django-pydantic-agent
The settings-agnostic agent-host substrate shared by Django's Pydantic-AI transports.
It takes an AgentConfig in and returns a built pydantic_ai.Agent — with
composed toolsets, audit, and user resolution — and reads no Django settings
of its own. Each transport owns its settings namespace, builds an AgentConfig,
and hands it down:
- django-ag-ui — AG-UI over SSE for a browser client.
- django-a2a — agent-to-agent (planned).
pip install django-pydantic-agent
Most projects install a transport and get this as a dependency. Reach for it directly when you are building a transport, sharing agent construction between two of them, or embedding an agent with no HTTP surface at all.
from django_pydantic_agent import AgentConfig, ToolRegistry, build_agent, tool
registry = ToolRegistry()
@tool(registry)
def list_orders(status: str) -> list[dict]:
"""List orders with the given status."""
...
agent = build_agent(registry, AgentConfig(model="anthropic:claude-sonnet-4-5"))
What it provides
- Agent construction —
AgentConfig→build_agent, model and provider resolution, and an escape hatch to replace construction entirely. - A tool registry —
@toolwithdestructive/category/confirm/summarymetadata, and JSON Schema derived from the signature. - Storage contracts —
ConversationStore/AttachmentStoreprotocols, reference implementations, and Django models incontrib.store. - Policy — an audit trail over every tool call, and a server-side approval gate for destructive tools. Both off by default.
- Integrations — optional bridges exposing
djangorestframework-servicesspecs or adrf-mcp-serverregistry as agent tools, in process.
Optional extras: [drf-mcp] (bridge a drf-mcp-server registry as a toolset),
[spec-tools] (drf-services specs as agent tools), [harness]
(pydantic-ai-harness capabilities), and the provider extras [anthropic] /
[openai] / [google].
Documentation
https://artui.github.io/django-pydantic-agent/
- Concepts — how a config becomes an agent, and what belongs here versus in a transport.
- Tools — registering tools and what the metadata does.
- Storage — the store contracts, owner scoping, and the reference models.
- Policy — audit and the destructive-tool gate.
- Integrations — DRF specs and MCP tools as agent tools.
- Reference — the full public API.
1 maintainer
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