Intent-addressable code for AI coding agents

Causari, an open-source tool from Croviatrust, records every action an AI agent takes on a codebase, including prompts, models, files read, and reasoning, without requiring agent permission. It uses a content-based join of LLM traffic and filesystem changes to provide provenance, enabling queries like 'why' and 'trace' for any line of code. The tool aims to solve the problem of untraceable AI-generated code changes and is available under BSL 1.1.

Intent-addressable code for AI agents. causari.dev https://causari.dev · Releases https://github.com/croviatrust/causari/releases · Discussions https://github.com/croviatrust/causari/discussions · MCP mcp-server · License BSL 1.1 /croviatrust/causari/blob/main/LICENSE Causari Latin, deponent verb :to plead a cause, to argue why.Because every line of AI-generated code deserves to be defended, traced, and understood. Causari records every action an AI agent takes on your codebase — not just the bytes that changed, but the prompt that asked , the model that answered , the files it read , and the reasoning behind the change . And it does so without asking the agent's permission : the built-in capture engine re proxy + re watch + re hook observes the LLM traffic and the filesystem independently, then joins them by content — the code that appears in your files is found inside the completion that produced it seconds earlier. Provenance becomes a fact, not a self-report. You can then ask questions no version control system has ever answered: re proxy local LLM proxy: every prompt, token and dollar flows through Causari on its way to the provider re watch passive recorder + causal join: file changes get attributed to the real prompt, model and cost re hook claude-code native capture via agent lifecycle hooks re why src/auth.ts:42 who/what produced this exact line? re trace src/auth.ts:42 full UPSTREAM causal cone: every event that contributed transitively, through reads/writes re impact <event-id full DOWNSTREAM cone: what flowed from this action, transitively causality-aware blast radius re lens src/auth.ts render a file with per-line provenance annotations re find "the JWT refactor" search every prompt, reasoning and message re bisect --test "npm test" find the agent action that broke the build re churn measure AI code survival: how much survived vs was rewritten, per agent, with wasted spend re report --open generate a shareable HTML dashboard of AI waste re skill distill turn verified events into signed, reusable skills re skill export <id portable Ed25519 bundle for teammates re skill pull <team-dir sync a shared folder Dropbox, git, NFS — no server re skill trust add <label trust an org signing key; unknown signers rejected re fork experiment-claude branch into a parallel timeline re revert <id undo an action with causal preview of what else you are implicitly undoing When an agent touches 30 files and something breaks, you don't need to read 4 000 lines of chat. You ask Causari why and when . Every provenance tool before Causari had the same fatal dependency: it only worked if the agent volunteered its own history. Agents don't. Harnesses don't expose reasoning. Nobody reports costs. Causari removes the dependency. Two independent observation streams, one causal join: ┌─────────────────────────┐ ┌─────────────────────────┐ │ re proxy │ │ re watch │ │ │ │ │ │ sees every prompt, │ │ sees every byte that │ │ completion, token and │ │ changes on disk │ │ dollar OpenAI- and │ │ snapshots, diffs │ │ Anthropic-compatible │ │ │ └────────────┬────────────┘ └────────────┬────────────┘ │ │ │ CONTENT-BASED JOIN │ └────────────────►◄────────────────┘ the lines inserted in your files are searched inside the completions captured moments before — a match is a causal fingerprint, with confidence A real session, end to end: bash $ re proxy causari: LLM capture proxy listening on http://127.0.0.1:4242 • gpt-4o 42→18 tok $0.0003 "Add JWT refresh logic that rotates every 24h" $ re watch in another terminal • 0d47599550 auth.py ↳ intent: "Add JWT refresh logic that rotates every 24h" gpt-4o confidence 100%, 3/3 lines $ re why auth.py:2 auth.py:2 token = issue token user, scope="session" introduced