Push vs. Pull Agent Memory?

Recall, a new open-source memory substrate for AI agents, introduces a push-based memory model where agents automatically check, update, and supersede facts without manual queries. The tool runs locally on SQLite, requires no cloud or server, and supports CLI, MCP, and TUI interfaces. It aims to give developers full control over agent memory with built-in provenance, confidence scoring, and rollback capabilities.

The memory layer your agent just uses. It remembers, corrects itself, and recalls across sessions on its own. Local, free, and yours. Install install · Quickstart the-60-second-tour · Demo demo · Agents & MCP hook-up-your-agent · How it works how-it-works · Why Recall why-recall · Compare how-recall-compares · Teams one-graph-many-writers · Checker, Solver & Lattice beyond-memory-checker-solver-and-lattice · Docs /H-XX-D/recall-memory-substrate/blob/main/docs/README.md · Roadmap /H-XX-D/recall-memory-substrate/blob/main/ROADMAP.md Most agent memory is pull: a store you query. You ask, it returns the closest matches, and it is on you to notice when a fact has gone stale. Recall is push : the agent and the substrate run a loop together. It checks what it already knows before it acts, does the work, and writes back what it learned, superseding the old fact when something changes and surfacing the contradiction without being asked. No reminding it to save, no separate cloud service mining your transcript after the fact. Under the hood an LLM proposes a structured write, an admission firewall validates it, and the compiler returns only the relevant subgraph, ranked by evidence, fit to a word budget, all in local SQLite: no server, no account, no cloud. The memory is yours, and every fact still carries provenance, confidence, and a one-command undo. One installable Node.js tool: CLI, read-only TUI, MCP server, quiet maintenance daemon, strict write schema, semantic search, encrypted secrets side graph, and a reproducible benchmark harness. npm install -g github:H-XX-D/recall-memory-substrate Or use the installer script, which clones, builds, and links recall + recall-mcp : curl -fsSL https://raw.githubusercontent.com/H-XX-D/recall-memory-substrate/main/scripts/install.sh | bash Requires Node.js 24+. Recall uses Node's built-in SQLite, so there is no database server, no native build step, no account, and no network dependency. CI runs the core suite on Linux, macOS, and Windows, plus a readiness lane for MCP smoke, Python hooks/toolkit checks, public benchmarks, and installer validation. Upgrades, uninstall, and troubleshooting: Installation Guide /H-XX-D/recall-memory-substrate/blob/main/docs/11 INSTALLATION.md . The installer above already wires up any agent CLI it finds. To do it yourself or after installing a new agent , one idempotent command sets up the skill, the MCP server, the consult-Recall hook, and makes Recall the durable memory layer: recall claude sync Claude Code: skill + MCP + SessionStart hook; turns OFF native auto-memory recall codex sync OpenAI Codex: skill + MCP in config.toml + a Recall directive in AGENTS.md recall claude status confirm what's wired recall codex status for Codex Restart your agent and it's armed: it reads memory before relying on recollection and writes durable findings back on its own; you never tell it to "save." Both syncs back up your config before editing, are safe to re-run, and are reversible recall claude enable-auto-memory . Using a different MCP client? See hook up your agent hook-up-your-agent . recall version confirm the installed package version recall init create the local graph in ./.recall recall status store health, counts, config Memory enters as structured, schema-validated proposals. Normally your agent submits these over MCP see below hook-up-your-agent , but the same path works from the shell: recall admit --json decision.json validated, provenance-stamped, rollbackable It comes back as a compiled context packet, not a dump of the store: recall compile "prepare the auth service deploy" --words 220 objective: prepare the auth service deploy compiler state: - retrieval=fts5-bm25; query="prepare the auth service deploy"; selected cells=3; budget words=220 - health=beliefs:0, contradictions:0, stale or low trust:0, critical warnings:0 relevant memory: - Cap the Postgres pool at 20 connections: Staging fell over at 35 concurrent connections during the load test on June 