Software Forgets

Coding agents leave a dense record of their work. They search codebases, test approaches, hit errors, and read documentation. This activity creates a log of not just what changed, but why. The problem is that these session logs remain an archive. You cannot search them to find the answer to “why did we move off the streaming parser?” across ten thousand turns. For an agent to use this history while it works, the data needs indexing, retrieval, ranking, and provenance.

funes provides a durable memory layer for your agents. It works with Claude Code, Codex, pi, and Hermes. It builds from sessions already on your machine. It runs locally and integrates with one command. You can also send it to a Hugging Face dataset you own, which remains private by default.

Add memory to the agent you already use

funes is a single binary. Its default inference backend has no machine learning runtime dependency. Embedding and reranking happen on your machine. Install it:

curl -fsSL https://huggingface.co/buckets/huggingface/funes/resolve/install.sh | sh

Then add it to an agent:

funes add claude    # or: codex, pi, hermes

That one command builds the first index, gives the agent recall and get tools, and installs the automation that indexes each completed turn. Indexing is incremental. New runs add new turns rather than embedding the whole history again. Older and deeper content can backfill in bounded steps.

From there, you just work. When a task touches a past decision, rationale, or finding, the agent can reach for recall itself. You do not need to remember the old session or paste its context into the new one.

With funes added, recall happens inside the conversation. The agent reaches for its memory on its own and names the session behind its answer.

recall returns the original text, not a summary. It shows exactly where it came from: the agent, timestamp, session, and turn. Each result includes a get command that opens the full turn and its surrounding context.

Underneath, one deterministic pipeline parses every supported trace into the same turn-and-block shape. It chunks the data, embeds it with a pinned local model, and writes it to a local Lance dataset. A query combines vector and BM25 search, fuses their rankings, reranks the candidates with a cross-encoder, reweights them by recency, and attaches neighboring chunks.

That design gives funes three important properties:

• One memory across agents: Claude Code, Codex, pi, and Hermes all write to the same shape. recall spans their histories, and every hit says which agent produced it.
• Raw evidence stays intact: Nothing is distilled into a fact at write time. A result can always lead back to the turn that produced it.
• recall is local by default: No account or Hub repository is required. A hosted model does not process your sessions for indexing. Embedding and reranking run on your machine, and your coding agent does the reasoning.

The agent as a stranger problem is already solved on one machine. But memory gets more useful when the next agent is running somewhere else.

A memory is a dataset, not a service

To make a memory follow your work, bind one when you add funes to an agent:

funes add codex acme/funes-memory

The bind publishes your current memory there. funes then keeps it current, indexing each turn locally and publishing at session boundaries. The agent recalls from it throughout. Run the same command on another machine and the memory follows you there.

Underneath, the local memory is a Lance dataset, and the shared memory is a Hugging Face dataset (private by default) you own.

Before anything reaches the Hub, credentials have already been redacted during indexing. Publishing then scans every chunk again and withholds anything that still looks like a secret. The scanner behind this is documented in SECURITY.md, including what it does and does not cover.

When an agent reads a remote memory, funes caches the dataset files locally. Warm queries return to local speed. The Hub supplies the ownership, access control, versioning, and distribution it already supplies for other datasets. Your memory does not become an account in a separate memory service, and you do not rent it back through an API.

Ask first, wire later

recall is shaped for agents. When you want to put a question to a memory yourself, use ask. It reads your local memory by default:

funes ask claude "what did we decide about the streaming parser"

Or point it at a shared memory. We published a memory of funes development, so you can ask why funes works the way it does without creating a memory of your own:

funes ask claude "why is funes append-only" --memory huggingface/funes-memory

funes ask is the read-only, one-question sibling of funes add. It recalls the passages, hands them to a coding agent, and returns a grounded answer that names its sources. It does not install an integration or change the agent’s persistent setup.

A retrieval miss is not papered over. If the passages do not support an answer, the agent says so. You can rephrase the question or add funes to the agent so it can search the memory iteratively during normal work.

Switching agents without losing the thread

A shared memory is not tied to the agent or model that created it. Start a task in Claude Code, continue it in Codex next week, and the second agent can recall the first agent’s reasoning. Use pi with a local model or one served through the Hugging Face router, then return to Claude.

Claude makes a decision; a hook indexes it; Codex recalls it in another session. The older hits in the demo are earlier recordings of the same experiment: an append-only memory remembered the rehearsals too.

This matters in a few different scopes:

• Across your machines: Bind each agent to one memory and recall the history from whichever host you are using.
• Across a team: A new teammate’s agent can retrieve months of decisions on day one, including dead ends and rationale that never made it into a pull request.
• Alongside an open-source project: A maintainer can publish the sessions behind a release, naming them on the push. Think of it as a searchable CLAUDE.md that holds the history of why the project is the way it is, instead of a page someone must keep rewriting. Anyone can read a public memory with –memory.

Published memories carry a dataset card and the funes tag, making them recognizable and discoverable on the Hub. The Hub already hosts open weights and datasets. funes adds open working memory. It holds the decisions, failed approaches, and rationale behind a project, queryable by another agent and traceable to the sessions that produced them.

The cheapest way out of a long session

A long investigation bloats a session until each turn costs more to carry the context than to do the work. The usual answers are to let the agent compact and carry on, or to write a handoff and start fresh. Recall is a third option. We measured them against each other on the handoff-vs-recall benchmark: two tasks whose answer cannot be reconstructed without the session prior knowledge.

Compaction is what most agents do by default, and it was the only one of the three whose result divided. It arrived on one task and never arrived on the other. Where it failed, its summary had flattened the findings that mattered. Recall returns the passages themselves, so a finding does not have to survive summarization.

Recall was the cheapest of the three on both tasks. It was 8x cheaper than a written handoff on one task and 4x on the other.

The lighter segment of each bar is the one-time charge for preparing the channel, the handoff, or the compaction. This is paid before the first question is asked and counted once. A cross marks a channel that never arrives, so it has no cost per success.

Stop starting from zero

“To think is to forget differences, generalize, make abstractions.” — Jorge Luis Borges, Funes the Memorious

Your agents already wrote the record. funes lives at github.com/huggingface/funes, one command away from turning that record into a memory the next agent can read, on whichever machine you happen to be on.

Built on open source

funes invents little of this. It leans on open-source embedding models good enough to run locally. It uses Lance’s append-only datasets with cheap incremental writes. It relies on the Hub’s caching and content-dedup for datasets. The work is in fitting them into a memory an agent can actually use.

funes is open source too. Open an issue for anything from an install snag to a recall that missed, or an agent you would like supported.