🗄️ DeepAgents Backend Configuration Guide

Complete guide to DeepAgents 0.2.0 pluggable backends for persistent memory, filesystem access, and advanced storage strategies.

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📖 Table of Contents

1. Introduction
2. Quick Start
3. Backend Types
4. Complete Examples
5. Advanced Patterns
6. Troubleshooting

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🎯 Introduction

What's New in DeepAgents 0.2.0?

DeepAgents 0.2.0 introduces a pluggable backend abstraction that transforms how agents store and access files. Instead of being limited to ephemeral virtual filesystems, you can now:

✅ Persist memory across conversations (StoreBackend)
✅ Access real files on your local filesystem (FilesystemBackend)
✅ Hybrid strategies with different storage for different paths (CompositeBackend)
✅ Custom backends for databases, S3, remote VMs, etc.

Source: LangChain Blog - Doubling Down on DeepAgents

Why Backends Matter

Before 0.2.0:

Agent writes to /notes.txt → Stored in LangGraph state
New conversation starts → /notes.txt is gone ❌

After 0.2.0:

Agent writes to /memories/notes.txt → Stored in persistent database
New conversation starts → /memories/notes.txt still there! ✅

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🚀 Quick Start

Basic Setup (Default Behavior)

If you don't specify a backend, DeepAgents uses StateBackend (ephemeral, same as 0.1.0):

apiVersion: agent/v1
kind: AgentSpec
metadata:
  name: Basic Research Agent
spec:
  target_framework: deepagents

  language_model:
    provider: google-genai
    model: gemini-2.5-flash

  # No backend config = uses StateBackend (default)

Enable Persistent Memory

Add just 2 lines to enable persistent memory:

spec:
  target_framework: deepagents

  language_model:
    provider: google-genai
    model: gemini-2.5-flash

  # Enable persistent storage
  backend:
    type: store  # ✨ That's it!

Now everything the agent writes persists across conversations!

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📦 Backend Types

1. StateBackend (Default)

Storage: LangGraph state (ephemeral, per-thread)
Persistence: Within conversation only
Best For: Temporary scratch space, single-conversation agents

Configuration:

backend:
  type: state

Behavior: - ✅ Fast (in-memory) - ✅ Checkpointed within thread - ❌ Lost when thread ends - ❌ Not shared across threads

Use Cases: - Simple Q&A agents - Temporary calculations - Draft generation - Single-session tasks

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2. StoreBackend (Persistent)

Storage: LangGraph store (persistent database)
Persistence: Across all conversations
Best For: Long-term memory, learning agents, chatbots

Configuration:

backend:
  type: store

Behavior: - ✅ Persistent across threads - ✅ Shared between conversations - ✅ Survives restarts - ⚡ Slightly slower (database)

Use Cases: - Chatbots with memory - Research assistants that learn - Agents that build knowledge over time - Multi-session projects

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3. FilesystemBackend (Local Files)

Storage: Actual local filesystem
Persistence: Real files on disk
Best For: Code analysis, file editing, project work

Configuration:

backend:
  type: filesystem
  root_dir: /Users/local/my_project

Behavior: - ✅ Access real project files - ✅ Changes immediately visible - ✅ Works with other tools (git, IDE, etc.) - ⚠️ Security: Agent can modify actual files!

Use Cases: - Code review agents - Documentation generators - File refactoring - Project analysis

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4. CompositeBackend (Hybrid)

Storage: Route different paths to different backends
Persistence: Mixed strategies
Best For: Complex agents with different storage needs

Configuration:

backend:
  type: composite
  default: state              # Default for unspecified paths
  routes:
    /memories/: store         # Persistent memories
    /project/: filesystem     # Real project files
    /cache/: state            # Temporary files

Behavior: - ✅ Best of all worlds - ✅ Optimized for each data type - ✅ Flexible and powerful - 🔧 Requires more configuration

Use Cases: - Development assistants - Complex research agents - Multi-domain agents - Production systems

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📚 Complete Examples

Example 1: Persistent Chatbot

Scenario: Build a chatbot that remembers user preferences and previous conversations.