3. Capped pool size at 20 in service config; raising it requires a load test sign-off. decision:07fbbfd9-… risks: - Auth tokens expire but never rotate: Access tokens have 24h expiry but no rotation path; a leaked token stays valid until expiry. risk:1cb991a1-… tasks: - Add a smoke check for the new rate limiter: The rate limiter shipped behind a flag; nobody has verified the 429 path end to end. task:8bddbb07-… expansion handles: - 07fbbfd9-… 1cb991a1-… 8bddbb07-… A packet holds ranked evidence, open risks and tasks, contradiction warnings when they exist, and expansion handles for drilling into any cell, all under a hard word budget. You can browse the graph at any time: recall tui read-only terminal dashboard recall search "rate limiter" FTS5 + BM25 lexical search recall semantic "token rotation" semantic search hash or real embeddings Every cell also carries an effective confidence next to the author's immutable stated confidence. It is recomputed on every read from incoming supports, challenges, and the writer's contradiction record. Write one contradiction and the number moves: before: the pool-cap decision stands alone decision:6eba1114… state=active/conf:0.7/eff:0.7/… after: one cell contradicts it nothing deleted, no model ran decision:6eba1114… state=active/conf:0.7/eff:0.29 challenged /… Challenged cells sink in ranking, supported cells hold, and writers with a record of overconfidence get discounted. All of it is deterministic and runs offline. Runtime state stays local and is git-ignored by default: .recall/recall.sqlite3 primary graph .recall/secrets.sqlite3 encrypted secrets side graph Back up or move a graph with the portable archive path: recall export recall-export.json recall import --json recall-export.json --db .recall/restored.sqlite3 Undo a bad write through the rollback journal: recall rollback list recall rollback show <journal-id recall rollback apply <journal-id See Backup And Recovery /H-XX-D/recall-memory-substrate/blob/main/docs/20 BACKUP AND RECOVERY.md for the full restore path, including file-level SQLite copies and upgrade safety. Change your mind without losing the past. Recall's moat is supersession: a correction is never an overwrite. You admit a new cell that --contradicts the old one, and every future read demotes the superseded value instead of deleting it; the conflict resolves automatically, at read time. ▶ Click the frame or open assets/recall-supersede-demo.mp4 /H-XX-D/recall-memory-substrate/blob/main/assets/recall-supersede-demo.mp4 for a ~17s screencast, a real, unedited recall run:: v1 "Cache TTL is 60s" lands at full strength eff:0.70 .: v2 --contradicts v1 "…is 300s." Read it back and compile doesn't return both as equals: v1 is demoted to eff:0.29 challenged , v2 stays high. The old value is still in the graph: preserved, queryable, just down-weighted.: a 3-link chain. v3 --contradicts v2 v1 and v2 collapse to eff:0 ; exactly one live answer remains, the why-we-changed trail intact.- The run asserts a tripwire: the real graph was untouched 334 → 334 , a throwaway db . Demotion-not-deletion is the line between memory that merely persists and memory that stays honest , the whole point of Recall over a flat note file. Every Recall capability has a short, honest screencast: real CLI, real output, an isolated graph, with the full script and the unedited transcript beside each clip. Browse them all in the companion gallery https://h-xx-d.github.io/recall-demos/ . | Screencast | What it shows | |---|---| Install in one command | recall claude sync / recall codex sync wires the skill, MCP, and consult-Recall hook, and turns off native note-memory | | three claude sessions, natural prompts, nobody says "save"; persist → supersede → cold-session recall | Supersession mechanics the clip above | the contradicts edge, read-time demotion, multi-link chains | Rollback a write | journaled, reversible undo; archives the node, strips its relations | Inception | grounded idea synthesis: a new hypothesis pre-linked depends on to its sources | Effective confidence | stated vs graph-computed trust, recomputed on every read | Compile | ids-first context packets; expand a cell or single field only when needed | Retrieval | FTS5 + BM25 with porter stemming and identifier-aware tokenization | Subgraph | slice the graph by structured facets tags , not just search | Memory health | recall beliefs / recall maintenance --derive audits between