diff --git a/features/acms_semantic_chunking.feature b/features/acms_semantic_chunking.feature
new file mode 100644
index 000000000..9c2d2bfa5
--- /dev/null
+++ b/features/acms_semantic_chunking.feature
@@ -0,0 +1,44 @@
+Feature: Semantic chunking context strategy for ACMS
+  As a context assembly system
+  I want to split files into semantic chunks
+  So that I can select individual chunks with relevance scoring
+
+  Background:
+    Given a semantic chunking strategy is initialized
+
+  Scenario: Chunk Python file into functions and classes
+    Given a Python file with functions and classes
+    When I chunk the Python file
+    Then I should get chunks for each function
+    And I should get chunks for each class
+    And I should get chunks for each method
+
+  Scenario: Chunk Markdown file into sections
+    Given a Markdown file with multiple sections
+    When I chunk the Markdown file
+    Then I should get chunks for each section
+    And each chunk should have the section title as name
+
+  Scenario: Handle Python file with syntax errors
+    Given a Python file with syntax errors
+    When I chunk the Python file
+    Then I should get a single chunk for the whole file
+
+  Scenario: Score chunk relevance
+    Given a list of semantic chunks
+    When I score the chunks for relevance
+    Then smaller chunks should have higher relevance scores
+    And chunks matching a query should have higher scores
+
+  Scenario: Select chunks within budget
+    Given a list of semantic chunks
+    And a context budget of 4096 characters
+    When I select chunks within the budget
+    Then only chunks that fit should be selected
+    And chunks should be sorted by relevance
+
+  Scenario: Convert chunks to context fragments
+    Given a list of semantic chunks
+    When I convert them to context fragments
+    Then each fragment should have the chunk content
+    And each fragment should have metadata about the chunk type
diff --git a/features/steps/acms_semantic_chunking_steps.py b/features/steps/acms_semantic_chunking_steps.py
new file mode 100644
index 000000000..6f4fff0a0
--- /dev/null
+++ b/features/steps/acms_semantic_chunking_steps.py
@@ -0,0 +1,237 @@
+"""Step definitions for semantic chunking BDD tests."""
+
+from behave import given, then, when
+
+from cleveragents.domain.models.acms.semantic_chunking import (
+    ChunkRelevanceScorer,
+    SemanticChunk,
+    SemanticChunkingStrategy,
+)
+
+
+@given("a semantic chunking strategy is initialized")
+def step_init_strategy(context):
+    """Initialize a semantic chunking strategy."""
+    context.strategy = SemanticChunkingStrategy()
+
+
+@given("a Python file with functions and classes")
+def step_python_file_with_functions_and_classes(context):
+    """Create a Python file with functions and classes."""
+    context.python_content = '''
+def hello_world():
+    """A simple function."""
+    return "Hello, World!"
+
+class MyClass:
+    """A simple class."""
+
+    def __init__(self):
+        """Initialize the class."""
+        self.value = 42
+
+    def get_value(self):
+        """Get the value."""
+        return self.value
+'''
+    context.python_file = "test.py"
+
+
+@when("I chunk the Python file")
+def step_chunk_python_file(context):
+    """Chunk the Python file."""
+    context.chunks = context.strategy.chunk_file(
+        context.python_file, context.python_content
+    )
+
+
+@then("I should get chunks for each function")
+def step_check_function_chunks(context):
+    """Check that function chunks were created."""
+    function_chunks = [c for c in context.chunks if c.chunk_type == "function"]
+    assert len(function_chunks) > 0, "No function chunks found"
+    assert any(c.name == "hello_world" for c in function_chunks)
+
+
+@then("I should get chunks for each class")
+def step_check_class_chunks(context):
+    """Check that class chunks were created."""
+    class_chunks = [c for c in context.chunks if c.chunk_type == "class"]
+    assert len(class_chunks) > 0, "No class chunks found"
+    assert any(c.name == "MyClass" for c in class_chunks)
+
+
+@then("I should get chunks for each method")
+def step_check_method_chunks(context):
+    """Check that method chunks were created."""
+    method_chunks = [c for c in context.chunks if c.chunk_type == "method"]
+    assert len(method_chunks) > 0, "No method chunks found"
+    assert any("__init__" in c.name for c in method_chunks)
+    assert any("get_value" in c.name for c in method_chunks)
+
+
+@given("a Markdown file with multiple sections")
+def step_markdown_file_with_sections(context):
+    """Create a Markdown file with multiple sections."""
+    context.markdown_content = '''# Introduction
+This is the introduction section.
+
+## Getting Started
+This section explains how to get started.
+
+### Installation
+Install the package using pip.
+
+## Usage
+This section explains how to use the package.
+
+### Examples
+Here are some examples.
+'''
+    context.markdown_file = "test.md"
+
+
+@when("I chunk the Markdown file")
+def step_chunk_markdown_file(context):
+    """Chunk the Markdown file."""
