> ## Documentation Index
> Fetch the complete documentation index at: https://restate-6d46e1dc-mintlify-35bb6672.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Parallel Agents
> Fan out work to multiple agents, then combine the results. Failed agents are retried independently while successful results are preserved.
export const GitHubLink = ({url}) => ;
Fan out work to multiple agents, then combine the results. Restate runs the agents in parallel with automatic retries and recovery. If one agent fails, only that agent is retried, the successful results are preserved.
```mermaid theme={null}
graph LR
A[Claim Request] --> B[Eligibility agent]
A --> C[Rate comparison agent]
A --> D[Fraud check agent]
B --> E[Decision agent]
C --> E
D --> E
```
## Example: parallel claim analysis
Three specialist agents analyze a claim concurrently. A decision agent combines their results.
```typescript workflow-parallel.ts {"CODE_LOAD::https://raw.githubusercontent.com/restatedev/ai-examples/refs/heads/main/vercel-ai/tour-of-agents/src/workflow-parallel.ts#here"} theme={null}
const run = async (ctx: restate.Context, claim: ClaimInput) => {
const [eligibility, rateComparison, fraudCheck] = await RestatePromise.all([
ctx.serviceClient(eligibilityAgent).run(claim),
ctx.serviceClient(rateComparisonAgent).run(claim),
ctx.serviceClient(fraudCheckAgent).run(claim),
]);
const model = wrapLanguageModel({
model: openai("gpt-5.4"),
middleware: durableCalls(ctx, { maxRetryAttempts: 3 }),
});
const { text } = await generateText({
model,
system: "You are a claim decision engine.",
prompt: `Decide about claim ${JSON.stringify(claim)}.
Base your decision on the following analyses:
Eligibility: ${eligibility}, Cost: ${rateComparison} Fraud: ${fraudCheck}`,
});
return text;
};
```
[Install Restate](/installation) and launch it:
```bash theme={null}
npm install --global @restatedev/restate-server@latest @restatedev/restate@latest
restate-server
```
Get the example:
```bash theme={null}
restate example typescript-vercel-ai-tour-of-agents && cd typescript-vercel-ai-tour-of-agents
npm install
```
Export your [OpenAI API key](https://platform.openai.com/api-keys) and run the agent:
```bash theme={null}
export OPENAI_API_KEY=sk-...
```
```bash theme={null}
npx tsx ./src/workflow-parallel.ts
```
Register the agents with Restate:
```bash theme={null}
restate deployments register http://localhost:9080 --force --yes # dev only: overrides previous registrations
```
Start a request for a claim that needs to be analyzed by multiple agents in parallel:
```bash theme={null}
curl localhost:8080/restate/call/ParallelAgentClaimApproval/run --json '{
"date":"2024-10-01",
"category":"orthopedic",
"reason":"hospital bill for a broken leg",
"amount":3000,
"placeOfService":"General Hospital"
}'
```
In the UI, you can see that the handler called the sub-agents in parallel.
Once all sub-agents return, the main agent makes a decision.
```python workflow_parallel.py {"CODE_LOAD::https://raw.githubusercontent.com/restatedev/ai-examples/refs/heads/main/openai-agents/tour-of-agents/app/workflow_parallel.py#here"} theme={null}
@agent_service.handler()
async def run(restate_context: restate.Context, claim: InsuranceClaim) -> str:
# Start multiple agents in parallel with auto retries and recovery
eligibility = restate_context.service_call(run_eligibility_agent, claim)
cost = restate_context.service_call(run_rate_comparison_agent, claim)
fraud = restate_context.service_call(run_fraud_agent, claim)
# Wait for all responses
await restate.gather(eligibility, cost, fraud)
# Run decision agent on outputs
result = await DurableRunner.run(
Agent(
name="ClaimApprovalAgent", instructions="You are a claim decision engine."
),
input=f"Decide about claim: {claim.model_dump_json()}. "
"Base your decision on the following analyses:"
f"Eligibility: {await eligibility} Cost {await cost} Fraud: {await fraud}",
)
return result.final_output
```
[Install Restate](/installation) and launch it:
```bash theme={null}
restate-server
```
Get the example:
```bash theme={null}
restate example python-openai-agents-tour-of-agents && cd python-openai-agents-tour-of-agents
```
Export your [OpenAI API key](https://platform.openai.com/api-keys) and run the agent:
```bash theme={null}
export OPENAI_API_KEY=sk-...