turns | Calibration | per-actor Brier scores; memory that learns who to trust | Watch programs | a non-LLM monitor that trips when the graph turns against a belief | Secrets | firewall refuses credential shapes; real secrets go in the encrypted side graph | Diff-aware resume | what's new, updated, and retracted since you left | Routine memory is agent-managed through MCP. Users should not have to hand-save ordinary observations, decisions, risks, or tasks. recall mcp config --db .recall/recall.sqlite3 print an MCP config block recall-mcp start the stdio MCP server Paste the config block into any MCP-capable client Claude Code, desktop apps, agent runtimes , then add the LLM System Prompt /H-XX-D/recall-memory-substrate/blob/main/docs/LLM SYSTEM PROMPT.md to your agent's instructions. The agent's loop becomes: compile, work, write back. | Tool | Purpose | |---|---| recall compile | Compile a compact context packet for a task. Start here | recall write | Submit a strict, evidence-aware memory proposal | recall search / recall semantic | Retrieve graph evidence by exact or semantic match | recall subgraph | Compose subgraphs from structured tags | recall daemon run once | Run one outside-the-LLM maintenance pass | There are 42 MCP tools in total, covering status, hyperedges, programs, DAGs, evals, ACP agent coordination, and calibration. The LLM Integration Guide /H-XX-D/recall-memory-substrate/blob/main/docs/LLM INTEGRATION.md is the full operating contract, including the proposal shape. The repo ships a Claude Code slash command at .claude/commands/recall.md /H-XX-D/recall-memory-substrate/blob/main/.claude/commands/recall.md . Copy it into your own project, or make it global, and /recall becomes a one-word way to wire a session to Recall: global available in every project : mkdir -p ~/.claude/commands && cp .claude/commands/recall.md ~/.claude/commands/ or per-project: cp .claude/commands/recall.md /path/to/your/project/.claude/commands/ After the one-time MCP setup above, typing /recall resolves your store RECALL DB if set, otherwise the local .recall/recall.sqlite3 , ensures it exists, compiles a context packet for what you are about to do, and hands the agent the compile, work, write-back loop. No schema to manage, no setup to repeat. /recall <topic compiles for that topic; bare /recall orients on recent state. If your memory already lives in a shared or global store, export RECALL DB so /recall targets it instead of minting an empty local db. The MCP-config-and-system-prompt setup above is also available as a single idempotent command per agent runtime. scripts/install.sh runs these automatically fail-soft when the corresponding CLI is present, and you can re-run them anytime to refresh to the latest bundled version: recall claude sync Claude Code recall codex sync OpenAI Codex recall claude status report which pieces are installed codex status likewise Claude Code recall claude sync installs a SessionStart/UserPromptSubmit hook that nudges the agent to consult Recall, copies the recall skill into ~/.claude/skills/ , registers the recall MCP server in ~/.claude.json , and sets CLAUDE CODE DISABLE AUTO MEMORY=1 so Recall is the durable memory layer instead of Claude Code's built-in note memory keep native auto-memory with RECALL KEEP AUTOMEMORY=1 ; revert with recall claude enable-auto-memory . Already accumulated native auto-memory? recall import auto-memory --root path --project name --apply --db path imports your ~/.claude/projects/<slug /memory/ .md files into Recall as calibrated cells dry-run by default; pass --apply to write . It is idempotent per file content, and a changed file supersedes its prior version via a contradicts edge, the migration wedge for owning your memory. Codex recall codex sync copies the recall skill into ~/.codex/skills/ , registers the recall MCP server under mcp servers.recall in ~/.codex/config.toml , and injects a marker-delimited Recall directive into ~/.codex/AGENTS.md , Codex's always-read global instructions, the analog of Claude Code's SessionStart hook. Codex exposes no native-memory kill switch, so Recall is positioned as the durable memory layer at the prompt level via that directive. All edits are backed up before write, preserve your existing config, and are idempotent. Propose. The LLM submits a recall.write.v1 proposal with content, evidence, confidence, provenance, and structured tags. Admit. Admission validates