Code Graph RAG vs Foglamp: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Code Graph RAG and Foglamp — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
C
Code Graph RAG
vitali87
Multi-language monorepo RAG: Tree-sitter parses your codebase into a Memgraph knowledge graph so you can query, edit, and refactor in plain English.
Key features
- Multi-Language Graph Ingest: Tree-sitter parses Python, TypeScript, TSX, JavaScript, Rust, Go, Java, C, C++, C#, PHP, Lua, and Dart into a single language-agnostic Memgraph schema.
- Natural-Language Cypher: The interactive CLI turns plain-English questions into Cypher queries and answers grounded in the real code structure, not vector-only guesses.
- AST-Based Surgical Editing: The agent edits code through structural patches with a diff preview before any change is applied.
- Structural Search & Replace: ast-grep is exposed as an agent tool, so you match and rewrite by AST pattern across the whole codebase instead of regex.
- Pluggable ast-grep Tier: Add a new language from a single YAML pattern file — Ruby was added this way with Module/Function/Class nodes plus import edges.
- Data-Flow Tracing: FLOWS_TO taint edges follow values through assignments, function calls, and I/O sinks across C, Java, C#, and Go.
- Dead-Code Detection: Walk call and reference edges from entry points to find functions and modules nothing reaches.
- Shared Graph Across Projects: Index many repos into one shared graph and query across them; a `clean` subcommand resets from scratch with confirmation.
Best for
- Monorepo Q&A: Ask 'where is refund logic in this monorepo?' and get grounded answers from a graph of the real code, not stale docs.
- AI-Assisted Refactoring: Rename or restructure APIs across languages with AST patches and a diff preview before commit.
- Cross-Language Data-Flow Audits: Trace a value through assignments and function calls to see where sensitive data ends up.
- Dead-Code Cleanup: Find unreachable functions and modules by walking call edges from entry points.
- Codebase Onboarding: Give a new engineer or agent a queryable graph they can explore in natural language.
- Structural Migrations: Use ast-grep to rewrite deprecated patterns (imports, error handling, config lookups) across a polyglot codebase.
Foglamp
Foglamp
Observability for AI agents: see the cost, latency, traces, and output quality of every LLM call with one SDK.
Key features
- Two-Line SDK Instrumentation: Wrap your model once and every generateText / streamText call is automatically instrumented.
- Per-Agent Spans and Spend: View per-agent spans, latency, and spend with the full call flow across orchestrator, researcher, writer, and critic.
- Evals: Score production traffic with code checks and LLM judges, including PII checks and pass-rate scoring.
- Distributed Traces: Waterfall every run with the exact prompt and response captured per span.
- Alerts: Set threshold rules on cost, latency, and error rate to catch problems early.
- Cost Intelligence: Know exactly what every call costs broken down by model, agent, and customer.
Best for
- Catching Cost Regressions: Detect a sudden 10x cost spike days after shipping before it drains the budget.
- Debugging Bad Output: Trace the exact prompt and response that produced a wrong or hallucinated answer.
- Quality Gating with Evals: Continuously score production traffic to verify agents stay accurate and PII-safe.
- Latency Monitoring: Alert when per-agent latency crosses a threshold so slow responses are caught fast.
- Per-Customer Spend Analysis: Break down LLM spend by customer and model to understand unit economics.