{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-codefuse-chatbot","slug":"codefuse-chatbot","name":"CodeFuse-ChatBot","type":"agent","url":"https://github.com/codefuse-ai/codefuse-chatbot","page_url":"https://unfragile.ai/codefuse-chatbot","categories":["ai-agents"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-codefuse-chatbot__cap_0","uri":"capability://planning.reasoning.multi.agent.task.orchestration.with.configurational.scheduling","name":"multi-agent task orchestration with configurational scheduling","description":"Coordinates multiple specialized AI agents through a declarative configuration framework (codefuse-muAgent) that decomposes complex development tasks into agent phases with explicit state transitions and tool bindings. Each agent receives role-specific prompts, tool access, and context routing based on task requirements, enabling sequential or parallel execution patterns without hardcoded workflows.","intents":["I need to break down a complex development task (e.g., 'design, implement, test, and deploy a feature') across multiple specialized agents","I want to define agent workflows declaratively so non-engineers can configure task pipelines","I need agents to hand off context and artifacts between phases without losing state"],"best_for":["teams building LLM-powered development automation platforms","organizations wanting to orchestrate agents without writing custom Python orchestration code","enterprises needing reproducible, auditable multi-step development workflows"],"limitations":["Configuration complexity grows with agent count and inter-agent dependencies","No built-in distributed execution — agents run sequentially or in-process, limiting parallelism","State management between agents requires explicit context passing; no automatic memory persistence across phases"],"requires":["Python 3.9+","codefuse-muAgent package installed","LLM endpoint configured (OpenAI API, local model, or compatible service)","Tool definitions registered in agent configuration"],"input_types":["YAML/JSON agent configuration","natural language task description","structured tool schemas"],"output_types":["agent execution logs with phase transitions","structured task artifacts (code, test results, deployment configs)","execution trace with decision points"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-codefuse-chatbot__cap_1","uri":"capability://code.generation.editing.repository.level.code.understanding.with.ast.based.indexing","name":"repository-level code understanding with ast-based indexing","description":"Analyzes entire codebases by parsing source files into Abstract Syntax Trees (AST) and building semantic indexes of functions, classes, dependencies, and call graphs. The system generates project structure summaries and enables agents to retrieve code context at repository, file, and function granularity, supporting multi-language analysis through language-specific parsers.","intents":["I want an agent to understand the full structure of my codebase before suggesting changes","I need to find all usages of a function across a large repository","I want to generate accurate code modifications that respect existing architecture and dependencies"],"best_for":["developers working with large, multi-file codebases (>10K LOC)","teams needing code-aware agents that can reason about architectural impact","organizations migrating or refactoring legacy systems with complex dependency graphs"],"limitations":["AST parsing adds initial indexing latency (proportional to codebase size); re-indexing required on file changes","Language support limited to explicitly implemented parsers; unsupported languages fall back to text-based analysis","Call graph analysis may miss dynamic calls (reflection, eval, dynamic imports) leading to incomplete dependency maps"],"requires":["Python 3.9+","Source code files in supported languages (Python, Java, JavaScript, Go, etc.)","Sufficient disk space for AST index (typically 2-5x source code size)","Language-specific parser libraries (tree-sitter or equivalent)"],"input_types":["source code files","repository directory structure","file paths for targeted analysis"],"output_types":["semantic code index (JSON/structured format)","dependency graphs","function/class definitions with signatures","code context snippets for agent consumption"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-codefuse-chatbot__cap_10","uri":"capability://memory.knowledge.conversation.context.management.with.multi.turn.memory","name":"conversation context management with multi-turn memory","description":"Maintains conversation history and context across multiple turns, allowing agents and users to reference previous messages, code snippets, and execution results. Implements context windowing strategies to fit long conversations into LLM context limits, prioritizing recent and relevant messages while pruning older context. Supports context summarization to compress long histories into concise summaries.","intents":["I want agents to remember previous conversation context when responding to follow-up questions","I need to maintain code snippets and execution results across multiple turns","I want to handle long conversations without exceeding LLM context limits"],"best_for":["users having extended conversations with agents","teams needing to maintain context across multiple development sessions","organizations using models with limited context windows (4K-8K tokens)"],"limitations":["Context windowing strategies may lose important information from earlier in conversation","Summarization adds latency and may introduce inaccuracies","No built-in