fabra-ai 2.1.0

Context infrastructure for AI applications

Fabra

Fabra records what your AI saw — so you can replay and debug it.

Fabra creates a replayable Context Record for every AI call: what data was used,
where it came from, what got dropped, and why.

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The Problem

ML Engineers: Feast needs Kubernetes and Spark. You have a 4-person team and a deadline.

AI Engineers: Legal asked what data your AI used for a decision last Tuesday. You had no answer.

Both problems have the same root cause: you don't own your data pipeline.

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30 Seconds to Proof

pip install fabra-ai && fabra demo

That's it. Server starts, makes a test request, shows you the result. No Docker. No config files. No API keys.

What you'll see

  Fabra Demo Server

  Testing feature retrieval...
  curl localhost:8000/features/user_engagement?entity_id=user_123

  Response:
  {
    "value": 87.5,
    "freshness_ms": 0,
    "served_from": "online"
  }

  Press Ctrl+C to stop, or visit http://localhost:8000/docs

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Two Entry Points, One Infrastructure

For ML Engineers "I need features in production, not a platform team." from fabra import FeatureStore, entity, feature from datetime import timedelta store = FeatureStore() @entity(store) class User: user_id: str @feature(entity=User, refresh=timedelta(hours=1)) def purchase_count(user_id: str) -> int: return db.query( "SELECT COUNT(*) FROM purchases WHERE user_id = ?", user_id ) fabra serve features.py curl localhost:8000/features/purchase_count?entity_id=u123 # {"value": 47, "freshness_ms": 0, "served_from": "online"} Python decorators. Not YAML.

For AI Engineers "Compliance asked what the AI knew. I need an answer." from fabra import FeatureStore from fabra.context import context, ContextItem store = FeatureStore() @context(store, max_tokens=4000, freshness_sla="5m") async def build_prompt(user_id: str, query: str): tier = await store.get_feature("user_tier", user_id) docs = await search_docs(query) return [ ContextItem(content=f"User tier: {tier}", priority=1), ContextItem(content=docs, priority=2), ] ctx = await build_prompt("user_123", "question") print(ctx.id) # ctx_018f3a2b-... (stable Context Record ID) print(ctx.lineage) # exact data used, full provenance Full audit trail. Not a black box.

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Core Concept: Context Record

A Context Record is an immutable snapshot of everything an AI knew at a specific moment in time:

• Structured features with freshness timestamps
• Retrieved documents with content hashes
• Token budget decisions (what was included vs dropped)
• Full lineage metadata (sources, versions, latencies)
• Cryptographic integrity (tamper-evident hashes)

Every Context Record has a stable ID (ctx_...) and can be replayed, diffed, or audited at any time:

# Replay any historical context
fabra context show ctx_018f3a2b-7def-7abc-8901-234567890abc

# Verify cryptographic integrity
fabra context verify ctx_018f3a2b-7def-7abc-8901-234567890abc

# Compare what changed between two decisions
fabra context diff ctx_abc123 ctx_def456

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Why It Works

1. You Own Your Data

LangChain queries your vector DB. Fabra is your vector DB. We ingest, index, track freshness, and serve. When someone asks "what did the AI know?", we have the answer because we never lost sight of the data.

# Replay any historical context
fabra context show ctx_018f3a2b-7def-7abc-8901-234567890abc

# Compare what changed between two decisions
fabra context diff ctx_abc123 ctx_def456

2. Same Code Everywhere

Development uses DuckDB (zero setup). Production uses Postgres + Redis (just add env vars). Your feature definitions don't change.

# Development (right now, on your laptop)
fabra serve features.py

# Production (same code, different backends)
FABRA_ENV=production \
FABRA_POSTGRES_URL=postgresql://... \
FABRA_REDIS_URL=redis://... \
fabra serve features.py

3. Point-in-Time Correctness

Training ML models? We use ASOF JOIN to ensure your training data reflects exactly what the model would have seen at prediction time. No data leakage. No training-serving skew.

4. Token Budgets That Work

No more prompt length errors in production. Set a budget, assign priorities, and low-priority items get dropped automatically.

