banna 0.2.3

A from-scratch, provider-agnostic reasoning agent with a typed state substrate and verifier-guided search. Primary benchmark: GAIA.

banna

A provider-agnostic reasoning agent built from scratch around a typed state substrate and a verifier-guided control loop. It is designed to study where ReAct-style agents fail on the GAIA benchmark and to address those failures structurally rather than through prompt patches.

The core has no agent-framework dependencies (no LangChain, LlamaIndex, or smolagents). The reasoning loop is a typed transition function over (state, action, observation) → state', and each control strategy is a small Policy implementation over that same substrate.

Installation

Requires Python 3.10+.

# From PyPI
pip install banna

# Or directly from GitHub
pip install git+https://github.com/siavashmonfared/banna.git

# Isolated CLI install
pipx install git+https://github.com/siavashmonfared/banna.git

# From a local clone (development)
git clone https://github.com/siavashmonfared/banna.git
cd banna
pip install -e ".[dev]"

Every install path provides a banna (and banna-agent) executable on your PATH.

Quickstart

On first run, banna launches a one-time setup wizard: choose a provider, supply an API key (or select a local Ollama model), and the choice is saved to ~/.config/banna/. Subsequent runs use the saved defaults.

# First run — the setup wizard launches automatically if no config exists
banna

# Override saved defaults with flags at any time
banna --policy react --provider openai --model gpt-5-nano

Example session

$ banna --policy react --provider openai --model gpt-5-nano

● banna · v0.2.2   provider=openai   model=gpt-5-nano   policy=react

> How many studio albums did Mercedes Sosa release between 2000 and 2009?

  thinking…
  ▸ search(query="Mercedes Sosa discography studio albums 2000-2009")
    ↳ 8 results · evidence_id ev_a3f
  ▸ read_url(url="https://en.wikipedia.org/wiki/Mercedes_Sosa")
    ↳ 12.4 kB · evidence_id ev_91c
  ▸ final_answer(answer="3", evidence_ids=["ev_a3f", "ev_91c"])
  verifiers: format ✓  citation ✓  coverage ✓  arithmetic skip

● banna
  3

  3 steps · 4.7s · 1840→210 tok · $0.0021

Subcommands

banna init                       # re-run the setup wizard
banna config get                 # show saved defaults
banna config set model gpt-4o    # change a single default
banna providers                  # list configured providers and status
banna providers --validate       # make a 1-token test call against each

MCP servers

Use tools served by an external MCP server as if they were native tools. Both stdio (local subprocess) and HTTP/SSE (remote) transports are supported.

# register a local stdio server (its tools appear namespaced, e.g. collab.collab_start)
banna config mcp add collab -- python3 /path/to/server.py
# register a remote HTTP server
banna config mcp add remote --http https://example.com/mcp
banna config mcp list            # show configured servers
banna config mcp remove collab   # drop one

Servers connect when the REPL starts and shut down on exit; a server that fails to start is reported and skipped rather than crashing the session. MCP tools run external code, so they go through the same per-call permission prompt as run_shell.

Sessions & resume

Every conversation is auto-saved to ~/.config/banna/sessions/ as it happens, so you can pick up where you left off.

banna --resume          # pick from a list of recent sessions
banna --resume last     # resume the most recent
banna --resume <id>     # resume a specific session

Inside the REPL, /sessions lists them and /resume [id|last] switches. The explicit /save <path> and /load <path> still work for hand-managed transcripts.

Memory

A persistent memory store (~/.config/myagent/memory.jsonl) survives across sessions. The agent can write and search it via the memory tool, and relevant entries are auto-recalled into context on each turn (gated by topical overlap so unrelated facts don't leak in).

Trace viewer

Turn any run's JSONL event log into a self-contained HTML report — every step's reasoning, tool calls and results, parallel batches, and the final answer, in one file with no external assets.

banna trace view runs/<id>/logs/<task>.jsonl        # writes <task>.html
banna trace view <log.jsonl> -o report.html         # custom output path

Policies

A Policy implements a single method, propose(state, llm, tools) → Action; the driver is agnostic to which strategy is running. Two policies are available from the CLI via --policy / /policy (react+ is the default):

Policy	Description
react	The core ReAct loop. One LLM call per tick; the model chooses THINK, TOOL_CALL, or FINAL_ANSWER. Fully autonomous, with no human in the loop. This is the benchmarked baseline.
react+ (default)	ReAct extended for interactive, human-in-the-loop use. Adds an ask_user clarifying-question affordance, a per-tool permission gate for shell commands, and error-scoping prompt guardrails. react+ subclasses react, so it inherits the entire engine unchanged.

react+ is the default because it is built for interactive sessions, where a person is present to answer clarifying questions and approve tool calls. The GAIA benchmark tests neither — there is no human in the loop — so the published numbers below are for the bare react engine.