persistence — context is lost if session ends; requires external storage for multi-session continuity"],"requires":["Python 3.9+","LLM with sufficient context window (8K+ tokens recommended)","Optional: external storage (database, file system) for session persistence"],"input_types":["user messages","agent responses","code snippets","execution results"],"output_types":["conversation history","context summaries","windowed context for LLM prompts"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-codefuse-chatbot__cap_11","uri":"capability://automation.workflow.docker.based.deployment.orchestration.with.service.composition","name":"docker-based deployment orchestration with service composition","description":"Provides Docker-based deployment configurations that orchestrate multiple services (LLM backend, knowledge base, web UI, sandbox environment) through docker-compose or Kubernetes manifests. Handles service initialization, networking, volume management, and health checks. Supports both local development deployments and production-scale distributed deployments.","intents":["I want to deploy CodeFuse-ChatBot locally for development and testing","I need to deploy CodeFuse-ChatBot to production with proper service orchestration","I want to scale individual components (e.g., multiple LLM backends) independently"],"best_for":["teams deploying CodeFuse-ChatBot as a self-hosted service","organizations needing reproducible deployment configurations","DevOps teams managing multi-service infrastructure"],"limitations":["Docker overhead adds ~500ms-2s per container startup; not suitable for serverless/function-as-a-service deployments","Service orchestration complexity grows with number of services; Kubernetes required for large-scale deployments","Resource requirements vary by component (LLM backend needs GPU, knowledge base needs persistent storage); configuration must account for hardware constraints"],"requires":["Docker and Docker Compose installed (or Kubernetes cluster)","GPU support for LLM backend (optional but recommended for performance)","Persistent storage for knowledge base and conversation history","Network connectivity between services"],"input_types":["docker-compose.yml or Kubernetes manifests","environment variables for configuration","service images"],"output_types":["running services","health check status","service logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-codefuse-chatbot__cap_12","uri":"capability://planning.reasoning.agent.phase.based.execution.with.explicit.state.transitions","name":"agent phase-based execution with explicit state transitions","description":"Structures agent execution as a sequence of explicit phases (e.g., analysis → design → implementation → testing → deployment) where each phase has defined inputs, outputs, and success criteria. Agents transition between phases based on completion conditions, maintaining state across phases. Supports conditional branching (e.g., 'if tests fail, return to implementation phase') and parallel phase execution.","intents":["I want to structure complex development tasks as explicit phases with clear transitions","I need agents to validate phase completion before moving to the next phase","I want to support conditional logic (e.g., 'if tests fail, retry implementation')"],"best_for":["teams with well-defined development processes (design → code → test → deploy)","organizations needing auditable, reproducible workflows","developers wanting explicit control over agent execution flow"],"limitations":["Phase-based execution requires upfront process definition; inflexible for ad-hoc or exploratory tasks","State management between phases adds complexity; requires explicit context passing","Conditional branching logic must be defined in configuration; complex conditions may require custom code"],"requires":["Python 3.9+","Phase definitions in configuration (YAML/JSON)","Success criteria for each phase","State management mechanism (in-memory or persistent)"],"input_types":["phase configuration","phase inputs (code, requirements, etc.)","success criteria"],"output_types":["phase outputs","execution trace with phase transitions","state at each phase"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-codefuse-chatbot__cap_2","uri":"capability://memory.knowledge.rag.enhanced.knowledge.base.retrieval.with.knowledge.graphs","name":"rag-enhanced knowledge base retrieval with knowledge graphs","description":"Integrates document knowledge bases with knowledge graph structures to improve retrieval accuracy and reasoning. Documents are chunked, embedded, and indexed in vector stores; knowledge graphs capture entity relationships and domain concepts. Retrieval combines semantic similarity search with graph-based relationship traversal, allowing agents to fetch contextually relevant information and follow entity connections across documents.","intents":["I want agents to retrieve relevant documentation and best practices when solving development tasks","I need to connect related knowledge artifacts (e.g., 'this API endpoint relates to this data model relates to this deployment pattern')","I want to reduce hallucinations by grounding agent responses in curated knowledge bases"],"best_for":["teams with large internal documentation or DevOps knowledge bases","organizations needing domain-specific reasoning (e.g., infrastructure patterns, API contracts)","enterprises wanting to enforce consistency by grounding agents in approved knowledge sources"],"limitations":["Knowledge graph construction is semi-manual; requires entity extraction and relationship definition","Retrieval quality depends on embedding model