+    context.chunks = context.strategy.chunk_file(
+        context.markdown_file, context.markdown_content
+    )
+
+
+@then("I should get chunks for each section")
+def step_check_section_chunks(context):
+    """Check that section chunks were created."""
+    section_chunks = [c for c in context.chunks if c.chunk_type == "section"]
+    assert len(section_chunks) > 0, "No section chunks found"
+
+
+@then("each chunk should have the section title as name")
+def step_check_section_names(context):
+    """Check that section chunks have proper names."""
+    section_chunks = [c for c in context.chunks if c.chunk_type == "section"]
+    names = [c.name for c in section_chunks]
+    assert "Introduction" in names or any("Introduction" in n for n in names)
+
+
+@given("a Python file with syntax errors")
+def step_python_file_with_syntax_errors(context):
+    """Create a Python file with syntax errors."""
+    context.python_content = '''
+def broken_function(
+    # Missing closing parenthesis
+    return "This won't parse"
+'''
+    context.python_file = "broken.py"
+
+
+@then("I should get a single chunk for the whole file")
+def step_check_single_chunk(context):
+    """Check that a single chunk was created for the whole file."""
+    assert len(context.chunks) == 1
+    assert context.chunks[0].chunk_type == "file"
+
+
+@given("a list of semantic chunks")
+def step_create_semantic_chunks(context):
+    """Create a list of semantic chunks."""
+    context.chunks = [
+        SemanticChunk(
+            file_path="test.py",
+            chunk_type="function",
+            name="small_func",
+            start_line=1,
+            end_line=3,
+            content="def small_func():\n    return 42\n",
+        ),
+        SemanticChunk(
+            file_path="test.py",
+            chunk_type="function",
+            name="large_func",
+            start_line=5,
+            end_line=50,
+            content="def large_func():\n" + "    x = 1\n" * 45,
+        ),
+    ]
+
+
+@when("I score the chunks for relevance")
+def step_score_chunks(context):
+    """Score the chunks for relevance."""
+    context.scorer = ChunkRelevanceScorer()
+    context.scored_chunks = [
+        (chunk, context.scorer.score(chunk, query=None, context_budget=4096))
+        for chunk in context.chunks
+    ]
+
+
+@then("smaller chunks should have higher relevance scores")
+def step_check_smaller_chunks_higher_score(context):
+    """Check that smaller chunks have higher scores."""
+    small_chunk_score = next(
+        score for chunk, score in context.scored_chunks if chunk.name == "small_func"
+    )
+    large_chunk_score = next(
+        score for chunk, score in context.scored_chunks if chunk.name == "large_func"
+    )
+    assert small_chunk_score > large_chunk_score
+
+
+@then("chunks matching a query should have higher scores")
+def step_check_query_matching_score(context):
+    """Check that chunks matching a query have higher scores."""
+    scorer = ChunkRelevanceScorer()
+    score_without_query = scorer.score(context.chunks[0], query=None)
+    score_with_query = scorer.score(context.chunks[0], query="small_func")
+    assert score_with_query > score_without_query
+
+
+@given("a context budget of {budget:d} characters")
+def step_set_context_budget(context, budget):
+    """Set the context budget."""
+    context.context_budget = budget
+
+
+@when("I select chunks within the budget")
+def step_select_chunks_within_budget(context):
+    """Select chunks within the budget."""
+    context.selected_chunks = context.strategy.select_chunks(
+        context.chunks, context_budget=context.context_budget
+    )
+
+
+@then("only chunks that fit should be selected")
+def step_check_chunks_fit_budget(context):
+    """Check that selected chunks fit within the budget."""
+    total_size = sum(len(chunk.content) for chunk in context.selected_chunks)
+    assert total_size <= context.context_budget
+
+
+@then("chunks should be sorted by relevance")
+def step_check_chunks_sorted_by_relevance(context):
+    """Check that chunks are sorted by relevance."""
+    scores = [chunk.relevance_score for chunk in context.selected_chunks]
+    assert scores == sorted(scores, reverse=True)
+
+
+@when("I convert them to context fragments")
+def step_convert_to_fragments(context):
+    """Convert chunks to context fragments."""
+    context.fragments = context.strategy.to_context_fragments(context.chunks)
+
+
+@then("each fragment should have the chunk content")
+def step_check_fragment_content(context):
+    """Check that fragments have the chunk content."""
+    for fragment, chunk in zip(context.fragments, context.chunks, strict=True):
+        assert fragment.content == chunk.content
+
+
+@then("each fragment should have metadata about the chunk type")
+def step_check_fragment_metadata(context):
+    """Check that fragments have metadata about the chunk type."""
