Python Delphi LSP

python-delphi-lsp parses Delphi/Object Pascal, builds semantic and project indexes, serves LSP, and provides bounded codebase navigation for agents. Version 2.0.5 is authored by Dark Light and supports Windows, macOS, and Linux.

Install and quick start

Install into the Python environment that will run the command:

python -m pip install python-delphi-lsp

On Windows, use py -m pip install python-delphi-lsp if that is your system convention. On macOS and Linux, use python3 -m pip install python-delphi-lsp when python is unavailable. Normal installed use needs neither a checkout nor PYTHONPATH.

from delphi_lsp import parse

result = parse("unit Unit1; interface implementation end.", "Unit1.pas")
print(result.root)

For semantic work across units, build_workspace_semantics returns per-file models and a shared symbol index. ProjectIndexer follows a project entry with explicit search paths, include paths, and defines.

from delphi_lsp import ProjectIndexer, build_workspace_semantics

workspace = build_workspace_semantics({
    "Unit1.pas": "unit Unit1; interface uses Unit2; implementation end.",
    "Unit2.pas": "unit Unit2; interface implementation end.",
})
print(workspace.index.lookup("Unit2"))

project = ProjectIndexer(
    search_paths=["src"], include_paths=["include"], defines=["DEBUG"]
).index("Main.dpr")
print(project.parsed_units)

Long-running discovery and indexing accept a keyword-only on_progress callback. It receives an immutable ProgressEvent with package-controlled phase, path, and monotonic counters; callback exceptions are not suppressed.

from delphi_lsp import ProjectIndexer, ProgressEvent

def report(event: ProgressEvent) -> None:
    print(event.phase, event.files_completed, event.path)

ProjectIndexer(on_progress=report).index("Main.dpr")

Architecture metrics

The public metrics API analyzes a single unit or aggregates a complete project:

from delphi_lsp import analyze_project, analyze_unit

unit = analyze_unit(
    "unit Alpha; interface implementation procedure Run; begin end; end.",
    "Alpha.pas",
)
print(unit.lines.total_lines, unit.cyclomatic.maximum)

project = analyze_project({
    "Main.dpr": "program Main; uses Alpha; begin end.",
    "Alpha.pas": "unit Alpha; interface implementation end.",
})
print(project.total_loc)

Line results distinguish total, source, blank, comment-only, mixed-comment, and compiler-directive lines. Project total_loc counts each .dpr, .dpk, and .pas source once; include_loc counts unique .inc inputs separately, and total_loc_with_includes combines both totals.

Cyclomatic complexity is reported per routine and as unit/project aggregates. The result also includes complete Halstead counts and derived values, a normalized 0–100 maintainability index, symbol counts, dependency edges, afferent coupling (fan-in), efferent coupling (fan-out), instability, abstractness, and distance from the main sequence. Coupling detail separates internal from external dependencies. Empty or partial inputs produce finite JSON values; unreadable agent-workspace inputs are reported as metric problems.

Run the stdio language server with delphi-lsp, or equivalently:

python -m delphi_lsp.lsp_server

OpenCode LSP configuration

The root opencode.json starts the installed package portably:

{
  "lsp": {
    "delphi": {
      "command": ["delphi-lsp"],
      "extensions": [".pas", ".dpr", ".dpk", ".inc"],
      "initialization": {"autoDiscoverPaths": true}
    }
  }
}

autoDiscoverPaths is the default. It discovers compiler context without an environment section; LSP remains available for normal editor and OpenCode use, including large sources.

The LSP builds its structural index through the same optimized outline path for every source, with no file-size threshold. Definition, hover, references, rename, completion, document symbols, workspace symbols, and diagnostics remain registered for every file size; source-aware fallbacks keep body-level queries available without returning the complete file as agent context.

Automatic discovery

Auto-discovery reads .dpr, .dpk, .dproj, .cfg, and .dof files. Its resolution order is:

1. An explicit project selection takes precedence.
2. Otherwise, .dpr and .dpk candidates are considered.
3. MainSource in a .dproj contributes its entry project.
4. A selected entry associates same-stem .dproj, .cfg, and .dof.
5. Unit search paths, include paths, and defines are accumulated from explicit settings, project metadata, and the associated settings files.
6. Direct Unit in 'path/Unit.pas' references contribute their parent directory to unit search paths.

A single discovered project is selected automatically. With no project entry, the server uses a synthetic workspace of supported sources. Scans skip build and cache directories such as build, dist, environments, VCS folders, node_modules, and tool caches. Missing paths and invalid metadata become problems; paths are not guessed.

