{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-docker-image","slug":"docker-image","name":"Docker Image","type":"repo","url":"https://hub.docker.com/r/krohling/bondai","page_url":"https://unfragile.ai/docker-image","categories":["automation"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-docker-image__cap_0","uri":"capability://automation.workflow.containerized.ai.agent.orchestration","name":"containerized-ai-agent-orchestration","description":"Packages BondAI agent framework into a Docker container that orchestrates multiple AI model integrations and tool bindings through a unified runtime environment. The container abstracts away dependency management, Python environment configuration, and model provider authentication by pre-installing all required libraries and exposing standardized interfaces for agent initialization, tool registration, and execution loops. This enables developers to deploy AI agents without managing conflicting dependencies or environment setup across different host systems.","intents":["I want to deploy an AI agent without worrying about Python dependency conflicts or environment setup","I need to run the same AI agent consistently across development, staging, and production environments","I want to quickly spin up an AI agent that can call multiple external tools and APIs without manual configuration"],"best_for":["DevOps teams deploying AI agents to Kubernetes or Docker Swarm clusters","Solo developers prototyping multi-tool AI agents locally without environment management overhead","Teams migrating AI agent workloads from laptops to cloud infrastructure (AWS ECS, GCP Cloud Run, Azure Container Instances)"],"limitations":["Container image size likely 1-3GB due to bundled ML libraries and model dependencies, increasing pull time and storage costs","No built-in persistence layer — agent state and conversation history require external databases or volume mounts","GPU support requires Docker with NVIDIA Container Runtime and host-level CUDA installation; CPU-only inference may be slow for large models","Model weights are not pre-downloaded in the image; first run requires downloading models from HuggingFace or OpenAI, adding startup latency"],"requires":["Docker 20.10+ or Docker Desktop 4.0+","API keys for integrated model providers (OpenAI, Anthropic, HuggingFace, etc.) passed as environment variables","Minimum 4GB RAM for CPU-based inference; 8GB+ recommended for concurrent agent instances","Network access to external model APIs and tool endpoints"],"input_types":["environment variables (API keys, model configuration)","JSON-formatted agent configuration files","text prompts and user queries","tool definitions and API schemas"],"output_types":["structured agent responses (JSON)","tool execution results","logs and telemetry data","container exit codes and status"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-docker-image__cap_1","uri":"capability://tool.use.integration.multi.provider.model.abstraction.layer","name":"multi-provider-model-abstraction-layer","description":"Provides a unified interface to multiple AI model providers (OpenAI, Anthropic, HuggingFace, local Ollama instances) through a standardized agent API, abstracting provider-specific authentication, request formatting, and response parsing. The container pre-installs SDKs for each provider and exposes configuration via environment variables, allowing developers to swap model providers without code changes. This abstraction handles differences in token counting, streaming response formats, and function-calling schemas across providers.","intents":["I want to test my agent with different model providers (GPT-4, Claude, open-source models) without rewriting agent code","I need to fall back to a secondary model provider if the primary API is rate-limited or unavailable","I want to use local models (Ollama) for privacy-sensitive workloads while keeping the same agent interface"],"best_for":["Teams evaluating multiple LLM providers for cost-performance tradeoffs","Developers building privacy-first agents that need to switch between cloud and local models","Startups prototyping with cheaper open-source models before scaling to proprietary APIs"],"limitations":["Provider-specific features (vision capabilities, structured output modes) may not be uniformly exposed across all providers","Token counting and cost estimation varies by provider; no unified billing or quota management","Streaming response handling differs across providers; some may buffer entire responses before returning","Function-calling schemas (OpenAI vs Anthropic vs Claude) require normalization, adding latency and potential format mismatches"],"requires":["API keys for at least one model provider (OpenAI, Anthropic, HuggingFace, etc.)","Network connectivity to provider APIs, or local Ollama instance running on accessible network interface","Docker environment variables configured with provider credentials and model selection"],"input_types":["agent prompts and system instructions","tool definitions with input/output schemas","model provider configuration (API endpoint, model name, temperature, max_tokens)"],"output_types":["normalized model responses (text, structured JSON)","token usage statistics","provider-agnostic function calls"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-docker-image__cap_2","uri":"capability://tool.use.integration.tool.binding.and.function.calling.registry","name":"tool-binding-and-function-calling-registry","description":"Implements a schema-based function registry that maps tool definitions (name, description, input schema, output schema) to executable Python functions or external API endpoints. The container exposes a registration interface where developers define tools declaratively (via JSON schemas or Python decorators), and the agent automatically generates function-calling prompts compatible with the selected model provider's format (OpenAI functions, Anthropic tools, etc.). At execution