S3 and PostgreSQL remote backends for LangChain Deep Agents
Project description
🗄️ Deep Agents Remote Backends
deepagents-backends provides production-ready implementations of the LangChain Deep Agents' BackendProtocol for remote file storage, allowing your agents to maintain state across restarts and share files in distributed environments.
Store agent files in S3 or PostgreSQL instead of ephemeral state, enabling persistent storage, distributed execution, and multi-agent file sharing.
🚀 Quickstart
pip install deepagents-backends
S3 Backend
Store agent files in AWS S3 or any S3-compatible storage (MinIO, DigitalOcean Spaces, etc.):
from deepagents import create_deep_agent
from deepagents_backends import S3Backend, S3Config
# Configure S3 (or MinIO for local development)
config = S3Config(
bucket="my-agent-bucket",
prefix="agent-workspace",
endpoint_url="http://localhost:9000", # Remove for AWS S3
access_key_id="minioadmin",
secret_access_key="minioadmin",
use_ssl=False,
)
# Create agent with S3 backend
agent = create_deep_agent(
backend=S3Backend(config),
system_prompt="You are a helpful assistant. Files persist in S3.",
)
# Run the agent - all file operations use S3
result = agent.invoke({
"messages": [{"role": "user", "content": "Create a Python calculator module in /src/"}]
})
PostgreSQL Backend
Store agent files in PostgreSQL with connection pooling for high-performance scenarios:
import asyncio
from deepagents import create_deep_agent
from deepagents_backends import PostgresBackend, PostgresConfig
async def main():
config = PostgresConfig(
host="localhost",
port=5432,
database="deepagents",
user="postgres",
password="postgres",
table="agent_files",
)
backend = PostgresBackend(config)
await backend.initialize() # Creates table + indexes
try:
agent = create_deep_agent(
backend=backend,
system_prompt="You are a data analyst. Files persist in PostgreSQL.",
)
result = await agent.ainvoke({
"messages": [{"role": "user", "content": "Create a data analysis project in /analysis/"}]
})
finally:
await backend.close() # Always close the connection pool
asyncio.run(main())
🔀 Composite Backend (Hybrid Storage)
Route different paths to different backends for optimal storage:
from deepagents import create_deep_agent
from deepagents.backends import CompositeBackend, StateBackend
from deepagents_backends import S3Backend, S3Config, PostgresBackend, PostgresConfig
# S3 for large files, PostgreSQL for structured data
s3_backend = S3Backend(S3Config(bucket="assets", ...))
pg_backend = PostgresBackend(PostgresConfig(...))
await pg_backend.initialize()
agent = create_deep_agent(
backend=CompositeBackend(
default=StateBackend(), # Ephemeral working files
routes={
"/assets/": s3_backend, # Large files → S3
"/data/": pg_backend, # Structured data → PostgreSQL
"/memories/": pg_backend, # Long-term memory → PostgreSQL
},
),
)
📚 Examples
See the examples/ directory for complete, runnable examples:
| Example | Description |
|---|---|
| s3_deep_agent.py | Full S3 backend with streaming and custom tools |
| postgres_deep_agent.py | PostgreSQL with multi-agent and sub-agent workflows |
| composite_backend.py | Hybrid S3 + PostgreSQL storage with routing |
| basic_usage.py | Low-level backend API operations |
Running Examples Locally
# Start MinIO and PostgreSQL
docker-compose up -d
# Run an example
python examples/s3_deep_agent.py
⚙️ Configuration
S3Config
S3Config(
bucket="my-bucket", # Required: S3 bucket name
prefix="agent-files", # Key prefix for all files
region="us-west-2", # AWS region (default: us-east-1)
endpoint_url=None, # Custom endpoint (MinIO, etc.)
access_key_id=None, # AWS credentials (or use IAM role)
secret_access_key=None,
use_ssl=True, # Use HTTPS
max_pool_connections=50, # Connection pool size
connect_timeout=5.0, # Connection timeout (seconds)
read_timeout=30.0, # Read timeout (seconds)
max_retries=3, # Retry attempts
)
PostgresConfig
PostgresConfig(
host="localhost", # PostgreSQL host
port=5432, # PostgreSQL port
database="deepagents", # Database name
user="postgres", # Username
password="postgres", # Password
table="agent_files", # Table name for file storage
min_pool_size=2, # Minimum connections in pool
max_pool_size=10, # Maximum connections in pool
sslmode="prefer", # SSL mode (use "require" in production)
)
🔧 Backend Protocol
Both backends implement the full BackendProtocol with sync and async methods:
| Method | Description |
|---|---|
read / aread |
Read file content (supports offset/limit pagination) |
write / awrite |
Create new file (fails if exists) |
edit / aedit |
Edit file with string replacement |
ls_info / als_info |
List directory contents |
glob_info / aglob_info |
Find files matching glob pattern |
grep_raw / agrep_raw |
Search files with line-numbered results |
upload_files / aupload_files |
Batch upload raw bytes |
download_files / adownload_files |
Batch download as bytes |
File Storage Format
Files are stored as JSON with line arrays for efficient line-based operations:
{
"content": ["line 1", "line 2", "line 3"],
"created_at": "2025-01-07T12:00:00Z",
"modified_at": "2025-01-07T12:30:00Z"
}
🧪 Development
# Install dev dependencies
uv sync
# Unit tests (mocked, no Docker)
uv run pytest -m unit
# Integration tests (Docker services started automatically via pytest-docker)
uv run pytest -m integration
# All tests
uv run pytest
Docker Services
| Service | Port | Credentials |
|---|---|---|
| MinIO (S3) | 9000 | minioadmin / minioadmin |
| MinIO Console | 9001 | minioadmin / minioadmin |
| PostgreSQL | 5432 | postgres / postgres |
🔒 Security
- Credentials: Use environment variables or IAM roles, never commit secrets
- PostgreSQL: Use
sslmode="require"in production - S3: Use
use_ssl=Truein production - Connection pooling: PostgresBackend maintains a connection pool—always call
close()
📄 License
MIT License - see LICENSE for details.
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