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Merge branch 'swiftyos/open-1920-marketplace-home-components' of github.com:Significant-Gravitas/AutoGPT into swiftyos/open-1920-marketplace-home-components

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SwiftyOS
2024-10-29 11:00:08 +01:00
parent d73acd13cb ebad48481e
commit e5a4b9a5ac
70 changed files with 2318 additions and 911 deletions
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2
.github/workflows/classic-frontend-ci.yml vendored
View File

@@ -49,7 +49,7 @@ jobs:
- name: Create PR ${{ env.BUILD_BRANCH }} -> ${{ github.ref_name }}
if: github.event_name == 'push'
uses: peter-evans/create-pull-request@v6
uses: peter-evans/create-pull-request@v7
with:
add-paths: classic/frontend/build/web
base: ${{ github.ref_name }}

8
.github/workflows/platform-autgpt-deploy-prod.yml vendored
View File

@@ -60,15 +60,15 @@ jobs:
fetch-depth: 0
- id: 'auth'
uses: 'google-github-actions/auth@v1'
uses: 'google-github-actions/auth@v2'
with:
workload_identity_provider: 'projects/638488734936/locations/global/workloadIdentityPools/prod-pool/providers/github'
workload_identity_provider: 'projects/1021527134101/locations/global/workloadIdentityPools/prod-pool/providers/github'
service_account: 'prod-github-actions-sa@agpt-prod.iam.gserviceaccount.com'
token_format: 'access_token'
create_credentials_file: true
- name: 'Set up Cloud SDK'
uses: 'google-github-actions/setup-gcloud@v1'
uses: 'google-github-actions/setup-gcloud@v2'
- name: 'Configure Docker'
run: |
@@ -78,7 +78,7 @@ jobs:
uses: docker/setup-buildx-action@v3
- name: Cache Docker layers
uses: actions/cache@v2
uses: actions/cache@v4
with:
path: /tmp/.buildx-cache
key: ${{ runner.os }}-buildx-${{ github.sha }}

6
.github/workflows/platform-autogpt-deploy.yaml vendored
View File

@@ -64,7 +64,7 @@ jobs:
fetch-depth: 0
- id: 'auth'
uses: 'google-github-actions/auth@v1'
uses: 'google-github-actions/auth@v2'
with:
workload_identity_provider: 'projects/638488734936/locations/global/workloadIdentityPools/dev-pool/providers/github'
service_account: 'dev-github-actions-sa@agpt-dev.iam.gserviceaccount.com'
@@ -72,7 +72,7 @@ jobs:
create_credentials_file: true
- name: 'Set up Cloud SDK'
uses: 'google-github-actions/setup-gcloud@v1'
uses: 'google-github-actions/setup-gcloud@v2'
- name: 'Configure Docker'
run: |
@@ -82,7 +82,7 @@ jobs:
uses: docker/setup-buildx-action@v3
- name: Cache Docker layers
uses: actions/cache@v2
uses: actions/cache@v4
with:
path: /tmp/.buildx-cache
key: ${{ runner.os }}-buildx-${{ github.sha }}

2
README.md
View File

@@ -109,7 +109,7 @@ This guide will walk you through the process of creating your own agent and usin
📦 [`agbenchmark`](https://pypi.org/project/agbenchmark/) on Pypi
 | 
📘 [Learn More](https://github.com/Significant-Gravitas/AutoGPT/blob/master/benchmark) about the Benchmark
📘 [Learn More](https://github.com/Significant-Gravitas/AutoGPT/tree/master/classic/benchmark) about the Benchmark
### 💻 UI

4
autogpt_platform/backend/backend/cli.py
View File

@@ -217,13 +217,13 @@ def websocket(server_address: str, graph_id: str):
"""
import asyncio
import websockets
import websockets.asyncio.client
from backend.server.ws_api import ExecutionSubscription, Methods, WsMessage
async def send_message(server_address: str):
uri = f"ws://{server_address}"
async with websockets.connect(uri) as websocket:
async with websockets.asyncio.client.connect(uri) as websocket:
try:
msg = WsMessage(
method=Methods.SUBSCRIBE,

336
autogpt_platform/backend/poetry.lock generated
View File

@@ -307,33 +307,33 @@ url = "../autogpt_libs"
[[package]]
name = "black"
version = "24.8.0"
version = "24.10.0"
description = "The uncompromising code formatter."
optional = false
python-versions = ">=3.8"
python-versions = ">=3.9"
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]
[package.dependencies]
@@ -347,7 +347,7 @@ typing-extensions = {version = ">=4.0.1", markers = "python_version < \"3.11\""}
[package.extras]
colorama = ["colorama (>=0.4.3)"]
d = ["aiohttp (>=3.7.4)", "aiohttp (>=3.7.4,!=3.9.0)"]
d = ["aiohttp (>=3.10)"]
jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
uvloop = ["uvloop (>=0.15.2)"]
@@ -1860,8 +1860,8 @@ python-dateutil = ">=2.5.3"
tqdm = ">=4.64.1"
typing-extensions = ">=3.7.4"
urllib3 = [
{version = ">=1.26.0", markers = "python_version >= \"3.8\" and python_version < \"3.12\""},
{version = ">=1.26.5", markers = "python_version >= \"3.12\" and python_version < \"4.0\""},
{version = ">=1.26.0", markers = "python_version >= \"3.8\" and python_version < \"3.12\""},
]
[package.extras]
@@ -1925,18 +1925,19 @@ testing = ["pytest", "pytest-benchmark"]
[[package]]
name = "poethepoet"
version = "0.26.1"
version = "0.29.0"
description = "A task runner that works well with poetry."
optional = false
python-versions = ">=3.8"
files = [
{file = "poethepoet-0.26.1-py3-none-any.whl", hash = "sha256:aa43b443fec5d17d7e76771cccd484e5285805301721a74f059c483ad3276edd"},
{file = "poethepoet-0.26.1.tar.gz", hash = "sha256:aaad8541f6072617a60bcff2562d00779b58b353bd0f1847b06d8d0f2b6dc192"},
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{file = "poethepoet-0.29.0.tar.gz", hash = "sha256:676842302f2304a86b31ac56398dd672fae8471128d2086896393384dbafc095"},
]
[package.dependencies]
pastel = ">=0.2.1,<0.3.0"
tomli = ">=1.2.2"
pyyaml = ">=6.0.2,<7.0.0"
tomli = {version = ">=1.2.2", markers = "python_version < \"3.11\""}
[package.extras]
poetry-plugin = ["poetry (>=1.0,<2.0)"]
@@ -2066,31 +2067,33 @@ files = [
[[package]]
name = "psutil"
version = "5.9.8"
version = "6.1.0"
description = "Cross-platform lib for process and system monitoring in Python."
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
files = [
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]
[[package]]
@@ -3719,4 +3741,4 @@ type = ["pytest-mypy"]
[metadata]
lock-version = "2.0"
python-versions = "^3.10"
content-hash = "f9293b504ef813f98f43a8c3ab1b779ff9d7dc2e3bd9412fccc6da5102915e6b"
content-hash = "ab3ae697e0be22e3ed20ae136db5b6805086279ebb57c99b60ed1c4d8d2dbbae"

16
autogpt_platform/backend/pyproject.toml
View File

@@ -29,7 +29,7 @@ ollama = "^0.3.0"
openai = "^1.35.7"
praw = "~7.7.1"
prisma = "^0.13.1"
psutil = "^5.9.8"
psutil = "^6.1.0"
pydantic = "^2.7.2"
pydantic-settings = "^2.3.4"
pyro5 = "^5.15"
@@ -37,24 +37,24 @@ pytest = "^8.2.1"
pytest-asyncio = "^0.23.7"
python-dotenv = "^1.0.1"
redis = "^5.0.8"
sentry-sdk = "1.45.0"
sentry-sdk = "2.17.0"
supabase = "^2.7.2"
tenacity = "^8.3.0"
tenacity = "^9.0.0"
uvicorn = { extras = ["standard"], version = "^0.30.1" }
websockets = "^12.0"
websockets = "^13.1"
youtube-transcript-api = "^0.6.2"
googlemaps = "^4.10.0"
replicate = "^0.34.1"
pinecone = "^5.3.1"
[tool.poetry.group.dev.dependencies]
poethepoet = "^0.26.1"
poethepoet = "^0.29.0"
httpx = "^0.27.0"
pytest-watcher = "^0.4.2"
requests = "^2.32.3"
ruff = "^0.5.2"
pyright = "^1.1.371"
ruff = "^0.7.1"
pyright = "^1.1.386"
isort = "^5.13.2"
black = "^24.4.2"
black = "^24.10.0"
aiohappyeyeballs = "^2.4.3"
[build-system]

