Julep Platform FAQ

This comprehensive FAQ document covers all aspects of the Julep platform, from architecture to troubleshooting. The information is organized by category for easy navigation.

Architecture & System Design
Data Model & Storage
Task Execution & Workflow
Agents API
Worker System & Integration
Development & Deployment
Performance & Optimization
Security & Compliance
Advanced Use Cases & Patterns
Troubleshooting & Common Issues

Architecture & System Design

Q: What is the overall system architecture of Julep, including all core components and their interactions?

Julep is a distributed system built on a microservices architecture designed to orchestrate complex AI workflows. The main components include:

Client Applications: Initiate requests to the Julep system
Gateway: Entry point for all API requests, handles authentication and load balancing (implemented using Traefik)
Agents API: Provides REST endpoints for managing agents, tasks, sessions, and documents; initiates workflows in Temporal
Temporal Workflow Engine: Orchestrates durable workflow execution, retries, and state management
Worker System: Executes workflows and activities defined in Temporal by polling for tasks
LiteLLM Proxy: Provides a unified interface for interacting with various LLM providers
Memory Store: Provides persistent storage using PostgreSQL/TimescaleDB for relational data and vector embeddings
Integration Service: Enables connections with external tools and APIs

Q: How does Julep handle distributed task execution and what role does Temporal play in the architecture?

Julep handles distributed task execution primarily through the Temporal Workflow Engine:

Workflow Orchestration: Temporal ensures durable execution of workflows, handling retries and maintaining state across failures
Task Queues: The Agents API initiates workflows by sending requests to Temporal, which places them on task queues like julep-task-queue
Worker Execution: Workers poll Temporal for tasks and execute activities like LLM calls, tool operations, and data interactions
State Management: Temporal persists workflow execution state in PostgreSQL, ensuring long-running processes can recover from failures

Q: What are the key design decisions behind separating agents-api, memory-store, integrations-service, and other components?

The separation follows microservices principles:

Modularity and Independent Scaling: Each service can be developed, deployed, and scaled independently
Separation of Concerns: Each component handles specific functionalities:
- agents-api: Manages agent definitions, tasks, sessions, and orchestrates workflows
- memory-store: Handles all data persistence including relational data and vector embeddings
- integrations-service: Provides standardized interface for external tool usage
- llm-proxy: Centralizes LLM interactions with a unified API
Resilience: Failures in one service are isolated from others
Technology Flexibility: Different services can use different technologies if needed

Q: How does the gateway component route requests between different services?

The Gateway component uses Traefik and routes requests based on defined rules:

Requests to /api/* are routed to the Agents API service
Requests to /tasks-ui/* go to the Temporal UI service
/v1/graphql requests are directed to the Hasura service
In multi-tenant setups, it enforces JWT-based authentication and forwards X-Developer-Id headers for resource isolation

Q: What is the role of the blob-store and how does it integrate with S3-compatible storage?

The blob-store is used for persistent storage of large data, specifically for Temporal workflow data when USE_BLOB_STORE_FOR_TEMPORAL is enabled. It integrates with S3-compatible storage through environment variables:

S3_ENDPOINT, S3_ACCESS_KEY, and S3_SECRET_KEY for connection
BLOB_STORE_BUCKET defines the bucket name
BLOB_STORE_CUTOFF_KB sets the size threshold for blob storage

Q: How does the llm-proxy (LiteLLM) handle different language model providers?

LiteLLM provides a unified interface to multiple LLM providers:

Supports providers like OpenAI, Anthropic, Gemini, Groq, and OpenRouter
Configuration defined in litellm-config.yaml with model names, parameters, and API keys
Handles response patching for consistency (e.g., changing finish_reason from “eos” to “stop”)
Tracks token usage and costs in PostgreSQL
Implements request caching using Redis and supports parallel forwarding

Q: What are the scalability patterns and limitations of the current architecture?

Julep’s architecture supports scalability through:

Agents API: Horizontal scaling via multiple instances, configurable with GUNICORN_WORKERS
Worker: Multiple workers with concurrency control using TEMPORAL_MAX_CONCURRENT_ACTIVITIES
Memory Store: PostgreSQL connection pooling with POOL_MAX_SIZE
LiteLLM: Request caching and parallel forwarding
Temporal: Durable workflow execution that scales to handle concurrent tasks

Q: How does Julep ensure high availability and fault tolerance across services?

