{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-nirdiamant--agents-towards-production","slug":"nirdiamant--agents-towards-production","name":"agents-towards-production","type":"repo","url":"https://github.com/NirDiamant/agents-towards-production","page_url":"https://unfragile.ai/nirdiamant--agents-towards-production","categories":["app-builders","deployment-infra"],"tags":["agent","agent-framework","agents","ai-agents","deployment","genai","generative-ai","langgraph","llm","llms","mlops","production","python","tutorials"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-nirdiamant--agents-towards-production__cap_0","uri":"capability://planning.reasoning.stateful.agent.orchestration.with.human.in.the.loop","name":"stateful-agent-orchestration-with-human-in-the-loop","description":"Implements complex task routing and state management using LangGraph's StateGraph and MemorySaver primitives, enabling agents to maintain conversation context across multiple turns while supporting human intervention checkpoints. The system uses a directed acyclic graph (DAG) pattern where each node represents a discrete agent action or decision point, with edges defining conditional routing logic based on agent output and external signals. State is persisted between invocations, allowing agents to resume interrupted workflows and maintain audit trails for compliance.","intents":["Build multi-turn agents that can pause for human approval before executing sensitive actions","Implement complex workflows where agent decisions branch into different execution paths based on context","Create agents that maintain conversation history and can reference previous interactions within a session","Deploy agents that require audit trails and can be debugged by replaying state transitions"],"best_for":["Enterprise teams building compliance-critical agents (finance, healthcare, legal)","Developers implementing approval workflows or multi-step task automation","Teams needing production-grade observability and debugging capabilities"],"limitations":["StateGraph adds ~50-100ms per state transition due to serialization overhead","Human-in-the-loop checkpoints require external notification/UI system (not built-in)","State size is limited by memory backend (Redis/PostgreSQL) — large conversation histories require pruning","No built-in distributed state locking — concurrent requests to same agent instance may cause race conditions"],"requires":["Python 3.9+","LangGraph library (0.0.x or later)","Persistent state backend (Redis, PostgreSQL, or in-memory for development)","LLM API key (OpenAI, Anthropic, or local Ollama)"],"input_types":["user messages (text)","structured task definitions (JSON)","external signals (approval decisions, tool results)"],"output_types":["agent actions (text, function calls)","state snapshots (JSON)","execution traces (structured logs)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--agents-towards-production__cap_1","uri":"capability://memory.knowledge.dual.memory.system.with.semantic.search","name":"dual-memory-system-with-semantic-search","description":"Combines short-term working memory (Redis-backed state store) with long-term semantic memory (vector database with embeddings) to enable agents to recall relevant historical context without token bloat. Short-term memory stores recent conversation turns and task state as structured JSON, while long-term memory indexes past interactions as embeddings, allowing semantic similarity search to retrieve relevant prior conversations. The system uses a retrieval-augmented generation (RAG) pattern where the agent queries long-term memory based on current context, then synthesizes retrieved memories into the prompt.","intents":["Build agents that remember user preferences and past interactions across multiple sessions","Implement semantic search over conversation history to find relevant prior context without manual tagging","Create personalized agents that adapt behavior based on historical patterns while keeping recent context fresh","Reduce token usage by selectively retrieving relevant memories instead of including full conversation history"],"best_for":["Customer service agents handling multi-session interactions","Personalization engines that need to recall user preferences from weeks of prior conversations","Teams building long-running assistants where token limits are a constraint"],"limitations":["Semantic search introduces ~200-500ms latency per memory retrieval (embedding + vector search)","Requires tuning of embedding model and similarity threshold — poor thresholds lead to irrelevant context retrieval","Memory eviction policies (TTL, size limits) must be manually configured; no automatic optimization","Vector database scaling requires separate infrastructure (Pinecone, Weaviate, Milvus) — no