{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-camel","slug":"camel","name":"CAMEL","type":"repo","url":"https://github.com/camel-ai/camel","page_url":"https://unfragile.ai/camel","categories":["automation"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-camel__cap_0","uri":"capability://automation.workflow.multi.agent.orchestration.with.workforce.coordination","name":"multi-agent orchestration with workforce coordination","description":"Orchestrates teams of autonomous agents through the Workforce class, which manages task distribution, agent lifecycle, and inter-agent communication using a centralized coordinator pattern. Agents are instantiated as Worker instances (SingleAgentWorker, GroupChatWorker) that execute tasks asynchronously and report results back to the workforce manager, enabling complex multi-agent workflows without manual choreography.","intents":["I need to coordinate 5+ agents working on different subtasks of a larger problem","I want agents to collaborate on a task with automatic task delegation and result aggregation","I need to manage agent lifecycle (creation, execution, termination) at scale"],"best_for":["teams building multi-agent research systems","developers implementing agent-based automation workflows","researchers studying agent scaling laws and collaboration patterns"],"limitations":["Workforce memory is in-memory only — no built-in persistence across sessions","Task distribution is sequential by default; parallel execution requires explicit async configuration","No automatic load balancing across heterogeneous agent types"],"requires":["Python 3.9+","At least one LLM provider API key (OpenAI, Anthropic, etc.)","camel-ai/camel installed via pip"],"input_types":["task descriptions (text)","agent role specifications (structured config)","tool/toolkit definitions (Python objects)"],"output_types":["task execution results (structured)","agent conversation logs (MessageList)","workflow execution traces (JSON)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-camel__cap_1","uri":"capability://tool.use.integration.unified.multi.provider.llm.model.abstraction.with.factory.pattern","name":"unified multi-provider llm model abstraction with factory pattern","description":"Abstracts 50+ LLM providers (OpenAI, Anthropic, Claude, Ollama, local models, etc.) through a ModelFactory and unified model interface, enabling agents to switch providers without code changes. Uses a factory pattern that maps UnifiedModelType enums to provider-specific backend implementations, handling authentication, API differences, and response normalization transparently.","intents":["I want to switch between OpenAI and Anthropic models without rewriting agent code","I need to use local LLMs (Ollama) alongside cloud providers in the same workflow","I want to abstract away provider-specific API differences in my agent implementation"],"best_for":["teams evaluating multiple LLM providers","developers building provider-agnostic agent systems","researchers comparing model performance across providers"],"limitations":["Not all providers support identical feature sets (e.g., vision, function calling) — fallback behavior varies","Token counting accuracy depends on provider's tokenizer; local approximations may differ from actual usage","Streaming support is provider-dependent; some backends have higher latency for streaming responses"],"requires":["Python 3.9+","API keys for desired providers (OpenAI, Anthropic, etc.) OR local Ollama instance","camel-ai/camel with model backends installed"],"input_types":["model type enum (UnifiedModelType)","provider credentials (API keys, endpoints)","model configuration (temperature, max_tokens, etc.)"],"output_types":["LLM responses (text, structured)","token usage metrics (input_tokens, output_tokens)","streaming response chunks (if enabled)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-camel__cap_10","uri":"capability://safety.moderation.observability.and.execution.tracing.with.structured.logging","name":"observability and execution tracing with structured logging","description":"Implements comprehensive observability through structured logging, execution tracing, and metrics collection at each step of agent execution. Captures agent decisions, tool calls, LLM responses, and errors in a queryable format, enabling debugging, monitoring, and analysis of agent behavior without code instrumentation.","intents":["I need to debug why an agent made a specific decision or tool call","I want to monitor agent performance and identify bottlenecks","I need to audit agent actions for compliance or security purposes"],"best_for":["teams operating agents in production","developers debugging complex multi-agent workflows","builders implementing agent monitoring and alerting"],"limitations":["Comprehensive tracing adds ~5-10% overhead to execution time","Trace storage can grow large for long-running agents — requires log rotation or external storage","Structured logging