High availability is achieved through:

Temporal’s durable execution model with automatic retries
Microservices architecture allowing independent service failures
PostgreSQL for persistent state storage
Worker pools for distributed task execution
Connection pooling and retry mechanisms

Data Model & Storage

Q: What is the complete data model including relationships between Agents, Tasks, Tools, Sessions, Entries, and Executions?

The core entities and their relationships:

Developer: Manages Agents, Users, and owns Tasks
Agent: Has Tasks, defines Tools, owns Docs, participates in Sessions
User: Owns Docs and participates in Sessions
Task: Contains WorkflowSteps and is executed as Executions
Execution: Logs Transitions and tracks task execution state
Session: Contains Entries (conversation history)
Entry: Individual messages within a Session
Tool: Capabilities available to Agents
Doc: Documents with embeddings for knowledge base

All entities use UUIDs for identification and include developer_id for multi-tenancy.

Q: How does the memory-store handle vector embeddings and similarity search?

The memory store provides vectorized document storage:

Documents have an embeddings field stored in docs_embeddings_store table
Supports three search types:
- Vector Search: search_docs_by_embedding for semantic similarity
- Text Search: search_docs_by_text using PostgreSQL full-text search
- Hybrid Search: search_docs_hybrid combining both approaches
Uses cosine similarity for vector comparisons
Implements Maximum Marginal Relevance (MMR) for result diversity

Q: What PostgreSQL and TimescaleDB features are leveraged for time-series data?

While PostgreSQL is the primary database, specific TimescaleDB features are not explicitly detailed in the codebase. The system uses:

Standard PostgreSQL timestamps (created_at, updated_at) for temporal data
Time-based filtering in queries (e.g., list_entries with date ranges)
No explicit TimescaleDB-specific features documented

Q: How are agent instructions and task definitions stored and versioned?

Agent Instructions: Stored as string or array[string] in the Agent entity
Task Definitions: Stored as Task entities with fields like name, description, input_schema, main (workflow steps), and tools
Versioning: Handled through UUID changes - altering a UUID creates a new entity while preserving the original
Timestamps (created_at, updated_at) provide implicit version tracking

Q: What is the schema for storing conversation history and context?

Conversation history uses two main entities:

Session: Contains id, user, agent, situation (context), system_template, and metadata
Entry: Contains id, session_id, role (user/assistant/system), content (string or JSON), source, and timestamp
Sessions group entries and maintain context across conversations

Q: How does Julep handle data partitioning and archiving for long-running agents?

The codebase does not contain explicit information about data partitioning or archiving strategies. The system uses:

Multi-tenancy through developer_id filtering
Pagination support for large datasets
Time-based filtering capabilities
No documented automatic archiving policies

Q: What are the indexing strategies for optimizing query performance?

Indexing strategies include:

Trigram indexes for text search (indicated by trigram_similarity_threshold parameter)
Vector indexes for embedding similarity searches
Document chunking and embedding storage for efficient retrieval
Use of prepared statements for query optimization

Task Execution & Workflow

Q: How does the TaskExecutionWorkflow handle complex multi-step operations?

The TaskExecutionWorkflow orchestrates multi-step operations by:

Processing different WorkflowStep types through dedicated handlers
Managing state transitions between steps
Integrating with Temporal for durability and reliability
Using handle_step method to process each step type
Evaluating expressions within steps using eval_step_exprs

Q: What are all the possible workflow step types and their configurations?

Basic Steps:

PromptStep: Sends prompts to LLMs and handles responses
ToolCallStep: Executes tool calls (functions, integrations, APIs, system operations)
EvaluateStep: Evaluates expressions and returns results
SetStep: Sets values in execution state
GetStep: Retrieves values from execution state
LogStep: Logs messages during execution
ReturnStep: Returns a value and completes workflow
ErrorWorkflowStep: Raises an error and fails the workflow
SleepStep: Pauses execution for specified duration
WaitForInputStep: Pauses for external input

Control Flow Steps:

IfElseWorkflowStep: Conditional branching based on condition
SwitchStep: Multi-way branching based on case evaluation
ForeachStep: Iterates over collections
MapReduceStep: Maps function over items with optional parallelism
YieldStep: Yields execution to another workflow
ParallelStep: Executes steps in parallel (not yet implemented)

Q: How does the state machine handle transitions between different execution states?