the schema, applies firewall checks, attenuates unsupported claims, warns on near-duplicates, blocks secret-looking content, and journals a rollback entry. Store. Memory persists as addressable cells and n-ary hyperedges in SQLite, reachable by address, tag, relation, or semantic search. Compile. The compiler builds a compact, task-specific packet under a word budget, listing each cell's challengers alongside it and computing each cell's effective confidence from the live graph. Maintain. A quiet daemon runs stale-memory, contradiction, derivation, and eval passes outside the LLM, writing back through the same admission path as everyone else. The base structure is a hypernetwork. DAGs are optional overlays for ordered workflows, evidence chains, and execution traces. Hyperedges can also carry programs: declared, versioned, sandboxed operations recall.program.v1 that run on demand. Bind a decision to its risks and verifications and the bundle can score itself. Because the score reads live effective confidence, it works as a tripwire: recall program run <program-id Friday deploy gate → averageEffectiveConfidence: 0.7, score: 0.827 new evidence contradicts the load-test verification recall program run <program-id same gate, no model ran → averageEffectiveConfidence: 0.322, score: 0.638 recall program run <program-id --derive → derives a witness cell, filed through the same admission gate as any other write Other memory systems store relations as passive records that only an external model can act on. Here, a deploy gate's score falls on its own when any member is contradicted, whether by a teammate, another agent, or a failing test wired in through test-contradicts edges. See Advanced Graph Operations /H-XX-D/recall-memory-substrate/blob/main/docs/06 ADVANCED GRAPH OPERATIONS.md . The watch operation turns a bundle into a standing reflex. It baselines against its own previous run run history is the state, so no extra machinery is needed , trips when the bundle's live effective confidence moves more than delta , and derives nothing otherwise. A quiet watch means the value was checked and had not moved. recall program add <hyperedge-id --json watch.json { "schemaVersion": "recall.program.v1", "operation": "watch", "params": { "delta": 0.15, "concernTarget": "<decision-cell-id " } } recall program run <program-id --derive → untripped: derives nothing → tripped: files a concern against the target decision, through the same admission gate as every other write, attributed to program:<id A tripped watch does not modify its target. It files a concern through admission like any other writer, which means reflexes carry produced by and accumulate a calibration record. A watcher whose concerns keep getting refuted gets discounted by the same loop that scores humans and LLMs. Watches can chain: a verification collapses, its watcher files a concern on the decision built on it, that decision's effective confidence falls, and a watcher on the decision can fire next. Run a watch from cron and a standing decision becomes a monitored service. Two more operations round out the set: drift is watch with attribution. A tripped run names which member moved topMover , plus a ranked movers list , so you can see what caused a gate to fall. quorum is k-of-m sign-off as a graph object. A member counts as an approval when its live effective confidence clears minEff , counted across distinct actors so one writer cannot stack the gate. If an approver's cell is later contradicted, its effective confidence drops and the approval stops counting, with no policy code involved. Quorum runs always derive their attestation. Compile packets carry a standing programs section listing the gates, watches, and quorums covering each returned cell, with program and bundle handles, so an agent writing new evidence can tie it into the existing bundle instead of orphaning it. Deeper concepts live in the docs /H-XX-D/recall-memory-substrate/blob/main/docs/README.md : the write schema /H-XX-D/recall-memory-substrate/blob/main/docs/02 WRITE SCHEMA.md , tagging & subgraphs /H-XX-D/recall-memory-substrate/blob/main/docs/03 TAGGING AND SUBGRAPHS.md , the context compiler /H-XX-D/recall-memory-substrate/blob/main/docs/04 CONTEXT COMPILER.md , and cells & graph views /H-XX-D/recall-memory-substrate/blob/main/docs/14 ADDRESSABLE CELLS AND GRAPH VIEWS.md . recall incept is an experimental primitive for synthesis rather