CodeCartographer

Understand an unfamiliar codebase with an AI agent — and get a validated spec you can rebuild from. CodeCartographer turns a repository into layered architecture, behavioral contracts, defect findings, and a language-agnostic reimplementation spec, with each phase validated before the next one runs. Works with Pi, Claude Code, Cursor, Codex, or any MCP-capable agent.

●  CodeCartographer
├─ ✓  architecture phase    ⟳ 25 · 76 tool uses · 1.0M tokens · 4m28s
├─ ✓  defect-scan-mech.     ⟳ 39 · 91 tool uses · 2.4M tokens · 7m05s
└─ ⠹  contracts phase       ⟳ 11 · 37 tool uses · 335.1k tokens · 40.1s
       ⎿ extracting behavioral contracts from server/index.ts…

Why CodeCartographer

Asking an LLM to "analyze this repo" loses context halfway through, hallucinates findings, and leaves no artifact the next session can pick up. CodeCartographer fixes three things:

1. The filesystem is the memory, not the conversation. Each phase writes a smaller, templated, evidence-tagged artifact to .codecarto/findings/. Later phases re-read the specific upstream files they need. A new session — or a context compaction — picks up from status.yaml without losing progress.

2. Every phase is validated before the pipeline advances. Completion criteria are real: a FAIL output stops the run. You can't accidentally build a reimplementation spec on top of hallucinated architecture.

3. The output is a spec, not a chat log. The final reimplementation-spec.md is language-agnostic, module-inventoried, and carries acceptance scenarios plus known unknowns. Hand it to another agent to rebuild from.

Every finding is tagged with an evidence level: observed fact, strong inference, portability hazard, or open question.

At a glance

Publish completed reimplementation specs from Pi or MCP, then run the synthesis pipeline to turn a product vision and explicitly confirmed library entries into a conflict-aware project-plan.md with a decision-level provenance ledger.

If CodeCartographer saves you a day of codebase archaeology, star the repo — it helps the next person find it.

OpenAI Build Week reviewers: see the new-vs-existing scope and one-command demo.

Install

Three surfaces, in recommended order. All three share the same core/ and produce byte-identical phase prompts. Pi provides the richest orchestration UX; Pi and MCP both support the executable library and synthesis workflows; drop-in mode provides the analysis framework without those runtime operations.

1. Pi extension — recommended for interactive use. First-class UX.
2. MCP server — for Claude Code, Codex, opencode, Cursor, Claude Desktop, and any other MCP-capable agent.
3. Drop-in template — pure .codecarto/ markdown + YAML for one-off evaluation or any LLM that can read and write files. Library and synthesis workflows are not available in pure drop-in mode; the analysis side works fully.

Pi extension (recommended)

Pi is a TUI coding agent. The CodeCartographer extension adds slash commands, a live agents widget, and the dashboard.

pi install npm:codecartographer-pi          # from the npm registry
pi install /absolute/path/to/CodeCartographer  # from a local checkout
pi install git:github.com/HuginnIndustries/CodeCartographer  # from a git URL

Don't run npm install codecartographer-pi for the Pi use case. Plain npm install puts the package on disk but doesn't register it with Pi. Use pi install npm:... so Pi writes the package into its own ~/.pi/agent/settings.json.

For extension development, point Pi directly at the entrypoint:

pi -e /absolute/path/to/CodeCartographer/extensions/codecarto/index.ts

MCP server (for other coding agents)

Use this when your coding agent isn't Pi — Claude Code, Codex, opencode, Cursor, Claude Desktop, or anything else that speaks MCP. The host drives the conversation and runs the LLM; CodeCartographer provides phase prompts, validation, and experimental library publish/list/reindex operations.

30-second setup for Claude Code, Cursor, Codex, and Claude Desktop: see the MCP quickstart.

npm install --global codecartographer-pi

Add to your host config (~/.config/claude-code/config.json, claude_desktop_config.json, etc.):

{
  "mcpServers": {
    "codecartographer": {
      "command": "codecarto-mcp"
    }
  }
}

Official MCP Registry listing: io.github.HuginnIndustries/codecartographer.

Drop-in template (one-off / evaluation)

Use this to try CodeCartographer in any repo without installing anything, or in environments where neither Pi nor an MCP-capable agent is available. Works with any LLM that can read and write files.

cp -r /path/to/CodeCartographer/.codecarto /path/to/your-repo/

Then in the LLM session: Read .codecarto/GUIDE.md and begin the analysis.

