "B", "COM", "C4", "RET", "SIM", "ICN", "Q", "RSE", "D", "E", "F", "I", "W", "N", "ERA", "PL"]
+ignore = [
+ "B905", # zip() without strict=
+ "E501", # line too long
+ "D203", # 1 blank line required before class docstring
+ "D213", # Multi-line docstring summary should start at the second line
+ "PLR2004", # Replace magic number with named constant
+ "PLR0913", # Too many arguments
+ "COM812", # Missing trailing comma
+ "ERA001", # Found commented-out code
+]
+
+[tool.ruff.lint.isort]
+lines-after-imports = 2
+
+[tool.ruff.lint.per-file-ignores]
+"tests/*" = ["D"]
+
+[tool.ruff.lint.pydocstyle]
+convention = "numpy"
+
+[tool.ruff.lint.pycodestyle]
+max-doc-length = 120
diff --git a/pyrightconfig.json b/pyrightconfig.json
new file mode 100644
index 0000000..248f094
--- /dev/null
+++ b/pyrightconfig.json
@@ -0,0 +1,7 @@
+{
+ "pythonVersion": "3.12",
+ "venvPath": ".",
+ "venv": ".venv",
+ "include": ["src", "examples", "tests"],
+ "extraPaths": ["src"]
+}
diff --git a/src/motive/__init__.py b/src/motive/__init__.py
index 92ac5d9..03b2b39 100644
--- a/src/motive/__init__.py
+++ b/src/motive/__init__.py
@@ -1,3 +1,8 @@
-"""motive — structured decision explainability for LLM agents."""
+"""motive — answer 'why did the agent do that?' with saliency, rationale, and counterfactuals."""
+
+from motive._types import SaliencyResult, SaliencyScore, Segment, SegmentLevel # noqa: E402
+from motive.saliency import SaliencyEngine # noqa: E402
+
__version__ = "0.0.1"
+__all__ = ["SaliencyEngine", "Segment", "SegmentLevel", "SaliencyScore", "SaliencyResult"]
diff --git a/src/motive/_types.py b/src/motive/_types.py
new file mode 100644
index 0000000..4dbb5c2
--- /dev/null
+++ b/src/motive/_types.py
@@ -0,0 +1,61 @@
+"""Core data types for motive."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from enum import Enum
+
+
+class SegmentLevel(str, Enum):
+ """Granularity level of a context segment."""
+
+ DOCUMENT = "document"
+ SENTENCE = "sentence"
+ TOKEN = "token"
+
+
+@dataclass
+class Segment:
+ """A labelled chunk of input context that can be masked."""
+
+ id: str
+ content: str
+ label: str = ""
+ level: SegmentLevel = SegmentLevel.DOCUMENT
+
+
+@dataclass
+class SaliencyScore:
+ """Importance score for a single segment."""
+
+ segment_id: str
+ label: str
+ importance: float # [0, 1] normalised across segments in this pass
+ decision_changed: bool
+ original_decision: str
+ masked_decision: str
+
+
+@dataclass
+class SaliencyResult:
+ """Full result of a hierarchical occlusion pass."""
+
+ scores: list[SaliencyScore]
+ original_decision: str
+ model: str
+ method: str = "hierarchical_occlusion"
+
+ @property
+ def top(self) -> list[SaliencyScore]:
+ """Segments sorted by importance descending."""
+ return sorted(self.scores, key=lambda s: s.importance, reverse=True)
+
+ def summary(self) -> str:
+ """One-line human-readable summary of the top driver."""
+ if not self.scores:
+ return "No segments scored."
+ best = self.top[0]
+ return (
+ f"Decision '{self.original_decision}' was most influenced by "
+ f"'{best.label}' (importance {best.importance:.2f})."
+ )
diff --git a/src/motive/saliency.py b/src/motive/saliency.py
new file mode 100644
index 0000000..937f501
--- /dev/null
+++ b/src/motive/saliency.py
@@ -0,0 +1,248 @@
+"""Hierarchical occlusion-based saliency engine."""
