{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-devika","slug":"devika","name":"Devika","type":"agent","url":"https://github.com/stitionai/devika","page_url":"https://unfragile.ai/devika","categories":["ai-agents"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-devika__cap_0","uri":"capability://tool.use.integration.multi.model.llm.abstraction.with.provider.agnostic.routing","name":"multi-model llm abstraction with provider-agnostic routing","description":"Devika abstracts multiple LLM providers (Claude 3, GPT-4, Gemini, Mistral, Groq, Ollama) behind a unified interface, allowing runtime selection and swapping without code changes. The LLM Integration Architecture uses a provider registry pattern where each backend implements a common interface for chat completion, token counting, and streaming. Configuration is externalized via config.py, enabling users to specify model, API keys, and provider settings without modifying agent code.","intents":["I want to switch between Claude and GPT-4 without rewriting my agent logic","I need to run Devika locally with Ollama instead of cloud APIs","I want to compare outputs across multiple models for the same task","I need to fall back to a local model if cloud APIs are unavailable"],"best_for":["teams evaluating multiple LLM providers for cost/performance tradeoffs","developers building on-premise AI systems with Ollama","organizations with multi-cloud or hybrid LLM strategies"],"limitations":["No automatic prompt adaptation across model families — Claude and GPT-4 may require different prompt engineering","Token counting varies by provider; context window management is provider-specific","Streaming support depends on provider implementation; some providers may have higher latency"],"requires":["API keys for selected providers (OpenAI, Anthropic, Google, Mistral, Groq) OR local Ollama instance","Python 3.9+","config.py properly configured with provider credentials"],"input_types":["natural language prompts","system messages","conversation history (chat format)"],"output_types":["text completions","streaming token chunks","structured JSON (if model supports)"],"categories":["tool-use-integration","llm-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-devika__cap_1","uri":"capability://planning.reasoning.hierarchical.agent.orchestration.with.specialized.agent.roles","name":"hierarchical agent orchestration with specialized agent roles","description":"Devika implements a multi-agent system coordinated by AgentOrchestrator that delegates tasks to specialized agents: PlannerAgent (task decomposition), ResearcherAgent (information gathering), CoderAgent (code generation), PatcherAgent (bug fixing), FeatureAgent (feature implementation), and ActionAgent (system actions). Each agent is stateless and receives context via the orchestrator, which maintains InternalMonologue for reasoning continuity. The orchestrator uses a workflow pattern where agents are invoked sequentially or conditionally based on task requirements and agent outputs.","intents":["I want the AI to break down a complex feature request into subtasks before coding","I need the AI to research APIs and documentation before writing integration code","I want the AI to automatically fix bugs it introduces during code generation","I need visibility into the AI's reasoning process as it works through a task"],"best_for":["teams building complex software features requiring research and planning","developers who want transparent, multi-step AI reasoning for code generation","organizations needing AI agents that can self-correct and iterate"],"limitations":["Agent coordination adds latency — each agent invocation requires LLM calls, potentially 5-10 round-trips for complex tasks","No built-in agent prioritization or resource limits — all agents run sequentially, no parallel execution","InternalMonologue context can grow unbounded; no automatic context pruning or summarization","Agent failures are not gracefully handled — if one agent fails, the orchestrator does not have fallback strategies"],"requires":["LLM provider configured and accessible","AgentOrchestrator instantiated with all agent classes","Task input in natural language or structured format"],"input_types":["natural language task descriptions","project context (file structure, existing code)","user feedback and corrections"],"output_types":["task decomposition plan","research findings and URLs","generated code","bug fixes and patches","internal reasoning trace (InternalMonologue)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-devika__cap_10","uri":"capability://automation.workflow.configuration.driven.deployment.with.multi.environment.support","name":"configuration-driven deployment with multi-environment support","description":"Devika uses a centralized configuration system (config.py) that externalizes all deployment settings: LLM provider selection, API keys, model names, project directories, and feature flags. The configuration supports multiple environments (development, staging, production) through environment-specific config files or environment variables. This allows the same codebase to be deployed across different environments without code changes, and enables users to customize Devika's behavior without modifying source code.","intents":["I want to deploy Devika with different LLM providers in different environments","I need to configure Devika without modifying source code","I want to use environment variables for sensitive configuration (API keys)","I need to switch between local Ollama and cloud LLMs based on deployment"],"best_for":["DevOps teams deploying Devika across multiple environments","organizations with strict configuration management