Agent Memory Bridge

简体中文

Give coding agents one shared, governed record of project decisions across tools and sessions.

Agent Memory Bridge is shared engineering memory for developers and teams that use more than one coding agent. It complements AGENTS.md, CLAUDE.md, and client-native preference memory rather than replacing them. SQLite/WAL is the durable authority, with FTS5 and optional local embeddings as derived indexes for lexical, semantic, or hybrid retrieval.

0.25.1 is the cross-platform integrity patch for Trustworthy Adaptive Retrieval. It rejects non-canonical base64url receipt aliases and removes a Python 3.11 command-provider test race while preserving the 13-tool MCP surface and the shadow-only feedback boundary.

Codex is the reference workflow, not the product boundary. AMB uses local stdio MCP; client integrations are documented or locally verified only where labeled below.

Conceptual visual only: a shared-memory workspace metaphor for AMB. It is not product evidence, identity evidence, certification, distribution, or use proof.

Try it locally after install: <venv-python> -m agent_mem_bridge first-run --client generic --example

Why It Exists

Most agent memory either feels too shallow or too heavy:

• summaries become stale blobs
• vector stores hide why something was recalled
• every new session starts cold or gets a stale context dump
• handoff state turns into ad hoc notes or a queue you did not want to build

AMB takes a smaller path: local SQLite authority, explicit namespaces, inspectable records, capability-labeled retrieval, and a signal lifecycle for lightweight coordination.

What You Get

• Durable memory: decisions, gotchas, procedures, concepts, beliefs, and supporting records.
• Coordination signals: claim -> extend -> ack / expire / reclaim without pretending to be a scheduler.
• Review-first writeback: learning candidates can be staged for human review before explicit promotion into durable records.
• Context assembly: startup and task-time context can be rendered from procedures, concepts, beliefs, gotchas, and linked support without adding more MCP tools.
• Governed change: explicit deletion, supersession, changed premises, and task-domain applicability are checked before guidance becomes actionable.
• Cross-client activation receipts: a read-only CLI receipt can show that two distinct declared client labels participated in one memory loop without exposing paths, content, session IDs, or model IDs.
• Retrieval feedback: callers can mark a receipt-bound result as helpful, misleading, outdated, not_applicable, or not_used without changing ranking or memory.
• Proof discipline: release contract checks, public-surface checks, onboarding checks, benchmark snapshots, visual inventory checks, and 604 tests collected.

How It Works

AMB keeps the runtime path small: MCP-compatible coding agents call 13 public MCP tools; SQLite/WAL remains the durable authority; FTS5 and optional local embeddings are derived indexes; governed context and CLI reports are rendered without automatic durable writeback. Release checks, benchmarks, and the visual claim inventory stay outside that runtime path.

Who It Is For

• You use more than one coding agent and want project decisions, gotchas, and handoffs to remain shared across them.
• You already use AGENTS.md, CLAUDE.md, or native preference memory and need a governed cross-agent layer alongside it.
• You want memory that is local and inspectable instead of a hosted platform or opaque vector stack.
• You run review, handoff, or multi-agent workflows and need coordination signals without building a full task queue.

Install

Requirements:

• Python 3.11+
• SQLite with FTS5 support; optional local embeddings are derived indexes, not durable authority
• any MCP-compatible client that can launch a local stdio server
• optional uv / uvx for a pinned one-command GitHub smoke test

Pinned GitHub install with Python:

python -m venv .amb-venv
python -c "import os; from pathlib import Path; print((Path('.amb-venv') / ('Scripts/python.exe' if os.name == 'nt' else 'bin/python')).absolute())"

Treat the printed value as <venv-python>. Keep that resolved local path out of commits and issue reports. In a POSIX shell, shell-quote that path when needed. In Windows PowerShell, invoke it as & "<venv-python>". Then run:

<venv-python> -m pip install "https://github.com/zzhang82/Agent-Memory-Bridge/archive/refs/tags/v0.25.1.zip"
<venv-python> -m agent_mem_bridge doctor
<venv-python> -m agent_mem_bridge verify

Optional pinned GitHub smoke test with uvx:

uvx --from git+https://github.com/zzhang82/Agent-Memory-Bridge@v0.25.1 agent-memory-bridge verify

Quick Start: Unified First-Run

Use first-run when you want a complete copy/paste setup guide for a client. It renders install steps, a placeholder-safe config snippet, verification commands, and a first Task Brief preview. It does not write client config files or durable memory records.