Playbook:

apiVersion: agent/v1
kind: AgentSpec
metadata:
  name: Persistent Chatbot
  description: Chatbot with long-term memory
spec:
  target_framework: deepagents

  language_model:
    provider: google-genai
    model: gemini-2.5-flash
    temperature: 0.7

  # Persistent memory across all conversations
  backend:
    type: store

  persona:
    role: Personal AI Assistant
    goal: Help users and remember their preferences over time
    system_prompt: |
      You are a personal AI assistant with long-term memory.

      IMPORTANT: Save important information to these files:
      - /user_profile.txt - User's name, preferences, interests
      - /conversation_history.txt - Key topics from past conversations
      - /reminders.txt - User's reminders and to-dos

      Before responding, ALWAYS:
      1. Check /user_profile.txt to personalize your response
      2. Check /conversation_history.txt for context
      3. Update files with new information learned

Usage:

# First conversation
super agent run persistent_chatbot --goal "Hi! My name is Alice and I love Python."

# Agent writes to /user_profile.txt:
# Name: Alice
# Interests: Python programming

# Days later, new conversation
super agent run persistent_chatbot --goal "What's my name?"

# Agent reads /user_profile.txt
# Response: "Your name is Alice! And I remember you love Python programming."

Why It Works: - StoreBackend persists files across all threads - Agent can read previous data - Builds knowledge over time

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Example 2: Code Review Agent

Scenario: Agent analyzes actual project files and suggests improvements.

Playbook:

apiVersion: agent/v1
kind: AgentSpec
metadata:
  name: Code Review Agent
  description: Analyzes real project files
spec:
  target_framework: deepagents

  language_model:
    provider: google-genai
    model: gemini-2.5-pro  # Pro for better analysis
    temperature: 0.3

  # Access real project files
  backend:
    type: filesystem
    root_dir: /Users/local/my_project

  persona:
    role: Senior Code Reviewer
    goal: Analyze code for bugs, security issues, and best practices
    system_prompt: |
      You are a senior code reviewer with access to the project files.

      Available in your filesystem:
      - /src/ - Source code
      - /tests/ - Test files
      - /README.md - Project documentation
      - /requirements.txt - Dependencies

      When reviewing:
      1. Read the relevant files using ls and read_file
      2. Analyze for:
         - Security vulnerabilities
         - Performance issues
         - Code style violations
         - Missing tests
      3. Provide specific, actionable feedback
      4. If appropriate, suggest or make improvements

Usage:

# Analyze a specific file
super agent run code_review_agent --goal "Review src/auth.py for security issues"

# Agent reads actual file from disk:
# - /src/auth.py
# Response: "Found 3 security concerns in src/auth.py..."

# Analyze entire codebase
super agent run code_review_agent --goal "Analyze the entire codebase and write a report to /code_review_report.md"

# Agent:
# 1. Lists files with ls /src/
# 2. Reads each file
# 3. Writes report to actual file: /Users/local/my_project/code_review_report.md

Why It Works: - FilesystemBackend gives access to real files - Changes are immediately visible in IDE - Can be used with git, etc.

⚠️ Security Note: Agent can modify actual files! Use carefully.

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Example 3: Research Agent with Hybrid Storage

Scenario: Advanced research assistant that uses optimal storage for each data type.

Playbook:

apiVersion: agent/v1
kind: AgentSpec
metadata:
  name: Advanced Research Agent
  description: Hybrid storage for optimal performance
spec:
  target_framework: deepagents

  language_model:
    provider: google-genai
    model: gemini-2.5-flash
    temperature: 0.7

  # Hybrid storage strategy
  backend:
    type: composite
    default: state                # Scratch space for current conversation
    routes:
      /memories/: store          # Long-term research knowledge
      /papers/: filesystem       # Access to local paper PDFs
      /cache/: state             # Temporary internet search results

  persona:
    role: AI Research Assistant
    goal: Conduct thorough research and build long-term knowledge
    system_prompt: |
      You are an AI research assistant with advanced storage capabilities.