requires compatible logging backend (JSON, structured formats)"],"requires":["Python 3.9+","Logging configuration (Python logging module or external service)","Optional: external tracing backend (e.g., Datadog, New Relic, OpenTelemetry)"],"input_types":["agent execution context","logging configuration (level, format, destination)","trace sampling rate (optional)"],"output_types":["structured logs (JSON)","execution traces (with timestamps, durations)","metrics (token usage, latency, error rates)","debug information (LLM prompts, responses, tool calls)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-camel__cap_11","uri":"capability://data.processing.analysis.synthetic.data.generation.from.agent.interactions","name":"synthetic data generation from agent interactions","description":"Leverages agent conversations and tool executions to generate synthetic training data for model fine-tuning or evaluation. Captures agent-generated examples with diverse reasoning patterns, tool usage, and error recovery, enabling creation of domain-specific training datasets without manual annotation.","intents":["I want to generate training data for fine-tuning models on agent-like behavior","I need diverse examples of how agents solve problems in my domain","I want to create evaluation datasets from agent interactions"],"best_for":["teams fine-tuning models for specific domains","researchers studying agent behavior and learning","builders creating domain-specific training datasets"],"limitations":["Generated data quality depends on agent quality — poor agents produce poor training data","Synthetic data may not cover edge cases or adversarial scenarios","Requires significant agent execution to generate large datasets (cost and time)"],"requires":["Python 3.9+","Running agents to generate interactions","LLM provider API keys for agent execution","Storage for generated datasets"],"input_types":["agent task descriptions (text)","number of examples to generate (int)","domain-specific constraints (optional)"],"output_types":["synthetic examples (structured format)","agent reasoning traces (for analysis)","tool usage patterns (for evaluation)","error recovery examples (for robustness)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-camel__cap_12","uri":"capability://planning.reasoning.task.decomposition.and.planning.with.hierarchical.execution","name":"task decomposition and planning with hierarchical execution","description":"Enables agents to decompose complex tasks into subtasks using chain-of-thought reasoning, with hierarchical execution where parent tasks coordinate child task execution. Agents can plan multi-step workflows, delegate subtasks to other agents, and aggregate results, enabling complex problem-solving without manual workflow definition.","intents":["I want agents to break down complex problems into manageable subtasks","I need hierarchical task execution where parent tasks coordinate children","I want agents to plan multi-step solutions before execution"],"best_for":["teams building agents for complex problem-solving","developers implementing hierarchical task systems","researchers studying agent planning and decomposition"],"limitations":["Task decomposition quality depends on LLM reasoning — may produce suboptimal plans","Hierarchical execution adds latency due to planning overhead (~500ms-2s per task)","No automatic validation that subtasks are solvable or independent"],"requires":["Python 3.9+","LLM provider API key with strong reasoning capabilities (GPT-4, Claude 3, etc.)","Task definitions with clear success criteria"],"input_types":["high-level task description (text)","constraints and requirements (text)","available tools and resources (toolkit definitions)"],"output_types":["task decomposition plan (structured)","subtask execution results (structured)","final solution (text or structured)","reasoning trace (for analysis)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-camel__cap_13","uri":"capability://search.retrieval.web.search.and.information.retrieval.integration","name":"web search and information retrieval integration","description":"Integrates web search capabilities through SearchToolkit, enabling agents to query search engines (Google, Bing, DuckDuckGo) and retrieve current information. Handles search result parsing, ranking, and deduplication, with automatic integration to agent tool-calling pipeline for seamless information retrieval during task execution.","intents":["I want agents to search the web for current information during task execution","I need agents to retrieve and summarize search results","I want agents to fact-check claims by searching for supporting evidence"],"best_for":["teams building agents that need current information","developers implementing fact-checking or research agents","builders creating agents for information retrieval tasks"],"limitations":["Search API rate limits may throttle agent execution — requires backoff or caching","Search result