{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"julep","slug":"julep","name":"Julep","type":"platform","url":"https://julep.ai","page_url":"https://unfragile.ai/julep","categories":["ai-agents","deployment-infra","automation"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"julep__cap_0","uri":"capability://memory.knowledge.stateful.agent.session.management.with.persistent.memory","name":"stateful agent session management with persistent memory","description":"Manages agent state across multiple conversation turns by persisting session data, conversation history, and agent context to a backend store. Uses session IDs to maintain continuity between API calls, enabling agents to recall previous interactions and maintain context without re-sending full conversation history. Implements automatic state serialization and retrieval patterns that abstract away session lifecycle management from the developer.","intents":["Build an agent that remembers user preferences and past interactions across multiple conversations","Maintain agent state without manually managing conversation history in application code","Enable long-running agents that can be paused and resumed without losing context"],"best_for":["Teams building production AI agents that need persistent user context","Developers creating multi-turn conversational experiences without session management infrastructure","Applications requiring agents to maintain state across days or weeks of interactions"],"limitations":["Session data storage has latency overhead — retrieving large conversation histories adds 100-500ms per API call","No built-in session expiration policies — requires manual cleanup of old sessions","State size limits may apply depending on backend storage tier"],"requires":["API key for Julep platform","Session ID generation and tracking in client application","Network connectivity to Julep backend service"],"input_types":["text (user messages)","structured metadata (user context, agent parameters)"],"output_types":["text (agent responses)","structured session state (JSON)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"julep__cap_1","uri":"capability://automation.workflow.workflow.execution.engine.with.step.based.task.orchestration","name":"workflow execution engine with step-based task orchestration","description":"Executes multi-step agent workflows by decomposing tasks into discrete steps, managing control flow (sequential, conditional, looping), and coordinating state between steps. Uses a declarative workflow definition format that maps to an execution runtime, enabling agents to perform complex sequences of actions (tool calls, LLM invocations, data transformations) with built-in error handling and step retry logic.","intents":["Define a multi-step workflow where an agent researches a topic, summarizes findings, and generates a report","Implement conditional logic in agent behavior based on intermediate results","Retry failed steps automatically without restarting the entire workflow"],"best_for":["Developers building complex agent workflows with multiple decision points","Teams needing declarative workflow definitions that non-engineers can review","Applications requiring deterministic, auditable agent execution paths"],"limitations":["Workflow definitions may become verbose for highly dynamic or data-driven branching logic","Limited support for real-time workflow modification during execution","Debugging complex workflows requires understanding the execution engine's state model"],"requires":["Workflow definition in Julep's workflow schema format","API key for Julep platform","Understanding of step-based execution model"],"input_types":["workflow definition (JSON/YAML)","step parameters (structured data)","tool schemas (for tool integration)"],"output_types":["step results (structured data)","workflow execution logs (JSON)","final agent output (text or structured)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"julep__cap_10","uri":"capability://automation.workflow.deployment.and.scaling.with.serverless.execution.model","name":"deployment and scaling with serverless execution model","description":"Deploys agents as serverless functions that scale automatically based on demand. Agents are invoked via API calls that trigger execution in isolated containers or functions. The platform handles infrastructure management, auto-scaling, and resource allocation. Supports both on-demand and scheduled execution patterns.","intents":["I want to deploy my agent without managing servers or infrastructure","I need my agent to scale automatically when traffic increases","I want to run agents on a schedule (e.g., daily reports) without keeping servers running"],"best_for":["Teams without DevOps expertise who want to deploy agents quickly","Startups and small teams with variable agent usage patterns","Organizations wanting to minimize infrastructure costs"],"limitations":["Cold start latency may impact response time for infrequently used agents (100-500ms)","Execution time limits (typically 15-30 minutes) may not suit long-running workflows","Debugging serverless execution is harder than local development","Vendor lock-in risk; migrating to different platforms requires code changes"],"requires":["Agent definition compatible with serverless runtime","API key and deployment permissions","Optional: custom dependencies or Docker image"],"input_types":["agent code/configuration","deployment manifest"],"output_types":["deployment status","execution logs","scaling metrics"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"julep__cap_2","uri":"capability://tool.use.integration.tool.integration.and.function.calling.with.schema.based.dispatch","name":"tool