Comparisons
An honest look at how Provasign — and its open-source components Grove, Prism, and Fuse — stack up against the tools you might already be using or evaluating. We name names, we link to their docs, and we tell you where they are stronger than us.
This is a living document. If you spot something inaccurate — especially about a competitor — open an issue.
Table of Contents
- Prism vs. Copilot, Claude Code, Cursor, Codex CLI context delivery
- Grove vs. LSP, Sourcegraph, ctags, Stack Graphs
- Fuse vs. git merge, IntelliMerge, Plastic SCM, AI-based merge resolvers
- Provasign vs. CI/CD, CodeRabbit, Greptile, Sigstore, SLSA
- Provasign vs. Devin, Cursor Background Agents, Copilot Workspace
Prism vs. Other Context Delivery
When an AI coding agent needs to know what code is relevant to a task, the agent’s host has to deliver context. Every tool does this differently.
| Capability | Prism | GitHub Copilot (semantic search) | Claude Code (filesystem tools) | Cursor (codebase indexing) | Codex CLI |
|---|---|---|---|---|---|
| Architecture | Graph-ranked + local | Embedding-based + cloud | File-by-file ad hoc | Embedding-based + cloud | File-by-file ad hoc |
| Where ranking happens | Your laptop | Microsoft servers | The model itself | Cursor servers | The model itself |
| Cross-file BFS traversal | Yes (Grove edges) | No (embeddings only) | No | No | No |
| Deterministic ranking | Yes (5 signal scores) | No (embedding cosine) | N/A | No | N/A |
| Token budget allocation | Transparent (35/25/20/10/10) | Opaque | None | Opaque | None |
| Progressive disclosure | Yes (full → sha-pointer) | No | No | No | No |
| Session deduplication | Yes (LRU keyed by content SHA) | Implicit (conversation history) | Implicit | Implicit | Implicit |
| Token savings (typical) | 35–92% first read · ~99% re-read | Not measurable | Not measurable | Not measurable | Not measurable |
| Code leaves your laptop | No | Yes (to Microsoft) | No (with local models) | Yes (to Cursor) | No (with local models) |
| Works with any agent | Yes (MCP + VS Code lm.registerTool) | No (Copilot only) | No (Claude only) | No (Cursor only) | No (Codex only) |
| Cost | Free (MIT) | $10–$39/user/month | Token billing | $20/user/month | Token billing |
Where Prism is stronger:
- Deterministic ranking. Every score is reproducible. You can debug why a symbol was or wasn’t included —
prism explainshows the 5-signal breakdown. With embedding-based retrieval, “why did the agent get this file?” is not an answerable question. - Cross-file graph traversal. Prism doesn’t just find similar files — it finds the call graph, the type graph, the test graph. When you ask “what does this function transitively call?”, embedding similarity gives you semantic neighbours; Grove gives you actual callers.
- Progressive disclosure across the session. Read a file three times in a conversation? First read is full. Second read is a sha-pointer (~10 tokens). Third read same — until the file changes or scrolls out. This alone saves ~99% on repeated reads, which is the dominant pattern in long agent sessions.
- Local everything. Your codebase never reaches a vendor’s servers. No telemetry. No “improving our models with your code.” Matters for proprietary codebases, regulated industries, and anyone who’s read their employer’s IP agreement carefully.
- Cross-agent. Prism works with Claude Code, Copilot, Cursor, Codex CLI, Windsurf, Zed, and any MCP client. If you change agents next quarter, your context layer stays.
Where the others are stronger:
- Cursor’s indexing is invisible to the user — it just works. Prism requires
prism initand anindexstep. We’re working on auto-indexing. - Copilot’s semantic search has been trained on a massive codebase corpus. For wildly heterogeneous queries against unusual codebases, embedding-based retrieval may surface things Prism’s BFS misses. We mitigate this with Model2Vec semantic similarity as one of the five signals, but it’s a 29 MB local model — not GPT-scale embeddings.
- Claude Code’s filesystem-direct approach is the simplest possible architecture. No index, no daemon, no port. Works great on small repos.
TL;DR: If you care about deterministic ranking, cross-file graph awareness, local-first operation, and using the same context layer across multiple AI tools, Prism wins. If you’re a Cursor-only shop with a small codebase and trust their cloud, you may not need Prism yet.