      STORAGE GUIDELINES:

      📁 /memories/ (Persistent Database)
      - Save important research findings
      - Store literature reviews
      - Keep track of research topics explored
      - Build cumulative knowledge over time

      📂 /papers/ (Local Filesystem)
      - Access PDF papers in /Users/local/research/papers/
      - Read abstracts and summaries
      - Reference actual academic papers

      💾 /cache/ (Temporary)
      - Store internet search results
      - Keep intermediate calculations
      - Temporary notes (cleared each conversation)

      🗂️ / (Scratch Space)
      - Current conversation context
      - Draft responses
      - Work in progress

      WORKFLOW:
      1. Check /memories/ for previous research on topic
      2. Search for new information
      3. Save results to /cache/ temporarily
      4. If information is important, save to /memories/
      5. Access /papers/ for academic sources

Advanced Configuration (in Python):

# For more control, configure in playbook
backend:
  type: composite
  default: state
  routes:
    /memories/: store
    /papers/: filesystem
    /cache/: state

  # Root dir for filesystem backend
  root_dir: /Users/local/research

Usage:

export GOOGLE_API_KEY="your-key"

# First research session
super agent run advanced_research_agent --goal "Research transformer architectures. Save key findings."

# Agent workflow:
# 1. Checks /memories/transformer_research.txt (not found)
# 2. Searches for information
# 3. Saves results to /cache/search_results.txt
# 4. Writes summary to /memories/transformer_research.txt (persisted!)
# 5. May read from /papers/attention_is_all_you_need.pdf

# Week later, follow-up research
super agent run advanced_research_agent --goal "What did I learn about transformers?"

# Agent workflow:
# 1. Reads /memories/transformer_research.txt (found - from last week!)
# 2. Response: "Based on your previous research, transformers..."
# 3. Can build upon previous knowledge

# Analyze specific paper
super agent run advanced_research_agent --goal "Summarize the paper in /papers/bert.pdf"

# Agent reads actual file from /Users/local/research/papers/bert.pdf

Why It Works: - /memories/ persists across weeks (StoreBackend) - /papers/ accesses real PDFs (FilesystemBackend) - /cache/ is fast and ephemeral (StateBackend) - / is conversation-scoped (StateBackend)

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🎓 Advanced Patterns

Pattern 1: Learning Agent

Goal: Agent that improves over time by learning from interactions.

backend:
  type: store

persona:
  system_prompt: |
    You are a learning assistant. After each interaction:

    1. Read /knowledge/learned_facts.txt
    2. Add new facts you learned
    3. Write back to /knowledge/learned_facts.txt

    Over time, your knowledge will grow!

Result: Agent builds cumulative knowledge base.

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Pattern 2: Project-Aware Agent

Goal: Agent that understands your entire project structure.

backend:
  type: composite
  default: state
  routes:
    /project/: filesystem

  root_dir: /Users/local/my_app

persona:
  system_prompt: |
    You have access to the entire project in /project/.

    Key files:
    - /project/src/ - Source code
    - /project/tests/ - Tests
    - /project/docs/ - Documentation

    You can read and modify these files directly!

Result: Agent sees actual project, can make real changes.

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Pattern 3: Multi-Session Research

Goal: Research project that spans multiple days.

backend:
  type: composite
  default: state
  routes:
    /research/: store
    /output/: filesystem

  root_dir: /Users/local/research_output

persona:
  system_prompt: |
    You are conducting a long-term research project.

    /research/ - Persistent research notes (cross-session)
    /output/ - Final reports written to disk
    / - Current session workspace

    At the end of each session, summarize findings to /research/session_N_summary.txt

Result: Research builds across sessions, final reports saved to disk.

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Pattern 4: Secure Sandbox

Goal: Agent can work freely but can't access sensitive files.

backend:
  type: filesystem
  root_dir: /tmp/agent_sandbox  # Isolated directory

persona:
  system_prompt: |
    You can create and modify files freely.
    All files are in a secure sandbox.

Result: Agent has filesystem access but can't touch important files.

