Prism

Graph-ranked code context for AI coding agents.

Prism turns a task plus a few precise anchors into the code, callers, callees,
tests, docs, and coverage gaps an agent needs to make a change safely.

What Prism is

Prism is a type-resolved code-graph engine for coding agents. It indexes a
repository into a compiler-grade graph (symbols, calls, overrides, implements,
test edges — via the embedded Grove
engine) and exposes that graph at task altitude: one deterministic call
answers a whole question an agent would otherwise spend dozens of turns
approximating. For bug-fix and implement tasks it delivers the answer as
edit-ready, line-numbered source — verbatim windows plus each anchor's
callers and covering tests — so the model edits without a second read
(prism_query, phase-aware; delivery="symbols" for the compact list).

The need. Agents gather context with text search and file reads. That works
for locating things, but it fails exactly where the stakes are highest:
enumerating everything a change touches. Overridden methods, interface
implementations, overload-specific callers, and indirect call chains are
invisible to grep — and an agent that misses one site ships a broken build.
Measured across 4 languages and blast radii of 1–310 sites, text-search agents
top out at 0.62–0.75 recall on change-impact tasks even on frontier models
(see provasign/research).

The principles.

1. Correctness and completeness first. A faster or cheaper incomplete
   answer is a faster broken build. Every design choice is subordinate to
   returning the complete, type-resolved answer.
2. Task altitude, not primitives. The graph is exposed as whole-task
   operations (change_impact, rename_plan, untested_surface, …), not as
   node/edge primitives the agent must orchestrate. Orchestrating traversals
   is itself a frontier-model skill; a task-level call works on any model.
3. Determinism. The engine solves the traversal; the agent relays the
   result. Same query, same index, same answer — testable without an LLM, and
   never re-filtered through grep/sed (measured to drop real sites).
4. Tier invariance. Because the hard part is done by the engine, the same
   completeness holds from a free local 30B model to a frontier model —
   measured at recall 1.00 on both, where orchestration-based approaches
   collapse on cheap models.
5. Each layer does what it's best at. Shell tools find the first anchor
   (they win at string location — Prism does not replace grep). Prism
   answers relationship and whole-task questions. The model reasons and edits.

Use cases — the questions Prism answers in one call:

Where Prism is the wrong tool (honesty is a feature): locating a string or
file (rg wins), languages outside the supported set below, dispatch wired at
runtime through frameworks/reflection/DI (Prism's edges are static and
type-resolved — it will show you nothing rather than a guess), and one-line
greppable changes where any approach ties.

Prism is not a better grep. Use rg/grep to find the first anchor. Use
Prism to answer the follow-up questions that usually cost several file reads:

• What calls this?
• What does this call?
• Which tests define the contract?
• What else is in the blast radius?
• Which nearby exported functions have no direct test coverage?

The recommended agent mode is both (MCP tools as primary surface, CLI
fallback for subagents that don't inherit the MCP session):

prism init . --mode both

Agents with an active MCP session call prism_query, prism_read, and
prism_lookup directly. For bug-fix and implement tasks prism_query
delivers verbatim line-numbered source windows plus each anchor's callers and
covering tests (edit-ready, phase-aware; --delivery symbols forces the
compact list). Subagents and CI scripts fall back to the CLI:

prism query "fix direct coverage gaps" --terms buildCoverageGaps --include graph,tests,coverage_gaps --format text
prism read internal/mcp/tools.go --format text
prism lookup github.com/provasign/prism/internal/mcp.buildCoverageGaps --format text

--format text avoids the large JSON metadata wrappers that made early MCP
benchmarks look expensive. Agents see plain source-like context with short
headers, and can ask for lean or json only when automation needs it.

Grove is embedded in the Prism binary. There is no separate daemon, token, or
grove_url setup in current releases.

Why Prism

Shell search gives pointers. Agents still have to chase those pointers by
reading files, guessing test names, and manually reconstructing call paths.

Prism precomputes the project graph and lets the agent ask for relationships:

rg buildCoverageGaps internal/
  -> prism query "write tests for buildCoverageGaps" \
       --terms buildCoverageGaps \
       --include graph,tests,coverage_gaps \
       --format text

On this repository, five real maintenance scenarios were run both ways on
2026-06-07. Shell-only baselines used rg plus targeted sed reads; Prism used
one CLI text command per scenario.