628
autogpt_platform/backend/target.prisma Normal file
View File

@@ -0,0 +1,628 @@
// We need to migrate our database schema to support the domain as we understand it now
// To do so requires adding a bunch of new tables, but also modiftying old ones and how
// they relate to each other. This is a large change, so instead of doing in in one go,
// We have created the target schema, and will migrate to it incrementally.
datasource db {
provider = "postgresql"
url = env("DATABASE_URL")
}
generator client {
provider = "prisma-client-py"
recursive_type_depth = 5
interface = "asyncio"
}
// User model to mirror Auth provider users
model User {
id String @id @db.Uuid // This should match the Supabase user ID
email String @unique
name String?
createdAt DateTime @default(now())
updatedAt DateTime @default(now()) @updatedAt
metadata Json? @default("{}")
// Relations
Agents Agent[]
AgentExecutions AgentExecution[]
AgentExecutionSchedules AgentExecutionSchedule[]
AnalyticsDetails AnalyticsDetails[]
AnalyticsMetrics AnalyticsMetrics[]
UserBlockCredit UserBlockCredit[]
AgentPresets AgentPreset[]
UserAgents UserAgent[]
// User Group relations
UserGroupMemberships UserGroupMembership[]
Profile Profile[]
StoreListing StoreListing[]
StoreListingSubmission StoreListingSubmission[]
StoreListingReview StoreListingReview[]
}
model UserGroup {
id String @id @default(uuid()) @db.Uuid
createdAt DateTime @default(now())
updatedAt DateTime @default(now()) @updatedAt
name String
description String
groupIconUrl String?
UserGroupMemberships UserGroupMembership[]
Agents Agent[]
Profile Profile[]
StoreListing StoreListing[]
@@index([name])
}
enum UserGroupRole {
MEMBER
OWNER
}
model UserGroupMembership {
id String @id @default(uuid()) @db.Uuid
createdAt DateTime @default(now())
updatedAt DateTime @default(now()) @updatedAt
userId String @db.Uuid
User User @relation(fields: [userId], references: [id], onDelete: Cascade)
userGroupId String @db.Uuid
UserGroup UserGroup @relation(fields: [userGroupId], references: [id], onDelete: Cascade)
Role UserGroupRole @default(MEMBER)
@@unique([userId, userGroupId])
@@index([userId])
@@index([userGroupId])
}
// This model describes the Agent Graph/Flow (Multi Agent System).
model Agent {
id String @default(uuid()) @db.Uuid
version Int @default(1)
createdAt DateTime @default(now())
updatedAt DateTime @default(now()) @updatedAt
name String?
description String?
// Link to User model
createdByUserId String? @db.Uuid
// Do not cascade delete the agent when the user is deleted
// This allows us to delete user data with deleting the agent which maybe in use by other users
CreatedByUser User? @relation(fields: [createdByUserId], references: [id], onDelete: SetNull)
groupId String? @db.Uuid
// Do not cascade delete the agent when the group is deleted
// This allows us to delete user group data with deleting the agent which maybe in use by other users
Group UserGroup? @relation(fields: [groupId], references: [id], onDelete: SetNull)
AgentNodes AgentNode[]
AgentExecution AgentExecution[]
// All sub-graphs are defined within this 1-level depth list (even if it's a nested graph).
SubAgents Agent[] @relation("SubAgents")
agentParentId String? @db.Uuid
agentParentVersion Int?
AgentParent Agent? @relation("SubAgents", fields: [agentParentId, agentParentVersion], references: [id, version])
AgentPresets AgentPreset[]
WebhookTrigger WebhookTrigger[]
AgentExecutionSchedule AgentExecutionSchedule[]
UserAgents UserAgent[]
UserBlockCredit UserBlockCredit[]
StoreListing StoreListing[]
StoreListingVersion StoreListingVersion[]
@@id(name: "agentVersionId", [id, version])
}
////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////
//////////////// USER SPECIFIC DATA ////////////////////
////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////
// An AgentPrest is an Agent + User Configuration of that agent.
// For example, if someone has created a weather agent and they want to set it up to
// Inform them of extreme weather warnings in Texas, the agent with the configuration to set it to
// monitor texas, along with the cron setup or webhook tiggers, is an AgentPreset
model AgentPreset {
id String @id @default(uuid()) @db.Uuid
createdAt DateTime @default(now())
updatedAt DateTime @default(now()) @updatedAt
name String
description String
// For agents that can be triggered by webhooks or cronjob
// This bool allows us to disable a configured agent without deleting it
isActive Boolean @default(true)
userId String @db.Uuid
User User @relation(fields: [userId], references: [id], onDelete: Cascade)
agentId String @db.Uuid
agentVersion Int
Agent Agent @relation(fields: [agentId, agentVersion], references: [id, version], onDelete: Cascade)
InputPresets AgentNodeExecutionInputOutput[] @relation("AgentPresetsInputData")
UserAgents UserAgent[]
WebhookTrigger WebhookTrigger[]
AgentExecutionSchedule AgentExecutionSchedule[]
AgentExecution AgentExecution[]
@@index([userId])
}
// For the library page
// It is a user controlled list of agents, that they will see in there library
model UserAgent {
id String @id @default(uuid()) @db.Uuid
createdAt DateTime @default(now())
updatedAt DateTime @default(now()) @updatedAt
userId String @db.Uuid
User User @relation(fields: [userId], references: [id], onDelete: Cascade)
agentId String @db.Uuid
agentVersion Int
Agent Agent @relation(fields: [agentId, agentVersion], references: [id, version])
agentPresetId String? @db.Uuid
AgentPreset AgentPreset? @relation(fields: [agentPresetId], references: [id])
isFavorite Boolean @default(false)
isCreatedByUser Boolean @default(false)
isArchived Boolean @default(false)
isDeleted Boolean @default(false)
@@index([userId])
}
////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////
//////// AGENT DEFINITION AND EXECUTION TABLES ////////
////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////
// This model describes a single node in the Agent Graph/Flow (Multi Agent System).
model AgentNode {
id String @id @default(uuid()) @db.Uuid
agentBlockId String @db.Uuid
AgentBlock AgentBlock @relation(fields: [agentBlockId], references: [id], onUpdate: Cascade)
agentId String @db.Uuid
agentVersion Int @default(1)
Agent Agent @relation(fields: [agentId, agentVersion], references: [id, version], onDelete: Cascade)
// List of consumed input, that the parent node should provide.
Input AgentNodeLink[] @relation("AgentNodeSink")
// List of produced output, that the child node should be executed.
Output AgentNodeLink[] @relation("AgentNodeSource")
// JSON serialized dict[str, str] containing predefined input values.
constantInput Json @default("{}")
// JSON serialized dict[str, str] containing the node metadata.
metadata Json @default("{}")
ExecutionHistory AgentNodeExecution[]
}
// This model describes the link between two AgentNodes.
model AgentNodeLink {
id String @id @default(uuid()) @db.Uuid
// Output of a node is connected to the source of the link.
agentNodeSourceId String @db.Uuid
AgentNodeSource AgentNode @relation("AgentNodeSource", fields: [agentNodeSourceId], references: [id], onDelete: Cascade)
sourceName String
// Input of a node is connected to the sink of the link.
agentNodeSinkId String @db.Uuid
AgentNodeSink AgentNode @relation("AgentNodeSink", fields: [agentNodeSinkId], references: [id], onDelete: Cascade)
sinkName String
// Default: the data coming from the source can only be consumed by the sink once, Static: input data will be reused.
isStatic Boolean @default(false)
}
// This model describes a component that will be executed by the AgentNode.
model AgentBlock {
id String @id @default(uuid()) @db.Uuid
name String @unique
// We allow a block to have multiple types of input & output.
// Serialized object-typed `jsonschema` with top-level properties as input/output name.
inputSchema Json @default("{}")
outputSchema Json @default("{}")
// Prisma requires explicit back-references.
ReferencedByAgentNode AgentNode[]
UserBlockCredit UserBlockCredit[]
}
// This model describes the status of an AgentExecution or AgentNodeExecution.
enum AgentExecutionStatus {
INCOMPLETE
QUEUED
RUNNING
COMPLETED
FAILED
}
// Enum for execution trigger types
enum ExecutionTriggerType {
MANUAL
SCHEDULE
WEBHOOK
}
// This model describes the execution of an AgentGraph.
model AgentExecution {
id String @id @default(uuid()) @db.Uuid
createdAt DateTime @default(now())
updatedAt DateTime @default(now()) @updatedAt
startedAt DateTime?
executionTriggerType ExecutionTriggerType @default(MANUAL)
executionStatus AgentExecutionStatus @default(COMPLETED)
agentId String @db.Uuid
agentVersion Int @default(1)
Agent Agent @relation(fields: [agentId, agentVersion], references: [id, version], onDelete: Cascade)
// we need to be able to associate an agent execution with an agent preset
agentPresetId String? @db.Uuid
AgentPreset AgentPreset? @relation(fields: [agentPresetId], references: [id])
AgentNodeExecutions AgentNodeExecution[]
// This is so we can track which user executed the agent.
executedByUserId String @db.Uuid
ExecutedByUser User @relation(fields: [executedByUserId], references: [id], onDelete: Cascade)
stats Json @default("{}") // JSON serialized object
}
// This model describes the execution of an AgentNode.
model AgentNodeExecution {
id String @id @default(uuid()) @db.Uuid
agentExecutionId String @db.Uuid
AgentExecution AgentExecution @relation(fields: [agentExecutionId], references: [id], onDelete: Cascade)
agentNodeId String @db.Uuid
AgentNode AgentNode @relation(fields: [agentNodeId], references: [id], onDelete: Cascade)
Input AgentNodeExecutionInputOutput[] @relation("AgentNodeExecutionInput")
Output AgentNodeExecutionInputOutput[] @relation("AgentNodeExecutionOutput")
executionStatus AgentExecutionStatus @default(COMPLETED)
// Final JSON serialized input data for the node execution.
executionData String?
addedTime DateTime @default(now())
queuedTime DateTime?
startedTime DateTime?
endedTime DateTime?
stats Json @default("{}") // JSON serialized object
UserBlockCredit UserBlockCredit[]
}
// This model describes the output of an AgentNodeExecution.
model AgentNodeExecutionInputOutput {
id String @id @default(uuid()) @db.Uuid
name String
data String
time DateTime @default(now())
// Prisma requires explicit back-references.
referencedByInputExecId String? @db.Uuid
ReferencedByInputExec AgentNodeExecution? @relation("AgentNodeExecutionInput", fields: [referencedByInputExecId], references: [id], onDelete: Cascade)
referencedByOutputExecId String? @db.Uuid
ReferencedByOutputExec AgentNodeExecution? @relation("AgentNodeExecutionOutput", fields: [referencedByOutputExecId], references: [id], onDelete: Cascade)
agentPresetId String? @db.Uuid
AgentPreset AgentPreset? @relation("AgentPresetsInputData", fields: [agentPresetId], references: [id])
// Input and Output pin names are unique for each AgentNodeExecution.
@@unique([referencedByInputExecId, referencedByOutputExecId, name])
}
// This model describes the recurring execution schedule of an Agent.
model AgentExecutionSchedule {
id String @id @default(uuid()) @db.Uuid
createdAt DateTime @default(now())
updatedAt DateTime @default(now()) @updatedAt
agentPresetId String @db.Uuid
AgentPreset AgentPreset @relation(fields: [agentPresetId], references: [id], onDelete: Cascade)
schedule String // cron expression
isEnabled Boolean @default(true)
// Allows triggers to be routed down different execution paths in an agent graph
triggerIdentifier String
// default and set the value on each update, lastUpdated field has no time zone.
lastUpdated DateTime @default(now()) @updatedAt
// Link to User model
userId String @db.Uuid
User User @relation(fields: [userId], references: [id], onDelete: Cascade)
Agent Agent? @relation(fields: [agentId, agentVersion], references: [id, version])
agentId String? @db.Uuid
agentVersion Int?
@@index([isEnabled])
}
enum HttpMethod {
GET
POST
PUT
DELETE
PATCH
}
model WebhookTrigger {
id String @id @default(uuid()) @db.Uuid
createdAt DateTime @default(now())
updatedAt DateTime @default(now()) @updatedAt
agentPresetId String @db.Uuid
AgentPreset AgentPreset @relation(fields: [agentPresetId], references: [id])
method HttpMethod
urlSlug String
// Allows triggers to be routed down different execution paths in an agent graph
triggerIdentifier String
isActive Boolean @default(true)
lastReceivedDataAt DateTime?
isDeleted Boolean @default(false)
Agent Agent? @relation(fields: [agentId, agentVersion], references: [id, version])
agentId String? @db.Uuid
agentVersion Int?
@@index([agentPresetId])
}
////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////
////////////// METRICS TRACKING TABLES ////////////////
////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////
model AnalyticsDetails {
// PK uses gen_random_uuid() to allow the db inserts to happen outside of prisma
// typical uuid() inserts are handled by prisma
id String @id @default(dbgenerated("gen_random_uuid()")) @db.Uuid
createdAt DateTime @default(now())
updatedAt DateTime @default(now()) @updatedAt
// Link to User model
userId String @db.Uuid
User User @relation(fields: [userId], references: [id], onDelete: Cascade)
// Analytics Categorical data used for filtering (indexable w and w/o userId)
type String
// Analytic Specific Data. We should use a union type here, but prisma doesn't support it.
data Json @default("{}")
// Indexable field for any count based analytical measures like page order clicking, tutorial step completion, etc.
dataIndex String?
@@index([userId, type], name: "analyticsDetails")
@@index([type])
}
model AnalyticsMetrics {
id String @id @default(dbgenerated("gen_random_uuid()")) @db.Uuid
createdAt DateTime @default(now())
updatedAt DateTime @default(now()) @updatedAt
// Analytics Categorical data used for filtering (indexable w and w/o userId)
analyticMetric String
// Any numeric data that should be counted upon, summed, or otherwise aggregated.
value Float
// Any string data that should be used to identify the metric as distinct.
// ex: '/build' vs '/market'
dataString String?
// Link to User model
userId String @db.Uuid
User User @relation(fields: [userId], references: [id], onDelete: Cascade)
}
////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////
//////// ACCOUNTING AND CREDIT SYSTEM TABLES //////////
////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////
enum UserBlockCreditType {
TOP_UP
USAGE
}
model UserBlockCredit {
transactionKey String @default(uuid())
createdAt DateTime @default(now())
userId String @db.Uuid
User User @relation(fields: [userId], references: [id], onDelete: Cascade)
blockId String? @db.Uuid
Block AgentBlock? @relation(fields: [blockId], references: [id])
// We need to be able to associate a credit transaction with an agent
executedAgentId String? @db.Uuid
executedAgentVersion Int?
ExecutedAgent Agent? @relation(fields: [executedAgentId, executedAgentVersion], references: [id, version])
// We need to be able to associate a cost with a specific agent execution
agentNodeExecutionId String? @db.Uuid
AgentNodeExecution AgentNodeExecution? @relation(fields: [agentNodeExecutionId], references: [id])
amount Int
type UserBlockCreditType
isActive Boolean @default(true)
metadata Json @default("{}")
@@id(name: "creditTransactionIdentifier", [transactionKey, userId])
}
////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////
////////////// Store TABLES ///////////////////////////
////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////
model Profile {
id String @id @default(uuid()) @db.Uuid
createdAt DateTime @default(now())
updatedAt DateTime @default(now()) @updatedAt
// Only 1 of user or group can be set.
// The user this profile belongs to, if any.
userId String? @db.Uuid
User User? @relation(fields: [userId], references: [id], onDelete: Cascade)
// The group this profile belongs to, if any.
groupId String? @db.Uuid
Group UserGroup? @relation(fields: [groupId], references: [id])
username String @unique
description String
links String[]
avatarUrl String?
@@index([username])
}
model StoreListing {
id String @id @default(uuid()) @db.Uuid
createdAt DateTime @default(now())
updatedAt DateTime @default(now()) @updatedAt
isDeleted Boolean @default(false)
// Not needed but makes lookups faster
isApproved Boolean @default(false)
// The agent link here is only so we can do lookup on agentId, for the listing the StoreListingVersion is used.
agentId String @db.Uuid
agentVersion Int
Agent Agent @relation(fields: [agentId, agentVersion], references: [id, version], onDelete: Cascade)
owningUserId String @db.Uuid
OwningUser User @relation(fields: [owningUserId], references: [id])
isGroupListing Boolean @default(false)
owningGroupId String? @db.Uuid
OwningGroup UserGroup? @relation(fields: [owningGroupId], references: [id])
StoreListingVersions StoreListingVersion[]
StoreListingSubmission StoreListingSubmission[]
@@index([isApproved])
@@index([agentId])
@@index([owningUserId])
@@index([owningGroupId])
}
model StoreListingVersion {
id String @id @default(uuid()) @db.Uuid
version Int @default(1)
createdAt DateTime @default(now())
updatedAt DateTime @default(now()) @updatedAt
// The agent and version to be listed on the store
agentId String @db.Uuid
agentVersion Int
Agent Agent @relation(fields: [agentId, agentVersion], references: [id, version])
// The detials for this version of the agent, this allows the author to update the details of the agent,
// But still allow using old versions of the agent with there original details.
// TODO: Create a database view that shows only the latest version of each store listing.
slug String
name String
videoUrl String?
imageUrls String[]
description String
categories String[]
isFeatured Boolean @default(false)
isDeleted Boolean @default(false)
// Old versions can be made unavailable by the author if desired
isAvailable Boolean @default(true)
// Not needed but makes lookups faster
isApproved Boolean @default(false)
StoreListing StoreListing? @relation(fields: [storeListingId], references: [id], onDelete: Cascade)
storeListingId String? @db.Uuid
StoreListingSubmission StoreListingSubmission[]
// Reviews are on a specific version, but then aggregated up to the listing.
// This allows us to provide a review filter to current version of the agent.
StoreListingReview StoreListingReview[]
@@unique([agentId, agentVersion])
@@index([agentId, agentVersion, isApproved])
}
model StoreListingReview {
id String @id @default(dbgenerated("gen_random_uuid()")) @db.Uuid
createdAt DateTime @default(now())
updatedAt DateTime @default(now()) @updatedAt
storeListingVersionId String @db.Uuid
StoreListingVersion StoreListingVersion @relation(fields: [storeListingVersionId], references: [id], onDelete: Cascade)
reviewByUserId String @db.Uuid
ReviewByUser User @relation(fields: [reviewByUserId], references: [id])
score Int
comments String?
}
enum SubmissionStatus {
DAFT
PENDING
APPROVED
REJECTED
}
model StoreListingSubmission {
id String @id @default(uuid()) @db.Uuid
createdAt DateTime @default(now())
updatedAt DateTime @default(now()) @updatedAt
storeListingId String @db.Uuid
StoreListing StoreListing @relation(fields: [storeListingId], references: [id], onDelete: Cascade)
storeListingVersionId String @db.Uuid
StoreListingVersion StoreListingVersion @relation(fields: [storeListingVersionId], references: [id], onDelete: Cascade)
reviewerId String @db.Uuid
Reviewer User @relation(fields: [reviewerId], references: [id])
Status SubmissionStatus @default(PENDING)
reviewComments String?
@@index([storeListingId])
@@index([Status])
}

9
autogpt_platform/frontend/next.config.mjs
View File

@@ -1,16 +1,7 @@
import { withSentryConfig } from "@sentry/nextjs";
import dotenv from "dotenv";
// Load environment variables
dotenv.config();
/** @type {import('next').NextConfig} */
const nextConfig = {
env: {
NEXT_PUBLIC_AGPT_SERVER_URL: process.env.NEXT_PUBLIC_AGPT_SERVER_URL,
NEXT_PUBLIC_AGPT_MARKETPLACE_URL:
process.env.NEXT_PUBLIC_AGPT_MARKETPLACE_URL,
},
images: {
domains: [
"images.unsplash.com",

26
autogpt_platform/frontend/src/components/agptui/PublishAgentAwaitingReview.stories.tsx Normal file
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import type { Meta, StoryObj } from "@storybook/react";
import { PublishAgentAwaitingReview } from "./PublishAgentAwaitingReview";
const meta: Meta<typeof PublishAgentAwaitingReview> = {
title: "AGPT UI/Publish Agent Awaiting Review",
component: PublishAgentAwaitingReview,
tags: ["autodocs"],
parameters: {
layout: "centered",
},
};
export default meta;
type Story = StoryObj<typeof PublishAgentAwaitingReview>;
export const Filled: Story = {
args: {
agentName: "AI Video Generator",
subheader: "Create Viral-Ready Content in Seconds",
description: "AI Shortform Video Generator: Create Viral-Ready Content in Seconds Transform trending topics into engaging shortform videos with this cutting-edge AI Video Generator. Perfect for content creators, social media managers, and marketers looking to capitalize on the latest news and viral trends. Simply input your desired video count and source website, and watch as the AI scours the internet for the hottest stories, crafting them into attention-grabbing scripts optimized for platforms like TikTok, Instagram Reels, and YouTube Shorts. Key features include: - Customizable video count (1-5 per generation) - Flexible source selection for trending topics - AI-driven script writing following best practices for shortform content - Hooks that capture attention in the first 3 seconds - Dual narrative storytelling for maximum engagement - SEO-optimized content to boost discoverability - Integration with video generation tools for seamless production From hook to conclusion, each script is meticulously crafted to maintain viewer interest, incorporating proven techniques like 'but so' storytelling, visual metaphors, and strategically placed calls-to-action. The AI Shortform Video Generator streamlines your content creation process, allowing you to stay ahead of trends and consistently produce viral-worthy videos that resonate with your audience.",
thumbnailSrc: "https://picsum.photos/seed/video/500/350",
onClose: () => console.log("Close clicked"),
onDone: () => console.log("Done clicked"),
onViewProgress: () => console.log("View progress clicked"),
},
};

