The AI Orchestration Platform
Deploy secure AI apps that run reliably, remember everything, and scale effortlessly.
focus on logic, not infrastructure.
Reliable Execution
Your AI apps run reliably, recovering automatically from failures and idle suspensions with no lost progress.
Persistent State
Your app state persists automatically, ensuring continuity through failures, suspensions, and runs.
Simple Logic
Your orchestration logic is simple, free from the complexity of persistence, retries, and recovery.
1class ResearchCoordinatorAgent:
2 def __init__(self):
3 # Automatically persisted
4 self.research_tasks = {}
5
6 async def conduct_research(self, topic):
7 task_id = generate_unique_id()
8
9 # Exactly-once calls to other agents
10 search_results = await search_agent.query(topic)
11 analysis = await analyst_agent.analyze(search_results)
12 report = await writing_agent.generate_report(analysis)
13
14 # State automatically preserved
15 self.research_tasks[task_id] = {
16 "topic": topic,
17 "status": "completed",
18 "report_id": report.id
19 }
20
21 return report1class ResearchCoordinatorAgent:
2 def __init__(self):
3 # Automatically persisted
4 self.research_tasks = {}
5
6 async def conduct_research(self, topic):
7 task_id = generate_unique_id()
8
9 # Exactly-once calls to other agents
10 search_results = await search_agent.query(topic)
11 analysis = await analyst_agent.analyze(search_results)
12 report = await writing_agent.generate_report(analysis)
13
14 # State automatically preserved
15 self.research_tasks[task_id] = {
16 "topic": topic,
17 "status": "completed",
18 "report_id": report.id
19 }
20
21 return report1class ResearchCoordinatorAgent:
2 def __init__(self):
3 # Automatically persisted
4 self.research_tasks = {}
5
6 async def conduct_research(self, topic):
7 task_id = generate_unique_id()
8
9 # Exactly-once calls to other agents
10 search_results = await search_agent.query(topic)
11 analysis = await analyst_agent.analyze(search_results)
12 report = await writing_agent.generate_report(analysis)
13
14 # State automatically preserved
15 self.research_tasks[task_id] = {
16 "topic": topic,
17 "status": "completed",
18 "report_id": report.id
19 }
20
21 return report1class ResearchCoordinatorAgent:
2 def __init__(self):
3 # Automatically persisted
4 self.research_tasks = {}
5
6 async def conduct_research(self, topic):
7 task_id = generate_unique_id()
8
9 # Exactly-once calls to other agents
10 search_results = await search_agent.query(topic)
11 analysis = await analyst_agent.analyze(search_results)
12 report = await writing_agent.generate_report(analysis)
13
14 # State automatically preserved
15 self.research_tasks[task_id] = {
16 "topic": topic,
17 "status": "completed",
18 "report_id": report.id
19 }
20
21 return reportHow It Works
Golem runs your AI agents, workflows, and agentic services in secure WASM sandboxes. With real-time state replication and supervision, your AI apps become invincible, instantly observable, undoable, and resilient, in an elastic deployment that scales effortlessly.
No timeouts. No manual recovery. No lost state.
Automatic state persistence through continuous replication
Instant recovery & resumption through deterministic replay
Zero infrastructure—no databases, queues, or retry logic
Explore the Docs →
See It in Action
Without Golem
Reliable AI apps require complex infrastructure and operations to handle failures, idle suspensions, handoffs, API usage, long runs, and state persistence.
Databases for
persistence
persistence
Message queues
for reliability
for reliability
Schedulers to
manage task
restarts
manage task
restarts
Distributed event
logs for tracking
state changes
logs for tracking
state changes
Retry logic
for failure
recovery
for failure
recovery
Distributed
consensus for
partitioning
consensus for
partitioning
With Golem
Golem runs your AI apps reliably, persisting state across failures and idle suspensions automatically. Exactly-once interactions with agents, workflows and tools simplify logic.
