Content
<p align="center">
<img src="assets/social-preview.png" alt="agent-lsp" width="600">
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<a href="#tools"><img src="https://img.shields.io/badge/CI--verified_tools-65%2F65-brightgreen.svg" alt="CI Coverage"></a>
<a href="#multi-language-support"><img src="https://img.shields.io/badge/languages-30_CI--verified-brightgreen.svg" alt="Languages"></a>
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<a href="https://agentskills.io"><img src="assets/badge-agentskills.svg" alt="Agent Skills"></a>
<a href="https://github.com/blackwell-systems/agent-lsp"><img src="https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/blackwell-systems/agent-lsp/badges/assets/downloads-badge.json" alt="downloads"></a>
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<a href="https://microsoft.github.io/language-server-protocol/specifications/lsp/3.17/specification/"><img src="https://img.shields.io/badge/LSP-3.17-blue.svg" alt="LSP 3.17"></a>
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**Code intelligence infrastructure for AI agents.** 65 tools, 30 CI-verified languages, 24 agent workflows. Single Go binary.
```bash
curl -fsSL https://raw.githubusercontent.com/blackwell-systems/agent-lsp/main/install.sh | sh && agent-lsp init
```
## 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](https://gcformat.com) instead of JSON. 30-84% fewer tokens depending on tool (up to 92.7% with session dedup). [100% LLM comprehension on every frontier model](https://gcformat.com/guide/benchmarks.html), 91.2% on complex code graphs where JSON averages 54.1%. See [below](#token-optimized-output-gcf) for measured savings per tool.
**How the pieces fit together:** [LSP](https://microsoft.github.io/language-server-protocol/) (Language Server Protocol) is how editors get code intelligence: completions, diagnostics, go-to-definition. [MCP](https://modelcontextprotocol.io/) (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 **30 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, and more. When we say "works with gopls," that's a verified, automated claim, not a hope.
### Speculative execution
Simulate changes in memory before writing to disk. No other MCP-LSP implementation has this.
`preview_edit` previews the diagnostic impact of any edit. You see exactly what breaks before the file is touched. `simulate_chain` evaluates a sequence of dependent edits (rename a function, update all callers, change the return type) and reports which step first introduces an error.
8 speculative execution tools. See [docs/guide/speculative-execution.md](./docs/guide/speculative-execution.md) for the full workflow.
### Token savings
Structured LSP responses use **5-34x fewer tokens** than grep/read on the same tasks. On HashiCorp Consul (319K lines), a blast-radius analysis uses 17.7MB via grep vs 841KB via LSP, reducing 5,534 tool calls to 119. Savings scale with codebase size. See [docs/guide/token-savings.md](./docs/guide/token-savings.md) for the full experiment across five codebases.
### Token-optimized output (GCF)
Tool responses are encoded in [GCF (Graph Compact Format)](https://gcformat.com) instead of JSON. GCF eliminates field-name repetition, identifier repetition, and per-record structural overhead.
| Profile | Tools | Savings vs JSON |
|---------|-------|----------------|
| Tabular | All 66 tools | **30-51%** |
| Graph | blast_radius, find_callers, explore_symbol, find_references, type_hierarchy, cross_repo, detect_changes, list_symbols | **79-84%** |
| Graph + session dedup | Same, via [gcf-proxy](https://github.com/blackwell-systems/gcf-proxy) `--session` | **92.7%** (5th call) |
GCF is enabled by default. To revert to JSON:
```bash
export AGENT_LSP_OUTPUT_FORMAT=json
```
Benchmark: `go run scripts/gcf-benchmark.go`. See [docs/guide/gcf-integration.md](./docs/guide/gcf-integration.md) for architecture details.