quality and chunking strategy; poor chunks lead to irrelevant results","Graph traversal adds latency (~50-200ms per query depending on graph size); not suitable for real-time, latency-critical paths"],"requires":["Vector database (Chroma, Weaviate, Milvus, or equivalent)","Embedding model (OpenAI, local sentence-transformers, or compatible)","Knowledge graph database (Neo4j, or in-memory graph structure)","Document corpus in supported formats (Markdown, PDF, plain text)"],"input_types":["documents (text, Markdown, PDF)","entity definitions and relationships","semantic queries from agents"],"output_types":["ranked document chunks with relevance scores","entity relationship paths","augmented context for agent prompts"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-codefuse-chatbot__cap_3","uri":"capability://automation.workflow.sandboxed.code.execution.with.isolated.environments","name":"sandboxed code execution with isolated environments","description":"Executes generated or user-provided code in isolated sandbox environments (Docker containers or local process isolation) with resource limits, preventing malicious or buggy code from affecting the host system. Agents can safely run code generation outputs, tests, and deployment scripts while capturing stdout, stderr, and exit codes for validation and debugging.","intents":["I want agents to execute generated code and validate it works before proposing changes","I need to run tests and deployment scripts safely without risking the production environment","I want to capture execution results and feed them back to agents for iterative refinement"],"best_for":["development teams automating code generation and testing workflows","organizations running untrusted or auto-generated code that requires validation","CI/CD pipelines needing safe code execution with feedback loops to LLM agents"],"limitations":["Sandbox overhead adds 500ms-2s per execution (container startup, cleanup); not suitable for high-frequency execution","Resource limits (CPU, memory, disk) must be pre-configured; runaway processes may still consume resources before termination","Network access within sandbox must be explicitly enabled; by default, sandboxes are network-isolated, limiting integration testing"],"requires":["Docker daemon running (for container-based sandboxes) OR local process isolation support","Python 3.9+ for sandbox orchestration","Resource quotas configured (CPU cores, memory limits, timeout thresholds)","Base images or runtime environments for target languages (Python, Node.js, Go, etc.)"],"input_types":["code snippets (Python, JavaScript, Bash, etc.)","test scripts","deployment configurations"],"output_types":["execution stdout/stderr","exit codes","execution duration","resource usage metrics"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-codefuse-chatbot__cap_4","uri":"capability://tool.use.integration.multi.model.llm.abstraction.with.provider.agnostic.routing","name":"multi-model llm abstraction with provider-agnostic routing","description":"Abstracts LLM provider differences (OpenAI, Anthropic, local models, proprietary APIs) behind a unified interface, allowing agents to route requests to different models based on task requirements, cost, or availability. Supports model-specific optimizations (e.g., function calling schemas, context window sizes) while maintaining consistent prompt formatting and response parsing across providers.","intents":["I want to use different LLM providers (OpenAI, local models, proprietary) without rewriting agent code","I need to optimize for cost by routing simple tasks to cheaper models and complex tasks to powerful models","I want to ensure my system works offline with open-source models and online with commercial APIs"],"best_for":["organizations wanting flexibility to switch LLM providers without code changes","teams needing cost optimization through intelligent model routing","enterprises requiring offline-capable systems with optional cloud fallback"],"limitations":["Model-specific features (e.g., vision capabilities, function calling) require conditional logic; not all models support all features","Response format differences between models may require post-processing; no guarantee of consistent output structure","Latency varies significantly across providers (OpenAI ~1-3s, local models ~5-30s depending on hardware); routing logic must account for SLA requirements"],"requires":["Python 3.9+","API keys for commercial providers (OpenAI, Anthropic) OR local model deployment (Ollama, vLLM, etc.)","Model configuration specifying endpoint, authentication, and capability flags","Sufficient GPU memory for local models (varies by model size: 8GB for 7B models, 20GB+ for 34B models)"],"input_types":["prompts (text)","model configuration (JSON/YAML)","tool/function schemas"],"output_types":["LLM responses (text, structured JSON)","token usage metrics","model routing decisions"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-codefuse-chatbot__cap_5","uri":"capability://memory.knowledge.devops.specialized.knowledge.base.construction","name":"devops-specialized knowledge base construction","description":"Provides self-service tools for building domain-specific knowledge bases focused on DevOps, infrastructure, and operations tasks. Includes pre-built templates for common DevOps patterns (deployment strategies, monitoring, incident response), document ingestion pipelines, and knowledge graph scaffolding. Teams can extend knowledge bases with custom patterns and best practices without ML expertise.","intents":["I want to build a knowledge base of our deployment patterns and infrastructure best