{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-chatdev","slug":"chatdev","name":"ChatDev","type":"agent","url":"https://github.com/OpenBMB/ChatDev","page_url":"https://unfragile.ai/chatdev","categories":["ai-agents"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-chatdev__cap_0","uri":"capability://automation.workflow.yaml.driven.multi.agent.workflow.orchestration","name":"yaml-driven multi-agent workflow orchestration","description":"Enables declarative workflow definition through YAML configuration files stored in yaml_instance/ directory, eliminating code-based agent choreography. The runtime dynamically parses YAML schemas to instantiate agent nodes, configure tool bindings, and manage context flow between agents without requiring Python/JavaScript programming. Uses a configuration-driven architecture where workflow topology, agent roles, and data dependencies are expressed as structured YAML, then executed by a domain-agnostic orchestration engine that interprets node definitions and manages inter-agent communication.","intents":["Define multi-agent workflows without writing code","Rapidly prototype agent orchestration patterns across different domains","Version control workflow definitions alongside application code","Enable non-technical users to compose agent pipelines"],"best_for":["Teams building domain-specific agent workflows (data viz, game dev, research)","Organizations seeking low-code/no-code agent composition","Rapid prototyping teams iterating on agent orchestration patterns"],"limitations":["YAML schema complexity grows with conditional logic — nested conditionals become difficult to maintain","No built-in version control or rollback for workflow definitions","Limited debugging visibility into YAML parsing errors at runtime","Schema validation happens at parse time, not design time"],"requires":["Python 3.9+","YAML files in yaml_instance/ directory with valid schema","LLM provider API keys (OpenAI, Anthropic, or compatible)","ChatDev runtime environment"],"input_types":["YAML configuration files","Environment variables for provider credentials","Workflow input parameters (strings, numbers, JSON objects)"],"output_types":["Structured workflow execution logs","Agent-generated artifacts (code, images, data)","Context state at each workflow step"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chatdev__cap_1","uri":"capability://automation.workflow.visual.workflow.canvas.with.drag.and.drop.node.composition","name":"visual workflow canvas with drag-and-drop node composition","description":"Provides a browser-based Web Console (port 5173) with interactive workflow canvas enabling visual agent node composition, connection, and parameter configuration through drag-and-drop UI. The frontend layer communicates with the backend API layer to persist workflow definitions, validate node connections, and preview execution flow. Users visually design agent topologies by placing nodes representing agents/tools, connecting them to define data flow, and configuring node parameters through form-based UI without touching YAML directly.","intents":["Visually design agent workflows without editing YAML","Quickly prototype multi-agent orchestration patterns","Validate agent connections and data flow before execution","Enable domain experts (non-engineers) to compose workflows"],"best_for":["Product managers and domain experts prototyping workflows","Teams wanting visual workflow design with instant feedback","Organizations with non-technical stakeholders defining automation"],"limitations":["Canvas UI abstracts YAML complexity — advanced conditional logic difficult to express visually","No collaborative real-time editing across multiple users","Visual representation may not scale to 50+ node workflows","Export to YAML may require manual refinement for complex scenarios"],"requires":["Web browser with ES6+ support","ChatDev backend API running on localhost:5173","Network connectivity to backend service"],"input_types":["Mouse/keyboard input for node placement and connection","Form inputs for node parameter configuration","Drag-and-drop file uploads for workflow templates"],"output_types":["YAML workflow definitions","Visual workflow diagrams","Execution preview/validation reports"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chatdev__cap_10","uri":"capability://memory.knowledge.memory.backend.abstraction.with.pluggable.persistence","name":"memory backend abstraction with pluggable persistence","description":"Provides an abstraction layer for memory/knowledge storage enabling pluggable backends (database, vector store, file system) without modifying workflow definitions. Agents can store and retrieve information through a unified memory interface, with the actual persistence mechanism configured at runtime. Supports both short-term context memory (within workflow execution) and long-term knowledge storage (across executions), enabling agents to build cumulative knowledge and reference historical information.","intents":["Store and retrieve agent knowledge across workflow executions","Enable agents to reference historical information and patterns","Implement long-term memory for agent learning and adaptation","Support different memory backends (database, vector store, file system) without code changes"],"best_for":["Workflows requiring agents to maintain long-term