+    for fragment, chunk in zip(context.fragments, context.chunks, strict=True):
+        assert "chunk_type" in fragment.metadata
+        assert fragment.metadata["chunk_type"] == chunk.chunk_type
+        assert "chunk_name" in fragment.metadata
+        assert fragment.metadata["chunk_name"] == chunk.name
diff --git a/src/cleveragents/domain/models/acms/semantic_chunking.py b/src/cleveragents/domain/models/acms/semantic_chunking.py
new file mode 100644
index 000000000..508fb7d59
--- /dev/null
+++ b/src/cleveragents/domain/models/acms/semantic_chunking.py
@@ -0,0 +1,305 @@
+"""Semantic chunking strategy for ACMS context assembly."""
+
+import ast
+import re
+from dataclasses import dataclass
+from pathlib import Path
+
+from cleveragents.domain.models.core.context_fragment import (
+    ContextFragment,
+    FragmentProvenance,
+)
+
+
+@dataclass
+class SemanticChunk:
+    """Represents a semantically meaningful chunk of code or text."""
+
+    file_path: str
+    chunk_type: str
+    name: str
+    start_line: int
+    end_line: int
+    content: str
+    relevance_score: float = 0.5
+
+
+class PythonSemanticChunker:
+    """Chunks Python files into semantic units using AST analysis."""
+
+    def __init__(self) -> None:
+        """Initialize the Python semantic chunker."""
+        self.chunks: list[SemanticChunk] = []
+
+    def chunk(self, file_path: str, content: str) -> list[SemanticChunk]:
+        """Chunk a Python file into semantic units."""
+        self.chunks = []
+
+        try:
+            tree = ast.parse(content)
+        except SyntaxError:
+            return [
+                SemanticChunk(
+                    file_path=file_path,
+                    chunk_type="file",
+                    name=Path(file_path).stem,
+                    start_line=1,
+                    end_line=len(content.splitlines()),
+                    content=content,
+                    relevance_score=0.5,
+                )
+            ]
+
+        lines = content.splitlines(keepends=True)
+
+        for node in ast.walk(tree):
+            if isinstance(node, ast.FunctionDef):
+                self._extract_function_chunk(node, lines, file_path)
+            elif isinstance(node, ast.ClassDef):
+                self._extract_class_chunk(node, lines, file_path)
+
+        if not self.chunks:
+            return [
+                SemanticChunk(
+                    file_path=file_path,
+                    chunk_type="file",
+                    name=Path(file_path).stem,
+                    start_line=1,
+                    end_line=len(lines),
+                    content=content,
+                    relevance_score=0.5,
+                )
+            ]
+
+        return self.chunks
+
+    def _extract_function_chunk(
+        self, node: ast.FunctionDef, lines: list[str], file_path: str
+    ) -> None:
+        """Extract a function as a semantic chunk."""
+        start_line = node.lineno
+        end_line = node.end_lineno or node.lineno
+
+        chunk_content = "".join(lines[start_line - 1 : end_line])
+
+        self.chunks.append(
+            SemanticChunk(
+                file_path=file_path,
+                chunk_type="function",
+                name=node.name,
+                start_line=start_line,
+                end_line=end_line,
+                content=chunk_content,
+                relevance_score=0.5,
+            )
+        )
+
+    def _extract_class_chunk(
+        self, node: ast.ClassDef, lines: list[str], file_path: str
+    ) -> None:
+        """Extract a class and its methods as semantic chunks."""
+        start_line = node.lineno
+        end_line = node.end_lineno or node.lineno
+
+        chunk_content = "".join(lines[start_line - 1 : end_line])
+
+        self.chunks.append(
+            SemanticChunk(
+                file_path=file_path,
+                chunk_type="class",
+                name=node.name,
+                start_line=start_line,
+                end_line=end_line,
+                content=chunk_content,
+                relevance_score=0.5,
+            )
+        )
+
+        for item in node.body:
+            if isinstance(item, ast.FunctionDef):
+                method_start = item.lineno
+                method_end = item.end_lineno or item.lineno
+                method_content = "".join(lines[method_start - 1 : method_end])
+
+                self.chunks.append(
+                    SemanticChunk(
+                        file_path=file_path,
+                        chunk_type="method",
+                        name=f"{node.name}.{item.name}",
+                        start_line=method_start,
+                        end_line=method_end,
+                        content=method_content,
+                        relevance_score=0.5,
+                    )
+                )
+
+
+class MarkdownSemanticChunker:
+    """Chunks Markdown files into semantic units based on sections."""
+
+    def __init__(self) -> None:
+        """Initialize the Markdown semantic chunker."""
+        self.chunks: list[SemanticChunk] = []
+
+    def chunk(self, file_path: str, content: str) -> list[SemanticChunk]:
+        """Chunk a Markdown file into semantic units."""