Limitation. Drop-in mode runs the analysis pipeline fully, but library + synthesis workflows require executable code through Pi or MCP.

Forward synthesis quickstart

Analysis turns repositories into reusable specifications. Synthesis runs the other direction: it combines a raw product vision with human-confirmed specifications and produces an implementation-ready plan without losing provenance.

1. Configure the library that contains specs published with /codecarto-publish or the MCP codecarto_publish tool:

   yaml

   Copy

   # ~/.codecarto/config.yaml or .codecarto/workflow/config.yaml
   library:
     path: /absolute/path/to/codecarto-library
     namespace: your-namespace # omit for a single-tenant library
     publish_confirm: true
   

2. Initialize a clean planning workspace and fill in its brief:

   text

   Copy

   /codecarto-init synthesis
   

   Edit .codecarto/inputs/vision.md with the audience, problem, desired outcome, constraints, and non-goals.

3. Run until CodeCartographer creates the candidate proposal:

   text

   Copy

   /codecarto-next --auto
   

   The run intentionally stops before merging. Review .codecarto/findings/goal-synthesis/proposal.md and change one or more candidate boxes from [ ] to [x].

4. Resume:

   text

   Copy

   /codecarto-next --auto
   

The final .codecarto/findings/goal-synthesis/project-plan.md contains product scope, architecture, work packages, acceptance gates, an unresolved-conflict register, and a provenance ledger mapping every load-bearing decision back to the vision or a confirmed specification. Runtime preflight checks prevent merging or finalization before explicit human confirmation.

How it works

The "code" is structured Markdown + YAML inside .codecarto/:

• GUIDE.md — LLM entry point. Every session reads this first.
• workflow/pipeline.yaml — phase definitions, dependencies, output paths.
• workflow/status.yaml — mutable per-project state. Single source of truth for progress.
• workflow/VALIDATE.md — validation protocol run after every phase.
• findings/<phase>/SKILL.md — detailed analysis instructions per phase.
• templates/ — output templates that enforce consistent structure.

Phases form a DAG: contracts and protocols can run in parallel after architecture; porting waits for both; reimplementation-spec is last. The host (Pi, MCP, or your shell) reads the active pipeline, finds the next phase whose dependencies are all complete, hands the LLM that phase's instructions, validates the output, and advances status.yaml.

For multi-session work, every new session reads .codecarto/GUIDE.md (or the lighter NEW_THREAD_BLURB.md), checks workflow/status.yaml, and picks up where the last session left off. You don't explain what happened in previous sessions.

Progressive distillation and context resilience

CodeCartographer is a progressive, evidence-tagged distillation of a codebase. It does not ask one context window to retain the entire investigation. Instead, each phase turns a large body of source evidence into a smaller, more task-specific artifact that the next phase can read:

source code
  → architecture map
  → behavioral contracts + protocols + defect findings
  → porting bundle
  → reimplementation spec

This is deliberate distillation, not incidental chat summarization. Each artifact follows a template, preserves evidence levels and known unknowns, and must pass validation before it becomes an input to downstream phases.

What happens when conversation context is compacted?

The filesystem, not the conversation, is the durable memory of a run:

• Each phase gets a fresh context window. In the Pi extension it runs as an isolated phase sub-agent; MCP and drop-in hosts should use the same one-session-per-phase pattern.
• Completed findings live under .codecarto/findings/. Later phases re-read the specific upstream artifacts declared by the active pipeline instead of relying on conversational recall.
• workflow/status.yaml records progress, terminal open_questions, in-pipeline carry_forward, and a separate post_pipeline backlog for optional spikes, amendments, deltas, decisions, and reruns after completion. Phase agents propose changes in .codecarto/scratch/handoffs/<phase>.yaml; completion validates and applies them under a lock with host timestamps, one canonical closeout, and an idempotent THREAD_LOG.md entry.
• Pi phase transcripts are file-backed and remain available through /resume, /tree, and /export, even when the active model context has been compacted.
• For isolated Pi phase sessions, compaction uses a phase-aware continuation summary that explicitly preserves evidence, files inspected, output progress, open questions, and validation gaps. The resulting summary is also checkpointed atomically at .codecarto/scratch/checkpoints/<phase>.md.
• Pi records successful, failed, and aborted compactions plus their trigger (threshold, overflow, or manual) in local usage data and exposes the totals in the widget, /codecarto-usage, completion summaries, and dashboard.