requirements","teams using infrastructure-as-code (Terraform, Kubernetes) for deployment"],"limitations":["Configuration validation is minimal — invalid settings may not be caught until runtime","No built-in configuration versioning or rollback; changes are immediate","Configuration is not encrypted; sensitive data (API keys) should be managed via secrets management","No UI for configuration management; all changes require file editing or environment variables"],"requires":["config.py file in the project root","Environment variables set for sensitive configuration (optional)","Python 3.9+ to parse configuration"],"input_types":["configuration files (YAML, JSON, or Python)","environment variables"],"output_types":["configuration objects","validated settings"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-devika__cap_11","uri":"capability://search.retrieval.real.time.browser.widget.for.monitoring.web.research","name":"real-time browser widget for monitoring web research","description":"Devika includes a browser widget in the web interface that displays web pages and search results as the ResearcherAgent performs web searches. The widget shows URLs, page content, and extracted information in real-time via Socket.IO updates. Users can see what the AI is researching and verify that the research is relevant and accurate. The widget also allows users to manually navigate to URLs or provide additional research context if needed.","intents":["I want to see what websites the AI is researching","I need to verify that the AI's research is accurate and relevant","I want to provide additional research context if the AI is going in the wrong direction","I need to understand what information the AI is using to inform code generation"],"best_for":["developers who want transparency into AI research and decision-making","teams using Devika for tasks requiring external knowledge (API integration, framework selection)","organizations needing audit trails of AI research for compliance"],"limitations":["Browser widget displays only text content; images and interactive elements are not rendered","Real-time updates can impact performance if research is very active","No built-in fact-checking or source validation; users must manually verify accuracy","Widget is read-only; users cannot interact with web pages directly"],"requires":["Web browser with JavaScript enabled","Flask backend with Socket.IO for real-time updates","Frontend browser widget deployed"],"input_types":["URLs from web search","page content from web scraping"],"output_types":["rendered web pages","extracted information","search result summaries"],"categories":["search-retrieval","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-devika__cap_12","uri":"capability://memory.knowledge.internal.monologue.and.reasoning.trace.persistence","name":"internal monologue and reasoning trace persistence","description":"Devika maintains an InternalMonologue component that records the agent's reasoning process throughout task execution. This includes planning decisions, research findings, code generation rationale, and bug-fixing logic. The monologue is persisted and displayed to users, providing a detailed trace of how the AI arrived at its conclusions. Users can review the monologue to understand the AI's decision-making and identify where it may have gone wrong. The monologue is also used by agents to maintain context across multiple LLM calls.","intents":["I want to understand why the AI made a particular decision","I need to debug the AI's reasoning if it produces incorrect code","I want to see the full reasoning trace for compliance or audit purposes","I need to provide feedback to the AI based on its reasoning"],"best_for":["developers debugging AI agent behavior","teams requiring transparency into AI decision-making","organizations with compliance requirements for AI systems"],"limitations":["InternalMonologue can grow very large for complex tasks, consuming significant memory and storage","No automatic summarization or pruning; users must manually review long monologues","Monologue is not structured; extracting specific decisions requires manual parsing","Persistence is not encrypted; sensitive information in the monologue is exposed"],"requires":["InternalMonologue component instantiated","Storage for persisting monologue (file system or database)","Frontend component to display monologue"],"input_types":["agent reasoning","planning decisions","research findings","code generation rationale"],"output_types":["structured reasoning trace","decision explanations","context for subsequent agents"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-devika__cap_2","uri":"capability://search.retrieval.contextual.web.research.and.semantic.keyword.extraction","name":"contextual web research and semantic keyword extraction","description":"The ResearcherAgent integrates a Search System that performs contextual keyword extraction from task descriptions and web browsing to gather relevant information. The system analyzes the user's request, identifies key concepts, and executes web searches to retrieve documentation, API references, and implementation examples. Results are cached and returned to the CoderAgent to inform code generation. The research capability is integrated into the agent workflow, allowing the system to pause code generation, research dependencies, and then resume with informed context.","intents":["I want the AI to look up API documentation before writing integration code","I need the AI to research best practices for a specific technology before implementing","I want the AI to find examples of similar implementations online","I need the AI to verify that a library or framework