Agent CLI and Interface/Protocol v2

delphi-lsp-agent has these subcommands and options:

delphi-lsp-agent cache start --root PATH [--project-file FILE] [--max-memory 512M]
                                  [--idle-timeout 1800]
delphi-lsp-agent cache status --root PATH [--format text|json]
delphi-lsp-agent cache stop --root PATH
delphi-lsp-agent view --root PATH [--project-file FILE] --layer LAYER
                      [--query TEXT] [--format markdown|json] [--deep-projects]
delphi-lsp-agent index --root PATH [--project-file FILE] [--out FILE]
delphi-lsp-agent query --root PATH ACTION [VALUE]
                      [--project-id FILE] [--detail summary|declaration|members|context|body|implementations]
                      [--relation references|callers|callees|uses|used_by|inherits|implements]
                      [--cursor TEXT] [--max-items INT] [--max-chars INT]
delphi-lsp-agent skill install [--target PATH] [--force]
delphi-lsp-agent opencode install [--target PATH] [--python PYTHON]
                                  [--force] [--write-agent|--write-config]
delphi-lsp-agent worker --root PATH [--project-file FILE]

The cache commands manage one daemon per canonical root. Use these:

delphi-lsp-agent cache start --root PATH
delphi-lsp-agent cache status --root PATH
delphi-lsp-agent cache stop --root PATH

cache start outputs cache lifecycle JSON; runtime warnings are still on stderr. cache status --format json outputs status JSON to stdout and the same warning stream on stderr. cache stop outputs stop status JSON and may include warnings on stderr. query outputs Protocol v2 JSON responses and writes warnings to stderr.

delphi-lsp-agent query --root PATH find TCustomer
delphi-lsp-agent query --root PATH focus TARGET_ID
delphi-lsp-agent query --root PATH inspect
delphi-lsp-agent query --root PATH trace TARGET_ID --relation callers
delphi-lsp-agent query --root PATH metrics
delphi-lsp-agent query --root PATH metrics UNIT_QUERY
delphi-lsp-agent cache status --root PATH --format json

inspect uses the currently focused target, so call focus TARGET_ID before inspect unless a previous request already selected it.

The cache daemon prewarms the navigation cache at startup so first find requests are fast. The cache retained-cache budget is 512 MiB by default and tracks retained cache usage only, not a hard RSS/parse peak. Warnings are emitted on stderr at or above 80 percent.

Eviction is ordered: auxiliary caches are evicted first, navigation caches second. If compaction removes navigable data, the daemon rebuilds the navigation state on demand while preserving focus state for the next request.

The daemon tracks a 30-minute idle timeout; idle state shows in JSON status (cache status). source revision changes on source edits and invalidate reused request caches. Workspace state appears in status as requests, warm_hits, rebuilds, invalidations, evictions, and cache_state.

Metadata is stored in .delphi-lsp/agent-cache/daemon.json with owner-only token and permissions (daemon.json mode 600 and parent 700). Do not copy or share this token outside the root workspace.

view --layer accepts overview, projects, units, unit, symbols, symbol, implementation, references, problems, and metrics. For example, delphi-lsp-agent view --layer metrics --format json returns a project summary and detailed unit metric objects; --query filters units by name or path. index materializes overview, projects, and problems JSON. skill install writes the skill; opencode install writes the package-named skill, Markdown agent, and plugin. The two deprecated write flags are harmless aliases and do not change user configuration. worker serves NDJSON over standard input/output.

Protocol v2 actions are open, find, inspect, trace, focus, problems, and metrics. A metrics request without a query returns the project summary followed by unit cards. A query filters units, while a unit target_id from open selects one unit; detail: "members" adds routine, Halstead, dependency, and symbol-count detail without returning source text. Detail values are summary, declaration, members, context, body, and implementations. Relations are references, callers, callees, uses, used_by, inherits, and implements.

A request requires action and can include query, target_id, project_id, detail, relation, cursor, max_items, and max_chars. Defaults are empty text fields, detail: "summary", no relation, max_items: 12, and max_chars: 12000. Ranges are 1–50 items and 256–40000 characters. A successful envelope has schema: 2, workspace_revision, focus (project, unit, and target IDs), result, page, and context; errors have schema: 2 and a code/message.

Focus preserves the selected project, unit, or target. Cursors bind a workspace revision and request fingerprint, so source changes and cross-target or cross-detail reuse invalidate them. max_items and max_chars bound each response. A sound_partial relation is sound but incomplete: unresolved and ambiguous relations are never fabricated. Unsupported relations are rejected.

For every source size the navigator builds an outline first, loads source detail lazily for a selected target, and returns only selected fragments. Typed source chunks are at most 6000 characters and also respect the response budget. This optimization does not remove LSP functionality.

OpenCode semantic navigator

Install the generated integration in a worktree:

delphi-lsp-agent opencode install --target .

It writes:

.agents/skills/python-delphi-lsp/SKILL.md
.opencode/plugins/delphi_codebase.ts
.opencode/agents/python-delphi-lsp.md

The package-named Markdown agent enables only the python-delphi-lsp skill and delphi_codebase. It denies bash, read, glob, grep, and lsp, along with edit/write and other raw source tools. The skill is enabled. The installer does not use the retired .opencode/tools path and never reads or changes opencode.json; that file remains entirely user-owned. The deprecated --write-config and --write-agent options are accepted harmlessly for compatibility.