by 0d47599550 agent: cursor model: gpt-4o prompt: Add JWT refresh logic that rotates every 24h Point any agent at the proxy and you're done: OPENAI BASE URL=http://127.0.0.1:4242/openai/v1 ANTHROPIC BASE URL=http://127.0.0.1:4242/anthropic Where the agent runtime exposes lifecycle hooks, capture is native and exact — no inference needed: re hook claude-code wires UserPromptSubmit + PostToolUse into .claude/settings.json: every prompt captured, every edit recorded as a full Causari event Everything stays on your machine: .causari/capture/ is a local, append-only ledger. No cloud, no telemetry, no API keys touched. Causari is the first production issuer of Crovia Seals — the open, IETF-drafted receipt format for AI outputs draft-crovia-seal-01 https://datatracker.ietf.org/doc/draft-crovia-seal/ . One flag turns the proxy into a sealing gateway: bash $ re proxy --seal causari: Crovia Seal issuer active — pubkey 3fa9c2… • gpt-4o 42→18 tok $0.0003 "Add JWT refresh logic" 🔏 cs 2026 Q7RM2KJ3VWXA5YBN4CDEFGH2I6 Every completion gets an Ed25519-signed, hash-chained, offline-verifiable receipt in .causari/seal/seals.jsonl . The seal commits to SHA-256 hashes of the exact request and response bytes — content never leaves your machine . Anyone holding your public key can verify the whole chain without a server, an account, or Causari itself: bash $ re seal verify ✓ 128 seal s verified — every signature valid, chain contiguous from genesis $ re seal issuer print the pubkey to share with auditors $ re seal list browse issued receipts The implementation is proven against the normative conformance vectors from croviatrust/crovia-seal https://github.com/croviatrust/crovia-seal CSC-1 canonicalization, domain-separated payloads, fail-closed verification . When a regulator, a customer or a court asks "which model wrote this code, and can you prove it?" — the answer is one file and one public key. Recording the past is half the job. The other half is making sure no agent ever pays for the same lesson twice. re skill distill walks the ledger and compresses every completed task — the prompt that triggered it, the steps that were taken, the files that changed — into a skill : a unit of experience an agent can recall before acting. Each skill is signed with the repository's Ed25519 key at the moment of distillation; edit one byte afterwards and re skill verify exposes it. Trust is earned, never claimed: ● recorded distilled from the ledger — no success signal yet ◆ verified evidence attached: exit code 0, or the work is still alive at the tip of the timeline it survived ★ proven verified AND recalled 3+ times by agents doing new work bash $ re skill distill distill: 128 event s scanned, 7 new skill s , 12 already distilled ◆ verified 2ce0c7bbda add retry with exponential backoff $ re skill verify ok 2ce0c7bbda add retry with exponential backoff verify: 7 skill s , every signature valid The loop closes through MCP: when an agent calls causari recall , signed skills are returned first, ranked by trust proven ×4, verified ×2 , and every recall bumps the skill's use counter — which is exactly how a verified skill earns the ★. Agents get measurably cheaper over time, and re churn shows you the savings in dollars. One engineer's verified fix becomes every agent's instinct — without a central SaaS: re skill export 2ce0c7bbda --output jwt-fix.json portable bundle re skill trust pubkey share your Ed25519 key re skill trust add platform <their-pubkey trust a teammate/org key re skill import jwt-fix.json verify signature + accept re skill pull ~/Dropbox/causari-skills/ sync a whole team folder Skills signed by unknown keys are rejected , not imported. The mesh is cryptographic: Dropbox, git, NFS, S3 — any folder works. Causari verifies Ed25519 on every file; tampered bundles fail closed. Like everything in Causari, skills are local files .causari/skills/ , self-contained and portable. The signature means a skill can be shared and verified by anyone — across repos, teams, and orgs, with no central server. Every repo can mint a signed proof of its AI provenance — how many agent actions, which agents and models, how much verified experience — bound to the exact ledger by a content