embedded option for production"],"requires":["Python 3.9+","Redis instance (for short-term state) or in-memory alternative","Vector database (Pinecone, Weaviate, Milvus, or Chroma for development)","Embedding model (OpenAI, Hugging Face, or local)","LLM API key"],"input_types":["user messages (text)","conversation history (structured JSON)","semantic queries (natural language)"],"output_types":["retrieved memories (text snippets with similarity scores)","augmented prompts (text with injected context)","memory metadata (timestamps, relevance scores)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--agents-towards-production__cap_10","uri":"capability://automation.workflow.cloud.deployment.with.infrastructure.as.code","name":"cloud-deployment-with-infrastructure-as-code","description":"Provides Infrastructure-as-Code (IaC) templates (Terraform, CloudFormation, or Pulumi) for deploying agents to cloud platforms (AWS, GCP, Azure) with all supporting infrastructure (databases, monitoring, networking). The system defines agent deployment as code, enabling version control, reproducible deployments, and easy scaling. Templates include best practices for security (IAM roles, secrets management), networking (VPCs, load balancers), and monitoring (CloudWatch, Datadog).","intents":["Deploy agents to cloud platforms with all supporting infrastructure in one command","Version control infrastructure changes and enable rollback if deployment fails","Scale agent deployments (add more instances, increase database capacity) by modifying IaC","Ensure consistent security configuration (IAM roles, secrets, networking) across deployments"],"best_for":["Teams with DevOps expertise using IaC for infrastructure management","Organizations requiring reproducible, auditable deployments","Scenarios where infrastructure changes must be version-controlled and reviewed"],"limitations":["IaC learning curve is steep — requires understanding of cloud provider services and networking","Debugging IaC errors is difficult — error messages are often cryptic and require cloud provider knowledge","State management (Terraform state) requires careful handling — state corruption can break deployments","IaC is cloud-specific — migrating between providers requires rewriting templates","Cost estimation is difficult — IaC doesn't provide upfront cost estimates before deployment"],"requires":["Cloud provider account (AWS, GCP, Azure)","IaC tool (Terraform, CloudFormation, Pulumi, or Ansible)","Cloud CLI tools (aws, gcloud, az)","Git repository for version control"],"input_types":["agent configuration (model, prompt, tools)","infrastructure requirements (compute, storage, networking)","security policies (IAM roles, secrets)"],"output_types":["deployed resources (compute instances, databases, load balancers)","deployment outputs (API endpoints, database URLs)","audit logs (resource creation, modifications)"],"categories":["automation-workflow","deployment-infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--agents-towards-production__cap_11","uri":"capability://code.generation.editing.model.customization.and.fine.tuning.pipeline","name":"model-customization-and-fine-tuning-pipeline","description":"Provides utilities for fine-tuning LLMs on agent-specific tasks (instruction following, tool use, output formatting) using training data collected from agent interactions. The system includes data collection (logging agent interactions), data preparation (filtering, formatting), and fine-tuning orchestration (calling OpenAI, Anthropic, or local fine-tuning APIs). Fine-tuned models can be deployed as drop-in replacements for base models, improving accuracy and reducing costs.","intents":["Improve agent accuracy by fine-tuning models on domain-specific tasks","Reduce token usage and costs by fine-tuning smaller models to match larger model performance","Customize model behavior (output format, tone, instruction following) for specific use cases","Create specialized models for specific agent roles (research, writing, analysis)"],"best_for":["Teams with sufficient training data (1000+ examples) and budget for fine-tuning","Cost-sensitive deployments where smaller fine-tuned models can replace larger models","Domain-specific agents where general models underperform"],"limitations":["Fine-tuning requires substantial training data (typically 1000+ examples) and is expensive ($100-10000+ per fine-tuning run)","Fine-tuning results are unpredictable — improvements vary widely depending on data quality and hyperparameters","Fine-tuned models are vendor-specific (OpenAI, Anthropic) — cannot be easily migrated","Evaluating fine-tuning improvements requires careful A/B testing — difficult to isolate fine-tuning impact","Fine-tuned models may overfit to training data — performance on out-of-distribution examples