bourdon 0.3.0

Cross-agent memory federation for human-AI collaboration

Bourdon

bourdon.ai · Cross-agent memory federation for human-AI collaboration.

Current AI memory systems are call-and-repeat — discrete turns with nothing happening in between. Real human language is concurrent — listeners recognize, recall, and formulate while speakers are still speaking. Bourdon is the engineering translation of that concurrent structure into AI systems.

"We used our minds to make minds that make our minds better."

Named for the bourdon — the deep continuous drone of a pipe organ, the foundational tone that holds every other voice in place. (The lineage continues from the original basso continuo metaphor — the Baroque bass accompaniment — chosen when the project was named Continuo before the 2026-05-05 rebrand. Same music-theory family, tighter metaphor.)

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Status: Pre-Alpha (v0.3.0, BSL 1.1)

Project renamed Continuo → Bourdon on 2026-05-05 (v0.1.0); relicensed MIT → Business Source License 1.1 on 2026-05-06 (v0.2.0). See release notes for migration. The version-by-version history below covers v0.0.1 through v0.0.7 in detail; for v0.0.8 and later see GitHub Releases.

Earlier version history

• v0.0.1 -- initial scaffold, Phase 1 orchestrator working standalone
• v0.0.2 -- L5 JSON Schema + adapter contract, base adapter module, first external adapter stub (Claude Code, discovers memory sources), test suite (49 tests), CI workflow (Windows + Ubuntu + macOS x Python 3.10-3.12)
• v0.0.3 -- Claude Code adapter full parsing: PROJECTS//OVERVIEW.md -> project entities, LOG/.md -> sessions, auto-memory frontmatter -> entities, memory.jsonl knowledge graph -> entities. Entity dedupe across sources. Conservative visibility policy.
• v0.0.4 -- L2 UltraRAG async integration. core/l2.py with L2Config (YAML + env-var overrides), L2Client Protocol, FastMCPL2Client, query_l2() that never blocks / never raises. Disabled by default; opt in via core/l2_config.yaml or BOURDON_L2_ENABLED=true. Optional extra: pip install 'bourdon[ultrarag]'.
• v0.0.5 -- L6 MCP server. The federation layer. core/l6_store.py loads every ~/agent-library/agents/*.l5.yaml, builds a cross-agent entity index, and exposes query primitives (list_agents, find_entity, list_recent_work, get_cross_agent_summary) with visibility filtering re-applied at query time. core/l6_server.py wraps the store in a fastmcp server exposing agent-library:// resources + query_agent_memory / list_recent_work / find_entity / get_cross_agent_summary MCP tools. Launch via python -m core.l6_server. Optional extra: pip install 'bourdon[server]'. 33 new tests (149 total): store query semantics, private-entity filter, reload behavior, lazy-import guard, server construction.
• v0.0.6 (this release) -- Codex adapter + atomic L5 write. adapters/codex.py reads ~/.codex/session_index.jsonl newest-first, resolves each entry's rollout file to pull session_meta.cwd, emits Session rows. Dedupes thread_names into topic-type Entity rows with last_touched preserved. Registered under bourdon.adapters entry point. New core/l5_io.py provides write_l5() / write_l5_dict() with tmp+rename atomic semantics so L6 file watchers never see half-written manifests. 39 new tests (188 total): session_index parsing, rollout resolution, timestamp normalization, dedupe, schema round-trip, Codex L5 round-tripped through L6Store end-to-end.
• v0.0.7 -- Generic Codex memory pipeline + first-class CLI. adapters/codex.py now treats ~/.codex/memories/* as the primary distilled source, enriches with rollout chronology and structured apply_patch file evidence, and defaults Codex-derived entities/sessions to team visibility. New bourdon codex export, bourdon codex build-context, and bourdon codex eval commands turn that normalized model into L5 federation output plus Codex-oriented L0/L1 timing artifacts. core/l6_store.py and core/l6_server.py now support access_level=public|team|private while preserving include_private compatibility. Plus agent.role_narrative -- new optional L5 schema field that differentiates agents sharing the same type slug (Claude Code = manager; Codex = lead author; Cursor = debugger; Cline = throwaway; Clyde = general-purpose). Inspired by Intrinsic Memory Agents. Both shipping adapters populate it; Clyde publisher does too. Plus temporal validity windows (valid_from / valid_to ISO 8601 dates on Entities, Zep-Graphiti-inspired) so federation queries can answer "what was active in Q1 2026?" not just "what's in memory?". Plus bourdon claude-code export subcommand designed for SessionEnd hook use -- writes the Claude Code L5 manifest to ~/agent-library/agents/claude-code.l5.yaml silently, never raises, exits 0 in all failure modes. Wire it into ~/.claude/settings.json:

{
  "hooks": {
    "SessionEnd": [
      { "command": "bourdon claude-code export" }
    ]
  }
}

Plus spec/POSITIONING.md stakes the recognition-first thesis publicly, and spec/RELATED_WORK.md maps Bourdon's vocabulary to the wider field (Mem0, Zep, Letta, Cognee, Memora, SCS, Intrinsic Memory Agents, G-Memory, H-MEM, MCP roadmap). And core/recognition_runtime.py ships the first concrete implementation of the recognition-first runtime: synchronous template-based recognition string + concurrent L1 hydration awaitable, ≤3s timeout budget, never raises. This is the headline behavior the FINDINGS_JOURNAL flagged on 2026-04-19.

Not ready for production use. Built in the open as a spec-and-reference-implementation for a convention we hope the ecosystem adopts.

What It Is

A tiered, timing-aware memory protocol for any human-AI collaboration where context matters over time:

• Developer workflows — memory across Claude Code, Codex, Cursor, Copilot
• Customer support operations — cross-tool customer intelligence
• Scientific research — lab notebook continuity across sessions and team members
• Creative writing, architecture, project management, education — and anywhere else context accrues

One architecture, many domains. Content is always domain-specific; cognition is universal.

The Memory Stack

Per-agent personal memory:
  L0 — Hot Cache          always in system prompt, ~3K tokens
  L1 — Entity Synopses    triggered on L0 keyword hit, parallel loaded
  L2 — Episodic Memory    async retrieval during human response time
  L3 — Indexed History    on-demand searchable session logs
  L4 — Raw Archive        verbatim conversation history

Cross-agent federation:
  L5 — Agent Memory Manifest    per-agent public glossary (a projection of L0-L4)
  L6 — Federation Library       aggregates all L5s, exposed as MCP server

See spec/ARCHITECTURE_v0.1.md for the full architecture doc.

Quick Start (Phase 1 Orchestrator)

# From a local clone:
cd core/
python -c "
import asyncio
from orchestrator import Bourdon

async def main():
    memory = Bourdon()
    base = 'You are a helpful AI assistant.'
    prompt = await memory.prepare('Let us work on Bourdon today', base)
    print(prompt)

asyncio.run(main())
"

This loads the L0 hot cache and any matching L1 synopses, then prints the fully-assembled system prompt ready to pass to an Ollama / OpenAI / Claude API call.

Quick Start (Codex CLI)

bourdon codex export --access-level team
bourdon codex build-context --out-dir ./build/codex-context
bourdon codex prepare-turn --memory-md "Can we keep working on Bourdon?"
bourdon codex eval --fixtures

This generic Codex path is designed for org-wide distribution: local Codex memories stay team by default, public federation requires explicit promotion, and generated L0/L1 artifacts live separately from the repo's static Clyde examples.

Quick Start (Cross-Agent Recognition)

bourdon prepare-turn "Can we keep working on Bourdon?" --access-level team
bourdon deeper-context "Can we keep working on Bourdon?" --access-level team
python -m core.l6_server --transport stdio

prepare-turn reads the L6 federation library and returns immediate recognition plus a bounded prompt fragment. deeper-context is the companion L2 retrieval surface; it returns empty context when L2 is disabled.