+
+from __future__ import annotations
+
+import asyncio
+import re
+from typing import Any, Literal, cast
+
+from openai import AsyncOpenAI
+from openai.types.chat import ChatCompletionMessageParam, ChatCompletionToolParam
+
+from ._types import SaliencyResult, SaliencyScore, Segment, SegmentLevel
+
+
+_MASK = "[CONTENT REDACTED]"
+_TOP_K_DEFAULT = 2
+
+ToolChoice = Literal["auto", "required", "none"]
+
+
+def _extract_tool_name(response: Any) -> str:
+ """Pull the called tool name out of a chat completion response."""
+ msg = response.choices[0].message
+ if msg.tool_calls:
+ return msg.tool_calls[0].function.name
+ return msg.content or ""
+
+
+def _logprob_for_tool(response: Any, tool_name: str) -> float | None:
+ """Sum logprobs over the tokens that spell out `tool_name` in the response.
+
+ Tool names are multi-token (e.g. "escalate_to_human" → ["escal","ate","_to","_human"]),
+ so we reconstruct the full generated string, locate the tool name span, and sum
+ the logprobs of every token that overlaps with it.
+ """
+ choice = response.choices[0]
+ if not getattr(choice, "logprobs", None):
+ return None
+ tokens_data = getattr(choice.logprobs, "content", None) or []
+ if not tokens_data:
+ return None
+
+ full_text = "".join(td.token for td in tokens_data)
+ start = full_text.find(tool_name)
+ if start == -1:
+ return None
+ end = start + len(tool_name)
+
+ total_lp = 0.0
+ pos = 0
+ found = False
+ for td in tokens_data:
+ tok_end = pos + len(td.token)
+ if pos < end and tok_end > start:
+ total_lp += td.logprob
+ found = True
+ pos = tok_end
+
+ return total_lp if found else None
+
+
+def _mask_messages(
+ messages: list[ChatCompletionMessageParam],
+ segments: list[Segment],
+ mask_idx: int,
+) -> list[ChatCompletionMessageParam]:
+ """Return a copy of messages with segment[mask_idx] replaced by the mask placeholder."""
+ target = segments[mask_idx]
+ result: list[dict[str, Any]] = []
+ for msg in messages:
+ raw = cast(dict[str, Any], msg)
+ content = raw.get("content") or ""
+ if isinstance(content, str) and target.content in content:
+ result.append({**raw, "content": content.replace(target.content, _MASK, 1)})
+ else:
+ result.append(raw)
+ return cast(list[ChatCompletionMessageParam], result)
+
+
+def _split_sentences(text: str) -> list[str]:
+ """Naive sentence splitter for agent context segments."""
+ parts = re.split(r"(?<=[.!?])\s+", text.strip())
+ return [p for p in parts if p.strip()]
+
+
+def _normalise(scores: list[SaliencyScore]) -> list[SaliencyScore]:
+ """Scale importance values to [0, 1] relative to the max in this pass."""
+ max_val = max((s.importance for s in scores), default=1.0)
+ if max_val == 0:
+ return scores
+ return [
+ SaliencyScore(
+ segment_id=s.segment_id,
+ label=s.label,
+ importance=s.importance / max_val,
+ decision_changed=s.decision_changed,
+ original_decision=s.original_decision,
+ masked_decision=s.masked_decision,
+ )
+ for s in scores
+ ]
+
+
+class SaliencyEngine:
+ """Hierarchical occlusion saliency for agent tool-call decisions.
+
+ Pass 1 masks at segment (document/message) level.
+ Pass 2 drills into the top_k segments at sentence level.
+
+ Importance is the drop in log-probability of the original tool choice
+ when a segment is masked. Falls back to binary (flipped = 1.0,
+ unchanged = 0.0) when logprobs are unavailable.
+ """
+
+ def __init__(
+ self,
+ client: AsyncOpenAI,
+ model: str,
+ top_k: int = _TOP_K_DEFAULT,
+ ) -> None:
+ self.client = client
+ self.model = model
+ self.top_k = top_k
+
+ def explain(
+ self,
+ messages: list[ChatCompletionMessageParam],
+ segments: list[Segment],
+ tools: list[ChatCompletionToolParam],
+ tool_choice: ToolChoice = "auto",
+ ) -> SaliencyResult:
+ """Run explanation synchronously."""
