惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

U
Unit 42
罗磊的独立博客
博客园 - 聂微东
T
The Blog of Author Tim Ferriss
博客园 - 司徒正美
Stack Overflow Blog
Stack Overflow Blog
F
Fortinet All Blogs
A
About on SuperTechFans
腾讯CDC
Apple Machine Learning Research
Apple Machine Learning Research
B
Blog RSS Feed
IT之家
IT之家
V
Visual Studio Blog
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
宝玉的分享
宝玉的分享
C
Check Point Blog
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Vercel News
Vercel News
爱范儿
爱范儿
Microsoft Security Blog
Microsoft Security Blog
月光博客
月光博客
T
Tailwind CSS Blog
The Cloudflare Blog
Hugging Face - Blog
Hugging Face - Blog

Hacker News: Show HN

PurrrrrFocus: Pomodoro Timer App - App Store Workflow Engine — Multi-Step Orchestration for Bun RapidPhoto: Pro Photo Editor App - App Store GitHub - DheerG/swarms: Achieve extraordinary results with claude code across a variety of tasks SPICE simulation → oscilloscope → verification with Claude Code — Lucas Gerads Show HN: VCoding – A 5 MB native Windows IDE with no dynamic dependencies Show HN: LLMs don't hallucinate because they're bad at math, it's the format GitHub - Agent-FM/agentfm-core: AgentFM is a peer-to-peer network that turns everyday computers into a decentralized AI supercomputer. AgentFM lets you run massive AI workloads directly across a global mesh of idle CPUs and GPUs. Show HN: Tracking Top US Science Olympiad Alumni over Last 25 Years GitHub - Potarix/agent-hub: One place to talk to all your agents Show HN: Runtime security for AI agents(injection,tool abuse, data exfiltration) GitHub - dubeyKartikay/lazyspotify: Terminal Spotify client for macOS and Linux GitHub - the-banana-tool/king-louie: Easy to use GUI Personal AI Assistant. Win/Linux/Mac. Show HN I made my vacation rental bookable by AI agents–no Airbnb, 0% commission GitHub - basteez/jsf-autoreload: maven plugin to enable hot reload on jsf projects uvm32/hosts/host-gdbstub at main · ringtailsoftware/uvm32 GitHub - labsai/EDDI: Config-driven engine that turns JSON into production-grade AI agents. Multi-agent orchestration, 12+ LLM providers, MCP/A2A protocols, RAG, persistent memory, and enterprise compliance (EU AI Act, GDPR, HIPAA). Built on Quarkus. GitHub - glitchnsec/fortyone-oss: AI Executive Assistant Platform Quickstart | Alien GitHub - muxshed/shed: One stream in, or many. Every destination, simultaneously. No cloud middleman, no per-channel fees, no limits. GitHub - ocrbase-hq/ocrbase: 📄 PDF/IMG ->.MD/JSON Document OCR API for PaddleOCR and GLMOCR. Self-hostable. GitHub - impactjo/home-memory: MCP server that lets your AI assistant remember everything about your home. GitHub - Sets88/dbcls: DbCls is a powerful terminal database client that supports various databases GitHub - neptun2000/heor-agent-mcp GitHub - SeanFDZ/macmind: Single-layer transformer in HyperTalk for the classic Macintosh RollQuation: Math Puzzles - Apps on Google Play GitHub - dropbox/witchcraft Show HN: Agent-cache – Multi-tier LLM/tool/session caching for Valkey and Redis GitHub - opentalon/opentalon: OpenTalon is an open-source platform built from the ground up in Go as a robust alternative to OpenClaw LinkedIn™ 职位抓取工具 - Chrome 应用商店
GitHub - adlternative/agent-historian: Search and read pa...
adltereturn · 2026-06-21 · via Hacker News: Show HN

agent-historian

English | 简体中文

npm skills.sh license

Search and read your past AI coding-agent conversation history from the command line — so your agent can recover earlier research, commands, errors, and decisions instead of repeating work.

Ships a small CLI (ochist) and an Agent Skill so agents like OpenCode and Claude Code can check history before doing fresh research.

  • Multi-agent. Reads OpenCode (opencode.db) and Claude Code (~/.claude/projects/*.jsonl) out of the box, plus additional locally detected agents. Pluggable: add a new agent by implementing one interface.
  • Project- or global-scoped. Searches default to the current project (current directory and below); --global widens to everything.
  • Read-only. Never modifies any data store.
  • Context-friendly. Plain, pipe-friendly output. Agents page with grep/head/wc/jq instead of dumping whole sessions into context.
  • Zero runtime dependencies. Uses Node's built-in node:sqlite (Node ≥ 22.5). No native modules.

Why this exists

AI coding agents are mostly stateless across sessions. Every new chat starts from zero, so the agent happily redoes investigation it already finished yesterday — re-reading the same files, re-running the same commands, re-deriving the same conclusions. That wastes your time, your tokens, and your patience.

agent-historian gives the agent (and you) a cheap, local way to ask:

"Have I solved this before? What command did I run? Which file did we change? What did we decide, and why?"