than retrieval. It compiles a slice of the graph for an open objective, then emits a write-back template whose depends on is pre-populated with the slice's cell ids. A model fills in the synthesis a method, a connection, an insight implied by a contradiction between cells and admits it. Because the template is pre-grounded, any cell created this way is born linked to the sources it was synthesized from. The generative step stays in the model on purpose. Recall gathers the slice, guarantees the grounding, and admits the result, but it does not synthesize, because putting a model in the runtime would break the no-model trust loop. The new cell lands as a hypothesis at conservative confidence, marked unverified, so it enters the graph and earns or loses trust over time through the same effective-confidence machinery as any other write. The model synthesizes, Recall grounds it and lets the graph vet it. recall incept "novel approaches given what we know about X and Y" Recall makes a few opinionated bets that most memory layers don't: | Most memory layers | Recall | | |---|---|---| Trust model | Append text, trust later | Every write passes an admission firewall: schema-validated, provenance-stamped, rollbackable | What returns to the model | The whole store, or a top-k blob | A compiled context packet: the relevant subgraph, ranked by evidence, fit to a word budget | Structure | Flat notes or a single knowledge graph | Addressable cells plus n-ary hyperedges, with optional DAG overlays for ordered work | Where it lives | A cloud service you send data to | Local-first. SQLite on your machine. No account, no network required | Secrets | Mixed into the same store | A separate encrypted side graph, opt-in, never in the primary graph | Mistakes | Overwrite and move on | Rollback instead of overwrite: supersede by relation, keep the audit trail | Maintenance | Manual curation, or none | A quiet daemon runs stale-memory, contradiction, and derivation passes outside the LLM | Calibration | Confidence is decoration | Closed-loop calibration: each actor's stated confidence is scored against survived contradictions | Confidence | A static number typed once | A living number: effective confidence is recomputed from supports, challenges, and the writer's track record on every read, with no LLM in the loop | The common thread is auditability: provenance on every cell, a firewall on every write, and a packet you can actually read. The main design split in agent memory is how trust changes when new information arrives. The field has three mechanisms: | Mechanism | Who uses it | What happens to a contested claim | |---|---|---| | A model decides | mem0, Zep, Letta, Hindsight | An LLM resolves the conflict at ingest, invalidates the fact, or rewrites beliefs. Opaque, non-reproducible, and the losing claim is often deleted | | The clock decides | decay-based systems | Importance fades on a forgetting curve whether or not any evidence arrived | | The evidence decides | Recall | Effective confidence is recomputed from typed supports, challenges, and the writer's contradiction record. Same graph, same number, every time | Other systems ask a model what to believe. Recall computes it. The rest are architectural design properties, not benchmark claims. Pick the tool that matches how much you care about auditability and local control. | Property | Vector RAG | Knowledge-graph memory | Cloud memory APIs | Recall | |---|---|---|---|---| | Runs fully local, no account | sometimes | sometimes | ✗ | ✅ | | Structured write schema enforced | ✗ | partial | varies | ✅ | | Admission firewall on every write | ✗ | ✗ | varies | ✅ | | Provenance + rollback per write | ✗ | partial | varies | ✅ | | N-ary hyperedges not just pairwise | ✗ | rare | ✗ | ✅ | | Word-budgeted compiled context | ✗ | ✗ | partial | ✅ | | Encrypted, segregated secrets store | ✗ | ✗ | varies | ✅ | | Single runtime, one memory API | ✗ | varies | n/a | ✅ | | Trust evolves with no LLM in the loop | ✗ | ✗ | ✗ | ✅ | | Tiered reads over trust-annotated claims title → peek → full cell | ✗ | partial | partial | ✅ | Tiered, agent-navigated retrieval is an emerging pattern across the field. Letta pages between memory tiers, and progressive-disclosure indexes are appearing elsewhere. Recall's difference is what sits at each tier: not auto-summaries of activity, but gate-vetted claims carrying live trust state, addressed in the same namespace