practices","I need agents to reference our runbooks and incident response procedures when troubleshooting","I want to capture and reuse our team's DevOps knowledge in a structured, searchable format"],"best_for":["DevOps and SRE teams wanting to codify operational knowledge","organizations standardizing infrastructure practices across teams","enterprises needing to reduce MTTR by grounding agents in curated runbooks"],"limitations":["Knowledge base quality depends on documentation quality; garbage in, garbage out","Maintaining knowledge base currency requires ongoing updates as infrastructure evolves","Pre-built templates are generic; customization to specific infrastructure (cloud provider, tools) requires manual effort"],"requires":["Python 3.9+","Vector database for document storage","Embedding model for document vectorization","Knowledge graph database (optional but recommended)","DevOps documentation in supported formats (Markdown, plain text, PDF)"],"input_types":["runbooks and procedures (Markdown, text)","infrastructure-as-code templates","incident reports and postmortems","monitoring and alerting configurations"],"output_types":["indexed knowledge base","knowledge graph with DevOps entity relationships","searchable documentation with semantic indexing"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-codefuse-chatbot__cap_6","uri":"capability://automation.workflow.web.ui.with.multi.mode.interaction.llm.kb.code.search.agent","name":"web ui with multi-mode interaction (llm, kb, code, search, agent)","description":"Provides a unified web interface supporting five distinct interaction modes: LLM chat (direct model interaction), KB search (knowledge base queries), Code analysis (repository exploration), Search (semantic search across all sources), and Agent (multi-agent task execution). Each mode has specialized UI components and context management, allowing users to switch modes without losing conversation state.","intents":["I want a single interface to chat with an LLM, search documentation, explore code, and run agents","I need to switch between different interaction modes (e.g., 'search the KB for this pattern, then ask an agent to implement it')","I want to see code context, documentation, and agent execution traces in a unified view"],"best_for":["development teams wanting a unified interface for AI-assisted development","organizations deploying CodeFuse-ChatBot as an internal tool","users preferring web UI over CLI or IDE integration"],"limitations":["Web UI latency depends on backend response times; agent execution traces may take 10-30s to display","Context switching between modes requires explicit user action; no automatic mode detection based on query intent","Code visualization limited to text-based display; no IDE-like features (syntax highlighting, refactoring tools)"],"requires":["Web server (Node.js, Python Flask/FastAPI, or equivalent)","Backend API endpoints for each mode (LLM, KB, Code, Search, Agent)","Modern web browser (Chrome, Firefox, Safari, Edge)","WebSocket support for real-time agent execution traces (optional but recommended)"],"input_types":["natural language queries","code snippets for analysis","file paths for code exploration","agent task descriptions"],"output_types":["LLM responses","search results with relevance scores","code context with syntax highlighting","agent execution logs and artifacts"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-codefuse-chatbot__cap_7","uri":"capability://code.generation.editing.agent.driven.code.generation.with.iterative.refinement","name":"agent-driven code generation with iterative refinement","description":"Agents generate code based on task descriptions, execute generated code in sandboxes, capture execution results, and iteratively refine code based on test failures or user feedback. The system maintains execution history and allows agents to learn from previous attempts, improving code quality across iterations without requiring manual debugging.","intents":["I want an agent to write code, test it, and fix bugs automatically","I need to generate code incrementally, validating each step before proceeding","I want agents to learn from test failures and improve code quality across iterations"],"best_for":["teams automating routine code generation tasks (boilerplate, CRUD operations, test generation)","developers wanting AI-assisted coding with automatic validation","organizations reducing manual code review burden through automated testing"],"limitations":["Iterative refinement adds latency (5-30s per iteration depending on code complexity and test suite size)","Agent-generated code may not match team coding standards; requires linting and style checking integration","Complex logic or domain-specific patterns may require multiple iterations; no guarantee of convergence to correct solution"],"requires":["Python 3.9+","Sandbox environment for code execution","Test framework compatible with generated code (pytest, Jest, etc.)","Linting and formatting tools (Black, Prettier, etc.)","LLM with sufficient context window to maintain execution history"],"input_types":["task description (natural language)","code context (existing codebase, function signatures)","test specifications or examples"],"output_types":["generated code","test execution results","refinement history","final validated code"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-codefuse-chatbot__cap_8","uri":"capability://data.processing.analysis.project.structure.generation.from.codebase.analysis","name":"project structure generation from codebase analysis","description":"Analyzes existing codebases to extract project