108
autogpt_platform/frontend/src/components/agptui/PublishAgentAwaitingReview.tsx Normal file
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import * as React from "react";
import { IconClose } from "../ui/icons";
import Image from "next/image";
import { Button } from "../agptui/Button";
interface PublishAgentAwaitingReviewProps {
agentName: string;
subheader: string;
description: string;
thumbnailSrc?: string;
onClose: () => void;
onDone: () => void;
onViewProgress: () => void;
}
export const PublishAgentAwaitingReview: React.FC<PublishAgentAwaitingReviewProps> = ({
agentName,
subheader,
description,
thumbnailSrc,
onClose,
onDone,
onViewProgress,
}) => {
return (
<div
className="inline-flex min-h-screen sm:h-auto sm:min-h-[824px] w-full sm:max-w-[670px] flex-col rounded-none sm:rounded-3xl border border-slate-200 bg-white shadow"
role="dialog"
aria-labelledby="modal-title"
>
<div className="w-full relative h-[180px] sm:h-[140px] rounded-none sm:rounded-t-3xl border-b border-slate-200">
<div className="w-full absolute left-0 top-[40px] sm:top-[40px] flex flex-col items-center justify-start px-6">
<div
id="modal-title"
className="text-neutral-900 text-xl sm:text-2xl font-semibold font-['Poppins'] leading-relaxed mb-4 sm:mb-2 text-center"
>
Agent is awaiting review
</div>
<div className="text-center text-slate-500 text-sm font-normal font-['Inter'] leading-relaxed max-w-[280px] sm:max-w-none">
In the meantime you can check your progress on your Creator Dashboard page
</div>
</div>
<button
onClick={onClose}
className="absolute right-4 top-4 w-[38px] h-[38px] rounded-full bg-gray-100 flex items-center justify-center hover:bg-gray-200 transition-colors"
aria-label="Close dialog"
>
<IconClose size="default" className="text-neutral-600" />
</button>
</div>
<div className="flex flex-1 flex-col items-center px-6 py-6 gap-8 sm:gap-6">
<div className="flex w-full flex-col items-center gap-6 sm:gap-4 mt-4 sm:mt-0">
<div className="flex flex-col items-center gap-3 sm:gap-2">
<div className="font-['Geist'] text-lg font-semibold leading-7 text-neutral-800 text-center">
{agentName}
</div>
<div className="font-['Geist'] text-base font-normal leading-normal text-neutral-600 text-center max-w-[280px] sm:max-w-none">
{subheader}
</div>
</div>
<div
className="w-full h-[280px] sm:h-[350px] bg-neutral-200 rounded-xl"
role="img"
aria-label={thumbnailSrc ? "Agent thumbnail" : "Thumbnail placeholder"}
>
{thumbnailSrc && (
<Image
src={thumbnailSrc}
alt="Agent thumbnail"
width={500}
height={350}
className="h-full w-full rounded-xl object-cover"
/>
)}
</div>
<div
className="w-full h-[150px] sm:h-[180px] overflow-y-auto font-['Geist'] text-base font-normal leading-normal text-neutral-600"
tabIndex={0}
role="region"
aria-label="Agent description"
>
{description}
</div>
</div>
</div>
<div className="w-full p-6 flex flex-col sm:flex-row items-center justify-center gap-4 border-t border-slate-200">
<Button
onClick={onDone}
variant="outline"
className="w-full sm:flex-1 h-12 rounded-[59px]"
>
Done
</Button>
<Button
onClick={onViewProgress}
variant="default"
className="w-full sm:flex-1 h-12 rounded-[59px] bg-neutral-800 hover:bg-neutral-900 text-white"
>
View progress
</Button>
</div>
</div>
);
};

65
autogpt_platform/frontend/src/components/agptui/PublishAgentSelect.stories.tsx Normal file
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import type { Meta, StoryObj } from "@storybook/react";
import { PublishAgentSelect } from "./PublishAgentSelect";
const meta: Meta<typeof PublishAgentSelect> = {
title: "AGPT UI/Publish Agent Select",
component: PublishAgentSelect,
tags: ["autodocs"],
};
export default meta;
type Story = StoryObj<typeof PublishAgentSelect>;
const mockAgents = [
{ name: "SEO Optimizer", lastEdited: "2 days ago", imageSrc: "https://picsum.photos/seed/seo/300/200" },
{ name: "Content Writer", lastEdited: "5 days ago", imageSrc: "https://picsum.photos/seed/writer/300/200" },
{ name: "Data Analyzer", lastEdited: "1 week ago", imageSrc: "https://picsum.photos/seed/data/300/200" },
{ name: "Image Recognition", lastEdited: "2 weeks ago", imageSrc: "https://picsum.photos/seed/image/300/200" },
{ name: "Chatbot Assistant", lastEdited: "3 weeks ago", imageSrc: "https://picsum.photos/seed/chat/300/200" },
{ name: "Code Generator", lastEdited: "1 month ago", imageSrc: "https://picsum.photos/seed/code/300/200" },
{ name: "AI Translator", lastEdited: "6 weeks ago", imageSrc: "https://picsum.photos/seed/translate/300/200" },
{ name: "Voice Assistant", lastEdited: "2 months ago", imageSrc: "https://picsum.photos/seed/voice/300/200" },
{ name: "Data Visualizer", lastEdited: "3 months ago", imageSrc: "https://picsum.photos/seed/visualize/300/200" },
];
const defaultArgs = {
onSelect: (agentName: string) => console.log(`Selected: ${agentName}`),
onCancel: () => console.log("Cancelled"),
onNext: () => console.log("Next clicked"),
onOpenBuilder: () => console.log("Open builder clicked"),
};
export const Default: Story = {
args: {
...defaultArgs,
agents: mockAgents,
},
};
export const NoAgents: Story = {
args: {
...defaultArgs,
agents: [],
},
};
export const SingleAgent: Story = {
args: {
...defaultArgs,
agents: [mockAgents[0]],
},
};
export const SixAgents: Story = {
args: {
...defaultArgs,
agents: mockAgents.slice(0, 6),
},
};
export const NineAgents: Story = {
args: {
...defaultArgs,
agents: mockAgents,
},
};

141
autogpt_platform/frontend/src/components/agptui/PublishAgentSelect.tsx Normal file
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import * as React from "react";
import Image from "next/image";
import { Button } from "../agptui/Button";
import { IconClose } from "../ui/icons";
interface Agent {
name: string;
lastEdited: string;
imageSrc: string;
}
interface PublishAgentSelectProps {
agents: Agent[];
onSelect: (agentName: string) => void;
onCancel: () => void;
onNext: () => void;
onClose: () => void;
onOpenBuilder: () => void;
}
export const PublishAgentSelect: React.FC<PublishAgentSelectProps> = ({
agents,
onSelect,
onCancel,
onNext,
onClose,
onOpenBuilder,
}) => {
const [selectedAgent, setSelectedAgent] = React.useState<string | null>(null);
const handleAgentClick = (agentName: string) => {
setSelectedAgent(agentName);
onSelect(agentName);
};
return (
<div className="w-full max-w-[900px] bg-white rounded-3xl shadow-lg flex flex-col mx-auto">
<div className="p-4 sm:p-6 border-b border-slate-200 relative">
<div className="absolute top-4 right-4">
<button
onClick={onClose}
className="w-8 h-8 rounded-full bg-gray-100 flex items-center justify-center hover:bg-gray-200 transition-colors"
aria-label="Close"
>
<IconClose size="default" className="text-neutral-600" />
</button>
</div>
<h2 className="text-neutral-900 text-xl sm:text-2xl font-semibold font-['Poppins'] leading-loose text-center mb-2">Publish Agent</h2>
<p className="text-neutral-600 text-sm sm:text-base font-normal font-['Geist'] leading-7 text-center">Select your project that you'd like to publish</p>
</div>
{agents.length === 0 ? (
<div className="h-[370px] px-4 sm:px-6 py-5 flex-col justify-center items-center gap-[29px] inline-flex">
<div className="w-full sm:w-[573px] text-center text-neutral-600 text-lg sm:text-xl font-normal font-['Geist'] leading-7">
Uh-oh.. It seems like you don't have any agents in your library.
<br />
We'd suggest you to create an agent in our builder first
</div>
<Button
onClick={onOpenBuilder}
variant="default"
size="lg"
className="text-white bg-neutral-800 hover:bg-neutral-900"
>
Open builder
</Button>
</div>
) : (
<>
<div className="flex-grow p-4 sm:p-6 overflow-hidden">
<h3 className="sr-only">List of agents</h3>
<div
className="h-[300px] sm:h-[400px] md:h-[500px] overflow-y-auto pr-2"
role="region"
aria-labelledby="agentListHeading"
>
<div id="agentListHeading" className="sr-only">Scrollable list of agents</div>
<div className="p-2">
<div className="grid grid-cols-1 sm:grid-cols-2 lg:grid-cols-3 gap-4">
{agents.map((agent) => (
<div
key={agent.name}
className={`rounded-2xl overflow-hidden cursor-pointer transition-all ${
selectedAgent === agent.name
? "ring-4 ring-violet-600 shadow-lg"
: "hover:shadow-md"
}`}
onClick={() => handleAgentClick(agent.name)}
onKeyDown={(e) => {
if (e.key === 'Enter' || e.key === ' ') {
e.preventDefault();
handleAgentClick(agent.name);
}
}}
tabIndex={0}
role="button"
aria-pressed={selectedAgent === agent.name}
>
<div className="relative h-32 sm:h-40 bg-gray-100">
<Image
src={agent.imageSrc}
alt={agent.name}
layout="fill"
objectFit="cover"
/>
</div>
<div className="p-3">
<h3 className="text-neutral-800 text-sm sm:text-base font-medium font-['Geist'] leading-normal">{agent.name}</h3>
<p className="text-neutral-500 text-xs sm:text-sm font-normal font-['Geist'] leading-[14px]">Edited {agent.lastEdited}</p>
</div>
</div>
))}
</div>
</div>
</div>
</div>
<div className="p-4 sm:p-6 border-t border-slate-200 flex justify-between gap-4">
<Button
onClick={onCancel}
variant="outline"
size="default"
className="w-full sm:flex-1"
>
Back
</Button>
<Button
onClick={onNext}
disabled={!selectedAgent}
variant="default"
size="default"
className="w-full sm:flex-1 text-white bg-neutral-800 hover:bg-neutral-900"
>
Next
</Button>
</div>
</>
)}
</div>
);
};

73
autogpt_platform/frontend/src/components/agptui/PublishAgentSelectInfo.stories.tsx Normal file
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import type { Meta, StoryObj } from "@storybook/react";
import { PublishAgentInfo } from "./PublishAgentSelectInfo";
const meta: Meta<typeof PublishAgentInfo> = {
title: "AGPT UI/Publish Agent Info",
component: PublishAgentInfo,
tags: ["autodocs"],
decorators: [(Story) => <div style={{ maxWidth: "670px", margin: "0 auto" }}><Story /></div>],
};
export default meta;
type Story = StoryObj<typeof PublishAgentInfo>;
export const Default: Story = {
args: {
onBack: () => console.log("Back clicked"),
onSubmit: () => console.log("Submit clicked"),
onClose: () => console.log("Close clicked"),
},
};
export const Filled: Story = {
args: {
...Default.args,
initialData: {
title: "Super SEO Optimizer",
subheader: "Boost your website's search engine rankings",
thumbnailSrc: "https://picsum.photos/seed/seo/500/350",
youtubeLink: "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
category: "SEO",
description: "This AI agent specializes in analyzing websites and providing actionable recommendations to improve search engine optimization. It can perform keyword research, analyze backlinks, and suggest content improvements.",
},
},
};
export const ThreeImages: Story = {
args: {
...Default.args,
initialData: {
title: "Multi-Image Agent",
subheader: "Showcasing multiple images",
thumbnailSrc: "https://picsum.photos/seed/initial/500/350",
youtubeLink: "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
category: "SEO",
description: "This agent allows you to upload and manage multiple images.",
additionalImages: [
"https://picsum.photos/seed/second/500/350",
"https://picsum.photos/seed/third/500/350",
],
},
},
};
export const SixImages: Story = {
args: {
...Default.args,
initialData: {
title: "Gallery Agent",
subheader: "Showcasing a gallery of images",
thumbnailSrc: "https://picsum.photos/seed/gallery1/500/350",
youtubeLink: "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
category: "SEO",
description: "This agent displays a gallery of six images.",
additionalImages: [
"https://picsum.photos/seed/gallery2/500/350",
"https://picsum.photos/seed/gallery3/500/350",
"https://picsum.photos/seed/gallery4/500/350",
"https://picsum.photos/seed/gallery5/500/350",
"https://picsum.photos/seed/gallery6/500/350",
],
},
},
};