is compatible with my project"],"best_for":["developers working with unfamiliar libraries or frameworks","teams building integrations that require external API knowledge","projects where implementation details depend on current documentation"],"limitations":["Web search results are not guaranteed to be current or accurate — may retrieve outdated documentation","No built-in fact-checking or result validation — the system trusts search results without verification","Search latency adds 2-5 seconds per research query; no caching across sessions","Keyword extraction is heuristic-based; may miss relevant concepts or extract noise"],"requires":["Internet connectivity for web searches","Search API configured (e.g., Google Search, Bing, or similar)","ResearcherAgent instantiated and integrated into AgentOrchestrator"],"input_types":["task description (natural language)","technology names or library names","API endpoint descriptions"],"output_types":["list of relevant URLs","extracted code snippets from search results","API documentation summaries","implementation examples"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-devika__cap_3","uri":"capability://code.generation.editing.multi.language.code.generation.with.syntax.aware.formatting","name":"multi-language code generation with syntax-aware formatting","description":"The CoderAgent generates code in multiple programming languages (JavaScript, Python, Java, C++, etc.) using LLM-based code synthesis. The system maintains language-specific templates and formatting rules to ensure generated code adheres to language conventions. Code is generated in response to task decomposition from the PlannerAgent and research context from the ResearcherAgent. The generated code is written to the project file system and displayed in the web-based editor widget for user review and modification.","intents":["I want the AI to write production-ready code in my project's primary language","I need the AI to generate code that follows my project's style and conventions","I want the AI to create code across multiple files and directories","I need the AI to generate code for different parts of the stack (frontend, backend, tests)"],"best_for":["developers using Devika as a pair programmer for feature implementation","teams with multi-language codebases needing AI assistance across all languages","projects where code generation quality is critical (production systems)"],"limitations":["Generated code quality depends on LLM capability and prompt engineering — no guarantee of correctness or best practices","No built-in linting or formatting — generated code may not match project style guides","No type checking or static analysis — generated code may have type errors or logical bugs","Context window limits prevent generation of very large files (>4000 lines); multi-file generation requires multiple LLM calls"],"requires":["LLM provider configured and accessible","CoderAgent instantiated","Project file system writable","Target language specified in task or project configuration"],"input_types":["task description","existing code context","API specifications","research findings"],"output_types":["source code files","test files","configuration files","documentation"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-devika__cap_4","uri":"capability://code.generation.editing.automated.code.patching.and.iterative.bug.fixing","name":"automated code patching and iterative bug fixing","description":"The PatcherAgent automatically identifies and fixes bugs in generated code by analyzing error messages, test failures, and code review feedback. When the CoderAgent generates code that fails tests or produces errors, the PatcherAgent receives the error context, analyzes the root cause, and generates corrective patches. This creates an iterative loop where code is generated, tested, and refined until it passes validation. The patcher maintains a patch history and can apply multiple fixes sequentially.","intents":["I want the AI to automatically fix bugs it introduces during code generation","I need the AI to iterate on code until tests pass","I want the AI to learn from errors and improve subsequent code generation","I need visibility into what bugs were found and how they were fixed"],"best_for":["teams using Devika for feature development where code quality is critical","projects with automated test suites that can provide feedback to the patcher","developers who want AI-assisted debugging and iterative refinement"],"limitations":["Patching effectiveness depends on error message clarity — cryptic errors may not be fixable automatically","No guarantee of convergence — patcher may enter infinite loop if error is fundamental","Patch history can grow large; no automatic cleanup or summarization","Patcher has no access to runtime debugging tools (debugger, profiler) — limited to static analysis and error messages"],"requires":["Test suite or error detection mechanism to provide feedback","PatcherAgent instantiated and integrated into AgentOrchestrator","Generated code accessible for analysis and modification"],"input_types":["error messages","test failure output","code review feedback","generated code"],"output_types":["patch files","corrected code","patch history and explanations","root cause analysis"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-devika__cap_5","uri":"capability://automation.workflow.dynamic.agent.state.tracking.and.visualization","name":"dynamic agent state tracking and visualization","description":"Devika maintains dynamic state tracking for each agent and the overall task execution, exposing this state through a web-based visualization dashboard. The state includes current agent, task