quality varies by query; agents may retrieve irrelevant results","Web content changes frequently — cached results may become stale"],"requires":["Python 3.9+","Search API credentials (Google Custom Search, Bing Search, etc.)","Internet connectivity for search queries"],"input_types":["search queries (text)","search parameters (number of results, language, etc.)","result filtering criteria (optional)"],"output_types":["search results (title, URL, snippet)","parsed web content (text)","result rankings (relevance scores)"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-camel__cap_14","uri":"capability://tool.use.integration.browser.automation.and.web.interaction.for.agents","name":"browser automation and web interaction for agents","description":"Enables agents to interact with web browsers through BrowserToolkit, supporting navigation, form filling, element interaction, and screenshot capture. Uses Selenium or similar automation libraries under the hood, with automatic error handling and recovery, enabling agents to perform complex web tasks without manual scripting.","intents":["I want agents to navigate websites and interact with web applications","I need agents to fill forms, click buttons, and extract data from web pages","I want agents to take screenshots and analyze visual content"],"best_for":["teams building web automation agents","developers implementing agents for web scraping or data extraction","builders creating agents for web-based task automation"],"limitations":["Browser automation is slow (~1-5s per action) compared to API calls","JavaScript-heavy websites may not render correctly in headless mode","Some websites block automated access; requires handling of anti-bot measures"],"requires":["Python 3.9+","Selenium or similar browser automation library","WebDriver (ChromeDriver, GeckoDriver, etc.) for target browser","Browser installation (Chrome, Firefox, etc.)"],"input_types":["URLs (text)","interaction commands (click, type, scroll, etc.)","CSS/XPath selectors (text)","form data (structured)"],"output_types":["page content (HTML, text)","screenshots (image)","extracted data (structured)","interaction results (success/failure)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-camel__cap_15","uri":"capability://tool.use.integration.terminal.command.execution.and.system.automation","name":"terminal command execution and system automation","description":"Enables agents to execute terminal commands and system operations through TerminalToolkit, with sandboxing, error handling, and output capture. Agents can run scripts, manage files, and interact with system tools, enabling automation of system administration and development tasks.","intents":["I want agents to execute shell commands and system operations","I need agents to manage files, directories, and system resources","I want agents to run scripts and development tools"],"best_for":["teams building DevOps or system administration agents","developers implementing agents for development task automation","builders creating agents for infrastructure management"],"limitations":["Terminal execution is inherently unsafe — requires careful sandboxing and permission management","Command output can be large; may exceed context window for verbose commands","Platform-specific commands (Windows vs Linux) require conditional logic"],"requires":["Python 3.9+","Shell access (bash, PowerShell, etc.)","Appropriate file system and system permissions","Optional: sandboxing/containerization for security"],"input_types":["shell commands (text)","working directory (path)","environment variables (dict)","input streams (optional)"],"output_types":["command output (stdout, stderr)","exit code (int)","execution time (float)","error messages (text)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-camel__cap_2","uri":"capability://text.generation.language.conversational.agent.with.streaming.and.tool.calling.orchestration","name":"conversational agent with streaming and tool-calling orchestration","description":"ChatAgent implements a single autonomous agent that maintains conversation state, executes tools via function calling, and streams responses incrementally. Uses a step() method that orchestrates the execution pipeline: message preprocessing → LLM inference → tool execution → memory updates → response formatting, with configurable hooks for each stage and native support for streaming output.","intents":["I need a single agent that can call tools/functions and maintain conversation context","I want to stream agent responses to the user in real-time rather than waiting for completion","I need to customize agent behavior at specific points in the execution pipeline"],"best_for":["developers building interactive chatbot applications","teams implementing single-agent automation tasks with tool use","builders prototyping agent-based UIs with real-time feedback"],"limitations":["Memory is in-memory only by default — no persistence without custom storage