+ return asyncio.run(self.explain_async(messages, segments, tools, tool_choice))
+
+ async def explain_async(
+ self,
+ messages: list[ChatCompletionMessageParam],
+ segments: list[Segment],
+ tools: list[ChatCompletionToolParam],
+ tool_choice: ToolChoice = "auto",
+ ) -> SaliencyResult:
+ """Async entry point."""
+ original_resp = await self._call(messages, tools, tool_choice, logprobs=True)
+ original_tool = _extract_tool_name(original_resp)
+ original_lp = _logprob_for_tool(original_resp, original_tool)
+
+ # Pass 1: segment level
+ pass1 = await self._occlusion_pass(messages, segments, tools, tool_choice, original_tool, original_lp)
+ pass1 = _normalise(pass1)
+
+ # Pass 2: sentence level on top_k segments that actually had non-zero importance
+ top_ids = {
+ s.segment_id
+ for s in sorted(pass1, key=lambda s: s.importance, reverse=True)[: self.top_k]
+ if s.importance > 0
+ }
+ pass2: list[SaliencyScore] = []
+ for seg in segments:
+ if seg.id not in top_ids:
+ continue
+ sentences = _split_sentences(seg.content)
+ if len(sentences) <= 1:
+ continue
+ sub_segments = [
+ Segment(
+ id=f"{seg.id}__s{i}",
+ content=s,
+ label=s[:80],
+ level=SegmentLevel.SENTENCE,
+ )
+ for i, s in enumerate(sentences)
+ ]
+ sub_scores = await self._occlusion_pass(
+ messages, sub_segments, tools, tool_choice, original_tool, original_lp
+ )
+ pass2.extend(_normalise(sub_scores))
+
+ return SaliencyResult(
+ scores=pass1 + pass2,
+ original_decision=original_tool,
+ model=self.model,
+ )
+
+ async def _occlusion_pass(
+ self,
+ messages: list[ChatCompletionMessageParam],
+ segments: list[Segment],
+ tools: list[ChatCompletionToolParam],
+ tool_choice: ToolChoice,
+ original_tool: str,
+ original_lp: float | None,
+ ) -> list[SaliencyScore]:
+ tasks = [
+ self._score_segment(messages, segments, i, tools, tool_choice, original_tool, original_lp)
+ for i in range(len(segments))
+ ]
+ return list(await asyncio.gather(*tasks))
+
+ async def _score_segment(
+ self,
+ messages: list[ChatCompletionMessageParam],
+ segments: list[Segment],
+ mask_idx: int,
+ tools: list[ChatCompletionToolParam],
+ tool_choice: ToolChoice,
+ original_tool: str,
+ original_lp: float | None,
+ ) -> SaliencyScore:
+ masked_msgs = _mask_messages(messages, segments, mask_idx)
+ resp = await self._call(masked_msgs, tools, tool_choice, logprobs=True)
+ masked_tool = _extract_tool_name(resp)
+
+ if original_lp is not None:
+ masked_lp = _logprob_for_tool(resp, original_tool)
+ importance = (
+ max(0.0, original_lp - masked_lp)
+ if masked_lp is not None
+ else (1.0 if masked_tool != original_tool else 0.0)
+ )
+ else:
+ importance = 1.0 if masked_tool != original_tool else 0.0
+
+ seg = segments[mask_idx]
+ return SaliencyScore(
+ segment_id=seg.id,
+ label=seg.label or seg.id,
+ importance=importance,
+ decision_changed=masked_tool != original_tool,
+ original_decision=original_tool,
+ masked_decision=masked_tool,
+ )
+
+ async def _call(
+ self,
+ messages: list[ChatCompletionMessageParam],
+ tools: list[ChatCompletionToolParam],
+ tool_choice: ToolChoice,
+ logprobs: bool = False,
+ ) -> Any:
+ return await self.client.chat.completions.create(
+ model=self.model,
+ messages=messages,
+ tools=tools,
+ tool_choice=tool_choice,
+ logprobs=logprobs,
+ top_logprobs=5 if logprobs else None,
+ temperature=0,
+ )
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/test_saliency.py b/tests/test_saliency.py
new file mode 100644
index 0000000..4e9655b
--- /dev/null
+++ b/tests/test_saliency.py
@@ -0,0 +1,462 @@
+"""Tests for motive.saliency — all pure logic, no network calls."""