the evidence machinery uses. The index layer tells the agent where digging is warranted, not just that it may dig. Recall trades turnkey cloud convenience for local control and an audit trail. If you want a hosted, batteries-included memory service, projects like mem0, Letta, and Zep are excellent. Recall is the other kind of tool: the graph lives on your disk, every write comes with a receipt, and nothing in it is above being challenged. Most memory layers assume one user and one assistant. Recall's write path was built for traffic. Humans, agents, CI jobs, the daemon, and tripped reflexes all write through the same admission gate, so every cell lands schema-checked, attributed, timestamped, and rollbackable no matter who sent it. That is what makes a single project graph safe to share: when a decision changes overnight, a teammate does not ask around in chat, they compile. The packet says who changed it, when, on what evidence, and what it contradicts. Run recall compile "what changed since Friday" on Monday morning and the briefing writes itself. The gate is also vendor-blind. A proposal from GPT, Claude, Gemini, or a local model is the same recall.write.v1 JSON, judged by the same rules, landing in the same graph. Calibration then scores every writer separately, human or model, on one ledger. Route work to whichever model is cheap this month, and let track records rather than logos decide how much to trust what comes back. It is tier-blind for the same reason. The write contract asks for discipline, not brilliance, so a small model can hold the same memory standards as a frontier one. And because the compile packet carries the team's accumulated judgment, including the calls, the risks, and the open contradictions, a cheap model starts its session with the same briefing an expensive one gets. The packet does the remembering so the model does not have to. Standing programs turn the shared graph into something a team can watch without opening a terminal. The fully actuated setup puts the store on a box everyone can reach and lets the tripwires do the talking. Point the whole team at one graph by setting RECALL DB once on the host: the CLI, the MCP server, and the helper scripts all read it, so agents, cron jobs, and exporters route to the same store with no per-command flag pass --db when you want to override it . Then let a scheduler run each standing gate on a heartbeat. Every program run prints plain JSON, so it wires into whatever the room already watches with no integration to install: export RECALL DB=/srv/recall/payments.sqlite3 one graph, whole team cron, every 10 min: trip the deploy gate, ping the channel recall program run <watch-id --derive \ | jq -e '.run.output.tripped' /dev/null && curl -s -X POST "$SLACK WEBHOOK" -H 'content-type: application/json' \ -d '{"text":"Deploy gate tripped: the load-test verification moved."}' run history is a time series of gate scores createdAt + output.current . An exporter scrapes it into Prometheus/Grafana, where alerting fires when a gate's effective confidence crosses your threshold. recall program runs --limit 200 A panel of gate scores is the project's live status, read straight off the trust graph: each score is its bundle's effective confidence, so the board shows at a glance what is currently believed, what is contested, and what just moved. The tripped run has already filed its concern in the graph through admission, so Slack and Grafana are only the echo into the room, not the source of truth. One caveat, stated plainly: today "shared" means the writers are processes on that one host. Team SSH sessions, CI jobs, agents running there, and the cron watchers all hit the same store over SQLite WAL, which handles the concurrency. Putting the file on a network share for many separate machines to write is not safe, by SQLite's own guidance. One graph served over HTTP with authenticated actor identity, so remote machines write without sharing the host, is next on the roadmap /H-XX-D/recall-memory-substrate/blob/main/ROADMAP.md . Recall is the memory layer of a four-part system. The other three parts plug into the same graph through the same admission gate. Checker is the verification layer, built on one rule: absence of refutation is not verification. It runs declared checks and stores honest verdicts verified / refuted / unverifiable / error / partial in a ledger, and its attestation is git-native: verify-commit