progress, generated code, research findings, and reasoning trace (InternalMonologue). State is updated in real-time via Socket.IO, allowing users to monitor agent activity as it happens. The visualization includes a chat interface, code editor, terminal output, and browser widget showing web research in progress.","intents":["I want to see what the AI is currently working on and why","I need to monitor the AI's progress on a long-running task","I want to understand the AI's reasoning and decision-making process","I need to pause or redirect the AI if it's going in the wrong direction"],"best_for":["developers debugging AI agent behavior and workflows","teams using Devika for complex tasks requiring human oversight","organizations needing transparency into AI decision-making for compliance"],"limitations":["State visualization adds overhead — Socket.IO updates can impact performance for long-running tasks","InternalMonologue can become very large and difficult to parse for complex tasks","No built-in state persistence — state is lost if the server restarts","Visualization is real-time only; no historical playback or state snapshots"],"requires":["Web browser with WebSocket support","Flask backend with Socket.IO configured","Frontend components (chat, editor, terminal, browser widgets) deployed"],"input_types":["agent state updates","task progress events","error messages"],"output_types":["real-time state visualization","chat messages","code editor content","terminal output","browser widget content"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-devika__cap_6","uri":"capability://memory.knowledge.project.based.file.and.context.management","name":"project-based file and context management","description":"Devika organizes work around projects, where each project has its own file system, configuration, and context. The system tracks project metadata (name, description, language, framework), maintains a file index for quick lookup, and manages context windows by selectively including relevant files in agent prompts. When agents need to reference existing code, the system retrieves relevant files based on semantic similarity or explicit imports, avoiding the need to include the entire codebase in every prompt.","intents":["I want to organize my AI-assisted development work into separate projects","I need the AI to understand my project structure and existing code","I want the AI to modify specific files without affecting the rest of the codebase","I need the AI to track dependencies and imports across my project"],"best_for":["developers working on multiple projects simultaneously","teams with large codebases where full-context inclusion is infeasible","projects requiring careful file management and version control integration"],"limitations":["File indexing is not real-time — changes made outside Devika may not be reflected immediately","Semantic similarity for file retrieval depends on embeddings quality; may miss relevant files","No built-in version control integration — file changes are not automatically committed","Context management is heuristic-based; may include irrelevant files or exclude necessary ones"],"requires":["Project directory structure on disk","Project metadata configuration (language, framework, etc.)","File system access for reading and writing"],"input_types":["project configuration","file paths","code queries"],"output_types":["file list","relevant code snippets","project metadata","dependency graph"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-devika__cap_7","uri":"capability://text.generation.language.natural.language.chat.interface.with.multi.turn.conversation","name":"natural language chat interface with multi-turn conversation","description":"Devika provides a web-based chat interface where users submit natural language instructions and receive responses from the agent system. The chat maintains conversation history, allowing users to provide follow-up instructions, corrections, and feedback. Each message is processed by the agent orchestrator, which determines which agents to invoke and in what order. The chat interface also displays agent reasoning, code snippets, and research findings inline, creating a conversational development experience.","intents":["I want to describe a feature in natural language and have the AI implement it","I need to provide feedback or corrections to the AI's work mid-task","I want to ask the AI questions about the code it generated","I need to see the AI's reasoning and ask follow-up questions"],"best_for":["developers who prefer conversational interaction over traditional IDEs","non-technical stakeholders describing features to be implemented","teams using Devika as a collaborative pair programming tool"],"limitations":["Natural language instructions are ambiguous — the AI may misinterpret intent","Conversation history can grow large, impacting context window usage","No built-in disambiguation or clarification prompts — the AI may proceed with incorrect assumptions","Chat interface is web-based only; no IDE integration or command-line interface"],"requires":["Web browser with JavaScript enabled","Flask backend with Socket.IO for real-time chat","Frontend chat component deployed"],"input_types":["natural language text","code snippets (pasted into chat)","file paths and references"],"output_types":["natural language responses","code snippets","reasoning explanations","task status updates"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-devika__cap_8","uri":"capability://code.generation.editing.integrated.code.editor.with.real.time.syntax.highlighting.and.terminal","name":"integrated code editor with real-time syntax highlighting and terminal","description":"Devika