integration and function calling with schema-based dispatch","description":"Integrates external tools and APIs by accepting tool schemas (function signatures, parameters, descriptions), automatically generating function-calling prompts for LLMs, and dispatching tool invocations based on LLM outputs. Supports multiple tool types (HTTP APIs, webhooks, internal functions) and handles parameter validation, error responses, and result formatting before returning to the agent for further processing.","intents":["Enable an agent to call external APIs (weather, search, CRM) based on user requests","Automatically validate tool parameters before execution to prevent malformed API calls","Chain multiple tool calls together where outputs from one tool feed into the next"],"best_for":["Developers integrating agents with existing API ecosystems","Teams building agents that need deterministic tool calling without hallucination","Applications requiring audit trails of which tools were called and with what parameters"],"limitations":["Tool schema definition requires manual specification of all parameters and types","No automatic API discovery — schemas must be manually created or imported","Tool execution timeout limits may apply depending on backend configuration"],"requires":["Tool schema definitions (JSON schema format)","API credentials or authentication tokens for external tools","Network connectivity to external tool endpoints"],"input_types":["tool schema (JSON schema)","LLM function-calling output (structured tool invocation)","tool parameters (structured data)"],"output_types":["tool execution result (JSON or text)","error responses (structured error objects)","execution metadata (latency, status codes)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"julep__cap_3","uri":"capability://planning.reasoning.agent.definition.and.configuration.with.role.based.context","name":"agent definition and configuration with role-based context","description":"Allows developers to define agents with specific roles, system prompts, model selection, and default parameters that persist across sessions. Agents are created as reusable configurations that can be instantiated multiple times with different session contexts, enabling consistent behavior while maintaining per-session state. Supports model switching, temperature/parameter tuning, and system prompt customization without code changes.","intents":["Create a customer support agent with a specific tone and knowledge base that can be reused across multiple customer conversations","Define multiple agent personas (analyst, writer, coder) that can be swapped in the same application","Adjust agent behavior (creativity, determinism) via parameters without redeploying code"],"best_for":["Teams managing multiple agent configurations in production","Applications needing to switch between different agent behaviors dynamically","Developers who want to separate agent configuration from application logic"],"limitations":["Agent definitions are immutable after creation — modifications require creating new agent versions","No built-in A/B testing framework for comparing agent configurations","System prompt changes don't retroactively affect existing sessions"],"requires":["Agent configuration in Julep's agent schema format","LLM model selection (OpenAI, Anthropic, etc.)","API key for Julep platform"],"input_types":["agent configuration (JSON)","system prompt (text)","model parameters (temperature, max_tokens, etc.)"],"output_types":["agent ID (string)","agent metadata (JSON)","agent execution results (text/structured)"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"julep__cap_4","uri":"capability://tool.use.integration.api.first.agent.invocation.with.request.response.patterns","name":"api-first agent invocation with request/response patterns","description":"Exposes agent execution through REST/HTTP APIs with standard request/response patterns, enabling agents to be called from any client (web, mobile, backend services) without SDK dependencies. Supports both synchronous (blocking) and asynchronous (webhook-based) invocation modes, with request queuing and response streaming for long-running operations. Handles authentication via API keys and provides structured response formats for easy integration.","intents":["Call an agent from a web frontend without installing a Python/Node SDK","Trigger agent execution asynchronously and receive results via webhook callback","Stream agent responses in real-time to a client application"],"best_for":["Teams with polyglot tech stacks that need language-agnostic agent access","Web applications requiring real-time agent response streaming","Microservices architectures where agents are called from multiple services"],"limitations":["API latency overhead for each request — network round-trip adds 50-200ms","Streaming responses require WebSocket or Server-Sent Events support in client","Rate limiting may apply depending on API tier"],"requires":["HTTP client library (curl, fetch, axios, requests, etc.)","API key for authentication","Network connectivity to Julep API endpoint"],"input_types":["JSON request body (agent ID, session ID, user message, parameters)","HTTP headers (authentication, content-type)"],"output_types":["JSON response (agent output, session state, metadata)","streaming response (Server-Sent Events or WebSocket frames)","webhook payload (for async invocations)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"julep__cap_5","uri":"capability://memory.knowledge.conversation.history.and.context.management","name":"conversation history and context