Grove vs. Other Code Intelligence
Grove is a persistent code knowledge graph. The alternatives target different problems.
| Capability | Grove | LSP (e.g. gopls, pyright) | Sourcegraph | ctags / GNU global | Stack Graphs (research) |
|---|---|---|---|---|---|
| Persistent storage | Yes (SQLite) | No (in-memory per session) | Yes (proprietary store) | Yes (flat file) | Yes |
| Cross-language graph in one query | Yes | No (one language per server) | Yes | Yes (regex-based) | Yes |
| Edge types | 8 typed edges | Reference + definition | Many | Reference only | Many |
| Delta indexing | Yes (git blob SHA) | N/A | Yes | Manual | Yes |
| BFS / graph queries | Yes | No (point queries) | Yes (GraphQL) | No | Yes |
| Local-only deployment | Yes | Yes | Self-host or cloud | Yes | Research-grade |
| MCP-accessible to agents | Yes (8 tools) | No | No (REST/GraphQL only) | No | No |
| Indexes non-code files (Markdown, YAML, etc.) | Yes (FTS5 + Model2Vec) | No | Yes | Some | No |
| Cost | Free (MIT) | Free (per-language) | $99+/user/month (cloud) or self-host | Free | N/A (not productized) |
Where Grove is stronger:
- MCP-first design. Grove exposes 8 tools (
grove_index,grove_query,grove_impact,grove_deps,grove_symbols,grove_tests,grove_icr,grove_conflicts) directly to AI agents. LSP and ctags were built for IDEs, not agents. - Eight typed edges, scoped.
callsanduses-typeedges are constrained to same-file and imported files — without that constraint, a function namedparsein one package would appear to callparsein unrelated packages, producing ~5× the false-positive edges. - Delta indexing by git blob SHA. Re-indexing a 5,000-file repo after a one-line change touches one file, not 5,000. LSP rebuilds in-memory per session; ctags requires manual re-run.
- Persistent and queryable from anywhere. Grove is a daemon with HTTP, gRPC, and MCP endpoints. Any tool can query the graph — not just the IDE that started the LSP.
Where the others are stronger:
- LSP is the gold standard for IDE-level correctness. Real-time hover, completion, refactoring. Grove does not aim to replace LSP — they solve different problems. In fact, Grove uses Tree-sitter for parsing, which is faster but less semantically precise than a full type-checker. If you need exact “find all references” with full type resolution, use LSP.
- Sourcegraph at enterprise scale handles things Grove doesn’t yet: cross-repo navigation, code review integrations, batch changes, owners search. Grove is single-repo and developer-laptop-first.
- ctags is universal. Decades old, every editor supports it. Grove is new.
TL;DR: Grove is the right tool when AI agents are the consumers. For IDE features, keep LSP. For cross-repo enterprise search, Sourcegraph remains best-in-class.
Fuse vs. Other Merge Tools
Most git merge conflicts in the AI-agent era are false conflicts — two structurally independent changes that git can’t distinguish from real ones.
| Capability | Fuse | git merge (default) | IntelliMerge (research) | Plastic SCM Semantic | AI-based merge resolvers (Continue, Cody) |
|---|---|---|---|---|---|
| Granularity | Symbol | Line | Symbol | Symbol | Line (AI resolves) |
| Deterministic | Yes | Yes | Yes (academic prototype) | Yes | No (LLM-driven) |
| Auto-resolution rate (incremental) | ~85% | Effectively 0% (false conflicts dominate) | ~70% (paper) | Similar | Varies wildly |
| Cross-file blast radius | Yes (Grove /impact) |
No | No | No | Sometimes |
| Breaking change detection | Yes (signature diff) | No | Partial | Yes | Varies |
| AI handoff prompt when ambiguous | Yes (.git/fuse/conflict-<hash>.md) |
No | No | No | N/A (always uses AI) |
| Languages | 11 (Tree-sitter) | All (line-level only) | 4 (Java focus) | Limited | Whatever the LLM knows |
| Latency per merge | < 1 s | < 100 ms | Seconds | < 1 s | Seconds (API roundtrip) |
| Cost | Free (MIT) | Free | Academic | $7/user/month | Token billing |
| Works with any git workflow | Yes (gitattributes driver) | N/A | No (not packaged) | Only with Plastic SCM | Editor plugin only |
Fuse vs. AI-based merge resolution: the important comparison
This deserves a section of its own because it’s the most common alternative being evaluated alongside Fuse.
