Pasr

[PASR — Provenance-Aware Span Recall]

Provenance-Aware Span Recall — local, token-budgeted, source-traceable context for coding agents.

[PyPI] [Python] [CI] [License]

[Historical CLI source-selection example with file:line provenance; not an answer-quality comparison]

The problem

Agents need enough source to answer correctly, while every retrieved observation can be carried into later model requests. Native grep and bounded file reads already provide useful source and provenance. PASR must justify its additional retrieval, selection, and tool-catalog complexity against that baseline—not against dumping the entire repository.

What PASR does

Budgeted

Live select_context budgets the returned context, including labels, headings, separators, and redaction—not the MCP JSON envelope or cumulative conversation. Whole computed spans are admitted only when that final representation fits; spans are not necessarily complete functions. Lossless selection uses the exact rendered cost, and optional headers cannot displace source that already fits.

[a budget bar: 2,718 tokens kept under a 3,000-token ceiling, 282 free]

Traceable

Selection receipts record each span's file:line, token count, retrieval signals, and why it was kept or dropped. Receipts are saved under .pasr/receipts; persistence is best-effort, and the response reports whether a receipt was written. They cover PASR-delivered context, not everything an agent reads or does.

[anatomy of one returned span: where it is (file:line), what it costs (tokens), why it was kept (retrieval signals + score)]

Honest

Receipts expose heuristic query classifications, keyword coverage, and routing advice. These are not calibrated correctness probabilities or proof that all necessary evidence was retrieved. Advice must not replace checking the source.

[Heuristic query routing and coverage scores, not correctness or completeness guarantees]

Local by default

No daemon, vector database, or manual index setup is required. Offline selection needs installed dependencies and cached tokenizer data; optional model weights are separate. The client can still forward returned source to a cloud model. Default redaction is a no-op, not automatic secret detection.

Install

0.3.0 is now available on PyPI. Install uv and make uvx available on your PATH; use Python 3.10+ or allow uv to provision it. No PASR checkout is required. Replace /absolute/path/to/project with the repository to inspect, then choose the command for your client:

claude mcp add pasr -- uvx --from pasr-mcp==0.3.0 pasr-mcp --workspace /absolute/path/to/projectcodex mcp add pasr -- uvx --from pasr-mcp==0.3.0 pasr-mcp --workspace /absolute/path/to/project

Alternatively, merge this MCP configuration into your client's config. Update any existing pasr entry rather than registering the same server twice:

{ "mcpServers": { "pasr": { "command": "uvx", "args": ["--from", "pasr-mcp==0.3.0", "pasr-mcp", "--workspace", "/absolute/path/to/project"] } } }

The separate v0.3.0 GitHub release also provides versioned wheel/sdist artifacts. To install its wheel instead:

python -m pip install https://github.com/Apheironn/pasr/releases/download/v0.3.0/pasr_mcp-0.3.0-py3-none-any.whl

After installing that wheel, configure your client to run the installed pasr-mcp executable with --workspace /absolute/path/to/project instead of the uvx launch.

Developers only — source checkout: replace pasr-mcp==0.3.0 after --from in your existing registration with /absolute/path/to/pasr. For example:

uvx --from /absolute/path/to/pasr pasr-mcp --workspace /absolute/path/to/project

Use this instead of the published-package launch, not a second pasr registration. It runs that checkout, which may differ from the release; editing it does not update an existing global installation.

After installation, PASR's tools are available alongside the agent's native tools. The model decides when to search, select context, or read files directly. Smaller selected context does not by itself establish cheaper or more accurate answers.

[Configure PASR alongside native tools; the host chooses tools and stopping policy]

Per-client setup notes: Claude Code · Cursor · Windsurf.

Without an agent: run uvx --from pasr-mcp==0.3.0 pasr explain "<question>" from the project to inspect to print a selection receipt. Five historical CLI examples against pinned public repos are in examples/.

Verified Codex onboarding, with explicit limits

On 2026-10-04, Codex CLI 0.160.0 + GPT-6 Luna used an installed 0.3.0 wheel to answer a source-reading question in an isolated workspace containing one unmodified PASR source file. The final recipe used three model-selected PASR calls, 364 selected-context tokens, and source-checked relative line citations. This is a controlled integration smoke, not independent user adoption or evidence of lower total model cost. Setup failures and earlier model turns are retained in the integration record.