218
autogpt_platform/frontend/src/components/agptui/PublishAgentSelectInfo.tsx Normal file
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import * as React from "react";
import Image from "next/image";
import { Button } from "../agptui/Button";
import { IconClose, IconPlus } from "../ui/icons";
interface PublishAgentInfoProps {
onBack: () => void;
onSubmit: () => void;
onClose: () => void;
initialData?: {
title: string;
subheader: string;
thumbnailSrc: string;
youtubeLink: string;
category: string;
description: string;
additionalImages?: string[];
};
}
export const PublishAgentInfo: React.FC<PublishAgentInfoProps> = ({
onBack,
onSubmit,
onClose,
initialData,
}) => {
const [images, setImages] = React.useState<string[]>(
initialData?.additionalImages
? [initialData.thumbnailSrc, ...initialData.additionalImages]
: initialData?.thumbnailSrc
? [initialData.thumbnailSrc]
: []
);
const [selectedImage, setSelectedImage] = React.useState<string | null>(initialData?.thumbnailSrc || null);
const thumbnailsContainerRef = React.useRef<HTMLDivElement | null>(null);
const handleRemoveImage = (indexToRemove: number) => {
console.log(`Remove image at index: ${indexToRemove}`);
// Placeholder function for removing an image
};
const handleAddImage = () => {
console.log("Add image button clicked");
// Placeholder function for adding an image
};
return (
<div className="w-full max-w-[670px] bg-white rounded-3xl shadow-lg border border-slate-200 flex flex-col mx-auto">
<div className="p-6 border-b border-slate-200 relative">
<div className="absolute top-2 right-4">
<button
onClick={onClose}
className="w-[38px] h-[38px] rounded-full bg-gray-100 flex items-center justify-center hover:bg-gray-200 transition-colors"
aria-label="Close"
>
<IconClose size="default" className="text-neutral-600" />
</button>
</div>
<h2 className="text-neutral-900 text-2xl font-semibold font-['Poppins'] leading-loose text-center">Publish Agent</h2>
<p className="text-neutral-600 text-base font-normal font-['Geist'] leading-7 text-center">Write a bit of details about your agent</p>
</div>
<div className="flex-grow p-6 space-y-5 overflow-y-auto">
<div className="space-y-1.5">
<label htmlFor="title" className="text-slate-950 text-sm font-medium font-['Geist'] leading-tight">Title</label>
<input
id="title"
type="text"
placeholder="Agent name"
defaultValue={initialData?.title}
className="w-full pl-4 pr-14 py-2.5 rounded-[55px] border border-slate-200 text-slate-500 text-base font-normal font-['Geist'] leading-normal"
/>
</div>
<div className="space-y-1.5">
<label htmlFor="subheader" className="text-slate-950 text-sm font-medium font-['Geist'] leading-tight">Subheader</label>
<input
id="subheader"
type="text"
placeholder="A tagline for your agent"
defaultValue={initialData?.subheader}
className="w-full pl-4 pr-14 py-2.5 rounded-[55px] border border-slate-200 text-slate-500 text-base font-normal font-['Geist'] leading-normal"
/>
</div>
<div className="space-y-2.5">
<label className="text-slate-950 text-sm font-medium font-['Geist'] leading-tight">Thumbnail images</label>
<div className="h-[350px] p-2.5 rounded-[20px] border border-neutral-300 flex items-center justify-center overflow-hidden">
{selectedImage ? (
<Image
src={selectedImage}
alt="Selected Thumbnail"
width={500}
height={350}
objectFit="cover"
className="rounded-md"
/>
) : (
<p className="text-neutral-600 text-sm font-normal font-['Geist']">No images yet</p>
)}
</div>
<div ref={thumbnailsContainerRef} className="flex items-center space-x-2 overflow-x-auto">
{images.length === 0 ? (
<div className="w-full flex justify-center">
<Button
onClick={handleAddImage}
variant="ghost"
className="w-[100px] h-[70px] bg-neutral-200 rounded-md flex flex-col items-center justify-center hover:bg-neutral-300"
>
<IconPlus size="lg" className="text-neutral-600" />
<span className="text-neutral-600 text-xs font-normal font-['Geist'] mt-1">Add image</span>
</Button>
</div>
) : (
<>
{images.map((src, index) => (
<div key={index} className="relative flex-shrink-0">
<Image
src={src}
alt={`Thumbnail ${index + 1}`}
width={100}
height={70}
objectFit="cover"
className="rounded-md cursor-pointer"
onClick={() => setSelectedImage(src)}
/>
<button
onClick={() => handleRemoveImage(index)}
className="absolute top-1 right-1 w-5 h-5 bg-white bg-opacity-70 rounded-full flex items-center justify-center hover:bg-opacity-100 transition-opacity"
aria-label="Remove image"
>
<IconClose size="sm" className="text-neutral-600" />
</button>
</div>
))}
<Button
onClick={handleAddImage}
variant="ghost"
className="w-[100px] h-[70px] bg-neutral-200 rounded-md flex flex-col items-center justify-center hover:bg-neutral-300"
>
<IconPlus size="lg" className="text-neutral-600" />
<span className="text-neutral-600 text-xs font-normal font-['Geist'] mt-1">Add image</span>
</Button>
</>
)}
</div>
</div>
<div className="space-y-1.5">
<label className="text-slate-950 text-sm font-medium font-['Geist'] leading-tight">AI image generator</label>
<div className="flex items-center justify-between">
<p className="text-slate-700 text-base font-normal font-['Geist'] leading-normal">You can use AI to generate a cover image for you</p>
<Button
variant="default"
size="sm"
className="text-white bg-neutral-800 hover:bg-neutral-900"
>
Generate
</Button>
</div>
</div>
<div className="space-y-1.5">
<label htmlFor="youtube" className="text-slate-950 text-sm font-medium font-['Geist'] leading-tight">YouTube video link</label>
<input
id="youtube"
type="text"
placeholder="Paste a video link here"
defaultValue={initialData?.youtubeLink}
className="w-full pl-4 pr-14 py-2.5 rounded-[55px] border border-slate-200 text-slate-500 text-base font-normal font-['Geist'] leading-normal"
/>
</div>
<div className="space-y-1.5">
<label htmlFor="category" className="text-slate-950 text-sm font-medium font-['Geist'] leading-tight">Category</label>
<select
id="category"
defaultValue={initialData?.category}
className="w-full pl-4 pr-5 py-2.5 rounded-[55px] border border-slate-200 text-slate-500 text-base font-normal font-['Geist'] leading-normal appearance-none"
>
<option value="">Select a category for your agent</option>
<option value="SEO">SEO</option>
{/* Add more options here */}
</select>
</div>
<div className="space-y-1.5">
<label htmlFor="description" className="text-slate-950 text-sm font-medium font-['Geist'] leading-tight">Description</label>
<textarea
id="description"
placeholder="Describe your agent and what it does"
defaultValue={initialData?.description}
className="w-full h-[100px] pl-4 pr-14 py-2.5 rounded-2xl border border-slate-200 text-slate-900 text-base font-normal font-['Geist'] leading-normal resize-none bg-white"
></textarea>
</div>
</div>
<div className="p-6 border-t border-slate-200 flex justify-between gap-4">
<Button
onClick={onBack}
variant="outline"
size="default"
className="w-full sm:flex-1"
>
Back
</Button>
<Button
onClick={onSubmit}
variant="default"
size="default"
className="w-full sm:flex-1 text-white bg-neutral-800 hover:bg-neutral-900"
>
Submit for review
</Button>
</div>
</div>
);
};

63
autogpt_platform/frontend/src/components/ui/icons.tsx
View File

@@ -1213,6 +1213,69 @@ export const IconTiktok = createIcon((props) => (
</svg>
));
/**
* Close (X) icon component.
*
* @component IconClose
* @param {IconProps} props - The props object containing additional attributes and event handlers for the icon.
* @returns {JSX.Element} - The close icon.
*
* @example
* // Default usage
* <IconClose />
*
* @example
* // With custom color and size
* <IconClose className="text-primary" size="lg" />
*/
export const IconClose = createIcon((props) => (
<svg
xmlns="http://www.w3.org/2000/svg"
viewBox="0 0 14 14"
fill="none"
stroke="currentColor"
strokeWidth="2"
strokeLinecap="round"
strokeLinejoin="round"
aria-label="Close Icon"
{...props}
>
<path d="M1 1L13 13M1 13L13 1" />
</svg>
));
/**
* Plus icon component.
*
* @component IconPlus
* @param {IconProps} props - The props object containing additional attributes and event handlers for the icon.
* @returns {JSX.Element} - The plus icon.
*
* @example
* // Default usage
* <IconPlus />
*
* @example
* // With custom color and size
* <IconPlus className="text-primary" size="lg" />
*/
export const IconPlus = createIcon((props) => (
<svg
xmlns="http://www.w3.org/2000/svg"
viewBox="0 0 28 28"
fill="none"
stroke="currentColor"
strokeWidth="2"
strokeLinecap="round"
strokeLinejoin="round"
aria-label="Plus Icon"
{...props}
>
<path d="M14 5.83334V22.1667" />
<path d="M5.83331 14H22.1666" />
</svg>
));
/**
* Globe icon component.
*

1
autogpt_platform/frontend/src/lib/utils.ts
View File

@@ -106,6 +106,7 @@ const exceptionMap: Record<string, string> = {
Http: "HTTP",
Json: "JSON",
Ai: "AI",
"You Tube": "YouTube",
};
const applyExceptions = (str: string): string => {

235
autogpt_platform/market/poetry.lock generated
View File

@@ -247,22 +247,23 @@ test = ["pytest (>=6)"]
[[package]]
name = "fastapi"
version = "0.109.2"
version = "0.115.4"
description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
optional = false
python-versions = ">=3.8"
files = [
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{file = "fastapi-0.109.2.tar.gz", hash = "sha256:f3817eac96fe4f65a2ebb4baa000f394e55f5fccdaf7f75250804bc58f354f73"},
{file = "fastapi-0.115.4-py3-none-any.whl", hash = "sha256:0b504a063ffb3cf96a5e27dc1bc32c80ca743a2528574f9cdc77daa2d31b4742"},
{file = "fastapi-0.115.4.tar.gz", hash = "sha256:db653475586b091cb8b2fec2ac54a680ac6a158e07406e1abae31679e8826349"},
]
[package.dependencies]
pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<2.0.0 || >2.0.0,<2.0.1 || >2.0.1,<2.1.0 || >2.1.0,<3.0.0"
starlette = ">=0.36.3,<0.37.0"
starlette = ">=0.40.0,<0.42.0"
typing-extensions = ">=4.8.0"
[package.extras]
all = ["email-validator (>=2.0.0)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)", "jinja2 (>=2.11.2)", "orjson (>=3.2.1)", "pydantic-extra-types (>=2.0.0)", "pydantic-settings (>=2.0.0)", "python-multipart (>=0.0.7)", "pyyaml (>=5.3.1)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0)", "uvicorn[standard] (>=0.12.0)"]
all = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.5)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)", "jinja2 (>=2.11.2)", "orjson (>=3.2.1)", "pydantic-extra-types (>=2.0.0)", "pydantic-settings (>=2.0.0)", "python-multipart (>=0.0.7)", "pyyaml (>=5.3.1)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0)", "uvicorn[standard] (>=0.12.0)"]
standard = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.5)", "httpx (>=0.23.0)", "jinja2 (>=2.11.2)", "python-multipart (>=0.0.7)", "uvicorn[standard] (>=0.12.0)"]
[[package]]
name = "fuzzywuzzy"
@@ -389,105 +390,103 @@ i18n = ["Babel (>=2.7)"]
[[package]]
name = "levenshtein"
version = "0.25.1"
version = "0.26.1"
description = "Python extension for computing string edit distances and similarities."
optional = false
python-versions = ">=3.8"
python-versions = ">=3.9"
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]
[package.dependencies]
rapidfuzz = ">=3.8.0,<4.0.0"
rapidfuzz = ">=3.9.0,<4.0.0"
[[package]]
name = "markupsafe"
@@ -635,13 +634,13 @@ testing = ["pytest", "pytest-benchmark"]
[[package]]
name = "prisma"
version = "0.13.1"
version = "0.15.0"
description = "Prisma Client Python is an auto-generated and fully type-safe database client"
optional = false
python-versions = ">=3.7.0"
python-versions = ">=3.8.0"
files = [
{file = "prisma-0.13.1-py3-none-any.whl", hash = "sha256:b79ad69bdf09b217431904c1250c36421233ea394a230f1665f5699fd842ea20"},
{file = "prisma-0.13.1.tar.gz", hash = "sha256:f0f86a67c38e6f08b53cce9272dd9c736f69f4fcbb94dbdfa87bf44f983e925d"},
{file = "prisma-0.15.0-py3-none-any.whl", hash = "sha256:de949cc94d3d91243615f22ff64490aa6e2d7cb81aabffce53d92bd3977c09a4"},
{file = "prisma-0.15.0.tar.gz", hash = "sha256:5cd6402aa8322625db3fc1152040404e7fc471fe7f8fa3a314fa8a99529ca107"},
]
[package.dependencies]
@@ -919,17 +918,17 @@ cli = ["click (>=5.0)"]
[[package]]
name = "python-levenshtein"
version = "0.25.1"
version = "0.26.1"
description = "Python extension for computing string edit distances and similarities."
optional = false
python-versions = ">=3.8"
python-versions = ">=3.9"
files = [
{file = "python-Levenshtein-0.25.1.tar.gz", hash = "sha256:b21e7efe83c8e8dc8260f2143b2393c6c77cb2956f0c53de6c4731c4d8006acc"},
{file = "python_Levenshtein-0.25.1-py3-none-any.whl", hash = "sha256:654446d1ea4acbcc573d44c43775854834a7547e4cb2f79f638f738134d72037"},
{file = "python_Levenshtein-0.26.1-py3-none-any.whl", hash = "sha256:8ef5e529dd640fb00f05ee62d998d2ee862f19566b641ace775d5ae16167b2ef"},
{file = "python_levenshtein-0.26.1.tar.gz", hash = "sha256:24ba578e28058ebb4afa2700057e1678d7adf27e43cd1f17700c09a9009d5d3a"},
]
[package.dependencies]
Levenshtein = "0.25.1"
Levenshtein = "0.26.1"
[[package]]
name = "rapidfuzz"
@@ -1086,13 +1085,13 @@ files = [
[[package]]
name = "sentry-sdk"
version = "2.12.0"
version = "2.17.0"
description = "Python client for Sentry (https://sentry.io)"
optional = false
python-versions = ">=3.6"
files = [
{file = "sentry_sdk-2.12.0-py2.py3-none-any.whl", hash = "sha256:7a8d5163d2ba5c5f4464628c6b68f85e86972f7c636acc78aed45c61b98b7a5e"},
{file = "sentry_sdk-2.12.0.tar.gz", hash = "sha256:8763840497b817d44c49b3fe3f5f7388d083f2337ffedf008b2cdb63b5c86dc6"},
{file = "sentry_sdk-2.17.0-py2.py3-none-any.whl", hash = "sha256:625955884b862cc58748920f9e21efdfb8e0d4f98cca4ab0d3918576d5b606ad"},
{file = "sentry_sdk-2.17.0.tar.gz", hash = "sha256:dd0a05352b78ffeacced73a94e86f38b32e2eae15fff5f30ca5abb568a72eacf"},
]
[package.dependencies]
@@ -1116,10 +1115,12 @@ falcon = ["falcon (>=1.4)"]
fastapi = ["fastapi (>=0.79.0)"]
flask = ["blinker (>=1.1)", "flask (>=0.11)", "markupsafe"]
grpcio = ["grpcio (>=1.21.1)", "protobuf (>=3.8.0)"]
http2 = ["httpcore[http2] (==1.*)"]
httpx = ["httpx (>=0.16.0)"]
huey = ["huey (>=2)"]
huggingface-hub = ["huggingface-hub (>=0.22)"]
langchain = ["langchain (>=0.0.210)"]
litestar = ["litestar (>=2.0.0)"]
loguru = ["loguru (>=0.5)"]
openai = ["openai (>=1.0.0)", "tiktoken (>=0.3.0)"]
opentelemetry = ["opentelemetry-distro (>=0.35b0)"]
@@ -1148,13 +1149,13 @@ files = [
[[package]]
name = "starlette"
version = "0.36.3"
version = "0.41.2"
description = "The little ASGI library that shines."
optional = false
python-versions = ">=3.8"
files = [
{file = "starlette-0.36.3-py3-none-any.whl", hash = "sha256:13d429aa93a61dc40bf503e8c801db1f1bca3dc706b10ef2434a36123568f044"},
{file = "starlette-0.36.3.tar.gz", hash = "sha256:90a671733cfb35771d8cc605e0b679d23b992f8dcfad48cc60b38cb29aeb7080"},
{file = "starlette-0.41.2-py3-none-any.whl", hash = "sha256:fbc189474b4731cf30fcef52f18a8d070e3f3b46c6a04c97579e85e6ffca942d"},
{file = "starlette-0.41.2.tar.gz", hash = "sha256:9834fd799d1a87fd346deb76158668cfa0b0d56f85caefe8268e2d97c3468b62"},
]
[package.dependencies]
@@ -1231,13 +1232,13 @@ zstd = ["zstandard (>=0.18.0)"]
[[package]]
name = "uvicorn"
version = "0.30.5"
version = "0.32.0"
description = "The lightning-fast ASGI server."
optional = false
python-versions = ">=3.8"
files = [
{file = "uvicorn-0.30.5-py3-none-any.whl", hash = "sha256:b2d86de274726e9878188fa07576c9ceeff90a839e2b6e25c917fe05f5a6c835"},
{file = "uvicorn-0.30.5.tar.gz", hash = "sha256:ac6fdbd4425c5fd17a9fe39daf4d4d075da6fdc80f653e5894cdc2fd98752bee"},
{file = "uvicorn-0.32.0-py3-none-any.whl", hash = "sha256:60b8f3a5ac027dcd31448f411ced12b5ef452c646f76f02f8cc3f25d8d26fd82"},
{file = "uvicorn-0.32.0.tar.gz", hash = "sha256:f78b36b143c16f54ccdb8190d0a26b5f1901fe5a3c777e1ab29f26391af8551e"},
]
[package.dependencies]
@@ -1295,4 +1296,4 @@ watchmedo = ["PyYAML (>=3.10)"]
[metadata]
lock-version = "2.0"
python-versions = "^3.10"
content-hash = "19c9ea01b42caa67bb7fffe4f66c8f208e874886cd97279dfdf1f06dd7e3acf1"
content-hash = "f94a7651b3ddc7819bafdc75384cb8ad34f7a5415e652ecb5bd82d632d26e693"