The state machine tracks execution through:

States: queued, starting, running, succeeded, failed, cancelled
Transitions: Record state changes with types: init, step, finish, error, cancelled
Each transition includes output, current, and next workflow steps
Transitions stored in execution_transitions table
create_execution_transition function records all state changes

Q: What happens when a task fails midway through execution?

When a task fails:

Execution status transitions to “failed”
An “error” transition is created with the error message
The error field of the Execution object is populated
The system tracks the failure point and error details
Workflow implements retry policies for retryable errors
Non-retryable errors cause immediate failure

Q: How are conditional branches and loops implemented in workflows?

Conditional Branches:

IfElseWorkflowStep: Evaluates condition and executes “then” or “else” branch
SwitchStep: Multi-way branching with multiple cases

Loops:

ForeachStep: Iterates over collections processing each item
MapReduceStep: Maps functions over collections with optional parallel execution

These are processed by dedicated handlers like _handle_IfElseWorkflowStep and _handle_ForeachStep.

Q: What is the retry and error handling strategy for failed steps?

Error Classification: Errors classified as retryable or non-retryable
Retry Policy: DEFAULT_RETRY_POLICY applied to retryable errors
Max Retries: Workflow fails if max retries exceeded
Error Transitions: “error” transitions record failure states
Last Error Tracking: last_error attribute stores recent errors

Q: How does Julep handle long-running tasks and prevent timeouts?

Julep uses Temporal’s timeout mechanisms:

schedule_to_close_timeout and heartbeat_timeout for activities
Activity and workflow heartbeats ensure progress tracking
Large data handling via RemoteObject pattern to optimize memory
Blob storage for data exceeding size thresholds

Q: What are the mechanisms for task cancellation and cleanup?

Workflow Cancellation: Handled through Temporal’s cancellation features
State Management: Execution can transition to “cancelled” state
Persistence: All transitions persisted for state restoration
Cleanup: Status updates and transition history provide cleanup context

Agents API

Q: What are all the endpoints available in the Agents API and their use cases?

The Agents API provides these endpoints:

/agents: Create, retrieve, update, delete agent definitions
/tasks: Define and execute tasks, retrieve workflow definitions
/sessions: Manage conversational sessions and conversation history
/executions: Track task executions and monitor status
/docs: Handle document storage, search, and retrieval with embeddings
/tools: Define and manage agent tools
/users: Manage user accounts and authentication
/responses: OpenAI-compatible interface for LLM responses

Q: How does session management work and what data is maintained per session?

Session management maintains conversation state:

Session Data: id, agent_id, user_id, created_at, situation context
Entries: Individual conversation turns with role, content, and timestamps
Sessions created with julep.sessions.create linking agent and user
Messages added via julep.sessions.chat with role and content
Pagination support for retrieving conversation history

Q: What are the different types of tools an agent can use and how are they configured?

Tool types available:

Web Search Tool: Performs web searches with domain filtering
Function Tools: OpenAI-compatible function calling format
System Tools: Internal Julep resources (e.g., create_julep_session, session_chat)
Integration Tools: External services (e.g., BrowserBase, email providers)

Configuration includes name, type, description, and type-specific parameters.

Q: How does document storage and retrieval work for agent knowledge bases?

Documents (Doc entities) provide agent knowledge:

Stored in PostgreSQL with embeddings in docs_embeddings_store
Owned by either Agent or User
Three search methods: text-based, embedding-based, hybrid
Document operations: create, retrieve, list, search
Supports metadata filtering and pagination

Q: What are the authentication and authorization mechanisms for the API?

Two authentication modes: Single-Tenant Mode:

Uses AGENTS_API_KEY for authentication
Default developer ID assumed
X-Auth-Key header required

Multi-Tenant Mode:

JWT-based authentication via Gateway
X-Developer-Id header for resource isolation
Developer-specific data access control
JWT must contain sub, email, exp, iat claims

Q: How are agent instructions processed and validated?

