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

Same primitives. Every model. Your infrastructure.

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.

Metric	Status
Total Downloads
Monthly Reach
Open Source Activity
Analytics	View Live Download Trends on ClickPy →

pip install projectdavid

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

import os from dotenv import load_dotenv from projectdavid import Entity load_dotenv() client = Entity( base_url=os.getenv("BASE_URL"), # default: http://localhost:80 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.

	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	❌	✅	✅
Community Adoption	Proprietary	High

Full list of supported providers and endpoints →

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

Full SDK documentation →

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

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