Autogpt Agents

AutoGPT - Autonomous AI Agent Platform

Comprehensive platform for building, deploying, and managing continuous AI agents through a visual interface or development toolkit.

When to use AutoGPT

Use AutoGPT when:

• Building autonomous agents that run continuously
• Creating visual workflow-based AI agents
• Deploying agents with external triggers (webhooks, schedules)
• Building complex multi-step automation pipelines
• Need a no-code/low-code agent builder

Key features:

• Visual Agent Builder: Drag-and-drop node-based workflow editor
• Continuous Execution: Agents run persistently with triggers
• Marketplace: Pre-built agents and blocks to share/reuse
• Block System: Modular components for LLM, tools, integrations
• Forge Toolkit: Developer tools for custom agent creation
• Benchmark System: Standardized agent performance testing

Use alternatives instead:

• LangChain/LlamaIndex: If you need more control over agent logic
• CrewAI: For role-based multi-agent collaboration
• OpenAI Assistants: For simple hosted agent deployments
• Semantic Kernel: For Microsoft ecosystem integration

Quick start

Installation (Docker)

# Clone repository
git clone https://github.com/Significant-Gravitas/AutoGPT.git
cd AutoGPT/autogpt_platform

# Copy environment file
cp .env.example .env

# Start backend services
docker compose up -d --build

# Start frontend (in separate terminal)
cd frontend
cp .env.example .env
npm install
npm run dev

Access the platform

• Frontend UI: http://localhost:3000
• Backend API: http://localhost:8006/api
• WebSocket: ws://localhost:8001/ws

Architecture overview

AutoGPT has two main systems:

AutoGPT Platform (Production)

• Visual agent builder with React frontend
• FastAPI backend with execution engine
• PostgreSQL + Redis + RabbitMQ infrastructure

AutoGPT Classic (Development)

• Forge: Agent development toolkit
• Benchmark: Performance testing framework
• CLI: Command-line interface for development

Core concepts

Graphs and nodes

Agents are represented as graphs containing nodes connected by links:

Graph (Agent)
  ├── Node (Input)
  │   └── Block (AgentInputBlock)
  ├── Node (Process)
  │   └── Block (LLMBlock)
  ├── Node (Decision)
  │   └── Block (SmartDecisionMaker)
  └── Node (Output)
      └── Block (AgentOutputBlock)

Blocks

Blocks are reusable functional components:

Execution flow

User/Trigger → Graph Execution → Node Execution → Block.execute()
     ↓              ↓                 ↓
  Inputs      Queue System      Output Yields

Building agents

Using the visual builder

1. Open Agent Builder at http://localhost:3000 2. Add blocks from the BlocksControl panel 3. Connect nodes by dragging between handles 4. Configure inputs in each node 5. Run agent using PrimaryActionBar

Available blocks

AI Blocks:

• AITextGeneratorBlock - Generate text with LLMs
• AIConversationBlock - Multi-turn conversations
• SmartDecisionMakerBlock - Conditional logic

Integration Blocks:

• GitHub, Google, Discord, Notion connectors
• Webhook triggers and handlers
• HTTP request blocks

Control Blocks:

• Input/Output blocks
• Branching and decision nodes
• Loop and iteration blocks

Agent execution

Trigger types

Manual execution:

POST /api/v1/graphs/{graph_id}/execute
Content-Type: application/json

{
  "inputs": {
    "input_name": "value"
  }
}

Webhook trigger:

POST /api/v1/webhooks/{webhook_id}
Content-Type: application/json

{
  "data": "webhook payload"
}

Scheduled execution:

{
  "schedule": "0 */2 * * *",
  "graph_id": "graph-uuid",
  "inputs": {}
}

Monitoring execution

WebSocket updates:

const ws = new WebSocket('ws://localhost:8001/ws');

ws.onmessage = (event) => {
  const update = JSON.parse(event.data);
  console.log(`Node ${update.node_id}: ${update.status}`);
};

REST API polling:

GET /api/v1/executions/{execution_id}

Using Forge (Development)

Create custom agent

# Setup forge environment
cd classic
./run setup

# Create new agent from template
./run forge create my-agent

# Start agent server
./run forge start my-agent

Agent structure

my-agent/
├── agent.py          # Main agent logic
├── abilities/        # Custom abilities
│   ├── __init__.py
│   └── custom.py
├── prompts/          # Prompt templates
└── config.yaml       # Agent configuration