As a result, compaction—or even replacement—of the orchestrator session does not erase pipeline progress. A new session can reconstruct the relevant state from disk and continue.

The remaining limit is within a single oversized phase. Even Pi's phase-aware summary is still a lossy distillation, and MCP/drop-in compaction remains entirely host-controlled. Phase instructions therefore prioritize targeted reads, durable checkpoints, and explicit coverage accounting; if full coverage will not fit, the phase records PARTIAL validation and places unresolved work in open_questions or carry_forward. Cross-phase context loss is largely designed out; intra-phase context pressure is observed and bounded rather than hidden.

The porting bundle is the final intentional compression boundary. It carries a source index, load-bearing invariants, defect dispositions, and deep-read triggers. reimplementation-spec reads that bundle by default and opens lower-level reports only for a named gap, conflict, missing acceptance detail, or defect rationale.

Phases produce these artifacts

Every finding is tagged with an evidence level: observed fact, strong inference, portability hazard, or open question. Every phase output is validated against explicit completion criteria before the pipeline advances.

Pipeline variants

The default is a 7-phase run that splits the defect scan into a mechanical early pass and a semantic late pass — the reimplementation phase then designs around defects with full contracts and protocols context. Scale back if you want less:

Switch the active pipeline with /codecarto-switch-pipeline <variant> (Pi) or codecarto_switch_pipeline (MCP). This rewrites status.yaml in-place without deleting findings, handoffs, usage data, or closeouts. Phases that exist in both the old and new pipelines preserve their completion status.

On disk:

The dashboard

Every state change re-renders .codecarto/dashboard.html — a self-contained single-file artifact you open in any browser. Aggregates everything a human wants to see at a glance:

• Pipeline progress strip with per-phase status badges
• Per-phase cards with output links, open questions, carry-forward routing, owner notes, last-run usage
• Aggregate token and compaction telemetry + per-phase breakdown
• Activity timeline with session-file links
• Open questions roll-up grouped by source phase
• Closeouts list (reverse-chronological) with relative-path links

No JavaScript. No external assets. Light/dark via prefers-color-scheme. Works opened directly from file://.

Opt-in narrative summary. /codecarto-dashboard --narrate runs the orchestrator's model as a one-shot session that writes a 200–400 word executive summary citing specific findings from recent closeouts. Cached to .codecarto/.dashboard-narration.local.md and preserved across deterministic re-renders with a "(N runs since)" staleness note.

Pi extension features

Beyond the slash commands, the Pi extension layers on:

Phase sub-agents. /codecarto-next spawns each phase as an isolated AgentSession. Tool calls, file reads, and reasoning live in the child's own context window — they never accumulate in the orchestrator. Your TUI stays on the orchestrator session and remains responsive while phases work in background.

Live agents widget above the editor showing tool count, token usage, elapsed time, and current activity.

●  CodeCartographer
└─ ⠹  architecture phase  ⟳ 3 · 5 tool uses · 12.3k tokens · 1m32s
       ⎿ reading…

File-backed phase sessions. Phase transcripts persist to the same Pi session directory the orchestrator uses, so /resume, /tree, and /export browse them as first-class sessions. Each appears as CodeCartographer phase: <id> with lineage back to the orchestrator's session.

Phase-aware compaction and checkpoints. Only isolated sessions named CodeCartographer phase: <id> receive the specialized compaction prompt. It preserves the phase goal, evidence, inspected files, output progress, open questions, and validation gaps, then writes the resulting summary to .codecarto/scratch/checkpoints/<phase>.md. Orchestrator and unrelated Pi sessions retain normal host compaction.

Phase-completion summary in the orchestrator transcript. When a phase finishes, a Markdown closeout block is appended to the orchestrator's session via pi.sendMessage(...). Visible in the TUI scrollback; available to the orchestrator's LLM as context on your next message. No auto-trigger — you stay in control.

Opt-in LLM-steered seed prompts. Set orchestrator.llm_steer_next_phase: true in .codecarto/workflow/config.yaml (or pass --llm-steer per invocation), and the orchestrator's LLM rewrites the next phase's seed prompt to highlight relevant prior findings. Off by default — extra orchestrator-side tokens, opt-in. The rewritten prompt is injected into the orchestrator transcript so you can audit what the rewriter chose to emphasize.

