Context7
Up-to-date docs for AI agents
Descripción del creador (original en inglés)
Session memory for AI coding agents. Search past sessions, extract rules, and track what works.
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
Elige la app de IA que usas y copia la configuración.
ANTHROPIC_API_KEY, OPENAI_API_KEYLos valores marcados como secretos son llaves de tu cuenta. Nunca los compartas ni los pegues en un chat.
Necesitas
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json
{
"mcpServers": {
"afterpaths": {
"command": "uvx",
"args": [
"afterpaths",
"afterpaths-mcp"
],
"env": {
"ANTHROPIC_API_KEY": "your-value",
"OPENAI_API_KEY": "your-value"
}
}
}
}Ejecuta esto en tu terminal
claude mcp add afterpaths --env ANTHROPIC_API_KEY=your-value --env OPENAI_API_KEY=your-value -- uvx afterpaths afterpaths-mcpCompleta los valores vacíos antes de ejecutarlo.
Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"afterpaths": {
"command": "uvx",
"args": [
"afterpaths",
"afterpaths-mcp"
],
"env": {
"ANTHROPIC_API_KEY": "your-value",
"OPENAI_API_KEY": "your-value"
}
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"afterpaths": {
"type": "stdio",
"command": "uvx",
"args": [
"afterpaths",
"afterpaths-mcp"
],
"env": {
"ANTHROPIC_API_KEY": "your-value",
"OPENAI_API_KEY": "your-value"
}
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 6 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub burnssa, dueña del repo
Todavía no hemos leído este repo en GitHub
Todavía no hemos leído este repo en GitHub
Este se ejecuta en tu computadora; no hay endpoint en internet
Se ejecuta en tu computadora y usa ajustes locales en su lugar
Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.
Debes completar: ANTHROPIC_API_KEY, OPENAI_API_KEY
Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).
El repo de GitHub cambió por última vez –, y la última versión (0.2.5) llegó al registro hace 6 meses.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Python (uv) en tu computadora.
En la misma categoría: Context7, Codebase Memory, Exa
Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.
Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi
Up-to-date docs for AI agents
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.
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:
You're managing multiple agents - retaining critical context and learning from sessions is painful:
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.
Your Sessions Afterpaths
─────────────── ────────────────────────────────────
Claude Code ──► ap log ──► Browse sessions across IDEs Cursor ap stats ──► Analytics: tokens, activity, errors Codex ap summarize──► Session summaries (what happened) ap rules ──► Rule files (what to remember) ap search ──► Find past discussions & discoveries │ ▼ .claude/rules/ · .cursor/rules/ │ ▼ Your next session is smarter
pip install afterpaths
cd ~/code/your-project
ap audit
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:
# 1. Browse sessions and find significant work
ap log
export ANTHROPIC_API_KEY="sk-ant-..." ap summarize 1
ap rules
ap search "database schema" ap search "auth" --deep # also search raw transcripts
ap stats ap stats --daily
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.
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:
## Discoveries
~/.claude/projects/ are encoded by replacing / with -, but underscores are ALSO converted to hyphens. Three different paths encode identically:/Users/Code/foo_bar → -Users-Code-foo-bar/Users/Code/foo-bar → -Users-Code-foo-bar/Users/Code/foo/bar → -Users-Code-foo-bar
`ap rules` extracted it into `.claude/rules/gotchas.md`:
- **Claude Code lossy path encoding**: Claude Code encodes project paths by
replacing both / and _ with -, making them indistinguishable when decoding. When decoding, try underscore variants alongside hyphen variants at each greedy step, preferring longer segments (single directories) over nested paths. _Source: 91b1ffbc_
Next time Claude works on path decoding in this codebase, it already knows about the lossy encoding—no need to rediscover it.
| Without | With Afterpaths |
|---|---|
| Discover gotcha, forget to document it | ap summarize captures it with full context |
| Hit the same issue 3 weeks later | Rule in .claude/rules/ prevents it |
| No idea what's working | ap stats shows tokens, sessions, error rates |
| Sessions scattered across IDEs | ap log unified view across Claude + Cursor |
| Learnings siloed per tool | Rules sync to .claude/rules/ and .cursor/rules/ |
| Category | What it captures | Example |
|---|---|---|
| Dead Ends | Approaches that failed | "Don't use X because Y" |
| Decisions | Architectural choices | "We chose Redis over Postgres because..." |
| Gotchas | Non-obvious warnings | "Watch out for X when doing Y" |
| Patterns | Techniques that worked | "For X, use pattern Y" |
Each rule includes source session references so you can trace back to the original context.
| Tool | Status | Location |
|---|---|---|
| Claude Code | ✅ Ready | ~/.claude/projects/*.jsonl |
| Cursor | ⚠️ Partial | ~/Library/Application Support/Cursor/User/workspaceStorage/ |
| Codex CLI | ✅ Ready | ~/.codex/ |
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 --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.
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.
# Install with MCP support
pip install afterpaths[mcp]
claude mcp add afterpaths -- afterpaths-mcp
python -m afterpaths.mcp_server
Tools exposed:
| Tool | Description |
|---|---|
afterpaths_list_sessions | List recent sessions for context recovery |
afterpaths_show_session | Read session summaries and transcripts |
afterpaths_show_artifacts | Show artifacts ledger: files written/edited with provenance |
afterpaths_summarize | Generate summaries for sessions |
afterpaths_search | Search across past sessions (auto-escalates to transcripts on 0 summary hits) |
afterpaths_get_rules | Get extracted rules (dead ends, decisions, etc.) |
Once configured, agents can ask "have we seen this before?" or "what were the dead ends?" and get answers from your session history.
.afterpaths/ excluded by defaultyour-project/
├── .afterpaths/ # Summaries (gitignored) │ ├── summaries/ │ └── meta.json ├── .claude/ │ └── rules/ # Generated rules (commit these!) │ ├── dead-ends.md │ ├── gotchas.md │ └── patterns.md └── src/
Each rule file is split into a manual section (preserved across ap rules runs) and an auto section (regenerated each run):
# Gotchas: Things to Watch For
_Add project-specific notes here — preserved across ap rules runs._
Auto-generated by
afterpaths distill| Updated: 2026-04-21 12:00
Sources: 3 session(s)
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.
ap search)--json flag)MIT
*Manage your AI coding agents. Learn what works. Stop repeating mistakes.*