Long-running 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	< 2-3 minutes	Standard streaming with extended timeouts + keepalive pings	Simpler implementation, easy to add to standard streaming
Variable length runs	3-10+ minutes	Background mode	Survives disconnects, resumable streams

Background Mode with Resumable Streaming

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.

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.

curl

curl --request POST \
  --url https://api.letta.com/v1/agents/$AGENT_ID/messages/stream \
  --header 'Authorization: Bearer $LETTA_API_KEY' \
  --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 https://api.letta.com/v1/runs/$RUN_ID/stream \
 --header 'Accept: text/event-stream' \
 --data '{
"starting_after": 57
}'

python

stream = client.agents.messages.create(
    agent_id=agent_state.id,
    messages=[
      {
        "role": "user",
        "content": "Run comprehensive analysis on this dataset"
      }
    ],
    streaming=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

const stream = await client.agents.messages.create(agentState.id, {
  messages: [
    {
      role: "user",
      content: "Run comprehensive analysis on this dataset",
    },
  ],
  streaming: 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, {
  starting_after: lastSeqId,
})) {
  console.log(chunk);
}

HITL in Background Mode

When Human‑in‑the‑Loop (HITL) approval 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.

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.

curl

# 1) Start background stream; capture approval request
curl --request POST \
  --url https://api.letta.com/v1/agents/$AGENT_ID/messages/stream \
  --header 'Authorization: Bearer $LETTA_API_KEY' \
  --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 https://api.letta.com/v1/agents/$AGENT_ID/messages/stream \
 --header 'Authorization: Bearer $LETTA_API_KEY' \
 --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 https://api.letta.com/v1/runs/$RUN_ID/stream \
 --header 'Accept: text/event-stream' \
 --data '{
"starting_after": 0
}'

python

# 1) Start background stream and capture approval request
stream = client.agents.messages.create(
    agent_id=agent.id,
    messages=[{"role": "user", "content": "Do a sensitive operation"}],
    streaming=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(
    agent_id=agent.id,
    messages=[{"type": "approval", "approve": True, "approval_request_id": approval_request_id}],
    streaming=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

// 1) Start background stream and capture approval request
const stream = await client.agents.messages.create(agent.id, {
  messages: [{ role: "user", content: "Do a sensitive operation" }],
  streaming: 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.create(agent.id, {
  messages: [{ type: "approval", approve: true, approval_request_id: approvalRequestId }],
  streaming: 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!, { starting_after: approveSeq });
for await (const chunk of resume) {
  console.log(chunk);
}

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

curl

# Step 1: Find active background streams for your agents
curl --request GET \
  --url https://api.letta.com/v1/runs/active \
  --header 'Authorization: Bearer $LETTA_API_KEY' \
  --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 https://api.letta.com/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

# 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

// 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, {
    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 await (const chunk of stream) {
    console.log(chunk);
  }
}

Alternate option: Standard Streaming with Keepalive Pings and Longer Timeouts

Best for: Usecases where you are already using the standard streaming code, 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.

This approach assumes a persistent HTTP connection. We highly recommend using background mode for long-running jobs, especially when your infrastructure uses aggressive proxy timeouts, you need to handle network interruptions gracefully, or operations might exceed 10 minutes.

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:

curl

curl --request POST \
  --url https://api.letta.com/v1/agents/$AGENT_ID/messages/stream \
  --header 'Authorization: Bearer $LETTA_API_KEY' \
  --header 'Content-Type: application/json' \
  --data '{
  "messages": [
    {
      "role": "user",
      "content": "Execute this long-running analysis"
    }
  ],
  "include_pings": true
}'

python

# Configure client with extended timeout
from letta_client import Letta
import os

client = Letta(api_key=os.getenv("LETTA_API_KEY"))

# Enable pings to prevent timeout during long operations
stream = client.agents.messages.create(
    agent_id=agent_state.id,
    messages=[
        {
            "role": "user",
            "content": "Execute this long-running analysis"
        }
    ],
    streaming=True,
    stream_tokens=True,
    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

// Configure client with extended timeout
import Letta from "@letta-ai/letta-client";

const client = new Letta({
  apiKey: process.env.LETTA_API_KEY,
});

// Enable pings to prevent timeout during long operations
const stream = await client.agents.messages.create(agentState.id, {
    messages: [
        {
            role: "user",
            content: "Execute this long-running analysis"
        }
    ],
    streaming: true,
    stream_tokens: true,
    include_pings: true,  // Sends periodic keepalive messages
    timeout_in_seconds: 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);
}

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