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🔧 Configuration Reference

Complete Playbook Schema

apiVersion: agent/v1
kind: AgentSpec
metadata:
  name: Example Agent
spec:
  target_framework: deepagents

  language_model:
    provider: google-genai
    model: gemini-2.5-flash

  # Backend configuration (NEW in 0.2.0)
  backend:
    type: state | store | filesystem | composite

    # For filesystem backend only:
    root_dir: /path/to/directory

    # For composite backend only:
    default: state | store | filesystem
    routes:
      /path1/: store
      /path2/: filesystem
      /path3/: state

Backend Type Matrix

Backend	Persistence	Speed	Shared	Use Case
state	Thread only	⚡⚡⚡ Very Fast	❌ No	Scratch space
store	Forever	⚡⚡ Fast	✅ Yes	Long-term memory
filesystem	Forever	⚡⚡ Fast	✅ Yes	Real files
composite	Mixed	⚡⚡ Fast	Mixed	Best of all

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🚨 Troubleshooting

Issue 1: "Backend import failed"

Error:

⚠️  Backend import failed: No module named 'superoptix.vendor.deepagents.backends'

Solution:

# Update SuperOptiX
cd /Users/local/superagentic/SuperOptiX
pip install -e .

# Verify backends are available
python -c "from superoptix.vendor.deepagents.backends import state; print('✅ Backends available')"

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Issue 2: Files not persisting

Problem: Files disappear between conversations

Check:

# Make sure you're using store, not state
backend:
  type: store  # NOT state!

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Issue 3: Can't access local files

Error:

FileNotFoundError: /Users/local/project/file.txt

Solution:

backend:
  type: filesystem
  root_dir: /Users/local/project  # Must specify root!

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Issue 4: Permission denied

Error:

PermissionError: /etc/password

Cause: FilesystemBackend respects OS permissions

Solution: - Use appropriate root_dir - Check file permissions - Don't point to system directories

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📊 Performance Tips

1. Choose the Right Backend

# Fast scratch space
backend:
  type: state

# Persistent but slightly slower
backend:
  type: store

# Real files (filesystem speed)
backend:
  type: filesystem

2. Use Composite for Optimal Performance

# Optimize for each data type
backend:
  type: composite
  default: state           # Fast default
  routes:
    /memories/: store     # Only what needs persistence
    /big_files/: filesystem  # Offload large files

3. Limit Filesystem Scope

# Good: Specific directory
backend:
  type: filesystem
  root_dir: /Users/local/project/src

# Bad: Entire filesystem
backend:
  type: filesystem
  root_dir: /

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🎯 Best Practices

✅ Do

1. Use StateBackend for temporary, single-conversation agents
2. Use StoreBackend for chatbots and learning agents
3. Use FilesystemBackend with specific root_dir
4. Use CompositeBackend for complex production agents
5. Document storage strategy in system prompt
6. Test with small root_dir first

❌ Don't

1. Don't use FilesystemBackend on root (/)
2. Don't store sensitive data without encryption
3. Don't assume infinite storage in StoreBackend
4. Don't mix backends without CompositeBackend
5. Don't give write access to critical files
6. Don't forget to backup persistent data

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🚀 Migration Guide

From 0.1.0 to 0.2.0

No breaking changes! Old playbooks work as-is.

To add persistence:

# Old (still works)
spec:
  target_framework: deepagents
  language_model:
    model: gemini-2.5-flash

# New (with persistence)
spec:
  target_framework: deepagents
  language_model:
    model: gemini-2.5-flash
  backend:
    type: store  # Add this!

Steps: 1. Add backend section to playbook 2. Recompile: super agent compile my_agent --framework deepagents 3. Test: super agent run my_agent --goal "test" 4. Verify persistence works

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📚 Resources

• DeepAgents 0.2.0 Announcement
• DeepAgents GitHub
• SuperOptiX DeepAgents Integration Guide
• Gemini Configuration Guide

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💡 Next Steps

1. Try the examples above
2. Experiment with different backends
3. Build a persistent chatbot
4. Create a code analysis agent
5. Design your hybrid storage strategy

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Questions? Check our FAQ or Troubleshooting Guide

Ready to build? Start with the DeepAgents Quick Start!