10
autogpt_platform/market/pyproject.toml
View File

@@ -10,13 +10,13 @@ readme = "README.md"
[tool.poetry.dependencies]
python = "^3.10"
prisma = "^0.13.1"
prisma = "^0.15.0"
python-dotenv = "^1.0.1"
uvicorn = "^0.30.3"
fastapi = "^0.109.0"
sentry-sdk = { extras = ["fastapi"], version = "^2.11.0" }
uvicorn = "^0.32.0"
fastapi = "^0.115.4"
sentry-sdk = { extras = ["fastapi"], version = "^2.17.0" }
fuzzywuzzy = "^0.18.0"
python-levenshtein = "^0.25.1"
python-levenshtein = "^0.26.1"
# autogpt-platform-backend = { path = "../backend", develop = true }
prometheus-fastapi-instrumentator = "^7.0.0"

29
docs/content/AutoGPT/index.md
View File

@@ -1,29 +0,0 @@
# AutoGPT Agent
[🔧 **Setup**](setup/index.md)
&ensp;|&ensp;
[💻 **User guide**](./usage.md)
&ensp;|&ensp;
[🐙 **GitHub**](https://github.com/Significant-Gravitas/AutoGPT/tree/master/autogpt)
**Location:** `classic/original_autogpt/` in the GitHub repo
AutoGPT was conceived when OpenAI published their GPT-4 model accompanied by a paper
outlining the advanced reasoning and task-solving abilities of the model. The concept
was (and still is) fairly simple: let an LLM decide what to do over and over, while
feeding the results of its actions back into the prompt. This allows the program to
iteratively and incrementally work towards its objective.
The fact that this program is able to execute actions on behalf of its user makes
it an **agent**. In the case of AutoGPT, the user still has to authorize every action,
but as the project progresses we'll be able to give the agent more autonomy and only
require consent for select actions.
AutoGPT is a **generalist agent**, meaning it is not designed with a specific task in
mind. Instead, it is designed to be able to execute a wide range of tasks across many
disciplines, as long as it can be done on a computer.
## Coming soon
* How does AutoGPT work?
* What can I use AutoGPT for?
* What does the future of AutoGPT look like?

0
docs/content/AutoGPT/configuration/options.md → docs/content/classic/configuration/options.md
View File

0
docs/content/AutoGPT/configuration/search.md → docs/content/classic/configuration/search.md
View File

0
docs/content/AutoGPT/configuration/voice.md → docs/content/classic/configuration/voice.md
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142
docs/content/classic/index.md Normal file
View File

@@ -0,0 +1,142 @@
# AutoGPT Agent
[🔧 **Setup**](setup/index.md)
&ensp;|&ensp;
[💻 **User guide**](./usage.md)
&ensp;|&ensp;
[🐙 **GitHub**](https://github.com/Significant-Gravitas/AutoGPT/tree/master/autogpt)
**Location:** `classic/original_autogpt/` in the GitHub repo
AutoGPT was conceived when OpenAI published their GPT-4 model accompanied by a paper
outlining the advanced reasoning and task-solving abilities of the model. The concept
was (and still is) fairly simple: let an LLM decide what to do over and over, while
feeding the results of its actions back into the prompt. This allows the program to
iteratively and incrementally work towards its objective.
The fact that this program is able to execute actions on behalf of its user makes
it an **agent**. In the case of AutoGPT, the user still has to authorize every action,
but as the project progresses we'll be able to give the agent more autonomy and only
require consent for select actions.
AutoGPT is a **generalist agent**, meaning it is not designed with a specific task in
mind. Instead, it is designed to be able to execute a wide range of tasks across many
disciplines, as long as it can be done on a computer.
## Coming soon
* How does AutoGPT work?
* What can I use AutoGPT for?
* What does the future of AutoGPT look like?
# AutoGPT Classic Documentation
Welcome to the AutoGPT Classic Documentation.
The AutoGPT project consists of four main components:
- The [Agent](#agent) &ndash; also known as just "AutoGPT"
- The [Benchmark](#benchmark) &ndash; AKA `agbenchmark`
- The [Forge](#forge)
- The [Frontend](#frontend)
To tie these together, we also have a [CLI] at the root of the project.
## 🤖 Agent
**[📖 About AutoGPT](#autogpt-agent)**
&ensp;|&ensp;
**[🔧 Setup](setup/index.md)**
&ensp;|&ensp;
**[💻 Usage](./usage.md)**
The heart of AutoGPT, and the project that kicked it all off: a semi-autonomous agent powered by LLMs to execute any task for you*.
We continue to develop this project with the goal of providing access to AI assistance to the masses, and building the future transparently and together.
- 💡 **Explore** - See what AI can do and be inspired by a glimpse of the future.
- 🚀 **Build with us** - We welcome any input, whether it's code or ideas for new features or improvements! Join us on [Discord](https://discord.gg/autogpt) and find out how you can join in on the action.
<small>* it isn't quite there yet, but that is the ultimate goal that we are still pursuing</small>
---
## 🎯 Benchmark
**[🗒️ Readme](https://github.com/Significant-Gravitas/AutoGPT/blob/master/classic/benchmark/README.md)**
Measure your agent's performance! The `agbenchmark` can be used with any agent that supports the agent protocol, and the integration with the project's [CLI] makes it even easier to use with AutoGPT and forge-based agents. The benchmark offers a stringent testing environment. Our framework allows for autonomous, objective performance evaluations, ensuring your agents are primed for real-world action.
<!-- TODO: insert visual demonstrating the benchmark -->
- 📦 [**`agbenchmark`**](https://pypi.org/project/agbenchmark/) on Pypi
- 🔌 **Agent Protocol Standardization** - AutoGPT uses the agent protocol from the AI Engineer Foundation to ensure compatibility with many agents, both from within and outside the project.
---
## 🏗️ Forge
**[📖 Introduction](../forge/get-started.md)**
&ensp;|&ensp;
**[🚀 Quickstart](https://github.com/Significant-Gravitas/AutoGPT/blob/master/QUICKSTART.md)**
<!-- TODO: have the guides all in one place -->
Forge your own agent! The Forge is a ready-to-go template for your agent application. All the boilerplate code is already handled, letting you channel all your creativity into the things that set *your* agent apart.
- 🛠️ **Building with Ease** - We've set the groundwork so you can focus on your agent's personality and capabilities. Comprehensive tutorials are available [here](https://aiedge.medium.com/autogpt-forge-e3de53cc58ec).
---
## 💻 Frontend
**[🗒️ Readme](https://github.com/Significant-Gravitas/AutoGPT/blob/master/classic/frontend/README.md)**
An easy-to-use and open source frontend for any Agent Protocol-compliant agent.
- 🎮 **User-Friendly Interface** - Manage your agents effortlessly.
- 🔄 **Seamless Integration** - Smooth connectivity between your agent and our benchmarking system.
---
## 🔧 CLI
[CLI]: #cli
The project CLI makes it easy to use all of the components in the repo, separately or
together. To install its dependencies, simply run `./run setup`, and you're ready to go!
```shell
$ ./run
Usage: cli.py [OPTIONS] COMMAND [ARGS]...
Options:
--help Show this message and exit.
Commands:
agent Commands to create, start and stop agents
benchmark Commands to start the benchmark and list tests and categories
setup Installs dependencies needed for your system.
```
Common commands:
* `./run agent start autogpt` &ndash; [runs](./usage.md#serve-agent-protocol-mode-with-ui) the AutoGPT agent
* `./run agent create <name>` &ndash; creates a new Forge-based agent project at `agents/<name>`
* `./run benchmark start <agent>` &ndash; benchmarks the specified agent
---
🤔 Join the AutoGPT Discord server for any queries:
[discord.gg/autogpt](https://discord.gg/autogpt)
### Glossary of Terms
- **Repository**: Space where your project resides.
- **Forking**: Copying a repository under your account.
- **Cloning**: Making a local copy of a repository.
- **Agent**: The AutoGPT you'll create and develop.
- **Benchmarking**: Testing your agent's skills in the Forge.
- **Forge**: The template for building your AutoGPT agent.
- **Frontend**: The UI for tasks, logs, and task history.

0
docs/content/AutoGPT/setup/docker.md → docs/content/classic/setup/docker.md
View File

0
docs/content/AutoGPT/setup/for-developers.md → docs/content/classic/setup/for-developers.md
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Welcome to the AutoGPT Documentation.
The AutoGPT project consists of four main components:
## What is the AutoGPT Platform?
- The [Server](#server) &ndash; known as the "AutoGPT Platform"
- The [Agent](#agent) &ndash; also known as just "AutoGPT"
- The [Benchmark](#benchmark) &ndash; AKA `agbenchmark`
- The [Forge](#forge)
- The [Frontend](#frontend)
The AutoGPT Platform is a groundbreaking system that revolutionizes AI utilization for businesses and individuals. It enables the creation, deployment, and management of continuous agents that work tirelessly on your behalf, bringing unprecedented efficiency and innovation to your workflows.
To tie these together, we also have a [CLI] at the root of the project.
### Key Features
## 🌐 Server
- **Seamless Integration and Low-Code Workflows**: Rapidly create complex workflows without extensive coding knowledge.
- **Autonomous Operation and Continuous Agents**: Deploy cloud-based assistants that run indefinitely, activating on relevant triggers.
- **Intelligent Automation and Maximum Efficiency**: Streamline workflows by automating repetitive processes.
- **Reliable Performance and Predictable Execution**: Enjoy consistent and dependable long-running processes.
<!-- Setup, then Advanced, then New Blocks -->
## Platform Architecture
**[📖 Setup](server/setup.md)**
&ensp;|&ensp;
**[📖 Advanced Setup](server/advanced_setup.md)**
&ensp;|&ensp;
**[📖 Making New Blocks](server/new_blocks.md)**
The AutoGPT Platform consists of two main components:
The server is the backbone of the New AutoGPT project. It provides the infrastructure for the agents to run, and the UI for you to interact with them. It integrates with the Forge, Agent, and a bespoke UI to provide a seamless experience.
### 1. AutoGPT Server
---
The powerhouse of our platform, containing:
## 🤖 Agent
- **Source Code**: Core logic driving agents and automation processes.
- **Infrastructure**: Robust systems ensuring reliable and scalable performance.
- **Marketplace**: A comprehensive marketplace for pre-built agents.
**[📖 About AutoGPT](AutoGPT/index.md)**
&ensp;|&ensp;
**[🔧 Setup](AutoGPT/setup/index.md)**
&ensp;|&ensp;
**[💻 Usage](AutoGPT/usage.md)**
### 2. AutoGPT Frontend
The heart of AutoGPT, and the project that kicked it all off: a semi-autonomous agent powered by LLMs to execute any task for you*.
The user interface where you interact with the platform:
We continue to develop this project with the goal of providing access to AI assistance to the masses, and building the future transparently and together.
- **Agent Builder**: Design and configure your own AI agents.
- **Workflow Management**: Build, modify, and optimize automation workflows.
- **Deployment Controls**: Manage the lifecycle of your agents.
- **Ready-to-Use Agents**: Select from pre-configured agents.
- **Agent Interaction**: Run and interact with agents through a user-friendly interface.
- **Monitoring and Analytics**: Track agent performance and gain insights.
- 💡 **Explore** - See what AI can do and be inspired by a glimpse of the future.
## Platform Components
- 🚀 **Build with us** - We welcome any input, whether it's code or ideas for new features or improvements! Join us on [Discord](https://discord.gg/autogpt) and find out how you can join in on the action.
### Agents and Workflows
<small>* it isn't quite there yet, but that is the ultimate goal that we are still pursuing</small>
In the platform, you can create highly customized workflows to build agents. An agent is essentially an automated workflow that you design to perform specific tasks or processes. Create customized workflows to build agents for various tasks, including:
---
- Data processing and analysis
- Task scheduling and management
- Communication and notification systems
- Integration between different software tools
- AI-powered decision making and content generation
## 🎯 Benchmark
### Blocks as Integrations
**[🗒️ Readme](https://github.com/Significant-Gravitas/AutoGPT/blob/master/classic/benchmark/README.md)**
Blocks represent actions and are the building blocks of your workflows, including:
Measure your agent's performance! The `agbenchmark` can be used with any agent that supports the agent protocol, and the integration with the project's [CLI] makes it even easier to use with AutoGPT and forge-based agents. The benchmark offers a stringent testing environment. Our framework allows for autonomous, objective performance evaluations, ensuring your agents are primed for real-world action.
- Connections to external services
- Data processing tools
- AI models for various tasks
- Custom scripts or functions
- Conditional logic and decision-making components
<!-- TODO: insert visual demonstrating the benchmark -->
You can learn more under: [Build your own Blocks](platform/new_blocks.md)
- 📦 [**`agbenchmark`**](https://pypi.org/project/agbenchmark/) on Pypi
## Available Language Models
- 🔌 **Agent Protocol Standardization** - AutoGPT uses the agent protocol from the AI Engineer Foundation to ensure compatibility with many agents, both from within and outside the project.
The platform comes pre-integrated with cutting-edge LLM providers:
---
- OpenAI
- Anthropic
- Groq
- Llama
## 🏗️ Forge
## License Overview
**[📖 Introduction](forge/get-started.md)**
&ensp;|&ensp;
**[🚀 Quickstart](https://github.com/Significant-Gravitas/AutoGPT/blob/master/QUICKSTART.md)**
We've adopted a dual-license approach to balance open collaboration with sustainable development:
<!-- TODO: have the guides all in one place -->
- **MIT License**: The majority of the AutoGPT repository remains under this license.
- **Polyform Shield License**: Applies to the new `autogpt_platform` folder.
Forge your own agent! The Forge is a ready-to-go template for your agent application. All the boilerplate code is already handled, letting you channel all your creativity into the things that set *your* agent apart.
This strategy allows us to share previously closed-source components, fostering a vibrant ecosystem of developers and users.
- 🛠️ **Building with Ease** - We've set the groundwork so you can focus on your agent's personality and capabilities. Comprehensive tutorials are available [here](https://aiedge.medium.com/autogpt-forge-e3de53cc58ec).
## Ready to Get Started?
---
## 💻 Frontend
**[🗒️ Readme](https://github.com/Significant-Gravitas/AutoGPT/blob/master/classic/frontend/README.md)**
An easy-to-use and open source frontend for any Agent Protocol-compliant agent.
- 🎮 **User-Friendly Interface** - Manage your agents effortlessly.
- 🔄 **Seamless Integration** - Smooth connectivity between your agent and our benchmarking system.
---
## 🔧 CLI
[CLI]: #cli
The project CLI makes it easy to use all of the components in the repo, separately or
together. To install its dependencies, simply run `./run setup`, and you're ready to go!
```shell
$ ./run
Usage: cli.py [OPTIONS] COMMAND [ARGS]...
Options:
--help Show this message and exit.
Commands:
agent Commands to create, start and stop agents
benchmark Commands to start the benchmark and list tests and categories
setup Installs dependencies needed for your system.
```
Common commands:
* `./run agent start autogpt` &ndash; [runs](./AutoGPT/usage.md#serve-agent-protocol-mode-with-ui) the AutoGPT agent
* `./run agent create <name>` &ndash; creates a new Forge-based agent project at `agents/<name>`
* `./run benchmark start <agent>` &ndash; benchmarks the specified agent
---
🤔 Join the AutoGPT Discord server for any queries:
[discord.gg/autogpt](https://discord.gg/autogpt)
### Glossary of Terms
- **Repository**: Space where your project resides.
- **Forking**: Copying a repository under your account.
- **Cloning**: Making a local copy of a repository.
- **Agent**: The AutoGPT you'll create and develop.
- **Benchmarking**: Testing your agent's skills in the Forge.
- **Forge**: The template for building your AutoGPT agent.
- **Frontend**: The UI for tasks, logs, and task history.
- Read the [Getting Started docs](https://docs.agpt.co/platform/getting-started/) to self-host
- [Join the waitlist](https://agpt.co/waitlist) for the cloud-hosted beta
- [Contribute](contribute/index.md)

