---
title: Long-Running Executions
slug: guides/agents/long-running
subtitle: How to handle long-running agent executions
---

When agents need to execute multiple tool calls or perform complex operations (like deep research, data analysis, or multi-step workflows), processing time can vary significantly.

Letta supports various ways to handle long-running agents, so you can choose the approach that best fits your use case:

| Use Case | Duration | Recommendedation | Key Benefits |
|----------|----------|---------------------|-------------|
| Few-step invocations | < 1 minute | [Standard streaming](/guides/agents/streaming) | Simplest approach |
| Variable length runs | 1-10 minutes | **Background mode** (Keepalive + Timeout as a second choice) | Easy way to reduce timeouts |
| Deep research | 10+ minutes | **Background mode**, or async polling | Survives disconnects, resumable streams |
| Batch jobs | Any | **Async polling** | Fire-and-forget, check results later |

## Option 1: Background Mode with Resumable Streaming

<Note>
**Best for:** Operations exceeding 10 minutes, unreliable network connections, or critical workflows that must complete regardless of client connectivity.

**Trade-off:** Slightly higher latency to first token due to background task initialization.
</Note>

Background mode decouples agent execution from your client connection. The agent processes your request on the server while streaming results to a persistent store, allowing you to reconnect and resume from any point — even if your application crashes or network fails.

<CodeGroup>
```curl curl maxLines=50
curl --request POST \
  --url http://localhost:8283/v1/agents/$AGENT_ID/messages/stream \
  --header 'Content-Type: application/json' \
  --data '{
  "messages": [
    {
      "role": "user",
      "content": "Run comprehensive analysis on this dataset"
    }
  ],
  "stream_tokens": true,
  "background": true
}'

# Response stream includes run_id and seq_id for each chunk:
data: {"run_id":"run-123","seq_id":0,"message_type":"reasoning_message","reasoning":"Analyzing"}
data: {"run_id":"run-123","seq_id":1,"message_type":"reasoning_message","reasoning":" the dataset"}
data: {"run_id":"run-123","seq_id":2,"message_type":"tool_call","tool_call":{...}}
# ... stream continues

# Step 2: If disconnected, resume from last received seq_id
curl --request GET \
  --url http://localhost:8283/v1/runs/$RUN_ID/stream \
  --header 'Accept: text/event-stream' \
  --data '{
  "starting_after": 57
}'
```
```python python maxLines=50
stream = client.agents.messages.create_stream(
    agent_id=agent_state.id,
    messages=[
      {
        "role": "user",
        "content": "Run comprehensive analysis on this dataset"
      }
    ],
    stream_tokens=True,
    background=True,
)
run_id = None
last_seq_id = None
for chunk in stream:
    if hasattr(chunk, "run_id") and hasattr(chunk, "seq_id"):
        run_id = chunk.run_id       # Save this to reconnect if your connection drops
        last_seq_id = chunk.seq_id  # Save this as your resumption point for cursor-based pagination
    print(chunk)

# If disconnected, resume from last received seq_id:
for chunk in client.runs.stream(run_id, starting_after=last_seq_id):
    print(chunk)
```
```typescript node.js maxLines=50
const stream = await client.agents.messages.createStream({
    agentId: agentState.id,
    requestBody: {
        messages: [
            {
                role: "user",
                content: "Run comprehensive analysis on this dataset"
            }
        ],
        streamTokens: true,
        background: true,
    }
});

let runId = null;
let lastSeqId = null;
for await (const chunk of stream) {
    if (chunk.run_id && chunk.seq_id) {
        runId = chunk.run_id;      // Save this to reconnect if your connection drops
        lastSeqId = chunk.seq_id; // Save this as your resumption point for cursor-based pagination
    }
    console.log(chunk);
}

// If disconnected, resume from last received seq_id
for await (const chunk of client.runs.stream(runId, {startingAfter: lastSeqId})) {
    console.log(chunk);
}
```
```python python maxLines=60
# 1) Start background stream and capture approval request
stream = client.agents.messages.create_stream(
    agent_id=agent.id,
    messages=[{"role": "user", "content": "Do a sensitive operation"}],
    stream_tokens=True,
    background=True,
)

approval_request_id = None
orig_run_id = None
last_seq_id = 0
for chunk in stream:
    if hasattr(chunk, "run_id") and hasattr(chunk, "seq_id"):
        orig_run_id = chunk.run_id
        last_seq_id = chunk.seq_id
    if getattr(chunk, "message_type", None) == "approval_request_message":
        approval_request_id = chunk.id
        break

# 2) Approve in background; capture the approval stream cursor (this creates a new run)
approve = client.agents.messages.create_stream(
    agent_id=agent.id,
    messages=[{"type": "approval", "approve": True, "approval_request_id": approval_request_id}],
    stream_tokens=True,
    background=True,
)

run_id = None
approve_seq = 0
for chunk in approve:
    if hasattr(chunk, "run_id") and hasattr(chunk, "seq_id"):
        run_id = chunk.run_id
        approve_seq = chunk.seq_id
    if getattr(chunk, "message_type", None) == "tool_return_message":
        # Tool result arrives here on the approval stream
        break

# 3) Resume that run to read follow-up tokens
for chunk in client.runs.stream(run_id, starting_after=approve_seq):
    print(chunk)
```
</CodeGroup>