Architecture

The agent is a typed transition function over an AgentState. A Policy proposes the next Action; the driver executes it (LLM call, tool invocation, or terminal commit); the resulting Observation is folded back into state; Verifiers score any proposed answer; a multi-axis Budget decides when to stop.

Action = THINK | TOOL_CALL(name, args) | ASK_USER(question) | FINAL_ANSWER(answer, evidence_ids)

run_policy : AgentState × Policy × ToolRegistry × LLMClient → AgentState
                ↑                                                ↓
                └────── Policy.propose → execute → observe ──────┘

State

AgentState is the single object every component reads and writes through:

Field	Type	Contents
trace	list[Step]	Append-only log of Step(idx, action, observation, wall_s, tokens, meta). The replay/audit primitive.
evidence	list[Evidence]	Tool-fetched material with an evidence_id: search hits, URL bodies, PDF pages, file reads. Citations point here.
claims	list[Claim]	Propositions the model has asserted, each with supports: list[evidence_id] and per-verifier verdicts.
budget	Budget	Multi-axis tracker: steps, repair_steps, wall_s, tokens, cost_usd. Each axis trips independently.
metadata	dict	Policy-private state (plans, retry counters, user replies, etc.).

Tools

Tools are Callable[[dict], dict] with a ToolSpec schema. Each writes evidence into state.evidence and returns a deterministic dict that the policy reads as its next observation.

Tool	Purpose
search	Web search (DuckDuckGo / Bing / SerpAPI / YaCy backends)
read_url	Fetch and clean HTML to text; HTTP-cache aware
read_file	Generic local file read with magic-byte sniffing
pdf_reader	pypdf text extraction with optional pdfplumber tables
xlsx_reader	openpyxl sheet/cell access
python_sandbox	Run model-emitted Python in a restricted namespace
calculator	Single-expression safe-AST evaluator
grep, list_files	Code- and repo-task primitives
run_shell	Allowlisted shell; gated by a permission prompt under react+
plan	Records a structured plan into state
memory	Reads/writes a persistent skill and fact store
final_answer	Terminal commit; takes answer, reasoning, evidence_ids

Verifiers

Verifiers grade output against checks that do not require an LLM. Each returns a list of ClaimCheck(claim_id, verdict ∈ {ok, fail, warn, skip}, detail, meta). On a fail, meta["nudge"] provides an actionable instruction surfaced to the model on a retry tick.

Verifier	Catches
FormatVerifier	Empty or malformed answer field
ArithmeticVerifier	Wrong math in claims or reasoning (re-evaluates each equality with a safe AST)
CitationVerifier	Claims whose cited evidence does not contain the claimed values; broken evidence_id references
CoverageVerifier	Factual claims with no supporting evidence
CommandVerifier (optional)	Code-task failures via pytest / mypy / ruff; off by default

CitationVerifier checks whether a claim is defensible against the evidence it cited, not whether that evidence is factually correct.

Budget

Budget has five independently-tripping axes so that stuck-loop behavior does not consume budget meant for productive work:

Axis	Bounds
steps_used / max_steps	Productive ticks
repair_steps_used / max_repair_steps	Empty-reply, retry, and forced-tool-choice escape ticks
wall_s	Wall-clock time (excludes time paused on an interactive prompt)
tokens_in + tokens_out	Cumulative LLM tokens
cost_usd	Provider-priced cost

When any axis trips without a committed answer, the driver calls policy.synthesize_on_exhaustion(state) — a time-bounded forced-final_answer call with a cheap fallback chain (last claim → last short text → none) — so the run commits something rather than returning null.

GAIA validation results

Measured with the bare react engine: a full run on gpt-5-nano (165 questions across Levels 1–3), plus a cross-model L3-only probe on claude-sonnet-4-5.