Instructions stored as string or array of strings
Validated by Pydantic models from OpenAPI schemas
Included in agent’s default_system_template
Dynamic rendering supports both single and array formats
TypeSpec definitions ensure consistent structure

Q: What are the rate limiting and quota management strategies?

Rate limiting and quota management are planned features:

max_free_sessions and max_free_executions environment variables defined
Implementation details not yet available
Listed as future enhancement in roadmap

Q: How does the API handle streaming responses for real-time interactions?

Streaming support is currently planned but not implemented:

Open Responses API designed for OpenAI compatibility
stream configuration option available
Full streaming implementation pending

Worker System & Integration

Q: How does the worker system integrate with different LLM providers?

The Worker integrates with LLMs through LiteLLM Proxy:

LiteLLM acts as unified interface to various providers
Supports OpenAI, Anthropic, Gemini, Groq, OpenRouter
Worker makes LLM calls via LiteLLM Proxy
Configuration in litellm-config.yaml
Handles authentication, routing, and caching

Q: What are the system activities available and how do they work?

System activities include:

LLM Calls: Through LiteLLM Proxy
Tool Operations: Via Integration Service
Data Operations: Reading/writing to Memory Store
PG Query Step: Direct PostgreSQL queries
Activities invoked with StepContext and appropriate definitions

Q: How does Julep handle tool execution and external API calls?

Tool execution handled by Integration Service:

Integration Tools: Connect to external services with provider/method specs
System Tools: Operate on internal Julep resources
ToolCallStep defines tool and arguments
Worker invokes Integration Service for execution
Examples: email sending, browser automation, document search

Q: What is the sandboxing mechanism for Python expression evaluation?

Python expression sandboxing:

validate_py_expression function validates expressions
Identifies expressions starting with $, _, or containing {{
Checks for syntax errors, undefined names, unsafe operations
Limited scope with allowed names: _, inputs, outputs, state, steps
Prevents dunder attribute access and unapproved function calls

Q: How are integration credentials managed and secured?

Integration credentials managed through:

Credentials stored in setup parameters of tool definitions
API keys provided in task definition YAML
Environment variables for service-level credentials
No explicit encryption details documented

Q: What are the patterns for building custom integrations?

Custom integrations defined as Tool entities:

Type set to integration
Specify provider, method, and setup parameters
Include provider-specific configuration (API keys, endpoints)
Used within Task workflows as ToolCallStep
Examples: Browserbase, email providers, Cloudinary

Q: How does the browser automation integration work?

Browser automation workflow:

Create Julep session for AI agent
Create browser session using Browserbase
Store session info (browser_session_id, connect_url)
Perform actions via perform_browser_action tool
Interactive loop with agent planning and execution
Screenshot capture for visual feedback

Q: What are the performance optimizations for worker pools?

Worker performance optimizations:

TEMPORAL_MAX_CONCURRENT_ACTIVITIES controls concurrency
TEMPORAL_MAX_ACTIVITIES_PER_SECOND limits activity rate
GUNICORN_WORKERS for integration service scaling
Connection pooling and timeout configurations
LiteLLM caching and parallel forwarding

Development & Deployment

Q: What is the recommended development workflow for building with Julep?

Development workflow uses Docker Compose with watch mode:

Changes in source directories trigger automatic sync/restart
agents-api: Watches ./agents_api and gunicorn_conf.py
worker: Watches ./agents_api and Dockerfile.worker
integrations: Watches its directory for changes
Lock file or Dockerfile changes trigger rebuilds

Q: How should developers set up their local environment for testing?

Local setup primarily uses Docker:

Create project directory: mkdir julep-responses-api
Download and edit .env file
Download Docker Compose file
Run containers: docker compose up --watch
Verify with docker ps

Alternative CLI installation available via npx or uvx.

Q: What are the deployment options and best practices?