For interactive use, approve the known PASR server when the client prompts. In unattended Codex runs, approval_policy = "never" does not grant MCP tool permission: the initial model call was blocked. Only for a reviewed, trusted server/workspace, set default_tools_approval_mode = "approve" inside the existing [mcp_servers.pasr] table. This is explicit preapproval of that server, not a reason to disable global safeguards. PASR may write receipts/cache in its workspace; the host's read-only shell sandbox does not sandbox the MCP process.

Ask for workspace-relative file:line ranges copied from PASR output, as plain text rather than invented absolute links. The earlier answer added a nonexistent /workspace prefix; the revised citation instruction produced matching references. When scripting codex exec, close unused stdin; otherwise it can wait for additional prompt input. None of these observations proves another client/model will behave identically.

To repeat this controlled task, build a wheel and run the checked-in driver from the checkout root. Install Codex first and supply OPENAI_API_KEY through your normal secret mechanism; this makes paid API requests.

python -m build --outdir dist/demopython scripts/demo_client.py --codex /absolute/path/to/codex --wheel dist/demo/pasr_mcp-0.3.0-py3-none-any.whl --output eval/agent_bench/results/client-demo

On Windows, pass the actual codex.exe, not its shell wrapper. The output directory must be new. The driver installs the wheel into a temporary environment, copies only src/pasr/source_text.py, isolates Codex configuration, and retains the answer, tool calls, usage, warnings and failures before cleanup. It does not grade answer correctness, and only the exact supplied API key is redacted: inspect evidence before sharing. The maintained driver was also exercised with a real model; see its repeat record.

In one call

One localized question — "how are redirects resolved and followed" — against psf/requests (verbatim transcript):

This is a source-selection illustration, not a native grep/read baseline, an answer-quality result, or the current MCP default cap of 1,500 context tokens.

Evidence and alternatives

Native grep and bounded reads are useful defaults. PASR adds explicit context budgets and selection receipts; that does not establish better answers or a lower total model bill. Aider RepoMap provides structural navigation, and Repomix packages source for a model. These are overlapping workflows, not interchangeable products.

Latest source-reviewed agent studies (2026-10-03): the combined quality/cost gates failed. The experimental split reader used search_code / read_code with a host-enforced four-call limit and conservative stopping instructions. That workflow is not the default installed product:

The exploratory native run has an interrupted request with unknown usage; its known subtotal is not a complete token total. The same study used actual upstream Aider RepoMap and Repomix context generation under a shared answering harness, not the full Aider coding agent. Results vary by model, and uncertainty does not support equivalent accuracy or universal superiority over native tools or competitors.

See the confirmation record, exploratory matrix, competitor methods and caveats, and pipeline audit, sections 23–24. Earlier selector evaluations and offline localization proxies use different protocols; they are not current end-to-end product wins.

How it works

query + files / line ranges / globs  → discover safe files, read requested sections, tokenize, line-aligned chunks  → lossless-under-budget check: does the whole thing already fit? return it  → range-only body read: ordered whole-line prefix + explicit remaining ranges  → otherwise, ranked selection:  → candidates: BM25 + lexical coverage + Python AST / JS/TS/Rust tree-sitter symbols                + optional hashing / MiniLM scorer  → fuse unique span ranks per signal         score(s) = Σ_r 1 / (k + rank_r(s)), k = 60  → optional single-source prefix/tail reserve (off in default MCP selection)  → greedy source-span packing with exact final-context cost ≤ budget  → classify the query, score confidence, write the receipt  → return spans + provenance + token accounting + advice

RRF combines rank orders rather than calibrating raw scores. Packing is greedy (coverage-aware or score-only), not an exact knapsack optimizer. Selected spans are dependency-ordered before their rendered cost is checked. Coverage-aware selection preserves literally requested, case-sensitive definitions before the fused-candidate cutoff and prices complete bodies against the actual rendered budget. Compatible overlapping source spans are unioned once instead of discarding the uncovered parts. Remaining choices favor marginal keyword coverage per token, then source diversity and rank. BM25 and lexical selection retain exact identifiers and also match dotted, hyphenated and snake-case components, so needle can match needle_worker inside raw chunks without treating it as a literal definition-name request. Component matching does not imply synonym understanding or complete mechanism coverage.

MCP tools

Locators return workspace paths and, where available, line positions or read_lines hints. select_context accepts paths and path:start-end ranges. A location hint does not prove that a narrow range contains every mechanism needed for the answer.

Embedders can publish just the two locators with create_server(root, expose=("find_symbols", "find_evidence")) alongside a host's native source reader. Their navigation advice does not call unexposed PASR tools. This is an experimental configuration, not a demonstrated accuracy/token win; the default five-tool catalog is unchanged.