knowledge","Teams wanting to switch memory backends without code changes","Production workflows needing persistent agent state"],"limitations":["Memory abstraction adds latency for storage/retrieval operations","No built-in memory consistency guarantees across concurrent workflows","Memory backend selection impacts performance — wrong choice can bottleneck workflows","No automatic memory cleanup — unbounded growth possible"],"requires":["ChatDev runtime with memory backend support","Configured memory backend (database, vector store, or file system)","Memory backend credentials/connection strings in environment"],"input_types":["Memory keys and values (strings, JSON, embeddings)","Query patterns for memory retrieval"],"output_types":["Retrieved memory values","Memory storage confirmations","Query results from memory backend"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chatdev__cap_11","uri":"capability://automation.workflow.domain.specific.workflow.templates.with.pre.configured.tool.bindings","name":"domain-specific workflow templates with pre-configured tool bindings","description":"Provides specialized workflow templates for software development, data visualization, 3D generation, game development, and research domains, each with pre-configured tool bindings, agent roles, and orchestration patterns. Templates encode domain expertise through predefined agent responsibilities (e.g., architect, developer, reviewer for software dev) and tool selections (e.g., code generation, testing, documentation tools). Users instantiate templates through YAML configuration, customizing domain-specific parameters while reusing proven orchestration patterns.","intents":["Quickly bootstrap domain-specific workflows with pre-configured agents and tools","Reuse domain expertise encoded in templates across projects","Reduce time-to-first-workflow by leveraging template patterns","Standardize agent orchestration patterns within a domain"],"best_for":["Teams building workflows in supported domains (software dev, data viz, 3D, games, research)","Organizations wanting to standardize domain-specific agent patterns","Rapid prototyping teams leveraging domain templates"],"limitations":["Templates are domain-specific — cross-domain workflows require manual composition","Template customization depth limited by YAML expressiveness","No template inheritance or composition — difficult to build on existing templates","Adding new domain requires modifying core runtime"],"requires":["ChatDev 2.0 runtime with domain templates","Domain-specific tool dependencies (e.g., Blender for 3D, game engines)","LLM provider API keys matching template requirements"],"input_types":["Template selection and domain-specific parameters","Domain input (code requirements, visualization specs, 3D models, game mechanics)"],"output_types":["Domain-specific artifacts (code, visualizations, 3D models, game assets, research reports)","Workflow execution logs with domain metrics"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chatdev__cap_12","uri":"capability://automation.workflow.batch.workflow.execution.with.parameter.variation.and.result.aggregation","name":"batch workflow execution with parameter variation and result aggregation","description":"Enables batch processing of multiple workflow instances with parameter variation through Python SDK, executing workflows across datasets or parameter ranges and aggregating results. The batch system manages workflow instance lifecycle (creation, execution, result collection), supports parallel execution with configurable concurrency, and provides structured result aggregation enabling analysis across batch runs. Supports parameter sweeps, dataset iteration, and conditional batch execution based on previous results.","intents":["Execute workflows across multiple datasets or parameter combinations","Batch process large-scale automation tasks (code generation, data processing)","Perform parameter sweeps to optimize workflow behavior","Aggregate and analyze results across batch executions"],"best_for":["Data teams batch processing workflows across datasets","Teams automating code generation or content creation at scale","DevOps teams running parameter sweeps for workflow optimization","Production automation requiring high-throughput workflow execution"],"limitations":["Batch execution adds ~200ms overhead per workflow instance for orchestration","Parallel execution concurrency limited by available resources and API rate limits","No built-in result caching — identical parameter sets re-execute","Large batch sizes may exhaust API quotas or memory"],"requires":["Python 3.9+","ChatDev Python SDK","YAML workflow definitions","Input dataset or parameter combinations","LLM provider API keys with sufficient quota"],"input_types":["Batch input datasets (CSV, JSON, list of dicts)","Parameter variation specifications","Workflow configuration overrides"],"output_types":["Aggregated batch results (CSV, JSON)","Per-instance execution logs","Batch execution summary and statistics"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chatdev__cap_13","uri":"capability://automation.workflow.tutorial.interface.for.interactive.workflow.learning.and.experimentation","name":"tutorial interface for interactive workflow learning and experimentation","description":"Provides an interactive tutorial interface within the Web