may degrade"],"requires":["Python 3.9+","Training data (1000+ examples of agent interactions)","Fine-tuning API access (OpenAI, Anthropic, or local fine-tuning framework)","Sufficient budget for fine-tuning costs"],"input_types":["training data (input/output pairs from agent interactions)","fine-tuning parameters (learning rate, epochs, batch size)","model selection (base model to fine-tune)"],"output_types":["fine-tuned model (model ID or weights)","fine-tuning metrics (loss, accuracy on validation set)","deployment configuration (model ID for agent to use)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--agents-towards-production__cap_12","uri":"capability://text.generation.language.tutorial.driven.learning.with.runnable.examples","name":"tutorial-driven-learning-with-runnable-examples","description":"Provides a structured tutorial system where each production capability is taught through hands-on, runnable Jupyter notebooks and Python scripts. Each tutorial follows a standardized pattern: conceptual explanation, code walkthrough, and a working example that developers can execute locally. Tutorials are organized by production layer (orchestration, memory, tools, security, deployment), enabling developers to learn incrementally from prototype to production.","intents":["Learn agent development patterns by running working examples locally","Understand production considerations (state management, security, monitoring) through hands-on practice","Adapt tutorial code to specific use cases without starting from scratch","Build mental models of agent architecture by seeing complete end-to-end examples"],"best_for":["Developers new to agent development wanting hands-on learning","Teams evaluating agent frameworks and needing working examples","Engineers building production agents who need reference implementations"],"limitations":["Tutorials may become outdated as libraries evolve — requires maintenance","Tutorials assume some Python knowledge — not suitable for complete beginners","Running tutorials locally requires setting up dependencies and API keys, which can be error-prone","Tutorials are sequential — skipping early tutorials may leave knowledge gaps","Tutorial code is simplified for clarity — production code may need additional error handling and optimization"],"requires":["Python 3.9+","Jupyter Notebook or JupyterLab","API keys for LLM providers (OpenAI, Anthropic, etc.)","Optional: Docker and docker-compose for running supporting services"],"input_types":["tutorial notebooks (Jupyter .ipynb files)","tutorial scripts (Python .py files)","configuration (environment variables, API keys)"],"output_types":["executed notebook outputs (text, tables, visualizations)","agent responses (text, structured data)","deployment artifacts (Docker images, IaC templates)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--agents-towards-production__cap_2","uri":"capability://tool.use.integration.multi.user.secure.tool.calling.with.oauth2.scoping","name":"multi-user-secure-tool-calling-with-oauth2-scoping","description":"Implements OAuth2-based permission scoping for agent tool invocations, ensuring agents can only call APIs on behalf of authenticated users with appropriate authorization. The system uses an ArcadeTool abstraction that wraps external APIs (Slack, GitHub, Google Workspace) with auth_callback hooks, intercepting tool calls to validate user credentials and enforce scope restrictions before execution. Each tool invocation is tagged with the calling user's identity and permission set, enabling fine-grained access control and audit logging.","intents":["Build agents that can safely call external APIs (Slack, GitHub, Google Workspace) without exposing user credentials","Implement per-user permission scoping so agents respect OAuth2 scopes and cannot escalate privileges","Create multi-tenant agents where different users see different tool capabilities based on their authorization","Audit all tool invocations with user identity and timestamp for compliance and security investigations"],"best_for":["SaaS platforms integrating agents with user-owned external accounts (Slack bots, GitHub automation)","Enterprise teams requiring fine-grained access control and audit trails for agent actions","Multi-tenant agent platforms where users must not access each other's data"],"limitations":["OAuth2 token refresh adds ~100-200ms per tool call if token is expired","Scope validation is application-specific — no universal scope standard across APIs (Slack vs GitHub vs Google have different permission models)","Requires maintaining OAuth2 client secrets securely — misconfiguration can leak credentials","No built-in rate limiting per user — agents can exhaust API quotas if not explicitly throttled"],"requires":["Python 3.9+","OAuth2 provider credentials (client