The equivalent stdio configuration is uvx --from pasr-mcp==0.3.0 pasr-mcp --workspace /absolute/path/to/project --tools find_symbols,find_evidence. Keep the host's native grep and source reader available; this catalog does not contain a source-reading tool.

include paths and globs are relative to the workspace root. *.py searches only root-level files; **/*.py includes nested Python files. Omit include rather than guessing the layout. Locator descriptions state this distinction; a matching but too-narrow scope is not automatically widened.

The first five tools are exposed by default; the others are opt-in.

Literal symbol names and single filename/path queries retain stopword components: Where and where.py remain searchable rather than disappearing as prose words. An exact literal symbol name suppresses partial namesakes; its underscore components remain available for fallback only when no exact definition matches. Only a blank or whitespace-only find_files query requests an unranked listing. Symbol-kind aliases are applied to both the requested filter and discovered kinds, so a native Python class remains findable with kinds=["class"].

For discovery, an explicit qualified name such as hooks.enforce or Controller.dispatch prioritizes the matching definition's file over mere mentions. The qualifier must match the module path or actual enclosing definitions; this does not resolve import aliases. Broad-query scoring, explicit scopes and context budgets are unchanged. The opt-in search_code uses the same discovery ordering before selecting from three files.

Join the path in a hit's provenance with its read_lines and pass that to select_context(query=..., files=["path.rs:42-56"]) rather than reading the whole file. A suggestion contains the enclosing function when it is at most 40 lines; otherwise it contains up to eight lines on each side of the hit. Large functions may require a wider explicit range or find_symbols to locate the complete definition. Existing snippets, ranking, counts and warnings are retained.

For an unknown location, call search_code(query="cert_verify") to discover real workspace-relative paths. Discovery accepts no files, include, or other scope argument; do not guess paths from package names. For a known location, call the separate opt-in reader: read_code(query="validation exit state", files=["src/attr/validators.py:73-88"]). Its files argument is required and non-empty, using the same path/range syntax as select_context. Empty, missing, directory, mixed-missing, and escaping scopes fail rather than widening the search. Both tools reject unknown arguments. Migration: replace search_code(query=..., files=...) with read_code(...); the old scoped discovery call is rejected, not silently treated as discovery.

Range-only replies include continuation and same-read source_fingerprint values. Pass remaining ranges to read_code until continuation.files is empty; do not submit the empty list. Stop if blocked is true. A non-empty query is still required; explicit ranges determine which lines are read. Plain-file or mixed scopes remain query-ranked, not sequential whole-file reads. Scope errors do not trigger automatic discovery, path correction, or retries. Each call is independent; compare shared-file fingerprints before joining pages.

Publish the compact pair with uvx --from pasr-mcp==0.3.0 pasr-mcp --workspace /absolute/path/to/project --tools search_code,read_code. The default five-tool catalog and select_context behavior are unchanged. This flag does not impose the experiments' four-call limit or stopping policy. Unlike select_context, the compact pair does not persist selection receipts or append usage-ledger entries. It returns raw source text with an optional one-line JSON metadata header, plus the full object in MCP structuredContent. Discovery charges JSON string quoting/escapes against its context cap; returned source stays unescaped text in the structured channel. The reader retains the previous explicit-follow-up pricing and continuation semantics. Advice, metadata, tool catalogs, and repeated conversation history still cost extra. This interface separation is not a demonstrated answer-accuracy or cumulative-provider-token win.

Multiple ranges from the same file are unioned: overlapping lines are returned only once, and gaps stay excluded. An explicitly listed whole file overrides its ranges. Adding an include pattern does not widen an explicitly ranged file. Ranges also constrain symbol candidates, outlines, maps and embedded dependency traces. Complete range reads mean the requested sections are included, not that caller or dependency behavior has been covered. Follow those relationships when the question requires them. expand_context increases the budget inside the same scope; request wider ranges explicitly to read surrounding code.

Range-only body reads are sequential, not query-ranked. When every resolved file has a range and outline=false, PASR returns a prefix in file-request order, with merged ranges in ascending line order. It never skips an over-budget line to select a later match. Whole-file or mixed whole-file/range requests retain ranked selection. Ranking/window settings do not change range-only body order; optional map/trace headers still consume budget and can repeat source separately from that body.