structure, file organization, and architectural patterns, then generates structured summaries (project trees, module hierarchies, dependency diagrams). Agents use these summaries to understand project layout and propose changes that respect existing architecture. Supports incremental updates as codebase evolves.","intents":["I want an agent to understand my project structure before suggesting changes","I need to generate documentation of my codebase architecture automatically","I want to ensure agents propose changes that fit within existing project organization"],"best_for":["teams with large, complex codebases needing architectural understanding","organizations automating documentation generation","developers onboarding new team members who need codebase overview"],"limitations":["Project structure analysis is language-specific; multi-language projects require multiple parsers","Generated summaries may be incomplete for dynamic or unconventional project layouts","Incremental updates require tracking file changes; full re-analysis may be needed for large refactorings"],"requires":["Python 3.9+","Source code files in supported languages","Language-specific parsers (tree-sitter or equivalent)","Sufficient disk space for analysis results"],"input_types":["source code directory","file paths for targeted analysis","configuration files (package.json, setup.py, etc.)"],"output_types":["project tree structure (JSON/YAML)","module hierarchy","dependency diagrams","architectural summary"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-codefuse-chatbot__cap_9","uri":"capability://tool.use.integration.tool.library.integration.with.schema.based.function.calling","name":"tool library integration with schema-based function calling","description":"Integrates external tools and APIs through a schema-based function calling system where tools are defined with JSON schemas specifying inputs, outputs, and behavior. Agents can discover available tools, understand their capabilities through schemas, and invoke them with proper parameter binding. Supports tool chaining where output from one tool becomes input to another.","intents":["I want agents to use external tools (APIs, CLIs, scripts) to accomplish tasks","I need agents to understand tool capabilities and invoke them correctly","I want to create tool chains where multiple tools work together to solve complex problems"],"best_for":["teams integrating agents with existing tool ecosystems (CI/CD, monitoring, deployment)","organizations building custom tool libraries for domain-specific tasks","developers wanting agents to interact with APIs and external services"],"limitations":["Tool schema definition is manual; requires careful specification of inputs, outputs, and error cases","Tool invocation latency depends on external service response times; no built-in timeout or retry logic","Tool chaining requires agents to understand data flow between tools; complex chains may require explicit orchestration"],"requires":["Python 3.9+","Tool definitions in JSON schema format","External tools/APIs accessible from agent environment","Network connectivity for remote tools"],"input_types":["tool schemas (JSON)","tool invocation requests from agents","tool parameters"],"output_types":["tool execution results","error messages and status codes","structured tool responses"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","codefuse-muAgent package installed","LLM endpoint configured (OpenAI API, local model, or compatible service)","Tool definitions registered in agent configuration","Source code files in supported languages (Python, Java, JavaScript, Go, etc.)","Sufficient disk space for AST index (typically 2-5x source code size)","Language-specific parser libraries (tree-sitter or equivalent)","LLM with sufficient context window (8K+ tokens recommended)","Optional: external storage (database, file system) for session persistence","Docker and Docker Compose installed (or Kubernetes cluster)"],"failure_modes":["Configuration complexity grows with agent count and inter-agent dependencies","No built-in distributed execution — agents run sequentially or in-process, limiting parallelism","State management between agents requires explicit context passing; no automatic memory persistence across phases","AST parsing adds initial indexing latency (proportional to codebase size); re-indexing required on file changes","Language support limited to explicitly implemented parsers; unsupported languages fall back to text-based analysis","Call graph analysis may miss dynamic calls (reflection, eval, dynamic imports) leading to incomplete dependency maps","Context windowing strategies may lose important information from earlier in conversation","Summarization adds latency and may introduce inaccuracies","No built-in persistence — context is lost if session ends; requires external storage for multi-session continuity","Docker overhead adds ~500ms-2s per container startup; not suitable for serverless/function-as-a-service deployments","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:02.371Z","last_scraped_at":"2026-05-03T14:00:10.321Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=codefuse-chatbot","compare_url":"https://unfragile.ai/compare?artifact=codefuse-chatbot"}},"signature":"50v1xN5K9LNXq66cm9oeE4urwyYJbS82NXDeuRcPityuaqs8O9vC699VBBMIe850+8f+GHUNXtfiHLEowLEqCA==","signedAt":"2026-07-08T12:38:08.026Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/codefuse-chatbot","artifact":"https://unfragile.ai/codefuse-chatbot","verify":"https://unfragile.ai/api/v1/verify?slug=codefuse-chatbot","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"}}