backend","Tool execution is sequential; parallel tool calls require manual async orchestration","Context window management is manual — no automatic context pruning or summarization"],"requires":["Python 3.9+","LLM provider API key (OpenAI, Anthropic, etc.)","Tool definitions as Toolkit objects or function schemas"],"input_types":["user messages (text)","tool/function schemas (JSON or Python objects)","agent role/system prompts (text)"],"output_types":["agent responses (text, streaming chunks)","tool execution results (structured)","conversation history (MessageList)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-camel__cap_3","uri":"capability://memory.knowledge.agent.memory.system.with.multi.backend.storage.and.retrieval","name":"agent memory system with multi-backend storage and retrieval","description":"Manages agent conversation history and context through a pluggable memory architecture supporting multiple backends (in-memory, vector databases, external storage). Memory system handles message storage, retrieval, and context window management, with support for semantic search via embeddings and structured message filtering, enabling agents to maintain long-term context and retrieve relevant past interactions.","intents":["I need agents to remember past conversations and retrieve relevant context for new tasks","I want to implement RAG-style retrieval where agents search their memory for relevant information","I need to persist agent conversations across sessions and query them later"],"best_for":["teams building long-running agent systems with persistent memory","developers implementing RAG-augmented agents","researchers studying agent learning and knowledge retention"],"limitations":["In-memory backend loses all history on process restart — requires external storage for persistence","Vector search requires embedding model setup; adds latency (~50-200ms per query)","No automatic memory consolidation or summarization — context window can grow unbounded"],"requires":["Python 3.9+","For vector search: embedding model (OpenAI, local, etc.) and vector DB (optional)","For persistence: external storage backend (database, file system, etc.)"],"input_types":["agent messages (MessageList)","search queries (text)","memory configuration (backend type, storage params)"],"output_types":["retrieved messages (MessageList)","search results with relevance scores (structured)","memory statistics (message count, token usage)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-camel__cap_4","uri":"capability://planning.reasoning.role.playing.dialogue.system.for.two.agent.interactions","name":"role-playing dialogue system for two-agent interactions","description":"Implements RolePlaying class that orchestrates structured dialogue between two agents with distinct roles, enabling controlled multi-turn conversations where each agent has a specific persona and objective. Manages turn-taking, message passing, and conversation termination conditions, useful for studying agent collaboration, debate, or adversarial interactions.","intents":["I want to simulate a conversation between two agents with different roles and objectives","I need to study how agents negotiate, debate, or collaborate on a task","I want to generate synthetic dialogue data for training or evaluation"],"best_for":["researchers studying multi-agent dialogue and negotiation","teams generating synthetic training data from agent interactions","developers building debate or adversarial agent systems"],"limitations":["Limited to exactly two agents — use Workforce for 3+ agent scenarios","No built-in termination condition beyond max turns — requires custom logic for early stopping","Message passing is synchronous; no parallel processing of agent responses"],"requires":["Python 3.9+","Two ChatAgent instances with distinct roles and system prompts","LLM provider API keys"],"input_types":["two agent instances (ChatAgent)","initial task/prompt (text)","max turns and termination criteria (config)"],"output_types":["dialogue transcript (MessageList)","conversation statistics (turn count, token usage)","final agent states (memory, tool calls)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-camel__cap_5","uri":"capability://tool.use.integration.toolkit.ecosystem.with.22.specialized.tool.integrations","name":"toolkit ecosystem with 22+ specialized tool integrations","description":"Provides a modular toolkit system where each Toolkit encapsulates a domain of functionality (search, terminal, browser, media, communication, etc.) with multiple tools exposed as callable functions. Tools are automatically converted to function schemas for LLM function calling, with built-in error handling, async support, and integration to agent execution pipeline.","intents":["I want agents to search the web, execute terminal commands, or manipulate files","I need to extend agent capabilities with domain-specific tools without modifying core agent code","I want to provide agents