CONTEXT FORGE
Formally-verified safety for multi-agent LLM pipelines. 0 / 1,210 INV-15 violations, Z3-proved in 10.08 ms. AMD MI300X-native.
−44.4 % prompt tokens3.55 × KV-cache VRAM
Docs
Apohara · honest tools, 2026
Honest tools for the
AI agents that
write your code.
Six offline Rust binaries and one proof layer for AI coding agents. Search, guardrails, receipts, compliance, verification, signal. No cloud. No telemetry. No model downloads.
Dual MIT/Apache-2.0
Open source. No SaaS lock-in. Fork it, ship it, own it.
Rust · deterministic
Same input, same output. No LLM in the hot path.
MCP-native
Plug into Claude Code, Cursor, OpenCode, Codex.
Two offline Rust binaries and one proof layer. EU AI Act Art. 12 L2 ready by default. Browse the toolkit →
APOHARAOFFLINE-FIRSTRUSTDETERMINISTICZERO TELEMETRYOPEN SOURCEMCP-NATIVE
APOHARAOFFLINE-FIRSTRUSTDETERMINISTICZERO TELEMETRYOPEN SOURCEMCP-NATIVE
01
The offline toolkit.
Six focused Rust binaries plus one proof layer. Each ships and stands alone.
ARGUS
Real-time signal & chain-of-custody for agent actions.
Open analyzerAGENTGUARD
Command-safety gate + seccomp / Landlock sandbox.
DocsCODESEARCH
Offline hybrid BM25 + vector MCP. One SQLite file.
DocsSEALCHAIN
HMAC + Ed25519 + C2PA receipts. Tamper-evident provenance for every agent action.
DocsCOMPLIANCE
OWASP-A + NIST + ISO mapping. SARIF, CI-ready.
Docs
02
Engineering positioning.
The role of AI coding agent implementer exists, pays between 45.000 € and 90.000 €/year in Spain, and nearly nobody can do it for real.
01
Criteria · design systems, isolate failures, define tests and audit decisions.
02
Honest level · start from zero with the tools, but familiar with informatics helps.
03
Depth · the included Python & JS course takes you further after.
03
The uncomfortable truth.
These numbers aren't ours. They come from people who actually measured.
95%
of generative-AI initiatives in enterprise fail to deliver measurable return.
› MIT study, 2025
+19%
slower. What senior devs took longer with AI "acceleration" in a controlled study.
› METR, 2025
40%
of agentic AI projects will be cancelled before 2028: cost and value unclear.
› Gartner
Top 10
LLM risks exist for a reason: prompt injection, data leaks, over-permissioned agents.
› OWASP LLM
The AI doesn't fail for lack of power. It fails for lack of criteria.
DeterministicNo LLM in the hot path — same input, same output, every time.
Air-gappedThe whole toolkit runs offline. No model downloads, no API calls.
Honest benchmarksPublished limits. The wins and the trade-offs, side by side.
Audited by usWe publish our own internal audit, not just our wins.
No magic. No marketing. Just tools that say what they do — and what they don't.
Apohara is built on one rule: claim only what the code can back. Every tool ships its benchmark, its threat model, and an honest scorecard of where it stops. Better to under-promise and let the code earn the trust.