The plugin maintains one worker per session/root, reusing focus and indexes. During compaction it restores the focus and summary into the new context. Transport failure, session deletion, and plugin disposal clean up the worker.

OpenCode history: 1.1.0 and 1.1.1 used a spawned view per call model. Persistent session/root worker support first shipped in 2.0.0. This is the same persistent session/root worker boundary. The OpenCode worker stays separate from CLI daemon, and current plugin behavior is unchanged.

A generated OpenCode agent starts with this Markdown frontmatter:

---
description: Inspect Delphi and Object Pascal codebases through python-delphi-lsp.
mode: all
temperature: 0
permission:
  delphi_codebase: allow
  skill:
    "*": deny
    python-delphi-lsp: allow
  lsp: deny
  bash: deny
  read: deny
  glob: deny
  grep: deny
  list: deny
  edit: deny
  write: deny
  patch: deny
  task: deny
  webfetch: deny
  websearch: deny
  question: deny
  todowrite: deny
  todoread: deny
  codebase_map: deny
  code_guidelines: deny
---

Select python-delphi-lsp, ask it to load the python-delphi-lsp skill, then use delphi_codebase actions such as open, find, focus, and inspect. Use semantic tool calls, not raw source tools.

For architecture questions, call metrics without a query to compare unit cards and read project LOC. Then select a returned unit ID with another metrics call and detail: "members" to inspect its routines and coupling.

For the root LSP configuration, a local model can be used as follows:

opencode run --dir . --model ollama/ornith-lspctx --agent vllm-lsp \
  "Find the declaration of a Delphi symbol through LSP."

Use --agent vllm-lsp-edit only for the separate, focused LSP/edit verification workflow; the semantic navigator agent above remains restricted to its named skill and tool.

That separate verification uses vllm/ornith-lspctx and accepts the focused LSP result edit:Edit applied successfully; it is not the semantic navigator workflow and does not grant the navigator any raw source tools.

Reproducible large-project vLLM proof

The proof generates a 117,511-line project. The verifier requires skill, open (Main.dpr evidence), find, focus, and inspect, checks MegaProc02500 and Value := Value + 40, and forbids raw bash, read, glob, and grep. It then waits for the final answer and requires the exact body range src/Mega100kUnit.pas:117464-117509 and the inspected statement in that answer. The prompt requires returned range metadata instead of a model-calculated line inside the source fragment. The proof uses the local Ornith vLLM OpenAI-compatible endpoint at http://127.0.0.1:8001/v1.

Default scripts are offline and must not redownload the model. First check the cache:

python scripts/check_ornith_cache.py --require-complete

With an already-running endpoint on any supported platform, run:

python scripts/bootstrap_vllm_codebase_skill_test.py --use-running-server

On macOS, the offline cached auto-start path is:

python scripts/bootstrap_vllm_codebase_skill_test.py --start-vllm

Automatic local vLLM startup is macOS-only. The package and OpenCode plugin are supported on Windows. On Windows, start an OpenAI-compatible vLLM endpoint, then run the Python bootstrap from PowerShell:

python .\scripts\bootstrap_vllm_codebase_skill_test.py --use-running-server

For an endpoint at another URL, add --base-url:

python .\scripts\bootstrap_vllm_codebase_skill_test.py --use-running-server --base-url http://127.0.0.1:9000/v1

--skip-install is an optional acceleration for an already prepared .venv; omit it on a clean checkout so the bootstrap installs .[dev]. The final-answer verifier defaults to a 420-second probe timeout. On a slower local model server, increase it explicitly with --probe-timeout SECONDS.

The bundled automatic helper is not a cross-platform startup mechanism.

The architecture-metrics proof uses a separate deterministic 34-LOC project. It requires the restricted model to load the skill, call metrics for the project and most-complex unit, and report exact LOC, cyclomatic maximum, and instability values. Raw source, search, shell, and write tools remain forbidden:

python scripts/bootstrap_vllm_codebase_skill_test.py --probe metrics --use-running-server

Use --probe metrics --start-vllm --max-model-len 24576 for the cached macOS auto-start path when no endpoint is already running.

Migration to 2.0

delphi_lsp is the only supported import namespace. Update imports directly; there is no compatibility import alias.

Verification and limitations

For a checkout:

python -m pip install -e ".[dev]"
python -m pytest
python -m build
python -m twine check dist/*

CI tests Ubuntu, macOS, and Windows on Python 3.10 and 3.14, then builds and smoke-installs the wheel on Ubuntu/Python 3.14. Results depend on available project files, defines, includes, and paths; unsupported compiler behavior and unresolvable references are reported as problems. The vLLM proof additionally requires OpenCode, a local endpoint, and a complete local cache.

License

Mozilla Public License 2.0. See LICENSE.