refuses dirty trees and non-HEAD SHAs, gate --ref answers whether a specific commit is verified, and a fail-closed pre-push hook gates pushes on that answer. Checker emits typed checker-supports and checker-contradicts edges into the Recall graph, where they already count for more than peer testimony in effective confidence, and compile packets surface checker challenges on every affected cell. Verification is tied to an exact commit on a clean tree, not to a claim that the tests passed at some point. Solver is the compute layer: a library of 96 small, fast, gated solvers spanning control theory, signal processing, estimation, and optimization. Kalman filtering, FIR/biquad, CORDIC, Goertzel, count-min sketches, matrix-free conjugate gradient, and an Ising/simulated-annealing tier that makes QUBO formulations practical where hand-rolled heuristics used to be the only affordable option. The CPU tier is plain C, the GPU tier is CUDA, and every solver is validated against a reference oracle before any speed number is trusted. Each solver carries an optimality contract declaring what class of claim its answers make: exact, tolerance-bounded, or heuristic. Results land in Recall as addressable, priced claims. The division of labor: the model formulates, Lattice maps the code, Solver computes, Recall remembers, Checker attests. Lattice is the code-analysis layer, and the one part that is an enterprise capability rather than open source: access-gated, vetted, and not bundled in the OSS distribution. It ingests a codebase over the Language Server Protocol into the same typed hypernetwork Recall uses symbols, modules, and the import, call, and reference edges between them , then runs structural analyses over that graph. impact returns a change's reverse-reachability blast radius before you edit; hunt ranks structural bugs, including cross-signal findings no single diagnostic shows, like an exported path that reaches unimplemented code; diagnose surfaces cycles, dead code, stubs, and coupling hotspots in one pass; plan lays out the ordered, verify-gated steps to land a change; and verify is a differential gate that reports only the structural regressions a change caused , measured against a git ref in a throwaway worktree. A gated security-audit mode maps attack surface and source-to-sink reachability for authorized review of your own code. Every finding carries the same explicit verified / not verified contract Checker enforces: reachability tells you where to look, never that a bug is proven. What sets it apart is where the findings go. Lattice grounds everything it computes: the hard combinatorial step the minimum feedback arc set that breaks a dependency cycle routes to Solver's gated QUBO tier and is verified locally before it is trusted, and results land in Recall as addressable, evidence-weighted cells through the same admission gate as every other write. A structural regression stops being a console warning that scrolls away and becomes a cell with provenance that a deploy gate can score and a teammate can compile tomorrow. It ships an MCP server built for the agent edit loop ask impact before an edit, gate with hunt after , served in milliseconds off a graph ingested once and cached. Together they cover memory, verification, computation, and code structure behind one write path, with no model in the loop. Checker org capabilities, Solver access, and Lattice enterprise : todd@hendrixxdesign.com mailto:todd@hendrixxdesign.com inspect recall version recall status recall tui --watch retrieve recall search "query" recall semantic "query" recall subgraph --project Recall --category memory --subject compiler recall compile "task description" --words 900 recall incept "open objective" compile a slice into a grounded synthesis template write agent/debug path; normal memory flows through MCP recall write recall validate --json proposal.json recall admit --json proposal.json backup / restore recall export recall-export.json recall import --json recall-export.json --db .recall/restored.sqlite3 recall import auto-memory --project name --apply import Claude Code auto-memory files as calibrated cells dry-run default undo recall rollback list recall rollback show <journal-id recall rollback apply <journal-id advanced graph runtime recall hyperedge add --json hyperedge.json recall program add <hyperedge-id --json program.json recall dag analyze <overlay-id --derive recall eval