includes a web-based code editor widget that displays generated code with syntax highlighting, line numbers, and code folding. The editor is integrated with a terminal widget that allows users to run commands, execute tests, and see output in real-time. When the CoderAgent generates code, it is automatically displayed in the editor. Users can edit code directly in the editor, and changes are reflected in the file system. The terminal allows execution of build commands, test runners, and other development tools.","intents":["I want to see and edit the code the AI generates","I need to run tests and see the output in the same interface","I want to execute build commands without leaving Devika","I need to debug code by running it and seeing the output"],"best_for":["developers who want an integrated development environment within Devika","teams using Devika for end-to-end feature development","projects where immediate code testing and iteration is important"],"limitations":["Web-based editor has limited performance compared to native IDEs (VS Code, IntelliJ)","Terminal execution is sandboxed and may not support all system commands","No built-in debugging tools (breakpoints, step-through, variable inspection)","Syntax highlighting is limited to common languages; custom or domain-specific languages may not be supported"],"requires":["Web browser with JavaScript enabled","Backend server with terminal execution capability","File system access for reading and writing code"],"input_types":["generated code from CoderAgent","user edits in the editor","terminal commands"],"output_types":["syntax-highlighted code","terminal output","error messages","test results"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-devika__cap_9","uri":"capability://tool.use.integration.extensible.agent.framework.with.custom.agent.creation","name":"extensible agent framework with custom agent creation","description":"Devika's agent system is designed to be extensible, allowing developers to create custom agents that inherit from a base Agent class and implement task-specific logic. Custom agents can be registered with the AgentOrchestrator and invoked as part of the workflow. The framework provides utilities for agent communication, state management, and LLM integration, reducing boilerplate code. Agents are stateless and receive context via the orchestrator, enabling easy testing and composition.","intents":["I want to create a custom agent for domain-specific tasks (e.g., database schema generation)","I need to extend Devika's capabilities without modifying core code","I want to integrate Devika with my own tools and services","I need to create agents that work with my project's specific conventions"],"best_for":["developers extending Devika for specialized use cases","teams building custom AI workflows on top of Devika","organizations integrating Devika with proprietary tools and services"],"limitations":["Custom agents must follow the base Agent interface; significant deviations require framework modifications","No built-in testing framework for agents; developers must write their own tests","Agent composition is orchestrator-driven; agents cannot directly communicate with each other","Documentation for custom agent development is minimal; requires reading source code"],"requires":["Python 3.9+","Understanding of Devika's agent architecture","Base Agent class and AgentOrchestrator available"],"input_types":["task description","context from orchestrator","LLM responses"],"output_types":["task results","state updates","messages for other agents"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["API keys for selected providers (OpenAI, Anthropic, Google, Mistral, Groq) OR local Ollama instance","Python 3.9+","config.py properly configured with provider credentials","LLM provider configured and accessible","AgentOrchestrator instantiated with all agent classes","Task input in natural language or structured format","config.py file in the project root","Environment variables set for sensitive configuration (optional)","Python 3.9+ to parse configuration","Web browser with JavaScript enabled"],"failure_modes":["No automatic prompt adaptation across model families — Claude and GPT-4 may require different prompt engineering","Token counting varies by provider; context window management is provider-specific","Streaming support depends on provider implementation; some providers may have higher latency","Agent coordination adds latency — each agent invocation requires LLM calls, potentially 5-10 round-trips for complex tasks","No built-in agent prioritization or resource limits — all agents run sequentially, no parallel execution","InternalMonologue context can grow unbounded; no automatic context pruning or summarization","Agent failures are not gracefully handled — if one agent fails, the orchestrator does not have fallback strategies","Configuration validation is minimal — invalid settings may not be caught until runtime","No built-in configuration versioning or rollback; changes are immediate","Configuration is not encrypted; sensitive data (API keys) should be managed via secrets management","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":"2026-06-17T09:51:03.038Z","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=devika","compare_url":"https://unfragile.ai/compare?artifact=devika"}},"signature":"ZYePKa7RTHG+jcNzCS3XIEeOD7B2y0V6RIuNfYlmV4l6ysU4WDQQMoELP2CSHToj0hGnAzEaMX6EVQJG2Mu5BQ==","signedAt":"2026-07-08T00:18:18.764Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/devika","artifact":"https://unfragile.ai/devika","verify":"https://unfragile.ai/api/v1/verify?slug=devika","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"}}