Implement custom ability

from forge import Ability, ability

@ability(
    name="custom_search",
    description="Search for information",
    parameters={
        "query": {"type": "string", "description": "Search query"}
    }
)
def custom_search(query: str) -> str:
    """Custom search ability."""
    # Implement search logic
    result = perform_search(query)
    return result

Benchmarking agents

Run benchmarks

# Run all benchmarks
./run benchmark

# Run specific category
./run benchmark --category coding

# Run with specific agent
./run benchmark --agent my-agent

Benchmark categories

• Coding: Code generation and debugging
• Retrieval: Information finding
• Web: Web browsing and interaction
• Writing: Text generation tasks

VCR cassettes

Benchmarks use recorded HTTP responses for reproducibility:

# Record new cassettes
./run benchmark --record

# Run with existing cassettes
./run benchmark --playback

Integrations

Adding credentials

1. Navigate to Profile > Integrations 2. Select provider (OpenAI, GitHub, Google, etc.) 3. Enter API keys or authorize OAuth 4. Credentials are encrypted and stored securely

Using credentials in blocks

Blocks automatically access user credentials:

class MyLLMBlock(Block):
    def execute(self, inputs):
        # Credentials are injected by the system
        credentials = self.get_credentials("openai")
        client = OpenAI(api_key=credentials.api_key)
        # ...

Supported providers

Deployment

Docker production setup

# docker-compose.prod.yml
services:
  rest_server:
    image: autogpt/platform-backend
    environment:
      - DATABASE_URL=postgresql://...
      - REDIS_URL=redis://redis:6379
    ports:
      - "8006:8006"

  executor:
    image: autogpt/platform-backend
    command: poetry run executor

  frontend:
    image: autogpt/platform-frontend
    ports:
      - "3000:3000"

Environment variables

Generate encryption key

cd autogpt_platform/backend
poetry run cli gen-encrypt-key

Best practices

1. Start simple: Begin with 3-5 node agents 2. Test incrementally: Run and test after each change 3. Use webhooks: External triggers for event-driven agents 4. Monitor costs: Track LLM API usage via credits system 5. Version agents: Save working versions before changes 6. Benchmark: Use agbenchmark to validate agent quality

Common issues

Services not starting:

# Check container status
docker compose ps

# View logs
docker compose logs rest_server

# Restart services
docker compose restart

Database connection issues:

# Run migrations
cd backend
poetry run prisma migrate deploy

Agent execution stuck:

# Check RabbitMQ queue
# Visit http://localhost:15672 (guest/guest)

# Clear stuck executions
docker compose restart executor

References

• [Advanced Usage](references/advanced-usage.md) - Custom blocks, deployment, scaling
• [Troubleshooting](references/troubleshooting.md) - Common issues, debugging

Resources

• Documentation: https://docs.agpt.co
• Repository: https://github.com/Significant-Gravitas/AutoGPT
• Discord: https://discord.gg/autogpt
• License: MIT (Classic) / Polyform Shield (Platform)

AutoGPT Advanced Usage Guide

Custom Block Development

Block structure

from backend.data.block import Block, BlockSchema, BlockType
from pydantic import BaseModel

class MyBlockInput(BaseModel):
    """Input schema for the block."""
    query: str
    max_results: int = 10

class MyBlockOutput(BaseModel):
    """Output schema for the block."""
    results: list[str]
    count: int

class MyCustomBlock(Block):
    """Custom block for specific functionality."""

    id = "my-custom-block-uuid"
    name = "My Custom Block"
    description = "Does something specific"
    block_type = BlockType.STANDARD

    input_schema = MyBlockInput
    output_schema = MyBlockOutput

    async def execute(self, input_data: MyBlockInput) -> dict:
        """Execute the block logic."""
        # Implement your logic
        results = await self.process(input_data.query, input_data.max_results)

        yield "results", results
        yield "count", len(results)

    async def process(self, query: str, max_results: int) -> list[str]:
        """Internal processing logic."""
        # Implementation
        return ["result1", "result2"]

Block registration

# backend/blocks/__init__.py
from backend.blocks.my_block import MyCustomBlock

# Add to block registry
BLOCKS = [
    MyCustomBlock,
    # ... other blocks
]

Block with credentials

from backend.data.block import Block
from backend.integrations.providers import ProviderName

class APIIntegrationBlock(Block):
    """Block that uses external API credentials."""

    credentials_required = [ProviderName.OPENAI]

    async def execute(self, input_data):
        # Get credentials from the system
        credentials = await self.get_credentials(ProviderName.OPENAI)