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# Advanced Setup
The advanced steps below are intended for people with sysadmin experience. If you are not comfortable with these steps, please refer to the [basic setup guide](setup.md).
The advanced steps below are intended for people with sysadmin experience. If you are not comfortable with these steps, please refer to the [basic setup guide](../platform/getting-started.md).
## Introduction
For the advanced setup, first follow the [basic setup guide](setup.md) to get the server up and running. Once you have the server running, you can follow the steps below to configure the server for your specific needs.
For the advanced setup, first follow the [basic setup guide](../platform/getting-started.md) to get the server up and running. Once you have the server running, you can follow the steps below to configure the server for your specific needs.
## Configuration

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# Setting up the server
# Getting Started with AutoGPT: Self-Hosting Guide
- [Introduction](#introduction)
- [Prerequisites](#prerequisites)
This tutorial will walk you through the process of setting up AutoGPT locally on your machine.
<center><iframe width="560" height="315" src="https://www.youtube.com/embed/4Bycr6_YAMI?si=dXGhFeWrCK2UkKgj" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe></center>
## Introduction
@@ -22,7 +23,7 @@ To setup the server, you need to have the following installed:
- [Docker](https://docs.docker.com/get-docker/)
- [Git](https://git-scm.com/downloads)
### Checking if you have Node.js & NPM installed
#### Checking if you have Node.js & NPM installed
We use Node.js to run our frontend application.
@@ -41,7 +42,7 @@ npm -v
Once you have Node.js installed, you can proceed to the next step.
### Checking if you have Docker & Docker Compose installed
#### Checking if you have Docker & Docker Compose installed
Docker containerizes applications, while Docker Compose orchestrates multi-container Docker applications.
@@ -60,7 +61,7 @@ docker-compose -v
Once you have Docker and Docker Compose installed, you can proceed to the next step.
## Cloning the Repository
### Cloning the Repository
The first step is cloning the AutoGPT repository to your computer.
To do this, open a terminal window in a folder on your computer and run:
```
@@ -70,7 +71,7 @@ If you get stuck, follow [this guide](https://docs.github.com/en/repositories/cr
Once that's complete you can close this terminal window.
## Running the backend services
### Running the backend services
To run the backend services, follow these steps:
@@ -94,7 +95,7 @@ To run the backend services, follow these steps:
This command will start all the necessary backend services defined in the `docker-compose.combined.yml` file in detached mode.
## Running the frontend application
### Running the frontend application
To run the frontend application, follow these steps:
@@ -116,11 +117,12 @@ To run the frontend application, follow these steps:
```
This command will install the necessary dependencies and start the frontend application in development mode.
## Checking if the application is running
### Checking if the application is running
You can check if the server is running by visiting [http://localhost:3000](http://localhost:3000) in your browser.
### Notes:
**Notes:**
By default the application for different services run on the following ports:
Frontend UI Server: 3000