<venv-python> -m agent_mem_bridge first-run --client generic --example
<venv-python> -m agent_mem_bridge first-run --client codex --example
<venv-python> -m agent_mem_bridge first-run --client opencode --example
<venv-python> -m agent_mem_bridge first-run --client hermes --example

If you only need the config snippet, use config directly:

<venv-python> -m agent_mem_bridge config --client generic --example
<venv-python> -m agent_mem_bridge config --client codex --example
<venv-python> -m agent_mem_bridge config --client opencode --example
<venv-python> -m agent_mem_bridge config --client hermes --example
<venv-python> -m agent_mem_bridge config --client cursor --example

Dockerized stdio works too when you want an isolated runtime:

docker build -t agent-memory-bridge:local .
docker run --rm -i -e AGENT_MEMORY_BRIDGE_HOME=/data/agent-memory-bridge -v /path/to/bridge-home:/data/agent-memory-bridge agent-memory-bridge:local

Client-specific notes live in docs/INTEGRATIONS.md. Runtime configuration lives in docs/CONFIGURATION.md. Authority and correction rules live in docs/AUTHORITY-CONTRACT.md. Security guidance lives in SECURITY.md. Agents that are installing the bridge should start with INSTALL_FOR_AGENTS.md.

The First Useful Loop

Session 1 discovers a project rule:

store(
  namespace="project:demo",
  kind="memory",
  content="claim: Use WAL mode for concurrent SQLite readers."
)

Session 2 asks about the same project:

recall(namespace="project:demo", query="SQLite concurrent readers")

The agent gets the rule back without the user typing it again.

For coordination, use signals:

store(namespace="project:demo", kind="signal", content="release note review ready")
claim_signal(namespace="project:demo", consumer="reviewer-a", lease_seconds=300)
extend_signal_lease(id="<signal_id>", consumer="reviewer-a", lease_seconds=300)
ack_signal(id="<signal_id>", consumer="reviewer-a")

For polling, use an empty query with kind="signal" and pass the previous next_since value back as since. Polling returns later insertions in ascending order. Missing, deleted, or cross-namespace anchors fail explicitly. The cursor does not report later claim or ack transitions on older Signals. Text and memory recall return next_since: null.

For a cross-client activation receipt, keep one correlation id across both clients:

# Client A stores one reviewed project memory.
store(
  namespace="project:demo",
  kind="memory",
  title="Reviewed SQLite guidance",
  content="record_type: gotcha\nclaim: Use WAL mode for concurrent SQLite readers.",
  tags=["workflow:cross-client-activation", "activation-role:writer", "reviewed:true"],
  correlation_id="activation-demo-001",
  source_client="client-a"
)

# Client B recalls it, then records and acknowledges the read signal.
recall(namespace="project:demo", query="SQLite concurrent readers", correlation_id="activation-demo-001")
store(
  namespace="project:demo",
  kind="signal",
  content="{\"observed_memory_id\":\"<writer_memory_id>\"}",
  tags=["workflow:cross-client-activation", "activation-role:reader"],
  correlation_id="activation-demo-001",
  source_client="client-b"
)
ack_signal(id="<reader_signal_id>")

Then render the local receipt:

<venv-python> -m agent_mem_bridge activation-receipt --namespace project:demo --correlation-id activation-demo-001 --format markdown

The receipt reports hashes and pass/review status. It does not print raw memory content, private paths, session ids, model ids, or authenticated identity claims.

The short version:

WITHOUT AMB
user> We hit this last time too: run the generator after schema edits.

WITH AMB
agent> I found the previous gotcha: run the generator after schema edits.

Task Briefs do not require Agent Memory Harness (AMH). The AMB CLI can render a derived task context report over recalled records, including what context was used, ignored, or marked for review. That brief is a derived view over AMB memory; it is not a second durable store and does not add MCP tools.

The terminal demo and the before/after gotcha story are in examples/demo, with the story source at examples/demo/before-after-gotcha.cast.md.

Client Support

Status labels are intentionally narrow.

