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
We proved 0\/1,210
INV-15 violations.
Z3-verified.<\/span>
0 / 1,210 violations
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 →
Z3-PROVEDAMD MI300XDOI 10.5281/ZENODO.20277875496 TESTSJUDGE-CLASS SAFE
Z3-PROVEDAMD MI300XDOI 10.5281/ZENODO.20277875496 TESTSJUDGE-CLASS SAFE
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.