Deployment options: Single-Tenant Mode:

All users share context
SKIP_CHECK_DEVELOPER_HEADERS=True
Requires AGENTS_API_KEY

Multi-Tenant Mode:

Isolated resources per developer
AGENTS_API_MULTI_TENANT_MODE: true
JWT validation via Gateway

Best Practices:

Use environment variables for configuration
Implement layered security (API keys, JWT tokens)
Enable independent component scaling
Configure appropriate connection pools

Q: How does the TypeSpec code generation work and when to use it?

TypeSpec code generation:

TypeSpec files define data models (e.g., models.tsp)
scripts/generate_openapi_code.sh generates code
Creates Pydantic models and OpenAPI schemas
Edit TypeSpec files, not generated code
Regenerate after model changes

Q: What are the testing strategies for agents and workflows?

Testing strategies include:

Unit tests for entities (agents, docs, sessions, etc.)
Integration tests simulating scenarios
Workflow tests using unittest.mock.patch
Test fixtures for consistent data
Ward framework for test organization

Q: How to debug failed task executions and trace through workflows?

Debugging approach:

Check Execution status and error field
List transitions to trace execution flow
Examine transition outputs and types
Use LogStep for workflow logging
Error transitions indicate failure points

Q: What are the monitoring and observability features?

Limited monitoring information available:

Execution state and transition logging
No explicit monitoring features documented
Prometheus and Grafana mentioned in architecture
Detailed observability features not specified

Q: How to handle database migrations in production?

Database migration information not explicitly documented:

PostgreSQL used as primary database
Migration files exist in memory-store
Production migration process not detailed

Performance & Optimization

Q: What are the performance characteristics of different operations?

Performance characteristics:

MapReduceStep supports sequential or parallel execution
API response times reduced by 15% (per changelog)
Parallel processing improves collection operations
Connection pooling optimizes database access
Caching planned for future optimization

Q: How does Julep handle concurrent agent executions?

Concurrent execution handled through:

TaskExecutionWorkflow with Temporal orchestration
Worker pools for distributed execution
State isolation per execution
Temporal manages workflow concurrency
StepContext provides execution isolation

Q: What are the caching strategies employed across the system?

Current caching:

LiteLLM Proxy implements request caching
Redis used for LiteLLM cache storage
Web Search Tool has result caching
Advanced caching mechanisms planned

Q: How to optimize memory usage for large conversation histories?

Memory optimization strategies:

Pagination with limit and offset parameters
Maximum limit of 1000 entries per request
search_window for time-based filtering (default 4 weeks)
Token counting per entry
RemoteObject pattern for large data

Q: What are the bottlenecks in the current architecture?

Potential bottlenecks:

Large data retrieval without proper pagination
Complex database queries on large tables
Multi-tenancy query filtering overhead
Temporal workflow state management at scale
JSON aggregation in history queries

Q: How does connection pooling work for database access?

Connection pooling configuration:

connection_lifetime: 600 seconds
idle_timeout: 180 seconds
max_connections: 50
retries: 1
use_prepared_statements: true
POOL_MAX_SIZE configurable (default: CPU count, max 10)

Q: What are the best practices for writing efficient task definitions?

Best practices:

Define clear input schemas
Use modular workflow steps
Implement proper error handling
Avoid infinite loops in recursive patterns
Use appropriate tool types
Monitor execution status and transitions
Leverage parallel processing where applicable

Security & Compliance

Q: How does Julep handle sensitive data and ensure data privacy?

Data privacy ensured through:

Multi-tenant architecture with resource isolation
Developer-specific data access via developer_id
X-Developer-Id header for request routing
Separate data storage per developer

Q: What are the security measures for multi-tenant deployments?

Multi-tenant security:

JWT-based authentication at Gateway
JWT validation with required claims (sub, email, exp, iat)
X-Developer-Id header enforcement
Developer ID verification against database
Resource isolation by developer

Q: How are API keys and secrets managed throughout the system?

Secret management:

Environment variables for service credentials
API keys in tool setup parameters
AGENTS_API_KEY and JWT_SHARED_KEY for auth
LLM provider keys as environment variables
No explicit encryption details provided

Q: What audit logging capabilities are available?