Console enabling users to learn ChatDev through guided workflows, interactive examples, and step-by-step agent execution visualization. The tutorial system walks users through workflow concepts (agents, tools, context flow) with executable examples, showing how agents collaborate and how data flows through workflows. Users can pause execution, inspect agent state, and modify workflows in real-time to understand ChatDev mechanics.","intents":["Learn ChatDev concepts through interactive guided tutorials","Understand agent orchestration through step-by-step execution visualization","Experiment with workflow modifications and see immediate results","Onboard new team members to ChatDev platform"],"best_for":["New users learning ChatDev platform","Teams onboarding developers to agent orchestration","Organizations wanting self-service learning resources"],"limitations":["Tutorial examples may not cover all advanced features","Interactive execution visualization adds latency to step-by-step execution","Tutorial content requires maintenance as platform evolves","No personalized learning paths — same tutorials for all users"],"requires":["Web browser with ES6+ support","ChatDev backend API running","Network connectivity to backend"],"input_types":["User interactions (button clicks, workflow modifications)","Tutorial selection"],"output_types":["Step-by-step execution visualization","Agent state inspection","Tutorial completion status"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chatdev__cap_14","uri":"capability://automation.workflow.launch.and.monitoring.dashboard.for.workflow.execution.tracking","name":"launch and monitoring dashboard for workflow execution tracking","description":"Provides a monitoring dashboard within the Web Console displaying real-time workflow execution status, agent progress, resource utilization, and execution metrics. The dashboard shows active workflows, completed executions with results, and historical execution trends. Users can launch new workflow instances, monitor execution progress, view agent logs, and retrieve results through a unified interface. Supports filtering, searching, and exporting execution history for analysis.","intents":["Monitor real-time workflow execution progress","Track agent execution and identify bottlenecks","View execution history and performance trends","Launch and manage multiple workflow instances"],"best_for":["Operations teams monitoring production workflows","Teams debugging workflow execution issues","Organizations tracking workflow performance metrics"],"limitations":["Dashboard refresh rate may lag behind actual execution state","Large execution histories may slow dashboard performance","No built-in alerting for workflow failures or anomalies","Limited customization of dashboard metrics and views"],"requires":["Web browser with ES6+ support","ChatDev backend API running","Workflow execution history stored in backend"],"input_types":["Workflow launch requests","Filter/search criteria for execution history"],"output_types":["Real-time execution status and progress","Agent logs and execution traces","Performance metrics and execution history"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chatdev__cap_2","uri":"capability://automation.workflow.multi.domain.agent.workflow.templates","name":"multi-domain agent workflow templates","description":"Provides pre-built workflow templates for five distinct domains: software development, data visualization, 3D generation, game development, and deep research/video generation. Each domain template encodes domain-specific agent roles, tool bindings, and orchestration patterns that can be instantiated and customized through YAML configuration. The runtime loads domain-specific tools and LLM provider configurations based on the selected template, enabling the same orchestration engine to execute fundamentally different workflows without domain-specific code branches.","intents":["Quickly bootstrap agent workflows for specific domains","Reuse proven agent orchestration patterns across projects","Extend domain templates with custom agents and tools","Reduce time-to-first-workflow by 80% using templates"],"best_for":["Teams building workflows in supported domains (dev, viz, 3D, games, research)","Organizations wanting to standardize agent patterns across teams","Rapid prototyping teams leveraging domain expertise"],"limitations":["Templates are domain-specific — cross-domain workflows require manual composition","Template customization depth limited by YAML expressiveness","No template versioning or inheritance mechanism","Adding new domain requires modifying core runtime"],"requires":["ChatDev 2.0 runtime with domain templates installed","Domain-specific tool dependencies (e.g., Blender for 3D, game engines for game dev)","LLM provider API keys matching template requirements"],"input_types":["Template selection parameter","Domain-specific input (code requirements, visualization specs, 3D models, game mechanics, research topics)"],"output_types":["Domain-specific artifacts (code, visualizations, 3D models, game assets, research reports)","Workflow execution logs with domain-specific metrics"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chatdev__cap_3","uri":"capability://automation.workflow.python.sdk.for.programmatic.workflow.execution.and.batch.processing","name":"python sdk for