blackwell-systems/agent-lsp

**Code intelligence infrastructure for AI agents.** 65 tools, 32 CI-verified languages, 24 agent workflows. Single Go binary.
What is it?
agent-lsp is an **MCP server** that orchestrates existing LSP servers (gopls, rust-analyzer, jdtls, etc.) into agent-native workflows.
**Not an LSP server** — it's an orchestration layer that manages language servers and exposes batch operations, speculative editing, and multi-step workflows via MCP tools.
**Architecture:**
- **Language servers** (gopls, rust-analyzer, etc.) → provide code intelligence
- **agent-lsp** (MCP server) → orchestrates workflows, maintains warm runtime
- **AI agents** → consume via MCP protocol
Why agent-lsp?
**Persistent warm runtime**
Language servers stay indexed across agent sessions. First session: indexes workspace (~10s for typical projects). Subsequent sessions: instant. No cold-start penalty on each request.
**Batch operations**
blast_radius → one call returns all exports + all callers (test vs non-test partitioned). Without orchestration: 20+ sequential LSP calls.
**Speculative editing**
simulate_edit → preview changes in memory, check diagnostic delta, apply or discard. Test edits before touching disk.
**Workflow orchestration**
24 skills that chain LSP operations into complete pipelines:
- /lsp-refactor → impact analysis → preview → apply → verify build → run tests
- /lsp-safe-edit → preview → diagnostic diff → apply if safe
- /lsp-verify → LSP diagnostics → build → test suite
**Multi-language, single session**
One agent-lsp process routes .go to gopls, .ts to tsserver, .py to pyright. No reconfiguration between projects. Session persists across files and repositories.
> [!TIP]
> **Token-optimized output:** Tool responses encoded in GCF instead of JSON. 30-84% fewer tokens depending on tool (up to 92.7% with session dedup). 100% LLM comprehension on every frontier model, 91.2% on complex code graphs where JSON averages 54.1%. See below for measured savings per tool.
**How the pieces fit together:** LSP (Language Server Protocol) is how editors get code intelligence: completions, diagnostics, go-to-definition. MCP (Model Context Protocol) is the standard way AI tools like Claude Code discover and call external tools. agent-lsp bridges the two: language server intelligence, accessible to AI agents.
Use it when
- Building agentic code generation systems
- Automating refactors across large codebases
- CI tooling that needs programmatic code intelligence
- Any workflow where sequential LSP calls are too slow or complex
What agents say
We asked AI agents to evaluate agent-lsp across 10 coding tasks (find callers, rename safely, preview edits, detect dead code) and write an honest assessment. Four different models, four independent evaluations, same conclusion:
> **Claude (Opus 4.6):** "I would recommend agent-lsp for any workflow involving refactoring, impact analysis, or safe editing. The standout tools are blast_radius (blast radius in one call, with test/non-test partitioning that would take 5-10 grep commands to replicate), go_to_implementation (type-checked interface satisfaction that grep simply cannot do), and the simulation session workflow (speculative type-checking without touching disk, which has no grep/read equivalent at all)."
> **Cursor (auto):** "I would recommend agent-lsp for heavy refactors and code navigation because the rename, references, implementations, call hierarchy, and simulation tools remove a lot of brittle grep/manual-edit work and make changes safer."
> **GPT-5.5 (via Codex):** "I would recommend agent-lsp for symbol-aware work: references, implementations, rename previews, diagnostics, and large-file structure are materially faster and less error-prone than grep/read loops."
> **Gemini 2.5 Pro (via Gemini CLI):** "I would highly recommend agent-lsp because it provides a level of semantic awareness that standard text-searching tools simply cannot match. The ability to perform high-confidence renames, find interface implementations, and preview the diagnostic impact of edits without writing to disk significantly reduces the risk of introducing regressions."
Tested, not assumed
Every other MCP-LSP implementation lists supported languages in a config file. None of them run the actual language server in CI to verify it works.
agent-lsp CI runs **32 real language servers** against real fixture codebases on every push: Go, Python, TypeScript, Rust, Java, C, C++, C#, Ruby, PHP, Kotlin, Swift, Scala, Zig, Lua, Elixir, Gleam, Clojure, Dart, Terraform, Nix, Prisma, SQL, MongoDB, MQL, and more. When we say "works with gopls," that's a verified, automated claim, not a hope.