with access to external APIs and services"],"best_for":["developers building agents with external tool access","teams extending agent capabilities with custom toolkits","builders implementing autonomous agents for real-world tasks"],"limitations":["Tool execution is sequential by default — parallel tool calls require explicit async configuration","Error handling is toolkit-specific; no unified error recovery strategy","Some tools require external service credentials (API keys, service accounts)"],"requires":["Python 3.9+","Toolkit-specific dependencies (e.g., selenium for BrowserToolkit, requests for SearchToolkit)","External service credentials for tools (API keys, OAuth tokens, etc.)"],"input_types":["tool parameters (text, URLs, commands, etc.)","toolkit configuration (API keys, endpoints)","function schemas (auto-generated from tool definitions)"],"output_types":["tool execution results (text, structured data)","error messages (text)","execution logs (structured)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-camel__cap_6","uri":"capability://data.processing.analysis.message.preprocessing.and.token.counting.with.context.management","name":"message preprocessing and token counting with context management","description":"Implements message preprocessing pipeline that normalizes, filters, and truncates conversation history before LLM inference, with provider-specific token counting to track context window usage. Handles context overflow by implementing strategies like sliding window, summarization, or selective pruning, ensuring agents stay within model token limits while preserving relevant context.","intents":["I need to ensure agent conversations don't exceed the LLM's context window","I want to track token usage across conversations for cost estimation","I need to intelligently prune old messages while keeping recent context"],"best_for":["teams managing long-running agent conversations","developers optimizing token usage and LLM costs","builders implementing context-aware agent systems"],"limitations":["Token counting accuracy varies by provider — local approximations may differ from actual usage by 5-10%","Context pruning strategies are heuristic-based; no guarantee of preserving semantically important messages","Preprocessing adds ~50-100ms latency per message depending on strategy complexity"],"requires":["Python 3.9+","LLM provider API key for accurate token counting","Message history (MessageList)"],"input_types":["message list (MessageList)","context window size (int)","preprocessing strategy config (enum or custom)"],"output_types":["preprocessed messages (MessageList)","token usage metrics (input_tokens, output_tokens)","pruning statistics (messages removed, tokens saved)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-camel__cap_7","uri":"capability://data.processing.analysis.structured.output.generation.with.schema.based.validation","name":"structured output generation with schema-based validation","description":"Enables agents to generate structured outputs (JSON, dataclasses, Pydantic models) by providing LLMs with output schemas and validating responses against them. Uses provider-specific structured output APIs (OpenAI JSON mode, Anthropic structured generation) when available, with fallback to prompt-based schema enforcement for other providers.","intents":["I need agents to return structured data (JSON, objects) instead of free-form text","I want to validate agent outputs against a schema before using them","I need to extract specific fields from agent responses reliably"],"best_for":["developers building agents that feed into downstream systems requiring structured data","teams implementing agent-based data extraction pipelines","builders creating agent APIs with typed responses"],"limitations":["Provider-native structured output (OpenAI JSON mode) only available on specific models","Fallback to prompt-based schema enforcement has lower success rate (~85-95%) than native support","Schema validation adds ~100-200ms latency for complex schemas"],"requires":["Python 3.9+","Output schema definition (Pydantic model, JSON schema, or dataclass)","LLM provider supporting structured output (OpenAI, Anthropic, etc.)"],"input_types":["output schema (Pydantic, JSON schema, dataclass)","agent prompt/task (text)","validation rules (optional)"],"output_types":["validated structured output (JSON, object, dataclass instance)","validation errors (if schema violated)","raw LLM response (for debugging)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-camel__cap_8","uri":"capability://data.processing.analysis.data.loader.system.for.multi.format.document.ingestion","name":"data loader system for multi-format document ingestion","description":"Provides DataLoader classes that ingest documents in multiple formats (PDF, CSV, JSON, Markdown, web pages, etc.) and convert them to a unified internal representation for agent processing. Loaders handle format-specific parsing, text