digest and signed with the repo's Ed25519 key. re proof generate → causari-proof.json + causari-proof.svg badge re proof verify checks the signature offline — no server, no account re proof verify --against-repo …and confirms it still matches the live ledger Anyone — a reviewer, an auditor, a stranger reading your PR — can run re proof verify and confirm the proof was not altered after signing . No Causari account, no network call, no trust in us. Tamper with a single number and verification fails closed. Drop the badge in your README and every visitor sees it: AI provenance — verified by Causari causari-proof.svg https://causari.dev/verify It is agent-agnostic by construction: the proof aggregates the ledger , so it covers every agent Causari captured — Claude Code, Cursor, Cline, Windsurf, a raw re proxy — not just one runtime. Free forever: generating and verifying proofs offline. Commercial Trust Plane : the hosted public verification page on causari.dev , the org-wide proof registry, RFC 3161 timestamp anchoring, and audit-grade compliance exports. Existing tools either track text git , track sessions IDE checkpoints , or track conversations LangSmith, Helicone . None of them connect a line of code to the intent that produced it — and none of them can do it without the agent's cooperation. Causari does both: | You ask… | Causari answers… | |---|---| re proxy + re watch | Zero-integration capture. Prompts, models, tokens and dollars joined to file changes by content correlation — no agent cooperation required. | re why src/auth.ts:42 | The prompt, model, agent, tool, and reasoning that wrote that line. | re trace src/auth.ts:42 | Upstream causal cone. Every prior event that contributed, transitively, through the files it read or wrote. The intellectual ancestry of a piece of code. | re impact <event | Downstream causal cone. Every later event that depended, transitively, on what this one produced. The blast radius of an action. | re lens src/auth.ts | The file rendered with per-line provenance annotations: each line painted with the event id that introduced it. | re find "the JWT refactor" | Signed skills first, then every event — prompt, message, reasoning — ranked by trust and relevance. | re bisect --test "<cmd " | The first agent action whose output fails your tests. | re churn | AI Waste Score. How much AI-written code survived vs was rewritten, per agent. With cost data: dollars spent on code that did not survive. | re report --open | A self-contained HTML dashboard you can paste into Slack, PRs, or board decks — zero external assets, zero cloud calls. | re skill distill | Signed experience. Verified past work compressed into Ed25519-signed skills, recalled by agents trust-ranked before they act — the same mistake is never paid twice. | re skill export / pull | Team skill mesh. Portable bundles + trusted org keys; sync any shared folder. Unknown signers and tampered files rejected. | re proof generate / verify | Trustless AI-provenance certificate. A signed, content-bound proof + embeddable badge that anyone can verify offline — no server, no account. Tampering fails closed. | re fork claude-attempt | A new timeline you can extend without touching the original. | re watch --session bot1 | Concurrent multi-agent recording. One session per agent, lock-serialized commits, shared ancestry — no agent can orphan another's events. | re sessions / re switch <name | The fleet overview: every session tip with agent and last activity; jump between timelines. | re log --all | The full event DAG across every session, with tip and fork-point markers. | re diff a..b | The exact file delta between two agent actions. | re revert <id | Workspace snapped back to the pre-state of that action, with a causal preview of every downstream event you are implicitly undoing. | Most version control is one-dimensional: a chain of commits. Causari is two-dimensional: PAST FUTURE ┌──────────────────────────┐ ┌──────────────────────────┐ │ │ │ │ │ re trace foo.rs:42 │ │ re impact <event │ │ │ │ │ │ ← prompts & events │ │ events & prompts → │ │ that produced this │ │ that flowed from this │ │ │ │ │ └──────────────┬───────────┘ └─────────────┬────────────┘ │ │ │ a single