CodeFuse-ChatBot

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.