run --derive recall operate once --derive health & trust recall beliefs recall calibration recall maintenance --derive recall repair --apply prune dangling/unresolvable trust edges dry-run default; --apply deletes daemon recall daemon run-once --derive recall daemon run --interval-ms 60000 secrets encrypted side graph, explicit confirmation required printf 'password\nsecret-value' | recall secrets save \ --title "service token" --confirm-secret-save --password-stdin --value-stdin Run recall help for the full command surface, or see the CLI & TUI reference /H-XX-D/recall-memory-substrate/blob/main/docs/05 CLI TUI.md . Recall ships a reproducible public benchmark: a synthetic corpus in a throwaway database, measuring latency and throughput across the operational surfaces admit write , search , semantic , compile , paging, daemon and operator passes, secrets : npm run bench:public Numbers vary by machine. The point is that anyone can rerun the measurement. See Public Benchmark /H-XX-D/recall-memory-substrate/blob/main/docs/19 PUBLIC BENCHMARK.md for methodology. Start with the docs index /H-XX-D/recall-memory-substrate/blob/main/docs/README.md , which routes by purpose and by audience. Highlights: Installation Guide /H-XX-D/recall-memory-substrate/blob/main/docs/11 INSTALLATION.md Architecture /H-XX-D/recall-memory-substrate/blob/main/docs/01 ARCHITECTURE.md Strict Write Schema /H-XX-D/recall-memory-substrate/blob/main/docs/02 WRITE SCHEMA.md , the recall.write.v1 contract Context Compiler /H-XX-D/recall-memory-substrate/blob/main/docs/04 CONTEXT COMPILER.md LLM Integration Guide /H-XX-D/recall-memory-substrate/blob/main/docs/LLM INTEGRATION.md · LLM System Prompt /H-XX-D/recall-memory-substrate/blob/main/docs/LLM SYSTEM PROMPT.md Secrets Side Graph /H-XX-D/recall-memory-substrate/blob/main/docs/12 SECRETS SIDE GRAPH.md Daemon, MCP & Semantic Search /H-XX-D/recall-memory-substrate/blob/main/docs/13 DAEMON MCP SEMANTIC SUBGRAPHS.md Public Benchmark /H-XX-D/recall-memory-substrate/blob/main/docs/19 PUBLIC BENCHMARK.md Backup And Recovery /H-XX-D/recall-memory-substrate/blob/main/docs/20 BACKUP AND RECOVERY.md npm install npm run build tsc npm test 162 unit/integration tests npm run e2e 94 end-to-end checks across user + agent workflows npm run smoke init + status on a throwaway db npm run smoke:mcp stdio MCP initialize + tools/list smoke npm run test:python npm run verify:full Contributions are welcome. See CONTRIBUTING.md /H-XX-D/recall-memory-substrate/blob/main/CONTRIBUTING.md and the Code of Conduct /H-XX-D/recall-memory-substrate/blob/main/CODE OF CONDUCT.md . Keep changes schema-first, small, tested, and aligned with the single-runtime architecture. A good first PR runs npm test && npm run e2e clean; release-readiness changes should run npm run verify:full . The Roadmap /H-XX-D/recall-memory-substrate/blob/main/ROADMAP.md lays out direction by ring: Foundation, Runtime, and Interfaces. Working in this repo with an AI agent? Point it at AGENTS.md /H-XX-D/recall-memory-substrate/blob/main/AGENTS.md . Read SECURITY.md /H-XX-D/recall-memory-substrate/blob/main/SECURITY.md before using Recall with sensitive data. Important defaults: - runtime databases and logs are git-ignored - primary-graph writes reject secret-looking content, matched against a broad set of credential shapes cloud keys, vendor tokens, JWTs, private-key blocks, and secret-named assignments and tuned to never trip on the graph's own cell ids - encrypted secret saves require explicit confirmation - primary-graph writes are schema-validated and rollbackable Report vulnerabilities via GitHub Security Advisories. See the policy for details. Recall is an early working runtime foundation. It is suitable for local experimentation and integration work, and it does not claim production-grade or state-of-the-art behavior without external benchmarks and deployment review. Interfaces may change before a stable release. Treat compiled context packets as evidence, not unquestionable truth, which is how Recall is designed to be used anyway. If Recall helps your work, please cite it. See CITATION.cff /H-XX-D/recall-memory-substrate/blob/main/CITATION.cff or use GitHub's "Cite this repository" button. Recall is licensed under the Apache License 2.0 /H-XX-D/recall-memory-substrate/blob/main/LICENSE . See NOTICE /H-XX-D/recall-memory-substrate/blob/main/NOTICE . Built for agents that should remember responsibly .