Helix Context

Coordinate-index engine for LLM agents. Retrieves, weighs, and compresses your codebase into a context window — without a single LLM call on the retrieval path.

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Proof (30 seconds)

WIP benchmark numbers — compressor disabled (default LLM-free config), N=15 query shapes, May 2026:

metric	tokens	vs standard RAG (top-5 @ 1500)
median	2,757	2.9× fewer tokens
best (focused query)	1,410	5.7×
worst (broad 12-doc)	3,755	2.1×

With the optional compressor enabled (Claude Haiku splice), median improves to ~5×. In multi-turn sessions, the session delivery register elides already-seen documents — observed 37× reduction on repeated retrievals within a conversation.

Reproducer: python benchmarks/bench_rag_vs_sike_tokens.py against your own genome.

Agent contract: every /context response carries know { found, confidence } (grounded — you may answer) or miss { reason, escalate_to } (not found — don't answer from genome). Stale results downgrade to miss(reason="stale"|"cold"|"superseded") via the freshness gate.

Get started (60 seconds)

# 1. Install
pip install helix-context
python -m spacy download en_core_web_sm

# 2. Ingest your codebase
helix ingest path/to/your/project/ --recursive

# 3. Query it
helix query "how does the splice step work?"

# 4. Or start the proxy for IDE integration
helix-server   # binds to 127.0.0.1:11437

For extras matrix, BGE-M3 backfill, and tray setup: docs/SETUP.md.

Agent surfaces

Three ways to drive Helix — same retrieval primitives, same JSON shapes:

Surface	Best for	Example
CLI	Scripts, CI, cold-start agents	helix query "..." --json
MCP	Claude Code, Cursor, Claude Desktop	Add to settings.json
HTTP proxy	Continue IDE, OPENAI_BASE_URL redirect	POST /context

# CLI — no server, no daemon, subprocess-drivable
helix query    "what does the splice step do?" --json
helix packet   "edit the splice step" --task-type edit --json
helix gene get abc123 --json
helix neighbors "splice step" --k 10 --json
helix refresh-targets "edit the splice step" --json
helix status
helix diag corpus

Full CLI reference: docs/clients/cli.md. MCP tool schemas: docs/api/mcp-tools.md.

Pipeline (2 minutes)

Seven stages per turn, all LLM-free except optional splice:

  query
    │
    ▼
┌──────────────┐
│ 0. Classify  │  rule-based: decoder mode + assembly cap
└──────┬───────┘
       ▼
┌──────────────┐
│ 1. Extract   │  heuristic keyword + entity extraction
└──────┬───────┘
       ▼
┌──────────────┐  FTS5 BM25 + BGE-M3 dense (1024-dim) + tags
│ 2. Retrieve  │  + synonym expansion + co-activation + SR
│              │  ranked via RRF or additive fusion
└──────┬───────┘
       ▼
┌──────────────┐
│ 3. Re-rank   │  CPU classifier scores (optional)
└──────┬───────┘
       ▼
┌──────────────┐
│ 4. Splice    │  Headroom Kompress (CPU) or LLM compressor
└──────┬───────┘
       ▼
┌──────────────┐  token budget + legibility headers (fired tiers,
│ 5. Assemble  │  confidence ◆/◇/⬦, compression ratio) +
│   + Stage 7  │  freshness gate (stale/cold/superseded → miss)
└──────┬───────┘  + session delivery (elide already-seen docs)
       ▼
┌──────────────┐
│ 6. Persist   │  query+response → knowledge store (background)
└──────┘───────┘
       ▼
   know { } or miss { }

• know/miss contract: know means the context is grounded, agent may answer. miss means don't answer from genome — escalate via escalate_to tools or refetch from refresh_targets.
• Caller model class: /context accepts caller_model_class: "generic" | "small_moe" | "frontier" to select render branch (ordering, assembly cap, decoder mode). See docs/api/context-endpoint.md §7.

Configuration (17 sections in helix.toml)

Section	Key settings
[ribosome]	enabled, backend ("none" / "litellm" / "claude" / "deberta"), query_expansion
[hardware]	Device auto-detection (CUDA → ROCm → MPS → CPU)
[budget]	expression_tokens (7k default), max_genes_per_turn, splice_aggressiveness, legibility_enabled, session_delivery_enabled
[session]	Synthetic session windows, default party_id
[genome]	path (genomes/main/genome.db), compact_interval, replicas
[server]	host, port, upstream
[headroom]	Optional Headroom proxy lifecycle
[ingestion]	backend ("cpu" / "ollama"), splade_enabled, entity_graph
[context]	Cold-tier retrieval: enabled, k, min_cosine
[cymatics]	Frequency-domain scoring, harmonic_links, distance_metric
[classifier]	Rule-based query classification thresholds
[retrieval]	fusion_mode ("additive" / "rrf"), SR, ray_trace_theta, seeded_edges
[plr]	Piecewise linear reranker model
[know]	Know/miss calibration: confidence_floor, margin_threshold
[mem_sync]	Auto-memory → helix sync: watch_dirs, interval
[synonyms]	Query expansion map (e.g., "cache" → ["redis", "ttl"])
[abstain]	Low-confidence abstention thresholds

Full reference: docs/config-reference.md.