+        self.chunks = []
+        lines = content.splitlines(keepends=True)
+
+        header_pattern = re.compile(r"^(#{1,6})\s+(.+)$")
+        headers: list[tuple[int, int, str]] = []
+
+        for i, line in enumerate(lines):
+            match = header_pattern.match(line)
+            if match:
+                _level = len(match.group(1))
+                title = match.group(2).strip()
+                headers.append((i, _level, title))
+
+        if not headers:
+            return [
+                SemanticChunk(
+                    file_path=file_path,
+                    chunk_type="file",
+                    name=Path(file_path).stem,
+                    start_line=1,
+                    end_line=len(lines),
+                    content=content,
+                    relevance_score=0.5,
+                )
+            ]
+
+        for i, (header_line, _level, title) in enumerate(headers):
+            end_line = headers[i + 1][0] if i + 1 < len(headers) else len(lines)
+
+            chunk_content = "".join(lines[header_line:end_line])
+
+            self.chunks.append(
+                SemanticChunk(
+                    file_path=file_path,
+                    chunk_type="section",
+                    name=title,
+                    start_line=header_line + 1,
+                    end_line=end_line,
+                    content=chunk_content,
+                    relevance_score=0.5,
+                )
+            )
+
+        return self.chunks
+
+
+class ChunkRelevanceScorer:
+    """Scores the relevance of chunks for context selection."""
+
+    def __init__(self) -> None:
+        """Initialize the chunk relevance scorer."""
+
+    def score(
+        self,
+        chunk: SemanticChunk,
+        query: str | None = None,
+        context_budget: int = 4096,
+    ) -> float:
+        """Score the relevance of a chunk."""
+        score = 0.5
+
+        chunk_size = len(chunk.content)
+        if chunk_size < context_budget * 0.1:
+            score += 0.2
+        elif chunk_size < context_budget * 0.5:
+            score += 0.1
+
+        if chunk.chunk_type in ("class", "function", "method"):
+            score += 0.1
+
+        if query and query.lower() in chunk.name.lower():
+            score += 0.3
+
+        return min(1.0, max(0.0, score))
+
+
+class SemanticChunkingStrategy:
+    """Context strategy that uses semantic chunking for context assembly."""
+
+    def __init__(self) -> None:
+        """Initialize the semantic chunking strategy."""
+        self.python_chunker = PythonSemanticChunker()
+        self.markdown_chunker = MarkdownSemanticChunker()
+        self.scorer = ChunkRelevanceScorer()
+
+    def chunk_file(self, file_path: str, content: str) -> list[SemanticChunk]:
+        """Chunk a file based on its type."""
+        path = Path(file_path)
+
+        if path.suffix == ".py":
+            return self.python_chunker.chunk(file_path, content)
+        elif path.suffix in (".md", ".markdown"):
+            return self.markdown_chunker.chunk(file_path, content)
+        else:
+            return [
+                SemanticChunk(
+                    file_path=file_path,
+                    chunk_type="file",
+                    name=path.stem,
+                    start_line=1,
+                    end_line=len(content.splitlines()),
+                    content=content,
+                    relevance_score=0.5,
+                )
+            ]
+
+    def select_chunks(
+        self,
+        chunks: list[SemanticChunk],
+        query: str | None = None,
+        context_budget: int = 4096,
+    ) -> list[SemanticChunk]:
+        """Select chunks based on relevance and budget constraints."""
+        scored_chunks = [
+            (chunk, self.scorer.score(chunk, query, context_budget))
+            for chunk in chunks
+        ]
+
+        scored_chunks.sort(key=lambda x: x[1], reverse=True)
+
+        selected: list[SemanticChunk] = []
+        total_size = 0
+
+        for chunk, score in scored_chunks:
+            chunk_size = len(chunk.content)
+            if total_size + chunk_size <= context_budget:
+                chunk.relevance_score = score
+                selected.append(chunk)
+                total_size += chunk_size
+
+        return selected
+
+    def to_context_fragments(
+        self, chunks: list[SemanticChunk]
+    ) -> list[ContextFragment]:
+        """Convert semantic chunks to context fragments."""
+        fragments: list[ContextFragment] = []
+
+        for chunk in chunks:
+            provenance = FragmentProvenance(
+                resource_uri=chunk.file_path,
+                location=f"{chunk.start_line}-{chunk.end_line}",
+                strategy="semantic_chunking",
+            )
+            fragment = ContextFragment(
+                uko_node=f"file://{chunk.file_path}#{chunk.name}",
+                content=chunk.content,
+                detail_depth=0,
+                token_count=len(chunk.content.split()),
+                relevance_score=chunk.relevance_score,
+                provenance=provenance,
+                metadata={
+                    "chunk_type": chunk.chunk_type,
+                    "chunk_name": chunk.name,
+                    "relevance_score": str(chunk.relevance_score),
+                },
+            )
+            fragments.append(fragment)
+
+        return fragments