event │ └────────────►●◄─────────────┘ This unlocks a question nothing else can answer: "If I revert this action, what else am I implicitly undoing?" re revert answers it before touching a single byte. Git blame names one author. re why names one event. re trace reconstructs the intellectual ancestry of a piece of code: calc.js:2 export function sum a, b { return a - b; } trace: 3 causal contributors found ● 0b8424ee83 align calc.js with updated spec agent: gpt-4o prompt: the spec was updated, make calc.js match because: wrote calc.js:2 └─ 45230e9cda update spec to redefine sum agent: gpt-4o prompt: the team decided sum should compute a-b, update the spec because: wrote spec.md which event 0b8424ee83 read └─ 55a6dd9392 implement calc per spec agent: claude-3.5 prompt: implement sum following the spec in spec.md because: wrote calc.js which event 0b8424ee83 read The buggy line is not the root cause — the prompt that asked the agent to redefine the spec is. Causari surfaces it. You can debug prompts, not just code. Every event is a content-addressable object BLAKE3 containing: pre snapshot and post snapshot — the workspace tree before and after agent , model , tool prompt — the user task that triggered the action reasoning — the agent's chain-of-thought when exposed reads , writes , tokens in , tokens out , cost usd parent — the previous event in the timeline Snapshots are incremental only changed files create new blobs, just like git's object store , so the storage cost is bounded by the delta , not the absolute size of the workspace. 09:23:01 re init → .causari/ repository initialized 09:24:33 re record -m "Add JWT refresh logic" → 12 lines in src/auth.ts → agent: claude-3.5-sonnet → prompt: "Add JWT refresh logic that rotates every 24h" 09:25:12 re guard ⚠ critical without test: src/auth.ts ⚠ 1 alert s , 0 warning s 09:25:45 re why src/auth.ts:5 → introduced by a3f7b2c9 → prompt: "Add JWT refresh logic that rotates every 24h" → reasoning: The spec calls for refresh tokens... → 3 seconds 09:26:18 re trace src/auth.ts:5 → a3f7b2c9 "Add JWT refresh" └─ e112706e "Update auth spec" └─ c13aa663 "Initial scaffold" 09:27:03 re guard --badge ✓ .causari/guard-badge.svg generated 09:27:44 re impact a3f7b2c9 → downstream: 2 events depend on this → c4d1e8f2 "Deploy to staging" → d5e2a1b3 "Fix OAuth scope" 09:28:19 re churn causari churn: code survival across 1,284 events AGENT INTRO SURVIVED WASTE WASTED $ claude-3.5 8,210 6,012 26.8% $164.10 gpt-4o 3,400 1,510 55.6% $116.90 cursor 1,120 980 12.5% $5.50 AI survival 66.8% · AI Waste Score 33.2% $286.50 of $866.90 spent on code that did not survive 09:29:02 re report --open ✓ report written to causari-report.html → opening in browser 09:29:33 re revert a3f7b2c9 ⚠ preview: 2 downstream events will lose context → confirm with --yes to proceed Next on the roadmap: ✓ shipped: Verified skills experience layer : events whose verification passed get promoted into signed, reusable skills the agent can recall before acting re skill distill/list/show/verify , Ed25519 signatures, trust ladder ● recorded → ◆ verified → ★ proven , trust-ranked causari recall via MCP✓ shipped: Multi-agent DAG timelines : concurrent agents, true branching history --session , re sessions , re switch , re log --all ✓ shipped: Team skill registry : share signed skills across an organization — one engineer's verified fix becomes every agent's instinct re skill export/import/pull , re skill trust — Ed25519 mesh, no server✓ shipped: Verifiable provenance certificate : a signed, content-bound proof of a repo's AI provenance that anyone can verify offline, with an embeddable badge re proof generate/verify , Ed25519, self-contained SVG, tamper-evident fails closed - Cryptographic timestamps RFC 3161 + Ed25519-signed events for audit-grade timelines EU AI Act, SOC2 for agentic development — the paid Trust Plane on top of re proof Agent Provenance Protocol : an open spec for the signed, content-addressed event format, so any tool can produce or verify it- TUI à la lazygit for visual exploration - Cross-event semantic search over prompts and diffs embeddings - Counterfactual re replay --with <model re-execute past events under different models Causari ships its