@context(store, max_tokens=4000)
async def build_prompt(user_id: str, query: str):
    return [
        ContextItem(content=system_prompt, priority=0, required=True),
        ContextItem(content=user_history, priority=1),  # dropped first if over budget
        ContextItem(content=docs, priority=2),
    ]

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What's Real

This isn't a framework that wraps other tools. This is infrastructure:

Capability	What It Does
Feature Store	@feature decorators, online/offline stores, point-in-time joins
Context Store	@context decorators, token budgeting, lineage tracking
Vector Search	Built-in pgvector, automatic chunking, freshness tracking
Context Replay	fabra context show <id> returns exact historical state
Context Diff	fabra context diff <id1> <id2> shows what changed
Freshness SLAs	freshness_sla="5m" fails if data is stale
Diagnostics	fabra doctor validates your setup

CLI

fabra serve features.py      # Start the server
fabra demo                   # Interactive demo (no setup)
fabra doctor                 # Diagnose configuration issues
fabra context show <id>      # Replay historical context
fabra context diff <a> <b>   # Compare two contexts
fabra context list           # List recent contexts
fabra context export <id>    # Export for audit
fabra deploy fly|railway     # Generate deployment config
fabra ui features.py         # Launch the dashboard (requires Node.js)

Note: fabra ui requires Node.js. Run npm install in src/fabra/ui-next/ if dependencies aren't installed.

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How Fabra Fits in Your Stack

Fabra is not a replacement for:

Tool	Purpose	Relationship to Fabra
Airflow / Dagster	Batch workflow orchestration	Use for pipelines that feed Fabra
MLflow / W&B	Model training & experiment tracking	Use for training; Fabra handles inference-time context
LangChain / LlamaIndex	LLM orchestration & chains	Use for orchestration; Fabra provides the data layer

Fabra replaces or complements:

Tool	Fabra Advantage
Feast	Simpler setup, built-in context assembly
Custom feature serving	Production-ready out of the box
Ad-hoc RAG pipelines	Lineage, freshness SLAs, token budgets

See full comparison guide for detailed breakdowns vs Feast, Tecton, LangChain, and Pinecone.

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Honest Comparison

vs Feast

	Feast	Fabra
Setup	Kubernetes + Spark	pip install
Config	YAML files	Python decorators
Local dev	Docker required	Works immediately
Context/RAG	Not supported	Built-in

Choose Feast if: You have a platform team and existing K8s infrastructure.

vs LangChain

	LangChain	Fabra
Architecture	Orchestration framework	Storage + serving infrastructure
Data ownership	Queries external stores	Owns the write path
Audit trail	None	Full lineage + replay
Token management	DIY	Built-in budgets

Choose LangChain if: You need agent chains and don't need compliance.

Note: You can use Fabra + LangChain together — Fabra for storage/serving, LangChain for orchestration.

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Production Checklist

• Observability: Prometheus metrics at /metrics, structured logging
• Reliability: Circuit breakers, fallback chains, health checks
• Security: Self-hosted, your data stays in your infrastructure
• Deployment: One-command deploy to Fly.io, Railway, Cloud Run, Render

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Quick Start (Detailed)

Feature Store

pip install fabra-ai
fabra serve examples/demo_features.py

Test it:

curl localhost:8000/features/user_engagement?entity_id=user_123

Response:

{"value": 87.5, "freshness_ms": 0, "served_from": "online"}

Context Store

pip install fabra-ai
fabra serve examples/demo_context.py

Test it:

curl -X POST localhost:8000/v1/context/chat_context \
  -H "Content-Type: application/json" \
  -d '{"user_id":"user_123","query":"how do features work?"}'

Response:

{
  "id": "ctx_018f3a2b-...",
  "content": "You are a helpful AI assistant...",
  "meta": {
    "freshness_status": "guaranteed",
    "token_usage": 150
  },
  "lineage": {
    "features_used": ["user_tier", "user_engagement_score"],
    "retrievers_used": ["demo_docs"]
  }
}

Replay it later:

fabra context show ctx_018f3a2b-...

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What Fabra Does Not Do

• Agent orchestration - Use LangChain
• Workflow scheduling - Use Airflow/Dagster
• High-QPS streaming inference - Use Tecton
• No-code builders - This is Python infrastructure

Fabra focuses on one thing: making inference context provable.

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Try in Browser · Quickstart · Docs

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Fabra · Apache 2.0 · 2025

Once context is a ledger, everything else follows.