It deliberately does not try to summarize sessions with brittle heuristics (regex-based "accomplishment extraction" breaks on non-English text and on any phrasing it didn't anticipate). Instead it lets the agent read the real text on demand, using a progressive-disclosure workflow (locate → orient → scan → read) so only the relevant lines enter the context window.

Who it's for

  • Developers who switch between projects and sessions and want their agent to remember prior work instead of starting over.
  • AI coding agents (OpenCode, Claude Code, Qoder, …) that should check history before doing fresh research — wired up via the bundled Agent Skill.
  • Tool builders who want a small, dependency-free, read-only way to query local agent transcripts across multiple tools through one interface.

How it differs from memory / RAG / other approaches

There are several ways to give an agent "memory." agent-historian is deliberately the simplest one — it doesn't build a memory, it reads the ground truth you already have on disk.

Approach What it stores Retrieval Cost / setup Faithfulness
agent-historian nothing — reads existing transcripts lexical (grep/substring), on demand zero index, zero deps, read-only exact original text
Memory layers (mem0, OpenMemory, MemGPT, "memory tools") LLM-distilled facts/summaries it decides to save semantic recall of summaries needs a store + write step; can drift/hallucinate lossy — a model's paraphrase
RAG / embeddings (vector DB over chat logs) chunked text + embedding vectors semantic (vector similarity) embedding model + vector DB + reindex pipeline exact chunks, but needs infra & re-indexing
Built-in --resume / --continue the agent's own session files reload one whole session into context free, but no search exact, but all-or-nothing
Auto-summary recall (regex/heuristic "what did I accomplish") extracted bullet points keyword over summaries cheap brittle; breaks on non-English / unusual phrasing

When to use which

  • Use agent-historian when you want to find and re-read what actually happened — the exact command, error, diff, or decision — across past sessions and across multiple agents, with no infra and no risk of a model rewriting history. It's a search tool over real transcripts, not a memory.
  • Add a memory layer (mem0, etc.) when you want the agent to carry forward distilled preferences and durable facts ("the user prefers pnpm", "deploys go through staging") that should persist as structured knowledge.
  • Use RAG/embeddings when you need semantic recall over a large corpus and can afford an embedding model + vector store + re-indexing.

They're complementary: agent-historian answers "show me the real thing I did," memory/RAG answer "recall the gist of what I know." Many setups use both — historian for exact recall, a memory layer for distilled facts.

Design choices that follow from this

  • No embeddings, no index, no background process — search is plain lexical matching that runs on demand, so there's nothing to build, sync, or keep warm.
  • Read-only — it never writes a "memory," so it can't drift from or corrupt the source of truth.
  • Progressive disclosure — instead of stuffing summaries into context, the agent pages through results (locate → orient → scan → read) and pulls only the exact lines it needs.

Why CLI + Skill instead of an MCP server

This started as an MCP server, then deliberately moved to a CLI (ochist) plus an Agent Skill. Reasons:

  • The agent already has a shell. With a CLI, the agent composes ochist grep … | head, | wc -l, | grep -i error, | jq itself. An MCP server would have to anticipate and hard-code every such option as tool parameters. The shell is the query language.
  • Context control belongs to the agent. Paging/filtering with head/grep lets the agent pull only what it needs. An MCP tool tends to return a fixed blob; you re-implement pagination server-side and still over- or under-fetch.
  • Zero resident cost. An MCP server is a long-lived process attached to the session (and its tool schemas occupy context every turn). The CLI runs only when invoked — no daemon, no idle token overhead.
  • A Skill teaches when and how. MCP exposes capabilities; it doesn't tell the agent the workflow. The bundled skill encodes "check history before re-researching" and the locate → orient → scan → read recipe — guidance MCP can't carry.
  • Portable & inspectable. One binary works in any agent that can run shell commands, plus humans can run the exact same commands and see the exact output. No transport, no protocol, no per-client wiring.
  • Easy to extend. Adding an agent or a flag is a normal code change; there's no tool-schema/permission round-trip.

MCP is a great fit for capabilities an agent can't otherwise reach (remote APIs, privileged actions). Here the data is local files the agent can already read with a shell, so a CLI + Skill is simpler, cheaper, and more flexible.


When this project becomes unnecessary (and that's fine)

agent-historian mostly exists to fill a gap: agents persist rich session data locally, but don't expose a first-class way to search and read it back. OpenCode has session list (no message/part reader); Claude Code only has interactive --resume; Qoder's SDK can resume/continue but not read history.

The cleanest end state is for the agents themselves to ship this:

  • A read command, e.g. opencode message get <session> / opencode session show (and equivalents for Claude Code / Qoder) that prints messages and tool I/O as plain, pipe-friendly text.
  • An official skill that teaches the agent to check its own history before re-researching.

If that happens, you won't need this project — and that would be a good outcome. Until then, agent-historian provides a uniform, read-only, cross-agent way to do it today. (And if it stays useful as the cross-agent layer — one tool that reads OpenCode + Claude Code + Qoder + … through one interface and one skill — that's a fine reason for it to stick around too.)