**GCF:** [gcformat.com](https://gcformat.com) · [Spec](https://github.com/blackwell-systems/gcf) · [Go](https://github.com/blackwell-systems/gcf-go) · [Python](https://github.com/blackwell-systems/gcf-python) · [TypeScript](https://github.com/blackwell-systems/gcf-typescript) · [Playground](https://gcformat.com/playground.html)
### Why orchestration matters
AI agents make incorrect code changes because they can't see the full picture: who calls this function, what breaks if I rename it, does the build still pass. Language servers have the answers, but raw LSP tools require 20+ sequential calls and complex orchestration logic.
agent-lsp solves this by encoding correct multi-step operations into single calls and skills. `blast_radius` does what would take an agent 20+ calls in one. `/lsp-refactor` chains impact → preview → apply → verify → test without per-prompt orchestration.
### Persistent daemon mode
Python and TypeScript projects need minutes of background indexing before `find_references` works. agent-lsp automatically spawns a persistent daemon broker that survives between sessions, so the workspace stays indexed. First session: daemon starts and indexes (~10s for FastAPI). Subsequent sessions: instant connection to the warm daemon. Auto-exits after 30 minutes of inactivity. Go, Rust, and other fast-indexing languages bypass this entirely (zero overhead).
### Phase enforcement
Skills tell agents the correct order of operations. Phase enforcement makes the runtime *block* violations instead of trusting the agent to follow instructions.
When an agent activates a skill, every tool call is checked against the current phase's permissions. Calling `apply_edit` during blast-radius analysis doesn't silently proceed; it returns an error with specific recovery guidance ("complete the blast_radius phase first, allowed tools: [blast_radius, find_references]"). Phases advance automatically as the agent calls tools from later phases.
No other MCP tool provider enforces workflow ordering at runtime. See [docs/guide/phase-enforcement.md](./docs/guide/phase-enforcement.md).
### Concurrency analysis
The inspector includes 4 concurrency checks that work across 25 languages in 4 concurrency families (goroutine, thread, async, actor):
- **Unrecovered concurrent entry**: goroutines/threads/tasks without recovery
- **Unchecked shared state**: bare type assertions on sync.Map, ConcurrentHashMap
- **Channel never closed**: channels/queues created but never closed (goroutine leaks)
- **Shared field without sync**: fields accessed from concurrent contexts without synchronization
`blast_radius` annotates symbols with `sync_guarded: true` when the parent type has a mutex. `find_callers` with `cross_concurrent: true` traces call chains through goroutine/thread boundaries. The `/lsp-concurrency-audit` skill produces a field-level safety report for any type.
### Auto-diagnostics
Symbol edit tools (`replace_symbol_body`, `insert_after_symbol`, `insert_before_symbol`, `safe_delete_symbol`) automatically return `errors_after` and `warnings_after` counts. Agents know immediately whether an edit broke something without a separate `get_diagnostics` call.
`safe_apply_edit` combines preview + apply in one call: previews speculatively, applies to disk only if `net_delta == 0` (no new errors). One tool call instead of three.
### Works with
| AI Tool | Transport | Setup |
|---------|-----------|-------|
| [Claude Code](https://docs.anthropic.com/en/docs/claude-code) | stdio | `agent-lsp init` |
| [Cursor](https://cursor.com) | stdio | `agent-lsp init` |
| [Windsurf](https://windsurf.com) | stdio | `agent-lsp init` |
| [Gemini CLI](https://github.com/google-gemini/gemini-cli) | stdio | `agent-lsp init` |
| [Continue](https://continue.dev) | stdio | `agent-lsp init` |
| [Cline](https://github.com/cline/cline) | stdio | `agent-lsp init` |
| Any MCP client | HTTP+SSE | `agent-lsp --http --port 8080` |
See [docs/getting-started/mcp-clients.md](./docs/getting-started/mcp-clients.md) for copy-paste configs.
## Skills
Raw tools get ignored. Skills get used. Each skill encodes the correct tool sequence so workflows actually happen without per-prompt orchestration instructions. Skills are available as [AgentSkills](https://github.com/anthropics/agent-skills) slash commands and as MCP prompts via `prompts/list` / `prompts/get` for any MCP client.
