dr-llm 0.2.0

Shared LLM calling and event-sourced storage primitive for benchmark systems.

dr-llm

dr-llm is a shared primitive for:

• provider-agnostic LLM calls (API and headless)
• canonical PostgreSQL recording/query storage
• event-sourced multistep sessions with tool-calling
• worker-safe parallel tool execution with queue claiming
• generic typed sample pools for benchmark storage
• isolated per-project databases with backup/restore

It is intentionally domain-neutral so repos like nl_latents and unitbench can reuse it.

Core Capabilities

• Unified call interface:
  • LlmClient.query(LlmRequest) -> LlmResponse
• Canonical storage (PostgreSQL):
  • runs, calls, request/response payloads, artifacts
• Session runtime:
  • start, step, resume, cancel
  • native tool strategy (if provider supports tools) + brokered fallback
• Tool queue + workers:
  • idempotent tool call enqueue
  • concurrent worker claims via FOR UPDATE SKIP LOCKED
• Replay:
  • reconstruct message history from session_events
• Sample pools:
  • schema-driven typed key dimensions with auto-generated DDL
  • no-replacement acquisition via claims table
  • pending sample lifecycle (claim/promote/fail with FOR UPDATE SKIP LOCKED)
  • top-up orchestration: acquire, wait for pending, generate, re-acquire
• Project management:
  • isolated per-project Postgres containers via Docker
  • backup/restore with atomic swap

Install

uv add dr-llm

Quick verification:

uv run python -c "import dr_llm"

For maintainers, see the release runbook: docs/releasing.md.

Quick Start

1. Query a provider (no database required)

uv run dr-llm query \
  --provider openai \
  --model gpt-4.1 \
  --message "Hello, what's 2+2?" \
  --no-record

The --no-record flag skips database recording, so you can test providers without Postgres.

2. Start Postgres (for catalog and recording)

source ./scripts/start-test-postgres.sh

This starts a local Postgres container, applies schema migrations, and exports DR_LLM_DATABASE_URL and DR_LLM_TEST_DATABASE_URL into your shell. Use source (not ./) so the env vars persist.

3. Sync and list models

uv run dr-llm models sync --provider openai
uv run dr-llm models list --provider openai

Use --json on models list for full metadata output.

Available Providers

Provider	Aliases	Type	API Key Env
openai	—	OpenAI API	OPENAI_API_KEY
openrouter	—	OpenRouter API	OPENROUTER_API_KEY
minimax	—	MiniMax OpenAI-compat API	MINIMAX_API_KEY
anthropic	—	Anthropic API	ANTHROPIC_API_KEY
google	—	Google Gemini API	GOOGLE_API_KEY
glm	—	GLM (ZAI) API	ZAI_API_KEY
codex	codex-cli	Codex CLI (headless)	OPENAI_API_KEY
claude-code	claude	Claude Code CLI (headless)	ANTHROPIC_API_KEY
claude-code-minimax	claude-minimax	Claude Code via MiniMax	MINIMAX_API_KEY
claude-code-kimi	claude-kimi	Claude Code via Kimi	KIMI_API_KEY

Headless providers shell out to CLI tools (codex, claude) and redirect API traffic via environment variables. The MiniMax and Kimi variants point Claude Code at third-party Anthropic-compatible endpoints.

Some providers (MiniMax, Codex, Claude Code, Kimi) use static model lists for models sync since they don't expose a /models endpoint. The CLI will note when a list may be out of date and link to the provider's docs.

Configuration

• Required for DB-backed workflows: DR_LLM_DATABASE_URL
• Provider API keys: see the table above
• GLM provider defaults to: https://api.z.ai/api/coding/paas/v4
• MiniMax API provider defaults to: https://api.minimax.io/v1
• Claude headless coding-plan presets:
  • claude-code-minimax: routes via https://api.minimax.io/anthropic
  • claude-code-kimi: routes via https://api.kimi.com/coding/
• Model catalog overrides: config/model_overrides.json (YAML also supported)

CLI Reference

dr-llm providers

dr-llm models sync
dr-llm models list --provider openai
dr-llm models list --supports-reasoning --json
dr-llm models show --provider openrouter --model openai/o3-mini

dr-llm query \
  --provider openai \
  --model gpt-4.1 \
  --message "hello" \
  --no-record

dr-llm query \
  --provider openai \
  --model gpt-4.1 \
  --reasoning-json '{"effort":"high"}' \
  --message "hello"

dr-llm run start --run-type benchmark
dr-llm run finish --run-id <run_id> --status success
dr-llm run benchmark \
  --workers 128 \
  --total-operations 200000 \
  --warmup-operations 10000 \
  --max-in-flight 128 \
  --operation-mix-json '{"record_call":2,"session_roundtrip":1,"read_calls":1}' \
  --artifact-path .dr_llm/benchmarks/release-baseline.json

dr-llm session start \
  --provider openai \
  --model gpt-4.1 \
  --message "You are helpful" \
  --message "Solve this task"

dr-llm session step --session-id <session_id> --message "next"
dr-llm session resume --session-id <session_id>
dr-llm session cancel --session-id <session_id> --reason "stopped"

# brokered tool calls are queued by default; use workers
dr-llm tool worker run --tool-loader mypkg.tools:register_tools
# optional synchronous override for a single step:
dr-llm session step --session-id <session_id> --inline-tool-execution

dr-llm replay session --session-id <session_id>

dr-llm project create my-experiment
dr-llm project list
dr-llm project use my-experiment    # prints export DR_LLM_DATABASE_URL=...
dr-llm project start my-experiment
dr-llm project stop my-experiment
dr-llm project backup my-experiment
dr-llm project restore my-experiment backups/my-experiment-20260325.sql.gz
dr-llm project destroy my-experiment --yes-really-delete-everything

Benchmark command output:

{
  "artifact_path": ".dr_llm/benchmarks/release-baseline.json",
  "failed_operations": 0,
  "operations_per_second": 4231.8,
  "p50_latency_ms": 20.0,
  "p95_latency_ms": 200.0,
  "run_id": "run_abc123",
  "status": "success"
}

Reasoning + cost notes:

• OpenAI-compatible adapters now accept LlmRequest.reasoning / --reasoning-json.
• Reasoning text/details and reasoning token counts are normalized on LlmResponse.
• Provider-returned cost fields (e.g. OpenRouter usage.cost variants) are normalized into LlmResponse.cost.
• These are persisted in llm_call_responses alongside standard token usage.