The average reduction was 29.6% with one Prism command instead of 5-6 shell
commands. The bigger correctness win is that Prism surfaces tests and coverage
gaps proactively; shell-only workflows often discover those after CI fails.

A controlled A/B re-run (2026-06-12, post Grove-v0.6.2 fixes) on the payflow
ground-truth project: zero coverage false positives at the tool level, total
agent-token parity with the shell baseline (the 2026-06-07 run had +27–147%
overhead), 47 vs 84 tool calls, and the baseline agent missing 3 of 12
designed coverage gaps that coverage_gaps reports mechanically. Repeat
reads cost 29 tokens (95% saved); a rename under the agent's feet is reported
as one breaking renamed entry for ~130 tokens. Full report:
docs/AB-Test-Payflow-2026-06-12.md.

More detail, including repeat-read savings: provasign.dev/prism.

How It Works

Task + anchor terms
      |
      v
Embedded Grove index
  - symbols
  - call edges
  - dependency edges
  - test edges
      |
      v
Prism ranking
  - graph distance
  - semantic similarity
  - recency
  - test relevance
  - edit frequency / learned weights
      |
      v
Budgeted text context
  - target symbols
  - callers/callees
  - tests
  - docs
  - coverage_gaps

Prism supports two distinct saving mechanisms:

1. Context gathering reduction: one graph-aware query replaces multiple
   shell searches and file reads. This is what CLI text-mode benchmarks measure.
2. Session deduplication: in persistent MCP transports, repeated reads of
   unchanged files can become a short SHA pointer. This is where the ~99%
   repeated-read savings come from.

Direct CLI invocations are process-per-command, so they should be evaluated on
context gathering and output wrapper size, not same-session re-read dedupe.

Installation

# Homebrew (macOS / Linux) — one tap for the family (prism, fuse, shale)
brew install provasign/shale/prism

# macOS / Linux script
curl -fsSL https://raw.githubusercontent.com/provasign/prism/main/install.sh | bash

# Windows PowerShell
irm https://raw.githubusercontent.com/provasign/prism/main/install.ps1 | iex

# Pin a version
VERSION=v0.7.0 curl -fsSL https://raw.githubusercontent.com/provasign/prism/main/install.sh | bash

The installer writes prism to ~/bin by default. Set
INSTALL_DIR=/usr/local/bin or another directory to override.

Build from source:

make build
make test
make install

Quick Start: Agent CLI Text Mode

Run this once at the project root:

prism init . --mode both
prism index .

This writes:

• prism.yaml with agent_mode: "both"
• .mcp.json wiring the MCP server for MCP-capable clients
• steering files such as AGENTS.md, CLAUDE.md, .cursorrules,
  .windsurfrules, .github/copilot-instructions.md, and others
• compatible tool config files where detected

The generated agent instructions tell agents to use commands like:

prism query "trace the payment refund flow" --terms RefundPayment --include graph,tests --format text
prism query "find direct coverage gaps" --terms UpdatePayment,RequireScope --include graph,coverage_gaps --format text
prism read internal/payment/service.go --format text
prism lookup github.com/example/payflow/internal/payment.(*Service).RefundPayment --format text

Recommended agent workflow:

1. Locate the first anchor with rg, grep, or find.
2. Run prism query with the same anchor terms.
3. Use prism read for whole files only when needed.
4. Use prism lookup for one known function or method.
5. Treat coverage_gaps as a terminal structured output, not the start of
   manual cross-referencing.

Other Modes

prism init supports three modes:

prism init . --mode both  # recommended: MCP primary + CLI fallback for subagents
prism init . --mode mcp   # MCP tools only: prism_query, prism_read, ...
prism init . --mode cli   # CLI only: for environments without MCP support

MCP

MCP advertises fifteen tools: the context surface (prism_query,
prism_read, prism_search, prism_lookup, prism_references,
prism_resolve, prism_edges), the task-shaped graph operations
(prism_change_impact, prism_missing_implementations,
prism_untested_surface, prism_dead_code, prism_rename_plan,
prism_affected), and session upkeep
(prism_index, prism_drift). The auxiliary tools (prism_savings,
prism_feedback, prism_compact, prism_evidence) stay available through
the CLI and HTTP server without spending schema tokens in every MCP session. Use MCP when the client has first-class MCP support and
you want persistent session deduplication.