See [docs/guide/skills.md](./docs/guide/skills.md) for full descriptions and usage guidance.
**Before you change anything**
| Skill | Purpose |
|-------|---------|
| `/lsp-impact` | Blast-radius analysis before touching a symbol or file |
| `/lsp-implement` | Find all concrete implementations of an interface |
| `/lsp-dead-code` | Detect zero-reference exports before cleanup |
**Editing safely**
| Skill | Purpose |
|-------|---------|
| `/lsp-safe-edit` | Speculative preview before disk write; before/after diagnostic diff; surfaces code actions on errors |
| `/lsp-simulate` | Test changes in-memory without touching the file |
| `/lsp-edit-symbol` | Edit a named symbol without knowing its file or position |
| `/lsp-edit-export` | Safe editing of exported symbols, finds all callers first |
| `/lsp-rename` | `prepare_rename` safety gate, preview all sites, confirm, apply atomically |
**Getting started**
| Skill | Purpose |
|-------|---------|
| `/lsp-onboard` | First-session project onboarding: detect languages, map packages, find entry points and hotspots, check diagnostics |
**Understanding unfamiliar code**
| Skill | Purpose |
|-------|---------|
| `/lsp-explore` | "Tell me about this symbol": hover + implementations + call hierarchy + references in one pass |
| `/lsp-understand` | Deep-dive Code Map for a symbol or file: type info, call hierarchy, references, source |
| `/lsp-docs` | Three-tier documentation: hover → offline toolchain → source |
| `/lsp-cross-repo` | Find all usages of a library symbol across consumer repos |
| `/lsp-local-symbols` | File-scoped symbol list, usage search, and type info |
**After editing**
| Skill | Purpose |
|-------|---------|
| `/lsp-verify` | Diagnostics + build + tests after every edit |
| `/lsp-fix-all` | Apply quick-fix code actions for all diagnostics in a file |
| `/lsp-test-correlation` | Find and run only tests that cover an edited file |
| `/lsp-format-code` | Format a file or selection via the language server formatter |
**Generating code**
| Skill | Purpose |
|-------|---------|
| `/lsp-generate` | Trigger server-side code generation (interface stubs, test skeletons, mocks) |
| `/lsp-extract-function` | Extract a code block into a named function via code actions |
**Full workflow**
| Skill | Purpose |
|-------|---------|
| `/lsp-refactor` | End-to-end refactor: blast-radius → preview → apply → verify → test |
| `/lsp-inspect` | Full code quality audit (12 checks): dead symbols, test coverage, error handling, doc drift, concurrency safety |
| `/lsp-concurrency-audit` | Field-level concurrency safety audit for a type: traces concurrent access, flags unsynced fields |
## Docker
**Stdio mode** (MCP client spawns the container directly):
```bash
# Go
docker run --rm -i -v /your/project:/workspace ghcr.io/blackwell-systems/agent-lsp:go go:gopls
# TypeScript
docker run --rm -i -v /your/project:/workspace ghcr.io/blackwell-systems/agent-lsp:typescript typescript:typescript-language-server,--stdio
# Python
docker run --rm -i -v /your/project:/workspace ghcr.io/blackwell-systems/agent-lsp:python python:pyright-langserver,--stdio
```
**HTTP mode** (persistent service, remote clients connect over HTTP+SSE):
```bash
docker run --rm \
-p 8080:8080 \
-v /your/project:/workspace \
-e AGENT_LSP_TOKEN=your-secret-token \
ghcr.io/blackwell-systems/agent-lsp:go \
--http --port 8080 go:gopls
```
Images run as a non-root user (uid 65532) by default. Set `AGENT_LSP_TOKEN` via environment variable, never `--token` on the command line. Images are also mirrored to Docker Hub (`blackwellsystems/agent-lsp`). See [DOCKER.md](./DOCKER.md) for the full tag list, HTTP mode setup, and security hardening options.