MCP Tools

The bridge exposes 13 public MCP tools:

Same tools by group:

• store, recall, browse, stats
• forget, feedback, promote, annotate, revise, export
• claim_signal, extend_signal_lease, ack_signal

annotate adds non-policy tags and provenance without rewriting the original content. revise creates a successor record and an auditable supersession receipt in one transaction. Both operations preserve the review boundary: callers cannot mint reserved governance tags or revise hidden learning candidates into authority.

The richer behavior stays behind that surface: reviewed promotion helpers, consolidation, startup/task-time assembly, procedure policies, telemetry summaries, signal contention checks, learning-candidate review queues, Task Brief reports, human review workflows, and activation receipts. There are no separate task_packet, startup_packet, learning_candidate, task_brief, review_queue, review_workflow, or activation_receipt MCP tools, and no rerank, auth, ACL, ANN, graph, or auto-policy interface.

For normal service use, log capture helpers, promotion helpers, and strong consolidation are disabled by default. During each cycle, every enabled lane has its own exception boundary: one lane failure is reported with a failure count and bounded retry delay without stopping its siblings. The lanes still execute sequentially, so a slow call can delay later lanes; lane duration and slow-lane warnings make that delay visible. Watcher, reflex, consolidation, governance, and embedding scheduler state use tolerant atomic JSON and reset when a restored database has a different epoch. The service writes service-health.json, holds a local bridge-home singleton lock, exits 1 from service --once when any enabled lane fails, and exits 3 when another service owns the lock. Use --allow-multiple-services only when duplicate processing is deliberate.

Restore is an offline maintenance operation. Stop the service and every MCP/client process that can write the database before restoring, and reopen clients only after verification completes. The service lock excludes the background daemon; arbitrary MCP writers do not participate in that lock.

Operator review work is available as CLI reports, not MCP tools:

<venv-python> -m agent_mem_bridge review-queue --namespace project:demo --format markdown
<venv-python> -m agent_mem_bridge review-workflow --namespace project:demo --format markdown
<venv-python> -m agent_mem_bridge task-brief --namespace project:demo --query "release handoff" --format markdown
<venv-python> -m agent_mem_bridge activation-receipt --namespace project:demo --correlation-id activation-demo-001 --format markdown

review-queue shows staged candidates, review receipts, tombstones, stale records, and quarantined claims. review-workflow turns those queue items into explicit human decision prompts and manual steps. task-brief composes existing task-memory assembly, review queue items, and active signals into Used, Ignored, and Needs Review sections. activation-receipt reads existing rows for one namespace and correlation id and emits a sanitized declared-provenance receipt. These reports perform no automatic durable writeback.

Static-schema client compatibility

Some MCP clients generate one static input schema per tool and may send signal-only fields on kind="memory" paths: for example ttl_seconds or expires_at on store, and signal_status on recall, browse, or export. AMB drops those fields at the MCP transport boundary before creating or querying memory records. The lower-level memory store contract stays strict: durable memory and coordination signals remain separate lanes, and real signal lifecycle fields still belong only to kind="signal" operations.

Proof Snapshot

0.25.1 hardens Trustworthy Adaptive Retrieval. Retrieval reports whether it is operating as lexical, hashed_lexical, or true semantic; explicit memory text recall can return a short-lived HMAC receipt; and feedback records receipt-bound retrieval outcomes as append-only shadow evidence. The patch additionally rejects non-canonical base64url receipt aliases and stabilizes command-provider validation on Python 3.11. Feedback is not a reranker, memory editor, authentication layer, ACL system, ANN index, graph engine, or automatic policy path.

question_count = 11
memory_expected_top1_accuracy = 1.0
memory_mrr = 1.0
file_scan_expected_top1_accuracy = 0.636
file_scan_mrr = 0.909

sample_count = 16
classifier_exact_match_rate = 0.875
fallback_exact_match_rate = 0.062
classifier_better_count = 13
fallback_better_count = 2
classifier_filtered_low_confidence_count = 2

case_count = 7
flat_case_pass_rate = 0.429
governed_case_pass_rate = 1.0
flat_blocked_procedure_leak_rate = 1.0
governed_blocked_procedure_leak_rate = 0.0
governed_governance_field_completeness = 1.0