Generation transcript logging (default on):

• DR_LLM_GENERATION_LOG_ENABLED=true
• DR_LLM_GENERATION_LOG_DIR=.dr_llm/generation_logs
• DR_LLM_GENERATION_LOG_ROTATE_BYTES=104857600
• DR_LLM_GENERATION_LOG_BACKUPS=10
• DR_LLM_GENERATION_LOG_REDACT_SECRETS=true
• DR_LLM_GENERATION_LOG_MAX_EVENT_BYTES=10485760

Python Example

from dr_llm import LlmClient, LlmRequest, Message, PostgresRepository, ToolRegistry

repo = PostgresRepository()
client = LlmClient(repository=repo)

response = client.query(
    LlmRequest(
        provider="openai",
        model="gpt-4.1",
        messages=[Message(role="user", content="hello")],
    )
)
print(response.text)

Adapter lifecycle note:

• If you instantiate provider adapters directly, call adapter.close() when done (or use context manager form with ... as adapter:) to release underlying HTTP connections.

Pool Example

Pools provide schema-driven sample storage with no-replacement acquisition for benchmarks.

from dr_llm import (
    ColumnType, KeyColumn, PoolSchema, PoolStore, PoolService,
    PoolAcquireQuery, PoolAcquireResult,
)
from dr_llm.pool.models import PoolSample
from dr_llm.storage._runtime import StorageConfig, StorageRuntime

# 1. Declare a pool schema with typed key dimensions
schema = PoolSchema(
    name="my_benchmark",
    key_columns=[
        KeyColumn(name="provider"),
        KeyColumn(name="difficulty", type=ColumnType.integer),
    ],
)

# 2. Connect and create tables
runtime = StorageRuntime(StorageConfig(dsn="postgresql://..."))
store = PoolStore(schema, runtime)
store.init_schema()  # idempotent CREATE TABLE IF NOT EXISTS

# 3. Insert samples
store.insert_samples([
    PoolSample(
        key_values={"provider": "openai", "difficulty": 1},
        sample_idx=0,
        payload={"prompt": "What is 2+2?", "expected": "4"},
    ),
    PoolSample(
        key_values={"provider": "openai", "difficulty": 1},
        sample_idx=1,
        payload={"prompt": "What is 3+3?", "expected": "6"},
    ),
])

# 4. Acquire samples (no-replacement within a run)
result = store.acquire(PoolAcquireQuery(
    run_id="run_001",
    key_values={"provider": "openai", "difficulty": 1},
    n=2,
))
for sample in result.samples:
    print(sample.payload)

# 5. Or use PoolService for automatic top-up generation
service = PoolService(store)
result = service.acquire_or_generate(
    PoolAcquireQuery(
        run_id="run_002",
        key_values={"provider": "openai", "difficulty": 2},
        n=5,
    ),
    generator_fn=lambda key_values, deficit: [
        PoolSample(key_values=key_values, payload={"generated": True})
        for _ in range(deficit)
    ],
)

Testing

uv run ruff format
uv run ruff check --fix src/ tests/ scripts/
uv run ty check src
uv run pytest tests/ -v

Integration tests

Integration tests require a running Postgres instance. If the test container (dr-llm-pg-test on port 5433) is already running, tests work automatically — conftest.py sets the default DR_LLM_TEST_DATABASE_URL.

To start the test container from scratch:

source ./scripts/start-test-postgres.sh

Then run integration tests:

uv run pytest tests/ -v -m integration

If integration tests are skipped unexpectedly, include skip reasons:

uv run pytest tests/ -v -m integration -rs

Demo Scripts

End-to-end query flow

Creates a project, records queries, verifies backup/restore:

./scripts/demo-query-flow.sh

Requires Docker and at least one of OPENAI_API_KEY or ANTHROPIC_API_KEY.

Pool provider demo

Queries all available LLM providers (API and headless) and stores results in a typed pool:

uv run python scripts/demo-pool-providers.py

Auto-detects available providers by checking API key env vars and CLI tool availability (claude, codex). For each provider: syncs the model catalog, selects a model, sends a query, and inserts the result into a pool. Prints a summary table at the end.

Options:

uv run python scripts/demo-pool-providers.py --project-name my-demo --prompt "Explain gravity in one sentence."

Requires Docker. Works with any combination of providers — set API keys and/or install CLI tools for the providers you want to test.

Milestone Closeout Artifacts

• Milestone status: docs/milestones.md
• Consumer rollout checklist: docs/consumer-rollout-checklist.md
• M2b operations checklist: docs/ops/m2b-hardening-checklist.md
• Compatibility contract: docs/compatibility-contract.md
• Migration guide: docs/migration-guide.md
• Integration notes:
  • docs/integrations/nl_latents.md
  • docs/integrations/unitbench.md
• Example gateways:
  • examples/nl_latents_gateway.py
  • examples/unitbench_gateway.py