Audit logging:

Currently limited implementation
Listed as planned feature in roadmap
Usage tracking for LLM calls (tokens and costs)
Comprehensive audit logging pending

Q: How does Julep ensure secure execution of user-provided code?

Secure code execution through:

Task-based workflow system with YAML definitions
Controlled tool invocation with explicit permissions
Python expression validation
Structured workflow steps
No arbitrary code execution

Q: What are the network security considerations for deployment?

Network security:

API key authentication for single-tenant
JWT tokens for multi-tenant
Gateway-level authentication
HTTPS/TLS support implied
Service-to-service communication within network

Advanced Use Cases & Patterns

Q: What are examples of complex multi-agent workflows?

Browser Use Assistant:

Session initialization
Browser session creation
Interactive agent-browser loop
Screenshot feedback
Goal-oriented task completion

Email Assistant:

Email input processing
Query generation
Documentation search
Response generation
Automated email sending

Video Processing:

Natural language instructions
Cloudinary integration
Transformation generation
Video processing execution

Q: How to implement human-in-the-loop patterns?

Human-in-the-loop not explicitly documented:

WaitForInputStep provides pause mechanism
Session-based interactions allow user input
No dedicated approval workflow patterns
Can be built using existing primitives

Q: What are the patterns for building conversational agents with memory?

Conversational memory patterns:

Session and Entry entities maintain history
previous_response_id links responses
Session metadata and custom templates
Persistent conversation state
Context maintained across interactions

Q: How to implement custom tool integrations?

Custom tool implementation:

Define Tool entity with type integration
Specify provider, method, setup parameters
Use @function_tool decorator for functions
Add to agent’s tool list
Invoke via ToolCallStep in workflows

Q: What are the best practices for handling structured data extraction?

Structured data handling:

Tools accept and return structured JSON
Evaluate steps process tool outputs
Response objects contain structured data
Pydantic models ensure data validation
TypeSpec defines consistent schemas

Q: How to build agents that can learn and adapt over time?

Learning/adaptation features limited:

Agent instructions can be updated
Metadata field allows dynamic information
No explicit learning mechanisms
Adaptation through instruction updates
Memory through conversation history

Q: What are the patterns for building agents that can collaborate?

Agent collaboration not documented:

Current model focuses on single agents
No inter-agent communication patterns
Sessions link one agent to users
Collaboration would require custom implementation

Troubleshooting & Common Issues

Q: What are the most common errors and how to resolve them?

Python Expression Errors:

Syntax errors: Fix malformed expressions
Undefined names: Use allowed names only
Unsafe operations: Avoid dunder attributes
Runtime errors: Check for division by zero
Unsupported features: Avoid lambdas, walrus operator

Schema Validation Errors:

Pydantic validation failures
Adjust JSON to match expected schema

Integration Errors:

ApplicationError for missing tools
Verify tool definitions and availability

Q: How to debug issues with task execution?

Debugging steps:

Check Execution status field
Review error messages in Execution object
List and examine transitions
Check transition outputs and types
Use LogStep for additional logging
Trace workflow step progression

Q: What are the common performance problems and solutions?

Performance issues and solutions:

Large collections: Use MapReduceStep parallelism
Rate limits: Implement SleepStep delays
Memory usage: Paginate large result sets
Database queries: Ensure proper indexing
Concurrent execution: Configure worker pools

Q: How to handle edge cases in agent conversations?

Edge case handling:

IfElseWorkflowStep for conditional logic
SwitchStep for complex branching
WaitForInputStep for user input
ErrorWorkflowStep for invalid states
Proper error handling in workflows

Q: What are the known limitations and workarounds?

Expression Limitations:

No set comprehensions, lambdas, walrus operator
Use alternative Python constructs

Backwards Compatibility:

Old expression formats supported
Use $ prefix for consistency

ParallelStep Not Implemented:

Use MapReduceStep with parallelism instead

Streaming Not Available:

Planned feature, not yet implemented

Rate Limiting:

Basic environment variables defined
Full implementation pending

Additional Resources

This FAQ is generated from the Julep platform documentation and codebase analysis. For the most up-to-date information, please refer to the official Julep documentation and repository.

​Julep Platform FAQ

​Table of Contents

​Architecture & System Design

​Q: What is the overall system architecture of Julep, including all core components and their interactions?