Quick Start (Hybrid Memory Cycle)

powershell -ExecutionPolicy Bypass -File scripts/bootstrap-bourdon-mcp.ps1 -WorkspaceRoot "."
powershell -ExecutionPolicy Bypass -File scripts/run_memory_cycle.ps1 -WorkspaceRoot "." -SchemaPath ".\spec\L5_schema.json"

What this does:

• Builds and validates hybrid memory indices.
• Exports L5 manifests to workspace + ~/agent-library/agents/.
• Runs MCP smoke assertions against the L6 server.
• Writes machine-readable reports:
  • .cursor/memory/reports/mcp-smoke-report.json
  • .cursor/memory/reports/memory-cycle-report.json

Docs:

• docs/getting-started-memory-cycle.md
• docs/good-first-issues.md
• docs/agent-integration-status.md

Hybrid Memory Tooling

Helper scripts:

• scripts/bootstrap-bourdon-mcp.ps1
• scripts/doctor.ps1
• scripts/migrate_short_index.py
• scripts/validate_short_index.py
• scripts/build_bourdon_l5.py
• scripts/mcp_smoke_test.py
• scripts/regression_matrix.ps1
• scripts/run_memory_cycle.ps1

CI guardrails:

• python scripts/migrate_short_index.py --workspace-root "." --check
• python scripts/validate_short_index.py --workspace-root "."
• powershell -ExecutionPolicy Bypass -File scripts/regression_matrix.ps1 -WorkspaceRoot "."

If CI fails on migration --check, run local migration and commit normalized files:

python scripts/migrate_short_index.py --workspace-root "."
python scripts/validate_short_index.py --workspace-root "."

Run one-command preflight before full cycle:

powershell -ExecutionPolicy Bypass -File scripts/doctor.ps1 -WorkspaceRoot "." -InstallMissingDeps -RunRegressionMatrix

Roadmap

• v0.0.1 (now) — Scaffold + Phase 1 orchestrator (L0 + L1, manual files, Ollama-compatible)
• v0.1.0 — L2 UltraRAG async integration + session-close L5 export
• v0.2.0 — Relicense MIT → BSL 1.1
• v0.3.0 — Codex operational layer: memory doctor + fallback recognition + L6 prep
• v0.4.0 — pip install bourdon + bourdon init/up/query CLI; Clyde + Clair native L5 publishers
• v1.0.0 — Docs site, community adapter contributions, public launch
• v1.x — Cursor adapter (SQLite reverse-engineering), Copilot adapter (partial coverage), framework adapters (LangChain, CrewAI, AutoGen)

Adapter Compatibility

Agent	Difficulty	Status
Clyde	Native	Planned
Clair	Native	Planned
Claude Code	Native + Adapter	Export hook available
Codex	Moderate	Fallback + prepare-turn available
Cursor	SQLite	Adapter available; bourdon cursor export
Cline	Unknown	Blocked pending native store path/schema
Copilot	Deferred v1.x	Encrypted reasoning, no session index

Philosophy

See spec/THESIS.md (canonical copy lives in the project's claude-brain repo) for the founding argument.

See spec/USE_CASES.md for eight worked domain scenarios beyond developer workflows.

Contributing

Bourdon is source-available under the Business Source License 1.1 (auto-converts to Apache 2.0 after four years per version). Free for solo developers, internal/non-competing commercial use, research, and education. Commercial license required for hosted-service offerings that compete with RADLAB LLC's paid versions. See LICENSE for the legal text and LICENSE_FAQ.md for plain-English guidance. Contributions welcome — see CONTRIBUTING.md.

About

Bourdon is an open-source memory protocol and reference implementation seeded by RADLAB LLC. Designed with Ryan Davis (RADMAN), with major research and implementation contributions from Claude and Codex.

Contributors

• Ryan Davis -- creator, thesis, architecture, implementation direction
• Claude -- thesis drafting, architecture planning, early implementation
• Codex -- Codex adapter expansion, CLI implementation, timing-artifact generation, access-level model
• OpenAI Codex 5.3 -- hybrid memory cycle tooling, MCP smoke assertions, CI/report automation, starter template packaging

Other RADLAB Projects

Because you found us here, you might like to check out:

• ILTT — AI fitness automation for personal trainers (iltt.app)
• PRUN — Privacy-first encrypted password manager (prunpassword.com)
• Castmore — Cross-platform streaming discovery
• OMNIVour — Universal file conversion with AI extras

License

Business Source License 1.1, auto-converts to Apache License 2.0 four years after each version is published. See LICENSE for the full text and LICENSE_FAQ.md for guidance on what's permitted. Commercial licensing inquiries: [e-mail].

Versions v0.0.1 through v0.1.0 were published under MIT and remain MIT in their distributed form. Relicensing to BSL 1.1 applies from v0.2.0 onward.