```
```bash theme={null}
uv run app/workflow_parallel.py
```
Register the agents with Restate:
```bash theme={null}
restate deployments register http://localhost:9080 --force --yes # dev only: overrides previous registrations
```
Start a request for a claim that needs to be analyzed by multiple agents in parallel:
```bash theme={null}
curl localhost:8080/restate/call/ParallelAgentClaimApproval/run --json '{
"date":"2024-10-01",
"category":"orthopedic",
"reason":"hospital bill for a broken leg",
"amount":3000,
"placeOfService":"General Hospital"
}'
```
In the UI, you can see that the handler called the sub-agents in parallel.
Once all sub-agents return, the main agent makes a decision.
```python workflow_parallel.py {"CODE_LOAD::https://raw.githubusercontent.com/restatedev/ai-examples/refs/heads/main/google-adk/tour-of-agents/app/workflow_parallel.py#here"} theme={null}
@agent_service.handler()
async def run(ctx: restate.ObjectContext, claim: InsuranceClaim) -> str | None:
# Start multiple agents in parallel with auto retries and recovery
eligibility = ctx.service_call(run_eligibility_agent, claim)
cost = ctx.service_call(run_rate_comparison_agent, claim)
fraud = ctx.service_call(run_fraud_agent, claim)
# Wait for all responses
await restate.gather(eligibility, cost, fraud)
# Get the results
eligibility_result = await eligibility
cost_result = await cost
fraud_result = await fraud
# Run decision agent on outputs
prompt = f"""Decide about claim: {claim.model_dump_json()}. Assessments:
Eligibility: {eligibility_result} Cost: {cost_result} Fraud: {fraud_result}"""
events = runner.run_async(
user_id=ctx.key(),
session_id=claim.session_id,
new_message=Content(role="user", parts=[Part.from_text(text=prompt)]),
)
return await parse_agent_response(events)
```
[Install Restate](/installation) and launch it:
```bash theme={null}
restate-server
```
Get the example:
```bash theme={null}
restate example python-google-adk-tour-of-agents && cd python-google-adk-tour-of-agents
```
Export your [Google API key](https://aistudio.google.com/app/apikey) and run the agent:
```bash theme={null}
export GOOGLE_API_KEY=your-api-key
```
```bash theme={null}
uv run app/workflow_parallel.py
```
Register the agents with Restate:
```bash theme={null}
restate deployments register http://localhost:9080 --force --yes # dev only: overrides previous registrations
```
Start a request for a claim that needs to be analyzed by multiple agents in parallel:
```bash theme={null}
curl localhost:8080/restate/call/ParallelAgentClaimApproval/user123/run --json '{
"amount": 3000,
"category": "orthopedic",
"date": "2024-10-01",
"placeOfService": "General Hospital",
"reason": "hospital bill for a broken leg",
"sessionId": "session-123"
}'
```
In the UI, you can see that the handler called the sub-agents in parallel.
Once all sub-agents return, the main agent makes a decision.
```python workflow_parallel.py {"CODE_LOAD::https://raw.githubusercontent.com/restatedev/ai-examples/refs/heads/main/pydantic-ai/tour-of-agents/app/workflow_parallel.py#here"} theme={null}
@agent_service.handler()
async def run(ctx: restate.Context, claim: InsuranceClaim) -> str:
# Start multiple agents in parallel with auto retries and recovery
eligibility = ctx.service_call(run_eligibility_agent, claim)
cost = ctx.service_call(run_rate_comparison_agent, claim)
fraud = ctx.service_call(run_fraud_agent, claim)
# Wait for all responses
await restate.gather(eligibility, cost, fraud)
# Run decision agent on outputs
result = await restate_decision_agent.run(
f"Decide about claim: {claim.model_dump_json()}. "
"Base your decision on the following analyses:"
f"Eligibility: {await eligibility} Cost {await cost} Fraud: {await fraud}",
)
return result.output
```
[Install Restate](/installation) and launch it:
```bash theme={null}
restate-server
```
Get the example:
```bash theme={null}
restate example python-pydantic-ai-tour-of-agents && cd python-pydantic-ai-tour-of-agents
```
Export your [OpenAI API key](https://platform.openai.com/api-keys) and run the agent:
```bash theme={null}
export OPENAI_API_KEY=sk-...