### HITL in Background Mode

When [Human‑in‑the‑Loop (HITL) approval](/guides/agents/human-in-the-loop) is enabled for a tool, your background stream may pause and emit an `approval_request_message`. In background mode, send the approval via a separate background stream and capture that stream’s `run_id`/`seq_id`.

<Note>
Approval responses in background mode emit the `tool_return_message` on the approval stream itself (with a new `run_id`, different from the original stream). Save the approval stream cursor, then resume with `runs.stream` to consume subsequent reasoning/assistant messages.
</Note>

<CodeGroup>
```curl curl maxLines=70
# 1) Start background stream; capture approval request
curl --request POST \
  --url http://localhost:8283/v1/agents/$AGENT_ID/messages/stream \
  --header 'Content-Type: application/json' \
  --data '{
  "messages": [{"role": "user", "content": "Do a sensitive operation"}],
  "stream_tokens": true,
  "background": true
}'

# Example stream output (approval request arrives):
data: {"run_id":"run-abc","seq_id":0,"message_type":"reasoning_message","reasoning":"..."}
data: {"run_id":"run-abc","seq_id":1,"message_type":"approval_request_message","id":"message-abc","tool_call":{"name":"sensitive_operation","arguments":"{...}","tool_call_id":"tool-xyz"}}

# 2) Approve in background; capture approval stream cursor (this creates a new run)
curl --request POST \
  --url http://localhost:8283/v1/agents/$AGENT_ID/messages/stream \
  --header 'Content-Type: application/json' \
  --data '{
  "messages": [{"type": "approval", "approve": true, "approval_request_id": "message-abc"}],
  "stream_tokens": true,
  "background": true
}'

# Example approval stream output (tool result arrives here):
data: {"run_id":"run-new","seq_id":0,"message_type":"tool_return_message","status":"success","tool_return":"..."}

# 3) Resume the approval stream's run to continue
curl --request GET \
  --url http://localhost:8283/v1/runs/$RUN_ID/stream \
  --header 'Accept: text/event-stream' \
  --data '{
  "starting_after": 0
}'
```
```python python maxLines=70
# 1) Start background stream and capture approval request
stream = client.agents.messages.create_stream(
    agent_id=agent.id,
    messages=[{"role": "user", "content": "Do a sensitive operation"}],
    stream_tokens=True,
    background=True,
)

approval_request_id = None
orig_run_id = None
last_seq_id = 0
for chunk in stream:
    if hasattr(chunk, "run_id") and hasattr(chunk, "seq_id"):
        orig_run_id = chunk.run_id
        last_seq_id = chunk.seq_id
    if getattr(chunk, "message_type", None) == "approval_request_message":
        approval_request_id = chunk.id
        break

# 2) Approve in background; capture the approval stream cursor (this creates a new run)
approve = client.agents.messages.create_stream(
    agent_id=agent.id,
    messages=[{"type": "approval", "approve": True, "approval_request_id": approval_request_id}],
    stream_tokens=True,
    background=True,
)

run_id = None
approve_seq = 0
for chunk in approve:
    if hasattr(chunk, "run_id") and hasattr(chunk, "seq_id"):
        run_id = chunk.run_id
        approve_seq = chunk.seq_id
    if getattr(chunk, "message_type", None) == "tool_return_message":
        # Tool result arrives here on the approval stream
        break

# 3) Resume that run to read follow-up tokens
for chunk in client.runs.stream(run_id, starting_after=approve_seq):
    print(chunk)
```
```typescript node.js maxLines=70
// 1) Start background stream and capture approval request
const stream = await client.agents.messages.createStream({
  agentId: agent.id,
  requestBody: {
    messages: [{ role: "user", content: "Do a sensitive operation" }],
    streamTokens: true,
    background: true,
  }
});

let approvalRequestId: string | null = null;
let origRunId: string | null = null;
let lastSeqId = 0;
for await (const chunk of stream) {
  if (chunk.run_id && chunk.seq_id) { origRunId = chunk.run_id; lastSeqId = chunk.seq_id; }
  if (chunk.message_type === "approval_request_message") {
    approvalRequestId = chunk.id; break;
  }
}

// 2) Approve in background; capture the approval stream cursor (this creates a new run)
const approve = await client.agents.messages.createStream({
  agentId: agent.id,
  requestBody: {
    messages: [{ type: "approval", approve: true, approvalRequestId }],
    streamTokens: true,
    background: true,
  }
});

let runId: string | null = null;
let approveSeq = 0;
for await (const chunk of approve) {
  if (chunk.run_id && chunk.seq_id) { runId = chunk.run_id; approveSeq = chunk.seq_id; }
  if (chunk.message_type === "tool_return_message") {
    // Tool result arrives here on the approval stream
    break;
  }
}

// 3) Resume that run to read follow-up tokens
const resume = await client.runs.stream(runId!, { startingAfter: approveSeq });
for await (const chunk of resume) {
  console.log(chunk);
}
```
</CodeGroup>