signal_contention_case_count = 5
signal_contention_case_pass_rate = 1.0
unique_active_claim_rate = 1.0
duplicate_active_claim_count = 0
active_reclaim_block_rate = 1.0
stale_ack_blocked_rate = 1.0
stale_reclaim_success_rate = 1.0
pending_under_pressure_claim_rate = 1.0
initial_hard_expiry_cap_rate = 1.0

adversarial_case_count = 6
adversarial_task_count = 7
adversarial_governed_task_pass_rate = 1.0
adversarial_governed_blocked_record_leak_rate = 0.0

memory_evolution_case_count = 6
memory_evolution_task_count = 7
memory_evolution_governed_task_pass_rate = 1.0
memory_evolution_governed_blocked_record_leak_rate = 0.0
memory_evolution_governed_disposition_reason_hit_rate = 1.0

review_queue_item_count = 6
review_queue_actionable_count = 6
review_queue_hidden_lane_count = 2
review_queue_writeback_plan_count = 6
review_queue_no_auto_mutation = true
review_queue_public_mcp_surface_change = false
review_queue_item_type_count = 6

review_workflow_source_queue_item_count = 6
review_workflow_item_count = 6
review_workflow_manual_step_count = 27
review_workflow_requires_human_count = 6
review_workflow_auto_write_count = 0
review_workflow_no_auto_writeback = true
review_workflow_public_mcp_surface_change = false
review_workflow_item_type_count = 6

task_brief_used_count = 2
task_brief_ignored_count = 1
task_brief_needs_review_count = 4
task_brief_review_queue_item_count = 2
task_brief_active_signal_count = 1
task_brief_no_auto_writeback = true
task_brief_public_mcp_surface_change = false
task_brief_needs_review_source_type_count = 3

v019_case_count = 12
v019_pass_count = 12
v019_pass_rate = 1.0
v019_retrieval_case_count = 4
v019_retrieval_pass_rate = 1.0
v019_task_brief_case_count = 4
v019_task_brief_pass_rate = 1.0
v019_first_run_adoption_case_count = 4
v019_first_run_adoption_pass_rate = 1.0
v019_public_mcp_tool_count = 10
v019_public_mcp_surface_change = false
v019_client_config_write_count = 0
v019_durable_writeback_count = 0
v019_amh_required = false
v019_native_memory_comparison_required = true

v020_case_count = 6
v020_pass_count = 6
v020_pass_rate = 1.0
v020_import_sanity_pass = true
v020_stdio_round_trip_pass = true
v020_first_run_pass = true
v020_task_brief_pass = true
v020_public_mcp_tool_count = 10
v020_public_mcp_surface_change = false
v020_client_config_write_count = 0
v020_explicit_demo_memory_write_count = 1
v020_explicit_demo_signal_write_count = 0
v020_non_demo_durable_writeback_count = 0
v020_amh_required = false
v020_external_vendor_adoption_claim = false

v021_case_count = 20
v021_category_count = 4
v021_flat_baseline_hazards = 17
v021_flat_baseline_hazards_expected = 17/20
v021_governed_case_pass_count = 20
v021_governed_failures = 0
v021_governed_failures_target = 0/20
v021_governed_checkpoint_passes = 40
v021_governed_checkpoint_passes_target = 40/40
v021_governed_checkpoint_result_count = 40
v021_useful_current_retention_pass = true
v021_suppress_all_can_pass = false
v021_public_mcp_tool_count = 10
v021_public_mcp_surface_change = false
v021_auto_writeback_count = 0
v021_config_write_count = 0
v021_durable_live_writeback_count = 0

Full proof details are in benchmark/README.md.

Boundaries

AMB is not a graph database, general unlearning system, hosted memory platform, scheduler, worker runtime, distributed lock, exactly-once coordination system, packet API, automatic policy engine, compliance certification, authenticated identity system, or unreviewed durable writeback path from raw transcripts. It is a small local bridge for reusable engineering memory and lightweight coordination. forget remains an explicit mutating operation; governed change makes that operation more conservative and auditable rather than automatic.

For alternatives and trade-offs, see docs/COMPARISON.md.

Docs

• Client integrations
• Configuration
• Authority contract
• Trust boundary
• Agent install protocol
• Benchmark and proof harness
• v0.25.1 announcement
• Release communications
• Context assembly
• Memory taxonomy
• Promotion rules
• Client provenance
• Examples
• Contributing
• Security

License

MIT. See LICENSE.