Full endpoint reference

Core retrieval:

Endpoint	Purpose
POST /context	know/miss + expressed_context (primary)
POST /context/packet	Agent-safe bundle: verified / stale_risk / refresh_targets
POST /context/refresh-plan	Refresh targets only (reread plan)
POST /fingerprint	Navigation-first payload (scores, no body)
GET /context/expand	1-hop neighborhood from a gene_id
POST /v1/chat/completions	OpenAI-compatible proxy

Ingestion + maintenance:

Endpoint	Purpose
POST /ingest	Add content to the knowledge store
POST /consolidate	Rewrite stale docs from source fingerprints
POST /admin/refresh	Force retrieval-layer refresh
POST /admin/vacuum	Reclaim SQLite pages
POST /admin/swap-db	Hot-swap the .db file without restart

Identity + sessions:

Endpoint	Purpose
POST /sessions/register	Register agent participant
GET /sessions	List registered participants
GET /session/{id}/manifest	Session delivery log
POST /hitl/emit	Record HITL pause event

Diagnostics:

Endpoint	Purpose
GET /stats	Corpus metrics + compression ratio
GET /health	Model, doc count, calibration provenance
GET /genes/{gene_id}	Single document detail
GET /debug/resonance	Tier activation profile
GET /metrics/tokens	Token usage counters

Full schema: docs/api/endpoints.md.

Package structure (16 packages, post-PR #90)

Package	Purpose
adapters/	Cache, DAL, external retriever protocol
backends/	Compressor, BGE-M3 codec, DeBERTa, NLI, SEMA, SPLADE
cli/	helix CLI: query, packet, gene, neighbors, ingest, diag, config, status
encoding/	Chunking, fragments, legibility headers, Headroom bridge
identity/	CWoLa logger, session delivery, registry, provenance, claims
pipeline/	Tier logic, stage helpers
retrieval/	Expand, freshness, RRF/additive fusion, PLR, intent router, SR, seeded edges, query classifier
scoring/	Cymatics, know-calibration, know-decision, ray-trace, TCM
server/	FastAPI app factory + route modules (context, ingest, registry, admin)
storage/	DDL, indexes, co-activation graph
telemetry/	OTel metrics, histogram instrumentation
vault/	Obsidian vault export (diagnostic traces)
launcher/	System-tray supervisor
mcp/	MCP tool surface for Claude Code / Desktop
integrations/	ScoreRift bridge

Back-compat shims: genome.py, ribosome.py, server.py, replication.py, hgt.py re-export from new locations. Lexicon: docs/ROSETTA.md.

IDE + MCP integration

MCP setup (Claude Code / Cursor / Claude Desktop)

{
  "mcpServers": {
    "helix-context": {
      "command": "python",
      "args": ["-m", "helix_context.mcp_server"],
      "cwd": "/absolute/path/to/your/project",
      "env": { "HELIX_MCP_URL": "http://127.0.0.1:11437" }
    }
  }
}

Continue IDE

models:
  - name: Helix (Local)
    provider: openai
    model: gemma3:e4b
    apiBase: http://127.0.0.1:11437/v1
    apiKey: EMPTY
    roles: [chat]
    defaultCompletionOptions:
      contextLength: 128000
      maxTokens: 4096

OpenAI-compatible proxy (zero code changes)

OPENAI_BASE_URL=http://localhost:11437/v1 your-app

Knowledge store management

[genome]
path = "genomes/main/genome.db"   # relative to helix run directory

Backup (safe while running — WAL mode):

cp genomes/main/genome.db backups/genome-$(date +%Y%m%d).db

BGE-M3 backfill (one-time, after install):

python scripts/backfill_bgem3_v2.py genomes/main/genome.db

Observability

scripts\setup-grafana-telem.ps1     # Windows
scripts/setup-grafana-telem.sh      # Linux / macOS

Dashboard: http://localhost:3000/d/helix-overview. Full surface: docs/architecture/OBSERVABILITY.md.

Gotchas

• Knowledge store path is genomes/main/genome.db (not project root). Delete to start fresh.
• BGE-M3 backfill is one-time post-install — embedding_dense_v2 IS NULL until you run scripts/backfill_bgem3_v2.py. Low retrieval rate without it.
• Fusion mode defaults to "additive" (back-compat). Flip to "rrf" in [retrieval] after running scripts/calibrate_thresholds.py.
• Session delivery (session_delivery_enabled = true) tracks delivered docs per session, elides repeats. ~40% token savings on multi-turn. Pass ignore_delivered: true in /context body for benchmarks.
• know/miss contract requires the agent prompt fragment to be honored — without it, frontier models confabulate. Import helix_context.agent_prompt.full_fragment().
• Naming lexicon: biology terms (gene, genome, ribosome) have canonical software equivalents (document, knowledge store, compressor). Both work in code; new code uses software terms. See docs/ROSETTA.md.

Testing

python -m pytest tests/ -m "not live" -v   # ~1950 tests, no external services

Documentation

Start here	Go deeper
Setup guide	Pipeline lanes
Troubleshooting	Retrieval dimensions
/context API	Knowledge graph
Config reference	Session registry
Agent SDK fragment	Observability
Operator runbooks	Launcher architecture

Acknowledgments

Built on: spaCy NER · Howard 2005 TCM · Stachenfeld 2017 SR · SQLite FTS5 BM25 · BGE-M3 · Kompress · Headroom

License

Apache-2.0. See NOTICE for third-party attributions.