Install

npm install -g agent-historian      # exposes the `ochist` command

Or run without installing:

npx agent-historian sources
From source (for development)
git clone https://github.com/adlternative/agent-historian.git
cd agent-historian
npm install
npm run build
npm link          # symlink `ochist` globally

Requires Node ≥ 22.5 (for built-in node:sqlite).


CLI usage

ochist sources                       # which agents are detected
ochist sessions --limit 10           # recent sessions across all agents
ochist grep "ssh" --limit 8          # search all history
ochist meta <session>                # reliable metadata card
ochist show <session>                # one-line-per-message outline
ochist part <part_id>                # full text of one message

By default all detected agents are queried. Restrict with --source:

ochist sessions --source claudecode
ochist grep "docker build" --source opencode

Project vs global scope

sessions and grep default to the current project — sessions whose working directory is the current dir or below. Widen as needed:

ochist sessions                 # current project (cwd and subdirs)
ochist sessions --global        # every project
ochist sessions --dir ~/code/x  # a specific directory
ochist grep "ssh" --global      # search all history

<session> / <part_id> accept an agent-native id, a slug/prefix, or latest. Add --json to any command for machine-readable output (pipe to jq).

Recommended workflow (page, don't dump)

ochist grep "authorized_keys" --limit 5            # find candidate + part_id
ochist meta silent-star                             # confirm the session
ochist show silent-star | grep -i ssh-copy-id       # locate exact part
ochist part prt_xxxxx                                # read full message

Use as an Agent Skill

The repo includes a skill at skills/agent-history/SKILL.md that teaches agents when and how to use ochist — so they check history before doing fresh research.

Option A — npx skills (recommended, cross-agent)

The standard community installer. No clone needed; works for OpenCode, Claude Code, Cursor, Codex, and more:

# Install into every detected agent, globally:
npx skills add adlternative/agent-historian -g

# Or target specific agents:
npx skills add adlternative/agent-historian -s agent-history -a opencode -a claude-code -g

Option B — ochist skill install (version-locked to the CLI)

If you already installed the CLI (npm i -g agent-historian / npm link), it can install its own bundled skill:

ochist skill install --global     # → ~/.claude/skills + ~/.config/opencode/skills
ochist skill install              # project-local: ./.claude/skills + ./.agents/skills
ochist skill uninstall --global   # remove
ochist skill path                 # print the bundled skill dir

Option C — manual symlink

mkdir -p ~/.claude/skills        # read by BOTH Claude Code and OpenCode
ln -s "$(pwd)/skills/agent-history" ~/.claude/skills/agent-history

Restart the agent; it will discover the agent-history skill and load it on demand when you reference earlier work ("我之前…", "what did we do before", …).


How it works

ochist (CLI)
  └─ sources/registry.ts        selects active sources (auto-detect or --source)
       ├─ OpenCodeSource        reads opencode.db via node:sqlite
       ├─ ClaudeCodeSource      reads ~/.claude/projects/*.jsonl
       └─ <your agent here>     implement HistorySource

Each source normalizes its agent's data into common Session / Part shapes, so the CLI is agent-agnostic. Search is lexical (regex/substring over message content) — no embeddings, no index, no background process.

Subagents are handled per agent. OpenCode subagents are recorded as their own sessions (the agent field is explore/general/…). Claude Code subagent ("sidechain") transcripts live in agent-*.jsonl files that reference their parent session; agent-historian folds their content into the parent session and prefixes it with [subagent …], so nothing is lost or duplicated.


Add a new agent

  1. Create src/sources/<agent>.ts implementing the HistorySource interface from src/sources/types.ts:

    export class MyAgentSource implements HistorySource {
      readonly name = 'myagent';
      readonly label = 'My Agent';
      isAvailable() { /* does its data store exist? */ }
      location() { /* where it reads from */ }
      listSessions(opts) { /* … */ }
      loadParts(sessionId?) { /* … */ }
      loadPartRaw(partId) { /* … */ }
      resolveSessionId(selector) { /* id | slug | prefix | "latest" */ }
      loadTodos(sessionId) { /* [] if unsupported */ }
    }
  2. Register it in src/sources/registry.ts by adding it to ALL_SOURCES.

  3. npm run build. That's it — every subcommand now works for your agent.


A note on data formats

agent-historian reads each agent's local session data: OpenCode's SQLite database and the per-session JSONL transcripts that Claude Code, Qoder, and similar CLIs persist on disk. These on-disk formats are largely not officially documented, so the readers are best-effort and may need updates across agent versions. Everything is strictly read-only — the tool never writes to any agent's data store.


Credits & acknowledgements

This project stands on the shoulders of others:

  • claude-historian by @Vvkmnn — the original inspiration. The core idea ("let the agent search its own past conversations so it stops repeating research"), the historian framing, and the on-demand transcript-search approach all trace back to it. agent-historian reimagines that idea as a multi-agent, CLI-first, skill-driven tool.
  • The progressive-disclosure / "page, don't dump" philosophy is shared with memory tools like claude-mem and mem0, which informed the design.
  • The agents whose local history this reads — OpenCode, Claude Code, and Qoder — for building tools worth remembering.

If your project belongs here and isn't credited, please open an issue.


License

MIT — see LICENSE.