Context delivery for autonomous agents. Not an ORM.
The reDB Python SDK is a client library for building autonomous agents and agent-based systems that need governed, explainable, real-time access to data across databases, data warehouses, streams, and APIs.
Instead of exposing raw tables or objects, the SDK delivers context:
- Typed and schema-aware
- Policy-enforced and purpose-bound
- Traceable with full provenance
- Consistent across the entire reDB mesh
This SDK is designed for AI agents, not applications that manually manage data access.
This is not:
- ❌ an ORM
- ❌ an object–table mapper
- ❌ a persistence framework
- ❌ a query builder
- ❌ a replacement for SQL
There are:
- no models
- no sessions
- no implicit joins
- no
.save()or.delete() - no database-specific abstractions
If you are looking for an ORM, this is the wrong tool.
Traditional tools ask:
“How do I map database rows into objects?”
reDB asks:
“What context does an agent need to reason and act correctly?”
The SDK delivers Context Bundles, not entities.
A Context Bundle includes:
- logical schema and full type information
- semantic and sensitivity classifications (PII, PHI, etc.)
- policy effects (what was masked or filtered, and why)
- provenance and lineage (where the data came from)
- time, freshness, and snapshot semantics
This allows agents to reason safely, reproducibly, and audibly.
The entire SDK revolves around three primitives:
ctx = client.context.fetch(
view="support_case_v3",
keys={"case_id": "C-123"},
purpose="support_assist"
)You do not fetch tables.
You request purpose-bound context views.
for task in client.agent(pool="fraud.reviewers", purpose="fraud_review"):
ctx = task.context(view="fraud_signals_v2")
decision = run_agent(ctx)
client.emit("orders.actions", decision, idempotency_key=task.id)
task.ack()The reDB mesh:
- ingests events
- load-balances them across agent instances
- expands context consistently
- handles retries, backpressure, and failures
client.emit(
product="orders.actions",
event={
"order_id": "...",
"action": "FLAG_FOR_REVIEW",
"confidence": 0.93
}
)Agents emit facts and decisions.
They do not mutate databases directly.
The reDB mesh is instance-aware.
Every SDK client:
- registers itself as an application instance
- is tracked individually by the mesh
- can be load-balanced automatically
- is auditable down to the exact runtime
This enables:
- horizontal scaling of agents
- built-in load balancing
- safe retries and reprocessing
- per-instance governance and quotas
No external schedulers required.
| Capability | ORMs | Data APIs | reDB SDK |
|---|---|---|---|
| Cross-database context | ❌ | ✅ | |
| Streams as first-class | ❌ | ✅ | |
| Purpose-bound access | ❌ | ❌ | ✅ |
| Policy-aware results | ❌ | ❌ | ✅ |
| Provenance & audit | ❌ | ❌ | ✅ |
| Agent load balancing | ❌ | ❌ | ✅ |
| Instance-level identity | ❌ | ❌ | ✅ |
This SDK is not a thinner abstraction over databases.
It is a new interface layer between data and autonomous systems.
- The SDK communicates with the reDB mesh over a typed, streaming protocol
- All governance, policy enforcement, and routing happen inside the mesh
- The SDK remains lightweight, declarative, and explicit
Agents describe intent.
The mesh handles execution, safety, and scale.
Use the reDB Python SDK if:
- you are building AI agents or agent systems
- data spans multiple databases, streams, or APIs
- governance, auditability, and explainability matter
- scaling agents is painful today
- you want a consistent interface across environments
Do not use this if:
- you only need CRUD access to a single database
- you want to manage joins, transactions, and persistence manually
- you are building a traditional request/response application
This SDK is under active development.
The public API is being designed carefully to avoid ORM-like patterns and to remain stable as the reDB mesh evolves.
reDB Python SDK delivers governed, explainable context to autonomous agents — and scales them by design.