## Setup
### Step 1: Install agent-lsp
```bash
curl -fsSL https://raw.githubusercontent.com/blackwell-systems/agent-lsp/main/install.sh | sh
```
<details>
<summary>Alternative install methods</summary>
**macOS / Linux**
```bash
brew install blackwell-systems/tap/agent-lsp
```
**Windows**
```powershell
# PowerShell (no admin required)
iwr -useb https://raw.githubusercontent.com/blackwell-systems/agent-lsp/main/install.ps1 | iex
# Scoop
scoop bucket add blackwell-systems https://github.com/blackwell-systems/agent-lsp
scoop install blackwell-systems/agent-lsp
# Winget
winget install BlackwellSystems.agent-lsp
```
**All platforms**
```bash
# pip
pip install agent-lsp
# npm
npm install -g @blackwell-systems/agent-lsp
# Go install
go install github.com/blackwell-systems/agent-lsp/cmd/agent-lsp@latest
```
</details>
### Step 2: Install language servers
Install the servers for your stack. Common ones:
| Language | Server | Install |
|----------|--------|---------|
| TypeScript / JavaScript | `typescript-language-server` | `npm i -g typescript-language-server typescript` |
| Python | `pyright-langserver` | `npm i -g pyright` |
| Go | `gopls` | `go install golang.org/x/tools/gopls@latest` |
| Rust | `rust-analyzer` | `rustup component add rust-analyzer` |
| C / C++ | `clangd` | `apt install clangd` / `brew install llvm` |
| Ruby | `solargraph` | `gem install solargraph` |
Full list of 30 supported languages in [docs/reference/language-support.md](./docs/reference/language-support.md).
### Step 3: Verify setup
```bash
agent-lsp doctor
```
Probes each configured language server and reports capabilities. Fix any failures before proceeding. See [language support](./docs/reference/language-support.md) for install commands and server-specific notes.
### Step 4: Configure your AI tool
```bash
agent-lsp init
```
Detects language servers on your PATH, asks which AI tool you use, writes the correct MCP config, and installs skill awareness rules for your AI provider (CLAUDE.md for Claude Code, `.cursor/rules/` for Cursor, `.clinerules` for Cline, `.windsurfrules` for Windsurf, `GEMINI.md` for Gemini CLI). For CI or scripted use: `agent-lsp init --non-interactive`.
The generated config looks like:
```json
{
"mcpServers": {
"lsp": {
"type": "stdio",
"command": "agent-lsp",
"args": [
"go:gopls",
"typescript:typescript-language-server,--stdio",
"python:pyright-langserver,--stdio"
]
}
}
}
```
Each arg is `language:server-binary` (comma-separate server args).
### Step 5: Install skills
```bash
git clone https://github.com/blackwell-systems/agent-lsp.git /tmp/agent-lsp-skills
cd /tmp/agent-lsp-skills/skills && ./install.sh --copy
```
Skills are prompt files copied into your AI tool's configuration. `--copy` means the clone can be safely deleted afterward.
Skills are also available as **MCP prompts**: any MCP client can discover them via `prompts/list` and retrieve full workflow instructions via `prompts/get`, with no manual installation required. The `install.sh` path is for AgentSkills-compatible clients (Claude Code slash commands).
### Step 6: Allow tool permissions (Claude Code)
For Claude Code, add `mcp__lsp__*` to your permissions allow list so all 65 tools are available without per-tool approval prompts:
```json
// ~/.claude/settings.json
{
"permissions": {
"allow": ["mcp__lsp__*"]
}
}
```
Without this, Claude Code will prompt for permission on each tool call. Other MCP clients handle permissions differently; check your client's documentation.
Skills are multi-tool workflows that encode reliable procedures: blast-radius check before edit, speculative preview before write, test run after change. See [docs/guide/skills.md](./docs/guide/skills.md) for the full list.
### Step 7: Start working
Your AI agent calls tools automatically. The first call initializes the workspace:
```
start_lsp(root_dir="/your/project")
```
This is what the agent does, not something you type. Then use any of the 65 tools. The session stays warm; no restart needed when switching files.
