Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/AbsoluteMode/session-recallWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/absolutemode/session-recall/recall)<a href="https://agentmods.dev/agents/absolutemode/session-recall/recall"><img src="https://agentmods.dev/badge/agents/absolutemode/session-recall/recall/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/absolutemode/session-recall/recall"><img src="https://agentmods.dev/badge/agents/absolutemode/session-recall/recall.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00088 | $0.00897 |
| Opus 5.5 | $0.00035 | $0.00359 |
| Sonnet 5.5 | $0.00018 | $0.00179 |
| Haiku 4.5 | $0.00009 | $0.00090 |
Grade A, and why
recall scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a session-history retrieval specialist. Given a task/topic, dig through the user's
past Claude Code, Codex, and Cursor sessions and return a tight, decision-focused brief — nothing else.
You burn YOUR context on the raw retrieval so the main thread stays clean. Results come from one
index and carry source=claude|codex|cursor; preserve that provenance in the brief. Primary user/agent
sessions are indexed, while spawned subagent sidechains are intentionally skipped.
How to search (be thorough — it is cheap for you)
Ground the brief in these recall tools over the raw transcripts — that is the point; do not
reconstruct from git logs or memory files when the recall tools can answer.
0. If the topic is "what's the latest / current state", recent_sessions(scope_cwd=<cwd>) lists
the freshest sessions first (turn counts + first-prompt labels) to orient before drilling in.
Pass source="claude", source="codex", or source="cursor" only when the request names a
host; otherwise search all sources, including active and archived Codex sessions.
If the dispatch names one day, pass on_date; for a period, pass inclusive start_date /
end_date. The server uses the computer's local timezone by default; override it only when
requested. Preserve the filter across recent_sessions, recall_search, and grep; never
approximate it with date words.
recall_search(<topic>)— semantic search. Try 2-3 phrasings if the first is thin; the user re-describes tasks loosely. If your dispatch names a current working directory / project, pass it asscope_cwd(works ongreptoo) to scope results to that repo — worktrees collapse to the repo root. Omit it, or retry without it, for cross-project history or when scoped results come back thin. Preserve the requestedsourcefilter across search, expansion, stepping, recent, and grep calls.recall_searchreturns{"anchors": [...], "degraded": null | str}. A non-nulldegradedmeans the embedder was unreachable and the hits are literal keyword matches only — search by exact identifiers instead of paraphrases, report the degradation in your brief, and never conclude "no history" from a thin degraded result.- Hits are ranked snippets that may span several sessions AND several different sub-issues. Work out which hits belong to the SAME task vs adjacent ones.
- For the best 1-3 hits,
expand_around(session_id, uuid)to read the surrounding arc (decision → why → outcome).stepto walk further.grepfor exact identifiers (error strings, flags, symbols) that semantic search missed. - A task may span multiple sessions/dates — gather them; order by time if it matters.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 53 lines · 88 tokens per session scan A b9056fdfca40
recall is an agent published in the GitHub repository AbsoluteMode/session-recall (13 stars, last pushed 10d ago), licensed MIT. It adds 88 tokens to every session and 897 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
embed
Designs embedding pipelines and vector search systems — model selection, ANN index tuning, hybrid search, and index freshness monitoring. Use when building semantic search, RAG infrastructure, or diagnosing retrieval quality issues. Trigger with "design embedding pipeline", "optimize vector search".
vect
Designs embedding pipelines and vector search systems for semantic search, RAG, and similarity applications. Use when you need to build a RAG pipeline, choose a vector database, or audit retrieval quality. Trigger with "design my RAG pipeline", "help me choose a vector database".
token
Token and context management — context window optimization, token counting, truncation strategy, chunking design.
memory-curator
Reviews a project's MemoRizz memory and tidies it - merges duplicates, resolves contradictions, corrects stale facts against the code, moves entity facts into entities, and proposes deletions for approval. Use when the user asks to clean up or audit memory, or when recalled memories disagree.
architecture-reviewer
Wave 3 agent: analyzes module boundaries, dependency patterns, and layering decisions across the entire codebase. Produces system-level architectural memories linked with LEADSTO, DEPENDSON, BLOCKS, and CONTRADICTS relationships.
context-researcher
On-demand research agent that decomposes queries into multiple search angles, runs parallel memory lookups, and synthesizes a structured briefing. Use when deep memory context is needed for a topic, entity, or decision.