programmatic workflow execution and batch processing","description":"Exposes a Python SDK enabling programmatic workflow instantiation, execution, and batch processing for CI/CD integration and production automation. The SDK provides APIs to load YAML workflows, inject runtime parameters, execute workflows synchronously or asynchronously, and retrieve structured execution results. Supports batch processing of multiple workflow instances with parameter variation, enabling automation of repetitive tasks like code generation, data processing, or content creation at scale without manual intervention.","intents":["Execute ChatDev workflows from Python scripts and CI/CD pipelines","Batch process multiple workflow instances with parameter variation","Integrate ChatDev workflows into existing Python applications","Automate repetitive agent-based tasks at scale"],"best_for":["DevOps teams integrating ChatDev into CI/CD pipelines","Data teams batch processing workflows across datasets","Backend engineers embedding ChatDev into production services","Teams automating code generation or content creation at scale"],"limitations":["SDK abstracts YAML complexity — advanced debugging requires direct YAML inspection","Batch processing adds ~200ms overhead per workflow instance for orchestration","No built-in result caching — repeated workflows with same parameters re-execute","Async execution requires external state management for long-running workflows"],"requires":["Python 3.9+","ChatDev Python SDK installed via pip","YAML workflow definitions in yaml_instance/ directory","LLM provider API keys in environment variables"],"input_types":["YAML workflow file paths","Runtime parameters (dict/JSON)","Batch input datasets (CSV, JSON, list of dicts)"],"output_types":["Structured execution results (dict/JSON)","Workflow artifacts (generated code, images, data files)","Execution metadata (duration, token usage, error logs)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chatdev__cap_4","uri":"capability://tool.use.integration.custom.node.and.agent.extension.system","name":"custom node and agent extension system","description":"Enables developers to define custom agent nodes, tool providers, and memory backends through a plugin architecture without modifying core runtime. Custom nodes are registered in the extension system and referenced in YAML workflows, allowing teams to extend ChatDev with domain-specific agents, proprietary tools, or specialized LLM providers. The runtime dynamically loads custom node implementations at workflow instantiation time, enabling composition of built-in and custom agents in the same workflow.","intents":["Integrate proprietary tools and APIs into ChatDev workflows","Create domain-specific agent implementations","Add custom LLM providers beyond OpenAI/Anthropic","Build reusable agent components for team standardization"],"best_for":["Teams with proprietary tools requiring ChatDev integration","Organizations building domain-specific agent libraries","Enterprises needing custom LLM provider support","Teams standardizing agent patterns across projects"],"limitations":["Custom node development requires Python expertise — no visual node builder","No type safety for custom node inputs/outputs — runtime errors possible","Custom nodes must follow specific interface contract or runtime fails silently","No built-in testing framework for custom node validation"],"requires":["Python 3.9+","Understanding of ChatDev node interface and lifecycle","Custom node implementation following extension API","Registration of custom nodes in extension configuration"],"input_types":["Python class definitions implementing node interface","Tool/provider configuration (API keys, endpoints)","Custom node registration metadata (name, inputs, outputs)"],"output_types":["Registered custom nodes available in YAML workflows","Custom agent outputs (domain-specific artifacts)","Execution logs with custom node metrics"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chatdev__cap_5","uri":"capability://memory.knowledge.context.flow.and.data.passing.between.agents","name":"context flow and data passing between agents","description":"Implements a context management system where agent outputs are automatically captured and passed as inputs to downstream agents based on workflow topology. The runtime maintains a context dictionary that flows through the workflow, with each agent node reading from context, executing its task, and writing results back to context. Supports variable injection and environment parameter passing, enabling agents to access workflow-level configuration, previous agent outputs, and external data without explicit parameter passing in YAML.","intents":["Pass data between agents without explicit parameter mapping","Maintain workflow state across multi-step agent chains","Inject environment variables and configuration into agent context","Enable agents to reference outputs from previous steps"],"best_for":["Multi-step workflows requiring data flow between agents","Teams building complex agent chains with interdependencies","Workflows requiring environment-specific configuration injection"],"limitations":["Context dictionary grows unbounded — no automatic cleanup of intermediate results","No built-in context versioning — difficult to debug which agent modified context","Context passing is implicit — difficult to trace data lineage in complex workflows","Large context objects (images, large datasets) may cause memory issues"],"requires":["YAML workflow with agent nodes that read/write context","Understanding of context variable naming conventions","Environment variables set for configuration injection"],"input_types":["Agent outputs (strings, JSON, structured data)","Environment variables","Workflow input parameters"],"output_types":["Context dictionary with all intermediate and final results","Agent-specific outputs extracted from context"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chatdev__cap_6","uri":"capability://planning.reasoning.experiential.co.learning.ecl.for.iterative.agent.improvement","name":"experiential co-learning (ecl) for iterative agent improvement","description":"Implements a learning mechanism where agents capture execution experiences (successful patterns, failure modes, optimization insights) and refine their behavior across multiple workflow runs. The ECL system maintains an experience repository that agents query during execution to inform decision-making, and updates this repository based on execution outcomes. Enables agents to improve their orchestration strategies and tool selection over time without explicit retraining, using reinforcement learning principles to optimize workflow execution.","intents":["Improve agent decision-making through accumulated execution experience","Optimize workflow execution patterns based on historical outcomes","Reduce agent errors by learning from past failures","Enable agents to discover better tool combinations over time"],"best_for":["Long-running workflows where iterative improvement is valuable","Teams wanting agents to learn from execution history","Production workflows where optimization ROI justifies learning overhead"],"limitations":["ECL adds computational overhead per workflow execution for experience lookup/update","Experience repository can become stale if workflow requirements change","No built-in mechanism to invalidate or prune outdated experiences","Learning convergence time unpredictable — may require hundreds of executions"],"requires":["ChatDev runtime with ECL enabled","Persistent storage for experience repository (database or file system)","Multiple workflow executions to accumulate meaningful experience"],"input_types":["Workflow execution traces","Execution outcomes (success/failure)","Agent decision logs"],"output_types":["Improved agent behavior based on learned experiences","Experience repository updates","Execution metrics showing improvement over time"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chatdev__cap_7","uri":"capability://planning.reasoning.macnet.multi.agent.architecture.with.role.based.coordination","name":"macnet multi-agent architecture with role-based coordination","description":"Implements a multi-agent architecture where agents are assigned specific roles (e.g., analyst, developer, reviewer) and coordinate through a structured communication protocol. The MacNet architecture defines role-specific responsibilities, communication patterns, and decision-making authorities, enabling complex multi-agent workflows where agents collaborate based on their assigned roles. Agents communicate through a message-passing system that respects role hierarchies and communication constraints, enabling emergent coordination without explicit choreography.","intents":["Coordinate multiple agents with distinct roles and responsibilities","Implement role-based decision-making and authority hierarchies","Enable agents to communicate through structured protocols","Build complex workflows with emergent agent coordination"],"best_for":["Complex workflows requiring multi-agent coordination (e.g., software development with architect, developer, reviewer roles)","Teams wanting to model organizational structures in agent workflows","Workflows where agent roles and responsibilities are well-defined"],"limitations":["Role definitions must be carefully designed — poor role separation leads to deadlocks","Communication overhead increases with number of agents and message complexity","No built-in conflict resolution when agents disagree on decisions","Role hierarchies can become rigid — difficult to adapt to changing requirements"],"requires":["YAML workflow defining agent roles and communication patterns","Clear role definitions with responsibilities and authorities","Message-passing infrastructure for inter-agent communication"],"input_types":["Role definitions (name, responsibilities, authorities)","Communication protocol specifications","Agent interaction patterns"],"output_types":["Coordinated multi-agent execution traces","Role-specific outputs and decisions","Communication logs showing inter-agent interactions"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chatdev__cap_8","uri":"capability://planning.reasoning.iterative.experience.refinement.ier.for.workflow.optimization","name":"iterative experience refinement (ier) for workflow optimization","description":"Implements an iterative refinement loop where workflows are executed, outcomes are analyzed, and workflow definitions are automatically adjusted to improve performance. The IER system compares actual execution results against expected outcomes, identifies bottlenecks or failure points, and suggests or applies YAML modifications