The response includes continuation, for example:

{"files": ["path.rs:57-120"], "blocked": false}

To advance, pass continuation.files as the next call's files, without include. An empty list means the extant requested ranges are exhausted, not that the answer is complete. Ranges are clipped to the current file's end. blocked=true means no body line advanced: increase the budget where possible or use a direct reader. The MCP selection cap remains 1,500 tokens; repeating a blocked request cannot help. Responses carry source_fingerprint; compare shared-file identities before combining pages. Source edits require a fresh read, not trusting old line coordinates. Receipts and saved packs retain continuation metadata, but do not freeze future reads.

Every selection is independent. Repeating a request returns the requested source again; PASR does not assume that a previous response remains in the model's context. There is no server-lifetime call ceiling, novelty refusal, or automatic continuation through previously unread lines. The host explicitly chooses whether to follow the returned remaining ranges; doing so is not a new ranking pass. The host owns question boundaries, stopping policy, and retained-context tracking.

Source reads accept UTF-8 with an optional BOM and normalize CRLF/CR to LF. Only physical newlines define source coordinates: Unicode separators and formfeeds inside source do not create extra line numbers. Explicit reads reject undecodable or NUL-bearing input; content searches skip it with diagnostics. Path discovery remains metadata-only. Root and nested .gitignore rules apply, including ignored parent barriers; external directory junctions are not traversed.

Snapshot and API migration

Packs and receipts now use format 2. Rebuild old packs and reselect sources to create new receipts; format-1 records are rejected, not silently certified against current files. Receipt IDs address the request, source fingerprints, and rendered evidence. Expansion rereads current source and reports expansion_changed_sources. These are per-file snapshots, not an atomic working-tree snapshot or a full source archive.

Staged review uses a pinned index tree; --range A..B and A...B use B's pinned source, including callers. External --diff uses working-tree source and cannot be combined with those Git modes. JSON source_revision identifies the chosen source.

The unused controller/context-order modules and legacy Python candidate API were removed. Use the supported selection/provider APIs; no compatibility shims remain. The unused tree-sitter-python dependency and redundant benchmark sweep.py launcher were also removed. Default MCP tools remain the same five.

CLI

pasr explain "how is the request rate limited"        # run a selection, print the receiptpasr trace enforce_per_user_request_quota src/        # a symbol's dependency closurepasr trace HTTPAdapter src/ --callers                 # who calls it — impact analysispasr pack auth "session + login + token" src/auth/    # save a committable Context Packpasr review --staged src/                             # touched defs + the callers they affectpasr context --issue "$(cat issue.txt)" src/ \        # headless slice for CI / agents  --format text --metrics-file metrics.jsonpasr report --price-per-mtok 3                        # source-context estimates, not API bills

The path is optional everywhere — with none, PASR scans the whole workspace (.gitignore-aware). Pass a directory or globs (src/, lib/ "**/*.py") only to scope it tighter or run faster.

Receipt persistence to .pasr/receipts/<id>.{json,md} is best-effort (gitignored). These records describe PASR selections, not a complete global agent audit. The usage ledger at .pasr/ledger.jsonl supports source-context estimates, not measured API savings or proven avoided round trips. Context Packs land in .pasr/packs/ (committable); review them for sensitive source before sharing. Load a named pack with select_context(query="", advanced={"pack": "auth"}). For CI, see docs/ci.md.

Capability boundary

Use PASR to locate source and select inspectable context within a rendered budget. Computed spans and bounded static dependency traces need not contain the full mechanism. Global aggregation, lexical mismatch, dynamic bindings, and cross-file state transitions can require additional native searches or reads. Heuristic routing cannot guarantee detection of these gaps. Research results from other datasets do not establish answer-quality parity for this product.

Docs

• examples/ — five verbatim CLI transcripts against pinned repos
• eval/RESULTS.md — the 50-task evaluation, pre-registered (pip install ./eval → pasr-bench)
• docs/competitors-benchmark.md — latest real-component comparison, failed gates, and historical proxy results
• docs/architecture.md — components and data flow
• docs/roadmap.md — shipped and next
• CHANGELOG.md · CONTRIBUTING.md

This productises the frozen researchv2 study (model-external context optimization); a comparative write-up is in preparation.

Development

python -m venv .venv && . .venv/bin/activate   # or .venv\Scripts\Activate.ps1pip install -e ".[dev]"pytest -q

The pure-logic core imports no torch / transformers (and no mcp SDK — that loads only under pasr.mcp):

python -c "import pasr.pipeline, sys; assert not {'torch','transformers'} & set(sys.modules)"

License

Apache-2.0.