morluto / flameox

flameox

Local runtime evidence for coding agents investigating performance, memory, execution, concurrency, and reliability.

Language / 语言: English | 简体中文

Flameox connects profilers, benchmark tools, and trace processors to a local evidence record. It preserves their native artifacts and provenance, then exposes bounded evidence to the agent. The agent states what it wants to test; Flameox captures the measurements and preserves the experiment record for review.

Quick start

Install the local runtime and connect a supported MCP client through the guided setup:

npx flameox@latest setup

Restart the client, open the project you intend to inspect, and ask it to:

Initialize Flameox in this project and list the available profiling capabilities.

The setup command installs a versioned local runtime and changes only approved client configuration. Project initialization is separate and creates .diagnostics/ only after the client calls the initialization workflow for its fixed project root.

For source development:

uv sync --extra dev
uv run flameox init .
uv run flameox status

Python 3.12 or newer and the committed uv.lock are required.

Investigation path

symptom → capture or import → bounded evidence → hypothesis
        → discriminating experiment → supported, refuted, or inconclusive finding

Evidence sources include pyperf, py-spy, pytest-reportlog, coverage.py, Memray, Perfetto, torch.profiler, Nsight Systems, Nsight Compute, ROCprofiler, Compute Sanitizer, NVBench, and typed inference-provider exports. Availability depends on the host, permissions, installed extras, and selected adapter. Flameox reports missing evidence instead of silently substituting a weaker source.

A profile helps explore a problem; it does not establish a performance or correctness conclusion. That requires a representative workload, a declared metric and estimand, compatible run identity, preserved samples, and an appropriate semantic oracle.

Named workloads

Commands live in flameox.toml as argument arrays. Parameters are declared scalars; there is no shell expansion.

schema_version = 1

[workloads.scan]
argv = ["python", "bench.py", "--implementation", "{implementation}"]
cwd = "."
timeout_seconds = 60

[workloads.scan.parameters]
implementation = ["baseline", "candidate"]

[workloads.scan.oracle]
strength = "cross_treatment_equivalence"
argv = ["python", "validate.py", "--implementation", "{implementation}"]

[experiments.scan_comparison]
workload = "scan"
design = "randomized_complete_blocks"
blocks = 10
treatment_factor = "implementation"
combination_policy = "cartesian"
primary_metric = "pyperf.workload"
polarity = "lower_is_better"
estimand = "median_paired_log_ratio"
practical_threshold = 0.05
confidence_level = 0.95
random_seed = 1984

[experiments.scan_comparison.factors]
implementation = ["baseline", "candidate"]

The MCP configure_workload tool validates and writes the canonical definition without executing it. A manually authored valid definition is active immediately; there is no approval copy or secondary workload registry.

uv run flameox workload show scan --json
uv run flameox capture plan pyperf --workload scan \
  --parameters '{"implementation":"baseline"}' --json
uv run flameox capture run pyperf --workload scan \
  --parameters '{"implementation":"baseline"}' --json

Planning resolves every executable once. The resulting binding contains the exact invocation path, canonical target, trust decision, and file identity. Execution revalidates that binding instead of searching PATH again. Plans are short-lived, single-use capabilities whose complete intent is retained in the workspace SQLite control plane.

Experiments and analysis

uv run flameox investigations create \
  '{"question":"Does the candidate remove reverse-scan overhead?"}' --json
uv run flameox hypotheses record @hypothesis.json --json
uv run flameox experiment plan scan_comparison \
  --investigation <investigation-id> --adapter pyperf --json
uv run flameox experiment run scan_comparison \
  --investigation <investigation-id> --adapter pyperf --json

Experiments retain randomized treatment order, attempted trials, failures, cancellations, validation receipts, and exclusions. Analyses resolve all input through one pinned corpus snapshot:

uv run flameox analyze hotspots <run-or-artifact>
uv run flameox analyze scaling <experiment-id>
uv run flameox analyze compare @comparison-request.json
uv run flameox analyze memory <run-or-artifact>
uv run flameox analyze execution <run-or-artifact>
uv run flameox analyze pytorch <run-or-artifact>
uv run flameox analyze failures

Read-only analysis does not create a durable claim. Use analyze record, analyze record-comparison, or findings record when the result should become part of the investigation history.

Data and safety boundaries

.diagnostics/ contains:

• control-plane.sqlite3 for plans, operations, runs, revisions, idempotency, and relationships;
• content-addressed native artifacts;
• immutable Parquet generations and corpus commits;
• a rebuildable catalog.duckdb analytical cache.

Large evidence does not live in SQLite. Deleting catalog.duckdb does not delete evidence; flameox catalog rebuild recreates it from committed generations.

The CLI and MCP server expose bounded task-shaped operations, not shell strings, raw SQL, or arbitrary artifact bytes. Workloads may access the network unless active containment denies it. The control process performs network I/O only for explicit setup, upgrade, approved provider acquisition, or explicitly enabled symbol services—not during ordinary capture or analysis.

The trusted-local capture path does not enforce containment for child processes; it records that limitation. Projects that require managed containment can select it explicitly. Planning refuses when the requested guarantee is unavailable.

CLI and MCP discovery

uv run flameox --help
uv run flameox mcp serve --project-root .
uv run flameox mcp inspect --project-root . --json

mcp inspect is the authoritative inventory of tool schemas, annotations, and resource templates for the installed version. See CLI and MCP boundaries for workflow and trust semantics.

Integrity and retention

uv run flameox validate
uv run flameox validate --full
uv run flameox catalog validate
uv run flameox catalog rebuild
uv run flameox recover
uv run flameox gc
uv run flameox gc --apply

Validation never repairs evidence. Garbage collection is a dry run unless --apply is supplied, and applied candidates first move to recoverable trash. Permanent purge requires a separate explicit command naming an expired trash manifest.

Documentation

• Architecture — authoritative module and process boundaries
• Storage and evidence — authority, snapshots, and publication
• Investigations — experiments, analysis, and claim quality
• Adapters — producer ownership and compatibility
• Runtime safety — execution, filesystem, cancellation, and retention
• CLI and MCP — public workflow and trust boundaries
• Testing — suite ownership and CI lanes
• Contributing — development and pull-request workflow

Development

uv sync --extra dev
uv run ruff check src tests tools
uv run mypy src tests tools
uv run pytest -q

See the testing guide for marker and provider commands. Flameox is available under the MIT License.