ID, client secret) for each integrated service","Secure credential storage (AWS Secrets Manager, HashiCorp Vault, or encrypted database)","User authentication system (Auth0, Okta, or custom JWT)","Arcade or similar tool integration framework"],"input_types":["user identity (JWT, session token)","tool invocation requests (structured JSON with tool name and parameters)","OAuth2 tokens (access token, refresh token)"],"output_types":["authorized tool results (API response)","audit logs (user ID, tool name, timestamp, parameters, result)","permission denial errors (structured error with required scope)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--agents-towards-production__cap_3","uri":"capability://search.retrieval.real.time.web.search.integration.for.agents","name":"real-time-web-search-integration-for-agents","description":"Integrates real-time search capabilities (via Tavily Search API) as a callable tool within agent workflows, enabling agents to fetch current web information and incorporate it into reasoning. The system wraps search queries in a TavilySearchResults tool that returns ranked, deduplicated results with source attribution, which the agent can then synthesize into its response. Search results are cached briefly to avoid redundant queries within the same conversation turn, and the agent can iteratively refine searches based on initial results.","intents":["Build agents that answer questions about current events, stock prices, or real-time data without hallucinating","Create research assistants that can fetch and cite sources from the web as part of their reasoning","Implement agents that can verify facts by searching the web and comparing results","Enable agents to break out of training data cutoff limitations by accessing live information"],"best_for":["News/research agents that need current information","Customer support agents answering questions about product updates or pricing","Fact-checking or verification workflows"],"limitations":["Search latency is ~1-3 seconds per query, blocking agent execution","Tavily API has rate limits (typically 100-1000 queries/month depending on tier) — high-volume agents will hit limits","Search results quality depends on query formulation — poorly phrased queries return irrelevant results","No built-in deduplication across multiple search queries — agent may retrieve same information multiple times","Search results are snapshots at query time — no historical tracking or change detection"],"requires":["Python 3.9+","Tavily API key (free tier available with limits)","LLM with function-calling support (OpenAI, Anthropic, or compatible)","Network access to Tavily API"],"input_types":["search queries (natural language text)","search parameters (max results, include_domains, exclude_domains)"],"output_types":["search results (list of {title, url, snippet, source})","result metadata (relevance score, timestamp)"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--agents-towards-production__cap_4","uri":"capability://safety.moderation.prompt.injection.and.pii.filtering.guardrails","name":"prompt-injection-and-pii-filtering-guardrails","description":"Implements multi-layer security guardrails using LlamaFirewall and QualifireGuard to detect and block prompt injection attacks and personally identifiable information (PII) leakage. The system operates at two checkpoints: (1) input validation filters user messages for injection patterns and PII before they reach the agent, and (2) output validation filters agent responses to prevent PII from being returned to users. Guardrails use pattern matching, regex, and LLM-based classification to identify threats, with configurable severity levels (block, redact, warn).","intents":["Prevent attackers from injecting malicious prompts that override agent instructions","Automatically redact or block PII (SSNs, credit card numbers, email addresses) from being exposed in agent responses","Detect and log security incidents (injection attempts, PII leakage) for compliance and forensics","Enforce data privacy regulations (GDPR, CCPA) by preventing sensitive data from leaving the system"],"best_for":["Customer-facing agents handling sensitive data (healthcare, finance, legal)","Multi-user systems where agents must not leak data between users","Compliance-heavy industries requiring PII protection and audit trails"],"limitations":["Pattern-based PII detection has high false-positive rates (e.g., flagging legitimate email addresses as PII)","LLM-based classification adds ~200-500ms latency per request and requires API calls","Guardrails can be bypassed by sophisticated prompt injection (e.g., encoding attacks, indirect references)","No built-in context awareness — guardrails cannot distinguish between legitimate and malicious PII references","Redaction is lossy — once