        # Use credentials
        client = OpenAI(api_key=credentials.api_key)

        response = await client.chat.completions.create(
            model="gpt-4o",
            messages=[{"role": "user", "content": input_data.prompt}]
        )

        yield "response", response.choices[0].message.content

Block with cost tracking

from backend.data.block import Block
from backend.data.block_cost_config import BlockCostConfig

class LLMBlock(Block):
    """Block with cost tracking."""

    cost_config = BlockCostConfig(
        cost_type="token",
        cost_per_unit=0.00002,  # Per token
        provider="openai"
    )

    async def execute(self, input_data):
        response = await self.call_llm(input_data.prompt)

        # Report token usage for cost tracking
        self.report_usage(
            input_tokens=response.usage.prompt_tokens,
            output_tokens=response.usage.completion_tokens
        )

        yield "output", response.content

Advanced Execution Patterns

Parallel node execution

from backend.executor.manager import ExecutionManager

async def execute_parallel_nodes(graph_exec_id: str, node_ids: list[str]):
    """Execute multiple nodes in parallel."""
    manager = ExecutionManager()

    tasks = [
        manager.execute_node(graph_exec_id, node_id)
        for node_id in node_ids
    ]

    results = await asyncio.gather(*tasks)
    return results

Conditional branching

from backend.blocks.branching import BranchingBlock

class SmartBranchBlock(BranchingBlock):
    """Advanced conditional branching."""

    async def execute(self, input_data):
        condition = await self.evaluate_condition(input_data)

        if condition == "path_a":
            yield "output_a", input_data.value
        elif condition == "path_b":
            yield "output_b", input_data.value
        else:
            yield "output_default", input_data.value

Loop execution

class LoopBlock(Block):
    """Execute a subgraph in a loop."""

    async def execute(self, input_data):
        items = input_data.items
        results = []

        for i, item in enumerate(items):
            # Execute nested graph for each item
            result = await self.execute_subgraph(
                graph_id=input_data.subgraph_id,
                inputs={"item": item, "index": i}
            )
            results.append(result)

            yield "progress", f"Processed {i+1}/{len(items)}"

        yield "results", results

Graph composition

Nested agents

from backend.blocks.agent import AgentExecutorBlock

class ParentAgentBlock(Block):
    """Execute child agents within a parent agent."""

    async def execute(self, input_data):
        # Execute child agent
        child_result = await self.execute_agent(
            agent_id=input_data.child_agent_id,
            inputs={"query": input_data.query}
        )

        # Process child result
        processed = await self.process_result(child_result)

        yield "output", processed

Dynamic graph construction

from backend.data.graph import GraphModel, NodeModel, LinkModel

async def create_dynamic_graph(user_id: str, template: str):
    """Create a graph dynamically based on template."""
    graph = GraphModel(
        name=f"Dynamic Graph - {template}",
        description="Auto-generated graph",
        user_id=user_id
    )

    # Add nodes based on template
    nodes = []
    if template == "research":
        nodes = [
            NodeModel(block_id="search-block", position={"x": 0, "y": 0}),
            NodeModel(block_id="summarize-block", position={"x": 200, "y": 0}),
            NodeModel(block_id="output-block", position={"x": 400, "y": 0})
        ]
    elif template == "code-review":
        nodes = [
            NodeModel(block_id="github-block", position={"x": 0, "y": 0}),
            NodeModel(block_id="review-block", position={"x": 200, "y": 0}),
            NodeModel(block_id="comment-block", position={"x": 400, "y": 0})
        ]

    graph.nodes = nodes

    # Create links between nodes
    for i in range(len(nodes) - 1):
        graph.links.append(LinkModel(
            source_id=nodes[i].id,
            sink_id=nodes[i+1].id,
            source_name="output",
            sink_name="input"
        ))

    return await graph.save()

Production deployment

Kubernetes deployment

# autogpt-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: autogpt-backend
spec:
  replicas: 3
  selector:
    matchLabels:
      app: autogpt-backend
  template:
    metadata:
      labels:
        app: autogpt-backend
    spec:
      containers:
      - name: rest-server
        image: autogpt/platform-backend:latest
        command: ["poetry", "run", "rest"]
        ports:
        - containerPort: 8006
        env:
        - name: DATABASE_URL
          valueFrom:
            secretKeyRef:
              name: autogpt-secrets
              key: database-url
        resources:
          requests:
            memory: "512Mi"
            cpu: "500m"
          limits:
            memory: "2Gi"
            cpu: "2000m"
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: autogpt-executor
spec:
  replicas: 5
  selector:
    matchLabels:
      app: autogpt-executor
  template:
    spec:
      containers:
      - name: executor
        image: autogpt/platform-backend:latest
        command: ["poetry", "run", "executor"]
        resources:
          requests:
            memory: "1Gi"
            cpu: "1000m"
          limits:
            memory: "4Gi"
            cpu: "4000m"