AI-based merge resolvers (e.g., Continue’s resolve-conflict, Cody’s smart merge, various agent CLIs) send the conflict region to an LLM and use the LLM’s response as the resolution. This works — sometimes — but has serious tradeoffs:
| Concern | AI-based merge | Fuse |
|---|---|---|
| Deterministic | No — same conflict can produce different resolutions across runs | Yes |
| Reviewable | Hard — you’re reviewing an LLM’s judgment, not a rule | Easy — strategies are named (Symbol / Import / Config / Line / Handoff) and confidence is scored |
| Auditable | Difficult — there’s no record of why the LLM chose a resolution | Yes — every decision is logged to .git/fuse/audit.json with class, strategy, confidence, symbols merged |
| Cost | API tokens per conflict | Zero per conflict |
| Wrong resolutions | LLM can confidently produce broken code that compiles | Fuse falls back to conflict markers rather than guess |
| Speed | Slow (network roundtrip) | Local, sub-second |
| Cross-file blast radius | Sometimes, depends on context window | Always, via Grove |
| Safety on bad inputs | LLM may resolve in a way that breaks distant callers | Grove /impact shows what would break before resolution is chosen |
The right mental model: AI merge is “have the LLM do my job.” Fuse is “use a deterministic algorithm where one exists, escalate to AI only when ambiguity is real.” Fuse’s handoff prompt at .git/fuse/conflict-<hash>.md is engineered for AI consumption — when Fuse can’t resolve, it hands the AI a structured packet (all three versions, symbol signatures, Grove blast radius, breaking change analysis, suggested approach). The AI gets to work with full context instead of just the diff hunk.
TL;DR: Fuse uses deterministic symbol comparison first, AI handoff only when genuinely ambiguous. AI-based tools use AI on every conflict, with corresponding cost, latency, and reproducibility problems.
Provasign vs. Other Delivery Tools
Provasign sits between an AI agent and your repository. The alternatives sit elsewhere in the pipeline.
| Capability | Provasign | Traditional CI (GitHub Actions, etc.) | CodeRabbit / Greptile | Sigstore / SLSA | Devin / Codex managed |
|---|---|---|---|---|---|
| Quality gates in the agent loop (pre-PR) | Yes | No (post-PR) | No (post-PR) | No | Internal to platform |
| Returns structured findings to the agent | Yes (file, line, rule, severity, fix-hint via MCP) | No | Partial (comments) | No | Internal |
| Build + test + coverage gate | Yes (Stage 1) | Yes | No | No | Internal |
| SAST + secrets + dep audit | Yes (Stage 2) | Configurable | Some | No (different layer) | Varies |
| Cryptographic signature on commit | Yes (Ed25519) | No (signed tags only via separate tooling) | No | Yes (artifact-level) | No |
| Replay / verify cert | Yes (provasign cert replay) |
No | No | Yes (artifact) | No |
| Captures original user prompt | Yes (YAML intent) | No | No | No | Internal |
| Audit trail per commit | Yes (Intent-ID + cert) | Logs (rolled off) | Review comments | Provenance attestation | Logs |
| Runs locally | Yes | Cloud + self-hosted runners | Cloud | Both | Cloud only |
| Cost | Free (MIT) | Free–$$$ depending on runners | $15+/user/month | Free (open source) | $20–$500/user/month |
Where Provasign is uniquely positioned:
- In the agent loop, not after the PR. Quality gates that run before the PR opens prevent the loop entirely. CI runs after the PR — by then the agent is gone and a human is back in the driver’s seat.
- The full audit triple. Provasign is the only tool that gives you: (1) the original prompt, (2) cryptographic proof of what gates passed, (3) byte-reproducible replay. Sigstore signs artifacts. Provasign signs commits and links them to the prompt.
- Same binary, three deployment modes. Laptop (SQLite, local key). Team (Postgres, Redis, shared key in KMS). Air-gapped. No vendor lock-in.
