
Afterpaths
io.github.burnssaPublished by GitHub account burnssav0.2.5Updated Oct 25, 2026
Session memory for AI coding agents. Search past sessions, extract rules, and track what works.
Overview
Local MCP server that gives an AI coding assistant access to past coding-session history: listing, reading, searching and summarizing sessions and extracting re
- What you can do
- List recent coding sessions across Claude Code, Cursor and Codex for context recovery and read their summaries or raw transcripts, plus an artifacts ledger of files written or edited with provenance. Search across past sessions, escalating to raw transcripts when summaries return nothing. Generate LLM summaries of sessions and retrieve extracted rules covering dead ends, decisions, gotchas and patterns.
- When to use it
- Useful when you work with several AI coding tools and want an assistant to recall earlier sessions, avoid repeating known dead ends, or pull project rules into a new session. Best for developers who want session history and distilled lessons available as native tools rather than a separate CLI.
- Requirements
- Runs locally as a Python package (uvx or pip, with the mcp extra) over stdio. Summarization needs an ANTHROPIC_API_KEY or OPENAI_API_KEY; search, list, show and rules tools work without one. Reads session data from local Claude Code, Cursor and Codex storage.
Installation
In SourceWeft
- Open Afterpaths in the dashboard and add it to a workspace.
- Enable the server for the chats that should use its tools.
Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.
Other MCP clients
Follow the launch instructions in the repository.
README
Afterpaths
Smarter with every session, automatically.
Extract rules from what worked. Track what didn't. Find the best models for your stack.
You're running Claude Code, Cursor and Codex, but which model actually works best for your stack? What approaches lead to breakthroughs vs. expensive dead ends? How do you stop your agents from making the same mistakes?
Afterpaths gives you a single view across all your AI coding tools: compare what's working, capture discoveries as rules, and guide your agent team away from costly diversions.
What you're seeing:
ap audit— Overview of your sessions, models used, and rule statusap show 5 --raw— Raw session transcript (the messy JSONL data, made readable)ap show 5— LLM-generated summary extracting discoveries, dead ends, and decisionshead .claude/rules/gotchas.md— Rules automatically extracted and ready for Claude's next session
The Problem
You're managing multiple agents - retaining critical context and learning from sessions is painful:
- Repeated mistakes — Your agents hit the same gotchas. Three weeks later, same dead end, same wasted tokens.
- No cross-tool visibility — Is Opus actually better than Sonnet for your codebase? Is Cursor outperforming Claude Code? You're guessing.
- Rules are tedious — After a costly diversion, the last thing you want is to write a CLAUDE.md rule. So you don't. And the knowledge evaporates.
- Sessions vanish — Session content is obscurely logged and hard to extract. Then it's often auto-deleted after 30 days. That breakthrough architecture decision? Context gone.
Afterpaths captures sessions across tools, surfaces what's working, and generates rules automatically—so your agents learn from every session, and you retain all your rich session context.
How It Works
Quick Start
The audit shows your sessions across all tools, model performance, and whether you have rules set up. No API key needed.
From there, the recommended flow:
Tip:
apis the short alias forafterpaths. Both work identically.
All commands support --json for structured output (e.g., ap log --json, ap show 1 --json, ap search "query" --json).
See docs/commands.md for the full command reference and recipes.
From Session to Rules
Real example: A bug causing 76 missing sessions became a rule that prevents the same mistake.
While building afterpaths, sessions for a project weren't showing up. The path (let's call it) /Users/Code/foo_bar was being decoded as /Users/Code/foo/bar. After investigation, we discovered Claude Code uses lossy path encoding—both / and _ become -.
The summary captured the discovery:
ap rules extracted it into .claude/rules/gotchas.md:
Next time Claude works on path decoding in this codebase, it already knows about the lossy encoding—no need to rediscover it.
Why Afterpaths
What Gets Extracted
Each rule includes source session references so you can trace back to the original context.
Supported Tools
Known Limitations
Cursor sessions: the adapter reads several known chat/composer formats from
state.vscdb (including workbench.panel.aichat.view.aichat.chatdata,
composer.composerData, and allComposers), but Cursor changes its storage
schema often and some sessions will return Entries: 0 even when the
state.vscdb file is present. When this happens, ap show <ref> --raw and the
afterpaths_show_session MCP tool emit a warning rather than silently
returning empty. The raw file is untouched — if you need that session, opening
state.vscdb directly with sqlite3 will still work.
Tool provenance features (ap show --artifacts, commit tracing, file
activity) only cover sessions whose entries the adapter can read.
MCP Server
Afterpaths includes an MCP server that puts session history directly into your agent's tool list. Instead of relying on agents to discover the CLI, the MCP server makes session search, summaries, and rules available as native tools.
Tools exposed:
Once configured, agents can ask "have we seen this before?" or "what were the dead ends?" and get answers from your session history.
Privacy
- All local — Summaries and rules stay in your project
- Your API key — Uses your Anthropic/OpenAI key
- Read-only — Never modifies your source code
- Gitignored —
.afterpaths/excluded by default
Storage
Hand-edited notes in rule files
Each rule file is split into a manual section (preserved across ap rules runs) and an auto section (regenerated each run):
Put hand-curated rules or project-specific reminders between the manual:start / manual:end markers — they'll survive every ap rules run. Content outside those markers (and outside the recognized boilerplate) blocks the run as a safety check. Pass --force to overwrite anyway, or wrap the content in manual markers to preserve it.
If you're upgrading from an earlier version and your rule files contain only auto-generated content, markers are inserted automatically on the next ap rules run with an empty manual section — no action required.
Roadmap
- Claude Code session parsing
- Cursor session support
- Session analytics (tokens, errors, daily trends)
- LLM summarization
- Automatic rule extraction
- Multi-target export (Claude, Cursor)
- Codex CLI support
- Cross-session search (
ap search) - JSON output (
--jsonflag) - MCP server for agent integration
- Semantic search across sessions
- Benchmarking and productivity insights
License
MIT
Manage your AI coding agents. Learn what works. Stop repeating mistakes.
Source: README.md at commit 1bdd717
Tools
0Version history
1- v0.2.5LatestSep 16, 2026
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