management","description":"Automatically maintains and retrieves conversation history for each session, managing message ordering, timestamps, and role attribution (user/agent/system). Implements context windowing strategies to keep conversation history within LLM token limits while preserving semantic relevance, and provides APIs to query, filter, and manipulate conversation history without affecting agent state.","intents":["Retrieve the full conversation history for a session to display in a UI","Automatically trim old messages from context to stay within token limits","Search conversation history for specific topics or user intents"],"best_for":["Applications with long-running conversations that need to manage token usage","Teams building chat UIs that display full conversation history","Agents that need to reference past interactions for context"],"limitations":["Context windowing strategies may lose important information from early conversations","Searching conversation history requires sequential scanning for large histories","No built-in semantic compression of conversation history"],"requires":["Session ID for the conversation","API key for Julep platform","LLM token limit awareness for context windowing"],"input_types":["session ID (string)","query filters (role, timestamp, content)","context window parameters"],"output_types":["conversation messages (array of message objects)","message metadata (timestamps, roles, token counts)","context summary (for windowing)"],"categories":["memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"julep__cap_6","uri":"capability://text.generation.language.multi.turn.conversation.with.context.preservation","name":"multi-turn conversation with context preservation","description":"Enables agents to engage in extended conversations where each turn maintains awareness of previous exchanges, user preferences, and conversation goals. Implements context preservation across turns by automatically passing relevant history to the LLM, managing token budgets, and updating session state after each turn. Supports interruption, clarification requests, and topic switching while maintaining coherent conversation flow.","intents":["Build a customer support agent that remembers the customer's issue across multiple exchanges","Enable a user to ask follow-up questions that reference earlier parts of the conversation","Allow agents to ask clarifying questions and adapt responses based on user feedback"],"best_for":["Applications building conversational AI experiences with natural dialogue flow","Customer support and helpdesk automation requiring context-aware responses","Interactive agents that need to maintain conversation state across multiple user turns"],"limitations":["Token budget constraints may limit how much history can be included in each turn","Context preservation adds latency as history must be retrieved and processed each turn","No built-in mechanism to detect and recover from conversation derailment"],"requires":["Session ID for conversation tracking","API key for Julep platform","LLM with sufficient context window for conversation history"],"input_types":["user message (text)","session ID (string)","conversation context (optional, auto-retrieved)"],"output_types":["agent response (text)","updated session state (JSON)","conversation metadata (turn count, tokens used)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"julep__cap_7","uri":"capability://automation.workflow.agent.execution.monitoring.and.logging","name":"agent execution monitoring and logging","description":"Provides detailed execution logs and monitoring for agent operations, including step-by-step execution traces, tool invocations, LLM calls, and error events. Logs are structured as JSON with timestamps, execution context, and performance metrics, enabling debugging, auditing, and performance analysis. Supports log filtering, search, and export for compliance and troubleshooting purposes.","intents":["Debug why an agent made an unexpected decision by reviewing execution logs","Audit which tools were called and with what parameters for compliance purposes","Identify performance bottlenecks in agent workflows by analyzing execution metrics"],"best_for":["Teams operating agents in production who need visibility into agent behavior","Compliance-heavy industries requiring audit trails of agent decisions","Developers debugging complex agent workflows with multiple steps and tool calls"],"limitations":["Logging overhead may add latency to agent execution","Log retention policies may limit how long execution history is available","Sensitive data in logs may require redaction or encryption"],"requires":["Agent execution to be tracked (automatic)","API key for Julep platform","Access to logging dashboard or log export APIs"],"input_types":["agent execution events (automatic)","log query filters (time range, agent ID, step name)"],"output_types":["execution logs (JSON)","performance metrics (latency, token usage)","error traces (stack traces, error messages)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"julep__cap_8","uri":"capability://safety.moderation.user.and.session.isolation.with.multi.tenancy.support","name":"user and session isolation with multi-tenancy support","description":"Isolates agent sessions and data by user/tenant, ensuring that one user's conversation history and state cannot be accessed by another user. Implements authentication and authorization checks at the API level, with support for multi-tenant deployments where multiple organizations use the same agent infrastructure. Session data is partitioned by user/tenant ID, and access controls are enforced on all