blueshark-forge 0.6.0

A model-agnostic agentic runtime for the terminal — any local model becomes a capable agent. The intelligence lives in the harness, not the weights.

blueshark-forge

A model-agnostic agentic runtime for the terminal. Any model, frontier or a small local one, becomes a capable agent, because the intelligence lives in the harness, not the weights. And every forge session is part of a fleet: they verify each other's work, coordinate, and share what they learn.

Not tied to any vendor. Runs on your machine, on your models.

---

Install

Requirements: Python 3.10+ and an inference engine (Ollama is the easy default).

# 1. install forge
pipx install blueshark-forge          # recommended (isolated); or: pip install blueshark-forge

# 2. install an engine to run models locally — Ollama is the simplest
#    macOS/Linux:  https://ollama.com  (download, then it runs in the background)
#    check it's up:  ollama --version

Set up (once per machine)

forge setup

This inspects your machine and configures forge for it:

• detects your RAM / chip / cores,
• picks a model ladder sized to your hardware (e.g. 8GB → a 3B; 16GB → 9B; 48GB Apple Silicon → qwen3-coder:30b → qwen3.6),
• pulls those models via Ollama,
• sizes the context window to your RAM,
• writes it all to ~/.forge/config.json.

Non-interactive: forge setup --auto.

Using something other than Ollama

forge speaks the OpenAI-compatible protocol that vLLM, llama.cpp, MLX, LM Studio, TGI, SGLang, and cloud APIs all serve — great for a workstation/cluster or remote inference. Choose it interactively in forge setup, or configure directly:

# point at a vLLM server (or any OpenAI-compatible endpoint)
forge setup --engine vllm \
  --url http://your-server:8000/v1 \
  --models "Qwen/Qwen2.5-Coder-32B-Instruct"

# a cloud API
forge setup --engine openai --url https://api.openai.com/v1 \
  --api-key sk-... --models "gpt-4o-mini,gpt-4o"

Engines: ollama (default) · vllm · llamacpp · mlx · lmstudio · tgi · sglang · openai. Set OPENAI_API_KEY in your env instead of --api-key if you prefer.

Use it

cd your-project
forge                       # interactive chat, oriented in this repo

Then just talk to it — it already knows your files, git state, and machine:

❯ what does this project do?
❯ read @src/auth.js and explain the login flow
❯ fix the failing tests
❯ add a --dry-run flag to the CLI and update the README

It works autonomously: it picks the files, makes the changes, runs the tests to verify, and reports back — only asking when it genuinely needs you.

In the chat:

• Esc — clear the input line, or (mid-run) stop the agent
• @path — pull a file's contents into your message
• /model — switch models live · /config — show settings · /plan — current plan
• Ctrl-D — quit

One-shot (non-interactive), great for scripts:

forge run "fix the type errors in src/ and run the build"

Commands

forge                       chat with an agent in the current repo
forge run "<task>"          run one task to completion, autonomously
forge setup                 detect hardware / choose engine / write config
forge status                show every live forge session and what it's doing
forge send <target> <msg>   message another running session
forge up  /  forge down     start / stop the fleet autopilot (verify + coordinate + learn)
forge receipts              trust audit trail — verdicts on "done" claims
forge learnings [dir]       durable facts forge has learned about a repo
forge trace [sid|last]      replay a session's step trace as a table
forge bench [--report]      harness-lift eval: same model bare vs full harness
forge replay [sid|last]     re-drive a recorded session through the harness, no model
forge --version

Flight recorder + replay

Every step's raw model output — malformed ones included — is logged into the session transcript. forge replay <sid> re-drives a real agent loop from those raws with no model and no GPU, then reports the first step where a changed harness diverges (a different gate/action/compaction point) and the terminal state — so a harness change is validated against real small-model behavior at zero inference cost. forge replay <sid> --to-fixture <name> snapshots the session's raws into tests/fixtures/<name>.jsonl, and tests/test_replay.py sweeps every fixture as a regression test. --strict also asserts each recorded prompt digest matches (loose, the default, is robust to prompt-wording changes). Set FORGE_RECORD=<path> to additionally mirror every model call into a {digest, raw, prompt_tokens} cassette. Replay reconstructs the harness-decision path; full fidelity of the file-system half needs a workspace snapshot (a setup.sh, like the bench fixtures), so replay runs in a throwaway dir and never touches your files.