+
+from __future__ import annotations
+
+from unittest.mock import AsyncMock, MagicMock
+
+import pytest
+
+from motive._types import Segment
+from motive.saliency import (
+ SaliencyEngine,
+ SaliencyScore,
+ _extract_tool_name,
+ _logprob_for_tool,
+ _mask_messages,
+ _normalise,
+ _split_sentences,
+)
+
+
+# ---------------------------------------------------------------------------
+# Helpers to build fake OpenAI responses
+# ---------------------------------------------------------------------------
+
+
+def _tool_response(tool_name: str, logprobs: list[dict] | None = None) -> MagicMock:
+ """Build a minimal fake chat completion response with a tool call."""
+ tool_call = MagicMock()
+ tool_call.function.name = tool_name
+
+ msg = MagicMock()
+ msg.tool_calls = [tool_call]
+ msg.content = None
+
+ choice = MagicMock()
+ choice.message = msg
+
+ if logprobs is not None:
+ lp_content = []
+ for entry in logprobs:
+ td = MagicMock()
+ td.token = entry["token"]
+ td.logprob = entry["logprob"]
+ td.top_logprobs = []
+ lp_content.append(td)
+ lp = MagicMock()
+ lp.content = lp_content
+ choice.logprobs = lp
+ else:
+ choice.logprobs = None
+
+ resp = MagicMock()
+ resp.choices = [choice]
+ return resp
+
+
+def _text_response(text: str) -> MagicMock:
+ """Build a response with no tool call (text only)."""
+ msg = MagicMock()
+ msg.tool_calls = None
+ msg.content = text
+
+ choice = MagicMock()
+ choice.message = msg
+ choice.logprobs = None
+
+ resp = MagicMock()
+ resp.choices = [choice]
+ return resp
+
+
+# ---------------------------------------------------------------------------
+# _extract_tool_name
+# ---------------------------------------------------------------------------
+
+
+class TestExtractToolName:
+ def test_returns_tool_name_from_tool_call(self) -> None:
+ resp = _tool_response("escalate_to_human")
+ assert _extract_tool_name(resp) == "escalate_to_human"
+
+ def test_falls_back_to_content_when_no_tool_calls(self) -> None:
+ resp = _text_response("send_reminder")
+ assert _extract_tool_name(resp) == "send_reminder"
+
+ def test_empty_content_returns_empty_string(self) -> None:
+ msg = MagicMock()
+ msg.tool_calls = None
+ msg.content = None
+ resp = MagicMock()
+ resp.choices = [MagicMock(message=msg)]
+ assert _extract_tool_name(resp) == ""
+
+
+# ---------------------------------------------------------------------------
+# _logprob_for_tool
+# ---------------------------------------------------------------------------
+
+
+class TestLogprobForTool:
+ def test_single_token_match(self) -> None:
+ logprobs = [{"token": "escalate_to_human", "logprob": -0.5}]
+ resp = _tool_response("escalate_to_human", logprobs)
+ result = _logprob_for_tool(resp, "escalate_to_human")
+ assert result == pytest.approx(-0.5)
+
+ def test_multi_token_match_sums_logprobs(self) -> None:
+ # "escalate_to_human" split as "escal" + "ate" + "_to" + "_human"
+ logprobs = [
+ {"token": "", "logprob": -0.01},
+ ]
+ resp = _tool_response("escalate_to_human", logprobs)
+ result = _logprob_for_tool(resp, "escalate_to_human")
+ assert result == pytest.approx(-0.1 + -0.2 + -0.3 + -0.4)
+
+ def test_returns_none_when_no_logprobs(self) -> None:
+ resp = _tool_response("tool_a")
+ assert _logprob_for_tool(resp, "tool_a") is None
+
+ def test_returns_none_when_tool_name_not_in_tokens(self) -> None:
+ logprobs = [{"token": "something_else", "logprob": -1.0}]
+ resp = _tool_response("tool_a", logprobs)
+ assert _logprob_for_tool(resp, "tool_a") is None
+
+ def test_returns_none_when_logprob_content_empty(self) -> None:
+ resp = _tool_response("tool_a", logprobs=[])
+ assert _logprob_for_tool(resp, "tool_a") is None
+
+
+# ---------------------------------------------------------------------------
+# _mask_messages
+# ---------------------------------------------------------------------------
+
+
+class TestMaskMessages:
+ def _segments(self) -> list[Segment]:
+ return [
+ Segment(id="system", content="You are a helpful agent."),
+ Segment(id="user", content="My account is locked."),
+ Segment(id="doc_1", content="Locked accounts require human review."),
+ ]
+
+ def test_masks_target_segment_content(self) -> None:
+ segments = self._segments()
+ messages = [
+ {"role": "system", "content": "You are a helpful agent."},
+ {"role": "user", "content": "My account is locked."},
+ ]
+ masked = _mask_messages(messages, segments, mask_idx=0)
+ assert "[CONTENT REDACTED]" in masked[0]["content"]
+ assert "You are a helpful agent." not in masked[0]["content"]
+
+ def test_leaves_other_messages_unchanged(self) -> None:
+ segments = self._segments()
+ messages = [
+ {"role": "system", "content": "You are a helpful agent."},
+ {"role": "user", "content": "My account is locked."},
+ ]
+ masked = _mask_messages(messages, segments, mask_idx=0)
+ assert masked[1]["content"] == "My account is locked."