```
```bash theme={null}
uv run app/workflow_parallel.py
```
Register the agents with Restate:
```bash theme={null}
restate deployments register http://localhost:9080 --force --yes # dev only: overrides previous registrations
```
Start a request for a claim that needs to be analyzed by multiple agents in parallel:
```bash theme={null}
curl localhost:8080/restate/call/ParallelAgentClaimApproval/run --json '{
"date":"2024-10-01",
"category":"orthopedic",
"reason":"hospital bill for a broken leg",
"amount":3000,
"placeOfService":"General Hospital"
}'
```
In the UI, you can see that the handler called the sub-agents in parallel.
Once all sub-agents return, the main agent makes a decision.
```python workflow_parallel.py {"CODE_LOAD::https://raw.githubusercontent.com/restatedev/ai-examples/refs/heads/main/langchain-python/tour-of-agents/app/workflow_parallel.py#here"} theme={null}
decision = create_agent(
model=init_chat_model("openai:gpt-5.4"),
system_prompt="You are a claim decision engine.",
middleware=[RestateMiddleware()],
)
agent_service = restate.Service("ParallelAgentClaimApproval")
@agent_service.handler()
async def run(ctx: restate.Context, claim: InsuranceClaim) -> str:
# Start multiple sub-agents in parallel with auto-retries and recovery.
eligibility = ctx.service_call(run_eligibility_agent, claim)
cost = ctx.service_call(run_rate_comparison_agent, claim)
fraud = ctx.service_call(run_fraud_agent, claim)
await restate.gather(eligibility, cost, fraud)
result = await decision.ainvoke(
{"messages": f"""Decide about claim: {claim.model_dump_json()}.
Base your decision on the following analyses:
Eligibility: {await eligibility} Cost {await cost} Fraud: {await fraud}"""}
)
return result["messages"][-1].content
```
[Install Restate](/installation) and launch it:
```bash theme={null}
restate-server
```
Get the example:
```bash theme={null}
restate example python-langchain-tour-of-agents && cd python-langchain-tour-of-agents
```
Export your [OpenAI API key](https://platform.openai.com/api-keys) and run the agent:
```bash theme={null}
export OPENAI_API_KEY=sk-...
```
```bash theme={null}
uv run app/workflow_parallel.py
```
Register the agents with Restate:
```bash theme={null}
restate deployments register http://localhost:9080 --force --yes # dev only: overrides previous registrations
```
Start a request for a claim that needs to be analyzed by multiple agents in parallel:
```bash theme={null}
curl localhost:8080/restate/call/ParallelAgentClaimApproval/run --json '{
"date":"2024-10-01",
"category":"orthopedic",
"reason":"hospital bill for a broken leg",
"amount":3000,
"placeOfService":"General Hospital"
}'
```
In the UI, you can see that the handler called the sub-agents in parallel.
Once all sub-agents return, the main agent makes a decision.
```typescript workflow-parallel.ts {"CODE_LOAD::https://raw.githubusercontent.com/restatedev/ai-examples/refs/heads/main/typescript-restate-only/tour-of-agents/src/workflow-parallel.ts#here"} theme={null}
async function run(ctx: Context, claim: ClaimInput) {
// Create parallel tasks - each runs independently
const claimJson = JSON.stringify(claim);
const eligibility = ctx.run(
"Eligibility agent",
async () => llmCall(
"Decide whether the following claim is eligible for reimbursement." +
"Respond with eligible if it's a medical claim, and not eligible otherwise." +
"\n\nClaim: " + claimJson,
),
{ maxRetryAttempts: 3 },
)
const fraud = ctx.run(
"Fraud agent",
async () => llmCall(
"Decide whether the claim is fraudulent." +
"Always respond with low risk, medium risk, or high risk." +
"\n\nClaim: " + claimJson,
),
{ maxRetryAttempts: 3 },
)
const cost = ctx.run(
"Rate comparison agent",
async () => llmCall(
"Decide whether the cost of the claim is reasonable given the treatment." +
"Respond with reasonable or not reasonable." +
"\n\nClaim: " + claimJson,
),
{ maxRetryAttempts: 3 },
)
// Wait for all tasks to complete and return the results
await RestatePromise.all([eligibility, cost, fraud]);
// Make final decision
const { text } = await ctx.run(
"Decision agent",
async () => llmCall( `Decide about claim ${JSON.stringify(claim)}.