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# Contributing to AutoGPT Agent Server: Creating and Testing Blocks
This guide will walk you through the process of creating and testing a new block for the AutoGPT Agent Server, using the WikipediaSummaryBlock as an example.
## Understanding Blocks and Testing
Blocks are reusable components that can be connected to form a graph representing an agent's behavior. Each block has inputs, outputs, and a specific function. Proper testing is crucial to ensure blocks work correctly and consistently.
## Creating and Testing a New Block
Follow these steps to create and test a new block:
1. **Create a new Python file** in the `backend/blocks` directory. Name it descriptively and use snake_case. For example: `get_wikipedia_summary.py`.
2. **Import necessary modules and create a class that inherits from `Block`**. Make sure to include all necessary imports for your block.
Every block should contain the following:
```python
from backend.data.block import Block, BlockSchema, BlockOutput
```
Example for the Wikipedia summary block:
```python
from backend.data.block import Block, BlockSchema, BlockOutput
from backend.utils.get_request import GetRequest
import requests
class WikipediaSummaryBlock(Block, GetRequest):
# Block implementation will go here
```
3. **Define the input and output schemas** using `BlockSchema`. These schemas specify the data structure that the block expects to receive (input) and produce (output).
- The input schema defines the structure of the data the block will process. Each field in the schema represents a required piece of input data.
- The output schema defines the structure of the data the block will return after processing. Each field in the schema represents a piece of output data.
Example:
```python
class Input(BlockSchema):
topic: str # The topic to get the Wikipedia summary for
class Output(BlockSchema):
summary: str # The summary of the topic from Wikipedia
error: str # Any error message if the request fails, error field needs to be named `error`.
```
4. **Implement the `__init__` method, including test data and mocks:**
!!! important
Use UUID generator (e.g. https://www.uuidgenerator.net/) for every new block `id` and *do not* make up your own. Alternatively, you can run this python code to generate an uuid: `print(__import__('uuid').uuid4())`
```python
def __init__(self):
super().__init__(
# Unique ID for the block, used across users for templates
# If you are an AI leave it as is or change to "generate-proper-uuid"
id="xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx",
input_schema=WikipediaSummaryBlock.Input, # Assign input schema
output_schema=WikipediaSummaryBlock.Output, # Assign output schema
# Provide sample input, output and test mock for testing the block
test_input={"topic": "Artificial Intelligence"},
test_output=("summary", "summary content"),
test_mock={"get_request": lambda url, json: {"extract": "summary content"}},
)
```
- `id`: A unique identifier for the block.
- `input_schema` and `output_schema`: Define the structure of the input and output data.
Let's break down the testing components:
- `test_input`: This is a sample input that will be used to test the block. It should be a valid input according to your Input schema.
- `test_output`: This is the expected output when running the block with the `test_input`. It should match your Output schema. For non-deterministic outputs or when you only want to assert the type, you can use Python types instead of specific values. In this example, `("summary", str)` asserts that the output key is "summary" and its value is a string.
- `test_mock`: This is crucial for blocks that make network calls. It provides a mock function that replaces the actual network call during testing.
In this case, we're mocking the `get_request` method to always return a dictionary with an 'extract' key, simulating a successful API response. This allows us to test the block's logic without making actual network requests, which could be slow, unreliable, or rate-limited.
5. **Implement the `run` method with error handling:**, this should contain the main logic of the block:
```python
def run(self, input_data: Input, **kwargs) -> BlockOutput:
try:
topic = input_data.topic
url = f"https://en.wikipedia.org/api/rest_v1/page/summary/{topic}"
response = self.get_request(url, json=True)
yield "summary", response['extract']
except requests.exceptions.HTTPError as http_err:
raise RuntimeError(f"HTTP error occurred: {http_err}")
```
- **Try block**: Contains the main logic to fetch and process the Wikipedia summary.
- **API request**: Send a GET request to the Wikipedia API.
- **Error handling**: Handle various exceptions that might occur during the API request and data processing. We don't need to catch all exceptions, only the ones we expect and can handle. The uncaught exceptions will be automatically yielded as `error` in the output. Any block that raises an exception (or yields an `error` output) will be marked as failed. Prefer raising exceptions over yielding `error`, as it will stop the execution immediately.
- **Yield**: Use `yield` to output the results. Prefer to output one result object at a time. If you are calling a function that returns a list, you can yield each item in the list separately. You can also yield the whole list as well, but do both rather than yielding the list. For example: If you were writing a block that outputs emails, you'd yield each email as a separate result object, but you could also yield the whole list as an additional single result object. Yielding output named `error` will break the execution right away and mark the block execution as failed.
### Blocks with authentication
Our system supports auth offloading for API keys and OAuth2 authorization flows.
Adding a block with API key authentication is straight-forward, as is adding a block
for a service that we already have OAuth2 support for.
Implementing the block itself is relatively simple. On top of the instructions above,
you're going to add a `credentials` parameter to the `Input` model and the `run` method:
```python
from autogpt_libs.supabase_integration_credentials_store.types import (
APIKeyCredentials,
OAuth2Credentials,
Credentials,
)
from backend.data.block import Block, BlockOutput, BlockSchema
from backend.data.model import CredentialsField
# API Key auth:
class BlockWithAPIKeyAuth(Block):
class Input(BlockSchema):
# Note that the type hint below is require or you will get a type error.
# The first argument is the provider name, the second is the credential type.
credentials: CredentialsMetaInput[Literal['github'], Literal['api_key']] = CredentialsField(
provider="github",
supported_credential_types={"api_key"},
description="The GitHub integration can be used with "
"any API key with sufficient permissions for the blocks it is used on.",
)
# ...
def run(
self,
input_data: Input,
*,
credentials: APIKeyCredentials,
**kwargs,
) -> BlockOutput:
...
# OAuth:
class BlockWithOAuth(Block):
class Input(BlockSchema):
# Note that the type hint below is require or you will get a type error.
# The first argument is the provider name, the second is the credential type.
credentials: CredentialsMetaInput[Literal['github'], Literal['oauth2']] = CredentialsField(
provider="github",
supported_credential_types={"oauth2"},
required_scopes={"repo"},
description="The GitHub integration can be used with OAuth.",
)
# ...
def run(
self,
input_data: Input,
*,
credentials: OAuth2Credentials,
**kwargs,
) -> BlockOutput:
...
# API Key auth + OAuth:
class BlockWithAPIKeyAndOAuth(Block):
class Input(BlockSchema):
# Note that the type hint below is require or you will get a type error.
# The first argument is the provider name, the second is the credential type.
credentials: CredentialsMetaInput[Literal['github'], Literal['api_key', 'oauth2']] = CredentialsField(
provider="github",
supported_credential_types={"api_key", "oauth2"},
required_scopes={"repo"},
description="The GitHub integration can be used with OAuth, "
"or any API key with sufficient permissions for the blocks it is used on.",
)
# ...
def run(
self,
input_data: Input,
*,
credentials: Credentials,
**kwargs,
) -> BlockOutput:
...
```
The credentials will be automagically injected by the executor in the back end.
The `APIKeyCredentials` and `OAuth2Credentials` models are defined [here](https://github.com/Significant-Gravitas/AutoGPT/blob/master/autogpt_platform/autogpt_libs/autogpt_libs/supabase_integration_credentials_store/types.py).
To use them in e.g. an API request, you can either access the token directly:
```python
# credentials: APIKeyCredentials
response = requests.post(
url,
headers={
"Authorization": f"Bearer {credentials.api_key.get_secret_value()})",
},
)
# credentials: OAuth2Credentials
response = requests.post(
url,
headers={
"Authorization": f"Bearer {credentials.access_token.get_secret_value()})",
},
)
```
or use the shortcut `credentials.bearer()`:
```python
# credentials: APIKeyCredentials | OAuth2Credentials
response = requests.post(
url,
headers={"Authorization": credentials.bearer()},
)
```
#### Adding an OAuth2 service integration
To add support for a new OAuth2-authenticated service, you'll need to add an `OAuthHandler`.
All our existing handlers and the base class can be found [here][OAuth2 handlers].
Every handler must implement the following parts of the [`BaseOAuthHandler`] interface:
```python title="autogpt_platform/backend/backend/integrations/oauth/base.py"
--8<-- "autogpt_platform/backend/backend/integrations/oauth/base.py:BaseOAuthHandler1"
--8<-- "autogpt_platform/backend/backend/integrations/oauth/base.py:BaseOAuthHandler2"
--8<-- "autogpt_platform/backend/backend/integrations/oauth/base.py:BaseOAuthHandler3"
--8<-- "autogpt_platform/backend/backend/integrations/oauth/base.py:BaseOAuthHandler4"
--8<-- "autogpt_platform/backend/backend/integrations/oauth/base.py:BaseOAuthHandler5"
--8<-- "autogpt_platform/backend/backend/integrations/oauth/base.py:BaseOAuthHandler6"
```
As you can see, this is modeled after the standard OAuth2 flow.
Aside from implementing the `OAuthHandler` itself, adding a handler into the system requires two more things:
- Adding the handler class to `HANDLERS_BY_NAME` under [`integrations/oauth/__init__.py`](https://github.com/Significant-Gravitas/AutoGPT/blob/master/autogpt_platform/backend/backend/integrations/oauth/__init__.py)
```python title="autogpt_platform/backend/backend/integrations/oauth/__init__.py"
--8<-- "autogpt_platform/backend/backend/integrations/oauth/__init__.py:HANDLERS_BY_NAMEExample"
```
- Adding `{provider}_client_id` and `{provider}_client_secret` to the application's `Secrets` under [`util/settings.py`](https://github.com/Significant-Gravitas/AutoGPT/blob/master/autogpt_platform/backend/backend/util/settings.py)
```python title="autogpt_platform/backend/backend/util/settings.py"
--8<-- "autogpt_platform/backend/backend/util/settings.py:OAuthServerCredentialsExample"
```
[OAuth2 handlers]: https://github.com/Significant-Gravitas/AutoGPT/tree/master/autogpt_platform/backend/backend/integrations/oauth
[`BaseOAuthHandler`]: https://github.com/Significant-Gravitas/AutoGPT/blob/master/autogpt_platform/backend/backend/integrations/oauth/base.py
#### Adding to the frontend
You will need to add the provider (api or oauth) to the `CredentialsInput` component in [`frontend/src/components/integrations/credentials-input.tsx`](https://github.com/Significant-Gravitas/AutoGPT/blob/master/autogpt_platform/frontend/src/components/integrations/credentials-input.tsx).
```ts title="frontend/src/components/integrations/credentials-input.tsx"
--8<-- "autogpt_platform/frontend/src/components/integrations/credentials-input.tsx:ProviderIconsEmbed"
```
You will also need to add the provider to the `CredentialsProvider` component in [`frontend/src/components/integrations/credentials-provider.tsx`](https://github.com/Significant-Gravitas/AutoGPT/blob/master/autogpt_platform/frontend/src/components/integrations/credentials-provider.tsx).
```ts title="frontend/src/components/integrations/credentials-provider.tsx"
--8<-- "autogpt_platform/frontend/src/components/integrations/credentials-provider.tsx:CredentialsProviderNames"
```
Finally you will need to add the provider to the `CredentialsType` enum in [`frontend/src/lib/autogpt-server-api/types.ts`](https://github.com/Significant-Gravitas/AutoGPT/blob/master/autogpt_platform/frontend/src/lib/autogpt-server-api/types.ts).
```ts title="frontend/src/lib/autogpt-server-api/types.ts"
--8<-- "autogpt_platform/frontend/src/lib/autogpt-server-api/types.ts:BlockIOCredentialsSubSchema"
```
#### Example: GitHub integration
- GitHub blocks with API key + OAuth2 support: [`blocks/github`](https://github.com/Significant-Gravitas/AutoGPT/tree/master/autogpt_platform/backend/backend/blocks/github/)
```python title="blocks/github/issues.py"
--8<-- "autogpt_platform/backend/backend/blocks/github/issues.py:GithubCommentBlockExample"
```
- GitHub OAuth2 handler: [`integrations/oauth/github.py`](https://github.com/Significant-Gravitas/AutoGPT/blob/master/autogpt_platform/backend/backend/integrations/oauth/github.py)
```python title="blocks/github/github.py"
--8<-- "autogpt_platform/backend/backend/integrations/oauth/github.py:GithubOAuthHandlerExample"
```
#### Example: Google integration
- Google OAuth2 handler: [`integrations/oauth/google.py`](https://github.com/Significant-Gravitas/AutoGPT/blob/master/autogpt_platform/backend/backend/integrations/oauth/google.py)
```python title="integrations/oauth/google.py"
--8<-- "autogpt_platform/backend/backend/integrations/oauth/google.py:GoogleOAuthHandlerExample"
```
You can see that google has defined a `DEFAULT_SCOPES` variable, this is used to set the scopes that are requested no matter what the user asks for.
```python title="blocks/google/_auth.py"
--8<-- "autogpt_platform/backend/backend/blocks/google/_auth.py:GoogleOAuthIsConfigured"
```
You can also see that `GOOGLE_OAUTH_IS_CONFIGURED` is used to disable the blocks that require OAuth if the oauth is not configured. This is in the `__init__` method of each block. This is because there is no api key fallback for google blocks so we need to make sure that the oauth is configured before we allow the user to use the blocks.
## Key Points to Remember
- **Unique ID**: Give your block a unique ID in the **init** method.
- **Input and Output Schemas**: Define clear input and output schemas.
- **Error Handling**: Implement error handling in the `run` method.
- **Output Results**: Use `yield` to output results in the `run` method.
- **Testing**: Provide test input and output in the **init** method for automatic testing.
## Understanding the Testing Process
The testing of blocks is handled by `test_block.py`, which does the following:
1. It calls the block with the provided `test_input`.
If the block has a `credentials` field, `test_credentials` is passed in as well.
2. If a `test_mock` is provided, it temporarily replaces the specified methods with the mock functions.
3. It then asserts that the output matches the `test_output`.
For the WikipediaSummaryBlock:
- The test will call the block with the topic "Artificial Intelligence".
- Instead of making a real API call, it will use the mock function, which returns `{"extract": "summary content"}`.
- It will then check if the output key is "summary" and its value is a string.
This approach allows us to test the block's logic comprehensively without relying on external services, while also accommodating non-deterministic outputs.
## Tips for Effective Block Testing
1. **Provide realistic test_input**: Ensure your test input covers typical use cases.
2. **Define appropriate test_output**:
- For deterministic outputs, use specific expected values.
- For non-deterministic outputs or when only the type matters, use Python types (e.g., `str`, `int`, `dict`).
- You can mix specific values and types, e.g., `("key1", str), ("key2", 42)`.
3. **Use test_mock for network calls**: This prevents tests from failing due to network issues or API changes.
4. **Consider omitting test_mock for blocks without external dependencies**: If your block doesn't make network calls or use external resources, you might not need a mock.
5. **Consider edge cases**: Include tests for potential error conditions in your `run` method.
6. **Update tests when changing block behavior**: If you modify your block, ensure the tests are updated accordingly.
By following these steps, you can create new blocks that extend the functionality of the AutoGPT Agent Server.
## Blocks we want to see
Below is a list of blocks that we would like to see implemented in the AutoGPT Agent Server. If you're interested in contributing, feel free to pick one of these blocks or chose your own.
If you would like to implement one of these blocks, open a pull request and we will start the review process.
### Consumer Services/Platforms
- Google sheets - [~~Read/Append~~](https://github.com/Significant-Gravitas/AutoGPT/pull/8236)
- Email - Read/Send with [~~Gmail~~](https://github.com/Significant-Gravitas/AutoGPT/pull/8236), Outlook, Yahoo, Proton, etc
- Calendar - Read/Write with Google Calendar, Outlook Calendar, etc
- Home Assistant - Call Service, Get Status
- Dominos - Order Pizza, Track Order
- Uber - Book Ride, Track Ride
- Notion - Create/Read Page, Create/Append/Read DB
- Google drive - read/write/overwrite file/folder
### Social Media
- Twitter - Post, Reply, Get Replies, Get Comments, Get Followers, Get Following, Get Tweets, Get Mentions
- Instagram - Post, Reply, Get Comments, Get Followers, Get Following, Get Posts, Get Mentions, Get Trending Posts
- TikTok - Post, Reply, Get Comments, Get Followers, Get Following, Get Videos, Get Mentions, Get Trending Videos
- LinkedIn - Post, Reply, Get Comments, Get Followers, Get Following, Get Posts, Get Mentions, Get Trending Posts
- YouTube - Transcribe Videos/Shorts, Post Videos/Shorts, Read/Reply/React to Comments, Update Thumbnails, Update Description, Update Tags, Update Titles, Get Views, Get Likes, Get Dislikes, Get Subscribers, Get Comments, Get Shares, Get Watch Time, Get Revenue, Get Trending Videos, Get Top Videos, Get Top Channels
- Reddit - Post, Reply, Get Comments, Get Followers, Get Following, Get Posts, Get Mentions, Get Trending Posts
- Treatwell (and related Platforms) - Book, Cancel, Review, Get Recommendations
- Substack - Read/Subscribe/Unsubscribe, Post/Reply, Get Recommendations
- Discord - Read/Post/Reply, Moderation actions
- GoodReads - Read/Post/Reply, Get Recommendations
### E-commerce
- Airbnb - Book, Cancel, Review, Get Recommendations
- Amazon - Order, Track Order, Return, Review, Get Recommendations
- eBay - Order, Track Order, Return, Review, Get Recommendations
- Upwork - Post Jobs, Hire Freelancer, Review Freelancer, Fire Freelancer
### Business Tools
- External Agents - Call other agents similar to AutoGPT
- Trello - Create/Read/Update/Delete Cards, Lists, Boards
- Jira - Create/Read/Update/Delete Issues, Projects, Boards
- Linear - Create/Read/Update/Delete Issues, Projects, Boards
- Excel - Read/Write/Update/Delete Rows, Columns, Sheets
- Slack - Read/Post/Reply to Messages, Create Channels, Invite Users
- ERPNext - Create/Read/Update/Delete Invoices, Orders, Customers, Products
- Salesforce - Create/Read/Update/Delete Leads, Opportunities, Accounts
- HubSpot - Create/Read/Update/Delete Contacts, Deals, Companies
- Zendesk - Create/Read/Update/Delete Tickets, Users, Organizations
- Odoo - Create/Read/Update/Delete Sales Orders, Invoices, Customers
- Shopify - Create/Read/Update/Delete Products, Orders, Customers
- WooCommerce - Create/Read/Update/Delete Products, Orders, Customers
- Squarespace - Create/Read/Update/Delete Pages, Products, Orders
## Agent Templates we want to see
### Data/Information
- Summarize top news of today, of this week, this month via Apple News or other large media outlets BBC, TechCrunch, hackernews, etc
- Create, read, and summarize substack newsletters or any newsletters (blog writer vs blog reader)
- Get/read/summarize the most viral Twitter, Instagram, TikTok (general social media accounts) of the day, week, month
- Get/Read any LinkedIn posts or profile that mention AI Agents
- Read/Summarize discord (might not be able to do this because you need access)
- Read / Get most read books in a given month, year, etc from GoodReads or Amazon Books, etc
- Get dates for specific shows across all streaming services
- Suggest/Recommend/Get most watched shows in a given month, year, etc across all streaming platforms
- Data analysis from xlsx data set
- Gather via Excel or Google Sheets data > Sample the data randomly (sample block takes top X, bottom X, randomly, etc) > pass that to LLM Block to generate a script for analysis of the full data > Python block to run the script> making a loop back through LLM Fix Block on error > create chart/visualization (potentially in the code block?) > show the image as output (this may require frontend changes to show)
- Tiktok video search and download
### Marketing
- Portfolio site design and enhancements
# Contributing to AutoGPT Agent Server: Creating and Testing Blocks
This guide will walk you through the process of creating and testing a new block for the AutoGPT Agent Server, using the WikipediaSummaryBlock as an example.
## Understanding Blocks and Testing
Blocks are reusable components that can be connected to form a graph representing an agent's behavior. Each block has inputs, outputs, and a specific function. Proper testing is crucial to ensure blocks work correctly and consistently.
## Creating and Testing a New Block
Follow these steps to create and test a new block:
1. **Create a new Python file** in the `backend/blocks` directory. Name it descriptively and use snake_case. For example: `get_wikipedia_summary.py`.
2. **Import necessary modules and create a class that inherits from `Block`**. Make sure to include all necessary imports for your block.
Every block should contain the following:
```python
from backend.data.block import Block, BlockSchema, BlockOutput
```
Example for the Wikipedia summary block:
```python
from backend.data.block import Block, BlockSchema, BlockOutput
from backend.utils.get_request import GetRequest
import requests
class WikipediaSummaryBlock(Block, GetRequest):
# Block implementation will go here
```
3. **Define the input and output schemas** using `BlockSchema`. These schemas specify the data structure that the block expects to receive (input) and produce (output).
- The input schema defines the structure of the data the block will process. Each field in the schema represents a required piece of input data.
- The output schema defines the structure of the data the block will return after processing. Each field in the schema represents a piece of output data.
Example:
```python
class Input(BlockSchema):
topic: str # The topic to get the Wikipedia summary for
class Output(BlockSchema):
summary: str # The summary of the topic from Wikipedia
error: str # Any error message if the request fails, error field needs to be named `error`.
```
4. **Implement the `__init__` method, including test data and mocks:**
!!! important
Use UUID generator (e.g. https://www.uuidgenerator.net/) for every new block `id` and *do not* make up your own. Alternatively, you can run this python code to generate an uuid: `print(__import__('uuid').uuid4())`
```python
def __init__(self):
super().__init__(
# Unique ID for the block, used across users for templates
# If you are an AI leave it as is or change to "generate-proper-uuid"
id="xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx",
input_schema=WikipediaSummaryBlock.Input, # Assign input schema
output_schema=WikipediaSummaryBlock.Output, # Assign output schema
# Provide sample input, output and test mock for testing the block
test_input={"topic": "Artificial Intelligence"},
test_output=("summary", "summary content"),
test_mock={"get_request": lambda url, json: {"extract": "summary content"}},
)
```
- `id`: A unique identifier for the block.
- `input_schema` and `output_schema`: Define the structure of the input and output data.
Let's break down the testing components:
- `test_input`: This is a sample input that will be used to test the block. It should be a valid input according to your Input schema.
- `test_output`: This is the expected output when running the block with the `test_input`. It should match your Output schema. For non-deterministic outputs or when you only want to assert the type, you can use Python types instead of specific values. In this example, `("summary", str)` asserts that the output key is "summary" and its value is a string.
- `test_mock`: This is crucial for blocks that make network calls. It provides a mock function that replaces the actual network call during testing.
In this case, we're mocking the `get_request` method to always return a dictionary with an 'extract' key, simulating a successful API response. This allows us to test the block's logic without making actual network requests, which could be slow, unreliable, or rate-limited.
5. **Implement the `run` method with error handling:**, this should contain the main logic of the block:
```python
def run(self, input_data: Input, **kwargs) -> BlockOutput:
try:
topic = input_data.topic
url = f"https://en.wikipedia.org/api/rest_v1/page/summary/{topic}"
response = self.get_request(url, json=True)
yield "summary", response['extract']
except requests.exceptions.HTTPError as http_err:
raise RuntimeError(f"HTTP error occurred: {http_err}")
```
- **Try block**: Contains the main logic to fetch and process the Wikipedia summary.
- **API request**: Send a GET request to the Wikipedia API.
- **Error handling**: Handle various exceptions that might occur during the API request and data processing. We don't need to catch all exceptions, only the ones we expect and can handle. The uncaught exceptions will be automatically yielded as `error` in the output. Any block that raises an exception (or yields an `error` output) will be marked as failed. Prefer raising exceptions over yielding `error`, as it will stop the execution immediately.
- **Yield**: Use `yield` to output the results. Prefer to output one result object at a time. If you are calling a function that returns a list, you can yield each item in the list separately. You can also yield the whole list as well, but do both rather than yielding the list. For example: If you were writing a block that outputs emails, you'd yield each email as a separate result object, but you could also yield the whole list as an additional single result object. Yielding output named `error` will break the execution right away and mark the block execution as failed.
### Blocks with authentication
Our system supports auth offloading for API keys and OAuth2 authorization flows.
Adding a block with API key authentication is straight-forward, as is adding a block
for a service that we already have OAuth2 support for.
Implementing the block itself is relatively simple. On top of the instructions above,
you're going to add a `credentials` parameter to the `Input` model and the `run` method:
```python
from autogpt_libs.supabase_integration_credentials_store.types import (
APIKeyCredentials,
OAuth2Credentials,
Credentials,
)
from backend.data.block import Block, BlockOutput, BlockSchema
from backend.data.model import CredentialsField
# API Key auth:
class BlockWithAPIKeyAuth(Block):
class Input(BlockSchema):
# Note that the type hint below is require or you will get a type error.
# The first argument is the provider name, the second is the credential type.
credentials: CredentialsMetaInput[Literal['github'], Literal['api_key']] = CredentialsField(
provider="github",
supported_credential_types={"api_key"},
description="The GitHub integration can be used with "
"any API key with sufficient permissions for the blocks it is used on.",
)
# ...
def run(
self,
input_data: Input,
*,
credentials: APIKeyCredentials,
**kwargs,
) -> BlockOutput:
...
# OAuth:
class BlockWithOAuth(Block):
class Input(BlockSchema):
# Note that the type hint below is require or you will get a type error.
# The first argument is the provider name, the second is the credential type.
credentials: CredentialsMetaInput[Literal['github'], Literal['oauth2']] = CredentialsField(
provider="github",
supported_credential_types={"oauth2"},
required_scopes={"repo"},
description="The GitHub integration can be used with OAuth.",
)
# ...
def run(
self,
input_data: Input,
*,
credentials: OAuth2Credentials,
**kwargs,
) -> BlockOutput:
...
# API Key auth + OAuth:
class BlockWithAPIKeyAndOAuth(Block):
class Input(BlockSchema):
# Note that the type hint below is require or you will get a type error.
# The first argument is the provider name, the second is the credential type.
credentials: CredentialsMetaInput[Literal['github'], Literal['api_key', 'oauth2']] = CredentialsField(
provider="github",
supported_credential_types={"api_key", "oauth2"},
required_scopes={"repo"},
description="The GitHub integration can be used with OAuth, "
"or any API key with sufficient permissions for the blocks it is used on.",
)
# ...
def run(
self,
input_data: Input,
*,
credentials: Credentials,
**kwargs,
) -> BlockOutput:
...
```
The credentials will be automagically injected by the executor in the back end.
The `APIKeyCredentials` and `OAuth2Credentials` models are defined [here](https://github.com/Significant-Gravitas/AutoGPT/blob/master/autogpt_platform/autogpt_libs/autogpt_libs/supabase_integration_credentials_store/types.py).
To use them in e.g. an API request, you can either access the token directly:
```python
# credentials: APIKeyCredentials
response = requests.post(
url,
headers={
"Authorization": f"Bearer {credentials.api_key.get_secret_value()})",
},
)
# credentials: OAuth2Credentials
response = requests.post(
url,
headers={
"Authorization": f"Bearer {credentials.access_token.get_secret_value()})",
},
)
```
or use the shortcut `credentials.bearer()`:
```python
# credentials: APIKeyCredentials | OAuth2Credentials
response = requests.post(
url,
headers={"Authorization": credentials.bearer()},
)
```
#### Adding an OAuth2 service integration
To add support for a new OAuth2-authenticated service, you'll need to add an `OAuthHandler`.
All our existing handlers and the base class can be found [here][OAuth2 handlers].
Every handler must implement the following parts of the [`BaseOAuthHandler`] interface:
```python title="autogpt_platform/backend/backend/integrations/oauth/base.py"
--8<-- "autogpt_platform/backend/backend/integrations/oauth/base.py:BaseOAuthHandler1"
--8<-- "autogpt_platform/backend/backend/integrations/oauth/base.py:BaseOAuthHandler2"
--8<-- "autogpt_platform/backend/backend/integrations/oauth/base.py:BaseOAuthHandler3"
--8<-- "autogpt_platform/backend/backend/integrations/oauth/base.py:BaseOAuthHandler4"
--8<-- "autogpt_platform/backend/backend/integrations/oauth/base.py:BaseOAuthHandler5"
--8<-- "autogpt_platform/backend/backend/integrations/oauth/base.py:BaseOAuthHandler6"
```
As you can see, this is modeled after the standard OAuth2 flow.
Aside from implementing the `OAuthHandler` itself, adding a handler into the system requires two more things:
- Adding the handler class to `HANDLERS_BY_NAME` under [`integrations/oauth/__init__.py`](https://github.com/Significant-Gravitas/AutoGPT/blob/master/autogpt_platform/backend/backend/integrations/oauth/__init__.py)
```python title="autogpt_platform/backend/backend/integrations/oauth/__init__.py"
--8<-- "autogpt_platform/backend/backend/integrations/oauth/__init__.py:HANDLERS_BY_NAMEExample"
```
- Adding `{provider}_client_id` and `{provider}_client_secret` to the application's `Secrets` under [`util/settings.py`](https://github.com/Significant-Gravitas/AutoGPT/blob/master/autogpt_platform/backend/backend/util/settings.py)
```python title="autogpt_platform/backend/backend/util/settings.py"
--8<-- "autogpt_platform/backend/backend/util/settings.py:OAuthServerCredentialsExample"
```
[OAuth2 handlers]: https://github.com/Significant-Gravitas/AutoGPT/tree/master/autogpt_platform/backend/backend/integrations/oauth
[`BaseOAuthHandler`]: https://github.com/Significant-Gravitas/AutoGPT/blob/master/autogpt_platform/backend/backend/integrations/oauth/base.py
#### Adding to the frontend
You will need to add the provider (api or oauth) to the `CredentialsInput` component in [`frontend/src/components/integrations/credentials-input.tsx`](https://github.com/Significant-Gravitas/AutoGPT/blob/master/autogpt_platform/frontend/src/components/integrations/credentials-input.tsx).
```ts title="frontend/src/components/integrations/credentials-input.tsx"
--8<-- "autogpt_platform/frontend/src/components/integrations/credentials-input.tsx:ProviderIconsEmbed"
```
You will also need to add the provider to the `CredentialsProvider` component in [`frontend/src/components/integrations/credentials-provider.tsx`](https://github.com/Significant-Gravitas/AutoGPT/blob/master/autogpt_platform/frontend/src/components/integrations/credentials-provider.tsx).
```ts title="frontend/src/components/integrations/credentials-provider.tsx"
--8<-- "autogpt_platform/frontend/src/components/integrations/credentials-provider.tsx:CredentialsProviderNames"
```
Finally you will need to add the provider to the `CredentialsType` enum in [`frontend/src/lib/autogpt-server-api/types.ts`](https://github.com/Significant-Gravitas/AutoGPT/blob/master/autogpt_platform/frontend/src/lib/autogpt-server-api/types.ts).
```ts title="frontend/src/lib/autogpt-server-api/types.ts"
--8<-- "autogpt_platform/frontend/src/lib/autogpt-server-api/types.ts:BlockIOCredentialsSubSchema"
```
#### Example: GitHub integration
- GitHub blocks with API key + OAuth2 support: [`blocks/github`](https://github.com/Significant-Gravitas/AutoGPT/tree/master/autogpt_platform/backend/backend/blocks/github/)
```python title="blocks/github/issues.py"
--8<-- "autogpt_platform/backend/backend/blocks/github/issues.py:GithubCommentBlockExample"
```
- GitHub OAuth2 handler: [`integrations/oauth/github.py`](https://github.com/Significant-Gravitas/AutoGPT/blob/master/autogpt_platform/backend/backend/integrations/oauth/github.py)
```python title="blocks/github/github.py"
--8<-- "autogpt_platform/backend/backend/integrations/oauth/github.py:GithubOAuthHandlerExample"
```
#### Example: Google integration
- Google OAuth2 handler: [`integrations/oauth/google.py`](https://github.com/Significant-Gravitas/AutoGPT/blob/master/autogpt_platform/backend/backend/integrations/oauth/google.py)
```python title="integrations/oauth/google.py"
--8<-- "autogpt_platform/backend/backend/integrations/oauth/google.py:GoogleOAuthHandlerExample"
```
You can see that google has defined a `DEFAULT_SCOPES` variable, this is used to set the scopes that are requested no matter what the user asks for.
```python title="blocks/google/_auth.py"
--8<-- "autogpt_platform/backend/backend/blocks/google/_auth.py:GoogleOAuthIsConfigured"
```
You can also see that `GOOGLE_OAUTH_IS_CONFIGURED` is used to disable the blocks that require OAuth if the oauth is not configured. This is in the `__init__` method of each block. This is because there is no api key fallback for google blocks so we need to make sure that the oauth is configured before we allow the user to use the blocks.
## Key Points to Remember
- **Unique ID**: Give your block a unique ID in the **init** method.
- **Input and Output Schemas**: Define clear input and output schemas.
- **Error Handling**: Implement error handling in the `run` method.
- **Output Results**: Use `yield` to output results in the `run` method.
- **Testing**: Provide test input and output in the **init** method for automatic testing.
## Understanding the Testing Process
The testing of blocks is handled by `test_block.py`, which does the following:
1. It calls the block with the provided `test_input`.
If the block has a `credentials` field, `test_credentials` is passed in as well.
2. If a `test_mock` is provided, it temporarily replaces the specified methods with the mock functions.
3. It then asserts that the output matches the `test_output`.
For the WikipediaSummaryBlock:
- The test will call the block with the topic "Artificial Intelligence".
- Instead of making a real API call, it will use the mock function, which returns `{"extract": "summary content"}`.
- It will then check if the output key is "summary" and its value is a string.
This approach allows us to test the block's logic comprehensively without relying on external services, while also accommodating non-deterministic outputs.
## Tips for Effective Block Testing
1. **Provide realistic test_input**: Ensure your test input covers typical use cases.
2. **Define appropriate test_output**:
- For deterministic outputs, use specific expected values.
- For non-deterministic outputs or when only the type matters, use Python types (e.g., `str`, `int`, `dict`).
- You can mix specific values and types, e.g., `("key1", str), ("key2", 42)`.
3. **Use test_mock for network calls**: This prevents tests from failing due to network issues or API changes.
4. **Consider omitting test_mock for blocks without external dependencies**: If your block doesn't make network calls or use external resources, you might not need a mock.
5. **Consider edge cases**: Include tests for potential error conditions in your `run` method.
6. **Update tests when changing block behavior**: If you modify your block, ensure the tests are updated accordingly.
By following these steps, you can create new blocks that extend the functionality of the AutoGPT Agent Server.
## Blocks we want to see
Below is a list of blocks that we would like to see implemented in the AutoGPT Agent Server. If you're interested in contributing, feel free to pick one of these blocks or chose your own.
If you would like to implement one of these blocks, open a pull request and we will start the review process.
### Consumer Services/Platforms
- Google sheets - [~~Read/Append~~](https://github.com/Significant-Gravitas/AutoGPT/pull/8236)
- Email - Read/Send with [~~Gmail~~](https://github.com/Significant-Gravitas/AutoGPT/pull/8236), Outlook, Yahoo, Proton, etc
- Calendar - Read/Write with Google Calendar, Outlook Calendar, etc
- Home Assistant - Call Service, Get Status
- Dominos - Order Pizza, Track Order
- Uber - Book Ride, Track Ride
- Notion - Create/Read Page, Create/Append/Read DB
- Google drive - read/write/overwrite file/folder
### Social Media
- Twitter - Post, Reply, Get Replies, Get Comments, Get Followers, Get Following, Get Tweets, Get Mentions
- Instagram - Post, Reply, Get Comments, Get Followers, Get Following, Get Posts, Get Mentions, Get Trending Posts
- TikTok - Post, Reply, Get Comments, Get Followers, Get Following, Get Videos, Get Mentions, Get Trending Videos
- LinkedIn - Post, Reply, Get Comments, Get Followers, Get Following, Get Posts, Get Mentions, Get Trending Posts
- YouTube - Transcribe Videos/Shorts, Post Videos/Shorts, Read/Reply/React to Comments, Update Thumbnails, Update Description, Update Tags, Update Titles, Get Views, Get Likes, Get Dislikes, Get Subscribers, Get Comments, Get Shares, Get Watch Time, Get Revenue, Get Trending Videos, Get Top Videos, Get Top Channels
- Reddit - Post, Reply, Get Comments, Get Followers, Get Following, Get Posts, Get Mentions, Get Trending Posts
- Treatwell (and related Platforms) - Book, Cancel, Review, Get Recommendations
- Substack - Read/Subscribe/Unsubscribe, Post/Reply, Get Recommendations
- Discord - Read/Post/Reply, Moderation actions
- GoodReads - Read/Post/Reply, Get Recommendations
### E-commerce
- Airbnb - Book, Cancel, Review, Get Recommendations
- Amazon - Order, Track Order, Return, Review, Get Recommendations
- eBay - Order, Track Order, Return, Review, Get Recommendations
- Upwork - Post Jobs, Hire Freelancer, Review Freelancer, Fire Freelancer
### Business Tools
- External Agents - Call other agents similar to AutoGPT
- Trello - Create/Read/Update/Delete Cards, Lists, Boards
- Jira - Create/Read/Update/Delete Issues, Projects, Boards
- Linear - Create/Read/Update/Delete Issues, Projects, Boards
- Excel - Read/Write/Update/Delete Rows, Columns, Sheets
- Slack - Read/Post/Reply to Messages, Create Channels, Invite Users
- ERPNext - Create/Read/Update/Delete Invoices, Orders, Customers, Products
- Salesforce - Create/Read/Update/Delete Leads, Opportunities, Accounts
- HubSpot - Create/Read/Update/Delete Contacts, Deals, Companies
- Zendesk - Create/Read/Update/Delete Tickets, Users, Organizations
- Odoo - Create/Read/Update/Delete Sales Orders, Invoices, Customers
- Shopify - Create/Read/Update/Delete Products, Orders, Customers
- WooCommerce - Create/Read/Update/Delete Products, Orders, Customers
- Squarespace - Create/Read/Update/Delete Pages, Products, Orders
## Agent Templates we want to see
### Data/Information
- Summarize top news of today, of this week, this month via Apple News or other large media outlets BBC, TechCrunch, hackernews, etc
- Create, read, and summarize substack newsletters or any newsletters (blog writer vs blog reader)
- Get/read/summarize the most viral Twitter, Instagram, TikTok (general social media accounts) of the day, week, month
- Get/Read any LinkedIn posts or profile that mention AI Agents
- Read/Summarize discord (might not be able to do this because you need access)
- Read / Get most read books in a given month, year, etc from GoodReads or Amazon Books, etc
- Get dates for specific shows across all streaming services
- Suggest/Recommend/Get most watched shows in a given month, year, etc across all streaming platforms
- Data analysis from xlsx data set
- Gather via Excel or Google Sheets data > Sample the data randomly (sample block takes top X, bottom X, randomly, etc) > pass that to LLM Block to generate a script for analysis of the full data > Python block to run the script> making a loop back through LLM Fix Block on error > create chart/visualization (potentially in the code block?) > show the image as output (this may require frontend changes to show)
- Tiktok video search and download
### Marketing
- Portfolio site design and enhancements

