crewai-endee 0.1.1b7

Endee vector database integration for CrewAI agent memory

crewai-endee

Endee vector database integration for CrewAI agent memory

crewai-endee connects Endee to CrewAI, giving your agents persistent memory with dense, hybrid, and filtered retrieval.

---

Installation

Requires Python 3.9+.

pip install crewai-endee

This installs endee, endee_model, crewai, and fastembed automatically.

Add your embedding provider (only install the one you use):

pip install cohere       # Cohere
pip install openai       # OpenAI
pip install google-genai # Google Gemini

---

Quick Start

from crewai_endee import EndeeVectorStore

store = EndeeVectorStore(
    type="my_index",
    embedder_config={"provider": "cohere", "config": {"model_name": "embed-english-light-v3.0"}},
)

store.save(value="Go is a statically typed language by Google.", metadata={"lang": "Go"})
results = store.search("static typing", limit=3)

---

Connect to Endee

First, configure your embedding provider:

embedder_config = {
    "provider": "cohere",
    "config": {"model_name": "embed-english-light-v3.0", "api_key": "YOUR_COHERE_API_KEY"},
}

With API token

Sign up at endee.io and get your token. See the Endee docs for details.

from crewai_endee import EndeeVectorStore

store = EndeeVectorStore(
    type="my_index",
    embedder_config=embedder_config,
    api_token="YOUR_ENDEE_API_TOKEN",
)

Without API token (local)

Run the open-source Endee server locally. See github.com/endee-io/endee for setup instructions. Then omit api_token:

store = EndeeVectorStore(
    type="my_index",
    embedder_config=embedder_config,
)

---

Dense Mode

from crewai_endee import EndeeVectorStore

store = EndeeVectorStore(
    type="demo_dense",
    embedder_config=embedder_config,
    api_token=ENDEE_API_TOKEN,
    space_type="cosine",
    precision="int8",
)

store.ensure_index()
store.save(value="Python is a dynamic language.", metadata={"lang": "Python"})
results = store.search("dynamic typing", limit=3)

---

Hybrid Mode

Add sparse_model_name to enable dense + sparse (BM25):

hybrid_store = EndeeVectorStore(
    type="demo_hybrid",
    embedder_config=embedder_config,
    api_token=ENDEE_API_TOKEN,
    sparse_model_name="endee/bm25",
)

hybrid_store.ensure_index()
hybrid_store.save(value="Go has native concurrency.", metadata={"lang": "Go"})

# Hybrid search — combines dense similarity with BM25 keyword matching
results = hybrid_store.search("concurrency", limit=3)

# Tune fusion weights (hybrid only)
results = hybrid_store.search("concurrency", limit=3, dense_rrf_weight=0.8, rrf_rank_constant=60)

---

Search with Filters

Endee uses MongoDB-style operator syntax for query filters:

# Filter by exact match
results = store.search("web language", limit=3, filter=[{"typing": {"$eq": "dynamic"}}])

# Filter with score threshold
results = store.search("systems programming", limit=3, filter=[{"typing": {"$eq": "static"}}], score_threshold=0.2)

# Include raw vectors in results
results = store.search("interpreted language", limit=1, include_vectors=True)

Supported operators: $eq, $ne, $gt, $gte, $lt, $lte, $in

---

Index Operations

# Describe index metadata (count, dimension, precision, etc.)
info = store.describe()

# Retrieve a single vector by ID
vec = store.get_vector("some_vector_id")

# Update filter metadata without re-embedding
store.update_filters([{"id": "some_vector_id", "filter": {"reviewed": "true"}}])

# Delete a single vector by ID
store.delete_vector("some_vector_id")

# Delete all vectors matching a filter
store.delete(filter=[{"category": {"$eq": "outdated"}}])

# Delete the entire index
store.reset()

---

CrewAI Integration

EndeeVectorStore extends CrewAI's RAGStorage. Wire it into a Crew via ShortTermMemory and EntityMemory:

from crewai import LLM, Agent, Crew, Process, Task
from crewai.memory.short_term.short_term_memory import ShortTermMemory
from crewai.memory.entity.entity_memory import EntityMemory
from crewai_endee import EndeeVectorStore

# Create Endee-backed stores for crew memory
stm_store = EndeeVectorStore(
    type="crew_short_term",
    embedder_config=embedder_config,
    api_token=ENDEE_API_TOKEN,
)

entity_store = EndeeVectorStore(
    type="crew_entity",
    embedder_config=embedder_config,
    api_token=ENDEE_API_TOKEN,
)

# Wrap as CrewAI memory objects
short_term_memory = ShortTermMemory(storage=stm_store)
entity_memory = EntityMemory(storage=entity_store)

# LLM + Agents
llm = LLM(model="gemini/gemini-2.5-flash", api_key=GOOGLE_API_KEY)

analyst = Agent(
    role="Software Analyst",
    goal="Extract programming language characteristics",
    backstory="You study programming language design and typing systems.",
    llm=llm,
)

# Tasks
analysis_task = Task(
    description="Analyse key characteristics of Python, Java, Go, Rust, and C++.",
    expected_output="Structured summary of each language.",
    agent=analyst,
)

# Crew with Endee-backed memory
crew = Crew(
    agents=[analyst],
    tasks=[analysis_task],
    process=Process.sequential,
    memory=True,
    short_term_memory=short_term_memory,
    entity_memory=entity_memory,
    embedder=embedder_config,
    verbose=True,
)

result = crew.kickoff()
print(result)

---

Supported Sparse Models

from crewai_endee import list_supported_models

for name, config in list_supported_models().items():
    print(f"  {name} — {config['description']}")

Model	Description
endee/bm25	BM25 sparse embeddings via endee_model
splade_pp	SPLADE++ for English — semantic + keyword hybrid search

---

API Reference

Constructor Parameters

Parameter	Type	Default	Description
type	str	(required)	Unique Endee index name
embedder_config	dict	None	Dense embedder config (provider, config)
api_token	str	None	Endee API token (omit for local mode)
space_type	str	"cosine"	Distance metric: cosine, l2, or ip
precision	str	server default	float32, float16, int16, int8, or binary
sparse_model_name	str	None	Set to enable hybrid mode (e.g. "endee/bm25")
ef_con	int	None	HNSW ef_construction for index build quality
text_key	str	"value"	Metadata key under which document text is stored
endee_index	Index	None	Pre-existing endee.Index object
allow_reset	bool	True	Whether reset() is permitted

Methods

Method	Description
save(value, metadata)	Embed text and upsert into the index
search(query, limit, filter, score_threshold, ...)	Semantic search with optional filters
ensure_index()	Create / verify the index and print status
describe()	Return index metadata (count, dimension, precision, etc.)
get_vector(id)	Retrieve a single vector by ID
delete_vector(id)	Delete a single vector by ID
delete(filter)	Delete all vectors matching a metadata filter
update_filters(updates)	Update filter metadata without re-embedding
reset()	Delete the entire index and reset state

Search Parameters

Parameter	Default	Description
query	(required)	Natural-language search query
limit	3	Max results (max 512)
filter	None	MongoDB-style filters: [{"field": {"$op": value}}]
score_threshold	0	Minimum similarity score to include
ef_search	None	HNSW ef parameter (default 128, max 1024)
include_vectors	False	Include raw vector data in results
dense_rrf_weight	None	Dense score weight in hybrid RRF fusion (0.0–1.0)
rrf_rank_constant	None	RRF constant k (default 60)

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Interactive Demo

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Full Endee documentation: docs.endee.io | GitHub: endee-io/endee | CrewAI docs: docs.crewai.com