Horizontal scaling

# autogpt-hpa.yaml
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: autogpt-executor-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: autogpt-executor
  minReplicas: 2
  maxReplicas: 20
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: External
    external:
      metric:
        name: rabbitmq_queue_messages
        selector:
          matchLabels:
            queue: graph-execution
      target:
        type: AverageValue
        averageValue: 10

Database optimization

-- Optimize for high-volume execution tracking
CREATE INDEX CONCURRENTLY idx_node_exec_graph_status
ON "AgentNodeExecution" ("graphExecutionId", "executionStatus");

CREATE INDEX CONCURRENTLY idx_graph_exec_user_status
ON "AgentGraphExecution" ("userId", "executionStatus", "createdAt" DESC);

-- Partition execution tables by date
CREATE TABLE "AgentGraphExecution_partitioned" (
    LIKE "AgentGraphExecution" INCLUDING ALL
) PARTITION BY RANGE ("createdAt");

-- Create monthly partitions
CREATE TABLE "AgentGraphExecution_2024_01"
PARTITION OF "AgentGraphExecution_partitioned"
FOR VALUES FROM ('2024-01-01') TO ('2024-02-01');

Monitoring and observability

Prometheus metrics

from prometheus_client import Counter, Histogram, Gauge

# Define metrics
EXECUTIONS_TOTAL = Counter(
    'autogpt_executions_total',
    'Total graph executions',
    ['graph_id', 'status']
)

EXECUTION_DURATION = Histogram(
    'autogpt_execution_duration_seconds',
    'Execution duration in seconds',
    ['graph_id'],
    buckets=[0.1, 0.5, 1, 5, 10, 30, 60, 120]
)

ACTIVE_EXECUTIONS = Gauge(
    'autogpt_active_executions',
    'Currently running executions'
)

# Use in executor
class ExecutionManager:
    async def execute_graph(self, graph_id, inputs):
        ACTIVE_EXECUTIONS.inc()
        start_time = time.time()

        try:
            result = await self._execute(graph_id, inputs)
            EXECUTIONS_TOTAL.labels(graph_id=graph_id, status='success').inc()
            return result
        except Exception as e:
            EXECUTIONS_TOTAL.labels(graph_id=graph_id, status='failed').inc()
            raise
        finally:
            ACTIVE_EXECUTIONS.dec()
            EXECUTION_DURATION.labels(graph_id=graph_id).observe(
                time.time() - start_time
            )

Grafana dashboard

{
  "dashboard": {
    "title": "AutoGPT Platform",
    "panels": [
      {
        "title": "Executions per Minute",
        "type": "graph",
        "targets": [
          {
            "expr": "rate(autogpt_executions_total[1m])",
            "legendFormat": "{{status}}"
          }
        ]
      },
      {
        "title": "Execution Latency (p95)",
        "type": "gauge",
        "targets": [
          {
            "expr": "histogram_quantile(0.95, rate(autogpt_execution_duration_seconds_bucket[5m]))"
          }
        ]
      },
      {
        "title": "Active Executions",
        "type": "stat",
        "targets": [
          {"expr": "autogpt_active_executions"}
        ]
      }
    ]
  }
}

Sentry error tracking

import sentry_sdk
from sentry_sdk.integrations.fastapi import FastApiIntegration
from sentry_sdk.integrations.asyncio import AsyncioIntegration

sentry_sdk.init(
    dsn=os.environ.get("SENTRY_DSN"),
    integrations=[
        FastApiIntegration(),
        AsyncioIntegration(),
    ],
    traces_sample_rate=0.1,
    profiles_sample_rate=0.1,
    environment=os.environ.get("APP_ENV", "development")
)

# Custom error context
with sentry_sdk.push_scope() as scope:
    scope.set_tag("graph_id", graph_id)
    scope.set_extra("inputs", sanitized_inputs)
    sentry_sdk.capture_exception(error)

API integration patterns

Webhook handling

from fastapi import APIRouter, Request
from backend.data.webhook import WebhookHandler

router = APIRouter()