HTTP Server

prism serve is optional. Use it for custom automation that wants HTTP instead
of CLI or MCP:

prism serve --port 8888 /path/to/project

It binds to 127.0.0.1.

CLI Reference

prism init [--global] [--mode cli|mcp|both] [dir]
prism index [dir]
prism status [dir]

prism query <task> [dir] \
  --terms a,b,c \
  --include graph,tests,docs,coverage_gaps \
  --delivery source|symbols \
  --max-files 5 \
  --depth 2 \
  --format text

prism read <file> [dir] --format text
prism lookup <name> [dir] --format text
prism search <keyword> [dir] --format text
prism references <name> [dir] --format text

# Task-shaped graph operations — one deterministic call each
prism change-impact 'Type.method(ParamType, ...)' [dir]   # declaration + override family + all resolved callers
prism rename-plan 'Type.method' NewName [dir]              # every concrete edit line, review-and-apply
prism missing-implementations 'Type.method' [dir]         # types claiming the contract that do not implement it
prism untested-surface 'Type.method' [dir]                # the change-set split covered/untested by test evidence
prism dead-code [dir] [--roots a,b]                       # unreachable production symbols (precision-first)
prism affected <file> [file ...] [dir]                    # tests covering the changed files (CI selection):
                                                          #   git diff --name-only | xargs prism affected

prism watch [dir]      # background file-watcher: delta-reindex on save, index always warm
prism drift [dir]
prism savings [dir]
prism compact [dir]
prism feedback --tool <name> --rating <0-5> [dir]
prism mcp [dir]
prism serve [--port 8888] [dir]
prism version

Output formats:

Configuration

prism.yaml is intentionally small:

version: 1
profile: "default"
agent_mode: "cli"

Optional keys:

model: "claude-sonnet-4-6"
grove_binary: "grove"
embeddings_backend: "tfidf"

Environment overrides include PRISM_MODEL, PRISM_PROFILE,
PRISM_GROVE_BINARY, and PRISM_EMBEDDINGS_BACKEND.

Language Support

Prism delegates parsing and graph construction to embedded Grove.

Markdown, YAML, JSON, shell scripts, Dockerfiles, Makefiles, SQL, GraphQL, and
other non-code files are indexed as document symbols and can be requested with
--include docs.

Benchmarks

One task, three ways to search — same agent, same frontier model, only the
tool changes. A signature change in jackson-databind: find all 8 call
sites it breaks, including callers not named after the method (invisible to
text search). Oracle-scored.

Fewer turns, fewer tokens, lower cost — and the only one that found every
site. Run the same task through Mason (Prism built in) on a free local
30B model: all 8, at $0 (0.997 mean recall across the 7-task
change-impact benchmark). Raw runs: provasign/research.

The headline numbers (context reduction per scenario, repeat-read savings by
project size, and the SHA-pointer dedup mechanism) are summarized with
methodology at provasign.dev/prism. The full
benchmark reports were trimmed from this repo to keep it lean; they remain
available in git history (git log --diff-filter=D -- docs/ to locate them).

Current practical summary:

• CLI --format text is the recommended default for shell-capable agents.
• Prism is strongest on graph/blast-radius/test/coverage-gap questions.
• Shell tools remain best for locating exact strings or filenames.
• MCP persistent transports add repeated-read deduplication that direct CLI
  invocations do not fully exercise.

Troubleshooting

prism query returns nothing: run prism index . from the project root.

Agent uses wrong steering: run prism init . --mode both (or your chosen mode) and verify prism.yaml has the correct agent_mode.

Wrong Prism binary: run command -v prism and prism version. Reinstall if
the version is old.

macOS quarantine:

xattr -d com.apple.quarantine "$(which prism)"
codesign -f -s - "$(which prism)"

MCP client does not connect: restart the coding tool after prism init, and
approve project MCP configuration if the tool prompts.