## What's unique about agent-lsp
| Capability | Details |
|------------|---------|
| Tools | **65** |
| Languages (CI-verified) | **30**, end-to-end integration tests on every push |
| Agent workflows (skills) | **24**, named multi-step procedures, discoverable via MCP `prompts/list` |
| Speculative execution | **8 tools**, simulate changes before writing to disk |
| Phase enforcement | **4 skills**, runtime blocks out-of-order tool calls with recovery guidance |
| Connection model | **persistent**, warm index across files and projects |
| Call hierarchy | **✓**, single tool, direction param |
| Type hierarchy | **✓**, CI-verified |
| Cross-repo references | **✓**, multi-root workspace |
| Auto-watch | **✓**, always-on, debounced file watching |
| HTTP+SSE transport | **✓**, bearer token auth, non-root Docker |
| Distribution | **single Go binary**, 10 install channels |
## Use Cases
- **Multi-project sessions**: point your AI at `~/code/`, work across any project without reconfiguring
- **Polyglot development**: Go backend + TypeScript frontend + Python scripts in one session
- **Large monorepos**: one server handles all languages, routes by file extension
- **Code migration**: refactor across repos with full cross-repo reference tracking
- **CI pipelines**: validate against real language server behavior
- **Niche language stacks**: Gleam, Elixir, Prisma, Zig, Clojure, Nix, Dart, Scala, MongoDB, all CI-verified
## Multi-Language Support
30 languages, CI-verified end-to-end against real language servers on every CI run. No other MCP-LSP implementation tests a single language in CI.
Go, Python, TypeScript, Rust, Java, C, C++, C#, Ruby, PHP, Kotlin, Swift, Scala, Zig, Lua, Elixir, Gleam, Clojure, Dart, Terraform, Nix, Prisma, SQL, MongoDB, JavaScript, YAML, JSON, Dockerfile, CSS, HTML.
See [docs/reference/language-support.md](./docs/reference/language-support.md) for the full coverage matrix.
## Tools
65 tools covering navigation, analysis, refactoring, symbol editing, composite exploration, safe editing, speculative execution, and session lifecycle. All CI-verified.
See [docs/reference/tools.md](./docs/reference/tools.md) for the full reference with parameters and examples.
## Further reading
### Documentation
- [Tools reference](./docs/reference/tools.md): full tool reference with parameters and examples
- [Skills reference](./docs/guide/skills.md): skill reference, workflows, use cases, and composition
- [Language support](./docs/reference/language-support.md): language coverage matrix
- [Architecture](./docs/architecture/architecture.md): system design and internals
- [Speculative execution](./docs/guide/speculative-execution.md): simulate-before-apply workflows
- [LSP conformance](./docs/reference/lsp-conformance.md): LSP 3.17 spec coverage
- [Docker](./DOCKER.md): Docker tags, compose, and volume caching
### Contributing
- [CI notes](./docs/architecture/ci-notes.md): CI quirks and test harness details
- [Distribution](./docs/architecture/distribution.md): install channels and release pipeline
## Development
```bash
git clone https://github.com/blackwell-systems/agent-lsp.git
cd agent-lsp && go build ./...
go test ./... # unit tests
go test ./... -tags integration # integration tests (requires language servers)
```
## Library Usage
The `pkg/lsp`, `pkg/session`, and `pkg/types` packages expose a stable Go API for using agent-lsp's LSP client directly without running the MCP server.
```go
import "github.com/blackwell-systems/agent-lsp/pkg/lsp"
client := lsp.NewLSPClient("gopls", []string{})
client.Initialize(ctx, "/path/to/workspace")
defer client.Shutdown(ctx)
locs, err := client.GetDefinition(ctx, fileURI, lsp.Position{Line: 10, Character: 4})
```
See [docs/architecture/architecture.md](./docs/architecture/architecture.md) for the full package API.
## License
MIT