data retrieval operations.","intents":["Build a SaaS application where each customer has isolated agent sessions","Ensure user conversations are private and cannot be accessed by other users","Support multi-tenant deployments where different organizations use the same agent infrastructure"],"best_for":["SaaS applications providing AI agents to multiple customers","Enterprise deployments requiring strict data isolation between users/departments","Applications handling sensitive user data that must be protected from unauthorized access"],"limitations":["Multi-tenancy adds complexity to agent configuration and session management","Cross-tenant queries or analytics require careful data aggregation to maintain isolation","Tenant-specific customizations may require separate agent configurations per tenant"],"requires":["User/tenant ID in API requests","API key with appropriate tenant scope","Authentication mechanism for user identification"],"input_types":["user/tenant ID (string)","session ID (string)","authentication token"],"output_types":["isolated session data (JSON)","authorization status (boolean)","access control metadata"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"julep__cap_9","uri":"capability://automation.workflow.agent.performance.optimization.and.cost.management","name":"agent performance optimization and cost management","description":"Provides tools to optimize agent execution for latency and cost, including token usage tracking, model selection guidance, and execution time metrics. Tracks token consumption per agent, per session, and per operation, enabling cost forecasting and budget management. Supports model switching recommendations based on task complexity and cost/performance tradeoffs.","intents":["Monitor how many tokens an agent is consuming and optimize prompts to reduce costs","Choose between faster/cheaper models (GPT-3.5) and more capable models (GPT-4) based on task requirements","Identify which agents or workflows are consuming the most resources and optimize them"],"best_for":["Teams running agents at scale who need to manage LLM API costs","Applications with cost-sensitive deployments requiring optimization","Organizations needing visibility into resource consumption per agent/user"],"limitations":["Token counting may not be perfectly accurate for all LLM providers","Cost optimization recommendations are heuristic-based and may not be optimal","Switching models may affect agent output quality or consistency"],"requires":["Agent execution tracking (automatic)","API key for Julep platform","LLM API credentials for cost tracking"],"input_types":["agent execution metrics (automatic)","model configuration (model name, parameters)"],"output_types":["token usage metrics (per agent, per session, per operation)","cost estimates (USD)","optimization recommendations (JSON)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"julep__headline","uri":"capability://automation.workflow.ai.agent.platform.with.long.term.memory","name":"ai agent platform with long-term memory","description":"Julep is a platform designed for building stateful AI agents that can remember context over time, enabling persistent conversations and enhanced user interactions through advanced workflow execution and tool integration.","intents":["best AI agent platform","AI agents for long-term memory","how to build stateful AI agents","top platforms for deploying AI agents","AI agent solutions for automation"],"best_for":["developers building conversational agents","teams needing persistent AI interactions"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":59,"verified":false,"data_access_risk":"high","permissions":["API key for Julep platform","Session ID generation and tracking in client application","Network connectivity to Julep backend service","Workflow definition in Julep's workflow schema format","Understanding of step-based execution model","Agent definition compatible with serverless runtime","API key and deployment permissions","Optional: custom dependencies or Docker image","Tool schema definitions (JSON schema format)","API credentials or authentication tokens for external tools"],"failure_modes":["Session data storage has latency overhead — retrieving large conversation histories adds 100-500ms per API call","No built-in session expiration policies — requires manual cleanup of old sessions","State size limits may apply depending on backend storage tier","Workflow definitions may become verbose for highly dynamic or data-driven branching logic","Limited support for real-time workflow modification during execution","Debugging complex workflows requires understanding the execution engine's state model","Cold start latency may impact response time for infrequently used agents (100-500ms)","Execution time limits (typically 15-30 minutes) may not suit long-running workflows","Debugging serverless execution is harder than local development","Vendor lock-in risk; migrating to different platforms requires code changes","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.35,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.15,"match_graph":0.25,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.327Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=julep","compare_url":"https://unfragile.ai/compare?artifact=julep"}},"signature":"jdAOufZj5etTnesEpMFGwGdmVXs+eJy7W/omdc7rDQtIyChAohpUZYXP4tQQU2Y3y5ESD8giTvtMiInqTmmJCQ==","signedAt":"2026-06-23T13:09:30.174Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/julep","artifact":"https://unfragile.ai/julep","verify":"https://unfragile.ai/api/v1/verify?slug=julep","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}