Harness-lift benchmark

forge bench measures what the harness buys, not the weights: it runs each task fixture (bench/<task>/ — prompt.txt + optional setup.sh/verify.sh) through the real agent loop twice, once bare (every scaffolding lever off) and once with the full harness, and prints the pass-rate lift. Per-lever ablation flags (--no-compact, --no-loop-detect, --no-read-gate, --single-rung) drop one lever from the full set so you can see which lever earned its complexity. Results append to ~/.forge/bench/results.jsonl.

Honest framing: "bare" turns off constrained decoding, but the loop still demands a JSON action every step and gives up after 5 malformed replies — so a bare pass-rate substantially measures format compliance (can the raw model hold the action contract at all). That is exactly the harness-lift story worth telling: the scaffolding is what makes a small local model usable.

In the chat

• Modes (shift+tab cycles, or /mode auto|plan|manual):
  • auto — acts freely, no questions (the default)
  • plan — read-only: investigates, then presents a plan for approval
  • manual — asks before every mutating action: y yes once · a always (saved — that action type won't ask again) · n no
• Queue messages while it works — just keep typing; Enter delivers your message to the agent between steps (it steers mid-task). Anything not absorbed becomes the next turn.
• /files — folder explorer — a three-pane Miller-column browser (parent · current · preview) right in the terminal: ↑↓ move, ←→ navigate, Enter on a file attaches it to your next message as @file, . shows hidden files, q closes.
• Esc clears the line, or stops the agent mid-run (twice force-returns).

One fleet with Claude Code

If Claude Code runs on the same machine with a fleet channel (~/.claude/fleet), forge joins that network automatically — no configuration:

• Unified board — forge status lists Claude Code sessions alongside forge sessions (and Claude Code's fleet board sees forge sessions).
• Cross-runtime messaging — forge send <target> <msg> and the agent's fleet_send action reach Claude Code sessions; Claude Code's fleet_send reaches forge sessions. Messages land mid-work, as if from a teammate.

forge speaks the Claude fleet's wire protocol directly: every forge session registers in the shared inbox registry (tagged kind: "forge") and accepts the fleet's authenticated POST /send. forge setup checks the interop on any machine and prepares what's safe (shared token), reporting exactly what works. Without Claude Code, forge's native fleet works standalone.

Why

Claude Code, Codex, and the rest are excellent, but each locks you to one provider's harness. forge is the harness itself, opened up: point it at Gemma, Qwen, your own model, or a frontier API, and you get the same agentic loop, tools, and multi-agent fabric.

The bet: move the agentic scaffolding out of the model's weights and into the harness, and even a 9B becomes a real agent. The levers:

• Constrained decoding — every model output is grammar-forced to a valid tool call (Ollama format schema). A small model literally cannot emit a malformed call.
• Bounded steps — the harness holds the loop; the model does one thing per turn.
• Loop detection — repeated no-progress actions are broken automatically.
• Autonomy scaffolding — task mode tells the model to act, not ask.
• Verify-on-done — a claim of "done" is checked, never trusted.

Workspace + computer awareness (like a real coding assistant): on start, forge builds a gitignore-aware map of the project, detects the language/project type, reads the git state, and learns the machine it's on (OS, shell, tool versions), all pinned into context. Say "fix the auth bug" or "read this @file" and it already knows where things are. It also inherits whatever the fleet has learned about the repo.

Frontier agent loop: a living plan (todo list the agent maintains and the harness pins each turn), surgical edit_file (not fragile full rewrites), self-correction (failed actions are flagged so the model diagnoses), loop-breaking, and context compaction for long sessions.

Local model router (escalation ladder): --model a,b,c is a ladder of local models, cheapest first. forge runs on the fast one and, when it detects it's stuck (the same command failing repeatedly), automatically escalates to a stronger LOCAL model with full context and keeps going — no cloud, no vendor. The default is gemma2:9b → qwen2.5-coder:7b → qwen3.6. Threshold tunable via FORGE_STUCK_THRESHOLD. This is the whole "local can be enough" bet: a smart harness routing across small models beats one big call for most work, and stays on your machine.