1class ResearchCoordinatorAgent:
2 def __init__(self):
3 # Automatically persisted
4 self.research_tasks = {}
5
6 async def conduct_research(self, topic):
7 task_id = generate_unique_id()
8
9 # Exactly-once calls to other agents
10 search_results = await search_agent.query(topic)
11 analysis = await analyst_agent.analyze(search_results)
12 report = await writing_agent.generate_report(analysis)
13
14 # State automatically preserved
15 self.research_tasks[task_id] = {
16 "topic": topic,
17 "status": "completed",
18 "report_id": report.id
19 }
20
21 return report1class ResearchCoordinatorAgent:
2 def __init__(self):
3 # Automatically persisted
4 self.research_tasks = {}
5
6 async def conduct_research(self, topic):
7 task_id = generate_unique_id()
8
9 # Exactly-once calls to other agents
10 search_results = await search_agent.query(topic)
11 analysis = await analyst_agent.analyze(search_results)
12 report = await writing_agent.generate_report(analysis)
13
14 # State automatically preserved
15 self.research_tasks[task_id] = {
16 "topic": topic,
17 "status": "completed",
18 "report_id": report.id
19 }
20
21 return report1class ResearchCoordinatorAgent:
2 def __init__(self):
3 # Automatically persisted
4 self.research_tasks = {}
5
6 async def conduct_research(self, topic):
7 task_id = generate_unique_id()
8
9 # Exactly-once calls to other agents
10 search_results = await search_agent.query(topic)
11 analysis = await analyst_agent.analyze(search_results)
12 report = await writing_agent.generate_report(analysis)
13
14 # State automatically preserved
15 self.research_tasks[task_id] = {
16 "topic": topic,
17 "status": "completed",
18 "report_id": report.id
19 }
20
21 return report1class ResearchCoordinatorAgent:
2 def __init__(self):
3 # Automatically persisted
4 self.research_tasks = {}
5
6 async def conduct_research(self, topic):
7 task_id = generate_unique_id()
8
9 # Exactly-once calls to other agents
10 search_results = await search_agent.query(topic)
11 analysis = await analyst_agent.analyze(search_results)
12 report = await writing_agent.generate_report(analysis)
13
14 # State automatically preserved
15 self.research_tasks[task_id] = {
16 "topic": topic,
17 "status": "completed",
18 "report_id": report.id
19 }
20
21 return reportExplore the Docs →
Why Golem?
AI applications demand long-running reliability, persistent state, precise coordination, and security, all in a package that scales elastically and efficiently. For the first time, Golem helps you tame this complexity with ease.
Feature
Golem
Self-Managed
Serverless (FaaS)
No-Code Agents
Fully Customizable
Complete code flexibility
Flexible but complex
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Limited runtime
Restricted workflows
Stateful Execution
Built-in state persistence
Requires external DBs
Stateless
Basic memory only
Survives Failures
Automatic recovery
Custom orchestration
No built-in recovery
Manual intervention
Long-Running Tasks
Unlimited task duration
Job schedulers needed
Short execution limits
Session-based limits
Reliable Task Handoff
Exactly-once delivery
Requires queues/events
No guarantees
Limited guarantees
Infrastructure Overhead
Zero operational burden
Heavy DevOps burden
Some operational tuning
Restricted workflows
With Golem, your AI apps run reliably, securely, and simply—no timeouts, no lost state, no operational headaches. Build with confidence, scale with ease.
Who’s Using Golem?
Developers and teams across industries rely on Golem to build secure, fault-tolerant, and scalable AI apps—without managing infrastructure.
"Golem is just magic in my book."
David Flanagan
Get Started
Golem makes it easy to deploy reliable, secure AI apps—no setup, no infrastructure, just code.
1. Install Golem CLI
Select your OS
cargo install golem-cli --force --locked2. Create an Agent
golem-cli app new simple-agent rust
cd simple-agent
golem-cli component new rust/simple-agent example:agent3. Deploy & Run
golem-cli app deploy
golem-cli worker invoke example:agent/agent1 do-workThat’s it! Your AI app is live—sandboxed, stateful, resilient, and fault-tolerant.