Base your decision on the following analyses:
- Eligibility: ${(await eligibility).text},
- Cost: ${(await cost).text},
- Fraud: ${(await fraud).text}`)
);
return text
}
```
[Install Restate](/installation) and launch it:
```bash theme={null}
restate-server
```
Get the example:
```bash theme={null}
restate example typescript-restate-tour-of-agents && cd typescript-restate-tour-of-agents
npm install
```
Export your API key:
```bash theme={null}
export OPENAI_API_KEY=sk-...
```
```bash theme={null}
npx tsx ./src/workflow-parallel.ts
```
Register the services with Restate:
```bash theme={null}
restate deployments register http://localhost:9080 --force --yes # dev only: overrides previous registrations
```
Send a request:
```bash theme={null}
curl localhost:8080/restate/call/ParallelAgentClaimApproval/run --json '{
"date":"2024-10-01",
"category":"orthopedic",
"reason":"hospital bill for a broken leg",
"amount":3000,
"placeOfService":"General Hospital"
}'
```
```python workflow_parallel.py {"CODE_LOAD::https://raw.githubusercontent.com/restatedev/ai-examples/refs/heads/main/python-restate-only/tour-of-agents/app/workflow_parallel.py#here"} theme={null}
parallelization_svc = restate.Service("ParallelAgentClaimApproval")
@parallelization_svc.handler()
async def run(ctx: restate.Context, claim: ClaimData) -> str | None:
"""Analyzes a claim in parallel with specialized agents."""
# Create parallel tasks - each runs independently
claim_json = json.dumps(claim.model_dump())
eligibility = ctx.run_typed(
"Eligibility agent",
llm_call,
RunOptions(max_attempts=3),
messages="Decide whether the following claim is eligible for reimbursement."
" Respond with eligible if it's a medical claim, and not eligible otherwise."
f"\n\nClaim: {claim_json}",
)
fraud = ctx.run_typed(
"Fraud agent",
llm_call,
RunOptions(max_attempts=3),
messages="Decide whether the claim is fraudulent."
" Always respond with low risk, medium risk, or high risk."
f"\n\nClaim: {claim_json}",
)
rate = ctx.run_typed(
"Rate comparison agent",
llm_call,
RunOptions(max_attempts=3),
messages="Decide whether the cost of the claim is reasonable given the treatment."
" Respond with reasonable or not reasonable."
f"\n\nClaim: {claim_json}",
)
# Wait for all tasks to complete
await restate.gather(eligibility, fraud, rate)
# Make final decision
decision = await ctx.run_typed(
"Decision agent",
llm_call,
RunOptions(max_attempts=3),
messages=f"Decide about claim: {claim.model_dump_json()}. "
"Base your decision on the following analyses:"
f"Eligibility: {(await eligibility).content} "
f"Cost: {(await rate).content} "
f"Fraud: {(await fraud).content}",
)
return decision.content
```
[Install Restate](/installation) and launch it:
```bash theme={null}
restate-server
```
Get the example:
```bash theme={null}
restate example python-restate-tour-of-agents && cd python-restate-tour-of-agents
```
Export your API key:
```bash theme={null}
export OPENAI_API_KEY=sk-...
```
```bash theme={null}
uv run app/workflow_parallel.py
```
Register the services with Restate:
```bash theme={null}
restate deployments register http://localhost:9080 --force --yes # dev only: overrides previous registrations
```
Send a request:
```bash theme={null}
curl localhost:8080/restate/call/ParallelAgentClaimApproval/run --json '{
"date":"2024-10-01",
"category":"orthopedic",
"reason":"hospital bill for a broken leg",
"amount":3000,
"placeOfService":"General Hospital"
}'
```