PII is redacted, the agent cannot reference it in follow-up turns"],"requires":["Python 3.9+","LlamaFirewall or QualifireGuard library","LLM API key (for LLM-based classification)","Regex patterns or PII detection models (built-in or custom)"],"input_types":["user messages (text)","agent responses (text)","PII patterns (regex, word lists)"],"output_types":["filtered messages (text with redactions or blocks)","security events (JSON with threat type, severity, timestamp)","audit logs (user ID, message, filter action, reason)"],"categories":["safety-moderation","security-guardrails"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--agents-towards-production__cap_5","uri":"capability://automation.workflow.serverless.agent.deployment.with.managed.runtime","name":"serverless-agent-deployment-with-managed-runtime","description":"Provides deployment abstractions (BedrockAgentCoreApp, @app.entrypoint decorator) that enable agents to run on serverless platforms (AWS Bedrock, Lambda) without managing infrastructure. The system handles request routing, state persistence, and scaling automatically, allowing developers to define agents as simple Python functions decorated with @app.entrypoint. The runtime manages cold starts, timeout handling, and integration with cloud logging/monitoring services.","intents":["Deploy agents to production without managing servers or containers","Scale agents automatically based on request volume without manual provisioning","Integrate agents with cloud-native services (Lambda, API Gateway, CloudWatch) with minimal boilerplate","Enable rapid iteration by deploying code changes without infrastructure changes"],"best_for":["Startups and small teams without DevOps expertise","Teams building event-driven agents (Slack bots, webhook handlers)","Cost-sensitive deployments where pay-per-invocation pricing is beneficial"],"limitations":["Cold start latency is 1-5 seconds for first invocation after idle period, unacceptable for real-time agents","Execution timeout is typically 15 minutes (AWS Lambda limit) — long-running agents must be refactored into smaller steps","State persistence requires external services (DynamoDB, S3) — no built-in local state","Vendor lock-in to AWS (Bedrock, Lambda) — difficult to migrate to other cloud providers","Debugging is harder in serverless environment — limited access to logs and runtime state"],"requires":["Python 3.9+","AWS account with Bedrock and Lambda access","AWS credentials configured (IAM role or access keys)","BedrockAgentCoreApp library","Deployment tool (AWS SAM, Serverless Framework, or manual CloudFormation)"],"input_types":["HTTP requests (JSON payload)","Lambda events (SNS, SQS, API Gateway)","agent parameters (structured JSON)"],"output_types":["HTTP responses (JSON)","Lambda response (structured format)","CloudWatch logs (agent execution traces)"],"categories":["automation-workflow","deployment-infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--agents-towards-production__cap_6","uri":"capability://planning.reasoning.multi.agent.communication.with.standardized.protocol","name":"multi-agent-communication-with-standardized-protocol","description":"Implements a standardized JSON-RPC communication protocol (A2AProtocol) for agents to invoke each other, enabling complex multi-agent workflows where specialized agents collaborate on tasks. Each agent is registered as an AgentCard with metadata (name, capabilities, input/output schema), and agents can discover and invoke other agents through a central registry. Communication is asynchronous with request/response tracking, allowing agents to wait for results or handle timeouts gracefully.","intents":["Build complex workflows where multiple specialized agents work together (e.g., research agent + writing agent + editor agent)","Enable agents to delegate subtasks to other agents without hardcoding dependencies","Create agent teams that can be composed dynamically based on task requirements","Implement agent-to-agent communication with error handling and timeout management"],"best_for":["Complex automation workflows requiring multiple specialized agents","Teams building agent platforms where agents need to be pluggable and composable","Scenarios where agents need to collaborate on long-running tasks"],"limitations":["JSON-RPC adds serialization overhead (~50ms per inter-agent call)","No built-in load balancing — if one agent is slow, dependent agents block waiting for results","Circular dependencies between agents can cause deadlocks — requires careful workflow design","Debugging multi-agent workflows is complex — requires tracing across multiple agent logs","No built-in versioning — breaking changes to agent schemas can break dependent agents"],"requires":["Python 3.9+","A2AProtocol library","Agent registry (in-memory, Redis, or