Per-phase usage tracking. Each phase run is appended to .codecarto/workflow/.usage.local.yaml. /codecarto-usage reports cumulative + per-phase token, runtime, tool-use, and compaction totals, including threshold/overflow/manual triggers and successful/failed/aborted outcomes.

Tool interception. bash is blocked outright; edit and write are confined to .codecarto/, plus the configured, marker-validated CodeCartographer library when one is configured. Same rules apply to phase sub-agents.

Slash commands

End-to-end auto mode (0.8.0+)

/codecarto-next --auto walks the entire pipeline without intervention. The loop spawns each next-eligible phase, auto-validates the output, auto-marks it complete, and advances until the pipeline finishes — or until something stops it (FAIL / MISSING validation, sub-agent error, or ctx.signal abort). The orchestrator's TUI stays responsive throughout; per-phase summaries land in the transcript as usual, and a final codecarto-auto-summary block reports the outcome with cumulative tokens, wall time, and a recovery hint if the run stopped early.

• Resumability is implicit: re-running --auto reads status.yaml and picks up from getNextEligiblePhase.
• --strict (requires --auto) treats PASS WITH GAPS as a stop — useful when you want to triage gaps before advancing.
• --auto --llm-steer runs the rewriter on every phase transition; the per-phase steering blocks land in the orchestrator transcript so the run is auditable.

Version history (Pi orchestration)

The current parallel-sub-agent design landed in 0.2.0 and has been incrementally enriched: file-backed sessions (0.3.0), summary injection (0.4.0), opt-in LLM steering (0.5.0), usage tracking (0.6.0), HTML dashboard (0.7.0), end-to-end auto mode (0.8.0), experimental library foundations plus MCP library tools (0.9.0), and Pi overlay activation gating (0.9.1). 0.1.x workspaces don't need migration — existing .codecarto/ directories work unchanged. See CHANGELOG.md for details.

MCP server

The same framework is packaged as a Model Context Protocol server. The MCP path returns prompt text for the host to dispatch and never runs sub-agents itself, so the Pi-only orchestration features (sub-agents, live widget, dashboard, usage tracking) don't apply — but phase prompts and validation are byte-identical with the Pi path because both import the same core/. v0.9.0 also exposes experimental library tools so MCP-capable hosts can publish, list, and reindex reusable reimplementation-spec.md artifacts.

Implements MCP spec revision 2025-11-25 via @modelcontextprotocol/sdk ≥ 1.29.0. The negotiated protocolVersion reflects whatever the connecting client requests; the server accepts every revision the SDK supports (currently 2025-11-25, 2025-06-18, 2025-03-26, 2024-11-05, 2024-10-07).

Each workflow tool accepts an absolute cwd for the target repository. codecarto_init requires force: true to overwrite an existing .codecarto/ (instead of Pi's interactive confirmation). The library tools accept an explicit absolute library_path or resolve library.path from .codecarto/workflow/config.yaml / ~/.codecarto/config.yaml. The library schema is experimental and may break before v2.

Compatible environments

Token usage and cost

CodeCartographer is token-intensive — it reads source code multiple times across phases and produces thousands of words of structured output. Plan accordingly.

Template overhead (fixed cost)

Every session reads the guide, pipeline definition, status, and validation protocol. Each phase additionally reads its own SKILL.md and output template. Fixed regardless of codebase size:

Source code reading (variable cost)

The dominant cost. Each phase reads some or all of your source code; the architecture phase reads the most. Rough guide: expect 1–3× your codebase size in tokens per phase. A 50k-token codebase might consume 100–200k input tokens across a full pipeline run.

Output generation

From a real 6-phase run (CodeCartographer analyzing itself — a small ~14k-word template):

Larger codebases produce proportionally larger outputs.

Cost estimates

For a medium-sized codebase (~100k tokens of source):

At current API pricing (~$3/M input, ~$15/M output for Claude Sonnet), a full 5-phase run on a 100k-token codebase costs roughly $2–4. Larger codebases scale linearly.