+
+ def test_masks_correct_index(self) -> None:
+ segments = self._segments()
+ messages = [
+ {"role": "user", "content": "My account is locked. Locked accounts require human review."},
+ ]
+ masked = _mask_messages(messages, segments, mask_idx=2)
+ assert "Locked accounts require human review." not in masked[0]["content"]
+ assert "My account is locked." in masked[0]["content"]
+
+ def test_no_match_leaves_messages_unchanged(self) -> None:
+ segments = self._segments()
+ messages = [{"role": "user", "content": "Something completely different."}]
+ masked = _mask_messages(messages, segments, mask_idx=0)
+ assert masked[0]["content"] == "Something completely different."
+
+ def test_returns_new_list_not_mutating_original(self) -> None:
+ segments = self._segments()
+ messages = [{"role": "system", "content": "You are a helpful agent."}]
+ original_content = messages[0]["content"]
+ _mask_messages(messages, segments, mask_idx=0)
+ assert messages[0]["content"] == original_content
+
+
+# ---------------------------------------------------------------------------
+# _split_sentences
+# ---------------------------------------------------------------------------
+
+
+class TestSplitSentences:
+ def test_splits_on_period(self) -> None:
+ result = _split_sentences("First sentence. Second sentence.")
+ assert len(result) == 2
+ assert result[0] == "First sentence."
+
+ def test_splits_on_exclamation(self) -> None:
+ result = _split_sentences("Watch out! Be careful.")
+ assert len(result) == 2
+
+ def test_splits_on_question_mark(self) -> None:
+ result = _split_sentences("Is this right? Yes it is.")
+ assert len(result) == 2
+
+ def test_single_sentence_returns_one_item(self) -> None:
+ result = _split_sentences("Just one sentence here.")
+ assert result == ["Just one sentence here."]
+
+ def test_empty_string_returns_empty_list(self) -> None:
+ result = _split_sentences("")
+ assert result == []
+
+ def test_filters_whitespace_only_parts(self) -> None:
+ result = _split_sentences(" First. Second. ")
+ assert all(s.strip() for s in result)
+
+
+# ---------------------------------------------------------------------------
+# _normalise
+# ---------------------------------------------------------------------------
+
+
+class TestNormalise:
+ def _score(self, sid: str, importance: float) -> SaliencyScore:
+ return SaliencyScore(
+ segment_id=sid,
+ label=sid,
+ importance=importance,
+ decision_changed=importance > 0,
+ original_decision="tool_a",
+ masked_decision="tool_b" if importance > 0 else "tool_a",
+ )
+
+ def test_max_becomes_one(self) -> None:
+ scores = [self._score("a", 4.0), self._score("b", 2.0), self._score("c", 1.0)]
+ normalised = _normalise(scores)
+ assert max(s.importance for s in normalised) == pytest.approx(1.0)
+
+ def test_relative_order_preserved(self) -> None:
+ scores = [self._score("a", 3.0), self._score("b", 1.0), self._score("c", 0.0)]
+ normalised = _normalise(scores)
+ by_id = {s.segment_id: s.importance for s in normalised}
+ assert by_id["a"] > by_id["b"] > by_id["c"]
+
+ def test_all_zeros_unchanged(self) -> None:
+ scores = [self._score("a", 0.0), self._score("b", 0.0)]
+ normalised = _normalise(scores)
+ assert all(s.importance == 0.0 for s in normalised)
+
+ def test_empty_list_returns_empty(self) -> None:
+ assert _normalise([]) == []
+
+ def test_preserves_segment_ids(self) -> None:
+ scores = [self._score("x", 2.0), self._score("y", 1.0)]
+ normalised = _normalise(scores)
+ assert {s.segment_id for s in normalised} == {"x", "y"}
+
+
+# ---------------------------------------------------------------------------
+# SaliencyEngine — async, mocked client
+# ---------------------------------------------------------------------------
+
+
+def _make_engine() -> tuple[SaliencyEngine, AsyncMock]:
+ """Return an engine wired to a mock OpenAI client."""