extraction, chunking, and metadata preservation, enabling agents to work with diverse data sources without format-specific code.","intents":["I want agents to read and process documents in various formats (PDF, CSV, JSON, etc.)","I need to extract text from web pages and PDFs for agent analysis","I want to chunk large documents for RAG-style retrieval"],"best_for":["teams building document processing agents","developers implementing RAG systems with multi-format inputs","builders creating agents that analyze diverse data sources"],"limitations":["PDF extraction quality varies with document structure; scanned PDFs require OCR (not built-in)","Chunking strategies are heuristic-based; semantic boundaries may not align with content structure","Large file processing (>100MB) may cause memory issues without streaming support"],"requires":["Python 3.9+","Format-specific libraries (pypdf for PDFs, pandas for CSVs, etc.)","File system or web access for document sources"],"input_types":["file paths (local or remote URLs)","file formats (PDF, CSV, JSON, Markdown, HTML, etc.)","chunking configuration (chunk size, overlap, strategy)"],"output_types":["parsed documents (unified Document objects)","extracted text (string)","metadata (source, page numbers, etc.)","chunks (for RAG)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-camel__cap_9","uri":"capability://automation.workflow.batch.processing.and.async.execution.for.scalable.agent.workflows","name":"batch processing and async execution for scalable agent workflows","description":"Enables agents and workflows to execute asynchronously using Python async/await patterns, with batch processing utilities for running multiple agent tasks in parallel. Provides decorators and utilities for converting synchronous code to async, managing concurrent execution, and aggregating results from parallel agent runs.","intents":["I want to run multiple agent tasks in parallel to improve throughput","I need to process large batches of inputs through agents efficiently","I want to avoid blocking on LLM API calls in my application"],"best_for":["teams building high-throughput agent systems","developers processing large datasets through agents","builders implementing non-blocking agent APIs"],"limitations":["Async execution adds complexity; debugging async code is harder than synchronous","Concurrent API calls may hit rate limits — requires manual throttling or backoff","Memory usage scales with number of concurrent tasks; no automatic resource pooling"],"requires":["Python 3.9+ with async/await support","asyncio event loop (built-in to Python)","Async-compatible LLM providers (most modern providers support this)"],"input_types":["agent tasks (list of inputs)","batch size (int)","concurrency limit (int)","async functions or agent instances"],"output_types":["aggregated results (list)","execution statistics (total time, throughput)","error logs (for failed tasks)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","At least one LLM provider API key (OpenAI, Anthropic, etc.)","camel-ai/camel installed via pip","API keys for desired providers (OpenAI, Anthropic, etc.) OR local Ollama instance","camel-ai/camel with model backends installed","Logging configuration (Python logging module or external service)","Optional: external tracing backend (e.g., Datadog, New Relic, OpenTelemetry)","Running agents to generate interactions","LLM provider API keys for agent execution","Storage for generated datasets"],"failure_modes":["Workforce memory is in-memory only — no built-in persistence across sessions","Task distribution is sequential by default; parallel execution requires explicit async configuration","No automatic load balancing across heterogeneous agent types","Not all providers support identical feature sets (e.g., vision, function calling) — fallback behavior varies","Token counting accuracy depends on provider's tokenizer; local approximations may differ from actual usage","Streaming support is provider-dependent; some backends have higher latency for streaming responses","Comprehensive tracing adds ~5-10% overhead to execution time","Trace storage can grow large for long-running agents — requires log rotation or external storage","Structured logging requires compatible logging backend (JSON, structured formats)","Generated data quality depends on agent quality — poor agents produce poor training data","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":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:02.371Z","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=camel","compare_url":"https://unfragile.ai/compare?artifact=camel"}},"signature":"QxrZBiPSlEfFjH7alHyXd5ryxAYkl5zaUmFKY6t//z1a47B77zcNcddPTkX/pAwo9t4mJX7ycBc/fr3tiXWdBQ==","signedAt":"2026-06-20T18:25:38.438Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/camel","artifact":"https://unfragile.ai/camel","verify":"https://unfragile.ai/api/v1/verify?slug=camel","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"}}