CodeGraph CLI MCP Server

# CodeGraph example configuration (copy to ~/.codegraph/config.toml or use CODEGRAPH_* env vars) [embedding] # provider options: ollama | openai | jina | lmstudio provider = "ollama" model = "qwen3-embedding:0.6b" dimension = 1024 batch_size = 32 normalize_embeddings = true cache_enabled = true ollama_url = "http://localhost:11434" # Optional: override chunk size for longer-context models # env: CODEGRAPH_CHUNK_MAX_TOKENS=2048 # skip_chunking = false [llm] provider = "ollama" # or openai / anthropic / openai-compatible / xai / lmstudio model = "qwen3:4b" context_window = 252000 max_retries = 3 [rerank] # Optional reranking provider: jina | lmstudio provider = "jina" model = "jina-reranker-v3" top_n = 10 candidates = 256 [database] backend = "surrealdb" [database.surrealdb] connection = "ws://localhost:3004" namespace = "codegraph" database = "main" # username = "root" # password is best set via env: CODEGRAPH__DATABASE__SURREALDB__PASSWORD strict_mode = false auto_migrate = true [server] host = "0.0.0.0" port = 3003 [performance] # Global knobs for indexing/embedding batch_size = 64 # embedding batch size (overrides via CODEGRAPH_EMBEDDINGS_BATCH_SIZE) workers = 4 # caps Rayon threads (leave one core free by default) max_concurrent = 4 # concurrent embedding requests max_texts_per_request = 256 [indexing] # Indexing tiers: fast | balanced | full tier = "fast" [monitoring] enabled = true metrics_enabled = true trace_enabled = false metrics_interval_secs = 60 [security] require_auth = false rate_limit_per_minute = 1200 [daemon] # Daemon mode for automatic file watching and re-indexing auto_start_with_mcp = true # Auto-start daemon when MCP server starts with --watch debounce_ms = 30 # Debounce file change events batch_timeout_ms = 200 # Wait for batch before processing exclude_patterns = ["**/node_modules/**", "**/target/**", "**/.git/**", "**/dist/**"] [agent] # Agentic tool configuration # Architecture options: # react - ReAct single-pass reasoning (default, fast) # lats - Language Agent Tree Search (complex problems, multi-path) # rig - Rig framework native orchestration (alternative backend) # Override at runtime: CODEGRAPH_AGENT_ARCHITECTURE=rig architecture = "react" timeout_secs = 300 # Total agent execution timeout max_steps = 15 # Maximum reasoning steps (tier-adjusted) memory_window = 40 # Conversation memory window # LATS-specific settings (only apply when architecture = "lats") [agent.lats] beam_width = 3 # Number of best paths to keep max_depth = 5 # Maximum search depth iteration_timeout_secs = 60 # Timeout per iteration # Multi-provider LATS (optional - uses main LLM provider if not set) # selection_provider = "openai" # selection_model = "gpt-5.1-codex-mini" # expansion_provider = "anthropic" # expansion_model = "claude-4.5" # evaluation_provider = "openai" # evaluation_model = "gpt-5.1" # Secrets are provided via environment variables; do not commit secrets to this file. # Examples: # OPENAI_API_KEY=sk-... # ANTHROPIC_API_KEY=... # JINA_API_KEY=... # XAI_API_KEY=xai-...