agent-historian
English | 简体中文
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);
--globalwidens to everything. - Read-only. Never modifies any data store.
- Context-friendly. Plain, pipe-friendly output. Agents page with
grep/head/wc/jqinstead 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-historianwhen 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,| jqitself. 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/greplets 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 → readrecipe — 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` commandOr 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 opencodeProject 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
-
Create
src/sources/<agent>.tsimplementing theHistorySourceinterface fromsrc/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 */ } }
-
Register it in
src/sources/registry.tsby adding it toALL_SOURCES. -
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-historianreimagines 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.

