Provasign vs. CodeRabbit / Greptile
These are AI code review tools. They sit after the PR opens and review the diff for issues. Useful — but a different role.
| Question | CodeRabbit / Greptile | Provasign |
|---|---|---|
| What problem does it solve? | “This PR has a bug” — caught by AI review | “This PR’s gates already passed” — provable before review |
| When does it run? | After the PR is opened | Before the PR is opened (in agent loop) |
| What does it produce? | Review comments | Cryptographic certificate |
| Can the agent use it? | The agent’s PR is the input | The agent calls it in its own loop |
| Is it adversarial-resistant? | LLM can be tricked into approving bad code | Build + tests + SAST run for real; LLM can’t fake them |
These tools complement each other. Run Provasign in the agent loop to prevent the CI-loop churn. Run an AI reviewer after the PR opens to catch things automated gates miss (logic bugs, architectural choices). They are not substitutes.
Provasign vs. Sigstore / SLSA
Sigstore signs artifacts. Provasign signs commits. SLSA defines provenance levels for artifacts; Provasign produces SLSA-compatible attestation but at commit time, not build time.
If your concern is “someone tampered with my build artifact between source and deployment,” you want Sigstore + SLSA. If your concern is “I need cryptographic proof of what gates passed against this exact change at commit time,” you want Provasign. Together: end-to-end provenance from prompt to production.
TL;DR: Provasign is the gate-the-agent-loop tool with the audit-trail-on-commit superpower. CI and CodeRabbit are the post-PR layer. Sigstore/SLSA are the post-build layer. All three layers exist for a reason; Provasign fills the one that didn’t exist before.
Provasign vs. End-to-End Agent Platforms
This is the comparison everyone asks for. “If I just use Devin / Cursor Background Agents / Copilot Workspace, do I need Provasign?”
| Capability | Provasign | Devin | Cursor Background Agents | GitHub Copilot Workspace |
|---|---|---|---|---|
| What it is | Open-source infrastructure beneath any agent | Managed autonomous agent | Managed agent in your IDE | Managed agent in your repo |
| Code stays local | Yes | No (Devin’s infrastructure) | No (Cursor’s infrastructure) | No (GitHub’s infrastructure) |
| Works with your existing agents | Yes — built for it | N/A (is the agent) | N/A (is the agent) | N/A (is the agent) |
| Self-hosted | Yes | No | No | No |
| Customizable policy gates | Yes (.provasign/policies/) |
Vendor-managed | Vendor-managed | Vendor-managed |
| Cryptographic commit signing | Yes | No | No | No |
| Intent capture / audit trail | Yes (committed YAML) | Internal logs | Internal logs | PR description |
| Open-source licensed | Yes (MIT for Grove/Prism/Fuse, AGPL-3.0 for Provasign) | No (closed) | No (closed) | No (closed) |
| Cost | Free | $500/agent/month | $40/user/month | Bundled with Copilot Enterprise |
The two paradigms:
Managed agent platforms (Devin, Background Agents, Workspace): You hand the platform a task; the platform writes the code, runs the gates, opens the PR. Convenient. Black-box. Doesn’t fit if you can’t ship source to a vendor.
Provasign: You hand any agent (Cursor, Claude Code, Copilot, your own) the task. Provasign delivers context, prevents false merge conflicts, certifies before PR, signs commits. The agent is yours to pick and swap.
Honest take: If your team is already all-in on a single managed platform and that platform’s governance story satisfies your compliance team, you may not need Provasign. The moment you want to use multiple agents, run locally, self-host, or own the audit trail — Provasign is the layer that makes that possible.
Summary
| If your top concern is… | Look at |
|---|---|
| Agents wasting context tokens | Prism |
| IDE-quality navigation | LSP (Grove complements, doesn’t replace) |
| Cross-repo enterprise search | Sourcegraph |
| False git conflicts on parallel agent work | Fuse |
| Agents in a loop fighting CI findings | Provasign |
| Cryptographic audit trail for AI-generated commits | Provasign |
| Post-PR AI code review | CodeRabbit / Greptile |
| Build provenance | Sigstore / SLSA |
| Fully managed autonomous agent | Devin / Cursor Background Agents / Copilot Workspace |
| Open-source, local-first, self-hosted, multi-agent | Provasign |
If we got something wrong about a competitor, file an issue. We’d rather correct an error here than have you make a buying decision against a misrepresentation.