+ client = MagicMock()
+ client.chat = MagicMock()
+ client.chat.completions = MagicMock()
+ client.chat.completions.create = AsyncMock()
+ return SaliencyEngine(client=client, model="test-model", top_k=2), client
+
+
+def _sample_segments() -> list[Segment]:
+ return [
+ Segment(id="system", content="You are a support agent. Escalate urgent issues.", label="System prompt"),
+ Segment(id="user_msg", content="My account is locked for 3 days.", label="User message"),
+ Segment(id="doc_1", content="Locked accounts require human review.", label="Escalation policy"),
+ ]
+
+
+def _sample_messages() -> list[dict]:
+ return [
+ {"role": "system", "content": "You are a support agent. Escalate urgent issues."},
+ {"role": "user", "content": "My account is locked for 3 days. Locked accounts require human review."},
+ ]
+
+
+def _sample_tools() -> list[dict]:
+ return [
+ {
+ "type": "function",
+ "function": {
+ "name": "escalate_to_human",
+ "description": "Escalate.",
+ "parameters": {"type": "object", "properties": {}, "required": []},
+ },
+ },
+ {
+ "type": "function",
+ "function": {
+ "name": "send_reminder",
+ "description": "Send reminder.",
+ "parameters": {"type": "object", "properties": {}, "required": []},
+ },
+ },
+ ]
+
+
+@pytest.mark.asyncio
+class TestSaliencyEngineAsync:
+ async def test_returns_saliency_result_with_correct_decision(self) -> None:
+ engine, client = _make_engine()
+ # All calls return the same tool — no flips
+ client.chat.completions.create.return_value = _tool_response("escalate_to_human")
+
+ result = await engine.explain_async(
+ messages=_sample_messages(), # type: ignore[arg-type]
+ segments=_sample_segments(),
+ tools=_sample_tools(), # type: ignore[arg-type]
+ )
+
+ assert result.original_decision == "escalate_to_human"
+ assert result.model == "test-model"
+
+ async def test_flipped_segment_gets_high_importance(self) -> None:
+ engine, client = _make_engine()
+ segments = _sample_segments()
+ responses = []
+ # Original call
+ responses.append(_tool_response("escalate_to_human"))
+ # Masking system → same
+ responses.append(_tool_response("escalate_to_human"))
+ # Masking user_msg → flips
+ responses.append(_tool_response("send_reminder"))
+ # Masking doc_1 → same
+ responses.append(_tool_response("escalate_to_human"))
+
+ client.chat.completions.create.side_effect = responses
+
+ result = await engine.explain_async(
+ messages=_sample_messages(), # type: ignore[arg-type]
+ segments=segments,
+ tools=_sample_tools(), # type: ignore[arg-type]
+ )
+
+ pass1 = [s for s in result.scores if "__s" not in s.segment_id]
+ by_id = {s.segment_id: s for s in pass1}
+ assert by_id["user_msg"].decision_changed is True
+ assert by_id["user_msg"].importance == pytest.approx(1.0)
+ assert by_id["system"].decision_changed is False
+
+ async def test_logprob_drop_used_as_importance_when_available(self) -> None:
+ engine, client = _make_engine()
+ segments = [
+ Segment(id="seg_a", content="important context", label="A"),
+ Segment(id="seg_b", content="irrelevant context", label="B"),
+ ]
+ messages = [{"role": "user", "content": "important context irrelevant context"}]
+
+ lp_tokens = [
+ {"token": "tool", "logprob": -0.01},
+ {"token": "_a", "logprob": -0.01},
+ ]
+ # Original: logprob of "tool_a" = -0.02
+ original_resp = _tool_response("tool_a", lp_tokens)
+ # Mask seg_a: logprob drops to -1.5 → importance = -0.02 - (-1.5) = 1.48
+ masked_a = _tool_response("tool_a", [{"token": "tool", "logprob": -0.8}, {"token": "_a", "logprob": -0.7}])
+ # Mask seg_b: logprob barely changes → low importance
