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// Pre-Launch · June 2026

The Multi-Agent
Platform
for Enterprises.

Federation · Memory · Belief · Replay

Live on GKE · MIT License · v0.1.1 · PyPI / GHCR / Helm
01 / Problem

Enterprise multi-agent AI
has a coordination crisis.

01

No shared memory

Every agent starts blank. Valuable context from Agent A never reaches Agent B. The fleet rediscovers facts on every call.

02

No audit trail

When something goes wrong, you cannot answer: what did the agent believe, from what source, at what time? Compliance teams are blocked.

03

No reconciliation

Two agents commit conflicting facts about inventory, a customer, or a forecast. There is no resolution layer. The error propagates silently.

04

Data access is unsafe

Agents query raw databases. Prompt injection attacks succeed because there is no policy layer between the model and the data.

02 / Solution

One runtime.
Three layers.
Every agent.

①
Federation

Pushdown execution to MongoDB, Snowflake, BigQuery, REST. Typed summaries with provenance. The model never touches raw rows.

②
Memory

Hot, warm, cold tiers. Automatic eviction, compaction, salience ranking. Shared across the fleet, scoped by permission.

③
Belief & Audit

Every fact attributed to an agent, a source, a timestamp. Queryable belief history. Branching replay for compliance and root-cause.

03 / Architecture

The 8-step router.
Per agent. <40ms.

01
Parse
Intent · entities
02
Policy
Scope check
03
Belief
Reconcile
04
Hydrate
Memory pull
05
Fetch
Mesh decision
06
Assemble
Context build
07
Call
LLM via litellm
08
Write
Beliefs · trace

Most platforms only do steps 1, 7, and 8. Steps 2–6 — policy, belief, memory, federation, assembly — are where Ezra sits.

04 / Why Ezra

Five things no other
platform ships.

i.
Pushdown execution The database does the math. The model gets typed summaries, never raw rows.
ii.
Versioned belief audit Every agent commitment, attributed by agent_id. Exportable at any timestamp.
iii.
Branching replay Reconstruct any agent state. Mutate it. Run forward. Diff. The compliance tool.
iv.
Time-travel federation Run any query as of any prior timestamp. Explicit provenance throughout.
v.
Cross-agent reconciliation Four strategies: last-write, highest-trust, escalate, or a custom resolver.
05 / Landscape

The layer under
the frameworks.

They do Ezra does
MemGPT · Letta Self-editing memory for a single agent. Shared, permission-scoped memory and reconciled beliefs for the entire fleet — with a versioned audit trail.
Langflow Visual builder for agent flows. The cloud runtime those flows need underneath — state, federated data, audit.
LangGraph · ADK Orchestration and control flow. Plugs in as their state plane — a first-class Google ADK toolset ships today.

What MemGPT did for one agent's context window, Ezra does for a fleet's shared state. Complementary to your orchestrator — not another one.

06 / The Ask

Join the
early access
program.

We are looking for two or three enterprise AI teams to co-build the production version. In exchange: direct influence on the roadmap, priority support, and pre-launch pricing.

pip install ezra-client docker pull ghcr.io/xavio2495/ezra-api:0.1.1 helm install ezra oci://ghcr.io/xavio2495/charts/ezra
Book a Briefing → Read the Docs → Quick Start →
2495.immanuel@gmail.com · github.com/xavio2495/Ezra · v0.1.1 · MIT