projectdavid 1.76.0

Python SDK for interacting with the Entities Assistant API.

projectdavid — Python SDK

The Python SDK for Project David — the open source, GDPR compliant successor to the OpenAI Assistants API.

Same primitives. Every model. Your infrastructure.

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What is Project David?

Project David is a full-scale, containerized LLM orchestration platform built around the same primitives as the OpenAI Assistants API — Assistants, Threads, Messages, Runs, and Tools — but without the lock-in.

• Provider agnostic — Hyperbolic, TogetherAI, Ollama, or any OpenAI-compatible endpoint. Point at any inference provider and the platform normalizes the stream.
• Every model — hosted APIs today, raw local weights tomorrow. Bring your own model.
• Your infrastructure — fully self-hostable, open source, GDPR compliant, security audited.
• Production grade — sandboxed code execution (FireJail), multi-agent delegation, file serving with signed URLs, real-time streaming frontend.

Project Uni5 — the next milestone. transformers, GGUF, and vLLM adapters that mean a model straight off a training run has a full orchestration platform in minutes. From the lab to enterprise grade orchestration — instantly.

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Installation

pip install projectdavid

Requirements: Python 3.10+ · A running Project David platform instance

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Quick Start

import os
from dotenv import load_dotenv
from projectdavid import Entity

load_dotenv()

client = Entity(
    base_url=os.getenv("BASE_URL"),        # default: http://localhost:9000
    api_key=os.getenv("ENTITIES_API_KEY"),
)

# Create an assistant
assistant = client.assistants.create_assistant(
    name="my_assistant",
    instructions="You are a helpful AI assistant.",
)

# Create a thread and send a message
thread = client.threads.create_thread()

message = client.messages.create_message(
    thread_id=thread.id,
    role="user",
    content="Tell me about the latest trends in AI.",
    assistant_id=assistant.id,
)

# Create a run
run = client.runs.create_run(
    assistant_id=assistant.id,
    thread_id=thread.id,
)

# Stream the response
stream = client.synchronous_inference_stream
stream.setup(
    user_id=os.getenv("ENTITIES_USER_ID"),
    thread_id=thread.id,
    assistant_id=assistant.id,
    message_id=message.id,
    run_id=run.id,
    api_key=os.getenv("PROVIDER_API_KEY"),
)

for chunk in stream.stream_chunks(
    model="hyperbolic/deepseek-ai/DeepSeek-V3-0324",
    timeout_per_chunk=15.0,
):
    content = chunk.get("content", "")
    if content:
        print(content, end="", flush=True)

See the Quick Start guide for the event-driven interface, tool calling, and advanced usage.

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Why Project David?

	OpenAI Assistants API	LangChain	Project David
Assistants / Threads / Runs primitives	✅	❌	✅
Provider agnostic	❌	Partial	✅
Local model support	❌	Partial	✅
Raw weights → orchestration	❌	❌	✅ (Uni5)
Sandboxed code execution	✅ Black box	❌	✅ FireJail PTY
Multi-agent delegation	Limited	❌	✅
Self-hostable	❌	✅	✅
GDPR compliant	❌	N/A	✅
Security audited	N/A	N/A	✅
Open source	❌	✅	✅

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Supported Inference Providers

Full list of supported providers and endpoints →

Works with any OpenAI-compatible endpoint out of the box — including Ollama for fully local inference.

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Environment Variables

Variable	Description
ENTITIES_API_KEY	Your Entities API key
ENTITIES_USER_ID	Your user ID
BASE_URL	Platform base URL (default: http://localhost:9000)
PROVIDER_API_KEY	Your inference provider API key

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Documentation

Topic	Link
Quick Start	sdk-quick-start.md
Assistants	sdk-assistants.md
Threads	sdk-threads.md
Messages	sdk-messages.md
Runs	sdk-runs.md
Inference	sdk-inference.md
Tools	sdk-tools.md
Function Calls	function-calling-and-tool-execution.md
Code Interpreter	sdk-code-interpreter.md
Files	sdk-files.md
Vector Store	sdk-vector-store.md

Full SDK documentation →

Full hosted docs coming at docs.projectdavid.co.uk

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Related Repositories

Repo	Description
platform	Core orchestration engine
entities-common	Shared utilities and validation
david-core	Docker orchestration layer
reference-frontend	Reference streaming frontend
entities_cook_book	Minimal tested examples — streaming, tools, search, stateful logic

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Project Uni5 — Roadmap

The next major milestone extends Project David to every model deployment scenario:

Got freshly trained weights?     →  transformers adapter     (Phase 1)
Got a quantized GGUF model?      →  GGUF / llama.cpp adapter (Phase 2)
Got a GPU cluster?               →  vLLM adapter             (Phase 3)
Want a hosted provider?          →  already works
Running Ollama locally?          →  already works

Five scenarios. One platform. From the lab to enterprise grade orchestration — instantly.

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License

PolyForm Noncommercial 1.0.0