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@@ -7,49 +7,46 @@ docs_dir: content
nav:
- Home: index.md
- The AutoGPT Server 🆕:
- Build your own Blocks: server/new_blocks.md
- Setup: server/setup.md
- Advanced Setup: server/advanced_setup.md
- Using Ollama: server/ollama.md
- Using D-ID: server/d_id.md
- Blocks: server/blocks/blocks.md
- The AutoGPT Platform 🆕:
- Getting Started: platform/getting-started.md
- Advanced Setup: platform/advanced_setup.md
- Build your own Blocks: platform/new_blocks.md
- Using Ollama: platform/ollama.md
- Using D-ID: platform/d_id.md
- Blocks: platform/blocks/blocks.md
- AutoGPT Agent:
- Introduction: AutoGPT/index.md
- AutoGPT Classic:
- Introduction: classic/index.md
- Setup:
- Setting up AutoGPT: AutoGPT/setup/index.md
- Set up with Docker: AutoGPT/setup/docker.md
- For Developers: AutoGPT/setup/for-developers.md
- Setting up AutoGPT: classic/setup/index.md
- Set up with Docker: classic/setup/docker.md
- For Developers: classic/setup/for-developers.md
- Configuration:
- Options: AutoGPT/configuration/options.md
- Search: AutoGPT/configuration/search.md
- Voice: AutoGPT/configuration/voice.md
- Usage: AutoGPT/usage.md
- Options: classic/configuration/options.md
- Search: classic/configuration/search.md
- Voice: classic/configuration/voice.md
- Usage: classic/usage.md
- Help us improve AutoGPT:
- Share your debug logs with us: AutoGPT/share-your-logs.md
- Share your debug logs with us: classic/share-your-logs.md
- Contribution guide: contributing.md
- Running tests: AutoGPT/testing.md
- Running tests: classic/testing.md
- Code of Conduct: code-of-conduct.md
- Benchmark:
- Readme: https://github.com/Significant-Gravitas/AutoGPT/blob/master/classic/benchmark/README.md
- Forge:
- Introduction: forge/get-started.md
- Components:
- Introduction: forge/components/introduction.md
- Agents: forge/components/agents.md
- Components: forge/components/components.md
- Protocols: forge/components/protocols.md
- Commands: forge/components/commands.md
- Built in Components: forge/components/built-in-components.md
- Creating Components: forge/components/creating-components.md
- Frontend:
- Readme: https://github.com/Significant-Gravitas/AutoGPT/blob/master/classic/frontend/README.md
- Benchmark:
- Readme: https://github.com/Significant-Gravitas/AutoGPT/blob/master/classic/benchmark/README.md
- Forge:
- Introduction: forge/get-started.md
- Components:
- Introduction: forge/components/introduction.md
- Agents: forge/components/agents.md
- Components: forge/components/components.md
- Protocols: forge/components/protocols.md
- Commands: forge/components/commands.md
- Built in Components: forge/components/built-in-components.md
- Creating Components: forge/components/creating-components.md
- Frontend:
- Readme: https://github.com/Significant-Gravitas/AutoGPT/blob/master/classic/frontend/README.md
- Docs: docs/index.md
- Contribute: contribute/index.md
# - Challenges:
# - Introduction: challenges/introduction.md

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