Run	Overall	L1	L2	L3	Cost
react · gpt-5-nano (full, 165 Q)	42.4% (70/165)	49.1%	46.5%	15.4%	~$0.87
react · claude-sonnet-4-5 (L3 only, 26 Q)	—	—	—	26.9% (7/26)	~$20.75

On gpt-5-nano, react finishes 92% of tasks through the normal commit path; the remaining 8% trip a budget axis. Median task finishes in 4 productive steps in under a minute. The Level-3 gap is the chained-reasoning regime: swapping in claude-sonnet-4-5 (same policy, same tools) nearly doubles L3 accuracy (26.9% vs 15.4%) at ~24× the cost — a single-set probe consistent with model capacity, not the scaffolding, being the L3 bottleneck.

Full per-level numbers, exit-reason distributions, operational statistics, reproduction instructions, and an evaluation-limitations section are in docs/evals/gaia_validation_report.md. The full validation runner is in experiments/02_gaia_full/run.py.

A 2×2 ablation crossing model capacity with intrinsic verification — showing that verifier_retry flips from net-negative on gpt-5-nano to net-positive on gpt-5-mini, tracking the verifier's false-positive rejection rate — is in docs/evals/ablation.md (with an explicit statistical-honesty section: the per-model effects are directional, not significant at n=165).

Repository layout

src/banna_agent/
├── core/          AgentState, Trace, Action, Budget, EventLog, run_policy
├── llm/           provider-agnostic LLMClient + adapters (anthropic, openai, gemini, ollama, bedrock)
├── tools/         search, read_url, read_file, pdf/xlsx, python_sandbox,
│                  calculator, run_shell, grep, list_files, plan, memory, final_answer
│   └── mcp/       MCP client (stdio + HTTP/SSE) + JsonTool bridge
├── policies/      react (engine, benchmarked) + react+ (default interactive CLI policy)
├── verifiers/     arithmetic, citation, coverage, format, command (+ base protocol)
├── benchmarks/    gaia/ (loader, runner, scorer, report)
├── memory/        in_memory_store, jsonl_store, skill_library, embeddings
├── trace/         render a run's JSONL event log to static HTML
└── cli/           Rich-based REPL: /policy /budget /show /sessions /resume /save /load …

Tests mirror src/ under tests/. Run them with:

pytest -q

Current status on this branch: 829 passed, 3 skipped (skips require the optional chromadb backend or real API keys).

Limitations

• Execution isolation is opt-in. Code-running tools dispatch through a SandboxBackend. The default process backend runs each call as a host subprocess (real timeout and memory separation, but it inherits the user's filesystem, network, and credentials) — fine for a research harness on your own machine, not for untrusted input. For untrusted input or shared infrastructure, start the agent with --sandbox=docker (or BANNA_SANDBOX=docker): every run_python / run_shell call then executes in a throwaway container with no network, a read-only root filesystem, dropped capabilities, and cpu/memory/pid limits. Because the container has no network, a missing third-party package can't be pip install-ed at runtime; instead the sandbox builds a derived image in a separate, network-enabled build step (which never runs model code) and re-runs the code against it. Packages on a trusted allowlist install with no prompt; anything else prompts for approval in interactive runs. The allowlist ships with a curated, version-pinned default set (numpy, pandas, scipy, sympy, matplotlib, scikit-learn, pillow, opencv, requests, lxml, openpyxl, …), and you can extend or override it with banna config packages add <import> <dist==version> (banna config packages list shows both). Override the base image with --sandbox-image (or BANNA_SANDBOX_IMAGE). Note that the docker backend is container-level isolation, not a security boundary against a determined adversary: containers share the host kernel, so a kernel-level exploit can still escape. For genuinely hostile code, run under a stronger runtime — a syscall-filtering sandbox such as gVisor (runsc) or a microVM such as Firecracker / Kata — which --sandbox-image and the SandboxBackend interface are designed to accommodate.
• Verifiers catch structural failures, not factual ones. A coherent answer grounded in an incorrect source passes the verifiers and still fails GAIA.
• Single-agent. There is no multi-agent delegation or coordination.
• Synchronous tools. Tools are dict → dict; long-running or streaming tools (headless-browser sessions, multi-turn shells) would require a redesign.
• GAIA-tuned. The verifiers, tool registry, and budget defaults target GAIA's distribution. Adapting to other benchmarks would require reworking the verifier set and adding domain tools.

License

MIT — see LICENSE.