+ masked_b = _tool_response("tool_a", [{"token": "tool", "logprob": -0.01}, {"token": "_a", "logprob": -0.02}])
+
+ client.chat.completions.create.side_effect = [original_resp, masked_a, masked_b]
+
+ result = await engine.explain_async(
+ messages=messages, # type: ignore[arg-type]
+ segments=segments,
+ tools=_sample_tools(), # type: ignore[arg-type]
+ )
+
+ pass1 = [s for s in result.scores if "__s" not in s.segment_id]
+ by_id = {s.segment_id: s for s in pass1}
+ assert by_id["seg_a"].importance > by_id["seg_b"].importance
+
+ async def test_no_pass2_when_all_segments_zero_importance(self) -> None:
+ engine, client = _make_engine()
+ # Every call returns the same tool — no flips, no logprobs → all zero
+ client.chat.completions.create.return_value = _tool_response("escalate_to_human")
+
+ result = await engine.explain_async(
+ messages=_sample_messages(), # type: ignore[arg-type]
+ segments=_sample_segments(),
+ tools=_sample_tools(), # type: ignore[arg-type]
+ )
+
+ sentence_scores = [s for s in result.scores if "__s" in s.segment_id]
+ assert sentence_scores == []
+
+ async def test_pass2_only_drills_into_nonzero_segments(self) -> None:
+ engine, client = _make_engine()
+ segments = [
+ Segment(id="system", content="You are an agent.", label="System"),
+ Segment(id="user_msg", content="Urgent issue. Account locked.", label="User"),
+ ]
+ messages = [
+ {"role": "system", "content": "You are an agent."},
+ {"role": "user", "content": "Urgent issue. Account locked."},
+ ]
+
+ responses = []
+ # Original
+ responses.append(_tool_response("escalate_to_human"))
+ # Pass 1: mask system → same; mask user_msg → flips
+ responses.append(_tool_response("escalate_to_human"))
+ responses.append(_tool_response("send_reminder"))
+ # Pass 2: two sentences in user_msg
+ responses.append(_tool_response("escalate_to_human")) # mask "Urgent issue."
+ responses.append(_tool_response("send_reminder")) # mask "Account locked."
+
+ client.chat.completions.create.side_effect = responses
+
+ result = await engine.explain_async(
+ messages=messages, # type: ignore[arg-type]
+ segments=segments,
+ tools=_sample_tools(), # type: ignore[arg-type]
+ )
+
+ sentence_scores = [s for s in result.scores if "__s" in s.segment_id]
+ # Only user_msg should be drilled (system had 0 importance)
+ assert all(s.segment_id.startswith("user_msg") for s in sentence_scores)
+ assert len(sentence_scores) == 2
+
+ async def test_call_count_is_1_plus_n_segments_for_pass1(self) -> None:
+ engine, client = _make_engine()
+ segments = _sample_segments() # 3 segments
+ client.chat.completions.create.return_value = _tool_response("escalate_to_human")
+
+ await engine.explain_async(
+ messages=_sample_messages(), # type: ignore[arg-type]
+ segments=segments,
+ tools=_sample_tools(), # type: ignore[arg-type]
+ )
+
+ # 1 original + 3 masked = 4 calls minimum (plus any pass2 calls)
+ assert client.chat.completions.create.call_count >= 4
+
+
+class TestSaliencyEngineSync:
+ def test_explain_sync_returns_result(self) -> None:
+ engine, client = _make_engine()
+ client.chat.completions.create.return_value = _tool_response("escalate_to_human")
+
+ result = engine.explain(
+ messages=_sample_messages(), # type: ignore[arg-type]
+ segments=_sample_segments(),
+ tools=_sample_tools(), # type: ignore[arg-type]
+ )
+
+ assert result.original_decision == "escalate_to_human"
+ assert len(result.scores) > 0
diff --git a/tests/test_types.py b/tests/test_types.py
new file mode 100644
index 0000000..778516f
--- /dev/null
+++ b/tests/test_types.py
@@ -0,0 +1,83 @@
+"""Tests for motive._types."""