@router.post("/webhooks/{webhook_id}")
async def handle_webhook(webhook_id: str, request: Request):
    """Handle incoming webhook."""
    handler = WebhookHandler()

    # Verify webhook signature
    signature = request.headers.get("X-Webhook-Signature")
    if not await handler.verify_signature(webhook_id, signature, await request.body()):
        return {"error": "Invalid signature"}, 401

    # Parse payload
    payload = await request.json()

    # Trigger associated graph
    execution = await handler.trigger_graph(webhook_id, payload)

    return {
        "execution_id": execution.id,
        "status": "queued"
    }

External API rate limiting

from asyncio import Semaphore
from functools import wraps

class RateLimiter:
    """Rate limiter for external API calls."""

    def __init__(self, max_concurrent: int = 10, rate_per_second: float = 5):
        self.semaphore = Semaphore(max_concurrent)
        self.rate = rate_per_second
        self.last_call = 0

    async def acquire(self):
        await self.semaphore.acquire()
        now = time.time()
        wait_time = max(0, (1 / self.rate) - (now - self.last_call))
        if wait_time > 0:
            await asyncio.sleep(wait_time)
        self.last_call = time.time()

    def release(self):
        self.semaphore.release()

# Usage in block
class RateLimitedAPIBlock(Block):
    rate_limiter = RateLimiter(max_concurrent=5, rate_per_second=2)

    async def execute(self, input_data):
        await self.rate_limiter.acquire()
        try:
            result = await self.call_api(input_data)
            yield "output", result
        finally:
            self.rate_limiter.release()

AutoGPT Troubleshooting Guide

Installation Issues

Docker compose fails

Error: Cannot connect to the Docker daemon

Fix:

# Start Docker daemon
sudo systemctl start docker

# Or on macOS
open -a Docker

# Verify Docker is running
docker ps

Error: Port already in use

Fix:

# Find process using port
lsof -i :8006

# Kill process
kill -9 <PID>

# Or change port in docker-compose.yml

Database migration fails

Error: Migration failed: relation already exists

Fix:

# Reset database
docker compose down -v
docker compose up -d db

# Re-run migrations
cd backend
poetry run prisma migrate reset --force
poetry run prisma migrate deploy

Error: Connection refused to database

Fix:

# Check database is running
docker compose ps db

# Check database logs
docker compose logs db

# Verify DATABASE_URL in .env
echo $DATABASE_URL

Frontend build fails

Error: Module not found: Can't resolve '@/components/...'

Fix:

# Clear node modules and reinstall
rm -rf node_modules
rm -rf .next
npm install

# Or with pnpm
pnpm install --force

Error: Supabase client not initialized

Fix:

# Verify environment variables
cat .env | grep SUPABASE

# Required variables:
# NEXT_PUBLIC_SUPABASE_URL=http://localhost:8000
# NEXT_PUBLIC_SUPABASE_ANON_KEY=your-key

Service Issues

Backend services not starting

Error: rest_server exited with code 1

Diagnose:

# Check logs
docker compose logs rest_server

# Common issues:
# - Missing environment variables
# - Database connection failed
# - Redis connection failed

Fix:

# Verify all dependencies are running
docker compose ps

# Restart services in order
docker compose restart db redis rabbitmq
sleep 10
docker compose restart rest_server executor

Executor not processing tasks

Error: Tasks stuck in QUEUED status

Diagnose:

# Check executor logs
docker compose logs executor

# Check RabbitMQ queue
# Visit http://localhost:15672 (guest/guest)
# Look at queue depths

Fix:

# Restart executor
docker compose restart executor

# If queue is backlogged, scale executors
docker compose up -d --scale executor=3

WebSocket connection fails

Error: WebSocket connection to 'ws://localhost:8001/ws' failed

Fix:

# Check WebSocket server is running
docker compose logs websocket_server

# Verify port is accessible
nc -zv localhost 8001

# Check firewall rules
sudo ufw allow 8001

Agent Execution Issues

Agent stuck in running state

Diagnose:

# Check execution status via API
curl http://localhost:8006/api/v1/executions/{execution_id}

# Check node execution logs
docker compose logs executor | grep {execution_id}

Fix:

# Cancel stuck execution via API
import requests

response = requests.post(
    f"http://localhost:8006/api/v1/executions/{execution_id}/cancel",
    headers={"Authorization": f"Bearer {token}"}
)

LLM block timeout

Error: TimeoutError: LLM call exceeded timeout

Fix:

# Increase timeout in block configuration
{
    "block_id": "llm-block",
    "config": {
        "timeout_seconds": 120,  # Increase from default 60
        "max_retries": 3
    }
}

Credential errors

Error: CredentialsNotFoundError: No credentials for provider openai

Fix: 1. Navigate to Profile > Integrations 2. Add OpenAI API key 3. Ensure graph has credential mapping

{
    "credential_mapping": {
        "openai": "user_credential_id"
    }
}

Memory issues during execution

Error: MemoryError or container killed (OOMKilled)

Fix:

# Increase memory limits in docker-compose.yml
executor:
    deploy:
        resources:
            limits:
                memory: 4G
            reservations:
                memory: 2G

Graph/Block Issues

Block not appearing in UI

Diagnose:

# Check block registration
from backend.data.block import get_all_blocks

blocks = get_all_blocks()
print([b.name for b in blocks])

Fix:

# Ensure block is imported in __init__.py
# backend/blocks/__init__.py
from backend.blocks.my_block import MyBlock

BLOCKS = [
    MyBlock,
    # ...
]

Graph save fails

Error: GraphValidationError: Invalid link configuration

Diagnose:

# Validate graph structure
from backend.data.graph import validate_graph

errors = validate_graph(graph_data)
print(errors)

Fix:

• Ensure all links connect valid nodes
• Check input/output name matches
• Verify required inputs are connected

Circular dependency detected

Error: GraphValidationError: Circular dependency in graph

Fix:

# Find cycle
import networkx as nx

G = nx.DiGraph()
for link in graph.links:
    G.add_edge(link.source_id, link.sink_id)

cycles = list(nx.simple_cycles(G))
print(f"Cycles found: {cycles}")

Performance Issues

Slow graph execution

Diagnose:

# Profile execution
import cProfile

profiler = cProfile.Profile()
profiler.enable()
await executor.execute_graph(graph_id, inputs)
profiler.disable()
profiler.print_stats(sort='cumulative')

Fix:

• Parallelize independent nodes
• Reduce unnecessary API calls
• Cache repeated computations

High database query latency

Diagnose:

# Enable query logging in PostgreSQL
docker exec -it autogpt-db psql -U postgres
\x
SHOW log_min_duration_statement;
SET log_min_duration_statement = 100;  -- Log queries > 100ms

Fix:

-- Add missing indexes
CREATE INDEX CONCURRENTLY idx_executions_user_created
ON "AgentGraphExecution" ("userId", "createdAt" DESC);

ANALYZE "AgentGraphExecution";

Redis memory growing

Diagnose:

# Check Redis memory usage
docker exec -it autogpt-redis redis-cli INFO memory

# Check key count
docker exec -it autogpt-redis redis-cli DBSIZE

Fix:

# Clear expired keys
docker exec -it autogpt-redis redis-cli --scan --pattern "exec:*" | head -1000 | xargs docker exec -i autogpt-redis redis-cli DEL

# Set memory policy
docker exec -it autogpt-redis redis-cli CONFIG SET maxmemory-policy volatile-lru

Debugging Tips

Enable debug logging

# Set in .env
LOG_LEVEL=DEBUG

# Or for specific module
LOG_LEVEL_EXECUTOR=DEBUG
LOG_LEVEL_BLOCKS=DEBUG

Trace execution flow

import logging

logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger("backend.executor")

# Add to executor
logger.debug(f"Executing node {node_id} with inputs: {inputs}")

Test block in isolation

import asyncio
from backend.blocks.my_block import MyBlock

async def test_block():
    block = MyBlock()
    inputs = {"query": "test"}

    async for output_name, value in block.execute(inputs):
        print(f"{output_name}: {value}")

asyncio.run(test_block())

Inspect message queues

# RabbitMQ management UI
# http://localhost:15672 (guest/guest)

# List queues via CLI
docker exec autogpt-rabbitmq rabbitmqctl list_queues name messages consumers

# Purge a queue
docker exec autogpt-rabbitmq rabbitmqctl purge_queue graph-execution

Getting Help

1. Documentation: https://docs.agpt.co 2. GitHub Issues: https://github.com/Significant-Gravitas/AutoGPT/issues 3. Discord: https://discord.gg/autogpt

Reporting Issues

Include:

• AutoGPT version: git describe --tags
• Docker version: docker --version
• Error logs: docker compose logs > logs.txt
• Steps to reproduce
• Graph configuration (sanitized)
• Environment: OS, hardware specs