time, the agent parses model-generated function calls, validates inputs against schemas, executes the bound function, and returns results back to the model for further reasoning.","intents":["I want to give my AI agent access to custom APIs or internal tools without manually writing prompt engineering for each tool","I need to validate tool inputs against schemas before execution to prevent malformed API calls","I want to add new tools to my agent without modifying the core agent code or redeploying the container"],"best_for":["Teams building enterprise AI agents that integrate with internal APIs, databases, and microservices","Developers creating tool-heavy agents (10+ tools) where manual prompt engineering becomes unmaintainable","Organizations needing strict input validation and audit trails for tool execution"],"limitations":["Schema validation adds latency (~50-200ms per function call) depending on schema complexity and validation library","Tool execution is synchronous by default; parallel tool calls require explicit async/await patterns","No built-in retry logic for failed tool calls; developers must implement custom error handling and fallback strategies","Tool definitions must be updated in the container or passed at runtime; no dynamic tool discovery from external registries"],"requires":["JSON Schema definitions for each tool's input and output","Python functions or HTTP endpoints that implement tool logic","Model provider support for function calling (OpenAI, Anthropic, Claude 3+)","Network access to external tool endpoints if tools are remote APIs"],"input_types":["tool definitions (JSON schemas, Python decorators)","tool metadata (name, description, category)","model-generated function calls (JSON)"],"output_types":["validated function call results","execution logs and error messages","tool usage statistics and audit trails"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-docker-image__cap_3","uri":"capability://memory.knowledge.agent.state.and.conversation.memory.management","name":"agent-state-and-conversation-memory-management","description":"Manages agent conversation history, execution state, and context windows through an in-memory or persistent storage backend. The container maintains a conversation buffer that tracks user messages, agent responses, and tool execution results, automatically managing token limits by summarizing or pruning older messages when approaching model context windows. Developers can configure memory strategies (sliding window, summary-based, vector-based retrieval) and optionally persist state to external databases (Redis, PostgreSQL) for multi-turn conversations across container restarts.","intents":["I want my agent to remember previous conversation context across multiple user interactions without losing information","I need to manage token limits intelligently so the agent doesn't exceed model context windows when conversations get long","I want to persist agent state so conversations survive container restarts or load balancing across multiple instances"],"best_for":["Teams building multi-turn conversational AI agents that need persistent memory","Applications with long-running agent sessions where context accumulation is a problem","Distributed deployments where agents run across multiple container instances and need shared state"],"limitations":["In-memory storage is lost on container restart; requires external persistence for production use","Token counting for context management is approximate; actual token usage may vary by model and encoding","Summarization-based memory strategies may lose nuanced context; vector-based retrieval requires embedding infrastructure","No built-in garbage collection for old conversations; developers must implement cleanup policies"],"requires":["External storage backend for persistence (Redis, PostgreSQL, DynamoDB, etc.) — optional for single-instance deployments","Embedding model for vector-based memory retrieval (if using semantic memory strategies)","Configured memory strategy and context window limits matching the selected model provider"],"input_types":["user messages and agent responses","tool execution results and side effects","memory configuration (strategy, max_tokens, persistence backend)"],"output_types":["conversation history (formatted for model input)","summarized context for long conversations","memory statistics (token usage, conversation length)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-docker-image__cap_4","uri":"capability://planning.reasoning.agent.execution.and.reasoning.loop","name":"agent-execution-and-reasoning-loop","description":"Implements the core agent loop that iteratively prompts the model, parses responses, executes tools, and incorporates results back into the conversation. The container orchestrates this loop with configurable stopping conditions (max iterations, tool call limits, timeout thresholds) and error handling strategies. The loop supports both synchronous execution (blocking until completion) and asynchronous patterns (streaming responses, background execution). Developers can hook into loop lifecycle events (before/after tool calls, on errors) for logging, monitoring, and custom business logic.","intents":["I want my agent to autonomously solve multi-step problems by reasoning, calling tools, and adapting based on results","I need to prevent infinite loops or runaway agent execution with configurable iteration limits and timeouts","I want to monitor and log what my agent is doing at each step for debugging and audit purposes"],"best_for":["Teams building autonomous agents that solve complex, multi-step tasks","Applications requiring transparent agent reasoning and decision-making trails","Developers needing fine-grained control over agent execution behavior and error recovery"],"limitations":["Synchronous execution blocks until agent completes; long-running agents may timeout in serverless environments","Each loop iteration adds latency (model inference + tool execution); complex tasks may require 10-30+ iterations","Error recovery is agent-dependent; if the agent gets stuck in a reasoning loop, manual intervention may be required","No built-in cost controls; runaway agents can incur significant API charges if iteration limits are not enforced"],"requires":["Model provider API with function-calling support","Tool definitions and implementations for the agent to call","Configured execution parameters (max_iterations, timeout, temperature, etc.)"],"input_types":["user query or task description","agent system prompt and instructions","tool definitions and current state"],"output_types":["final agent response or task result","execution trace (all iterations, tool calls, reasoning steps)","metrics (iterations count, total tokens, execution time)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-docker-image__cap_5","uri":"capability://automation.workflow.containerized.deployment.and.scaling","name":"containerized-deployment-and-scaling","description":"Packages BondAI as a Docker image that can be deployed to container orchestration platforms (Kubernetes, Docker Swarm, AWS ECS) with built-in support for horizontal scaling, health checks, and resource limits. The container exposes standard interfaces (HTTP API, gRPC, or message queues) for agent invocation, allowing multiple instances to run in parallel and handle concurrent requests. Developers can configure resource requests/limits (CPU, memory, GPU), health check endpoints, and graceful shutdown behavior for production deployments.","intents":["I want to scale my AI agent horizontally across multiple containers to handle concurrent user requests","I need to deploy my agent to Kubernetes with proper resource management and health monitoring","I want my agent to handle graceful shutdown and request draining when scaling down"],"best_for":["Teams deploying AI agents to production Kubernetes clusters","Applications with variable load that need auto-scaling based on request volume","Organizations requiring multi-region or multi-cloud agent deployments"],"limitations":["Horizontal scaling is limited by stateless design; agents with persistent memory require external state stores","Cold starts (container initialization) add 5-30 seconds latency; not suitable for sub-second response requirements","GPU sharing across containers is complex; each container typically needs exclusive GPU access","Model weights are not pre-downloaded; first request on a new container instance incurs model download latency"],"requires":["Container orchestration platform (Kubernetes 1.20+, Docker Swarm, AWS ECS, etc.)","Container registry for storing and distributing the Docker image","Resource quotas and limits configured in the orchestration platform","Health check endpoint and graceful shutdown signal handling in the agent"],"input_types":["container configuration (resource limits, environment variables)","orchestration platform manifests (Kubernetes YAML, Docker Compose, etc.)","health check probes and readiness/liveness criteria"],"output_types":["container status and health metrics","scaling events and load balancer decisions","resource utilization telemetry"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":21,"verified":false,"data_access_risk":"high","permissions":["Docker 20.10+ or Docker Desktop 4.0+","API keys for integrated model providers (OpenAI, Anthropic, HuggingFace, etc.) passed as environment variables","Minimum 4GB RAM for CPU-based inference; 8GB+ recommended for concurrent agent instances","Network access to external model APIs and tool endpoints","API keys for at least one model provider (OpenAI, Anthropic, HuggingFace, etc.)","Network connectivity to provider APIs, or local Ollama instance running on accessible network interface","Docker environment variables configured with provider credentials and model selection","JSON Schema definitions for each tool's input and output","Python functions or HTTP endpoints that implement tool logic","Model provider support for function calling (OpenAI, Anthropic, Claude 3+)"],"failure_modes":["Container image size likely 1-3GB due to bundled ML libraries and model dependencies, increasing pull time and storage costs","No built-in persistence layer — agent state and conversation history require external databases or volume mounts","GPU support requires Docker with NVIDIA Container Runtime and host-level CUDA installation; CPU-only inference may be slow for large models","Model weights are not pre-downloaded in the image; first run requires downloading models from HuggingFace or OpenAI, adding startup latency","Provider-specific features (vision capabilities, structured output modes) may not be uniformly exposed across all providers","Token counting and cost estimation varies by provider; no unified billing or quota management","Streaming response handling differs across providers; some may buffer entire responses before returning","Function-calling schemas (OpenAI vs Anthropic vs Claude) require normalization, adding latency and potential format mismatches","Schema validation adds latency (~50-200ms per function call) depending on schema complexity and validation library","Tool execution is synchronous by default; parallel tool calls require explicit async/await patterns","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.22,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"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":"2026-06-17T09:51:03.038Z","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=docker-image","compare_url":"https://unfragile.ai/compare?artifact=docker-image"}},"signature":"JFKsH5fnlwaseHAG8IOro5fTSkSJVRwN4jQQl9XpT9HyrYZARryHqkqTrbwGQB6YW1mafMKKvYC9r/5qhb10Cw==","signedAt":"2026-06-21T05:59:52.481Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/docker-image","artifact":"https://unfragile.ai/docker-image","verify":"https://unfragile.ai/api/v1/verify?slug=docker-image","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"}}