### Discovering and Resuming Active Streams

When your application starts or recovers from a crash, you can check for any active background streams and resume them. This is particularly useful for:
- **Application restarts**: Resume processing after deployments or crashes
- **Load balancing**: Pick up streams started by other instances
- **Monitoring**: Check progress of long-running operations from different clients

<CodeGroup>
```curl curl maxLines=50
# Step 1: Find active background streams for your agents
curl --request GET \
  --url http://localhost:8283/v1/runs/active \
  --header 'Content-Type: application/json' \
  --data '{
  "agent_ids": [
    "agent-123",
    "agent-456"
  ],
  "background": true
}'
# Returns: [{"run_id": "run-abc", "agent_id": "agent-123", "status": "processing", ...}]

# Step 2: Resume streaming from the beginning (or any specified seq_id)
curl --request GET \
  --url http://localhost:8283/v1/runs/$RUN_ID/stream \
  --header 'Accept: text/event-stream' \
  --data '{
  "starting_after": 0, # Start from beginning
  "batch_size": 1000   # Fetch historical chunks in larger batches
}'
```
```python python maxLines=50
# Find and resume active background streams
active_runs = client.runs.active(
    agent_ids=["agent-123", "agent-456"],
    background=True,
)

if active_runs:
    # Resume the first active stream from the beginning
    run = active_runs[0]
    print(f"Resuming stream for run {run.id}, status: {run.status}")

    stream = client.runs.stream(
        run_id=run.id,
        starting_after=0,  # Start from beginning
        batch_size=1000    # Fetch historical chunks in larger batches
    )

    # Each historical chunk is streamed one at a time, followed by new chunks as they become available
    for chunk in stream:
        print(chunk)
```
```typescript node.js maxLines=50
// Find and resume active background streams
const activeRuns = await client.runs.active({
    agentIds: ["agent-123", "agent-456"],
    background: true,
});

if (activeRuns.length > 0) {
    // Resume the first active stream from the beginning
    const run = activeRuns[0];
    console.log(`Resuming stream for run ${run.id}, status: ${run.status}`);

    const stream = await client.runs.stream(run.id, {
        startingAfter: 0,   // Start from beginning
        batchSize: 1000     // Fetch historical chunks in larger batches
    });

    // Each historical chunk is streamed one at a time, followed by new chunks as they become available
    for await (const chunk of stream) {
        console.log(chunk);
    }
}
```
</CodeGroup>

## Option 2: Async Operations with Polling

<Note>
**Best for:** Usecases where you don't need real-time token streaming.
</Note>

Ideal for batch processing, scheduled jobs, or when you don't need real-time updates. The [async SDK method](/api-reference/agents/messages/create-async) queues your request and returns immediately, letting you check results later:

<CodeGroup>
```curl curl maxLines=50
# Start async operation (returns immediately with run ID)
curl --request POST \
  --url http://localhost:8283/v1/agents/$AGENT_ID/messages/async \
  --header 'Content-Type: application/json' \
  --data '{
  "messages": [
    {
      "role": "user",
      "content": "Run comprehensive analysis on this dataset"
    }
  ]
}'

# Poll for results using the returned run ID
curl --request GET \
  --url http://localhost:8283/v1/runs/$RUN_ID
```
```python python maxLines=50
# Start async operation (returns immediately with run ID)
run = client.agents.messages.create_async(
    agent_id=agent_state.id,
    messages=[
      {
        "role": "user",
        "content": "Run comprehensive analysis on this dataset"
      }
    ],
)

# Poll for completion
import time
while run.status != "completed":
    time.sleep(2)
    run = client.runs.retrieve(run_id=run.id)

# Get the messages once complete
messages = client.runs.messages.list(run_id=run.id)
```
```typescript node.js maxLines=50
// Start async operation (returns immediately with run ID)
const run = await client.agents.createAgentMessageAsync({
    agentId: agentState.id,
    requestBody: {
        messages: [
            {
                role: "user",
                content: "Run comprehensive analysis on this dataset"
            }
        ]
    }
});

// Poll for completion
while (run.status !== "completed") {
    await new Promise(resolve => setTimeout(resolve, 2000));
    run = await client.runs.retrieveRun({ runId: run.id });
}

// Get the messages once complete
const messages = await client.runs.listRunMessages({ runId: run.id });
```
</CodeGroup>

## Option 3: Configure Streaming with Keepalive Pings and Longer Timeouts

<Note>
**Best for:** Usecases where you are already using the standard [streaming code](/guides/agents/streaming), but are experiencing issues with timeouts or disconnects (e.g. due to network interruptions or hanging tool executions).

**Trade-off:** Not as reliable as background mode, and does not support resuming a disconnected stream/request.
</Note>

<Warning>
This approach assumes a persistent HTTP connection. We highly recommend using **background mode** (or async polling) for long-running jobs, especially when:
- Your infrastructure uses aggressive proxy timeouts
- You need to handle network interruptions gracefully
- Operations might exceed 10 minutes
</Warning>

For operations under 10 minutes that need real-time updates without the complexity of background processing. Configure keepalive pings and timeouts to maintain stable connections:

<CodeGroup>
```curl curl maxLines=50
curl --request POST \
  --url http://localhost:8283/v1/agents/$AGENT_ID/messages/stream \
  --header 'Content-Type: application/json' \
  --data '{
  "messages": [
    {
      "role": "user",
      "content": "Execute this long-running analysis"
    }
  ],
  "include_pings": true
}'
```
```python python
# Configure client with extended timeout
from letta_client import Letta

client = Letta(
    base_url="http://localhost:8283",
)

# Enable pings to prevent timeout during long operations
stream = client.agents.messages.create_stream(
    agent_id=agent_state.id,
    messages=[
      {
        "role": "user",
        "content": "Execute this long-running analysis"
      }
    ],
    include_pings=True,  # Sends periodic keepalive messages
    request_options={"timeout_in_seconds": 600}  # 10 min timeout
)

# Process the stream (pings will keep connection alive)
for chunk in stream:
    if chunk.message_type == "ping":
        # Keepalive ping received, connection is still active
        continue
    print(chunk)
```
```typescript node.js maxLines=50
// Configure client with extended timeout
import { Letta } from '@letta/sdk';

const client = new Letta({
    baseUrl: 'http://localhost:8283',
});

// Enable pings to prevent timeout during long operations
const stream = await client.agents.createAgentMessageStream({
    agentId: agentState.id,
    requestBody: {
        messages: [
            {
                role: "user",
                content: "Execute this long-running analysis"
            }
        ],
        includePings: true  // Sends periodic keepalive messages
    }, {
        timeoutInSeconds: 600  // 10 minutes timeout in seconds
    }
});

// Process the stream (pings will keep connection alive)
for await (const chunk of stream) {
    if (chunk.message_type === "ping") {
        // Keepalive ping received, connection is still active
        continue;
    }
    console.log(chunk);
}
```
</CodeGroup>

### Configuration Guidelines

| Parameter | Purpose | When to Use |
|-----------|---------|------------|
| Timeout in seconds | Extends request timeout beyond 60s default | Set to 1.5x your expected max duration |
| Include pings | Sends keepalive messages every ~30s | Enable for operations with long gaps between outputs |