database)","Message broker or HTTP transport for inter-agent communication"],"input_types":["agent invocation requests (JSON-RPC with method, params)","agent metadata (AgentCard with schema)"],"output_types":["agent responses (JSON-RPC result or error)","execution traces (request/response history)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--agents-towards-production__cap_7","uri":"capability://automation.workflow.observability.and.monitoring.with.structured.logging","name":"observability-and-monitoring-with-structured-logging","description":"Provides structured logging and monitoring infrastructure that captures agent execution traces (state transitions, tool calls, LLM invocations) in a queryable format. The system logs each step of agent execution with timestamps, input/output, latency, and error information, enabling developers to debug issues, analyze performance, and detect anomalies. Logs are exported to cloud monitoring services (CloudWatch, Datadog, New Relic) for centralized analysis and alerting.","intents":["Debug agent behavior by replaying execution traces and inspecting state at each step","Monitor agent performance (latency, error rates, token usage) in production","Detect anomalies (unusual patterns, repeated failures) and trigger alerts","Analyze agent behavior patterns to optimize prompts and improve accuracy"],"best_for":["Production agents requiring operational visibility","Teams debugging complex agent workflows","Compliance-heavy environments requiring detailed audit trails"],"limitations":["Structured logging adds ~10-50ms per step due to serialization and I/O","High-volume agents generate large log volumes, increasing storage costs","Log retention policies must be manually configured — no automatic cleanup","Querying logs requires knowledge of log schema and query language (CloudWatch Insights, Datadog QL)","Real-time alerting requires separate configuration (CloudWatch alarms, Datadog monitors)"],"requires":["Python 3.9+","Logging library (Python logging, structlog, or similar)","Cloud monitoring service (AWS CloudWatch, Datadog, New Relic, or self-hosted ELK)","Credentials for monitoring service"],"input_types":["agent execution events (state transitions, tool calls, LLM invocations)","performance metrics (latency, token count, cost)"],"output_types":["structured logs (JSON with timestamp, level, message, context)","metrics (latency percentiles, error rates, token usage)","traces (execution flow with parent-child relationships)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--agents-towards-production__cap_8","uri":"capability://planning.reasoning.agent.evaluation.and.testing.framework","name":"agent-evaluation-and-testing-framework","description":"Provides a testing framework for evaluating agent behavior against defined criteria (accuracy, latency, cost, safety). The system allows developers to define test cases with expected outputs, run agents against test suites, and measure performance metrics. Evaluation supports both deterministic assertions (output matches expected value) and probabilistic metrics (accuracy across multiple runs, cost per invocation). Results are aggregated and compared across agent versions to track improvements.","intents":["Validate agent behavior before deploying to production","Measure agent accuracy, latency, and cost across different prompts and configurations","Compare agent versions to ensure improvements don't regress other metrics","Identify edge cases where agents fail and add test cases to prevent regressions"],"best_for":["Teams building production agents requiring quality gates","Iterative prompt engineering where changes must be validated","Cost-sensitive deployments where token usage must be tracked"],"limitations":["Deterministic testing is difficult with LLMs — same input may produce different outputs","Probabilistic evaluation requires multiple runs, increasing cost and latency","Defining meaningful evaluation metrics is domain-specific and requires manual effort","No built-in comparison with human baselines — difficult to assess if agent is 'good enough'","Evaluation results are snapshots — model updates or prompt changes can invalidate previous results"],"requires":["Python 3.9+","Test framework (pytest, unittest, or custom)","Test data (input/output pairs or scenarios)","Evaluation metrics (custom or from library)"],"input_types":["test cases (input, expected output, criteria)","agent configurations (model, prompt, tools)","evaluation metrics (accuracy, latency, cost)"],"output_types":["test results (pass/fail, actual vs expected)","performance metrics (accuracy %, latency ms, cost $)","comparison reports (version A vs version B)"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nirdiamant--agents-towards-production__cap_9","uri":"capability://automation.workflow.containerized.agent.deployment.with.docker","name":"containerized-agent-deployment-with-docker","description":"Provides