Alive terminal: a spinner while it thinks, a live plan panel, and clean per-step rendering with timing and pass/fail.

Proven: Gemma-9B, fully local, autonomously fixes a multi-bug repo through forge (read → fix → run tests → confirm). The reliability tracks task crispness — a clear verification signal (tests) makes small models solid; open-ended judgement still wants a bigger model, which is why the fleet's verifier routes to one.

Use

forge                          chat with an agent in the cwd (default model)
forge --model gemma2:9b        pick any Ollama model, or openai:model@url
forge run "<task>"             one-shot: run a task to completion, autonomous
forge status                   autopilot state + live sessions

The fleet (multi-agent) layer — native, because forge owns its own sessions:

forge up                       start the autopilot (TRUST + COORDINATE + LEARN)
forge down                     stop it
forge send <target> <msg>      message another session (it absorbs it mid-work)
forge receipts                 trust audit trail — verdicts on "done" claims
forge learnings [dir]          durable facts learned in a repo
forge trace [sid|last]         replay a session's per-step trace as a table
forge bench [--report]         harness-lift eval: same model bare vs full harness
forge replay [sid|last]        re-drive a recorded session through the harness, no model

Architecture

forge (one per terminal)
  repl / run  →  agent loop (the harness brain)
     · backend:  any model (Ollama · OpenAI-compatible · your own)
     · tools:    bash / read_file / write_file / list_files
     · levers:   constrain · bounded steps · loop-break · autonomy
     · session:  transcript + registry + native inbox
        │  many forge sessions
        ▼
forged (the fleet autopilot, native to forge)
     TRUST      independent verifier agent disproves "done" claims (routes to
                a capable model; read-only, cannot edit what it judges)
     COORDINATE warns two sessions editing the same file
     LEARN      harvests durable repo facts, shares them across sessions
     MESSAGE    session-to-session, via each session's inbox

Because forge owns the transcript format, the registry, and the inbox, the fleet is built in, no external channel API, no reading someone else's logs. This is the same fleet system first prototyped on Claude Code, now native and vendor-free.

Layout

forge/
  backends.py   model-agnostic backends (Ollama + OpenAI-compatible) + routing
  tools.py      tools (bash/read/write/edit/grep/glob/fleet_send) + action schema
  agent.py      the agent loop (harness brain) + levers + context management
  workspace.py  workspace + machine awareness (file tree, project type, git, tools)
  session.py    transcript · registry · token-authed inbox · locking
  repl.py       interactive chat + slash menus
  tui.py        raw-mode line editor (Esc to clear/stop) + interrupt watcher
  fleet.py      verify · coordinate · learn · message primitives
  daemon.py     forged — the autopilot loop
  config.py     per-machine config (~/.forge/config.json)
  setup.py      the installer (hardware detection, engine choice, model pulls)
  __main__.py   the CLI
~/.forge/       runtime: sessions/ · registry.json · learn/ · verdicts.jsonl (mode 0700)

Development

git clone https://github.com/hackspaces/blueshark-forge && cd blueshark-forge
python -m unittest discover -s tests    # 34 tests, stdlib only, no deps
./forge-cli                             # run from the checkout without installing

CI runs the suite on every push across Python 3.10–3.13. Contributions welcome.

Security & trust model

forge runs on your machine with your privileges — treat it like any coding assistant that can edit files and run commands.

• The file tools (read/write/edit/grep/glob) are confined to the working directory. The bash tool is intentionally not sandboxed — it runs arbitrary shell commands as you, on purpose (that's what a coding agent needs). Run forge in repos you trust, or use OS-level sandboxing for untrusted code.
• The fleet inbox (session-to-session messaging) is localhost-only and token-authenticated: only real forge sessions (which can read the private ~/.forge/registry.json, mode 0600) can message each other. ~/.forge is 0700.
• The autopilot (forge up) runs a repo's own test command to verify "done" claims. It does this on an isolated copy, but it does execute the project's test script — only run forge up over repos you trust.

Found a security issue? Please open an issue (or email the maintainer).

Contributing

See CONTRIBUTING.md. In short: fork, branch, add tests, open a PR against main. main is protected — changes land through reviewed PRs with green CI, not direct pushes.