​Q: How does Julep handle distributed task execution and what role does Temporal play in the architecture?

​Q: What are the key design decisions behind separating agents-api, memory-store, integrations-service, and other components?

​Q: How does the gateway component route requests between different services?

​Q: What is the role of the blob-store and how does it integrate with S3-compatible storage?

​Q: How does the llm-proxy (LiteLLM) handle different language model providers?

​Q: What are the scalability patterns and limitations of the current architecture?

​Q: How does Julep ensure high availability and fault tolerance across services?

​Data Model & Storage

​Q: What is the complete data model including relationships between Agents, Tasks, Tools, Sessions, Entries, and Executions?

​Q: How does the memory-store handle vector embeddings and similarity search?

​Q: What PostgreSQL and TimescaleDB features are leveraged for time-series data?

​Q: How are agent instructions and task definitions stored and versioned?

​Q: What is the schema for storing conversation history and context?

​Q: How does Julep handle data partitioning and archiving for long-running agents?

​Q: What are the indexing strategies for optimizing query performance?

​Task Execution & Workflow

​Q: How does the TaskExecutionWorkflow handle complex multi-step operations?

​Q: What are all the possible workflow step types and their configurations?

​Q: How does the state machine handle transitions between different execution states?

​Q: What happens when a task fails midway through execution?

​Q: How are conditional branches and loops implemented in workflows?

​Q: What is the retry and error handling strategy for failed steps?

​Q: How does Julep handle long-running tasks and prevent timeouts?

​Q: What are the mechanisms for task cancellation and cleanup?

​Agents API

​Q: What are all the endpoints available in the Agents API and their use cases?

​Q: How does session management work and what data is maintained per session?

​Q: What are the different types of tools an agent can use and how are they configured?

​Q: How does document storage and retrieval work for agent knowledge bases?

​Q: What are the authentication and authorization mechanisms for the API?

​Q: How are agent instructions processed and validated?

​Q: What are the rate limiting and quota management strategies?

​Q: How does the API handle streaming responses for real-time interactions?

​Worker System & Integration

​Q: How does the worker system integrate with different LLM providers?

​Q: What are the system activities available and how do they work?

​Q: How does Julep handle tool execution and external API calls?

​Q: What is the sandboxing mechanism for Python expression evaluation?

​Q: How are integration credentials managed and secured?

​Q: What are the patterns for building custom integrations?

​Q: How does the browser automation integration work?

​Q: What are the performance optimizations for worker pools?

​Development & Deployment

​Q: What is the recommended development workflow for building with Julep?

​Q: How should developers set up their local environment for testing?

​Q: What are the deployment options and best practices?

​Q: How does the TypeSpec code generation work and when to use it?

​Q: What are the testing strategies for agents and workflows?

​Q: How to debug failed task executions and trace through workflows?

​Q: What are the monitoring and observability features?

​Q: How to handle database migrations in production?

​Performance & Optimization

​Q: What are the performance characteristics of different operations?

​Q: How does Julep handle concurrent agent executions?

​Q: What are the caching strategies employed across the system?

​Q: How to optimize memory usage for large conversation histories?

​Q: What are the bottlenecks in the current architecture?

​Q: How does connection pooling work for database access?

​Q: What are the best practices for writing efficient task definitions?

​Security & Compliance

​Q: How does Julep handle sensitive data and ensure data privacy?

​Q: What are the security measures for multi-tenant deployments?

​Q: How are API keys and secrets managed throughout the system?

​Q: What audit logging capabilities are available?

​Q: How does Julep ensure secure execution of user-provided code?

​Q: What are the network security considerations for deployment?

​Advanced Use Cases & Patterns

​Q: What are examples of complex multi-agent workflows?

​Q: How to implement human-in-the-loop patterns?

​Q: What are the patterns for building conversational agents with memory?

​Q: How to implement custom tool integrations?

​Q: What are the best practices for handling structured data extraction?

​Q: How to build agents that can learn and adapt over time?

​Q: What are the patterns for building agents that can collaborate?

​Troubleshooting & Common Issues

​Q: What are the most common errors and how to resolve them?