own MCP server. Any agent runtime that speaks MCP Claude Desktop, Claude Code, Cursor, Cline, Windsurf, … can register Causari and get three new tools for free: | Tool | What the agent uses it for | |---|---| causari record | Record one of its own actions into the ledger after each tool call. | causari recall | Find past similar events before acting, to avoid repeating mistakes. | causari why | Inspect the provenance of a line before modifying code it didn't write. | Get the JSON snippet to paste into your agent's config: re mcp --install Then in any conversation the agent can call those tools by name. Causari silently builds a complete, queryable, causally-linked history of the session. Add the Causari Guard action to any repo and every pull request gets a risk summary — zero cloud, zero configuration: .github/workflows/guard.yml name: Causari Guard on: pull request: jobs: guard: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - uses: croviatrust/causari-guard-action@v1 It installs re , runs re guard --summary , and posts a Markdown table of alerts directly into the PR thread. Block merges on risky patterns bulk edits, auth without tests, missing tests, etc. . Keep the badge green on main : re guard --badge .causari/guard-badge.svg Linux & macOS: curl -sSf https://causari.dev/install.sh | sh Windows PowerShell : iwr -useb https://causari.dev/install.ps1 | iex Installs a SHA256-verified, ~800 KB pre-built binary into ~/.local/bin or %LOCALAPPDATA%\Programs\causari on Windows . Prefer building from source? cargo install --git https://github.com/croviatrust/causari or, with a local clone: cargo build --release ./target/release/re --help Scripted demos live in scripts/ : — the capture engine end to end: mock LLM upstream, demo-capture.ps1 re proxy , re watch , content-based causal join mock-llm.py included / demo.sh — full happy-path with demo.ps1 re why and re bisect / demo-trace.sh — upstream causal cone demo-trace.ps1 re trace / demo-bidir.sh — bidirectional causality demo-bidir.ps1 re impact , re lens , causality-aware re revert / demo-mcp.sh — MCP server end-to-end via JSON-RPC demo-mcp.ps1 Causari runs natively on Linux, macOS, and Windows — the binary is a single ~2 MB executable with no runtime dependencies. Causari is released under the Business Source License 1.1 see LICENSE . In plain English: Free for you, forever , in all of these cases:- personal use, including paid client work; - any organization's own internal development, in production and CI/CD, at any scale; - use by AI agents acting on infrastructure you control laptop, server, CI runner — this is the default way Causari is meant to be used; - academic research, teaching, non-commercial open-source projects; - redistribution of unmodified binaries via cargo / brew / apt / choco / nix / winget. Not free if you want to resell Causari itself to third parties as a hosted or managed service whose primary value is version control, causality or provenance tracking for AI agents. For that, talk to us. Becomes Apache 2.0 automatically four years after each version is published. The change is mechanical: nothing the project maintainers can prevent or accelerate. The experience layer skills is and will remain free in the re binary — distillation, Ed25519 signing, verification and recall all run locally and cost nothing. What will be commercial is the Trust Plane built on top of it: organization-wide signed skill registries, RFC 3161 timestamping, fleet dashboards and audit-grade compliance exports. The free tool creates the experience; the paid plane lets a company trust it at scale. Why BSL? Causari is meant to be widely used and modified, but we are not interested in subsidizing a future closed-source clone built by a better-distributed competitor. This is the same model that lets Sentry, HashiCorp and CockroachDB stay genuinely useful and contributable while sustaining the people who build them. Contributing? See CLA.md . Causari is built by Croviatrust https://croviatrust.com — the team behind Crovia , the cryptographically signed, Bitcoin-anchored public ledger of AI training-data transparency. Same DNA, different layer: Crovia proves what models learned; Causari proves what agents did.