Purpose-built backend for AI agents. Production infrastructure, zero configuration.

A Purpose-Built Backend for Agents

Agentfield provides everything you need to deploy autonomous agents in production. No assembly required. Write standard Python functions, and get a complete microservices architecture automatically.

Built for AI Backends

Functions → REST APIs

@app.reasoner()
async def analyze(text: str):
    return await app.ai("Analyze", text)

# → POST /execute/agent.analyze

Instant microservices from Python functions.

Auto-Routing & Discovery

# Call by name - no URLs or config
res = await app.call("analyst.summarize")

# → Control plane routes to available nodes
# → Load balances automatically

Service mesh scaling without config.

Runtime Capability Discovery

# Discover available capabilities
caps = app.discover(tags=["research"])

# AI selects tool at runtime
for reasoner in caps.capabilities[0].reasoners:
    if matches_query(reasoner):
        return await app.call(reasoner.target)

Build AI orchestrators with dynamic tool selection.

Zero-Config Memory

# Agent A writes
await app.memory.set("prefs", data)

# Agent B reads (any server)
val = await app.memory.get("prefs")

Shared state without Redis setup.

Built-in Vector Search

# Store with embeddings
await app.memory.set_vector("doc1", vector)

# Search semantically
results = await app.memory.search(query)

Semantic search without vector DBs.

Frontend-Friendly APIs

// Call standard REST from React/Vue
await fetch('/execute/agent.process', {
  method: 'POST',
  body: JSON.stringify({input: "Hello"})
});

Call agents like standard APIs.

Structured AI Output

class Analysis(BaseModel):
    sentiment: str

# Returns typed object, not string
res = await app.ai("Analyze", text, schema=Analysis)

Type-safe, validated AI responses.

Multi-Modal Support

# Process images & audio natively
res = await app.ai(
    "Describe this",
    "https://example.com/img.jpg"
)

Vision, audio, and text in one API.

Production Without Setup

Async + Webhooks

curl -X POST /v1/execute/async \
  -d '{
    "agent": "research.report",
    "webhook": "https://api.com/cb"
  }'
# → Get ID, receive webhook when done

Serverless-style async tasks via HTTP.

Real-Time Streaming

app.note("📊 Analyzing...")
app.note("✅ Found 23 items")

# → Streams to frontend via SSE
# → No WebSocket setup needed

Live progress updates to UI.

Automatic DAGs

# Workflow history modeled automatically
summary = await app.call("analyst.run")
post = await app.call("writer.write", summary)

# → UI visualizes this graph instantly

Visual execution graphs, zero config.

Polyglot Deployment

# Python for AI Logic
docker run agentfield/python-agent

# Go for High-Perf Routing
docker run agentfield/go-agent

Mix Python AI and Go infrastructure.

Auto-Retry & Backoff

@app.reasoner()
async def process_payment(id: str):
    # Fails? System retries automatically
    # Exponential backoff built-in
    return await api.charge(id)

Resilience without try/catch logic.

Event-Driven Memory

# Agent B reacts to Agent A
@app.memory.on_change("status")
async def handle_change(event):
    if event.data == "critical":
        await alert_team()

Reactive workflows without queues.

Prometheus Metrics

# Automatic metrics:
# - Execution time & Queue depth
# - Success/failure rates
# - Memory operations
# → Scrape at /metrics

Observability from day one.

K8s Health Checks

# Kubernetes probes ready
livenessProbe:
  httpGet:
    path: /health
    port: 8080

Native Kubernetes integration.

Pluggable Storage

# Dev: SQLite (Zero config)
af run

# Prod: PostgreSQL
export STORAGE_MODE=postgres
af run

SQLite for dev, Postgres for prod.

Trust & Compliance

Agent Identity

# Every agent gets an ID
# → did:agentfield:agent-123

# Every execution is signed
# → Verifiable, tamper-proof

Cryptographic decentralized identity for agents.

Verifiable Credentials

# Execution Result = W3C Credential
{
  "type": "VerifiableCredential",
  "issuer": "did:agentfield:agent-1",
  "proof": { ...signature... }
}

Tamper-proof execution proofs.

Offline Verification

# Export audit trail
curl /api/audit > trail.json

# Verify anywhere (no internet)
af verify trail.json

Verify audits without internet.

Chain Provenance

# Agent A → Agent B → Agent C
# VCs linked automatically:
# Proof(C) contains hash(Proof(B))
# Complete provenance chain

Complete history of "Who called whom".

Infrastructure Comparison

See how much boilerplate Agentfield eliminates compared to assembling your own stack.

What You Want	Traditional Approach	AgentField
REST APIs	Write Flask/FastAPI routes manually	@app.reasoner() decorator`@app.reasoner()`
Shared Memory	Setup Redis, write sync code	Built-in scoped memory`await app.memory.set()`
Vector Search	Configure Pinecone/Weaviate separately	Built-in semantic search`await app.memory.similarity_search()`
Service Discovery	Manual routing, hardcoded URLs	Automatic service mesh`await app.call("agent.function")`
Async Execution	Setup Celery + RabbitMQ infrastructure	Built-in async endpoints`POST /v1/execute/async`
Webhooks	Write custom webhook handlers + retry logic	Native webhook support`Built-in with WebhookConfig`
Workflow Visualization	Add disjointed instrumentation, build custom UI	Automatic DAG generation
Audit Trails	Hope unstructured text logs are enough	W3C Verifiable Credentials
Real-time Updates	Setup separate WebSocket server	SSE streaming built-in
Metrics	Add Prometheus instrumentation manually	/metrics endpoint automatic

Swipe left to see full comparison

Ready to Build?

Quick Start

Build and deploy your first agent in 5 minutes

Core Concepts

Deep dive into Agentfield's architecture and capabilities

Features

Control Plane for Production AI