to optimize the workflow. Enables workflows to self-optimize over multiple iterations without manual intervention, using feedback loops to discover better agent orderings, tool selections, or parameter configurations.","intents":["Automatically optimize workflow definitions based on execution outcomes","Identify and eliminate workflow bottlenecks through iterative refinement","Discover better agent orderings and tool selections over time","Reduce manual workflow tuning through automated optimization"],"best_for":["Production workflows where optimization ROI justifies refinement overhead","Teams wanting to automate workflow tuning","Workflows with clear success metrics enabling outcome analysis"],"limitations":["IER requires clear success metrics — difficult to apply to subjective outcomes","Refinement iterations add latency to workflow execution","No rollback mechanism if refinement degrades performance","Convergence to optimal workflow configuration unpredictable"],"requires":["ChatDev runtime with IER enabled","Clear success metrics for workflow outcome evaluation","Multiple workflow executions to enable iterative refinement"],"input_types":["Workflow execution traces","Success metrics and outcome evaluations","Performance baselines"],"output_types":["Optimized YAML workflow definitions","Refinement suggestions and applied modifications","Performance improvement metrics"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-chatdev__cap_9","uri":"capability://planning.reasoning.puppeteer.rl.based.orchestration.for.dynamic.agent.scheduling","name":"puppeteer rl-based orchestration for dynamic agent scheduling","description":"Implements a reinforcement learning-based orchestration system (Puppeteer) that dynamically schedules agent execution and resource allocation based on workflow state and execution history. The Puppeteer system learns optimal scheduling policies through RL training, enabling dynamic agent ordering, parallel execution decisions, and resource prioritization without explicit YAML choreography. Agents are scheduled based on learned policies that maximize workflow efficiency metrics (latency, cost, quality), adapting to changing conditions and agent capabilities.","intents":["Dynamically schedule agent execution based on workflow state","Optimize resource allocation across multiple agents","Adapt agent scheduling to changing conditions and capabilities","Maximize workflow efficiency through learned scheduling policies"],"best_for":["Complex workflows with flexible agent ordering and parallelization opportunities","Resource-constrained environments where dynamic allocation is critical","Production workflows where scheduling optimization ROI justifies RL overhead"],"limitations":["RL training requires extensive workflow execution history — slow convergence","Scheduling decisions are opaque — difficult to debug why agents execute in specific order","RL policies may be unstable if workflow characteristics change significantly","Puppeteer adds computational overhead for policy evaluation per workflow step"],"requires":["ChatDev runtime with Puppeteer RL orchestration enabled","Workflow execution history for RL policy training","Clear optimization metrics (latency, cost, quality)"],"input_types":["Workflow state (completed agents, pending agents, resource availability)","Agent capability profiles","Execution history and performance metrics"],"output_types":["Dynamic agent scheduling decisions","Resource allocation recommendations","Scheduling policy updates from RL training"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","YAML files in yaml_instance/ directory with valid schema","LLM provider API keys (OpenAI, Anthropic, or compatible)","ChatDev runtime environment","Web browser with ES6+ support","ChatDev backend API running on localhost:5173","Network connectivity to backend service","ChatDev runtime with memory backend support","Configured memory backend (database, vector store, or file system)","Memory backend credentials/connection strings in environment"],"failure_modes":["YAML schema complexity grows with conditional logic — nested conditionals become difficult to maintain","No built-in version control or rollback for workflow definitions","Limited debugging visibility into YAML parsing errors at runtime","Schema validation happens at parse time, not design time","Canvas UI abstracts YAML complexity — advanced conditional logic difficult to express visually","No collaborative real-time editing across multiple users","Visual representation may not scale to 50+ node workflows","Export to YAML may require manual refinement for complex scenarios","Memory abstraction adds latency for storage/retrieval operations","No built-in memory consistency guarantees across concurrent workflows","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:02.371Z","last_scraped_at":"2026-05-03T14:00:10.321Z","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=chatdev","compare_url":"https://unfragile.ai/compare?artifact=chatdev"}},"signature":"NwzPw/rct07wKLd2qlsY3ygTzsS4ay3wIZ8pA9T2Rgjc3pMT7nzXF8YMlfPClkAJU+hhHiylltQXWvAZHlMUDA==","signedAt":"2026-06-19T22:52:01.987Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/chatdev","artifact":"https://unfragile.ai/chatdev","verify":"https://unfragile.ai/api/v1/verify?slug=chatdev","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"}}