Tips to reduce token usage

• Start with architecture-only to see if the output quality is useful before committing to a full run.
• One LLM session per phase — each phase gets a fresh context window so you're not paying to carry stale context.
• For very large codebases (500k+ tokens of source), the LLM can't read everything anyway. It uses the architecture map to prioritize and produces partial results. open_questions in status.yaml shows what was skipped.
• The lite pipeline (3 phases) gives 80% of the value for understanding a codebase without porting-specific phases.
• Skip --llm-steer unless you're hitting cross-phase coherence issues — the rewriter costs orchestrator-side tokens per phase.

Model compatibility

LLM-agnostic by design, but model choice affects both what you can analyze and how good the results are. Two independent constraints: context window size and model capability.

Context window

Each phase runs in its own session, so the context window limits how much source code can be read per phase — not across the whole pipeline. After template overhead, prior-phase findings, and output generation:

Practical limits by codebase size:

The pipeline handles context exhaustion gracefully: phases write PARTIAL validation and log remaining work in open_questions.

Model capability

The harder constraint. Tasks that degrade fastest on weaker models:

1. Evidence classification (high risk) — distinguishing observed fact from strong inference from open question requires calibrated self-awareness about certainty. Weaker models over-classify inferences as facts and skip open question tagging.
2. Defect scan (high risk) — the multi-pass scan demands domain-specific reasoning (concurrency, security, API contracts). Weaker models produce more false positives, miss subtle bugs, and over-report style issues as defects.
3. Architecture synthesis (medium-high risk) — abstracting a coherent layer map from many files is high-order reasoning.
4. Structured output adherence (medium risk) — filling templates correctly with all required sections and consistent formatting.
5. Cross-phase coherence (medium risk) — later phases build on earlier findings. Weak architecture compounds errors downstream.

Recommended model tiers

If you're testing a new model, start with pipeline-architecture-only.yaml on a codebase you already understand and compare the output against your own knowledge. Fast signal on whether to trust the model with deeper phases.

Repository structure

.codecarto/                  # The drop-in template (Markdown + YAML).
  GUIDE.md                   # LLM entry point.
  findings/
    architecture/            # System structure, layers, dependency direction.
    defect-scan/             # Multi-pass defect report with severity and actions.
    contracts/               # User-visible behavior, defaults, acceptance checks.
    protocols/               # Event streams, state machines, persistence formats.
    porting/                 # Reverse-engineering synthesis bundle.
    reimplementation-spec/   # Language-agnostic build spec.
  scratch/                   # Disposable notes plus checkpoints and structured phase handoffs.
  templates/                 # Output structure templates.
  workflow/                  # Pipeline definitions, status, validation, config.
  closeouts/                 # Per-session closeout files.
  THREAD_LOG.md              # Cross-session summary log.
  dashboard.html             # Generated; gitignored.
core/                        # Pipeline state machine, validators, prompt assembly,
                             # dashboard renderer, usage log, orchestrator config.
extensions/codecarto/        # Pi extension surface (slash commands, widget,
                             # tool gating, dashboard writer + narrator).
mcp-server/                  # MCP server surface (workflow tools + experimental library tools).
tests/                       # Invariant tests catching cross-wrapper drift.
docs/                        # Roadmap, design notes.

The .codecarto/.gitignore excludes generated findings, scratch files, the dashboard, and the local usage / narration caches. Template files (workflow definitions, skills, output templates) are safe to commit so teammates can run their own analyses.

For automated agents

1. Load the active pipeline YAML and workflow/status.yaml.
2. Select the first phase whose status is not complete and whose dependencies are all complete.
3. Feed the phase's skill_path and required_reads to the agent.
4. Write outputs to the declared paths. Run validation. Update status.
5. Repeat until all phases are complete. Set current_phase to complete when done.

The MCP server does steps 1–3 directly; the Pi extension wraps them as slash commands plus the parallel-sub-agent runner described above.

Design principles

• LLM-agnostic — works with any model that can read and write files.
• Phase-gated — one phase per session, validated before advancing.
• Single source of truth — status.yaml tracks progress; no duplicated state.
• Evidence-classified — every finding tagged as observed fact, strong inference, portability hazard, or open question.
• Template-driven — consistent output structure across projects and sessions.
• Drop-in — lives inside your repo as .codecarto/. No symlinks, no copying source code, no runtime daemon.

Contributing

Bug reports, feature requests, and pull requests are welcome. See CONTRIBUTING.md for development setup, branch model, and the maintainer release process. All participants are expected to follow the Code of Conduct. For security issues, follow SECURITY.md instead of filing a public issue.

License

MIT — see LICENSE.