Docker containerization templates and best practices for packaging agents with all dependencies, enabling reproducible deployment across environments (development, staging, production). The system includes Dockerfile templates optimized for agent workloads (minimal base images, multi-stage builds, layer caching), and docker-compose configurations for local development with supporting services (Redis, vector DB, monitoring). Containers can be deployed to Kubernetes, ECS, or other container orchestration platforms.","intents":["Package agents with all dependencies for reproducible deployment","Run agents locally with supporting services (Redis, vector DB) using docker-compose","Deploy agents to Kubernetes or ECS with minimal configuration","Ensure consistency between development and production environments"],"best_for":["Teams with DevOps expertise wanting container-based deployment","Organizations using Kubernetes or ECS for orchestration","Scenarios requiring reproducible, version-controlled deployments"],"limitations":["Container startup time is 2-10 seconds, slower than serverless for bursty workloads","Requires container registry (Docker Hub, ECR, GCR) and CI/CD pipeline for image management","Kubernetes deployment requires expertise in cluster management, networking, and resource allocation","Container size can be large (500MB-2GB) if dependencies are not optimized, increasing deployment time","Debugging containerized agents is harder — requires exec into container or remote debugging setup"],"requires":["Docker 20.10+","Docker Compose 2.0+ (for local development)","Container registry (Docker Hub, AWS ECR, Google GCR)","Kubernetes or ECS cluster (for production deployment)","CI/CD pipeline (GitHub Actions, GitLab CI, Jenkins)"],"input_types":["agent source code (Python)","dependencies (requirements.txt, pyproject.toml)","configuration (environment variables, config files)"],"output_types":["Docker image (OCI format)","container logs (stdout/stderr)","deployment manifests (Kubernetes YAML, ECS task definition)"],"categories":["automation-workflow","deployment-infrastructure"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":54,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","LangGraph library (0.0.x or later)","Persistent state backend (Redis, PostgreSQL, or in-memory for development)","LLM API key (OpenAI, Anthropic, or local Ollama)","Redis instance (for short-term state) or in-memory alternative","Vector database (Pinecone, Weaviate, Milvus, or Chroma for development)","Embedding model (OpenAI, Hugging Face, or local)","LLM API key","Cloud provider account (AWS, GCP, Azure)","IaC tool (Terraform, CloudFormation, Pulumi, or Ansible)"],"failure_modes":["StateGraph adds ~50-100ms per state transition due to serialization overhead","Human-in-the-loop checkpoints require external notification/UI system (not built-in)","State size is limited by memory backend (Redis/PostgreSQL) — large conversation histories require pruning","No built-in distributed state locking — concurrent requests to same agent instance may cause race conditions","Semantic search introduces ~200-500ms latency per memory retrieval (embedding + vector search)","Requires tuning of embedding model and similarity threshold — poor thresholds lead to irrelevant context retrieval","Memory eviction policies (TTL, size limits) must be manually configured; no automatic optimization","Vector database scaling requires separate infrastructure (Pinecone, Weaviate, Milvus) — no embedded option for production","IaC learning curve is steep — requires understanding of cloud provider services and networking","Debugging IaC errors is difficult — error messages are often cryptic and require cloud provider knowledge","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7365744225049922,"quality":0.5,"ecosystem":0.7000000000000001,"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":"2026-05-24T12:16:22.062Z","last_scraped_at":"2026-05-03T13:58:21.998Z","last_commit":"2026-04-22T18:20:10Z"},"community":{"stars":19017,"forks":2536,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=nirdiamant--agents-towards-production","compare_url":"https://unfragile.ai/compare?artifact=nirdiamant--agents-towards-production"}},"signature":"ug8geoTLrDJ3tm8xIukWHJcS6gP8JIEndZz9sYqX6tKnPYW2HyFnRmET1p0Dd5hs4duMJFvJGF5GFovF6FIZDg==","signedAt":"2026-06-21T08:54:01.116Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/nirdiamant--agents-towards-production","artifact":"https://unfragile.ai/nirdiamant--agents-towards-production","verify":"https://unfragile.ai/api/v1/verify?slug=nirdiamant--agents-towards-production","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"}}