diff --git a/agentstack/_tools/chroma_vectordb/__init__.py b/agentstack/_tools/chroma_vectordb/__init__.py
new file mode 100644
index 0000000..bc44418
--- /dev/null
+++ b/agentstack/_tools/chroma_vectordb/__init__.py
@@ -0,0 +1,162 @@
+import os
+import chromadb
+from chromadb.config import Settings
+from chromadb.utils.embedding_functions import EmbeddingFunction
+from chromadb.utils.embedding_functions.openai_embedding_function import OpenAIEmbeddingFunction
+from typing import List, Dict, Any, Optional, Sequence, Mapping, Union
+from dotenv import load_dotenv
+from typing import cast
+
+# Load environment variables
+load_dotenv()
+
+def create_collection(
+    collection_name: str = "default_collection",
+    persist_directory: str = "chroma_db"
+) -> str:
+    """
+    Creates a new Chroma collection with OpenAI embeddings.
+    
+    Args:
+        collection_name: Name for the collection
+        persist_directory: Directory to store the database
+        
+    Returns:
+        str: Success message with collection details
+    """
+    openai_api_key = os.getenv("OPENAI_API_KEY")
+    if not openai_api_key:
+        raise ValueError("OPENAI_API_KEY environment variable is not set!")
+        
+    client = chromadb.Client(Settings(
+        persist_directory=persist_directory
+    ))
+    
+    embedding_function = cast(
+        EmbeddingFunction,
+        OpenAIEmbeddingFunction(
+            model_name="text-embedding-ada-002",
+            api_key=openai_api_key
+        )
+    )
+    
+    collection = client.get_or_create_collection(
+        name=collection_name,
+        embedding_function=embedding_function
+    )
+    
+    return f"Created collection '{collection_name}' in {persist_directory}"
+
+def add_documents(
+    collection_name: str,
+    documents: List[Dict[str, str]],
+    persist_directory: str = "chroma_db"
+) -> str:
+    """
+    Adds documents to a Chroma collection.
+    
+    Args:
+        collection_name: Name of the collection to add documents to
+        documents: List of documents, each with "content" and "url" keys
+        persist_directory: Directory where the database is stored
+        
+    Returns:
+        str: Success message with number of documents added
+    """
+    openai_api_key = os.getenv("OPENAI_API_KEY")
+    if not openai_api_key:
+        raise ValueError("OPENAI_API_KEY environment variable is not set!")
+        
+    client = chromadb.Client(Settings(
+        persist_directory=persist_directory
+    ))
+    
+    embedding_function = cast(
+        EmbeddingFunction,
+        OpenAIEmbeddingFunction(
+            model_name="text-embedding-ada-002",
+            api_key=openai_api_key
+        )
+    )
+    
+    collection = client.get_collection(
+        name=collection_name,
+        embedding_function=embedding_function
+    )
+    
+    docs: List[str] = []
+    metadatas: List[Mapping[str, Union[str, int, float, bool]]] = []
+    ids: List[str] = []
+    
+    for i, doc in enumerate(documents):
+        docs.append(doc.get("content", ""))
+        metadatas.append({"url": doc.get("url", "")})
+        ids.append(f"doc_{i}")
+    
+    collection.add(
+        documents=docs,
+        metadatas=metadatas,
+        ids=ids
+    )
+    
+    return f"Added {len(documents)} documents to collection '{collection_name}'"
+
+def query_collection(
+    collection_name: str,
+    query_text: str,
+    n_results: int = 3,
+    persist_directory: str = "chroma_db"
+) -> str:
+    """
+    Query a Chroma collection using natural language.
+    
+    Args:
+        collection_name: Name of the collection to query
+        query_text: The search query
+        n_results: Number of results to return
+        persist_directory: Directory where the database is stored
+        
+    Returns:
+        str: Query results including document content and metadata
+    """
+    openai_api_key = os.getenv("OPENAI_API_KEY")
+    if not openai_api_key:
+        raise ValueError("OPENAI_API_KEY environment variable is not set!")
+        
+    client = chromadb.Client(Settings(
+        persist_directory=persist_directory
+    ))
+    
+    embedding_function = cast(
+        EmbeddingFunction,
+        OpenAIEmbeddingFunction(
+            model_name="text-embedding-ada-002",
+            api_key=openai_api_key
+        )
+    )
+    
+    collection = client.get_collection(
+        name=collection_name,
+        embedding_function=embedding_function
+    )
+    
+    results = collection.query(
+        query_texts=[query_text],
+        n_results=n_results
+    )
+    
+    # Format results nicely
+    formatted_results = []
+    for i, (doc, metadata, distance) in enumerate(zip(
+        results['documents'][0],  # type: ignore
+        results['metadatas'][0],  # type: ignore
+        results['distances'][0]   # type: ignore
+    )):
+        formatted_results.append(
+            f"Result {i+1}:\n"
+            f"Content: {doc}\n"
+            f"URL: {metadata['url']}\n"
+            f"Relevance Score: {1 - float(distance):.2f}\n"
+        )
+    
+    return "\n".join(formatted_results)
diff --git a/agentstack/_tools/chroma_vectordb/config.json b/agentstack/_tools/chroma_vectordb/config.json
new file mode 100644
index 0000000..5302f28
--- /dev/null
+++ b/agentstack/_tools/chroma_vectordb/config.json
@@ -0,0 +1,21 @@
+{
+    "name": "chroma_vectordb",
+    "url": "https://www.trychroma.com/",
+    "category": "vector-store",
+    "env": {
+        "OPENAI_API_KEY": null
+    },
+    "dependencies": [
+        "chromadb>=0.4.0",
+        "openai>=1.0.0",
+        "python-dotenv>=1.0.0",
+        "pytest>=7.0.0",
+        "pytest-mock>=3.10.0"
+    ],
+    "tools": [
+        "create_collection",
+        "add_documents",
+        "query_collection"
+    ],
+    "cta": "Make sure you have an OpenAI API key set in your environment variables."
+}
\ No newline at end of file