+
+import pytest
+
+from motive._types import SaliencyResult, SaliencyScore, Segment, SegmentLevel
+
+
+def _score(segment_id: str, importance: float, changed: bool = False) -> SaliencyScore:
+ return SaliencyScore(
+ segment_id=segment_id,
+ label=segment_id,
+ importance=importance,
+ decision_changed=changed,
+ original_decision="tool_a",
+ masked_decision="tool_b" if changed else "tool_a",
+ )
+
+
+class TestSegment:
+ def test_defaults(self) -> None:
+ seg = Segment(id="s1", content="hello")
+ assert seg.label == ""
+ assert seg.level == SegmentLevel.DOCUMENT
+
+ def test_explicit_fields(self) -> None:
+ seg = Segment(id="s1", content="hello", label="My label", level=SegmentLevel.SENTENCE)
+ assert seg.label == "My label"
+ assert seg.level == SegmentLevel.SENTENCE
+
+ def test_segment_level_values(self) -> None:
+ assert SegmentLevel.DOCUMENT == "document"
+ assert SegmentLevel.SENTENCE == "sentence"
+ assert SegmentLevel.TOKEN == "token"
+
+
+class TestSaliencyScore:
+ def test_fields(self) -> None:
+ score = _score("doc_1", 0.9, changed=True)
+ assert score.segment_id == "doc_1"
+ assert score.importance == pytest.approx(0.9)
+ assert score.decision_changed is True
+ assert score.masked_decision == "tool_b"
+
+ def test_unchanged(self) -> None:
+ score = _score("doc_2", 0.1, changed=False)
+ assert score.decision_changed is False
+ assert score.original_decision == score.masked_decision
+
+
+class TestSaliencyResult:
+ def _result(self) -> SaliencyResult:
+ return SaliencyResult(
+ scores=[
+ _score("user_msg", 1.0, changed=True),
+ _score("system", 0.0),
+ _score("doc_1", 0.5),
+ ],
+ original_decision="escalate_to_human",
+ model="test-model",
+ )
+
+ def test_top_sorted_descending(self) -> None:
+ result = self._result()
+ importances = [s.importance for s in result.top]
+ assert importances == sorted(importances, reverse=True)
+
+ def test_top_first_is_highest(self) -> None:
+ result = self._result()
+ assert result.top[0].segment_id == "user_msg"
+
+ def test_summary_names_top_segment(self) -> None:
+ result = self._result()
+ summary = result.summary()
+ assert "escalate_to_human" in summary
+ assert "user_msg" in summary
+
+ def test_summary_empty_scores(self) -> None:
+ result = SaliencyResult(scores=[], original_decision="tool_a", model="m")
+ assert result.summary() == "No segments scored."
+
+ def test_default_method(self) -> None:
+ result = self._result()
+ assert result.method == "hierarchical_occlusion"
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..f17488d
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,1011 @@
+version = 1
+revision = 1
+requires-python = ">=3.11"
+resolution-markers = [
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+ "python_full_version >= '3.15' and sys_platform == 'emscripten'",
+ "python_full_version >= '3.15' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+ "python_full_version == '3.12.*' and sys_platform == 'win32'",
+ "python_full_version == '3.12.*' and sys_platform == 'emscripten'",
+ "python_full_version == '3.12.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+ "python_full_version < '3.12' and sys_platform == 'win32'",
+ "python_full_version < '3.12' and sys_platform == 'emscripten'",
+ "python_full_version < '3.12' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+ "python_full_version == '3.14.*' and sys_platform == 'win32'",
+ "python_full_version == '3.14.*' and sys_platform == 'emscripten'",
+ "python_full_version == '3.14.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+ "python_full_version == '3.13.*' and sys_platform == 'win32'",
+ "python_full_version == '3.13.*' and sys_platform == 'emscripten'",
+ "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+]
+
+[[package]]
+name = "annotated-types"
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+name = "anyio"
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